This lesson will discuss the way that energy decision makers and stakeholders look at development of new projects and technologies. We will define the motivation for the entire EME 801 Course. We will also hopefully allow students to be able to take a holistic view of energy markets and place them in a broader context to understand how various stakeholders will act in various situations. By training ourselves to ask these questions, we will be able to participate in and even help drive the energy transition.
Please note that we will be convening as a team to discuss this course's subject matter as well as any other energy transition matters the cohort would like to cover.
By the end of this lesson, you should be able to:
Each of the reading assignments below is meant to introduce the importance of -- and give a taste of -- the structure of global energy markets. While reading these, please think about how to describe how you think energy questions affect the near and long-term future of your business and our society.
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. Specific directions for assignments are in the Lesson 1 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
As we walk through our daily lives, it becomes very easy to take for granted the almost complete dependence we have, as humans in a post-industrial economy, on the incredible supply of inexpensive and convertible energy. If we live in anything other than a tropical climate, we require heating so that we don’t freeze. We use energy to till our fields and harvest our food. We also use energy to provide light during non-daylight hours as well as power machines that allow for inexpensive manufactures such as clothing and other household items. We move about, except when walking or bicycling, through the conversion of some sort of potential energy into the kinetic energy of the plane, train, automobile, or boat. And finally, we use energy to communicate and store and manipulate data. This vast network of comfort, nourishment, nutrition, transportation and information would be utterly impossible without inexpensive and readily dispatchable energy. It is literally the life’s blood of the entire economic system.
The resemblance between the human circulatory system and the natural gas grid (or the electric transmission and distribution grid) is similar, and the energy delivery system is almost as crucial to the economy as the circulatory system is to the human.
This chart shows the dependence on energy of various economies around the world. This is directly showing Tons of Oil equivalent to make $1,000 of GDP. A tonne of oil is equivalent to 7.44 barrels or 11,630 kWh. The implications are massive. Every 1,000 dollars of GDP in the US has at least 1 MWh or almost a barrel of oil embedded in it. More importantly, this offers no quantification of the risk to the economy should this energy supply be disrupted. Then the energy intensity would increase massively and whole supply chains and communication networks would be disrupted.
The constituents of global greenhouse gas emissions (those gasses which contribute to global warming) can be seen below:
To see just how much the energy sector contributes to these emissions, please see below.
Note: The pie chart below is interactive. You can click on sectors of the chart to see the proportions within a sector or category.
Hopefully, it becomes apparent that to address the threat of global warming, addressing the greenhouse gas intensity of the energy sector is paramount. In order to address this intensity, we must understand how the markets for energy work. This understanding helps us to make better decisions as we are confronted with choices to make in our professions.
Energy dependence drives geopolitics significantly. Many (if not most) of the world conflicts since the early 1980s either have been directly caused by a scramble for energy or had significant energy undertones. The flashpoints for global conflict include Iraq, Iran, Libya, Syria, Ukraine, Russia, Nigeria, and Venezuela. Each of these nations has significant energy wealth. The recent and deadly conflict in Ukraine has significant energy implications. The Donbas region is a part of Ukraine with large oil and gas reserves. This potential supply, when interwoven with greater integration of Ukraine’s economy, represents a threat to Russia’s position as the EU’s energy supplier. Please read the article "6 Ways Russia's Invasion of Ukraine Has Reshaped the Energy World" for a view into the conflict and the effect on energy throughout the world.
There are many other examples of how energy and its uninterrupted supply influence geopolitics and national security strategy. These include a huge and continuous US Navy presence in the Persian Gulf and the Mediterranean Sea, as well as the long-term contracting of China for Petroleum products. Please read the article called "BP Signs Long-Term LNG Contract with China's Shenzhen Energy."
The current energy transition away from fossil fuels is dramatic until it is placed in a historical context. Let’s look at the graph below for a great visualization:
Make sure to read the following articles, which are all listed on the Lesson 1 Overview page:
Please see the figures below to visualize the various sources and sinks of energy in the US economy. Both of these figures show the original source of energy, whether it be direct radiation from the sun, from combustion of natural gas, nuclear fission, or some other source. From there, we see how the sources "move" through the various conversion systems to provide energy services to the various sectors like transportation or residences (sinks):
One of the most interesting sinks in the US economy is rejected energy. Rejected energy is that which does not provide useful work. Rejection is usually in the form of heat, but also in the form of transportation losses. What does this imply for the potential to decarbonize?
We discuss sources and sinks here to illustrate the level of energy consumption across the US and also how complex the overall market is.
We will discuss supply and demand in greater detail toward the end of the course. Understanding how these forces interact in the present as well as how they will interact in the future will help inform all the decisions we make in our professions.
A stakeholder is defined as, "a person or entity with an interest or concern in something.” Further elucidation might say “Individuals and organizations who are actively involved in the enterprise, or whose interests may be positively or negatively affected as a result of the enterprise.” An addition to this definition would also include those who believe their interest may be affected by the enterprise. This is a very useful definition as it allows us to cast a very wide net to make sure that all the people and institutions that will be affected by our decisions are considered.
On a global scale this is clearly a very “wide net.” It includes the entire earth and many different people and entities. And many entities will have more than one stakeholder identification. Below are some obvious energy stakeholders in the global market:
Please see if you can double or even triple the number of stakeholders globally as you consider the energy market.
Hopefully in this lesson you learned how energy decision makers and stakeholders look at development of new projects and technologies. We defined the motivation for the entire EME 801 Course. We also hopefully allowed you to be able to take a holistic view of energy markets and place them in a broader context to understand how various players will act in various situations. By training you to ask these questions, you are becoming able to participate in and even help drive the energy transition.
Please double check the What is Due for Lesson 1? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 2.
This Module introduces the Final Project in full detail. You will learn what is required to complete the project and place it in the context of the global energy market. The learning objectives of the course include not just exposure to the structure and performance of energy markets, but most importantly, the determination of what the structure and performance of these markets mean for the business in which you might be engaged. To create this “laboratory” where you can test out you hypothesis of viability, we need to create a “project” against which we can test certain assumptions. For this purpose, we will spend a week understanding just what is expected for the project as well as identifying some of the major parameters of the project.
By the end of this lesson, you should be able to:
Please watch the following video interview with Nicolle Natali. If this video is slow to load here on this page, you can always access it and all course videos in the Media Gallery in Canvas.
Once the video begins to play, you can access the transcript for this video by choosing the transcript icon, to the right of the magnifying glass icon, in the upper right corner of the video player.
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. Specific directions for the assignment below are in the Lesson modules in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
This course offers students a broad introduction to project decision-making in the energy industries, and the impacts that markets, institutions and regulations have on investments and decisions made by firms in the energy industries. Topics to be covered include a discussion of market structures for natural gas, crude-oil, transportation fuels and electric power; basic project evaluation and decision-making for energy projects; and the impacts of major environmental regulations at the state and federal level of the functioning of energy markets and energy project decision-making. - Directly from the course catalog.
As this course is part of a Master's Degree in professional Studies, real-world application of the concepts in the course is paramount to the design of this course. Consequently, we introduce the project in the first few weeks of the course so that the student can immediately begin to apply the course content presented in the following modules to a real-world problem, which the student hopefully either finds applicable to his current or future profession or just particularly interesting. Either way (or potentially both) the student will be learning the content with an eye to how the content about market structure and financial principles may have on the chosen project. The intent of the project is to allow you to define an interesting or important problem and learn how the dynamic regulatory and market landscape will affect outcomes. You will not be penalized for taking on too grand or too small a project. You will learn, however, as you go through the project, that you may have chosen too grand or too small a project... but, that is important learning, too. The design of the course and the intent of the instructor are to allow for corrections should this become a problem. Please understand that you are being granted some serious agency, here, and that should help you gain control of your own learning and success. But also understand that the cohort and your instructor are available to help should the lack of "guardrails" be more of a hindrance than a help. The energy transition is very dynamic at this point in time. Therefore, the opportunity to generate a dynamic project without overwhelming constraints most closely reflects the industry that we are studying.
The project will be defined as such:
Investigate an energy project that you as a developer or corporate decision maker would like to construct or replace. This investigation will include the development of a full financial model to inform investment decisions as well as test various scenarios for the project’s development. We will also develop a full stakeholder register and a review of how governmental policy would affect your project. The project will be worth 700 points of your grade. They will be divided scored as shown below:
Task number | Task | Points |
---|---|---|
1 | Project Charter and Stakeholder Register | 75 |
2 | Development of Revenue Time Series | 50 |
3 | Development of Operating Expense Time Series | 50 |
4 | Development of Capital Costs | 50 |
5 | Development of Financial Metrics | 50 |
6 | Program Excel Financial Model | 50 |
7 | Development of Risk Management Measures | 50 |
8 | Development of Policy Sensitivities | 50 |
9 | Development of Electricity Market Sensitivities | 50 |
10 | Development of Fossil Fuel Market Sensitivities | 50 |
11 | Non-Financial Costs and Benefits | 75 |
12 | Final Presentation of Results | 100 |
Total | -- | 700 |
Each of the tasks will require some quantification to ultimately assess the financial value of your project, but there will also be a required write-up of the derivation of your values. Instructions will be provided in each module on what is required.
Client: Someone who pays for goods or services.
Locale: A place where something happens.
In the case of your project, you will be required to identify exactly who is going to pay for the energy that your project is going to produce. This “who” can be many different people or entities, but determining this upfront will be very important. For example, we could have a client be a residential customer installing a solar rooftop, a large commercial building upgrading its HVAC system, a big box retail customer wanting to “green up” its energy purchases, a large manufacturer who wants to install a combined heat and power system or a government agency who wants to take advantage of latent energy in its wastewater system to provide heat to district energy. The possibilities are really quite broad for defining your client.
Client definition is important though because the client will be the entity paying for the energy that is generated (saved) from your project. This may be different than the actual end-user of the energy and it is important to make sure that the client’s requirements are kept utmost in mind for the greatest success of the project.
The course is designed so that the student can be very creative with respect to client selection. There will not be specific prohibitions on client selection, however, the knowledge base of the evaluator is limited and the further the student strays from conventional technologies as well as political and economic regimes that are familiar, the more difficult will it be for there to be accurate feedback and evaluation. Innovation is encouraged, but please be ready to defend and justify your assumptions and parameters.
The locale is the “where.” But the “where” has many characteristics. For example:
This is not an extensive list, but gives one a general idea of what will need to be researched to develop your project.
A project charter is a formal, typically short document that describes your project in a general fashion — including what the objectives are, how it will be carried out, and who the stakeholders are. It is a crucial ingredient in planning the project because it is used throughout the project lifecycle and is a living breathing document that may change from start to finish. It provides a very simple guideline to which the project manager and team members can refer for clear guidance when reaching or (not reaching) milestones. It can be modified as the project evolves, but will always provide the direction to ensure that the project does not drift. Typical elements of the project charter will include the following elements:
As we will be going into a great deal of detail on many of these elements as that is the point of the course, the only elements that the Project Charter that must be included for now are:
In Canvas, please take a look at the example project charter and stakeholder register submitted by your peers in a previous semester. They provide great examples for how to think about this.
Stakeholder: A person with an interest or concern in something, especially a business.
So this is the rest of the “who” after we define the client.
According to the Project Management Institute, project stakeholders are defined as: “Individuals and organizations who are actively involved in the project, or whose interests may be positively or negatively affected as a result of project execution or successful project completion.” An addition to this definition would also include those who believe their interest may be affected by the project. This is a very useful definition as it allows us to cast a very wide net to make sure that all the people and institutions that will be affected by our project are considered.
While the stakeholder identification process is begun and hopefully very close to completed at the beginning of project development, it is an on-going process with certain stakeholders leaving and potentially appearing as the project evolves. It is therefore very important to be flexible and alert for the disappearance and emergence of stakeholder as the project develops. The specific steps we can take in the Identification Process are:
Let’s take a look at each of the steps to define what we must do to complete these steps:
A full development of the stakeholder register along with the actual information for your locale, technology and client for your project will be required as a completion requirement for the final Project write-up.
Let us now take a look at who the most likely stakeholders for a solar project might be:
See if you can increase the list. Remember to appropriately de-risk the project, the identification of, and communication with, the important stakeholders is paramount from the very beginning.
Certain Stakeholder Roles May Include:
The developer is responsible to devise and manage the whole process from ideation to delivery of the project and in many cases through portions of the active life of the project.
The lender provides capital dollars to the project. The lender will require statements of satisfactory financial viability ( a Pro Forma among other documents) as well as proof of other approvals (environmental, utility, zoning…)
Each of these may have to weigh in or grant certain approvals.
There are some different facets to the labor question. Making sure that there is appropriate human resources to complete the specific project is important, but there may also be certain areas of backlash as other workers could be displaced by solar energy. There is a significant push to ensure that re-training of energy workers occurs to ensure a renewable energy workforce that is fit for purpose.
There may be local interest groups who are very strong supporters of sustainable development for instance; there may also be local interest groups opposed to your development for reasons such as clashing with the natural landscape.
Similar to the local groups support and opposition can be found for solar development which could impact your project.
Supply chain issues are much more prevalent with the current level of solar penetration than they have ever been. Special care to treat these very important stakeholders well will mitigate significant risks in delivery of the project.
See if you can develop these roles further in your own thinking as well as identify more as we work through the course. Please take a look at the sample stakeholder register that has been submitted by a student in AE 878 that is a great example of how to think about this:
A good way to think about stakeholder management in particular is to look at the following image:
Here we see the intersection of interest and influence. As we work through the course, see if you can think of the stakeholders that will be introduced and how you might manage them. While this is not a course in project management, we are learning about energy markets through the lens of developing a project.
Here we want to be sure that as we talk about energy markets we become aware that there may be benefits of energy development outside of the localized strict financial analysis There may be incremental jobs as a result of the development. There could be reduction in carbon footprint as a result of the project. There could be many others. There may also be costs associated with the project that are not captured in the strict localized financial analysis such as increased carbon, flicker from a wind turbine, or the fouling of the view through a solar development.
This Module introduced the Final Project in full detail. We hope you learned what is required to complete the project and place it in the context of the global energy market. The learning objectives of the course include not just exposure to the structure and performance of energy markets, but most importantly, the determination of what the structure and performance of these markets mean for the business in which you might be engaged. To create this “laboratory” where you can test out you hypothesis of viability, we are creating a “project” against which we can test your assumptions. For this purpose, we spent this week understanding just what is expected for the project as well as identifying some of the major parameters of the project.
You have reached the end of Lesson 2! Double-check the What is Due for Lesson 2? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 3.
Steve Jobs and Bill Gates started their respective companies (Apple and Microsoft) in their garages. That makes for a nice story, but how exactly did these companies go from garage-band material to global behemoths? They had to raise money or "capital" somehow - there was only so far that Steve Jobs' own bank account (or his parents' bank account) was going to take him. Eventually, both Jobs and Gates needed to seek additional capital from various sorts of investors to help their companies grow - there is, after all, an old saying that it "takes money to make money." True enough; in this lesson, we will take a bit of a closer look at the process of raising capital, from venture capital to stocks to bonds. The world of corporate finance can get very murky very fast; and, in some ways, it's more of a legal practice than a business practice. Our focus is going to be on understanding the various mechanisms that are used to finance energy projects and the implications of those funding mechanisms on overall project costs.
Where we will ultimately wind up is at this mysterious quantity called the "discount rate." Where does that come from? When we are looking at social decisions that involve common costs and benefits, the discount rate is usually more of a matter of debate than anything else. But when a business decision is involved (and that business is a for-profit entity), then there is a rhyme and reason behind the determination of the discount rate as the "opportunity cost" of its investors. There are many different types of investors in a typical firm or project, all of whom face different opportunity costs, so we will encapsulate these in a single number called the "weighted average cost of capital" (WACC). The WACC turns out to be the correct discount rate for a company or a project.
Finally, most of the material that we will develop in this lesson is targeted toward for-profit companies making investments that are expected to earn some sort of positive return over a relevant time horizon. We won't talk much about the non-profit sector except at the very end, when we discuss how the deregulation of commodity markets has changed the investment game for many for-profit firms, but not necessarily for publicly-owned or cooperative firms. If you are interested specifically in project finance concepts for non-profit firms, the Non-profit Finance Fund [25] has some good resources available.
By the end of this lesson, you should be able to:
We will draw on sections from several readings. In particular, there are a number of good online tutorials on the weighted average cost of capital. If you want to get deeper into this subject, there is no substitute for a good textbook on corporate finance. The all-time classic is Principles of Corporate Finance by Brealy, Myers, and Allen (672 pp., McGraw Hill). This book has gone through a number of editions, so earlier editions are probably available online for relatively little cost.
Please watch the following video interview with Elise Zoli. If this video is slow to load here on this page, you can always access it and all course videos in the Media Gallery in Canvas.
Once the video begins to play, you can access the transcript for this video by choosing the transcript icon, to the right of the magnifying glass icon, in the upper right corner of the video player.
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. Specific directions for the assignment below can be found within this lesson.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
A lot of what we will be studying in this lesson falls under the umbrella of "corporate finance," even though our focus is actually individual energy projects, not necessarily the companies that undertake those projects. Still, there are a number of parallels and many concepts of how companies should finance their various activities are immediately relevant to the analysis of individual projects. After all, like companies as a whole, individual projects have capital, staffing, and other costs that need to be met somehow. And a company can sometimes be viewed as simply a portfolio of project activities. Similarly, an individual project can be viewed as being equivalent to a company with one single activity. (Following deregulation in the 1990s, a number of major energy projects, such as power plants, were actually set up as individual corporate entities under a larger "holding company.") A lot of the emphasis in the corporate finance field is how companies should finance their various activities. (For example, in the readings and external references, you will see a lot of mention of "target" financial structures.) That isn't really our focus - we are more concerned with understanding the various options that might be available to finance project activities. The "right" financing portfolio is ultimately up to the individuals or companies making those project investment decisions.
Project financing options are numerous and sometimes labyrinthine. You may not be surprised that lawyers play an active and necessary role (sometimes the most active role) in structuring financial portfolios for a project or even an entire company. While individual finance instruments span the range of complexities, the basics are not that difficult. For an overview, let's go back to the fundamental accounting identity:
The balance sheet for any company or individual project must obey this simple equation. So, if an individual or company wants to undertake an investment project (i.e., to increase the assets in its portfolio), then it needs some way to pay for these assets. Remembering the fundamental accounting identity, if Assets increase, then some combination of Liabilities and Owner Equity must increase by the same dollar amount. Herein lies the fundamental tenet of all corporate and project finance: financing activities that increase the magnitude of Assets must be undertaken through the encumbrance of more debt (which increases total Liabilities) or through the engagement of project partners with an ownership stake (which increases total Owner Equity).
Hence, all projects must be financed through some combination of "debt" (basically long-term loans by parties with no direct stake in the project other than the desire to be paid back) and "equity" (infusions of capital in exchange for an ownership stake or share in the project's revenues).
The following video introduces debt and equity in a little more detail. The article from Business Week [27], while it goes more into the specifics for small businesses than our purposes require, also has a nice overview of debt and equity concepts.
Debt and equity each have costs. The cost of debt is pretty explicit - lenders typically charge interest. The cost of equity is a little more complex since it represents an "opportunity cost." If an equity investor (like a potential holder of stock) buys into Acme PowerGen Amalgamated, that investor is foregoing the returns that it could have earned from some other investment vehicle. The attitude of most investors, in the immortal words of Frank Zappa, is "we're only in it for the money." Those foregone returns represent the opportunity cost of investing in Acme PowerGen Amalgamated. If we weight these costs by the proportion of some project that is financed through debt and equity means, we have a number that is known as the "weighted average cost of capital" or WACC. The general equation for WACC is:
Here, the "costs" are generally in terms of interest rates or rates of return. So, a company facing a 5% annual interest rate would have a "cost of debt" equal to 5% or 0.05. We'll get into these pieces in more depth, and will explain the strange tax term in the WACC equation after we gain more of an understanding of debt and equity, and how the costs of debt and equity might be determined.
The term "equity" in corporate or project finance jargon indicates some share of ownership in a company or project - i.e., some level of entitlement to some slice of the revenues brought in by the company or project. There are different priority levels of this entitlement - typically operating costs must be paid (including, in some cases borrowing costs) before equity investors can get their slice of the net revenues. There are also multiple priority levels of equity investors, which determines who gets paid first if profits are scarce.
The simplest, and one of the most common, forms of equity ownership is through the ownership of company stock. A share of stock is simply an ownership right to a portion of the company's profits. When public stock is initially issued by a company (called an "initial public offering"), the price paid for that stock is effectively a capital infusion for the company.
When you think about shares of stock, you may have in your mind things that are traded on the New York Stock Exchange. Not all stock is traded or issued this way, at least not initially. Often, company founders or owners will decide to sell limited amounts of stock in a company without that stock being available for the general public to purchase or being traded on an exchange like the New York Stock Exchange. A company that issues stock in this way is often referred to as being a "private" company, which means that its stock is held and traded (if it's ever traded) in the hands of individuals or institutions selected by the company. Often times, stock in a private company comes with some sort of voting right or other representation into how the company runs its operations.
A company that issues shares of stock to the general public is called a "publicly-traded" or "public" (for short) company. The term "public" in this case should not be confused with ownership by any government or the mission of the company - the term simply refers to the availability of the company's stock. The decision to "go public" is complicated and has costs as well as benefits. The obvious benefit is that issuing public stock is a relatively straightforward way to raise large amounts of capital. Owners of private stock that allow their stock to be sold in the public offering can also make substantial amounts of money if the demand for the stock among the public is high. There are, however, a couple of big down sides. First, issuing more shares of stock effectively dilutes the value of existing shares. If a company has $1 million in profits over a time period and increases the number of shares of stock from 1 million to 2 million, then the earnings per share of the company drops from $1 per share to $0.50 per share over that time period. People are sometimes willing to pay large sums for company stock if they believe that profits will increase in the future. Second, the more equity investors there are (public or private), the larger the loss of control by the company's initial shareholders.
Raising equity capital can happen through a number of different channels. Brief descriptions of a few of the major channels follow:
The "cost of equity" for a project or company represents the return that an equity investor would need in order to judge that project or company a worthwhile investment. Remember that the cost of equity is really an opportunity cost. Individual investors may have their own criteria for judging opportunity cost, and we can't get into their heads all of the time. So, how do we estimate opportunity cost for a particular project or company? The most common framework is to use a framework called the "Capital Asset Pricing Model" (CAPM). Investopedia has a nice introduction to this framework [31] that includes both the intuition and the equations. Here, we will stick mostly to the intuition.
Using the CAPM to determine the cost of equity, the equation is:
For project evaluation, it is common to use the beta and risk premium relevant to the industry in which the project is going to operate (e.g., utilities for a power plant or gasoline for a refinery). This web page has a nice table estimating the cost of equity for different industries [32].
Debt financing refers to capital infusions by entities that do not take any ownership or equity stake in the company or project. Debt financing is like a loan - in fact, bank loans are among the most common forms of debt financing for projects and companies. Most debt is "private," in that it is held in the hands of a single entity (like a bank) or group of entities, and transferring that debt to another party is time-consuming. Just as with stocks, there is "public" debt that is traded openly. Many corporations issue various types of bonds that can be traded no differently than stocks.
The big difference between debt and equity financing has to do with repayment. Equity financing is essentially a loan that is "repaid" through entitlements to a stream of future company or project profits. Debt financing involves various terms of repayment. In many cases, holders of debt have priority on repayment before holders of equity interests in a company.
The cost of debt is determined primarily by how likely or unlikely the lender is to be paid back. If a project goes into bankruptcy, for example, holders of debt may not earn back their entire investments. (One of the advantages of being a lender is that in the case of bankruptcy, lenders often have a higher priority for repayment than equity shareholders.) Rating agencies such as Moody's, S&P, and Fitch use ratings as a general indication of the riskiness of debt.
Wikipedia has nice overview tables and charts [33]of what the various ratings mean. For our purposes, the long-term column is more important than the short-term column. The Federal Reserve Bank of St. Louis [34] is a nice resource for finding returns or "yields" on corporate bonds of various grades. Often times, for each step away from the top rating (AAA, for example), investors demand a roughly 0.25% to 0.5% increase in yield in exchange for that increased risk of default, but this is not always the case. (You may see examples when you click on different links for corporate bond return data.) The list of available corporate bond rates from the St. Louis Fed can be dizzying. If you search on the website for the bond grade that you're looking for (like "AAA" or "BB") then you can find information more easily. You can also try using a web search engine by typing in "FRED AAA Corporate bond yield" if you were looking for the AAA bond. A couple of specific examples that you can look at to compare yields are:
Now that we've covered the basics of equity and debt financing, we can return to the Weighted Average Cost of Capital (WACC). Recall the WACC equation from the beginning of the lesson:
Evaluating the WACC for a company is different than evaluating the WACC for an individual energy project. When the WACC for a company is evaluated, we are often trying to determine (under imperfect information) what a company's costs of capital are. In this case, we would utilize as much financial data as possible in order to estimate the various terms in the WACC equation.
For an individual energy project, the various terms in the WACC equation are determined in large part by the type of investment being made, the type of market (regulated versus deregulated) in which the investment is occurring and the individual company or group of companies making the investments.
The tax rate term in the WACC equation may seem odd. Why discount the tax rate from the cost of debt financing? The reason is that from the perspective of a company, the interest on debt (i.e., the cost of debt) is tax-deductible, so interest payments are offset by tax savings.
Let's go through a hypothetical example to see how this works. Suppose that Mark Linguine PetroServices Amalgamated wanted to invest in a new oil refinery. What is the discount rate that Mark Linguine PetroServices should use in evaluating the NPV of the refinery project? The answer is that the discount rate is equal to the firm's Weighted Average Cost of Capital! So we need to calculate the WACC to determine the discount rate that Mark Linguine PetroServices should use.
Let's assume that 15% of Mark Linguine PetroServices' refinery activity would be financed by debt (just to use a single number). Oil company bonds have historically had very high ratings, so we'll assume that Mark Linguine PetroServices has a long-term bond rating of AAA. Looking online at corporate bond yields [38], we see that a 20-year AAA corporate bond would have a yield of 2.5% (as of the time of this writing - keep in mind that these rates can and do change frequently).
If Mark Linguine PetroServices faced a 35% marginal tax rate, then its cost of debt financing would be 0.025 × (1-0.35) = 0.02, or 2% (I'm rounding up here - the answer to more significant digits is 1.6%).
Turning now to the cost of equity financing, we need the return on the safe asset; the market risk premium; and the beta for the petroleum industry. The yield on the 30-year treasury bond [39] was 2.34% at the time of this writing. We will assume a risk premium of 5%, and from the "Cost of Capital by Sector [32]" web page we see that the beta for the petroleum industry is between 1.30 and 1.45 (the beta is in the second column of the table; we'll use 1.45 for this example). Thus, the cost of equity for Mark Linguine PetroServices would be 0.0234 + (1.45 × 0.05) = 0.1, or 10% (Again, I'm rounding here - the answer to more significant digits is 9.59%).
Assuming that 15% of the refinery was financed through debt and 85% through equity, the WACC for the Mark Linguine PetroServices refinery project would be:
The WACC represents the discount rate that a company should use in conducting a discounted cash flow analysis of a given energy project. The reason is that the discount rate represents the opportunity cost of getting something in the future relative to getting something today. Since the WACC represents the average return for an energy project (remembering that that average is weighted across both debt and equity investors), it represents a kind of average opportunity cost for investment in a project.
