Solar Photovoltaics

When most people think of “solar power,” they think about one of two things – vast arrays of solar collectors laid out in hot deserts (the left-hand panel below) or smaller arrays on rooftops or highways (the right-hand panel below). This is perhaps the most ubiquitous method of converting solar energy into electricity, but it is not the only method. These arrays of solar collectors are known as “solar photovoltaic” installations or Solar PV for short.

Solar photovoltaics, large and small

Credit: Left-hand panel is US Air Force, right-hand panel is licensed under CC-BY-SA-3.0

Solar PV installations consist of individual collectors called cells, which are packaged together in bundled modules. An individual cell does not generate enough electricity to power much of anything, which is why they must be bundled together. A single module might be enough to provide electricity for a single parking meter or roadside telephone. A number of modules can be further bundled together to form an array (see below). Multiple arrays might be needed to provide electricity for a building or a house.

Solar cell, module, and array

Credit: Argonne National Laboratory [3]

There are many different kinds of solar PV cells in existence (and even more being developed in research laboratories), but they all work in more or less the same way. Unlike virtually any other type of power plant (be it coal, natural gas or wind), there is no turbine in a Solar PV cell. In fact, there are basically no moving parts at all. .

Solar PV cells harvest solar energy through a phenomenon called the photovoltaic effect, discovered in 1839 by the French physicist Bequerel. Photons of solar energy interact with electrons to “excite” them, causing them to move through conductors, thus producing an electric current. The first solar PV module was made at Bell Labs in the 1950s, but was too expensive to be more than a curiosity; in the 1960’s NASA started to use PV modules in spacecraft and by the 1970s, people started to explore their use in a wider range of terrestrial applications.

This following video explains a bit more about how Solar PV cells work and describes the different Solar PV technologies in use today. One of the potentially most important evolutions in Solar PV technology is the use of semiconducting materials other than silicon in Solar PV cells. These materials are of interest because they could, in concept, allow more of the sun’s energy to be captured on a single array. But they face barriers in the form of high costs and, in some cases, questions about the availability of raw materials.

Video: Photovoltics, a Diverse Technology (4:26)

Most modern Solar PV technologies are relatively inefficient compared to other forms of electricity generation. Remember here that “efficiency” refers to how much of the fuel that is injected into an electricity generation system is actually converted into useful electricity, versus being rejected as waste heat or otherwise escaping from the generation system. While modern coal-fired and gas-fired power plants can have efficiencies as high as 60% (or sometimes even higher), most Solar PV cells convert sunlight to electricity with an efficiency of 20% or less (see below), though this number has been rising over time.

Efficiency of different Solar PV technologies over time

Click for a text description of the Solar PV technologies graph.

This chart from NREL (National Renewable Energy Laboratory) tracks the best research efficiencies of various solar cell technologies over time. Below is a detailed breakdown of each dataset, categorized by solar cell type, along with trends and notable efficiency milestones.

Multijunction Cells (Purple)

• Description: High-efficiency solar cells made from multiple semiconductor materials that capture different parts of the solar spectrum.
• Types:
  • Three-junction (concentrator)
  • Three-junction (non-concentrator)
  • Two-junction (concentrator)
• Trends:
  • Rapid efficiency gains from the 1980s onward.
  • 1995: First exceeded 30% efficiency.
  • 2005–2015: Surpassed 40% efficiency, reaching 45.5% in 2015 (record efficiency).
  • Key contributors: Spectrolab, Fraunhofer ISE, Boeing-Spectrolab, NREL.

Single-Junction GaAs Cells (Pink)

• Description: Made from Gallium Arsenide (GaAs), known for high efficiency.
• Types:
  • Concentrator
  • Thin-film crystal
• Trends:
  • 1980s: Early developments showed steady progress.
  • 1995–2005: Efficiency surpassed 25%.
  • 2010–2015: Efficiency reached 29–30%.
  • Key contributors: NREL, Radboud, Alta Devices.

Crystalline Silicon (Blue)

• Description: The most widely used commercial solar cell technology.
• Types:
  • Single crystal (Monocrystalline)
  • Multicrystalline (Polycrystalline)
  • Thin-film Silicon
  • Silicon Heterojunction (HIT)
• Trends:
  • 1980s: Efficiency ranged from 10% to 15%.
  • 1990s: Research pushed efficiency beyond 20%.
  • 2015: Efficiency peaked at about 26.6%.
  • Key contributors: SunPower, Panasonic, UNSW, Fraunhofer ISE.

Thin-Film Technologies (Green)

• Description: Low-cost, lightweight solar cells using non-silicon materials.
• Types:
  • Cu(In,Ga)Se₂ (CIGS)
  • CdTe (Cadmium Telluride)
  • Amorphous Si:H (stabilized)
  • Nano-, Micro-, Poly-Si
  • Multijunction Polycrystalline
• Trends:
  • 1975–1990: Slow improvements, with efficiencies around 5–10%.
  • 2000s: Notable breakthroughs, pushing CdTe and CIGS beyond 15% efficiency.
  • 2015: Best CIGS efficiency reached 22.3%; CdTe around 21.5%.
  • Key contributors: First Solar, NREL, ZSW, Solar Frontier, GE.

Emerging PV Technologies (Red)

• Description: Next-generation solar technologies still in early development.
• Types:
  • Organic solar cells
  • Organic-inorganic perovskites
  • Quantum dot solar cells
• Trends:
  • 2000s: Initial efficiencies below 5%.
  • 2010s: Perovskite solar cells saw rapid efficiency increases, reaching over 20% by 2015.
  • Key contributors: Oxford PV, UCLA, EPFL, University of Valencia.

Overall Observations

• Multijunction Cells remain the most efficient, exceeding 45%.
• Crystalline Silicon dominates commercial markets, reaching 26.6%.
• Thin-Film and Emerging PV show promise for future cost-effective solutions.
• Perovskites and Quantum Dots could revolutionize the field if efficiency gains continue.

Credit: National Renewable Energy Laboratory [6]

Whether the efficiency of Solar PV cells is all that important is a matter of some debate. On the one hand, higher-efficiency cells would require less land or space to produce a given amount of electricity. Land use (or the number of rooftops) can be a significant limiting factor in the deployment of Solar PV. On the other hand, fuel from the sun is free and there is no scarcity of sunlight, so whether Solar PV cells can achieve 30% efficiency versus 20% efficiency may not be such a big deal, and may not be worth the extra economic cost to produce such high-efficiency cells.

Concentrating Solar Power

If you have ever left a cold drink out in the sun during the summertime (or if you have children, if you have ever left water in the kiddie pool out in the sun for a long time), you would notice that the formerly cold water gets warm – maybe even hot. If it happens to be summertime where you are living right now, try it! Whether you realize it or not, this little science experiment is the basis for a second way of harnessing the sun’s energy to produce electricity, called “concentrated solar power” or CSP. (This technology is also sometimes called “solar thermal.”)

The Gemasolar CSP plant in Spain is using molten salt to collect solar energy concentrated by an array of mirrors. This molten salt acts as a thermal