Please read Section II (Problems in Managing the Restructured Industry) from Morgan, et al. [40] and have a look at the presentation from Gary Krellenstein [28]. Krellenstein's presentation in particular, while focused mostly on transmission investment, raises a number of really important issues regarding how changes in market institutions - such as decontrols on wellhead prices for natural gas, or electricity deregulation - have changed the investment environment for large energy projects. These impacts have perhaps been most evident in electricity, but are not confined to just the electricity sector.
The main idea from both readings is that deregulation has done two things simultaneously. First, it has removed the guarantee of cost recovery from major energy projects such as power plants. (Krellenstein's presentation is focused on transmission because, at the time that it was written, the U.S. federal government was considering proposals to deregulate transmission the same way that it had deregulated generation. In the end, it did not, and most transmission lines continue to enjoy cost recovery ensured by the state or federal government.) This means that energy projects need to earn sufficient revenues to cover costs, plus meet the rate of return demanded by investors. Second, commodity prices in deregulated energy markets are volatile - we have seen this in previous lessons with oil, petroleum products, natural gas, and electricity. Since the revenue stream for energy projects became more volatile, the projects themselves are viewed as being increasingly risky.
The point that Krellenstein makes on slide 13 of his presentation about "system" versus "project" financing sums it up nicely. From an investor's point of view, regulated energy markets are safe because the risks of cost recovery are effectively shifted from the project's owners to the people who pay energy bills. This may not be economically efficient, but from capital's point of view it is safe. Deregulation has shifted some of this risk back to the project's owners (i.e., the company building/operating the plant and its equity shareholders). This is why Krellenstein points out that the majority of "project financed" energy investments are heavily weighted towards debt financing. Since this debt financing is not highly rated (see Figure 3.1, from Krellenstein's presentation), it carries higher yields. The equity component of that financing also becomes more expensive since investors demand returns over a shorter time horizon, as shown in Figure 3.2 (also from Krellenstein).
Project Finance is the funding of a specific project or group of projects (like a PV Solar electricity generation project) on a non-recourse basis through a special purpose entity. What does that exactly mean?
In this lesson, we took closer look at the process of raising capital, from venture capital to stocks to bonds. The world of corporate finance can get very murky very fast; and, in some ways, it's more of a legal practice than a business practice. Our focus was on understanding the various mechanisms that are used to finance energy projects and the implications of those funding mechanisms on overall project costs.
When we are looking at social decisions that involve common costs and benefits, the discount rate is usually more of a matter of debate than anything else. But when a business decision is involved (and that business is a for-profit entity), then there is a rhyme and reason behind the determination of the discount rate as the "opportunity cost" of its investors. There are many different types of investors in a typical firm or project, all of whom face different opportunity costs, so we will encapsulate these in a single number called the "weighted average cost of capital" (WACC). The WACC turns out to be the correct discount rate for a company or a project.
You have reached the end of Lesson 3! Double-check the What is Due for Lesson 3? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 4.
The next series of lessons, starting with this one, will develop quantitative tools that will allow you to prepare an assessment of the profitability of an energy project, whether that project falls into a conventional energy category (e.g., a natural gas power plant) or a sustainable energy category (e.g., solar hot water heating). While our focus here will be on profitability as a performance metric for project evaluation, it is possible to use other performance metrics (such as life-cycle impacts; embedded energy, carbon equivalent or water). In the investment world, these alternative metrics are used less frequently in project evaluation.
This lesson will build up to the construction of the "pro forma" financial statement for a single energy project. Along the way, we will learn a little about the unique language of accounting, what "depreciation" means to a tax accountant, and how a "10K" is not just a road race.
By the end of this lesson, you should be able to:
Basic accounting concepts and the items on the pro forma income and cash flow statements will be explained using the following resources
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 4 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
This is not a course in accounting. But I really recommend that you take one. Accounting often gets a reputation as a monotonous and boring field, and since there is so much number-crunching involved in accounting, there are times when this is true. But to really understand the drivers of how business decisions get made, you need to start with some accounting – because the process of accounting provides the basic language and building blocks for determining which companies, technologies, and projects will ultimately be viewed as successes and failures.
The goal in this section is to familiarize you with some of the most basic concepts in accounting. It's meant to get you comfortable with the language of ledgers, not necessarily to convert you into one of those guys with the green eyeshades [44]. And you might even understand why your taxes are so complicated (at least, if you live in the United States).
The website Investopedia has a very nice introduction to accounting concepts. [45] At this point, you should go ahead and read it, especially the first five parts (through the section on financial statements). Once you are comfortable with that material, go ahead and have a look at Alison Kirby Jones's accounting tutorial [43], which focuses nicely on financial accounting. The second part of the tutorial, which focuses on the accounting statements of income and cash flows, is most relevant to us.
What we will focus on in this lesson is the practice of financial accounting - the preparation of a synopsis of a company's financial health. The primary tools in financial accounting are a series of tables or "statements" that convey specific information about the financial position of the company. The most important of these statements are:
The readings for this lesson and some of the discussion should focus on the company as the unit of analysis for accounting. But virtually all of the concepts that we develop can be applied to a specific project as well. In fact, the main purpose for going through these accounting concepts is so that you can apply them to the evaluation of energy projects, through a series of weekly assignments and (eventually) the final semester project.
The remainder of this section will be devoted to understanding the Balance Sheet and the Fundamental Accounting Identity. The Balance Sheet is just a tabular summary of the net financial position of a company at some point in time (note that this is a fundamental difference between the Balance Sheet and the P&L or Cash Flow Statements, which usually capture financial positions over a period of time, rather than just a snapshot). The Balance Sheet has just two columns - one which measures what the company owns (or its assets, sometimes called credits) and the other that measures what the company owes (or its liabilities, sometimes called debits).
Assets and liabilities, from an accounting standpoint, are just two sides of the same coin. When a company has possession of something that it hopes will net it a future benefit (an asset), that future benefit generates a "claimant" (liability). After all, if a company purchases equipment, supplies or labor, the money for those assets had to come from somewhere. Typical examples of claimants would include lenders (like a bank), shareholders in the company, or even the company's owners if they are the ones that put up money. Table 4.1 summarizes things that would go in the Asset and Liability sides of the Balance Sheet (note that this is just a summary of the figures on pages 3 and 4 of the Jones reading).
Assets | Liabilities |
---|---|
Cash | Accounts Payable (what the company owes its suppliers) |
Accounts Receivable (what the company is owed by customers) |
Claims by Lenders (what the company owes its creditors) |
Inventory | Owner's Equity (anything that the owner or shareholders put up to fund the company) |
Property, Plant and Equipment (PP&E basically any physical plant assets) |
One important feature of the Liabilities side of the Balance Sheet is how creditors (lenders) and suppliers are separated from equity owners (the company's employees and shareholders). Creditors and suppliers have claims to fixed sums of money from the company, equivalent to whatever they are owed. Equity owners have a claim to any "residual" income of the company after the creditors and suppliers have been paid. For this reason, equity owners are often referred to as "residual claimants."
Since every asset generates its own future liability, every entry on the Asset side of the Balance Sheet needs to have a corresponding entry on the Liability side. Put another way, Assets (the left side of the Balance Sheet) generally describe investing activities, which are undertaken by the company in order to make more money in the future (well, we hope). But the funds for those investing activities have to come from somewhere. So, the Liabilities (the right side of the Balance Sheet) describe the financial activities that are undertaken to support the investments. The Liabilities side of the balance sheet describes not only the total sum of what the company will owe to various parties in the future, but also what the distribution of those parties is (banks, shareholders, company owners, and so forth).
Owner's equity is the simplest example. If I set up a company with $10,000 of my own money, then the balance sheet on the first day of the company's operations would look like the one shown in Table 4.2:
Assets | Liabilities |
---|---|
Cash: $10,000 | Accounts payable: $0 (what the company owes its suppliers) |
Accounts Receivable : $0 (what the company is owed by customers) |
Claims by lenders: $0 (what the company owes its creditors) |
Inventory: $0 | Owner's equity: $10,000 (anything that the owner or shareholders put up to fund the company) |
Property, Plant and Equipment : $0 (PP&E basically any physical plant assets) |
Here is another example, which builds upon Table 4.2. Suppose now I issue shares of stock in my company, to the tune of another $10,000. I use $5,000 of that money to purchase a piece of office equipment. On the Assets side, I would then have $15,000 in cash and $5,000 worth of PP&E. On the Liabilities side, I would have $20,000 worth of owner's equity. This is shown in Table 4.3.
Assets | Liabilities |
---|---|
Cash: $15,000 | Accounts payable: $0 (what the company owes its suppliers) |
Accounts Receivable: $0 (what the company is owed by customers) |
Claims by Lenders: $0 (what the company owes its creditors) |
Inventory: $0 | Owner's Equity: $20,000 (anything that the owner or shareholders put up to fund the company) |
Property, Plant and Equipment: $5,000 (PP&E basically any physical plant assets) |
As one final example, suppose that instead of using the cash I raised to purchase the $5,000 worth of office equipment, I purchased the equipment half with cash and half on credit from an office supply store. This credit is a promise to pay the office supply store $2,500 at some future point. So, at that point, I would have $17,500 in cash and $5,000 worth of PP&E in the Assets column. As Liabilities, I would have the $2,500 of credit from the office supply store as Accounts Payable, plus the $20,000 in owner's equity. This situation is shown in Table 4.4.
Assets | Liabilities |
---|---|
Cash: $17,500 | Accounts payable: $2,500 (what the company owes its suppliers) |
Accounts Receivable: $0 (what the company is owed by customers) |
Claims by lenders: $0 (what the company owes its creditors) |
Inventory: $0 | Owner's equity: $20,000 (anything that the owner or shareholders put up to fund the company) |
Property, Plant and Equipment: $5,000 (PP&E basically any physical plant assets) |
You might have noticed that the Assets and Liabilities columns both add up to the same number ($10,000 in Table 4.1, or $22,500 in Table 4.4). This is no accident - the examples collectively illustrate what is known as the Fundamental Accounting Identity, which states that the sum of Liabilities to creditors and owner's equity (which, remember, includes shareholders) must equal the company's total Assets.
The term "depreciation" usually refers to the physical degradation of some capital asset, like wear and tear. My car, for example, doesn't drive as smoothly with 120,000 miles on it as it did when it had 12,000 miles. As some piece of capital gets older, you might naturally expect it to be worth less, either because it requires more maintenance or because it cannot be resold for as high a price. (Though the concept of "value" of physical plant can be a bit tricky, as we will learn in the next section.) It is often said that the value of a new car drops by 20% as soon as it is driven off the lot, even though it's basically the same vehicle that was bought for a new-car price. This illustrates the difference that can sometimes arise between physical depreciation and the depreciation in a capital asset's store of value.
Depreciation of an asset's store of value has substantial implications for the financial analysis of energy projects. You might recall from Lesson 5 that the profits of a regulated public utility are determined in large part by its total stock of non-depreciated capital. Who determines the rate at which a power plant, substation or other asset depreciates in value? Similarly, tax authorities in many countries allow companies to "write off" the depreciated value of assets when calculating their total income on which they are subject to paying tax. The term "write off" here refers to a deduction from total taxable income, rather than a deduction in the total tax bill, per se. For example, if you can claim that some capital asset has depreciated in value by $100 over the course of some year, and the tax rate is 35%, then that $100 asset depreciation will ultimately lower your tax bill by $35 (35% of $100), not by $100. This is the difference between a "tax deduction" and a "tax credit." Tax credits will appear later in the course when we discuss financial subsidies for energy projects.
The rate at which an asset is financially depreciated for tax, regulatory, or other financial purposes may be very different than the rate at which the asset actually physically depreciates in value. It is even possible that an asset could be treated as completely depreciated in the eyes of a regulator or the tax authority, yet could still be generating a lot of value for its owner. Many power plants or natural gas mains in the U.S., for example, are several decades old - well beyond their intended 30 to 40 year life spans. These assets are mostly considered to be depreciated assets, yet some continue to be highly profitable.
Depreciation allowances are usually determined by the regulator, tax authority or other relevant oversight body in order to allow the owners of depreciable capital assets to recover the costs of those assets through a series of tax deductions or other gains over the course of some number of years. The idea here is to encourage investment by allowing companies to use investment vehicles to reduce their tax burden. Our discussion here will focus on depreciation allowances that are allowed by tax authorities, since those allowances are ultimately the most important for development of energy project financial statements. The U.S. Internal Revenue Service, if you happen to be interested, maintains a mind-bogglingly complex list of types of property that are eligible for different depreciation schedules and methods. Here is an "introduction" to depreciation [46] from the IRS.
A depreciation schedule lists the percentage of the original (so-called "book") value of a piece of property that can be claimed as a depreciation allowance for tax reporting purposes. Broadly, there are two types of depreciation schedules: straight-line depreciation and so-called accelerated depreciation methods.
Before we get into the mechanics of depreciation, we need to develop some notation.
Based on these definitions, we can immediately see that B(t) is just equal to the original book value (P) less all of the cumulative depreciation allowances from year 1 to year t. In mathematical terms,
Table 4.5 provides the mathematical formulas for some common depreciation methods. Note that Modified Accelerated Cost Recovery Systems (MACRS), which have become more commonplace, are not included in Table 4.5 but will be discussed below.
Depreciation Method | D(t) | B(t) |
---|---|---|
Straight Line | ||
Sum of the Year's Digits | ||
Declining Balance | ||
Note: |
The most straightforward depreciation method is straight-line depreciation. Under straight-line depreciation, the book value of an asset (less its salvage value, if any) can be depreciated evenly over some number of years. For example, if you had an asset with a book value of $1,000; no salvage value; and a ten-year depreciation horizon, you could claim $100 each year for ten years as a depreciation expense and tax deduction.
The other three depreciation methods that we will discuss here - sum of the year's digits, declining balance, and MACRS - are all forms of "accelerated depreciation." Under accelerated depreciation systems, a larger proportion of the asset's book value is allowed to be depreciated in the earlier years of its use, with smaller proportions depreciated in later years of use. This allows the asset owner to enjoy a lower tax burden earlier in the asset's life. Other things being equal, this leads to higher profits in the years immediately following investment. Accelerated depreciation can substantially affect the value of an asset to its owner; we will see later just how this re-allocation of tax burden and profits across the useful life of an asset increases the asset's lifetime benefit to its owner.
The three accelerated depreciation methods that we will illustrate in this lesson are:
As a means of comparison between all of these methods, let's take a hypothetical asset with a book value of $1,000 and zero salvage value, and depreciate that asset over a ten-year time horizon. Table 4.6 and Figure 4.1 show the values of B(t) during each year for each of the four methods. For declining balance, we will use 25% per year. For MACRS we are using the 10-year table in Appendix 1 of Publication 496 [47].
As an exercise, see if you can reproduce the table and the figure.
Year | Straight-Line | SYD | MACRS | Balance |
---|---|---|---|---|
0 | $1,000.00 | $1,000.00 | $1,000.00 | $1,000.00 |
1 | $900.00 | $818.18 | $900.00 | $750.00 |
2 | $800.00 | $654.55 | $720.00 | $562.50 |
3 | $700.00 | $509.09 | $576.00 | $421.88 |
4 | $600.00 | $381.82 | $460.80 | $316.41 |
5 | $500.00 | $272.73 | $368.60 | $237.30 |
6 | $400.00 | $181.82 | $294.90 | $177.98 |
7 | $300.00 | $109.09 | $229.40 | $133.48 |
8 | $200.00 | $54.55 | $163.90 | $100.11 |
9 | $100.00 | $18.18 | $98.30 | $75.08 |
10 | $ --- | $ --- | $32.70 | $ --- |
The Balance Sheet that was discussed earlier in this lesson provides a snapshot in time of the financial health of a firm or the valuation (again, at a snapshot in time) of a specific investment project. The last two financial statements - the P&L and the cash flow statement - are used in two ways, depending on whether the entity under analysis is a company or a specific project. Either way, they have roughly the same format.
At this point, please read "A Primer on Financial Statements. [48]" The first big table in the article lays out the structure of the P&L statement pretty nicely, at least up until the row that is labeled "Net Income." The rows below Net Income pertain to calculating financial metrics for valuation of specific companies. The P&L and cash flow statements for U.S. companies, at least those that are publicly traded, are laid out in a mandatory quarterly filing to the Securities and Exchange Commission called the Form 10K. Some companies publish similar information in their annual reports to shareholders, but these annual reports are not subject to any sort of regulatory scrutiny, whereas 10K filings can be audited if necessary. So, the 10K is the real deal as far as determining the financial position of a company. This is not to say that all companies massage the numbers in their annual shareholder reports, only that you may find differences between the annual report and the 10K, if you look hard enough. 10K filings in the U.S. are public information, so you should be able to easily find them, as long as the company is required to file one.
Individual energy projects are often evaluated using P&L and Cash Flow statements that jointly are known as the "pro forma." Unlike the P&L and Cash Flow statements for a company, which should represent actual historical data, the pro forma represents the analyst's evaluation of the financial worthiness of a potential energy project. (It is possible to put together a historical pro forma for an individual energy project, but we'll focus on the pro forma for evaluation of potential energy projects.)
As we go through the various parts of the pro forma, it will be useful to refer to a numerical example, to keep things a little less abstract. I have posted a simple pro forma statement for a hypothetical natural gas power plant, in Microsoft Excel format. Look for the Pro Forma Example.xlsx file in the
in Canvas. Please download the spreadsheet for reference (for those who do not have access to Excel, the spreadsheet should be easily opened in Open Office or in a Google Spreadsheet). The spreadsheet has four tabs:Our hypothetical natural gas power plant has the following properties that are shown in Table 4.7 (from the Plant Properties tab). Some of these plant properties aren't relevant to us right now, but we will come back and use this hypothetical plant as an example in future lessons.
Properties | Unit |
---|---|
Capital Cost | $500,000 |
Annual Discount Rate | 10% |
Decision Horizon (N) | 10 years |
Annual Output | 4500 MWh |
Marginal Cost | $60 per MWh |
Variable O&M | $5 MWh |
Fixed O&M | $10,000 per year |
Tax Rate | 35% |
Below this table on the Plant Properties tab, you will notice a set of assumed annual sales prices, in $ per MWh, for the fifteen-year operational period.
Table 4.8 shows the first few years of the P&L statement (not all fifteen; for the full P&L statement, please refer to the Excel spreadsheet in Canvas). We will refer to Table 4.8 as we go through the items on the P&L statement.
First, note that we label the construction year as "Year 0" and the operational years as "Year 1-15." That is just a numbering convention indicating that we assume that the power plant is built this year and is operated beginning the following year for fifteen subsequent years.
The first section of the P&L, on lines (1) through (5), outlines the costs and revenues of the power plant project.
step | topic | Year 0 | Year 1 | Year 2 | Year 3 |
---|---|---|---|---|---|
(1) | Construction Cost | $500,000 | $ | $ | $ |
(2) | Annual Operating Revenue | - | $428,175 | $305,332 | $383,445 |
(3) | Annual Variable Operating Cost | - | $292,500 | $292,500 | $292,500 |
(4) | Annual Fixed Operating Cost | - | $10,000 | $10,000 | $10,000 |
(5) | Annual Net Operating Revenue | - | $125,675 | $2,832 | $80,945 |
(6) | |||||
(7) | Depreciation Expense | - | $50,000 | $90,000 | $72,000 |
(8) | |||||
(9) | Taxable Net Income | - | $75,675 | $(87,168) | $8,945 |
(10) | |||||
(11) | Taxes | - | $26,486 | $------ | $3,131 |
(12) | |||||
(13) | Income Net of Taxes | - | $49,189 | $(87,168) | $5,814 |
Lines (7) and (9) incorporate the depreciation allowance. The depreciation allowance is calculated using the $500,000 book value of the plant (i.e., the construction cost) and the annual depreciation allowance percentages from the MACRS table (the Depreciation tab in the spreadsheet). Taxable net income on Line (9) is calculated as the net operating revenue or EBIDTA, less the allowable tax deduction for depreciation.
Taxes owed by the plant are shown in Line (11), equal in this example to 35% of taxable net income, as long as taxable net income is positive. In year 2, for example, you will notice that the plant has a large negative taxable net income. This is not because the plant did not make any money (it made a little bit as you could see from Line (5) in Table 8.8) but because the allowable depreciation expense is so large that for tax purposes it appears as though the plant lost money. In this case, there is no income to be taxed, and the plant would not pay any taxes that year. On the P&L statement, its income net of taxes (Line (13) in Table 4.8) would be negative.
Of course, the plant did not really lose money in year 2, because the depreciation expense is not a real expense, in the sense of representing a cash outlay by the company that owns the power plant. For the purposes of calculating tax liability, however, the depreciation allowance is treated as a real expense.
Finally, we move onto the Cash Flow statement. In this example, the Cash Flow statement is much easier to put together than the P&L statement. Table 4.9 shows the first few years of the Cash Flow statement for purposes of illustration.
step | Activity | Year 0 | Year 1 | Year 2 | Year 3 |
---|---|---|---|---|---|
(1) | Investment Activities | $(500,000) | |||
(2) | |||||
(3) | Net Income from Operating | - | $49,189 | $(87,168) | $5,814 |
(4) | |||||
(5) | Depreciation Expenses | - | $50,000 | $90,000 | $72,000 |
(6) | |||||
(7) | Net increase or decrease | ||||
(8) | in cash | $(500,000) | $99,189 | $2,832 | $77,814 |
The first line on the Cash Flow statement lists all investment activities in that year - these represent outlays of cash (even if the Balance Sheet for that year would indicate that the investment activities were financed through debt or owner/shareholder equity). In our example, there is only one year - the construction year - with any investment activities. Line (3) is the final line item from the P&L statement, showing the post-tax net income from operating activities. The crucial point to remember here is that this figure includes the depreciation expense, which is not a real expense in the sense of any cash outlays. So, to get a real sense of how the project's cash holdings have changed throughout the year we need to add the depreciation expenses (Line (5) in Table 4.9) back to the net income (Line (3) from Table 4.9). The resulting sum is the "Net increase or decrease in cash," and it shows the end-of-year cash holdings for the power plant project. These cash holdings are used to pay back creditors and are disbursed among equity shareholders (i.e., the project's owners).
Accounting is the language that is used to describe business transactions and the financial viability of companies and projects. This same language is used to determine the viability of any type of for-profit energy company and project, whether based on conventional or alternative energy resources or technologies. We learned about the three fundamental accounting statements – the Balance Sheet, the P&L, and the Cash Flow. The latter two of these make up the “pro forma” evaluation of a potential energy project.
You have reached the end of Lesson 4! Double check the What is Due for Lesson 4? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 5.
Many major energy projects last a long time. Even those that aren't quite so long-lived are built with the intention that they will operate for many years. Moreover, different energy technology options have different construction and operating costs. The finances of energy projects can be difficult to evaluate for just this reason - they often involve large immediate capital outlays, followed by a stream of revenues (or costs, if the project is uneconomical) over a long period of time. The process of "discounting" is the way that we think about future costs and revenues in terms of decisions that we are forced to make today.
This lesson will be the most mathematically-intensive of the semester. We will learn about the "net present value" as a way of measuring the benefits versus the costs of a long-lived energy project. We will also discuss several other metrics that can be used to evaluate energy projects, and how these metrics are complementary — and can sometimes cause confusion. These metrics represent the “language” of the energy finance community who is responsible to make decisions about financing projects. The market participants use these metrics to compare energy capital investments against each other as well as against other ways to invest capital. For this reason it is very important for the energy professional to master them.
By the end of this lesson, you should be able to:
There are lots of good online resources for understanding net present value. Our primary external resource will be the article Have We Caught Your Interest? [49] This article goes deeper into the math than we will need to, but is nice and concise, and has all of the relevant information on discounting. You should skim this piece before you start in on the lesson material online. You can then return to the reading as a reference.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 5 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Suppose, hypothetically (or perhaps not), that you really like chicken wings. Now, suppose that I were going to give you chicken wings right now. If you are hungry, then you will be happy since I am giving you something that: (a) sates your hunger; and (b) tastes yummy. And I'm giving them to you right now. No waiting.
Now, suppose that I told you that I would give you the same chicken wings, but one hour from now. Assume that I just bought the chicken wings, and I'm going to let them sit in my car for the next hour. How happy are you right now about the prospect of getting these particular chicken wings in one hour? Well, maybe they will get a little bit cold, and maybe in the meantime you would eat something else to satisfy your hunger, but in one hour they will seem like pretty much the same chicken wings.
Now, suppose that I buy chicken wings right now, but I'm going to wait until tomorrow (24 hours from now) to give it to you. How happy are you right now about these chicken wings that you will get tomorrow? Probably less so - first of all, you are hungry now. Sure, you will be hungry again tomorrow, but will those chicken wings taste quite as good after sitting in my car for a day? Probably not.
Now, (really, we're almost to the point), suppose that I buy chicken wings right now, but I'm going to wait six months to give them to you. How happy are you right now about these chicken wings that will sit in my car for six months before being given to you? What will happen to those chicken wings between now and six months from now? Will they still even be recognizable as chicken wings? I won't even mention the special sauce…
On the other hand, they’re chicken wings. Why is it any different to you whether you get it now, a day from now, or six months from now? Basically, there are three reasons:
Evidently, the same chicken wings (whether they will be fresh or had sat in my car for some unfathomable period of time) are worth less to you if you get them in the future than if you get them now. The process of measuring how fast those chicken wings lose value to you, relative to their value right now, is called discounting. The "discount rate" measures how quickly or slowly something loses value over time. A high discount rate means that something loses value quickly relative to its value at the present time. A low discount rate means that something loses value slowly relative to its value at the present time. A discount rate of zero means that something does not lose any value over time - it's worth just as much in a day, week, year, decade, or century as it is right now. Remember: the discount rate measures how quickly something loses value over time. It does not measure whether something is valuable or not valuable in absolute terms.
The particular method of discounting that we will cover in this lesson is known as "exponential discounting" because of the assumption that something loses value at a constant rate (percent per year) over time. An alternative discounting method that we won't cover, but you can look at in your spare time, is known as "hyperbolic discounting." Wikipedia has a nice article on hyperbolic discounting. [50]
Before we can dive in, we need some terminology:
Suppose that you were to put some amount PV in a simple investment vehicle, like a bank account, that paid back your money plus a rate of return r every year. The future value of that amount in one year (t=1) would be:
If you kept your money in the account for another year, in that second year you would earn a rate of return r on all the money that was in your account the first year:
Note that you have effectively earned interest in the second year on the interest that you earned in the first year. This phenomenon is called "compounding." Since interest is calculated once per year in this model, we call this "annual compounding."
There is a detailed section in the "Have We Caught Your Interest" reading that discusses compounding at intervals more frequent than annually, all the way up to continuous compounding. Please read this section carefully, although in this class we will use annual compounding almost exclusively.
Going back to our little bank account, more generally, in year t the future value of the investment that you make today is:
This equation is an indifference condition - it says that you would be equally happy getting some amount of money PV right now, or if you had to wait t years to get your money. The factor just measures your opportunity cost for having to wait t years.
Now, we will flip the equation on its head. Suppose that you were promised some amount of money FV(t) in year t, sometime in the future. What is that promise of future wealth worth to you today? In other words, what would you need to be paid today in order to be equally happy between getting money today and getting the amount FV(t) in the future? We can find this by manipulating the previous equation to solve for PV. Dividing both sides by , we get:
Assuming that r is greater than zero, this means that the value in the present of some amount of money that you are going to get in the future is lower than the face value of that amount of money. In other words, you would be willing to accept less money right now in exchange for not needing to wait to get it. This is sensible, if you think about it. First, people are impatient. Second, there is an opportunity cost to waiting. If I got some amount PV right now (rather than some larger amount FV(t) in the future), then I could take that amount PV and do something with it that might increase its worth t years from now.
The term is known as the "discount factor" and it measures how quickly something has declined in value between now and t periods from now.
Here are two examples that illustrate the mechanics of discounting.
Example #1: Suppose that r = 0.04 (4%), t=20. The discount factor is equal to:
Thus, PV = 0.46×FV. What this says is that the present value is less than half of the future value after 20 years.
Example #2: Now suppose that t = 100. The discount factor is equal to:
Thus, PV = 0.02×FV. What this says is that the present value is only 2% of the future value.
These two examples, although simple and without much context, illustrate two important properties of exponential discounting. First, at any positive discount rate, if you go far enough into the future, then the future is worthless, and you would never consider it when making decisions in the present. Second, the higher the discount rate, the faster the future becomes worthless relative to the present. Remember that the discount rate does not determine whether one thing will be worth more or less in the future than another thing. It only measures the value of something in the future relative to that same thing in the present (like chicken wings six months from now versus the exact same chicken wings today).
There is almost nothing that puts students to sleep faster than the discount rate. Frankly, it's a dry topic. But the discount rate is actually front and center - in some ways, much more so than any science - in the debate over climate change and how societies should respond. The reason for the controversy is simple - if the worst impacts of climate change are going to be felt even two generations from now, then unless we use a discount rate of zero or near zero (meaning that we value the future exactly as much as the present) it is difficult to justify large and costly climate-related interventions in the present, in order to yield substantial benefits far in the future. But such a low discount rate flies in the face of what economists actually know about preferences that people seem to have for happiness now versus happiness (or wealth) in the future. If you are really interested in this topic from the perspective of climate change, Grist has a nice piece [51] (from a while ago). Since our focus in this course is on commodity markets and business decisions, we'll find (in the next couple of lessons) that there is a logical way to determine the "right" discount rate for those types of decisions.
Suppose that you were an electric utility considering two potential generation investment opportunities: A natural gas-fired generator with a capital cost of 600 dollars per kW and operating costs of 50 dollars per MWh, or a wind farm with a capital cost of 1,200 dollars per kW and operating costs of 5 dollars per MWh. For the purposes of this example, assume that either power plant could meet the same reliability goals that your utility had. But your regulator wants you to minimize economic costs to consumers. Which should you pick?
This is a difficult choice because it involves tradeoffs over time. The natural gas generator has a low up-front cost but a higher operating cost. The wind generator has a higher up-front cost but a very low operating cost, because fuel from the wind is free (at the margin). This tradeoff is shown visually in Figure 5.1, for a 1 MW natural gas and wind power plant — for the sake of comparison, we assume that each of the plants operates 30% of the time, so each produces 1 MW × 8,760 × 0.3 = 2,628 MWh per year.
This 30% figure is called the capacity factor, and it measures how much electricity a power plant produces in a year versus how much it could produce each year if it operated all the time at 100% capacity.
Capacity Factor = (Annual Production) ÷ (Capacity × 8,760 hours per year).
Figure 5.1 illustrates the tradeoff but doesn't say much about which decision you should make. Part of the problem is that by investing in the wind turbine, you are accepting a high cost right now in exchange for a stream of operating cost savings in the future. Is this stream of operating cost savings worth it?
One of the most frequently-used metrics to compare projects with different capital and operating cost streams is the net present value, which incorporates the present discounted value of all project costs and revenues.
The net present value (NPV) is defined by two terms: the present discounted value of costs and the present discounted value of revenues. If we let Bt be the (undiscounted) revenues (benefits) of some project during year t and we let Ct be the (undiscounted) costs of the same project during year t, then we can calculate the NPV as follows:
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In the equations, T is the time horizon for the project and r is the discount rate. If the net present value of a project is greater than zero, then the project is said to break even or be "feasible" in present discounted value terms. If you are in a position where you are evaluating multiple alternatives, you would normally want to choose the alternative with the highest net present value.
Sometimes, we will be interested in the net present value of a project as of some year prior to T. We will call this the "Cumulative NPV" as of year X. Mathematically, this is defined as:
Where X is some number smaller than T.
To illustrate, let's go back to one of our examples from earlier in this lesson. Our wind power plant had a capital cost of $1.2 million and annual operations costs of $13,140. Suppose that it had annual revenues of $100,000 and faced a discount rate of 10% per year. Each year, it thus has annual profits of $86,860.
Assuming that all of the capital costs were incurred in Year 0 and the plant began operating in Year 1, the present discounted value of each of the first three years of operation would be:
PV(0) = -$1.2 million (this is just the capital cost, note that this is a negative number)
PV(1) = $86,860÷(1.1)1 = $78,963.64
PV(2) = $86,860÷(1.1)2 = $71,785,12
PV(3) = $86,860÷(1.1)3 = $65,259.20
The Cumulative NPV for the first three years would be:
Cumulative NPV(0) = PV(0) -$1.2 million
Cumulative NPV(1) = PV(0) + PV(1) = -$1.12 million
Cumulative NPV(1) = PV(0) + PV(1) + PV(2) = -$1.05 million
Cumulative NPV(1) = PV(0) + PV(1) + PV(2) + PV(3) = -$983,992
Calculating NPV is reasonably straightforward in a spreadsheet program such as Excel. There are two functions in Excel, PV and NPV, that will calculate net present value for you. The difference between the PV and NPV functions is that the PV function assumes that you have the same profits in each period (as in our example above), while the NPV function does not make this assumption.
The video below shows an example of evaluating NPV using Excel. The example in the video is our natural gas plant from the previous lesson. Registered students can access the Excel file discussed in the video in Canvas.
There are two special cases where calculating the NPV is especially simple - the "annuity" and the "perpetuity." When a project's annual cash flow is the same in each year, not unlike a fixed-rate mortgage, we refer to this type of project as having an annuity structure. In this case, because the cash flow is the same each year, the summation term in the NPV can be greatly simplified. The formula for the present value of an annuity is shown below; if you are interested you can find the derivation in the reading accompanying this lesson.
where a is the annual cash flow. To illustrate this, let's calculate the present value of the wind power plant over a three year operating time horizon (T=3), using the annuity formula. One of the tricks here is that you need to take the Year 0 cash flow (the 1.2 million dollars capital cost) outside of the formula.
, which is just what we got using the NPV formula.
If an annuity involves identical payments over a long period of time (tens of decades, for example), we can approximate the NPV of the annuity using an extremely simple formula called a perpetuity. If we take the annuity formula and let T get really really large (tending towards infinity) then the "-1" term from the numerator becomes meaningless relative to the term, and the present value becomes:
.
As a rule of thumb, if the annuity lasts longer than 30 years, you can probably approximate the present value reasonably well using the perpetuity formula.
The internal rate of return (IRR) is one of the most frequently used metrics for assessing investment opportunities. The IRR is defined as the discount rate for which the NPV of a project is zero. The definition is simple, but the IRR is generally impossible to calculate without a computer.
If you use Excel, there is a built-in IRR function that will calculate the IRR for you, given a stream of costs and benefits over time.
Unlike the NPV, which takes units of dollars, the IRR is given in percentage terms (% discount rate per year such that the project NPV is zero). We call this a "yield" measure of return. This can be very convenient when comparing different types of projects. In many cases, the project with the largest yield (i.e., highest IRR) will be the most desirable. The IRR can also be compared to the investor's "hurdle rate," which is the lowest return that an investor is willing to accept before putting money into a project. Energy projects that will sell their output into competitive markets often need a yield higher than, say, a 15% hurdle rate over a five-year period. This means that the project's return on investment must be at least 15%, and that this yield must be realized within five years. If the IRR of a prospective project is higher than the hurdle rate, the project could be considered attractive to an investor.
While the IRR is often an appropriate measure for determining whether an individual project is worthwhile, there are three cases where IRR may not be very useful or may yield misleading information.
First, if a project has some years with positive cash flow and some years with negative cash flow (an example is on the power plant pro forma from the previous lesson), then it is possible that the IRR may have multiple values. Mathematically, this arises because a high-order polynomial equation may have multiple (non-unique) roots. As a simple example, if the cash flows for a project over three years are -$10, $21 and -$11, then there would be two IRRs for this potential project: 0% (because it just breaks even) and 10%.
Second, if a project has negative total undiscounted cash flows over its lifetime, then the IRR is mathematically undefined.
Third, it is possible that the project with the highest IRR may not be the project with the largest NPV. In other words, some types of projects can see their NPV increase when the discount rate goes up, which is the opposite of what we would expect to happen. This situation occurs when projects have large cash outlays at their end-of-life. Nuclear power plants are a good example — at the end of a nuclear power plant's life (at least in most developed countries), the owner must pay a large amount to have the plant decommissioned. If the discount rate used by the plant's owner is low, this increases the contribution of that end-of-life cash outlay to the NPV. If the discount rate used by the plant's owner is high, then that end-of-life cash outlay is not very important, in present value terms.
Here is a simple example. Suppose that a hypothetical investment requires a $5 cash outlay in Year 0, then earns $5 per year in Years 1 through 5. In Year 6, the project requires a cash outlay of $22.50. As an exercise, calculate the NPV assuming a discount rate of 10% per year and 5% per year. You should get NPVs of $1.25 and -$0.14.
Let's return to our wind power and natural gas power plant example from earlier in this lesson. Suppose that both power plants were selling electricity into the same deregulated generation market and both had the same expected operational life. Which plant would be more profitable? Since both plants would be facing the same market price for the electricity that they sell, the more profitable plant would be the one that had the lower average cost per Megawatt-hour of electricity over its entire lifetime.
The Levelized Cost of Energy (LCOE) can be used to help evaluate problems like this one, and is one of the most commonly used metrics for assessing the financial viability of energy projects. It is used particularly often in situations like the one we just discussed - comparing the lifetime costs of different technologies for electric power generation. The LCOE can, however, be applied to other energy projects as well (like oil and gas wells, or refineries).
The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero.
The LCOE is thus the average revenue per unit of energy output (so this would be $/MWh for a power plant, or $/barrel for an oil well, for example) over a project's lifetime such that the plant breaks even. The LCOE is sometimes called the Unit Technical Cost (UTC). It represents the lifetime average cost of energy for a specific project.
We will now get into the mathematics of calculating the LCOE. We will first present the most generic LCOE formula, and then we will discuss some simplifications of the formula.
LCOE is defined as the solution to the equation:
where Ct represents all capital costs incurred in year t (these may be zero except during the first few years of the project); Mt represents all operational costs incurred in year t, and Qt represents the total output of the project in year t. The term Ct + Mt represents the annual costs of the project (which may include payments on capital, fuel, labor, land leases and so forth). The term Qt represents the annual energy output of the plant.
Note that if all capital costs are incurred in year zero, then the term Ct factors out of the LCOE equation. In this case, you will sometimes see the capital cost term referred to as "Total Installed Cost" (TIC) or "Overnight Cost" (OC). In this case, we write the LCOE equation as:
In some other contexts (for those of you taking AE 878 through the RESS program, for example), you may see the discount rate r referred to as the "Weighted Average Cost of Capital" (WACC). We will devote an entire lesson later in the term to the relationship between the discount rate and the WACC (sneak preview: if the entity making the project investment is a for-profit entity, then the discount rate and WACC should be the same thing), and methods for calculating what the WACC should be.
We can thus solve for LCOE as:
There are a couple of ways to make this calculation easier. Often times when evaluating prospective energy projects we make two assumptions:
In this case, the Q and M terms from the LCOE equation are the same in each year, and we can write the LCOE as the sum of two terms:
If the variable cost of production (this would include fuel, labor and any variable operations/maintenance costs) don't change, then the LVC is just equal to this total variable cost per unit of output. Referencing the LCOE equations above, LVC would just be equal to M ÷ Q. (The LVC may also just be given in the problem statement, as in the examples below.)
Calculating LFC is a little bit more complicated. Assume that the project involves a discount rate r; the life of the project is T years; and the capital costs are paid in one lump sum TIC at the beginning of the project. LFC solves the equation:
which we can rewrite as:
Using some mathematics of finite sums (if you are really curious, Wikipedia has a detailed article on the "geometric series [52]," which describes the denominator of the LFC equation), since r is less than one, we can rewrite the denominator as
Thus,
and finally, we have our expression for LCOE:
Along these same lines, another (less messy) way to write the LCOE when output and variable costs are constant over time uses the "fixed charge rate" (FCR). The FCR is just the fraction of the Total Installed Cost (TIC) that must be set aside each year to retire capital costs (which includes interest on debt, return on equity and so forth - we'll discuss these in more detail in future lessons). Thus, TIC × FCR is the annuity payment (the sum of principal plus interest payments, like you would have with a home mortgage or a college loan) needed to pay off the investment's capital cost. The FCR is calculated as:
(You may see or have seen the FCR equation written with the WACC rather than the discount rate r. Remember that, for our purposes, there is really no difference between the two.)
Using the fixed charge rate, the LCOE can be written as:
In this simpler version of the LCOE equation, note that the first term is just the Levelized Fixed Cost (LFC) and the second term (M/Q) is just the Levelized Variable Cost (LVC).
Here is an example: Suppose that a power plant costs $10 billion to build and has an expected life of 30 years. The variable cost of producing one MWh of electricity is $20. It will operate 24 hours a day, 360 days a year at a capacity of 1000 MW. (Note: to get output, multiply capacity and hours of operation over the plant's life). What is the levelized cost of energy if the interest rate is 5%?
Here is the answer: First, we calculate the total amount of electricity produced annually:
(360 days per year) × (24 hours per day) × 1000 MW=8.64 million MWh per year.
This is Q in our LCOE formula. LVC is equal to $20 per MWh. So, we calculate LCOE as:
As an exercise for yourself, calculate the LCOE for the natural gas power plant and the wind power plant that we laid out earlier in this lesson. As a reminder, the wind plant has a capital cost of $1.2 million and a variable cost of $5/MWh. The natural gas plant has a capital cost of $600,000 and a variable cost of $50/MWh. Each plant produces 2,628 MWh per year. Assume a 10% annual discount rate and a 20-year life for each project. You should find that the wind plant has LCOE = $58.63/MWh and the gas plant has LCOE = $76.82 per MWh.
The LCOE can be used to compare energy projects to prevailing market prices. If the market price is higher than the LCOE, then the margin per unit of output is positive (Market price - LCOE is greater than zero) and the project should be profitable. If the market price is lower than the LCOE, then the project will have negative margins and will not be profitable. There are some pitfalls to using LCOE in this way to evaluate variable renewables like wind and solar, since the LCOE is often compared to the average electricity price. If you think about it, this comparison is biased against solar and biased towards wind because solar is more likely to be producing electricity during the daytime (when prices are high) and wind is more likely to be producing electricity during the nighttime (when prices are usually low). A more consistent approach, which is just as relevant for fossil-fired power plants as for renewables, would be to compare the LCOE to the average price when you would expect the power plant to be generating electricity.
Most energy projects involve large capital outlays at the beginning of project life, followed by a stream of costs and benefits during the project's years in operation. (Some types of energy projects would end their lives with large capital outlays as well, to handle decommissioning or other environmental issues.) Since waiting to enjoy future benefits from an energy project involves opportunity costs, a dollar of benefit in the future is worth less than a dollar of benefit now. These streams of future costs and benefits need to be expressed in terms of value at the time that the project decision is being considered or initiated. This process is called "discounting."
Project alternatives may have different capital and operating costs, even if they ultimately produce the same product. Electricity is probably the best example of this - power plants generally exhibit a tradeoff between capital and operating cost. We developed three different but related metrics to evaluate stand-alone projects and to compare the relative economic merits of project alternatives. The net present value will tell you which project will be the most profitable in absolute present-value dollar terms. The internal rate of return can be useful in comparing percentage returns or "yields" on different projects, or for checking whether a proposed investment exceeds the hurdle rate set by an individual investor or company. The internal rate of return cannot, however, always identify the most profitable project. The levelized cost of energy will tell you the average revenue per unit of output required for a proposed investment to break even, in present value terms. Levelized costs are often compared to prevailing market prices to estimate margins, but this comparison needs to be done with care if energy projects are not expected to run around the clock.
You have reached the end of Lesson 5! Double-check the What is Due for Lesson 5? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 6.
In one sense, energy projects are no different than playing the roulette wheel in Las Vegas. Both are inherently risky, and you may well not have the shirt left on your back at the end. Actually, in one important sense, you should feel more confident about the roulette wheel than with energy projects – with roulette at least you know how terrible your odds are, and nothing is going to change those terrible odds. Because the financial fate of energy projects winds up so often in the hands of market and political factors, both of which can turn on a dime (just ask the coal industry following the publication of the Clean Power Plan [53], or electricity demand response companies like EnerNOC when a major aspect of its business model was overturned by the DC Circuit Court [54] in 2014 - even though that ruling was eventually overturned by the Supreme Court), or even the renewable energy industry during 2022 when Build Back Better was defeated, but the inflation Reduction Act passed "Building A Clean Energy Economy," it can be difficult to know whether your project has a good or bad chance of succeeding. Given the nature of energy project investing – large up-front capital costs that must be recovered over a long duration – it’s sometimes a wonder that anything gets built at all!
The basic problem here is that the future is uncertain. In this lesson, we will look at a few different ways of considering uncertainty in project evaluation. Uncertainty has lots of different sources – markets may shift during a project cycle, making commodities more or less valuable; regulations may change; or public opposition to a project may be stronger than initially anticipated, making project permitting and siting all but impossible. Our focus here primarily will be on quantitative methods for incorporating uncertainty into project analyses, rather than on the sources of uncertainty themselves.
By the end of this lesson, you should be able to:
We will draw on sections from the following readings. The readings on coal-fired power plants and wind plants are a bit out of date but are useful illustrations of the types of uncertainty that energy projects face in the real world.
Registered students can access copies of the readings in the Lesson 6 module.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 6 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Before we get into quantitative methods for considering uncertainty in energy project evaluation, have a look at the piece from the EIA on the wind production tax credit [56], as well as the following story from CNN on nuclear energy [58]. (Note that the EIA story is still relevant, even though the debate on the wind production tax credit has now come and gone.)
There are certainly enough differences between the situations being faced by the wind and nuclear industries. But the commonality among them here is that both of these low-carbon power generation technologies face substantial uncertainty that will affect project investment decisions going forward. Wind energy has enjoyed a number of years of rapid growth in the U.S. and in Europe, but it is unclear how long that level of growth can be sustained. Nuclear energy appears to be in a period of decline. Whatever happened to the “energy transition” towards low-carbon electricity generation?
Figure 6.1 shows a screenshot from the PJM Renewable Dashboard [59], which illustrates all renewable energy projects within the PJM footprint that are currently in the queue for permission to interconnect to the PJM transmission grid. (There isn’t anything particularly special about PJM here, except that they have this nice graphical display of proposed and pending renewable energy projects.) The figure, which changes frequently as new projects are added and withdrawn from the queue, suggests a lot of enthusiasm for renewable electricity generation in PJM, particularly solar and wind. In fact, until coal-fired power plants started announcing their intention to retire a couple of years back, wind energy represented the largest source of planned new generation capacity of any type in the PJM system (yes, even more than natural gas, though that is no longer true as gas power plant projects position themselves to replace retiring coal plants). While there is plenty of interest, the reality is that few of these projects will actually get built, because their revenue streams depend on a number of factors that are highly uncertain.
Virtually all investments are always made under the shroud of uncertainty, but the problem is particularly acute for energy investments. The development of energy resources typically requires large up-front capital outlays, so investors need some assurance that revenue streams over time will be sufficient for cost recovery. You can get something of a sneak peek of how changes in market institutions in the electricity industry have increased both the potential financial rewards from low-cost electricity generation but also uncertainty in power generation investment by reading the selection from Morgan, et al., “The U.S. Electric Power Sector and Climate Change Mitigation.” [40]
Returning to the nuclear and wind energy examples for a moment, these two technologies nicely represent two distinct but equally important sources of uncertainty facing energy projects.
The example with nuclear energy is primarily an illustration of market uncertainty. Just a few years ago, before the natural gas supply boom, electricity prices were high and nuclear power (having very high reliability and low production costs) was highly profitable. Companies that ran large fleets of nuclear power plants well – the biggest in the United States was Exelon – earned large returns for shareholders. Many existing nuclear power plants continue to make money, though the number of nuclear power plant retirements has accelerated in recent years. The biggest problem facing new nuclear plants, as well as some existing plants, is uncertainty in the market price of electricity. In deregulated electricity markets, the price of electricity is most often driven by the price of natural gas. As gas prices have plummeted with the unconventional natural gas boom, so have electricity prices. This squeezes profit margins for existing plants and makes the financial prospects for new plants look bleak. Will natural gas prices rise again in five to ten years (roughly the timeline for a new nuclear build in the United States)? Who knows – current markets are not betting on it, as you could observe by looking at the futures curve for natural gas.
The example with wind energy is primarily an illustration of regulatory uncertainty. As we have discovered, wind energy is not always competitive with other forms of power generation without some form of subsidy or incentive (the same could actually be said for most low-carbon power generation technologies, including nuclear). The production tax credit (PTC) was implemented in order to provide a boost to the wind industry. The PTC basically amounts to a direct payment of a bit more than $20 per MWh for all qualifying wind energy plants. Other countries have implemented subsidy programs similar to the PTC, such as feed-in tariffs. But as the EIA article points out, since the PTC does not represent a long-term commitment by the U.S. government (it needs to be renewed periodically), when the PTC is not renewed, wind energy investments shrivel because the future availability of subsidies is uncertain. The piece by Morgan, et al., points out that the lack of a commitment to a carbon policy by the United States has been a major barrier to the development of a variety of low-carbon energy resources – not just wind and solar but also low-carbon bio-energy and technologies that can capture and store greenhouse gas emissions.
Other major sources of uncertainty for the energy project include:
Uncertainty cannot be eliminated, but we can incorporate it into our project analyses in a few different ways, as we’ll discuss in the next several components of the lesson. Our use of the word “uncertainty” here rather than “risk” is purposeful. Use of the term “risk” implies some knowledge about the probabilities of uncertain events, like a coin flip or the roll of some dice. But placing probabilities on specific events isn’t always straightforward or even possible. So, we use the broader term “uncertainty” to describe situations where some set of future outcomes is unknown. In particular (for our context), these outcomes are unknown at the time that some decision is to be made. Broadly, we can identify a few different types of such decision situations:
We will first discuss decision problems of the second type, then move on to the third type.
Before we dive into probability and expected monetary value (EMV), we will introduce a motivational problem from the petroleum industry. You are exploring the possibility of drilling a potential new oil field. You can either do the drilling yourself, or you can “farm out” the drilling operation to a partner. The field in which you are proposing to drill may or may not have oil – you don’t know until you drill and find out. If you drill yourself, you take on the risk if the field is not a producer, but if the field is a producer, you don’t need to share your profits with anyone. If you farm out the drilling operation, you are not exposed to any losses if the field is not a producer, but if the field is a producer, the drilling company will take the lion’s share of the profits. Table 6.1 shows the net present value of the prospective oil field.
Activity | Field is Dry | Field is a Producer |
---|---|---|
Drill Yourself | -$250,000 | $500,000 |
Farm Out | $0 | $50,000 |
Clearly, if you knew the field was a producer you would want to drill yourself (and if you knew it wasn’t, then you would not want to drill at all). But you don’t know this before you make your drilling decision. What should you do?
Suppose that you had enough information on the productivity of wells drilled in a similar geology to estimate that the probability of a dry hole was 65% and the probability of a producing well was 35%. Do these probabilities make your life any easier?
These decision problems can be solved by calculating a quantity known as the “expected monetary value” (EMV) – basically a probability-weighted average of net present values of different outcomes. Formally, the EMV is defined by determining probabilities of each distinct or “mutually exclusive” outcome, determining the NPV under each of the possible outcomes, and then weighting each possible value of the NPV by its probability. In mathematical terms, if Z is some alternative; Y1, Y2, …, Yn represent a set of possible outcomes of some uncertain variable; X1, X2, …, Xn represent the NPVs associated with each of the possible outcomes; and P(Y1), P(Y2), …, P(Yn) represent the probabilities of each of the outcomes, then the EMV is defined by:
The alternative with the highest EMV would be the option chosen. A decision-maker who chooses among alternatives in this way would be called an “expected-value decision-maker.”
Some things to remember about probabilities:
Now, back to our oil field problem. We’ll describe the problem again, using the language of expected monetary value. There are two alternatives – to drill yourself or to farm-out. The uncertainty is in the outcome of the drilling process. The set of mutually exclusive and exhaustive outcomes is {dry hole, producer}. These are the only two possible outcomes, regardless of whether you choose to drill yourself or farm-out. The NPVs of each alternative, under each possible outcome, are shown in Table 10.1. To decide whether to drill or farm out, you would calculate the EMV of each option as follows:
In this case, you should choose to farm out the drilling operation.
The basic idea behind EMV is fairly straightforward, assuming that you can actually determine the relevant probabilities with some precision. But the meaning of the EMV is a little bit subtle and requires some degree of care in interpretation. Let’s take a very basic situation – a coin flip. Suppose that we were to flip a coin. If it shows heads, you must pay me $1. If it shows tails, then I must pay you $1. The EMV of this game, assuming that heads and tails have equal probabilities, is $0. (See if you can figure out why, based on the EMV equation and the fact that P(heads) = 0.5 and P(tails) = 0.5.) But if you think about this for a minute, how useful is the EMV? If you play the coin-flipping game once, you will never ever have an outcome where the payoff to you is $0. The payoff will either be that you gain or lose one dollar.
If you look at the EMVs from the oil-field problem, you will see the same thing. The EMV of drilling is $12,500 but there is no turn of events under which you would wind up earning $12,500 – if you drill, it would either be that you lose $250,000 or gain $500,000. Similarly, if you farm out you will never earn exactly $17,500. You will either lose nothing (payoff of $0) or you will gain $50,000. So what does the EMV mean when it tells you that farming-out is the better option?
It’s important to remember that the EMV is a type of average. If you were to play the coin-flip game or the oil-field game a large number of times under identical circumstances, and make the same decision each time (i.e., to drill or to farm out), then over the long run you would expect to wind up with $12,500 if you choose to drill and $17,500 if you choose to farm out. While the EMV may be useful for gamblers or serial investors, using EMV needs to be done with some care for stand-alone projects in the face of uncertainty.
One potential alternative to calculating EMV when probabilities are known (or can be estimated) is to use those probabilities to describe the likelihoods of gaining or losing certain amounts of money. This is the idea behind the “value at risk” for an investment project or a portfolio of projects. The value at risk (VaR) describes the amount of money that will be gained or lost with some probability, typically worst-case situations (like describing the amount of money that would be gained or lost with a 5% probability). From a decision perspective, you might want to avoid investment opportunities with a large VaR (given some probability). Many times, VaR will have a duration associated with it, most often when calculated in reference to portfolios of financial assets (like stocks or bonds).
Wikipedia has a very descriptive entry for VaR [60]. The introductory section of the Damadoran VaR paper [57] is written in clearer English, but gets into the details quickly after the first few pages.
To force the example a little bit, here is how VaR might be applied in our simple oil field problem. The field will either yield a producer (probability 35%) or a dry hole (probability 65%). Those are the only two outcomes and probabilities for this problem. If we think about the negative outcome – the dry hole – and ask how much of the value of the project might be at risk if the hole turns out to be dry, then we can calculate VaR just by using the NPVs from Table 10.1. (In this example, there is no time dimension to VaR since the project is a one-shot deal.)
If we choose the option to drill ourselves, the 65% VaR would be -$250,000. If we choose to farm-out the drilling operation, the 65% VaR would be $0. We might choose to farm out simply because the VaR technique tells us that our extreme losses would be lower if we farm-out than if we drill the well ourselves.
In the oil-field example from the previous section, the probabilities of a dry hole and a producer were known with confidence. This isn’t always true in the real world. Moreover, sometimes in real-world situations, there are so many possible outcomes that is impossible to assign a specific probability to each outcome. Suppose, just hypothetically, that oil prices could vary between $50 and $100 per barrel over the next five years. What is the probability that oil prices will average $50.03 per barrel? $50.04 per barrel? $99.30 per barrel?
In cases where determining probabilities explicitly is not possible or practical, threshold analysis and sensitivity analysis can be useful in understanding how the net present value of different alternatives may vary with some variation in key variables. Both of these techniques are useful in identifying situations under which one alternative is better than another. This can make even complex decision problems much more tractable for the decision-maker, since it reduces the problem from needing to calculate net present values for a large number of alternative outcomes to a judgment of whether one or another set of outcomes is more likely.
As we go through these techniques, we will often refer to something called a “parameter” in the decision problem. In this case, a parameter refers to a variable whose value affects the outcome of one or more alternatives – so a parameter is different from an alternative. In the oil field problem, the major parameter would be whether the field is a dry hole or whether it is a producer (so the parameter itself would be the probability of a producer versus a dry hole). The price of oil or the quantity of oil (if any) might be other important parameters for a problem such as this one.
Sensitivity analysis proceeds by selecting one parameter, changing its values, and observing how these new values change the net present value (or EMV) of some alternative. If sensitivity analysis is conducted using a small range of alternative values, or if the alternative values represent different scenarios, then it is sometimes (aptly) called “scenario analysis.” There's more material on scenario analysis further down this page.
We’ll illustrate this using the oil-field problem, performing a sensitivity analysis on the EMV of the drilling option as we vary the dry-hole probability. In this case the outcome is the EMV and the parameter that we are varying is the probability of a dry hole. This analysis is pretty straightforward since we can write an expression for the EMV of drilling. We will need to use the fact that P(producer) = 1 – P(dry hole). (What rules of probability tell us that this is true?)
We can rearrange terms in the equation to get:
This equation tells us a couple of things. First, the EMV of drilling is going to decline as the probability of a dry hole increases. This makes sense, since we lose money if we drill ourselves and the field is not a producer. Second, the relationship between EMV and the dry hole probability is linear, so the EMV falls at a constant rate as the dry hole probability increases. We can also use the equation to find the dry hole probability where the EMV is equal to zero. We do this by setting EMV(drill) equal to zero and manipulating the equation as follows:
Normally, sensitivity analysis is utilized to visualize the change in net present value or EMV with the change in some parameter of interest. For the drilling option, this is shown in Figure 10.2, which plots the EMV versus the dry hole probability. Also shown in Figure 6.2 is the dry-hole probability where the EMV is equal to zero.
As an exercise for yourself, perform the same sensitivity analysis on the option to farm-out. The parameter is still the same (the probability of a dry hole) but the outcome is different (the option to farm-out versus the option to drill). Your graph should look like the one in Figure 6.3.
Threshold analysis (also called break-point analysis) seeks to identify the value of a parameter where the best decision changes. Instead of asking what the probability of a producer versus a dry hole might be (and what are the associated EMVs of the option to drill or farm-out), a threshold analysis would ask how likely would it need to be for the field to be a producer for the expected-value decision-maker to choose the option to drill.
Threshold analyses can proceed graphically or algebraically. We will use the oil field example to illustrate both. Remember that the EMV of both the drilling and the farm-out options are functions of the dry-hole probability and of the NPVs for drilling and farming-out. Holding the NPVs constant as in Table 10.1, we can write a mathematical expression for the EMV of each option as a function of the dry-hole probability P(dry hole). We will need to use the fact that P(producer) = 1 – P(dry hole). (What rules of probability tell us that this is true?)
If we graph the EMVs together on the same set of axes (as in Figures 6.2 and 6.3), the point at which the two lines cross would be the threshold. Figure 6.4 illustrates this crossing point. Note that the scale of the axes, especially the horizontal axis, is different than in Figures 6.2 or 6.3.
Looking carefully at Figure 6.4, we can see that at a dry hole probability of around 65% or lower, the EMV of drilling is higher than the EMV of the farm-out option (this is why the drilling curve is above the farm-out curve). If the dry hole probability is above 65%, then the farm-out option has a higher EMV.
Algebraically, we can solve explicitly for the threshold value of the dry hole probability, by setting EMV(drill) equal to EMV(farm-out) and solving for the dry hole probability that makes these EMVs identical. Here we go!
Threshold analysis in particular can be a very powerful way of making difficult decisions seem more tractable. Even in the simple oil-field problem, the relevant question for the investor evaluating the oil-field decision is not to determine the exact probability of the field being a producer. The threshold analysis approach asks the potential investor whether they believe that there is more than a 65% chance of the field being dry. If so, then they should farm out drilling or not drill at all. If not, then the investor should choose to drill themselves.
Scenarios about future outcomes or states of the world can be powerful tools for getting decision-makers to think about uncertainty. While scenario analysis is simpler than a sensitivity or a threshold analysis, scenarios can often be made up of specific values of multiple parameters. Scenario analysis is not about predicting the future or making projections. Like sensitivity and threshold analysis, it is a way to get decision-makers to consider different possible future states of the world, to identify the drivers that might lead to those states of the world, and to make plans for those possible states of the world.
In the energy world, one of the pioneers of scenario-based planning has been Shell [62], which started using scenario-based planning in the 1960s, as computer-aided decision-making was emerging among large businesses. Shell adopted scenario planning when it (and other large energy companies) were surprised by the emergence of environmentalism and the OPEC cartel as unforeseen but potentially disruptive forces to their business. The Harvard Business Review [63] has a nice article about Shell's development and use of scenario planning. (The article is also available through Canvas.)
The process of scenario planning has a number of steps:
Scenario planning has its critics - one thing about scenarios, for example, is that good scenarios are based around plausibility and not probability. This can lead to different decision-makers having different views of likely versus unlikely states of the world. Regardless, scenario planning is still a widely-used tool for decision-making.
Uncertainty is a critical problem in the evaluation of energy projects, owing to the capital-intensive nature of most projects and the need for cost recovery over long time frames. Projecting the market and regulatory conditions over such long time frames is often fraught with error and, in hindsight, bad decisions. Two of the major sources of uncertainty facing energy projects are market uncertainty (market prices or price volatility are not known with certainty into the future) and regulatory uncertainty (relevant policy measures cannot be anticipated with perfect foresight). Since low-carbon energy projects in particular need policy support to achieve widespread market deployment, regulatory uncertainty in the form of a lack of climate-change policy or inconsistent application of subsidies and incentives can be especially difficult for investors in low-carbon energy technologies. Even if the future cannot be predicted perfectly, there are ways to incorporate uncertainty into project evaluation. Calculation of expected monetary values (EMV) is possible when all possible outcomes can be identified and probabilities assigned, but since the EMV represents the average payoff over a large number of trials, it may not be appropriate for single energy project evaluation. Sensitivity analysis and threshold analysis are ways of visualizing how the NPV or EMV of a project may change under different values of key parameters.
You have reached the end of Lesson 6! Double check the What is Due for Lesson 6? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 7.
As Energy Market Professionals, we run across many different types of risk that we will need to manage going forward. Certain of these risks are very obvious - like the cost of the natural gas commodity increasing as the weather gets colder leading into October. Other risks may be less obvious - like the long term cost of the natural gas commodity remaining at long term historically low levels and thereby placing the value proposition of your utility scale solar power plant at risk. Other risks are seemingly less direct - such as the potential for the US Legislature to reduce or eliminate the Investment Tax Credit for a solar project or the imposition of tariffs on certain components. In this lesson, we will talk about identifying risks and ways to mitigate (or exacerbate) them through market-based and non-market-based instruments. We will further our understanding of risk management and how certain instruments can be used to de-risk our projects.
By the end of this lesson, you should be able to:
Video interview with Jayson Kaminsky, CEO of kWh Analytics. If this video is slow to load here on this page, you can always access it and all course videos in the Media Gallery in Canvas.
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Registered students can also access videos in the Media Gallery in Canvas.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 7 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
I typed "financial risk" into the search engine (Google.com search for "types of finanical risk") as I was putting this module together, and this is the list that confronted me:
Woah! Well yes, the management of each of these risks is important in your energy project. But don’t fret, many of these can be mitigated with simple measures and others might be ignored. First, let's get a definition (or two):
Financial Risk: "The possibility of losing money on an investment or business venture" (Investovepia.com [66]).
Risk can also be defined as: "The probability that actual results will differ from expected results" (Corporate Finance Institute.com [67]).
We'll take a look first at market risk.
Market Risk: "The possibility that an individual or other entity will experience losses due to facturs that affect the overall performance of investments" (Investopedia.com [68]).
The factors that affect the overall market are things like demographics, the overall inflation and interest rates, and a very recent and obvious one: the COVID 19 pandemic. All investments are subject to these kinds of factors and our energy projects will certainly have their returns affected by these macroeconomic parameters. Some of these parameters can be mitigated or “hedged” while others may not be able to be covered. For instance, if a developer were worried about rising inflation causing his returns to erode in the upcoming years, they might decide to include an escalator in the price of the output of the project. A more subtle form of risk mitigation for long term projects is to borrow money (take on debt) at a fixed interest rate for the term of the loan. This will shield the project developer against the potentiality of an increase in interest rates that might be catastrophic for returns in the future. Of course, when the developer decided to do a deal for debt at a fixed interest rate, they also removed the potential upside of a decrease in interest rates in the future, which could cause returns to be significantly higher than expected.
Doing a deal that removes pricing volatility for a period of time and for a known quantity is what is known in the financial community as a fixed for float swap. We will see this again as we move through the commodity markets as well as markets for other inputs into our project.
What is Commodity Price Risk?
Commodity Price Risk: "The possibility that commodity price changes will cause financial losses for either commodity buyers or producers" (Investopedia.com [69]).
Let us think about some examples of commodity price risk. Think, for example, about a natural gas consumer in the month of October. This company is thinking about how there is a heating season coming up and how natural gas might be trading at $3.00 per MMBtu and how he knows that natural gas prices can go up significantly during the winter months because demand for natural gas increases significantly in the heating season. Now the natural gas consumer in this situation is what is called a “natural short” - meaning that the consumer has a requirement that has not been contracted for or fulfilled. This natural short position places them at significant budgetary risk should prices increase, as they can, in the winter period. Should prices rise from $3.00 per MMBtu to $4.50 per MMBtu, the commodity portion of their natural gas exposure would rise 50%.
As you can see from the graph above, the volatility of the natural gas market could definitely produce this kind of risk for the consumer.
Another example of commodity price risk is that which might be experienced by a producer of a commodity. Imagine, for instance, if one were a solar developer. The solar developer wants to sell his electricity into the market to be able to pay his creditors and earn a return. If the revenue (which is equal to the quantity of electricity times the price of the electricity for the period in question) is subject to large swings in the price, the entire value proposition of the project could be at risk. This is one of the most important risks to any energy project and one that the savvy project developer (you, in this course) will want to mitigate.
Above is some significant electric price volatility over a 20 year period (remember - your project is likely 25-30 years). Mitigating this risk would be very important for investors in your project. Mitigating Commodity price risk can take on many forms (and we will go into more detail at the end of this lesson), but probably the simplest form would be to engage in a transaction to fix the price. The natural gas consumer above would likely estimate their usage (or burn) for the volatile period and fix the price for that volume in $/MMBtu. This consumer would then have much less heartburn over the winter period, since the indigestion of worrying about gas prices would not arise. The consumer simply buys a contract for delivery of natural gas at the current price. Similarly, the power developer would want to fix the price for its output for the term of the contract in $/MWh. Both of these simple strategies allow for more stable cash flows over a season or a project life.
Basis Risk: "The financial risk that offsetting investments in a hedging strategy will not experience price changes in entirely opposite directions from each other." (Investopedia.com [71]).
This type of risk takes place when the risk manager is not able to buy or sell a financial product that is an exact match for the product he needs to buy or sell. For instance, the gas purchaser in the example on the last page might not have enough load to buy a contract for each month in the winter. So he might buy just a January contract. If the other contracts were to settle differently than the January contract, this purchaser would have the basis risk associated with the mismatched purchases. In the energy market, there is a very specific basis risk that we call “locational bass risk.” This risk comes from the fact that a commodity may be produced or consumed at a point on the energy transportation network that is different from where the commodity is typically traded. In the second example on the previous page, where the developer is producing electrical energy, the price he would receive at his delivery point into the grid might be less (or more) than is paid at a liquid trading point. This potential differential is called “location” or "transportation” basis risk. (insert diagram here)
Counterparty Risk: "The probability that the other party in an investment, credit, or trading transaction may not fulfill its part of the deal and may default on the contractual obligations." (Investopedia.com [72]).
In our example from the previous page, the seller of the natural gas or the buyer of the developer’s solar output could default on the obligation it has taken on to sell or buy the product in each transaction. This means that when looking at mitigating these risks, it is very important to ensure that the party on the other side of the transaction is credit worthy and will perform their obligations. This phenomenon was of particular concern in late 2001 when Enron started to default on its many obligations to the energy market as it could no longer maintain liquidity. If you’ve never read or seen “The Smartest Guys in the Room” - It is highly recommended. Here's a trailer:
Operational Risk: Operational Risk "summarizes the uncertainties and hazards a company faces when it attempts to do its day-to-day business activities within a given field or industry." A type of business risk [73], it can result from breakdowns in internal procedures, people, and systems. (Investopedia.com [74]).
There are two specific types of operational risk that should accounted for in your project or in an energy enterprise: Production Risk and Expense Risk.
Production risk is the uncertainty associated with the energy production of your project. In renewable energy projects like wind and solar projects, the variability and intermittency of wind and sunlight can significantly affect the amount of energy produced. In oil and gas production, hydrocarbon reservoirs can produce more or less petroleum than expected. These uncertainties create uncertainty in the revenue profile for the project. If revenue is uncertain, then returns will be as well.
The operational risk faced by projects due to the uncertainty of the intermittent resource is significant, and without understanding and accounting for this risk, project stakeholders will not invest in the project. To account for the variation in the resource, the industry utilizes a probabilistic approach. The analyst cannot predict with 100% certainty what the output will be. The analyst can, however, use past data to predict with a level of certainty that an array will produce at least a certain amount of power. The analyst does this by developing P-values. For instance, a P50 value is that level of production that would expect to be exceeded annually at least 50% of the time. For a P90 value, we have a level of production that we expect to exceed 90% of the time or 9 out of 10 years.
Typically, lenders are interested in P90 or P95 values because they want to make sure that the debt payment will be made without interruption every year that the loan is outstanding. Developer and equity investors tend to look at the rosier P50 values, as these values are what should give the best estimates for equity returns.
For a really great exposition of how to account for variation in the solar resource, please read the blog by Solar GIS entitled How to Calculate P90 (or Other Pxx) PV Energy Yield Estimates [75], where there are a number of techniques to develop Pxx values discussed.
Expense risk is the risk that input values like fuel or maintenance costs will be different than projected. Obviously, unexpected increases in expenses will put pressure on the returns of a project.
One can mitigate risks through a number of different measures:
It is always important to understand that as the developer or operator moves to reduce risk in certain areas, they may be taking on unidentified risk in another area. Please keep this in mind as you work to de-risk your projects in this class and in your career. As Stephen Covey has said, “When you pick up one end of the stick, you pick up the other.”
One of the simplest ways to remove price and performance risk is to enter into a contract for services or a commodity. These types of arrangements take on many forms and can be very simple and also very complex. Natural gas and power purchase arrangements can be 50-100 pages in length, as many different terms and conditions need to be agreed upon. Typical terms that are covered in energy contracts include:
Contracts for services can include many other provisions as well, such as:
The general idea of a commodity or performance contract is to remove certain risks that one counterparty has to another counterparty who is better able to mitigate that risk. For instance, I would probably hire a plumber if my garbage disposal were clogged. This would allow me to remove the risk of flooding my kitchen and/or injuring my hand. The plumber gets paid to take on that risk, which he or she is much better at mitigating because he or she is a trained professional. These types of arrangements occur all the time even though we may not be aware that they are risk management measures. It is important, though, to make sure that when one mitigates risk with a contract that one is confident in the performance of the counterparty. The best contract doesn’t mitigate any risk if the party on the other side can’t or won’t perform.
A futures contract [76] is an agreement traded on an organized exchange to buy or sell assets, especially commodities or shares, at a fixed price but to be delivered and paid for later. The most relevant futures contract that the North American energy market deals with is the NYMEX Natural Gas Futures Contract. One can trade natural gas on the New York Mercantile Exchange in contracts of 10,000 MMBtu per month on a fairly liquid basis out about 3 years and in less liquid manner about 10 years beyond that. Buying a futures contract obligates one to take delivery of that gas in the month specified for the price agreed to. Selling a futures contract obligates one to deliver gas in the month specified for the price agreed to.
A forward contract is an informal agreement traded through a broker-dealer [77] network to buy and sell specified assets or commodities.
There are two basic options (and many other options) in the energy markets. The put and the call. The put allows but does not obligate the owner to deliver energy in a certain quantity at a certain price for a certain period. This type of contract is very helpful to a generator or producer because they can lay off the risk of prices falling below a certain level and not having enough revenue for the project.
The call allows but does not obligate the owner to receive energy in a certain quantity at a certain price for a certain period. This type of contract is very helpful to a consumer because they can lay off the risk of prices rising above a certain level and not having enough revenue for the project.
When one sells a put and buys a call, they are then a buyer in a forward contract. When one sells a call and buys a put, they are then a seller in a forward contract.
I hope you enjoyed the exploration of risk in the energy markets. Please remember to:
You have reached the end of Lesson 7! Double check the What is Due for Lesson 7? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 8.
Low-carbon energy in many regions has started to become competitive with fossil-fuel generation on an LCOE basis when including certain incentives we will discuss in this section. The social costs of some harmful pollutants emitted when fossil fuels are burned for useful energy are not reflected in the price of the fuel itself or the price of the final energy output. It is also important to consider that while subsidies are given to renewable sources of energy, certain in-kind subsidies to fossil fuels such as low cost mineral rights and the ability to socialize environmental clean-up do not make it into the LCOEs of these sources.
One solution to this problem is to place a price on pollution, which would make non-polluting alternatives look more economically attractive. The United States and many other countries actually do this with some types of pollutants - sulfur dioxide and oxides of nitrogen, for example. The European Union (and some areas of the U.S.) impose a price on carbon dioxide emissions through a system of tradeable emissions permits. Whether those prices are sufficiently high as to reflect the true social cost of pollution is subject to a lot of debate, some of it quite heated. But in concept, it is possible to level the playing field by taxing or pricing pollution.
We'll talk a bit about taxes in this lesson, but will spend more time on the other alternative, which is to subsidize or incentivize energy sources that don't pollute. Many jurisdictions actually do a bit of both. The motivation for these subsidies can be as much about politics as environmental quality - in many cases renewable energy subsidies are a form of industrial policy, meant to promote growth in a specific sector (like wind or solar), rather than about reducing pollution per se. These subsidies and incentives can take several different forms, from providing payments for each unit of energy generated to lowering capital or financing costs for new investments.
Our focus here is really on how these subsidies and incentives affect the financial analysis of power plants. We won't go too deeply into the mechanisms behind each specific type of incentive program, nor will we talk too much about how effective the incentive programs might be. (Another course offered through the RESS program, EME 803, touches on these issues.) We'll also limit ourselves to those types of incentives that directly affect project financing or financial analysis. There are a very wide variety of policy options, apart from direct taxes, subsidies or incentives, that can have the effect of shifting investment decisions towards renewable energy.
By the end of this lesson, you should be able to:
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. See the specific directions for the assignments below.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Before you get too deeply into this lesson, have a look at the Glossary page from the DSIRE website [83]. Focus on the first section of the page, which defines a number of types of financial incentives. The definitions here aren't too in-depth, but reviewing them will get you familiar with the language of different types of incentives. If you are interested, the section of the page on "Rules, Regulations and Policies" is also worth a look to give you some appreciation for the truly dizzying array of ways that governments at various levels (state, federal, and local) are trying to encourage the use of renewable energy resources, particularly for the production of electricity.
In general, DSIRE is a very good resource for learning about renewable energy and energy-efficiency incentives in the United States. The following video provides a quick tour of some of the most useful information on the DSIRE website. (Note: as of this writing the DSIRE website was undergoing some major revisions, but the structure of the website itself does not appear to have changed that much. It is worth some time wandering around that website to see what information is available.)
We'll categorize subsidies and incentives into a few broad categories:
Because the credit is applied either to the tax rate itself or post-tax, tax credits are often more generous than tax deductions. In some circumstances, a company or project may even claim a tax credit if the income for the project during the relevant year was negative (so the project owner would get paid in the form of the tax credit, even if her project did not make any money and would not have paid any taxes in the first place).
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The Renewable Portfolio Standard (RPS, sometimes called Clean Energy Standards) also deserves some mention, since it has been a popular mechanism to support renewable energy development in the United States in particular. Please have a look at Section III of "The Cost of the Alternative Energy Portfolio Standard in Pennsylvania" (reading on Canvas), which describes the RPS in one particular U.S. state, Pennsylvania. Pennsylvania's RPS is typical of how many such portfolio standards work. The reading also provides an overview of RPS policies in some other states. (You might notice that Pennsylvania qualifies some fossil fuels as "alternative energy sources" under its RPS, which demonstrates that RPS policies are not always equivalent to low-carbon policies.)
The RPS is basically a quota system for renewable energy and has been applied mostly to electricity. More than half of U.S. states have adopted some form of RPS, as shown in Figure 8.1, taken from the DSIRE website (note: look on the DSIRE web site for the most up to date version of this picture, as state programs change structure rapidly). Some countries have adopted policies for blending petroleum-based transportation fuels with biomass-based fuels (such as the Renewable Fuels Standard [85] in the U.S., which you can read about through the U.S. Environmental Protection Agency, but our discussion here will focus on portfolio standards for alternative electricity generation technologies. Rather than subsidizing renewables through financial mechanisms as we have already discussed, RPS sets quantity targets for market penetration by designated alternative electricity generation resources within some geographic territory, like a state or sometimes an entire country. Typical technologies targeted under RPS include wind, solar, biomass, and so forth. The way that RPS systems typically work in practice is that electric utilities can either build the required amount of alternative power generation technologies themselves, or they can contract with a separate company to make those investments. Companies that build renewable energy projects can register those projects in a jurisdiction with the RPS to generate tradeable renewable energy credits (RECs). The RECs can then be sold to electric utilities who can use those credits to meet their renewable energy targets.
You can find more information on REC prices through the NREL REC report assigned as part of this week's reading [79]. The data in the report makes a distinction between "voluntary" REC prices and "compliance" prices. Some states or areas have RPS targets that are not mandated by law, while in other states utilities face penalties if they do not comply with meeting their RPS targets.
Incentives and subsidies, if structured correctly, should act to make the technologies that qualify for those subsidies more competitive (compared to technologies that do not qualify). One mechanism that we can use to measure how this works, and whether the subsidy or incentive is likely to be effective, is by looking at how the incentive measure affects the levelized cost of energy (LCOE) for different projects.
Incentives and subsidies can affect the LCOE in one of two basic ways: they can reduce the LCOE directly through tax credits or feed-in tariff type structures, or they can reduce the WACC faced by the project developer (i.e., through loan guarantees or low/zero-interest loans).
The production tax credit (PTC) is one of the most well-known incentive programs for renewable energy in the United States. The structure of the PTC has changed over time, but it currently provides a tax credit equal to $22 per MWh for each MWh of electricity generated by a qualifying wind facility. The PTC does not actually extend over the entire life of the wind facility but, for the purposes of this example, we'll ignore that detail.
Suppose that we had a single 1 MW wind turbine that cost $1,200 per kW (or $1.2 million total) to construct. The wind turbine produces 3,000 MWh of electricity each year and the developer faces a WACC of 15% per year. The operations cost for the wind turbine is $5 per MWh. The lifetime of the project is assumed to be ten years. Remember our formula for the LCOE:
Here the LVC is $5/MWh. Looking back to the formula from Lesson 9, verify for yourself that the LFC for our hypothetical wind turbine is $79.70 per MWh, so the LCOE is $79.70 + $5 = $84.70 per MWh.
Recall that the way the production tax credit works is that it acts like a rebate to the owner of the project receiving the credit, for each MWh of electricity generated (note that not all tax credits work like this - some tax credits are based on capacity or on the amount invested). That rebate is functionally like a discount on the LCOE. So, to incorporate the impact of the PTC, we just subtract it from the LCOE. Thus, the impact of the PTC on our hypothetical wind turbine is:
Recall from the introduction to the lesson that there is some equivalence, in terms of encouraging low-carbon energy resources, between providing subsidies or incentives for those resources and imposing a tax or a price on emissions from polluting resources. You can see how this might work using the LCOE equation. If we were to take a hypothetical 1 MW coal plant with a capital cost of $2,000 and a marginal operations cost of $20 per MWh, assuming that the coal plant produced 7,000 MWh each year under the same financial terms (WACC and time horizon) as the wind plant, you would get the LCOE for that power plant to be $76.93 per MWh (try it yourself). Without any taxes, subsidies or incentives, the coal plant would look cheaper than the wind plant. But with the PTC, the LCOE for the wind plant would fall below that of the coal plant.
Now, let's see what happens if we were to impose a carbon tax of $10 per MWh on the coal plant (one MWh of coal-fired electricity has about one tonne of embedded CO2, so a tax of $10 per MWh is roughly equivalent to a carbon tax of $10 per tonne of CO2). The carbon tax is just a variable cost of operation, so it increases the LVC for the coal plant from $20 per MWh to $30 per MWh. The LCOE rises by $10 per MWH to $86.93 per MWh. With the carbon tax, the coal plant now looks to be more expensive than the wind plant without the PTC. (The wind plant looks a lot better if it happened to get the PTC at the same time that the coal plant was being taxed for its carbon emissions.)
Next, we'll take a look at the impact that a loan guarantee or low-interest loan might have on the LCOE for our hypothetical wind project. Suppose that we got to our 15% WACC for the wind plant through the following calculation: the wind plant is financed 50% by debt and equity, with costs of 15% and 20%, respectively. If the tax rate is 35%, then we would get a WACC of approximately 15% (try it yourself).
Now, suppose that the wind project is able to obtain a loan guarantee that lowers its cost of debt to 3% (assume for the purposes of this example that the share of debt and equity financing stays constant). This would lower the WACC to 11.2% and would lower the LCOE to $73.49 per MWh (again, you should try these calculations yourself). In this case, comparing the impact of the loan guarantee to the impact of the PTC, we can see that the PTC is more advantageous (i.e., it yields a greater reduction in LCOE).
In this section, we'll take our hypothetical wind project and look more closely at how subsidies and incentives can be incorporated into the pro forma financial statements. We'll focus on the PTC for this example. The project parameters for the wind turbine will remain the same, except that we'll extend the operational time horizon to 15 years from 10 years and assume fixed operations and maintenance costs of $10,000 per year. We'll also assume a sales price of $60 per MWh over the entire life of the wind project. The project parameters are shown in Table 8.1.
Capital Cost | 1,200,000 dollars |
---|---|
Annual discount rate | 15 percent |
Decision Horizon (N) | 10 years |
Annual Output | 3,000 MWh |
Marginal Cost | $0 per MWh |
Variable O&M | $5 per MWh |
Fixed O&M | $10,000 per year |
Tax Rate | 35 percent |
Sales Price | $60 per MWh |
The analysis that we are describing here is included in an Excel file posted on the Lesson 8 Overview page, called "Wind Pro Forma.xlsx (no PTC)." The tables below will show some excerpts from the full P&L and Cash Flow tables.
First, we'll take a look at the pro forma without the production tax credit. Tables 8.2 and 8.3 show excerpts from the P&L and Cash Flow statements (Years 0 through 3 for each statement). You could also try to reproduce these yourself as an exercise.
Project | Year 0 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|
Construction Cost | $1,200,000 | 0 | 0 | 0 |
Annual Operating Revenue | $0 | $180,000 | $180,000 | $180,000 |
Annual Variable Operating Cost | $0 | $15,000 | $15,000 | $15,000 |
Annual Fixed Operating Cost | - | $10,000 | $10,000 | $10,000 |
Annual Net Operating Revenue | - | $155,000 | $155,000 | $155,000 |
Depreciation Expense | - | $120,000 | $216,000 | $172,800 |
Taxable Net Income | - | $35,000 | $(61,000) | $(17,800) |
Taxes | - | $12,250 | $ - | $ - |
Income Net of Taxes | - | $22,750 | $(61,000) | $(17,800) |
Activities | Year 0 |
Year 1 |
Year 2 |
Year 3 |
|
---|---|---|---|---|---|
(1) | Investment Activities | $(1,200,000) | - | - | - |
(2) | - | - | - | - | |
(3) | Net Income from Operating | - | $22,750 | $(61,000) | $(17,800) |
(4) | - | - | - | - | |
(5) | Depreciation Expenses | $120,000 | $216,000 | $172,800 | |
(6) | - | - | - | - | |
(7) | Net increase or decrease | - | - | - | - |
(8) | in cash | $(1,200,000) | $142,750 | $155,000 | $155,000 |
Based on the cash flow statement, we can calculate the net present value and IRR for this project. If you make the calculation yourself or look at the Excel sheet, you will find that the project has a 15-year NPV that is negative - over that time period the project loses $400,440 in present discounted value terms. The IRR is calculated to be 7%.
Here the IRR is itself useful information - it tells you how low the WACC would need to be in order for the project to break even. You can thus use the IRR in evaluating the usefulness of loan guarantees or low-interest loans in making renewable energy projects profitable. In this case, if the wind project has a 50% debt and 50% equity financing structure, then even if the cost of debt were driven down to 0%, the project would still face a WACC of around 10%. Since this is higher than the IRR, we can conclude that unless the mix of debt and equity financing can be adjusted (more debt, less equity) then a zero-interest loan will not be sufficient to make our wind project profitable.
Now, we'll incorporate the production tax credit for wind into our analysis. This section of the discussion will reference a different Excel file, "Wind Pro Forma.xlsx (with PTC)," which is also posted and can be downloaded from the Lesson 8 Overview page. Here we'll simply assume that our project is eligible for the PTC, and that the PTC is set at $23 per MWh.
To incorporate the PTC into the pro forma, we need to add an extra line to the P&L statement, which calculates the amount of the tax credit. Since the tax credit is calculated as a direct deduction from taxes paid, it is possible (and you'll see it happen in this example) that the Taxes line item in the P&L actually turns out to be negative. This indicates that the wind project receives a check from the IRS each year, because of the production tax credit. (The depreciation allowances also contribute here.) Table 8.4 shows Years 0 through 4 of the P&L statement with the PTC included. Please have a look at the Excel file in Canvas as well - you will notice that there is a line item for the tax credit only for years 1 through 10 (after which the project is no longer eligible for the PTC).
Project | Year 0 |
Year 1 |
Year 2 |
Year 3 |
---|---|---|---|---|
Construction Cost | $1,200,000 | 0 | 0 | 0 |
Annual Operating Revenue | - | $180,000 | $180,000 | $180,000 |
Annual Variable Operating Cost | - | $15,000 | $15,000 | $15,000 |
Annual Fixed Operating Cost | - | $10,000 | $10,000 | $10,000 |
Annual Net Operating Revenue | - | $155,000 | $155,000 | $155,000 |
Depreciation Expense | - | $120,000 | $216,000 | $172,800 |
Taxable Net Income | - | $35,000 | $(61,000) | $(17,800) |
Tax Credit | - | $69,000 | $69,000 | $69,000 |
Taxes | - | $(56,750) | $(69,000) | $(69,000) |
Income Net of Taxes | - | $91,750 | $8,000 | $51,200 |
Table 8.5 shows the cash flow statement for Years 0 through 3. There is no real adjustment needed for the cash flow statement since the cash flow statement starts with the "Income Net of Taxes" line item from the P&L.
Activity | Year 0 |
Year 1 |
Year 2 |
Year 3 |
|
---|---|---|---|---|---|
(1) | Investment Activities | $(1,200,000) | - | - | - |
(2) | - | - | - | - | |
(3) | Net Income from Operating | - | $91,750 | $8,000 | $51,200 |
(4) | - | - | - | - | |
(5) | Depreciation Expenses | $120,000 | $216,000 | $172,800 | |
(6) | - | - | - | - | |
(7) | Net increase or decrease | - | - | - | - |
(8) | in cash | $(1,200,000) | $211,750 | $224,000 | $224,000 |
We can again go and calculate the net present value and IRR of the plant. The PTC in this case does not make the NPV positive - the project loses $54,145 in present discounted value terms - but the PTC does increase the NPV by close to $350,000 over the life of the plant. It also raises the IRR to 14%. In this case, if the PTC could be coupled with RECs for the project (even if those RECs were not worth very much individually), then the NPV would likely increase to positive territory.
Many renewable energy projects are currently at a cost disadvantage to conventional energy projects that do not account for in-kind subsidies to conventional projects for two reasons. First, many renewable energy technologies are not as mature as conventional technologies. Second, the social costs of pollution associated with fossil-fuel usage are not always fully incorporated into the prices charged in the marketplace. If the avoidance of those social costs could be monetized, then perhaps renewables would be more competitive with conventional energy resources. There are two basic ways to incorporate those social costs. First, polluting energy resources could be taxed (or a price put on their polluting emissions). Second, non-polluting energy resources could be given incentives or subsidies in some way, which lowers their costs compared to conventional energy resources. In this lesson, we discussed four basic mechanisms for providing subsidies and incentives. Tax credits and feed-in tariffs act as direct subsidies for each unit of energy produced. Rebates and grants can offset capital costs of new investments. Loan guarantees or low/zero-interest loans can substantially lower a project's WACC, increasing the present discounted value. Renewable portfolio standards or clean energy standards offer quota-based mechanisms for drawing alternative energy resources into the marketplace.
You have reached the end of Lesson 8! Double-check the What is Due for Lesson 8? list on the first page of this lesson to make sure you have completed all of the activities listed there before we begin the next lesson.
We started the course with a discussion of how important energy is to the economy and the society. WIth such an important (some would say existential) dependence on reliable, affordable and safe energy, it is little wonder that the government might have something to say about how these important products and services are provided. There are some great books and even treatises that have been written by economists and attorneys on the regulation of the energy industry in the US and globally. This lesson will focus only on the United States because a global survey would be too broad for inclusion and because many of the lessons learned in the US will be applicable to the rest of the developed world.
By the end of this lesson, you should be able to:
Please watch the following interview with Frank Lacey, which is borrowed from EME 897: Solar Energy Integration and Economics. If this video is slow to load here on this page, you can always access it and all course videos in the Media Gallery in Canvas.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 9 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
In theoretical economics there is the idea that certain services and goods are most efficiently provided by what is defined as a “natural monopoly.” A natural monopoly is a type of monopoly [88] in an industry or sector with high barriers to entry and start-up costs that prevent any rivals from competing. As such, a natural monopoly has only one efficient player. This company may be the only provider of a product or service in an industry or geographic location. Here's a nice definition of a natural monopoly [89]. One can see that there are places in the energy value chain where it might be most efficient to have a natural monopoly. For instance, it does not make sense from an efficiency standpoint to have more than one set of electricity distribution wires in the street. This also makes sense for natural gas distribution. Electric and natural gas transmission also are more efficient with fewer rather than many players. In the case of natural gas transmission, regulators allowed for limited competition among certain pipeline companies to many city gates in the Northeast and Midwest while there is pretty unbridled competition in the Texas and Louisiana markets. Regardless, large infrastructure projects are usually more efficient if not duplicated. For instance, do we need more than one wastewater system or more than one transit authority in a particular geographic region?
While it is quite likely that the best outcome from an efficiency perspective comes from a natural monopoly, there is an asymmetry that occurs with respect to pricing power particularly when the good or service provided is considered to be very important by the end user like heat or water or power. This price “inelasticity” for the consumer sets up the owner of the natural monopoly with significant pricing power. This pricing power over time could make the goods and services unaffordable to the consumer and allow for significant monopoly rents to be earned by the supplier.
This is where utility regulation seeks to “level the playing field” by regulating the prices (rates) of these granted natural monopolies. We will further discuss this process in section 9.4
Environmental regulations are governmental incentives or laws passed in attempts to protect the environment in a variety of ways: such as by reducing waste, requiring companies to cut back on production in order to reduce nation-wide emissions, or banning the use of harmful chemicals and substances to protect the environment, human health, and biodiversity.
In general, environmental regulation is dedicated towards the initial activities at the production site and how the waste is handled following the manufacturing process of a product.
The main goal of environmental regulation is to require strict rules on the measures necessary in order to reduce emissions and protect the environment – many of which would not occur if it weren’t for environmental regulations. Environmental regulations do the same: many wouldn’t commit to the environmental measures necessary on their own, but with the environmental regulations – it no longer becomes a choice, but a legal requirement that makes a global difference. There are many other courses in the RESS curriculum that cover Environmental regulation and policy. It is an extremely important topic and compliance with these regulations can be costly and is something that in a more complex development of a project like the ones we are doing here would require significant due diligence. Further - there are environmental policies which promote the development of renewable energy like Renewable Portfolio Standards (RPS), carbon taxes and feed-in tariffs. Please check out this link if you want more information about this: Our 2023 Guide to Environmental Regulations in the US [90].
Municipalities, State and the Federal Government also have other regulations such as labor, zoning, procurement and land use regulations. Think about some other types of regulation your project may come under as you work through it.
Authorities Having Jurisdiction (AHJ) are governmental or non-governmental entities responsible for enforcing building codes, fire codes, and other regulations in a given jurisdiction. These entities can have a significant impact on your project. You may want to circle back to your Stakeholder Register to see if you’ve covered the important AHJ’s in that document. The AHJs we will be covering in this lesson have mostly to do with the regulation of the various functions of the energy business. There are other AHJs that are important like environmental regulators, taxing authorities and zoning boards, but these AHJs are outside the scope of EME801.
In the United States there are three levels of government: Federal, State and Local. Each of these levels of government regulates the various aspects or functions of the energy delivery systems. When it comes to the economic regulation of the energy business, the Federal and State governments provide the vast majority of economic regulation. At the federal level, we have the Federal Energy Regulatory Commission (FERC), which is responsible for regulating the interstate sale and transmission of crude oil, refined products, natural gas and electricity. We will mostly focus on the regulation of the transportation of natural gas and electricity. FERC ensures the proper operation, expansion, abandonment and rate structure and level for interstate natural gas pipelines, which move gas from production areas like Texas, Louisiana, and the Appalachian regions of Pennsylvania, West Virginia and Ohio to load centers like New York City, Chicago and San Francisco. FERC also regulates interstate electric transmission through its regulation of Regional Transmission Organizations like PJM, ISONE and CalISO.
At the state level, each state has a utility commission which ensures the safe operation and fair rates for natural gas and electricity. These bodies include the Pennsylvania Public Utility Commission, The Illinois Commerce Commission and the Massachusetts Department of Public Utilities. These agencies provide important oversight to gas and electric utilities due to the nature of most utility service being a natural monopoly.
Remember back in the beginning of the course when we started talking about certain accounting terms and definitions? Well here we go again. In the first sections, we focused on starting with developing a time series of revenue and expenses by assuming some prices and volumes and costs (both capital and O&M). Using some financial analysis techniques we determined whether we had a viable project or not. Well, the way that the utility makes its rates is a little sideways from that. The utility starts with certain assumptions about the return required to allow investors to buy equity in the enterprise. This cost of equity is a heavily debated topic in a rate case because there are many views on how to determine how risky an enterprise is. But let’s just say that we agree that this cost of equity for the utility can be determined. Once this cost of equity is determined, we can then determine the total cost of capital because the debt used to finance the capital in the utility is already a known quantity and the capital structure has been set by how much capital is financed through debt vs equity. Now the next step, once we know the cost of capital, is to determine the amount of net operating income (NOI) that is required to be earned by the utility to recover its cost of capital. This is derived by multiplying the cost of capital by the “rate base” for the utility. The utility’s rate base is defined as the total value of a utility’s assets (e.g., plant, equipment, working capital, and deductions for accumulated depreciation). This then gives us our targeted NOI. We then add to NOI all the expenses of the utility. This includes taxes, depreciation and amortization, operating expenses and administrative and general expenses. The sum of these is equal to the revenue requirement for the utility. This revenue requirement is then divided by the billing determinants (in energy this could be kWh, MMBtu or MCF). Dividing the revenue requirement by the volumes then gives a rate in $/kWh or $/MMBtu. This is called then the rate for service.
Rates are set in quasi-judicial proceedings called rate cases. These are usually presided over by administrative law judges at the state and federal level. The utility “puts on” its rate case through a very large filing of a lot of data. Other parties to the case like consumer advocates, Commission Staff, Industrial Intervenors and activists will also file evidence in the rate case. These parties will typically represent the interests of their constituencies. You could imagine a large industrial customer saying his rate was too high so that the other customers should pay more. Consumer advocates will say that residential customers are paying too much so those folks will advocate for higher costs to other rate classes. All the intervenors will likely say that the utility is asking too much in general and will try to argue for a lower cost of equity, a smaller rate base and higher volumes, as each of these factors would keep rates lower overall.
Please be sure to read the assigned readings, complete the deliverable, complete the quiz, and play around in the utility rate-making spreadsheet.
You have reached the end of Lesson 9! Double check the What is Due for Lesson 9? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 10.
This lesson will introduce you to the global markets for the petroleum commodities (crude oil, refined products, and natural gas). These commodities, along with coal, are commonly called fossil fuels. The inherent value of these fuels comes from their relative ease of transportation, their energy density, and their ability to be inventoried and dispatched as needed to meet demand. These characteristics should always be kept in mind when trying to understand the challenges associated with a transition away from fossil fuels.
There is a great deal of complexity in the world of petroleum markets. There are entire 3- and 6-credit courses offered on the subject. We will cover a lot of ground in this single module. There is also supplementary material from the previous iteration of EME 801 (written and developed by Dr. Seth Blumsack) which is attached as an appendix. You are strongly encouraged to read this material and digest it, as further background on energy markets provides great insight to inform decisions that each of you will be called on to make.
By the end of this lesson, you should be able to:
Aside from the online materials, you will need to access the websites of the EIA and NYMEX (CME Group).
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. See the specific directions for the assignments below.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Crude oil is one of the most economically mature commodity markets in the world. Even though most crude oil is produced by a relatively small number of companies, and often in remote locations that are very far from the point of consumption, trade in crude oil is robust and global in nature. Nearly 80% of international crude oil transactions involve delivery via waterway in supertankers. Oil traders are able to quickly redirect transactions towards markets where prices are higher.
Global supply and demand determines prices for fossil fuels. Events around the world can affect our prices at home for oil-based energy such as gasoline, heating oil and natural gas. Political unrest in some oil-producing nations also contributes to high prices - basically, there is a fear that political instability could shut down oil production in these countries. OPEC, the large oil-producing cartel, does have some ability to influence world prices, but OPEC's influence in the world oil market is shrinking rapidly as new supplies in non-OPEC countries are discovered and developed.
Our discussion of the world market for crude oil will be broken into a few sections. We will first focus on what's called the "spot" and "forward" market for crude oil. The term "spot market" generally refers to a short-term commodity transaction where the physical commodity changes hands very soon after the seller receives payment. Most retail consumer purchases are examples of spot transactions. When I buy a newspaper at the convenience store or a slice of pizza at the pizza shop, I get the product right after I pay for it. Forward markets refer to contracts where buyers and sellers agree up-front on a price for a commodity that will be delivered at some point in the future. When I subscribe to the newspaper and have it delivered to my house every day, I am signing a type of forward contract with the newspaper company. When I call the pizza shop and ask them to deliver a pizza to my house one hour or one day from now, then I am also engaging in a type of forward contract.
We will also discuss "futures" markets for crude oil. The difference between futures and forward markets can be confusing at times. The primary difference is that a futures contract is a highly standardized commodity sold through a financial exchange, rather than a highly customizable contract bought and sold through one-on-one transactions. Futures markets do have the advantage that they have been able to attract many more buyers and sellers than forward markets.
Finally, we will spend time in this lesson learning where to find data on global oil markets and discussing the issue of "peak oil" - whether world oil reserves are dwindling or whether there is actually enough to support rising oil consumption over the coming decades. There is more data on the web than you might think, but the difficulty is in knowing where to find exactly what you are looking for.
Through much of the first half of the 20th Century, the United States was the dominant world oil producer and was, in fact, a net exporter. By the early 1900s, a bilateral "world" market for oil had developed, but the world price was simply the U.S. price plus the cost of freight. Russia's production did not lag far behind U.S. production during this time, but their relatively closed economy precluded much trade with the rest of the world. Table 10.1 shows the leading crude oil producers up to 1970.
Year | U.S. | Mexico | Venezuela | Russia | Indonesia | Middle East |
---|---|---|---|---|---|---|
1910 | 575 | 10 | 0 | 170 | 30 | 0 |
1920 | 1,214 | 430 | 1 | 70 | 48 | 31 |
1930 | 2,460 | 408 | 374 | 344 | 114 | 126 |
1940 | 3,707 | 12 | 508 | 599 | 170 | 280 |
1950 | 5,407 | 198 | 1,498 | 729 | 133 | 1,755 |
1960 | 7,055 | 271 | 2,854 | 2,957 | 419 | 5,255 |
1970 | 9,637 | 487 | 3,708 | 6,985 | 854 | 13,957 |
Unlike OPEC, the market for crude oil in the U.S. was generally competitive since mineral rights did not belong to the state but to the landholder. If it was under your property, you owned it. Since oil (and gas) reservoirs often crossed property boundaries, "rights" were hard to specify and enforce. If an oil reservoir was split between my property and yours, the money would flow to whoever could suck all the oil out first. Overdrilling was the result, as was the birth of "wildcatters," individuals who would dig oil wells on unproven land, hoping to strike it rich.
During the period up to 1970 (and even beyond), the "market" for crude oil was characterized largely by within-company exchanges. Most oil companies were "vertically integrated," meaning that the company operated all the way down the value chain - crude oil would go from the field to the refiner to the marketer (and then to the retailer, like a gas station) while staying within company borders. There was a small number of market transactions at what were referred to as "posted prices." Posted prices are essentially fixed offer prices posted by companies in advance of transactions. Posted prices were originally painted on wooden signs and hung on posts (hence the name), each remaining in effect until it was replaced by a new one. Now, posted prices take the form of electronic bulletins issued by major oil producers. (The spot market for crude oil is still not very large, so posted prices are not always representative of market conditions.) An example of a posted price bulletin is shown in Figure 10.1.
OPEC was formed in 1960, largely as a way for governments of oil-producing nations to capture oil revenues that, at the time, were going to foreign-producing firms. The motivation for founding OPEC was not market power, but rather a tax dispute. Oil was originally taxed as income. Thus, as prices fell with increased competition, the tax collected by the oil-producing nations also fell. The members of OPEC decreed that oil taxes would now come in the form of excise taxes, levied on a per-barrel basis. In other words, they succeeded in separating tax revenue from the value of the taxable commodity. The rise in oil prices around this time (see Table 10.1 on the previous page) reflects the fact that spot prices reflected taxes collected by OPEC governments, rather than market transactions since excise taxes act as a floor on prices. As a cartel attempting to coordinate actions among its members, OPEC has had only mixed success, as we will discuss below. Two incidents, one in 1973 and one in 1979, however, did impact the world oil market substantially, as shown in the middle section of Table 10.1, and cemented OPEC's reputation into place for the following decades.
In 1971 and 1972, fears began to grow in the developed world that if we were not already running out of energy supplies, we would soon, as additional nations adopted Western industrial structures. Then-president Richard Nixon appeared particularly concerned that Arab nations might impose a selective embargo on the United States for its pro-Israel policy. Such a selective embargo could not have worked; the world crude-oil market was too large, and replacement oil could have been found in too many places. The energy crisis was largely hysteria - production was increasing with no end in sight, and imports, particularly from Saudi Arabia, were rising.
It is important to separate the energy crisis from the Arab oil embargo of 1973. The two are separate but related events. The Arab oil embargo was successful only because of the price controls and rationing that occurred as a result of the energy crisis. It is possible (but perhaps a stretch) that the high prices and lines at the gas pumps may have happened even without cutbacks in supply from the Arab oil producers. The oil embargo officially started in October 1973, when a group of Middle East countries announced a 5% production cut per month as a reaction to the Yom Kippur War between Egypt and Israel (or, more precisely, Israel's victory in that war, aided by nations such as the United States). The embargoing nations said that the cuts would be restored once Israel withdrew from Palestine and Jerusalem.
The oil embargo of 1979 was not really much of an embargo at all, at least not in the sense of the 1973 embargo. The output cuts in 1979, however, were much larger, and the overall effect was more lasting than its predecessor six years earlier. The primary player in the 1979 embargo was Saudi Arabia, which cut production following a strike by Iranian oil workers. The production cuts were an attempt to raise prices. This they did, but they also reawakened fears of an energy crisis, with politicians muttering I-told-you-sos about how the world was in for a severe energy shortage. The cutbacks by Saudi Arabia only lasted three months, but the damage was done, and Saudi Arabia was recognized as the only single player that had the capability to move the world oil market. The following year, the Saudis found themselves in the enviable position of being able to raise prices without lowering output.
The current market for crude oil is truly global in reach. Oil cargoes move with relative ease between countries and across oceans. While most U.S. oil imports come from a relatively small group of countries, it is misleading to think that only those countries have an impact on oil prices in the United States. Because oil can and does move so freely from one area to another across the globe, it is better to think of the oil market as a global pool, rather than as a network of suppliers and buyers. If one supplier shrinks the overall depth of the pool by withholding supply (or floods the pool by producing a lot of oil), then the impact will be felt uniformly throughout the pool.
At this point, you are encouraged to listen to episodes 1, 2, and 3 of the Planet Money Buys Oil podcast [93]. This podcast is very entertaining and will give you a sense of what the "physical" market for crude oil is like. The physical market is what we've been talking about so far in this lesson - the part of the oil market where buyers and sellers exchange money for crude oil. In the next part of the lesson, we'll move into the "futures market" for crude oil, which is where all sorts of different market players hedge and speculate on the physical price of crude oil. Episode 3 gets into the refining area which we are also covering here.
The pricing of oil is determined largely by a mix of fundamental supply and demand factors, as well as expectations. How much of any given oil-price movement is due to each of these three factors is an eternal mystery that keeps a small army of editorial columnists and television talking heads in business. The supply-demand balance is perhaps the easiest piece to explain - when demand is high (for example, during the wintertime when heating oil demands are high or during the summer when people tend to drive more often and further distances), consumers are willing to pay more for refined petroleum products, and higher-cost oil supplies must be brought online. Thus, the price goes up. Similarly, when accidents, political strife, or war keep supplies offline, higher-cost replacements must be found, and the price goes up. Expectations can play a large role in pricing outcomes in the oil (as well as gas and refined products) markets. The role of people’s expectations can be traced back to OPEC's successes in the 1970s in increasing world oil prices, even for brief periods. Believing that OPEC had the power to do pretty much whatever it wanted, market participants began engaging in a series of self-fulfilling prophecy games. They worked something like this. First, one or more market participants would believe that OPEC would act to increase prices or reduce supply. Afraid of getting caught short or unable to fulfill contracts, stockpiling commenced, pushing up spot prices. Thus, all OPEC needed to do was cause panic in the markets by spreading rumors of policy changes. The gains were nearly always short-lived as the high cost of inventories would result in sell-offs, bringing oil prices down to pre-panic levels. Nowadays, broader geopolitical concerns, particularly in the Middle East, Africa, and Russia/Ukraine, have replaced the grumblings of OPEC as the source for expectations-induced spikes in fossil fuel prices including natural gas.
OPEC was mentioned earlier as an entity that has been able to exert substantial influence on global markets for crude oil. OPEC operates as a cartel - a group of producing countries that attempt to coordinate supply decisions in order to exert some influence on prices. OPEC does not try to set prices directly, as is often believed. What OPEC countries try to do is to expand or contract oil production in order to keep the world price within some band that the countries collectively deem desirable.
OPEC's actual ability to manipulate oil prices is not all that clear, and its influence has dwindled as more "unconventional" petroleum resources have been developed, including the oil sands in Canada and shale oil in the United States. Most cartels are difficult to sustain since each member of the cartel has the incentive to cheat - in OPEC's case, this means that countries have often produced more oil than they were supposed to under the quota system, as shown in Figure 10.2 (the most consistent cheater seems to have been the country of Algeria). Even during the 1973 embargo, none of the OPEC nations approached the formal 5% production cut mentioned in the embargo. Saudi Arabia's production decreased by 0.8%. Iraq and Oman saw the biggest percentage cuts in production at 1%. Prices did indeed go up, but largely as a result of fear and higher taxes rather than actual supply shortages. The actual production cuts lasted only three weeks; the embargo fell apart in December when Saudi Arabia raised production.
NOTES:
While OPEC has been viewed historically as a cartel that keeps oil prices high, its members have, more recently, probably been at least partially responsible for the rapid decline in oil prices. The Economist has a nice and recent article [99] describing the factors that have been contributing to the slide in oil prices. This has been partly due to sluggish economies in developing countries, energy efficiency in rich countries, the boom in shale-oil production in the United States (which we will come back to in a few weeks), and a strategic decision by Saudi Arabia to maintain high oil production levels even in the face of low prices (this is perhaps an attempt to inflict economic pain on the shale-oil business in the U.S.).
Earlier, it was discussed that when demand increases, higher-cost supplies must be brought online to meet that higher demand. Prices for oil have certainly been on a roller-coaster ride over the past few years. Does this mean that a few years ago, we thought all of the cheap oil in the world was gone, but we have now discovered new supplies of cheap oil? And if not, then what explains the price movements that we have seen in the oil market in recent years?
The answer depends on some understanding of the cost of supplying crude oil. Figure 10.3 provides a rough idea of the cost of extracting different types of oil resources. The low-cost resources are conventional oil fields that have been operating for decades. The higher-cost resources are so-called "unconventional" sources of oil, including deepwater or Arctic drilling; the oil sands of Alberta, Canada; and extraction of oil from shale formations (one of the best-known examples is the Bakken shale in North Dakota, whose extraction costs are somewhere in the lower end of the range shown - perhaps around $50 to $60 per barrel). If the producers of conventional oil were to flood the market, then the price would drop so low that unconventional players would be forced to shut down. This would be good for consumers right now, but bad for the producers of conventional oil (and eventually for consumers), since there would be less oil to sell later on. Thus, conventional oil producers hold some output back, leaving the unconventional producers to serve the leftover or "marginal" demand. This is good for conventional oil producers in both the short and long term (because they earn larger profits), but is bad for consumers in the short term. (In the long term, this strategy keeps prices from rising to even higher levels in the future.)
Part of the reason that crude-oil prices have been so high for so long is the increased role that unconventional oil is playing in world oil supply. This is due in some part to the natural decline in output that is expected from conventional oil fields as they mature (more on this later when we talk about "peak oil"). The growth in unconventional oil supplies has been so rapid that countries with large reserves of unconventional oil, such as the United States, have become large oil producers in a very short period of time.
Not much has been made here of long-term contracts for oil. This is because up until the early 1980s there were not very many. Production continued as long as the extracted oil could find a home in the spot market. The oil-producing countries, recognizing their market power, either implicitly or explicitly avoided long-term contracts in pursuit of volatile and largely lucrative spot prices.
We have already explained the difference between spot markets and forward markets. Both are "physical" markets, in that their main purpose is to exchange commodities between willing buyers and sellers. A spot or forward transaction typically involves the exchange of money for a physical commodity. Within the past few decades, entities with sufficiently large exposure in the physical market (i.e., the need to buy or sell lots of physical barrels of crude oil) have developed financial instruments that can help them "hedge" or control price volatility. By far the most important of these financial instruments is the "futures" contract.
Futures contracts differ from forward contracts in three important ways. First, futures contracts are highly standardized and non-customizable. The NYMEX futures contract is very tightly defined, in terms of the quantity and quality of oil that makes up a single contract, the delivery location and the prescribed date of delivery. Forward contracts are crafted between a willing buyer and seller and can include whatever terms are mutually agreeable. Second, futures contracts are traded through financial exchanges instead of in one-on-one or "bilateral" transactions. A futures contract for crude oil can be purchased on the NYMEX exchange and nowhere else. Third, futures contracts are typically "financial" in that the contract is settled in cash instead of through delivery of the commodity.
A simple example will illustrate the difference. Suppose I sign a forward contract for crude oil with my neighbor, where she agrees to deliver 100 barrels of crude oil to me in one month, for 50 dollars per barrel. A month from now arrives and my neighbor parks a big truck in my front yard, unloads the barrels, and collects 5000 dollars from me. This is a "physical" transaction. If I were to sign a futures contract with my neighbor, then in one month instead of dropping off 100 barrels of crude oil in front of my house, she pays me the value of that crude oil according to the contract (this is called "settlement" rather than "delivery"). So, I would receive 5000 dollars. I could then go and buy 100 barrels of crude oil on the spot market. If the price of crude oil on the spot market was less than $50 per barrel, then in the end I would have made money. If it was more than 50 dollars per barrel, then in the end I would have lost money, but not as much as if I had not signed the futures contract.
This simple example illustrates the primary usefulness of futures contracts, which is hedging against future fluctuations in the spot price. A hedge can be thought of as an insurance policy that partially protects against large swings in the crude oil price.
NYMEX (now called the "CME Group") provides a platform for buying and selling crude oil contracts from one month in advance up to eight and a half years forward. The time series of futures prices on a given date is called the "forward curve," and represents the best expectations of the market (on a specific date) as to where the market will go. The value of these expectations, naturally, depends on the number of market participants or "liquidity." For example, on a typical day, there are many thousands of crude oil futures contracts traded for delivery or settlement one month in advance. On the other hand, there may be only a few (if any) futures contracts traded for delivery or settlement eight years in advance.
The NYMEX crude oil futures contract involves the buying and selling of oil at a specific location in the North American oil pipeline network. This location, in Cushing Oklahoma, was chosen very specifically because of the amount of oil storage capacity located there and the interconnections to pipelines serving virtually all of the United States.
There is a wealth of data out there on crude oil markets. The following video will help orient you toward a website where you can find information on crude oil prices, demand, shipments and other data. Although this is a U.S. website, there is a good amount of data on global markets.
If you look at Figure 10.4, you might notice that the price of crude oil generally declines as you move farther out into the future. This is called "backwardation." The opposite, in which the price of crude oil increases as you move farther out into the future, is called "contango." In general, we expect the crude oil market to be in backwardation most of the time; that is, we expect the future price to be lower than the current (spot) price. This can be true even if we expect demand for crude oil to increase in the future. Why would this be the case? If demand is expected to rise in the future, shouldn't that bring the market into contango?
Sometimes this does happen. Most often, the crude oil market is in backwardation because storing crude oil generally involves low costs and has some inherent value. Suppose you had a barrel of crude oil. You could sell it now, or store it to sell later (maybe the price will be higher). The benefit to storing the barrel of oil is the option to sell it at some future date, or to keep on holding on to the barrel. This benefit is known as the "convenience yield." Now, suppose that hundreds of thousands of people were storing barrels of oil to sell one year from now. When one year comes, all those barrels of oil will flood the market, lowering the price (a barrel in the future is also worth less than a barrel today; a process called discounting that we will discuss in a later lesson). Thus, because inventories of crude oil are high, the market expects the price to fall in the future.
The ability to store oil implies that future events can impact spot prices. A known future supply disruption (such as the shuttering of an oil refinery for maintenance) will certainly impact the futures price for oil, but should also impact the spot price as inventories are built up or drawn down ahead of the refinery outage.
Our transportation systems are highly dependent on petroleum. High and volatile prices for oil (and at the pump) naturally give rise to suggestions that oil production has peaked, or that we are running out of oil. Concern over oil supplies is not new. Looking back over the past hundred years, there have been five or six projections [101] from governments, researchers and other groups suggesting that the world was about to run out of oil, precious metals or other valuable commodities.
In the 1950s, a geologist named M. King Hubbert looked at oil production data from all of the major oil-producing countries in the world (at that time). Based on his statistical analysis of the data, he projected that U.S. oil production would peak in the 1970s and that world oil production would peak during the first decade of the 21st century. These projections came to be known as "Hubbert's Peak." And it turns out that Hubbert's projections were highly accurate - U.S. oil production did peak in the 1970s, and the collection of oil-producing countries that Hubbert originally studied did see their collective oil production peak in the early 2000s. So, maybe Hubbert had a point, and maybe there is something to the "peak oil" paranoia.
The reality of the "amount of oil" is more complex. When Hubbert made his predictions in the 1950s, the oil industry was still in its technical infancy. Most oil production came from so-called "elephant" oil fields, tremendously large reservoirs of easily-accessible oil. To imagine what these "elephant" fields were like, think about the theme song to the Beverly Hillbillies [102], when Jed Clampett shoots a hole in the ground and oil comes spouting up. The elephant oil fields that represented most oil production during Hubbert's time were basically like "Jed Clampett oil." What Hubbert was predicting was really the decline in Jed Clampett oil.
While Hubbert was right about Jed Clampett oil, his analysis did not consider the advances in technology that would make extraction of oil possible from less-accessible reservoirs. Nor did he consider that a rise in the price of oil would make oil extraction from so-called "unconventional" reservoirs profitable enough to undertake. Deepwater drilling (like in the Gulf of Mexico), the Canadian oil sands, and even extraction of oil from shales via hydraulic fracturing in North Dakota are all examples of unconventional oil production.
In fact, most geologists now believe that the amount of unconventional oil is much larger than all of the Jed Clampett oil fields put together. A recent study from University of California at Berkeley ("Risks of the Oil Transition" by Alex Farrell and Adam Brandt, Environmental Research Letters (2006)) estimated that the world has used up only about 5% of known technically recoverable oil reserves. A future supply curve for liquid hydrocarbons (crude oil and usable synthetic liquid fuels), as shown in Figure 10.5 and adapted from the Brandt/Farrell paper, demonstrates this shift from "conventional" resources (i.e., Jed Clampett oil) to unconventional resources. These unconventional resources include the oil sands for which Alberta is now well known; bitumen-laden "heavy oils" (for which Venezuela is also known); enhanced oil recovery from conventional wells (EOR); synthetic fuels manufactured using natural gas or coal as a feedstock; and oil shales, which includes both naturally occurring deposits of oil in low-porosity shale formations (typically requiring hydraulic fracturing to extract) and hydrocarbon-rich shales that are used to produce a synthetic crude oil.
The reality is not that we are "running out of oil," but rather that we are transitioning from a period of easily-accessible oil at low prices to an era of increasingly unconventional production, which has higher costs. Companies will not try to develop these unconventional resources unless consumers are willing to pay the price (economic and environmental) or governments heavily subsidize oil production or consumption. So far, the world has found a way to consume plenty of $100-per-barrel oil. At some point, unconventional oil exploration will get so expensive that consumers will look to lower-cost alternatives. Oil will price itself out of the market before the world truly runs out. The increasing popularity of hybrid vehicles, electric vehicles, bicycle transportation in urban areas and even natural gas vehicles are examples of such a shift, even if government policies are required to affect the decisions that consumers make.
Sheik Ahmed Zahi Yamani, the longtime Saudi oil minister and a key founder of OPEC, has perhaps summed up the world oil market the most nicely. He said, "The Stone Age came to an end, not for lack of stones, and the oil age will end, but not for lack of oil."
Despite all of the attention that it gets, no one actually wants crude oil – what people want are the various products that are derived from crude oil. These products include not only gasoline, heating oil, and so forth (which will be the focus of our discussions) but everyday consumer items like plastics, long underwear, and crayons. In this lesson, we’ll discuss markets for energy commodities that are refined from petroleum. While conditions in the crude oil market are highly influential in determining the prices of these products, in some sense each petroleum product market has a life of its own.
Once crude oil is extracted from the ground, it must be transported and refined into petroleum products that have any value. Those products must then be transported to end-use consumers or retailers (like gasoline stations or the company that delivers heating oil to your house, if you have an oil furnace). The overall well-to-consumer supply chain for petroleum products is often described as being segmented into three components (shown graphically in Figure 10.6).
Some companies in the petroleum industry have activities that would fall into upstream, midstream and downstream segments. ExxonMobil is one example of such a firm. Others have activities that fall primarily into only one segment. The KinderMorgan pipeline company is an example of a specialized petroleum firm, in this case belonging to the midstream segment. Many regions have local gas station brands that would specialize in the downstream segment of the industry. One of the best-known regional examples is the WaWa chain of gas stations and convenience stores in eastern Pennsylvania, but large grocery stores and retailers like Costco and Wal-Mart are increasingly involved in downstream sales of petroleum products.
Petroleum refineries are large-scale industrial complexes that produce saleable petroleum products from crude oil (and sometimes other feedstocks like biomass). The details of refinery operations differ from location to location, but virtually all refineries share two basic processes for separating crude oil into the various product components. Actual refinery operations are very complicated, but the basic functions of the refinery can be broken down into three categories of chemical processes:
The link below will take you to a 10-minute video that provides more details on the various refining processes.
The first process is known as distillation. In this process, crude oil is heated and fed into a distillation column. A schematic of the distillation column is shown in Figure 10.7. As the temperature of the crude oil in the distillation column rises, the crude oil separates itself into different components, called “fractions.” The fractions are then captured separately. Each fraction corresponds to a different type of petroleum product, depending on the temperature at which that fraction boils off the crude oil mixture.
The second and third processes are known as cracking and reforming. Figure 10.8 provides a simplified view of how these processes are used on the various fractions produced through distillation. The heaviest fractions, including the gas oils and residual oils, are lower in value than some of the lighter fractions, so refiners go through a process called “cracking” to break apart the molecules in these fractions. This process can produce some higher-value products from heavier fractions. Cracking is most often utilized to produce gasoline and jet fuel from heavy gas oils. Reforming is typically utilized on lower-value light fractions, again to produce more gasoline. The reforming process involves inducing chemical reactions under pressure to change the composition of the hydrocarbon chain.
The production of final petroleum products differs from refinery to refinery, but in general, the oil refineries in the U.S. are engineered to produce as much gasoline as possible, owing to high demand from the transportation sector. Figure 10.9 shows the composition of output from a typical U.S. refinery.
Nearly half of every barrel of crude oil that goes into a typical U.S. refinery will emerge on the other end as gasoline. Diesel fuel, another transportation fuel, is generally the second-most-produced product from a refinery, representing about one-quarter of each barrel of oil.
Gasoline, diesel fuel, and heating oil are volatile commodities – prices fluctuate up and down, sometimes dramatically, as shown in Figure 10.10 which compares spot prices for crude oil with retail prices (i.e., what you would pay at the pump) for gasoline and diesel fuel. Focusing on gasoline and diesel fuel, you will notice that there is some pattern to all of the noisiness in the graphic – prices for both fuels tend to rise starting in the spring and tend to decline in the late summer and fall. This reflects the increased demand for transportation (primarily driving) during the summer months.
Why are prices for petroleum products so volatile? The easy (but not that satisfying) answer is that the price of crude oil can be volatile. In the United States, about 65 cents of every dollar we pay for diesel fuel or gasoline represents the cost of crude oil. The rest represents taxes assessed at the state and federal level. (In European countries, taxes are a much bigger component of gasoline costs.) You can see the volatility visually through the use of the crude oil price graphing tool [107] from the U.S. Energy Information Administration. It allows you to plot the price of West Texas Intermediate (the benchmark crude oil price in the U.S.) on a daily, monthly, and annual basis (depending on how much variability you want to see).
Starting in early 2008, prices climbed very rapidly, reaching nearly $150 per barrel in early July. But by December of that year, the price had dropped by almost 75%! Meanwhile, more recently, the price has dropped to $40 per barrel or even lower! What on earth is going on?
That supply and demand forces were at work is the primary reason, but the nature of the supply and demand forces are worth some time discussing. Suppose that the demand for crude oil or a refined petroleum product were to increase by some amount. How would that demand be met? There are basically two options: existing idle capacity (oil wells or petroleum refineries, for example) could be restarted, or that demand could be met by drawing down crude oil or petroleum product storage. If additional capacity could be restarted, the increase in demand would lead to an increase in the market price due to the higher costs of the capacity brought back online.
But what is the cost of releasing additional supply from storage? Here, we have a tricky economics problem – the actual cost (in accounting terms) of releasing additional supply is probably pretty small. But if you are the owner of some crude oil or petroleum products in storage, if you release supply from storage now, then you can’t release those same barrels again at some point in the future unless you replenish your storage facility (which requires buying oil or petroleum products on the open market and then putting them in storage). So, by releasing from storage now, you are giving up the opportunity for some future profit. This is referred to as “opportunity cost.” (Another term used in the finance field is the “convenience yield.”) Whether the opportunity cost is high or low depends on your expectations about the future price in the crude oil or petroleum products market. If you think demand is going to continue to be high tomorrow, then you face a high opportunity cost by releasing supply from your storage today. If you think that the increase in demand is transient, then that opportunity cost is low (because you think that demand is going to go back down tomorrow).
As it turns out, the opportunity cost of releasing supply from storage is related to the amount of spare production capacity, as shown in Figure 10.11. Generally, as the demand for oil or refined petroleum products rises, more expensive capacity (whether it’s oil wells or refineries) must be brought online, so the market price rises as demand goes up. As you get close to the total capacity constraint (the dashed vertical line in the figure), there is literally no more capacity to be brought online, so the price must rise rapidly in order for one of three things to happen: (i) additional supply is released from storage; (ii) new capacity can be constructed, which of course is expensive; or (iii) demand can go down because the price is too high. This transition from a slow and steady price increase to a regime of substantial volatility is endemic to the market for nearly every energy commodity, and the kink in the supply curve shown in Figure 10.11 is appropriately known as the “devil’s elbow” in the energy commodity business.
Bringing on new capacity will eventually lead to lower prices if demand stays constant – all it does is to shift the capacity constraint and the devil’s elbow in Figure 10.11 further to the right (this is shown in Figure 10.12). But this may have a self-reinforcing effect of keeping new capacity out of the market. Since new capacity in the refining business is generally built in large chunks (so-called “lumpy” investment – no one is going to build a refinery that processes a few marginal barrels of oil per day), if a new refinery is built without an increase in demand for petroleum products, the net effect may be to reduce the price for petroleum products so much that the new refinery is not profitable. Because of the devil’s elbow and the lumpy nature of bringing new sources online, energy commodity markets generally do not have a stable equilibrium like you might find in economics textbooks.
NYMEX (now called the "CME Group") offers futures contracts for several refined petroleum products - the most frequently-traded are heating oil and gasoline. The video below shows you where to find these prices on the CME Group website.
There's an old joke from the oil industry that goes something like this:
An oil company executive walks into a bar and sees a wildcatter slouched over the bar, staring into his drink.
"What's the matter?" says the oil company executive. "Another dry hole?"
"Worse," says the wildcatter, "we found gas!"
For many decades, natural gas was the poor cousin to crude oil. Often found alongside crude oil in reservoirs, natural gas was considered to be a low-value waste product that was often flared or vented into the atmosphere in very large quantities (enough to supply several European countries for an entire year), in order to more easily access the high-value crude. Natural gas is extremely important in the North American energy market for two distinct but interrelated reasons. First, natural gas has become the fuel of choice for dispatchable electricity generation. This means that all alternative forms of generation are compared on a cost basis with the constriction and operation of a gas-fired power plant. Second, natural gas is the major source of heating for the built environment throughout North America. It represents a very important source of energy storage that can be dispatched upon very little notice. Particularly with the rapid emergence of unconventional natural gas resources (these are often grouped into a catch-all category of “shale gas” but include natural gas found in sandstone formations, coal beds, and other types of geologic formations other than shales), there are lots of perceived opportunities in natural gas. We will discuss shale gas in the next lesson, but first, we will walk through an introduction to natural gas as a commodity and the functioning of North American markets for natural gas.
Like crude oil, natural gas is an energy source based on hydrocarbon chains, but the composition of natural gas is generally different than the composition of crude oil. Natural gas is primarily composed of methane, though some natural gas deposits also contain substantial fractions of other hydrocarbon gases or liquids such as ethane and propane (these are longer hydrocarbon chains that have substantial value as chemical feedstocks). Most gas deposits also contain impurities such as sulfur or other carbon compounds that must be separated prior to the gas being injected into transmission or distribution pipelines. Gas deposits that consist primarily of methane are known as “dry” gas deposits, while those with larger fractions of other hydrocarbons are known as “wet” or “rich” gas deposits.
Unlike oil, natural gas is essentially wedded to its transportation system – without pipelines (and liquefied natural gas tankers, which we’ll discuss later), there is no economical way to get large quantities of gas to market. Moreover, natural gas pipelines generally need to be dedicated assets. Using oil or petroleum product pipelines to move natural gas is not really possible, and moving other products in natural gas pipelines is not possible without completely repurposing the pipeline (and the injection/withdrawal infrastructure on either end). This asset specificity and complementarity between natural gas and the pipeline transportation infrastructure has been a significant factor in the development of the natural gas market. Each has little use without the other.
Natural gas is used in industrial, commercial, residential, and electric power applications. Natural gas demand in the United States has remained virtually flat for the better part of a decade, although, as shown in Figure 10.14, there has been a shift in the composition of that demand, away from industrial utilization and towards the electric power sector. Expectations are that the emergence of low-priced gas supplies from unconventional sources will change the drivers of natural gas in two ways – first, demand among both the industrial and electric power sector is anticipated to increase. Second, surplus natural gas supplies may open up an entirely new export sector for North American natural gas.
Each demand sector has its own intra-annual pattern of natural gas demand. Residential demand, for example, tends to be highest in the winter (because of demand for space heating), while demand in the electric power sector tends to be highest in the summer, due to higher demand for electricity (for air conditioning). Overall, natural gas demand in the United States peaks in the wintertime, with a lesser peak during the summer. A typical set of annual demand profiles is shown in Figure 10.15. There are some shifts occurring here as well, particularly as demand from the electric power sector continues to climb.
Beginning in the mid to late 1990s, the U.S. electric power sector started undergoing a transformation away from building new coal-fired power plants and towards building new natural gas plants. This shift took place for a variety of reasons, including increasingly stringent environmental requirements for power plants and a shift in the market for electricity induced by deregulation and restructuring. This shift towards more gas-fired power generation was associated with a pronounced increase in both the average spot price of natural gas and the volatility of natural gas prices, as shown in Figure 10.16. This association seems to have broken down since 2009 – there has been additional investment in, and utilization of, gas-fired power generation but a pronounced decline in natural gas prices. This trend reflects the influence of additional gas supplies from unconventional deposits (primarily shales and tight sandstone formations) coming online. Referencing our discussion of capacity constraints in the lesson on petroleum refining, the price and utilization trends in Figure 10.16 reflect textbook energy economics – the build-out in natural gas generation represented increased demand without a corresponding increase in natural gas supply, pushing the global demand curve towards the capacity constraint and rapidly increasing prices (some price spikes such as those observed in 2000, 2005, and 2008 are associated with extreme weather events like hurricanes or interruptions to gas pipeline networks, such as an explosion in the Western U.S. in 2000).
Please read Chapter 5 of the van Vactor reading (Flipping the Switch: The Transformation of Energy Markets [91]), along with the two RBN Energy articles (Henry the Hub, I am I am [96] and The Way She Moves [97]), before you proceed with the written material here. Sections 5.3 to 5.5 of the van Vactor reading are particularly important.
The North American natural gas market is structured based on what has been called the “hub” model of gas pricing. In markets with hub pricing, the interaction of supply and demand sets prices at a small number of specific locations. These locations are the "hubs." (In the van Vactor reading and in one of the assignment questions for this week, there is some discussion about the properties of a good "hub" trading point.) The "local" price at any other location or points of consumption in the commodity network is thus the hub price plus the cost of transportation from the hub. Price differences between any two points in the gas pipeline network represent just the cost of transportation between those two points.
We can also describe this using some terminology from the natural gas industry (this terminology is also commonplace in the oil industry). If the hub represents some point where it is easy to obtain natural gas without much transportation, this would be referred to as the wellhead price. The point where the natural gas enters the distribution system for local delivery (see Figure 5.1 from the van Vactor reading) is known as the citygate. The citygate price should generally be higher than the wellhead price because of the transportation cost associated with moving the gas from the wellhead to the citygate. The wellhead price simply represents the market equilibrium for the natural gas commodity, not including any transportation costs.
One implication of the hub pricing model is that the entire gas market should roughly obey the law of one price, which says that if the gas market is working efficiently, then price differences should reflect transportation costs. A change in the wellhead price of natural gas would be reflected everywhere else in the network.
Here is a simple illustration of the law of one price, using the small network shown in Figure 10.17. In this example, p represents the price at a given location and c represents the transportation cost to get from the supply point (wellhead) to a specific location. Gas fields A and B are connected via pipeline to market centers (you can think of these as citygates) 1 and 2. A and B can deliver to both markets, but the transportation cost c is higher to get to market 2 than market 1. Thus, c1 < c2 and in equilibrium, p1 < p2. Thus, gas at Market 1 will be cheaper than gas at Market 2.
Further, in equilibrium p2 = p1 + (c2 – c1), which in words says that the price at Market 2 would be equal to the price at Market 1 plus the transportation cost to Market 2 from Market 1 (the transportation cost to Market 1 from the wellhead is already reflected in the price at Market 1). Using number, suppose that the price of natural gas at the wellhead (either field A or B) was $5 per million BTU ($/MMBTU). The transportation cost to market 1 is $1/MMBTU and the transportation cost to market 2 is $3/MMBTU. If this gas market obeyed the law of one price, then the citygate price at market 1 would be $5/MMBTU + $1/MMBTU = $6/MMBTU. The citygate price at market 2 would thus be $6/MMBTU + ($3/MMBTU - $1/MMBTU) = $8/MMBTU.
One implication of the law of one price is that changes in supply or demand at one location can affect pricing at all locations in the network. Here is another example, again using the same network (see Figure 10.18). Suppose that demand in market 2 were to increase. Suppliers in both fields would increase their offer prices so that p2 > p1 + (c2 – c1). Assuming that there are no constraints on the size of the pipeline, that increased demand at market 2 would bid up the price for all market points in the network, since all suppliers would try to move their gas to market 2. (Of course, there is some limit to this redirection of supply, since demand in market 2 is finite.) Supply to market 1 would decrease, raising prices in market 1 to restore equilibrium (where the price difference between markets is equal to the difference in transport costs).
It is possible for markets connected by pipelines to depart from the law of one price. The simplest example of how this might happen is if there is a constraint or an interruption on a pipeline. For example, suppose that the pipeline between markets 1 and 2 were to be removed from service, isolating market 2. What would happen to prices at market 1? Market 2? In the assignment for this lesson, you will get some practice looking at the law of one price in the North American natural gas market using data from the U.S. Energy Information Administration.
Every natural gas field within some geographic area has the potential to be a hub pricing point. Locations that are close to producing areas and are also connected to a large number of gas transmission pipelines also have the potential to be good hub pricing points. In general, hub pricing points need to be highly connected to the rest of the network – it should be easy to move gas from the hub point to any other location in the network.
The major hub pricing point in North America is the “Henry Hub,” which is a physical location in Louisiana, indicated in Figure 10.19. Henry Hub is well-connected to the rest of the North American market (as indicated by the flows of gas through North America as shown graphically in Figure 10.20).
The Henry Hub is also the point at which the NYMEX futures contracts for natural gas are priced. The following video shows how to access natural gas pricing data from EIA and NYMEX.
This lesson introduced you to the global markets for the petroleum commodities (crude oil, refined products, and natural gas). These commodities, along with coal, are commonly called fossil fuels. The inherent value of these fuels comes from their relative ease of transportation, their energy density, and their ability to be inventoried and dispatched as needed to meet demand. These characteristics should always be kept in mind when trying to understand the challenges associated with a transition away from fossil fuels.
There is a great deal of complexity in the world petroleum markets. There are entire 3- and 6-credit courses offered on the subject. We covered a lot of ground in this single module. There is also supplementary material from the previous iteration of EME 801 (written and developed by Dr. Seth Blumsack) which is attached as an appendix. You are strongly encouraged to read this material and digest it as further background on energy markets provides great insight to inform decisions that each of you will be called on to make.
You have reached the end of Lesson 10! Double-check the What is Due for Lesson 10? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 11.
As shown in Figure 10A.1, the development of our current globalized oil market can be broken down into a few different stages. The first phase was marked largely by intra-company transactions, with occasional inter-company "spot" sales. The second was defined by the emergence of OPEC and its attempts to influence an increasingly global oil trade for political ends. The third is defined by the commoditization of oil markets, with regional prices linked by inter-regional trade and the development of sophisticated financial instruments such as futures and option contracts, which we'll discuss in more detail below.
While the market for oil is global in reach, trade has clustered itself into several primary regions. This has happened despite shipping costs that are generally low (only a few dollars per barrel) and the ease with which oil cargoes can be directed and redirected towards the highest-priced buyers (in financial terms, oil is "fungible"). Nevertheless, prices in these regions tend to move in tandem.
One reason for regional pricing of crude oil is that it is a heterogeneous commodity - not all crude oils are alike. Some oil can be extracted at a cost of a few dollars per barrel, and flows like water (it would look like Coca-Cola coming out of the ground). Other oil requires sophisticated equipment, techniques and processing to extract, and is thick as tar, requiring special methods to transport it to the refinery (and to refine into saleable petroleum products). In general, oil with a low viscosity is referred to as "light," while thicker, higher-viscosity crude oils are referred to as "heavy." Light oils are generally valued higher than heavy oils. The viscosity of crude oil is measured on a scale known as the API gravity (API stands for "American Petroleum Institute"). The API gravity scale measures how heavy or light a crude oil is, relative to water (thus the terms heavy and light oil). The API gravity of a crude oil is measured by taking its specific gravity (density relative to water), and calculating:
Sulfur content is another important determinant of value; the lower the sulfur content the better. So-called "sweet" oils are low in sulfur, while "sour" oils have a higher sulfur content. There are some differences in crude oil quality among the major trading regions. Pricing of heterogeneous commodities often involves establishing a benchmark or "marker" price that is used to track general price movements. Pricing in any particular transaction is based on the marker price, with adjustments for location and quality. Below are some brief descriptions of some of the major global benchmark oil streams, and the Energy Information Administration has a very nice article on the most important global marker prices [108]. The first two, West Texas Intermediate (WTI) and Brent, are the most important benchmark oil prices in the world.
A very interesting and entertaining thing happened in the crude oil market in April 2020, when the price of oil on the futures market went negative, trading at -$37.63 per barrel. This means that if you were a potential buyer of crude oil, someone would have paid you $37.63 for every barrel of oil you agreed to buy. I don't know about you, but no one has ever paid me to fill my car's gas tank. It usually works the other way around. What on earth happened here? This short blog post from RBN Energy [109] has a good explanation. The reason for this price craziness has to do a little bit with panic in the oil market because of the coronavirus pandemic and a little bit with how futures markets work. Basically, what happened was that there were a bunch of crude oil traders who had contracts to buy crude oil for delivery in May 2020. Those traders either had to take physical delivery of a bunch of barrels of crude oil in May, or find someone else to assume their contract (this is called "closing one's position" in commodity market parlance). Well, since the pandemic had hit and crude oil demand had collapsed, there was no one in the market who really wanted to buy crude oil from these traders. And, there was no place for these traders to physically put the crude oil that they were obligated to take in May 2020. So these traders got caught in a market squeeze and had to pay others to close their positions. It's crazy, and hasn't happened in the crude oil market before...but it makes perfect sense when you realize how this market actually works!
Please read this short but informative background on the PADD system [110] from the Energy Information Administration:
Originally created during World War II for the purposes of regional rationing of gasoline supplies, the Petroleum Administration for Defense Districts (PADDs) are still utilized today to track regional movements of crude oil and (particularly) petroleum products in the United States. While the PADD system might seem a bit archaic, studying the movements of petroleum products between the PADD regions is useful for understanding how these markets are segmented in the United States. Figure 10A.2 shows a map of the PADDs with the locations of oil refineries. Note that the figure shows operable refinery locations and capacity volumes as of January 1, 2012 (a long time ago!). This is because the last full year for which data was published was 2010.
The description of the PADD system from the EIA includes some data on inter-PADD shipments of petroleum products (remember that most of these will be gasoline and the “distillates” – diesel fuel and heating oil). Figure 10A.3 shows this data in visual form, again using the PADD designation map from the EIA. The figure indicates that there is substantial inter-PADD trade between the eastern states, the Gulf Coast and the Midwest. The Rocky Mountain states and the U.S. West Coast, on the other hand, are largely isolated from the rest of the United States and even from one another. Because of a lack of refinery capacity and pipeline capacity, the U.S. West Coast, in particular, has a gasoline and diesel market that is largely separate from the rest of the country. (This is also due in part to California’s gasoline standards, which are more stringent than in the rest of the U.S.)
Petroleum product pipeline maps are not available in the public domain, but you can view an image online at the following websites.
Oil refineries produce value-added petroleum products from crude oil. Profitability is thus determined by several different variables:
Determining profitability for a specific refinery is very difficult, since data on operational and environmental compliance costs are generally not available. A rough measure could be obtained by calculating the cost of crude-oil feedstock (though to do this with precision would require knowledge of the crude blends used in a specific refinery) and comparing that cost with the market value of the suite of products produced at the refinery. This still requires more information than might be publicly available for a typical refinery, and is subject to market conditions for the various products produced.
A useful but simplified measure of refinery profitability is the “crack spread.” The crack spread is the difference in the sales price of the refined product (gasoline and fuel oil distillates) and the price of crude oil. An average refinery would follow what is known as the 3-2-1 crack spread, meaning for every three barrels of oil, the refinery produces an equivalent of two barrels of gasoline and one barrel of distillate fuels (diesel and heating oil). This ratio of refined product output closely mirrors the composition in Figure 2.4, but remember that the crack spread is only a first-order approximation of how profitable a refinery would be at the margin! The higher the crack spread, the more money the refinery will make, so it will be utilizing as much capacity it has available. Inversely, at some lower crack spread prices, it actually may be in the refinery’s best interest, due to costs for the plant, to scale back the amount of capacity utilized. Please see the appendix for an example and further elucidation of the crack spread.
Calculating the 3-2-1 crack spread typically uses published prices for crude oil, gasoline and distillates. These prices are typically taken from the New York Mercantile Exchange. The NYMEX has traded contracts for crude oil and gasoline, but no contract for diesel fuel (the most-produced of the distillate fuel oils). In calculating the 3-2-1 crack spread, prices for heating oil futures are typically used instead. Below is an example of how to calculate the crack spread, using data from 2012.
The crack spread, of course, is not a perfect measure of refinery profitability. What it really measures is whether the refinery will make money at the margin – i.e., whether an additional barrel of crude oil purchased upstream will yield sufficient revenues from saleable products downstream. In reality, existing refineries must consider their refining costs in addition to just the cost of crude oil. These costs include labor (though that is generally a small part of refinery operations); chemical catalysts; utilities; and any short-term financial costs such as borrowing money to maintain refinery operations. These variable costs of refining may amount to perhaps $20 per barrel (depending on conditions in utility pricing and financial markets). In the example above, the true margin on refining would be $6.58 per barrel of crude oil – much lower than the simple crack spread would suggest.
The crack spread tends to be sensitive to the slate of products produced from the refinery. In the example above, we used gasoline and distillate fuel oil (heating oil) because those are two typically high-valued products, and U.S. refineries are generally engineered to maximize production of gasoline and fuel oil.
The crack spread is also sensitive to the selection of the oil price used. In the example above, we used the NYMEX futures price for crude oil, which recall is based on the West Texas Intermediate blend - a fairly light crude oil. Many U.S. refineries, however, are engineered to accept heavier crude oils as feedstocks. If there are systematic differences in the prices of heavy crude oils versus West Texas Intermediate, then the crack spread calculation (while illustrative) may not be sensible for a particular refinery.
The Energy Information Administration recently published a couple of good articles describing how the U.S. refinery fleet has been adjusting to changes in U.S. crude oil production. Not only has the quantity of crude oil produced in the U.S. been increasing rapidly, but the oil coming out of the large shale plays (like the Bakken in North Dakota) is much lighter than the crude oils typically accepted by U.S. refineries.
The first article, Regional Refinery Trends Continue to Evolve [114], published on 7 January 2015
The second article, Regional Refinery Trends Evolve to Accommodate Increased Domestic Crude Oil Production [115], published on 15 January 2015
The regulated electric utility model served the industry and U.S. consumers well for nearly a century. Costs and prices fell nearly continuously, and service expanded to nearly every corner of the U.S. While other countries experimented with their own regulatory systems, for the most part, these involved national electric utilities, not the regulated private enterprises found in the United States.
Beginning in the 1970s, the trend of falling prices suddenly reversed, and the decades of technological progress in power generation slowed. The rapid pace of technological advance had masked some fundamental problems with the way in which electric utilities were regulated. Dissatisfaction among electricity consumers grew, paving the way for the grand experiments in electricity deregulation and restructuring that continue to this day. This lesson and the following lesson will provide an in-depth discussion of how these new markets are structured and will describe some fundamental changes in how the price of electricity is determined. The basic market concepts will be discussed in this lesson, while the next lesson will examine how these markets are changing in response to the emergence of large-scale renewable power generation.
By the end of this lesson, you should be able to:
Please watch the following video interview. If this video is slow to load here on this page, you can always access it and all course videos in the Media Gallery in Canvas.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 11 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Technology improvements and economies of scale caused electric rates to fall until 1970, which made industry and residential customers happy. In 2002 dollars, prices fell from about $5.15 per kWh in 1892 to about 9.7 cents per kWh in 1970. The highly regulated structure of the electric utility business created a stable environment for expansion of access to electric power. Beginning in 1970, however, prices for electric power began to rise sharply: 320% in current dollars from 1970 to 1985 (28% in inflation-adjusted prices), as shown in Figure 11.1.
The oil embargo of 1973-74 affected both the production and consumption of electricity. Petroleum fueled 17% of US electricity generation at that time, and so the curtailment in supply reduced the ability to generate needed electricity (Today, petroleum supplies only 2%). The uncertain supply and much higher prices (which jumped from $3 in September 1973 to $12 a barrel in January 1974) had a devastating effect on the economy directly and the demand for electricity indirectly. Electric power generation in the United States in the period 1949 through 1973 increased exponentially (at the compounded rate of 7 ¾ % per year), and linearly since 1973 (with annual increases of 70 billion kilowatt-hours per year, the equivalent of roughly 15 new large generation plants per year), as shown in Figure 11.2. The transition from exponential to linear growth was unanticipated and led to the decade-long over-capacity of generation discussed above. The industry went through a difficult time after 1973, adjusting to the slower rate of growth utilities had in startup, construction, or planning a doubling of generation capacity. No one knew how long the decreased demand would last and, given the penalties of delaying or canceling construction, much of this capacity was built. If the industry had continued to grow exponentially, it would be almost twice as large today as it actually is.
Price increases were driven by higher petroleum, coal, and natural gas prices, rate increases to cover the cost of over-capacity, particularly in generation, reduced rates of technology improvement, and by investments in coal and nuclear generation plants of a size that stretched available technology beyond cost effectiveness. In the 1970s, nuclear power plants promised low-cost, environmentally benign power. Many utilities started the construction of new nuclear plants. Unfortunately, the utilities learned that building and operating nuclear plants was much more difficult than operating dams and fossil fuel plants. As a result, there were vast cost overruns in construction, e.g., Diablo Canyon and the Washington Public Power System, and poor plant operation, e.g., the fuel rod meltdown at Three Mile Island.
Building excess capacity, eliminating some plants that were in planning or early stages of construction, and having nuclear plants that turned out to be much more expensive than estimated and which didn’t operate well generated tremendously high costs. Public Utility Commissions and consumers resisted putting these costs into the rate base since they raised costs (and prices) markedly. However, since the PUCs had generally approved the investments, there was little alternative to reimbursing the utilities for the majority of these costs.
The electricity price increases came at a time when the deregulation of the airlines (1978), railroads, and the trucking industry (1980) were reducing prices substantially. The price increases upset consumers and generated intense political pressure to hold down electricity rates. One proposed answer was deregulating electricity, fostering competition and lower prices
As a reaction to the 1973 energy crisis, Congress passed the 1978 Public Utilities Regulatory Policies Act (PURPA), eliminating, at least in principle, protected monopolies for electric generation. The success of early non-utility generation facilities and of deregulation in other industries led to provisions in the 1992 Energy Policy Act encouraging wholesale and retail choice in electricity. Over the next decade, nineteen states and the District of Columbia enacted some form of electric restructuring legislation.
As shown in Figure 11.1, the price of electricity rose 50% from 1970 to 1975. The “minor” issues in the Rate of Return Regulation (RORR) structure that had been ignored now became major problems. The defects had been hidden by rapidly evolving generation technology that continually lowered generation costs.
Clearly, there were problems with RORR. One alternative would have been to reform the regulatory process, as was originally tried in the United Kingdom. The other alternative, which was embraced first within Chile and the United Kingdom before spreading to the United States, was a package of reforms that would change the way that some parts of the industry were regulated, and loosen regulations in other parts of the industry.
The terms “deregulation” and “restructuring” are sometimes used interchangeably to describe the changes in the electric power industry starting in the 1990s. While the terms are not interchangeable, both in fact are correct. The generation segment of the electricity supply chain has been progressively deregulated, with power plants competing with one another in many areas of the world to provide service to a regional grid operator. Electric transmission has largely been restructured, not deregulated, with transmission regulation shifting from a local to a regional scale. In the U.S., this has meant that the regulation of electric transmission has shifted from state to federal authorities. Electric distribution has, with few exceptions, retained the same regulated structure that it has always had. This “last mile” of wires service is provided by a public utility that is granted a local monopoly in exchange for rate of return regulation by the state. In states that have undertaken deregulation and/or restructuring, the “electric utility” is in many cases just a distribution company.
Please read the first two sections from Blumsack, Measuring the Benefits and Costs of Regional Electric Grid Integration [119], which describes in more detail the process of deregulation/restructuring and some of the important U.S. federal policy initiatives that have pushed the industry towards its new state. It is important to realize that some aspects of restructuring have essentially arisen from federal initiatives and some from state initiatives. The most important aspects of electricity restructuring are:
Not all areas of the U.S. have adopted deregulation or restructuring. Figures 11.3 and 11.4 show the areas of the U.S. that have adopted competitive wholesale markets for electric energy generation and those individual states that have taken on power-sector reforms at the retail level.
Regional Transmission Organizations (RTOs) are non-profit, public-benefit corporations that were created as a part of electricity restructuring in the United States, beginning in the 1990s. Some RTOs, such as PJM in the Mid-Atlantic states, were created from existing “power pools” dating back many decades (PJM was first organized in the 1920s). The history of the RTO dates back to FERC Orders 888 and 889, which suggested the concept of the “Independent System Operator” (ISO) to ensure non-discriminatory access to transmission systems. FERC Order 2000 encouraged, but did not quite require, all transmission-owning entities to form or join a Regional Transmission Organization to promote the regional administration of high-voltage transmission systems. The difference between RTO and ISO is, at this point, largely semantic. Order 2000 contains a set of technical requirements for any system operator to be considered a FERC-approved RTO.
RTOs are regulated by FERC, not by the states (i.e., RTO rules are determined by a FERC-approved tariff and not by state Public Utility Commissions) and membership in a RTO by any entity is voluntary. Including Texas (which is technically outside of FERC’s jurisdiction), there are seven RTOs in the U.S., covering about half of the states and roughly two-thirds of total U.S. annual electricity demand. Each RTO establishes its own rules and market structures, but there are many commonalities. Broadly, the RTO performs the following functions:
In many ways, RTOs perform the same functions as the vertically-integrated utilities that were supplanted by electricity restructuring. There are, however, a number of important distinctions between RTOs and utilities.
The set of NETL power market primers zipped file [121] contains more information on specific differences between the various RTO markets.
The separation of ownership from control in RTO markets raises some interesting complications for planning. RTOs have responsibility for ensuring reliability and adequacy of the power grid. They must perform regional planning, meaning that they determine where additional power lines and generators are required in order to maintain system reliability. But RTOs generally cannot require that member companies make any investments. They generally rely on a variety of market mechanisms to create financial incentives for member firms to invest in generation. Many transmission investments needed for reliability are eligible for fixed rates of return set by FERC.
Operating a power system requires making decisions on time scales covering fifteen orders of magnitude prior to real-time dispatch, as shown in Figure 11.5.
Since the RTO does not own any physical assets, it must effectively sign contracts with generation suppliers to provide needed services. The market mechanisms run by the RTO are used to procure generation supplies needed to maintain reliability. Once generation supplies are procured by the RTO, it can dispatch generation as needed to meet demand.
RTOs run three types of markets that enable them to manage the power grid over time scales ranging from cycles (one cycle = 1/60th of one second) to several years in advance of real-time dispatch, as shown in Figure 11.6.
Capacity Markets are meant to provide financial incentives for suppliers to keep generation assets online and to induce new investment in generation. Capacity markets are generally forward markets to have generation capacity online and ready to produce electricity at least one year ahead of time. PJM’s capacity market is run three years ahead of time. For example, a generator that participates in the PJM capacity market in 2023 is effectively making a promise to have generation capacity online and ready to produce in 2026. Capacity markets are thought to be necessary because prices in other RTO markets are not always sufficiently high to keep existing generation from shutting down or to entice new generators to enter the market. Not all RTOs have forward capacity markets. Texas, for example, does not operate a capacity market. We will discuss capacity markets in more detail in Lesson 12.
Energy Markets are perhaps the most well-known of all market constructs run by RTOs. Like capacity markets, energy markets are forward markets but are used by the RTO to ensure that enough generation capacity is online and able to produce energy on a day-ahead (24-hour ahead) to one-hour-ahead basis. RTOs run two types of energy markets. The first, the “day-ahead market” is used to determine which generators are scheduled to operate during each hour of the following day (and at what level of output), based on a projection of electricity demand the following day. The second, the “real-time market” is somewhat poorly named; this market is used by the RTO to adjust which generators are scheduled to run on an hour-ahead basis. A better term for the real-time market (which is used in some cases) would be “adjustment market” or “balancing market” since supplies for this so-called real-time market are actually procured one day in advance (but after supplies are procured through the day-ahead market). The prices prevailing in the day-ahead and real-time markets are the most commonly referenced and quoted of all markets run by the RTOs.
Ancillary Services Markets allow the RTO to maintain a portfolio of backup generation in case of unexpectedly high demand or if contingencies, such as generator outages, arise on the system. There are many different types of ancillary services, corresponding to the speed with which the backup generation needs to be dispatched. “Reserves” represent capacity that can be synchronized with the grid and brought to some operating level within 60, 30, or 15 minutes. “Regulation” represents capacity that can change its level of output within a few seconds in response to fluctuations in the system frequency. Ancillary services are increasingly important for renewable energy integration, so we will discuss those markets in Lesson 12.
Suppliers may participate in multiple markets. For example, a 100 MW generator could offer 80 MW to the day-ahead market, 10 MW to the real-time market, and 5 MW each to the regulation and reserves markets. The generator would earn different payments for each type of service provided to the grid. Thus, while the day-ahead or real-time price is often referred to as “the” market price of electricity, in reality, there are many different prices in the RTO market at any given time, each representing a different type of service offered to the RTO.
Because the RTO operates its entire system in an integrated way, even though its footprint may encompass many different utility territories and transmission owners, RTO type markets are sometimes referred to as “power pools” or simply “pools.” The following video contains more information about how the pool-type markets are structured, using the largest pool type model (PJM, in the Mid-Atlantic U.S.).
If you are interested, the following two videos discuss electricity market structures that are alternatives to the pool:
Virtually all RTO markets are operated as “uniform price auctions.” Under the uniform price auction, generators submit supply offers to the RTO, and the RTO chooses the lowest-cost supply offers until supply is equal to the RTO’s demand. This process is called “clearing the market.” The last generator dispatched is called the “marginal unit” and sets the market price. Any generator whose supply offer is below the market-clearing price is said to have “cleared the market,” and is paid the market-clearing price for the amount of supply that cleared the market. Generators with marginal operating costs below the market-clearing price will earn profits. In general, if the market is competitive (all suppliers offer at marginal operating cost) the marginal unit does not earn any profit.
The uniform price auction is illustrated in Figure 11.7. There are five suppliers, each of which offers its capacity to the market at a different price. These supply offers are shown in Table 11.1. Here we will assume that supply offers are equal to the marginal costs of each generator, but in the deregulated generation market suppliers are not really obligated to submit offers that are equal to costs. The RTO aggregates these supply offers to form a single market-wide “dispatch stack” or supply curve. Demand is represented by a vertical line (the RTO assumes that demand is fixed, or “perfectly inelastic” with respect to price). In this case, demand is 55 MWh. Generators A, B, C and D clear the market. Generator E does not clear the market since its supply offer is too high. The market-clearing price, known as the “system marginal price (SMP)” would be $40 per MWh. Generators A, B, C, and D would each be paid $40 per MWh. Generators A, B and C would earn profit. Generator D is the marginal unit so it earns zero profit.
Supplier | Capacity (MW) |
Marginal Cost ($/MWh) |
---|---|---|
A | 10 | $10 |
B | 15 | $15 |
C | 20 | $30 |
D | 25 | $40 |
E | 10 | $70 |
Let’s calculate the profits for each of our generators. Remember that each generator that clears the market (in this case, it would be A, B, C, and D; E does not clear the market) earns the SMP for each unit of electricity they sell. Total profits are thus calculated as:
Since the SMP in our example is equal to $40, profits are calculated as:
Note in particular that Firm D, which is the “marginal unit” setting the SMP of $40/MWh, clears the market but does not earn any profits. We will come back to this case when we discuss capacity markets in Lesson 7.
Unlike petroleum pipelines or natural gas pipelines, which in most cases require compression to maintain sufficient pressure to move product from the wellhead to the sales point, the cost of moving additional electrons through a network of conductors is essentially zero, since there is no fuel cost for “compression” in electrical networks.
(Actually, this isn’t quite true for a couple of reasons. First, transmission lines aren’t perfect conductors, so there is some resistance in the network. Because of this resistance, some of the electricity injected into the transmission grid by power generators is lost as heat between the power plant and the customer. The magnitude of these “resistive losses” is around 10% in a modern power grid like North America’s. What this means is that the system has to generate more power than is actually demanded, to account for these losses. For example, if demand in a system with 10% losses is 100 MW, then the system will need to generate around 111 MW. Second, the transmission grid needs to maintain a certain voltage level, and maintaining this level sometimes involves output adjustments at the power plant location, which does impose an economic cost on the system. In the discussion here, we will ignore these two costs to focus on the effects of transmission congestion.)
In the market-clearing example that we just went through, all suppliers were paid the market-clearing “system marginal price,” and the problem did not really say anything about where the generators or customers were located, or what the transmission network looked like. We just assumed that power produced at any one plant could be delivered to a customer at any location in the transmission network. Thus, electricity markets should exhibit the law of one price, just as we saw in natural gas networks.
While electricity markets should exhibit the law of one price, the reality is that they often do not. Figure 11.8 shows a contour map of electricity prices in the PJM electricity grid on a warm, but not terribly hot day in June 2005. You can find more up-to-date maps [122] on the PJM website [123]. You can also find a really nifty animation of LMPs [124] from the MISO market [125]. If you look carefully at the MISO animation, you may see some negative prices, meaning that someone using electricity gets paid to use it, and someone producing electricity actually pays to generate power! While this may seem strange, it is actually a natural economic outcome of a market with fluctuating supply and demand, and no storage. We'll get to the negative-price phenomenon later in the course.
Getting back to our picture of prices in the PJM system, you can see that prices in the western portion of PJM’s grid are an order of magnitude lower than prices in the eastern portion of the grid. This means that a power plant located in, say, Pittsburgh could make a lot of money by selling electricity to customers in Washington, DC. The demand from Washington should bid up the price in Pittsburgh as more generation in Pittsburgh comes online to serve the Washington market. This is just what we saw in our study of natural gas markets. But this doesn’t happen in electricity. Why?
The basic answer is “transmission congestion.” Conductors cannot hold an infinite number of electrons. At some point, the resistive heat would just cause the conductor to melt. (Remember that materials expand when they get hot. When you hear about power lines “sagging” into trees, that is what’s going on.) So, power system engineers place limits on the amount of power that a transmission line can carry at any point in time. When a line’s loading hits its rated capacity, we say that the line is “congested” and it can’t transfer any additional power. So, the system has to find another way to meet demand, without additionally loading congested lines. Usually, this involves reducing output at low-cost generators and increasing output at higher-cost generators. This process, known as “out of merit dispatch,” imposes an economic cost on the system.
After correcting for transmission congestion by adjusting the dispatch of power plants, the cost of meeting demand in one location (e.g., Washington) may be substantially higher than the cost of meeting demand in another location (e.g., Pittsburgh). The locational marginal price (LMP) at some particular point in the grid measures the marginal cost of delivering an additional unit of electric energy (i.e., a marginal MWh) to that location.
We will illustrate the concept of LMP using the two-node network shown in Figure 11.9. Node 1 has 100 MWh of demand, while node 2 has 800 MWh of demand. There are two generators in the system – one at node 1 with a marginal cost of $20/MWh and one at node 2 with a marginal cost of $40/MWh. A transmission line connects the two nodes. For the purposes of this example, assume that either of the generators could produce 1,000 MWh, and we will ignore any issues with transmission losses. Both generators submit supply offers to the electricity market that are equal to their marginal costs.
Suppose that the transmission line could carry an infinite amount of electricity. How should the system operator dispatch the generators to meet demand? Either generator by itself could meet all 900 MWh of demand in the system, so the system operator would dispatch generator 1 at 900 MWh and generator 2 would not be dispatched. The SMP would be equal to $20/MWh.
Now, suppose that the transmission line could carry only 500 MWh. In this case, the system operator could supply all of the demand at node 1 with generator 1, but only 500 MWh of demand at node 2 with generator 1. The remaining demand at node 2 would need to be met by dispatching generator 2. Since there is plenty of generation capacity left at generator 1, if demand at node 1 were to increase by 1 MWh, it could be met using generator 1 and thus the LMP at node 1 is $20 per MWh. If demand at node 2 were to increase, that demand would need to be met by increasing output at generator 2. Thus, the LMP at node 2 would be $40 per MWh.
Next, we'll look at how much the Generators are paid and how much the Customers pay. Customers at Node 1 are charged $20 per MWh, while customers at Node 2 are charged $40 per MWh for all energy consumed. Generator 1 is paid $20 per MWh, while Generator 2 is paid $40 per MWh for all energy produced. You may notice here that while customers at Node 2 pay $40 per MWh for all 800 MWh that they consume, some of that energy was imported from Node 1 at a much lower cost. This is not a mistake - it's how the LMP pricing system works in the US.
The RTO collects revenue from the customers as follows:
From Node 1: 100 MWh × $20 per MWh = $2,000
From Node 2: 800 MWh × $40 per MWh = $32,000
Total Collections = $34,000
The RTO pays the generators as follows:
To Node 1: 600 MWh × $20 per MWh = $12,000
From Node 2: 300 MWh × $40 per MWh = $12,000
Total Collections = $24,000
The RTO collects excess revenue in the amount of $34,000 - $24,000 = $10,000. It collects this excess revenue because customers at Node 2 pay $40/MWh for all energy consumed, while some of those megawatt-hours only cost $20/MWh to produce.
This excess revenue is called "congestion revenue." In general, when there is congestion in the network and LMPs differ, then there will be some congestion revenue. We will discuss later in the course what the RTO does with this extra revenue. For now, just remember that whenever there is transmission congestion and LMPs at different nodes of the network aren't equal, the RTO will usually wind up with some congestion revenue.
As an exercise for yourself, calculate the LMPs and congestion revenue under a second two-node example. Demand at Node 1 is 5 MWh, while demand at Node 2 is 10 MWh. The transmission line connecting Nodes 1 and 2 has a capacity of 5 MWh. The marginal cost of Generator 1 is $10/MWh while the marginal cost of Generator 2 is $15/MWh.
You should find that the LMP at Node 1 is $10/MWh; the LMP at Node 2 is $15/MWh; and that 5 MW of power is exported from Node 1 to Node 2 on the transmission line.
You should also find that congestion revenue is equal to $25.
While rate of return regulation served the electricity industry and consumers well for many decades, it had embedded in it a number of incentive problems that made for inefficient operation of electric utilities and some behavior by regulators that was not always in the public interest. In particular, rate of return regulation gave electric utilities incentives to over-invest in capital - the so-called "Averch Johnson" effect. Regulators, in turn, labored under incomplete information regarding the state of the utilities that they were supposed to regulate. Even on their best day, the utilities knew more about the power grid than their regulators, so utilities could more easily get regulators to approve investments. Public utility commissioners are aided by large technical staffs, but at the end of the day, these staff members had expert knowledge but still had less data and system information than the utilities.
Recognizing that generation could be competitive over large regional areas, and that transmission and distribution needed to retain some form of regulation, the restructuring of the electricity industry consisted of the following fundamental changes:
In the United States in particular, restructuring has proceeded unevenly. Around half of U.S. states now have a power sector that has been restructured in some way. The other half still operate following the regulated utility structure that we discussed in Lesson 5.
You have reached the end of Lesson 11! Double check the What is Due for Lesson 11? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 12.
For many decades, the companies that operated electricity grids had to do so with two objectives in mind: reliability (keeping the lights on) and cost (keeping electricity affordable). Despite the complexities of the electric grid, these two objectives were relatively simple to meet, in part because the regulatory system shifted a lot of the financial risk away from companies and onto ratepayers. It isn’t that hard to keep a power grid reliable by overbuilding it.
This lesson builds on the previous two objectives by adding a third to power grid operations – lowering the environmental footprint of power generation by adding large amounts of low-emissions generation resources. This is, very broadly, sometimes called the “renewable integration” challenge. The technical challenges in maintaining the balance of supply and demand are immense, but our focus is on the economic challenges, particularly in the context of electricity deregulation.
By the end of this lesson, you should be able to:
There are a lot of good resources that describe the renewables integration challenge. We will lean on external readings more heavily in this lesson than in previous lessons.
This lesson will take us one week to complete. Please refer to the Course Calendar in Canvas for specific due dates. Specific directions and grading rubrics for assignment submissions can be found in the Lesson 12 module in Canvas.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
Before you get into the details of the lesson, please have a look at the following story from the New York Times about wind farms in Vermont: Intermittent Nature of Green Power is Challenge for Utilities [127].
As you are reading the story, think about the following questions (these would apply to solar as well as wind, but since the story is specifically about wind, we’ll pick on wind to frame the questions). Clearly, the integration of wind into the Vermont electric grid (which is interconnected to the rest of the New England grid) has not gone as smoothly as we might have hoped.
Please read the “Overview” section (through page 14) from " Managing Variable Energy Resources to Increase Renewable Electricity’s Contribution to the Grid [135]."
The terms “renewable energy resources” and “variable energy resources” are often used interchangeably when applied to electric power generation. The two are, in fact, not the same, although there is some overlap. The term “Variable Energy Resource” (VER) refers to any generation resource whose output is not perfectly controllable by a transmission system operator, and whose output is dependent on a fuel resource that cannot be directly stored or stockpiled and whose availability is difficult to predict. Wind and solar power generation are the primary VERs since the sun does not shine all the time (even during the day, clouds and dust can interfere with solar power generation in surprising ways) and the wind does not blow all the time. In some cases, hydroelectricity without storage (so-called “run of river” hydro) could be considered a VER since its output is dependent on streamflows at any given moment. VERs are, in some sense, defined in relation to so-called “dispatchable” or “controllable” power generation resources, which encompass fossil-fuel plants, nuclear, and some hydroelectricity (with reservoir storage). The VER concept is pretty vague if you think about it – after all, coal or natural gas plants can sometimes break and so don’t have perfect availability. Fuel supply chains can also be disrupted for fossil plants. In addition, the “variable” aspect is, at least in concept, nothing new for system operators. Demand varies all the time and system operators are able to handle it without substantial negative impacts.
Keep in mind that the “variability” of VERs is different over different time scales. Figures 7-9 of “Managing Variable Energy Resources to Increase Renewable Electricity’s Contribution to the Grid” show how wind and solar are variable over time scales of days or fewer. Figure 12.2, below, shows wind energy production in the PJM RTO every five minutes over a period of two years. This figure shows how wind production varies seasonally and also inter-annually, with windier and less windy years.
Variability with respect to electricity demand is also important. If you think about it, electric system operators don’t really care about variability in demand or in VERs per se – what they care about is being able to match supply and demand on a continuous basis. If variability between VERs and demand were perfectly synchronous, so that VERs would increase (or decrease) in output right at the moments when demand increased or decreased in output, then there would be no problems. If VERs and demand are anti-correlated or perfectly asynchronous, that poses more of a challenge. Part of the challenge with the wind in Vermont, as you have read, is that the wind tends to blow most strongly at night when there is less electricity demand and the power plants that are serving that demand are inflexible “base load” units that are difficult to ramp down. This is typical of wind – Figure 12.3 shows (normalized) wind and electricity demand by season in the PJM RTO. Solar, on the other hand, is much more highly correlated with electricity demand (at least on a day-to-day basis).
Whether or not you believe that there is anything “special” about VERs, electric grid operators around the world are rethinking the way that they plan and operate their systems and markets in order to accommodate various forms of policy that are promoting investment in VERs. Read the introductory portion of Chapter 4 of Energy Storage on the Grid and the Short-term Variability of Wind and pp.15-20 of “Managing Variable Energy Resources to Increase Renewable Electricity’s Contribution to the Grid,” which discuss various types of strategies that grid operators have been using to manage large-scale VERs on the grid. As you may have gathered from the Vermont article, some of the control strategies used most often by grid operators (such as manually curtailing wind energy output during periods when supply exceeds demand) have also been the most controversial.
More generally, there are three economic challenges relevant to VER integration, each of which we will discuss in a bit more detail.
Many restructured electricity markets offer power generators payments for the capacity that they have ready to produce electricity, not just for the electricity that they actually produce. For example, if you owned a 100 MW (100,000 kW) power plant and the capacity payment was $10 per kW per month, you would earn $12 million per year regardless of how much electricity you actually produced. In areas that have them, capacity payments have become a major portion of a generator’s revenue stream.
Capacity markets are a little bit odd. Almost no other market for any commodity, anywhere in the world, has them. (There is a capacity market for natural gas pipelines, but it is operated differently than electricity capacity markets.) In markets for other non-storable goods, like hotel rooms and seats on airplanes, any fixed costs of operations are rolled into the room rate or ticket price. If there is enough unused capacity, then it will exit the market (the hotel will close, or the airline will go bankrupt). Yet, this doesn’t really happen in electricity.
There are three features in electricity markets that have justified the need for capacity markets – two are regulatory interventions. The first goes back to 1965, when a large blackout crippled much of the U.S. Northeast [139] [13]. Rather than being saddled with additional regulations imposed by an angry government, the electricity industry adopted a set of then-voluntary standards [140] [14] for reliability. (The standards are now mandatory.) One of these standards was called the “installed capacity” requirement, which stated that electric grid operators needed to control more capacity than they thought they would need to meet peak demand. For example, if annual peak electricity demand in your system was 100 MW, you might be required to own or control 120 MW of capacity, just in case your power plants broke; your estimate of demand was wrong; or some combination of both. This extra capacity requirement is sometimes called the “capacity margin.”
The second intervention goes back to California’s power crisis of 2000 and 2001. California was one of the first states to deregulate its electricity industry, and it got a lot wrong in the deregulation process. In particular, as firms like Enron found out, the markets created in California were ridiculously simple to manipulate. Prices could easily be driven to levels 100 times higher than normal. Following California’s debacle, other regions did push forward with deregulation, but no one wanted to be the next California. So, virtually all restructured markets instituted “caps” (or upper limits) on electricity prices. In PJM, for example, the price cap is set at $1,000 per MWh. This is the maximum amount that any company may charge for electricity. The markets also instituted watchdogs known as “market monitors” who are tasked with reviewing supply bids submitted by generating companies and flagging those that are deemed to be manipulative. California showed us that electricity markets (even those that are well-designed) are susceptible to manipulation, so many of these market monitors are quite aggressive, punishing firms that submit bids that are higher than marginal costs.
The final regulatory intervention is retail electric rates that are fixed and do not reflect fluctuations in the cost of generating electricity. This has partly been justified on the grounds of protecting consumers from volatile energy prices (the fact is that no energy commodity has more price volatility than does electricity), and also due to the fact that the electric meters that most customers have are still based on a century-old analog technology that does not allow the utility to bill customers based on time of use.
So, suppose that the electric system operator decided that a new power plant needed to be built “for reliability” (i.e., in order to meet the installed capacity requirement). In a market environment, all you would need is for someone to build the plant, operate it during times of peak demand when prices are relatively high, and rake in the dough. Sounds simple enough, right?
Figure 12.4 shows why, in fact, the situation is not so simple. The figure shows the average cost of producing one megawatt-hour from a new natural gas power plant (the “levelized cost of energy,” which we will meet in detail in a future lesson), as a function of how often the plant operates. The more hours the plant operates, the more productive hours over which it can spread its capital cost, so the lower price it would need to charge in order to be profitable. A typical power plant that would operate only during the highest-priced hours would need to charge a price higher than the price cap in order to remain financially solvent. Furthermore, consumers buying energy from that plant would pay the fixed retail rate, not the plant’s levelized cost of energy.
Figure 12.4 basically illustrates the conundrum: someone needs to build the plant for reliability reasons. But because of price caps and fixed retail pricing, no one would ever make money operating this plant. We are stuck in a contradiction – the system operator needs to maintain its installed capacity margin, but no power generation company has any incentive to build the plants that will meet this regulatory requirement.
In electric systems that have not restructured, this isn’t so much of a problem since the regulated utility can ask its public utility commission to pass through the costs of this plant to its ratepayers. But in a deregulated generation environment, there is no guarantee of cost recovery. This conundrum is sometimes called the “missing money” problem. Capacity payments are supposed to solve this problem by providing additional revenue to power plants to keep them operating. It is important to realize that capacity payments have been incredibly controversial since they are sometimes viewed as windfall profits for power generators.
To see the relevance to VER integration, let’s take our example of the uniform price auction from the previous lesson. In that example, we have five generators and demand is 55 MWh. Generator D clears the market and the SMP is $40/MWh. Profits are:
You can see how Firm D might have a “missing money” problem since it produces electricity but earns no profits. If Firm D is the “marginal generator” too often, it will not be able to cover any fixed costs and will eventually go out of business. (We didn’t have fixed costs in the example, but most power plants do have some fixed costs of operation, like land leases or other rental payments.)
Now suppose that a 20 MW wind plant was built in this market. Wind energy has a very low marginal cost – so close to zero that we can just call it zero for this example. This new wind energy plant moves the entire dispatch curve to the right by 20 MW. If the wind is producing at full capacity and demand is 55 MWh, then Firm C becomes the market-clearing generator and the SMP falls to $30/MWh. (The 20 MW of wind with zero marginal cost has the same effect on Firms A through E as a 20 MW decline in electricity demand – why?) Try recalculating profits yourself under this scenario. You should get:
While it is nice that the wind farm’s profits are so high and that the wholesale price of power has fallen, three of the five existing generating firms are not making any profit and might consider shutting down altogether. But according to reliability rules, we need those plants to be operating in order to have enough bulk generation capacity. Because of this requirement, the wind farm has effectively instigated a “missing money” problem for some of the other generators in the system. Hence, there is some need for a capacity payment or other revenue stream, but only because of the regulatory requirements for installed capacity.
To figure out what the capacity payment needs to be for a specific generator, we can compare its revenues to its total costs. The difference represents the capacity payment needed for the power plant to break even. Consider two cases as an illustration. First, suppose that Firm C in our example above had fixed costs of $100. With the wind plant in the market, Firm C breaks even on its operating costs (the SMP is $30 per MWh and its production cost is also $30 per MWh) but covers zero of its fixed costs. So, the capacity payment needed for Firm C would be $100. As a second example, suppose that Firm A had fixed costs of $250. With the wind plant in the market, Firm A earns $200 in operating profit, but its fixed costs are $250. Its total profit would thus be -$50 and Firm A would need a capacity payment of $50 in order to break even.
Finally, suppose that Firm B had fixed costs of $100. With operating profits of $225, Firm B earns a total profit of $225 - $100 = $125. Thus, Firm B is profitable even without a capacity payment. Does that mean that Firm B is not allowed to earn a capacity payment in the electricity market? No - Firm B can compete in the capacity market and earn a capacity payment just like any other power plant. While the capacity market was set up because some power plants (like A and C in our illustrations) have revenue shortfalls, participation in the capacity market is not limited just to those plants who "need" a capacity payment to avoid losing money.
You might be wondering what happened to the 20 MW of new wind – doesn’t that count as “capacity?” The answer is that different regions have very different ways of allocating capacity credits to VERs. Typically, the system operator will let a VER count for a fraction of its capacity. So, our 20 MW wind farm in this example would count for perhaps 2 or 3 MW towards the system-wide installed capacity requirement.
In some areas with a high penetration of VERs, particularly wind, the price of electricity has started to become negative, meaning that suppliers must pay the system operator in order to keep producing electricity. This also means that consumers get paid to use more electricity, which sounds like a terrific deal. Here’s how such a thing is possible. Mount Storm Lake in West Virginia is home to both a large coal plant (one of the biggest in the state) and a large wind farm. These plants are connected to the same transmission line that winds its way towards Washington D.C. This transmission line is almost always congested, so the Mount Storm group of power plants cannot always produce at full combined capacity. During an autumn evening, when electricity demand is low, the Mount Storm coal plant is producing at full capacity and meeting electricity demand. All of a sudden, the wind picks up and the Mount Storm wind farm starts to generate a lot of power and there is excess supply at Mount Storm. What options does the system operator have? It could simply shut down the coal plant (which would be hard to do quickly since coal plants are inflexible) or shut down the wind farm (which, as you saw in Vermont, is not without its own problems). Or it could allow the price to go negative and charge either (or both) plants to continue to produce electricity. If there happened to be some electricity consumer at Mount Storm, that consumer could get paid for absorbing that excess supply locally.
In a power grid, supply and demand must be matched at every second. In order to keep the grid operating reliably, system operators need a portfolio of backup resources in case of unplanned events. These backup resources are collectively known as “ancillary services.” These are purchased by the system operator through a type of option arrangement. The system operator might have an agreement with a power plant that gives the system operator the right to start up or shut down that plant if a contingency arises on the power grid, or if demand increases or decreases in ways that the system operator did not predict.
A very simple example of how this might work is shown in Figures 12.6 and 12.7. Figure 12.6 illustrates a hypothetical power system with some amount of predicted or “scheduled” demand. Actual demand deviates from this scheduled demand by small amounts – sometimes higher and sometimes lower. Figure 12.7 shows how a generator providing ancillary services would change its output in response to demand fluctuations – a practice known as “load following.” Ancillary services are the primary mechanisms currently in place for system operators to accommodate unanticipated variations in VER output.
There are many different flavors of ancillary services. Please read the “Ancillary Services Primer” which provides some more detailed information. Two types of ancillary services are of particular relevance for VER integration – reserves and regulation.
“Reserves” represent backup generation that can be called upon in a certain amount of time in case of a contingency on the power grid, like the unexpected loss of a generator or transmission line (interestingly enough, it’s not clear whether or not the unanticipated loss of a VER for resource reasons, like the wind stops blowing, is considered a “contingency” in the eyes of reliability regulators). There are two types of reserves:
“Frequency regulation” or just “regulation” refers to the generation that can respond automatically to detected deviations from the frequency at which all generators in a synchronous AC system are rotating (in the US, this is 60 Hertz; some other countries use 50 Hertz). Regulation is sometimes called “automatic generation control” since the response is typically too fast for a human being to initiate.
Regulation is, at this point in time, the most relevant ancillary service for VER integration and is also one of the most difficult to understand. The frequency of the power grid needs to remain constant at 50 or 60 Hertz (depending on the country). That frequency is related to the demand-supply balance. It may be helpful to think about frequency as analogous to the water level in a bathtub, as illustrated in Figure 12.8. The balance of demand and supply is like the tub having an identically-sized faucet and drain. If the faucet is larger than the drain, the water level rises – and in a power grid if the supply is larger than the demand, then the frequency will go up. If the drain is larger than the faucet (or if demand exceeds supply), then the system frequency declines.
Frequency regulation as an ancillary service corrects for frequency deviations by increasing or decreasing the output of specific generators, usually by small amounts, in order to effectively increase or decrease the size of the faucet relative to the drain. Response times for generators providing regulation are typically on the order of seconds, which is primarily why frequency regulation is used as a way for system operators to ride through unanticipated fluctuations in VER output.
Recall from the Ancillary Services discussion that generators are used to provide frequency regulation by effectively increasing the size of the faucet relative to the drain. There is no particular reason that generators need to have a monopoly on this. If we were to change the size of the drain relative to the faucet, we would accomplish the same thing, right?
The idea of balancing supply and demand on the demand side rather than solely on the supply side is not that new – electric utilities have been paying their customers to put timers on thermostats or hot water heaters for decades. Some utilities have even figured out how to charge customers more for electricity during the day than at night. But following an order from the Federal Energy Regulatory Commission in 2012 (Order 745), there has been a renewed interest in developing mechanisms to pay people and businesses not to consume electricity. “Demand Response” refers to end-use customers adjusting demand in response to price signals, or energy conservation during periods of high demand (to prevent blackouts). Many electricity systems, particularly those with active wholesale markets, have implemented wholesale demand response programs that allow customers to offer demand reduction on the same basis that generators offer supply. There are two basic flavors of demand response:
Many electricity systems, both regulated and restructured, are also experimenting with allowing demand-side participants to offer ancillary services such as frequency regulation (remember the bathtub analogy and you will see that, at least in concept, this is a perfectly sensible idea). But to date, the vast majority of demand-side participation, and over 90% of all of the profits from demand response, has been through the capacity market. Figure 12.9 illustrates the rapid growth in demand-side capacity market participation. In recent capacity auctions, the largest source of new “capacity” was actually commitments to reduce demand rather than increase supply. This figure shows how the components of the PJM Base Residual Auction (BRA) for installed capacity (ICAP) changed over the years 2007-2015. The left side of the figure shows the components of capacity reductions in the form of retirements, derates, et cetera. The right side shows the components of the capacity additions in the form of new generation, demand response, et cetera.
The result has been to introduce volatility into capacity market pricing. Just as the introduction of VERs lowered the price in the electricity market in our previous example, so too has demand response lowered prices in the capacity market, to the point where generators have started to complain of a “missing money” problem in the capacity market as well!
The primary piece of regulation that has enabled demand response in electricity markets is known as FERC Order 745 [141], which mandated that Regional Transmission Organizations compensate demand response at the prevailing market price under most conditions. This means that if you successfully offer electricity demand reduction to the RTO, you benefit twice. First, you benefit by not having to pay for the electricity that you did not consume. Second, you benefit because the RTO pays you the prevailing market price for all of that foregone consumption.
Because of the way that Order 745 has mandated that demand response be paid, it has been very controversial, so much so that it was successfully challenged in May of 2014 before the U.S. Circuit Court in Washington, DC. The gist of the arguments against Order 745 was as follows:
The D.C. Circuit Court agreed with both of these arguments and overturned FERC Order 745, effectively removing economic demand response as a participant in U.S. electricity markets. In a moment that captured the attention of electric power industry geeks everywhere, the D.C. Circuit Court decision was appealed to the U.S. Supreme Court [142], which overturned the Circuit Court decision [143] and allowed demand response to be paid the same way that power plants get paid. If you happen to be a legal junkie, you might like to read the Supreme Court's decision [144], which has had implications for a lot of different smart grid technology programs run by regional power markets.
Variable Energy Resources (VERs) - specifically wind and solar - do introduce some special economic challenges to power grids and electricity markets. Since VERs have such low marginal costs (remember that fuel from the wind and sun is free) they do have the potential to depress prices in the day-ahead and real-time energy markets, even producing negative prices in some cases. This creates challenges for system operators in restructured areas in particular, since they are charged with having enough generation capacity on the grid to meet reliability requirements, but individual generation owners will stay in the market based only on business criteria - whether they earn enough money to make continued operations worthwhile. The resulting conflict between market and regulatory signals has been called the "missing money" problem, and capacity payments have been introduced, albeit controversially, as a mechanism to make up for the "missing money." Energy output from VERs can also fluctuate faster than system operators are able to adjust for, so large-scale VER integration will almost certainly increase the demand for existing ancillary services (particularly frequency regulation) and may necessitate new types of ancillary services, which could be provided by either demand-side or supply-side resources.
You have reached the end of Lesson 12! Double check the What is Due for Lesson 12? list on the first page of this lesson to make sure you have completed all of the activities listed there before you begin Lesson 13.
In our last Lesson, we touched on the challenges of the integration of intermittent generating resources into the power grid. We will continue the discussion of these challenges, add some more concepts, and discuss some real world examples of the types of issues we now confront and some of those we will confront in the future. The lesson is pretty heavy on readings from research and academia this week. This is to be expected, as we are dealing with major transitions, here. One thing you can be sure of - the readings will be foundational, but also out of date fairly shortly. Certain problems will persist, while others will be solved. The motivation for the inclusion of these papers is to show the current state of affairs while also allowing you to place the best thinking about these problems in context.
By the end of this lesson, you should be able to:
There are a lot of good resources
This lesson will take us one week to complete. Please refer to the Course Calendar for specific due dates. Specific directions for the assignment below can be found within this lesson.
If you have any questions, please post them to our Questions? discussion forum (not email). I will not be reviewing these. I encourage you to work as a cohort in that space. If you do require assistance, please reach out to me directly after you have worked with your cohort --- I am always happy to get on a one-on-one call, or even better, with a group of you.
The notion of energy storage is fairly straight forward and simple from a business context. In business we would call it “inventory.” This is the notion that we should have enough supply in stock to meet demand on a timely basis. In the context of a supermarket, the grocer would have a certain number of cartons of eggs, for instance, so that throughout the day as customers showed up, there would be eggs available to be bought. Energy as a good is slightly different from eggs, though. Certain forms of energy can be stockpiled or inventoried like gasoline in your car’s tank or natural gas in a storage reservoir. Other forms of energy must be produced at exactly the same time as they are consumed. This is mainly true for electricity, which must be balanced on any hourly basis. Natural gas loads are balanced with supply on an hourly basis as the commodity can be stored and pressures can fluctuate to allow for peaks and troughs in delivery. The storage of energy allows us to match supply with demand on a timely basis (instantaneously, hourly, daily, seasonally, annually).
In the case of natural gas we have a storage network by state that looks like this in the US:
Check out the US Energy Information Administration site's Natural Gas page [154] for some interesting discussion about natural gas storage.
In terms of the types of storage and the locations, please see the map below (Figure 13.2).
Natural gas can be stored in underground caverns for release on a seasonal, monthly, daily and intra-day basis. The need to balance the system is not necessarily instantaneous, but there does need to be gas available to meet sudden increases in demand (usually due to cold weather).
The electricity grid, though, must be able to meet increases and decreases in demand on an instantaneous basis. This means that until recently, almost all fluctuations in electricity demand were met by ramping a power plant up or down. With the advent of intermittency on the generation side, the value of the ability to meet less predictable fluctuations has increased the interest in electricity storage. Please see the diagram below for insight into the technologies being considered as we transition to a more flexible energy delivery system. The diagram shows how various technologies are positioned to provide services to the electricity grid. On the x-axis is depicted the scale in terms of the capacity of the resource in Ws. On the y-axis, we see the temporal ability of the resource in terms of seconds, minutes, and hours. The selected technology then appears at an intersection of those two variables (capacity and size). For instance, a nickel-cadmium battery can provide up to 200 kW in the second-to-minute range, making it a good resource to provide Uninterrupted Power Supply (UPS) or Power Quality services.
One of the most important issues in the renewable energy transition is that posed by the ability of the grid to accept the great quantity of renewable resources required for decarbonization. The issues can be broken down into two major categories. The first is location. We have discussed transportation basis differential in previous lessons, and it plays an important role in the integration of renewable resources as well. It is simply a fact that the renewable resources are not necessarily located near the energy loads. Supply and demand are separated by space. (Much of the electric load in the US is located near the coasts, while much of the wind resource is located in the Mid-Continent or off-shore. There is a great solar resource in the desert of Arizona without a really significant load there.) Consequently, the interstate electric transmission grid will need to be built out to a significant degree for the loads intended to be met with electricity to be served. This is not always an easy process as transmission paths can be disputed and new transmission is expensive to build.
The second issue is variability. Intermittent renewable resources like wind and solar do not respond to grid dispatch commands the way that on-demand combustion and steam turbines do. Since most renewable resources are not currently dispatchable and actually may become available in large quantities at inconvenient times, there comes a place for increased levels of ancillary services provided by the transmission grid. These services take the form of frequency response, spinning and non-spinning reserve. Sometimes these services can be supplied by shifting loads, electric and thermal storage, but very often by dispatchable gas or diesel fired combustion turbines. These combustion turbines are often very carbon intensive, and if we are to reduce the carbon footprint of the electric grid, it becomes essential to try to move to supplying these ancillary services from other sources. This is one of the reasons we are starting to see greater deployment of battery storage with solar and wind.
The Duck Curve is a classic example of having a lot of something when you might not need it as much as you used to. In California, electricity loads peak in the mid-day when commercial and residential air conditioning peaks as a result of warm weather. This mid- to late-afternoon peak coincides quite nicely with the fact that the solar resource is also at its greatest when the sun is high in the sky. The electricity generation eco-system in California responded by building a lot of roof-top, commercial and utility scale solar to capture this resource as well as meet environmental energy generation targets. This led to a significant proliferation of solar generation, which provided huge amounts of energy in the middle of the day, thereby depressing the mid-day peak and moving it to the late afternoon with a significant ramp. Please see the figure below, which shows the net loads on the CAISO during the March-May time frame. Kind of looks like a duck, no?
This phenomenon poses two significant problems for the energy market. First, grid stress - the system has to be able to meet that ramp in net usage from HE1600-HE1900 which is very steep. Second, economics - how does the market price a resource for which there may be zero demand, as is shown above?
The Falcon Curve is a potential problem that we could face as we start to electrify heating and domestic hot water in the cold climates around the world. A significant portion of the energy consumed in the northern states and in Canada goes to provide heating in cold climates. One very simple decarbonization strategy for the decarbonization of heating is the installation of air source heat pumps. This strategy appears attractive currently because the electric system is built to provide enough electricity for a currently high summer peak. So currently, there is enough electric system transmission and distribution capacity to meet demand for the addition of a certain level of air source heat pumps. Unfortunately, these air source heat pumps do not operate as well when temperatures get lower. As we approach a greater saturation rate with air source heat pumps, virtually all systems will become winter peaking, which will cause the annual energy demand to be much higher than it currently is in the winter months. This phenomenon was actually experienced in Texas in February 2021 during Winter Storm Uri when electric heating demand skyrocketed when temperatures plummeted. If there had not been blackouts, it is entirely possible that the Texas grid would have posted an all-time peak in the winter even in a climate as usually warm as Texas is. The curve below kind of looks like a Falcon, eh?
The challenges of renewable energy integration are many, but these examples provide a look at how people are thinking about them. I challenge you as a student of EME 801 to begin to use some of the tools we developed in class and during the project to think about challenges you face and how you might meet them. Enjoy.
In this lesson, we have presented a few of the problems with the integration and development of renewables as they exist in the current environment. As the energy transition continues, some of these will be solved, some will be exacerbated and others will emerge. The skills developed and honed in this course will allow you to think critically about them and help to solve them.
You have reached the end of Lesson 13! Double check the What is Due for Lesson 13? section of the Lesson 13 Course Overview page. Complete the quiz and remember and rejoice - there is no deliverable due for Lesson 13.... but please continue to work on your project!
Note: Lesson 13 is the final lesson of new content for EME 801.
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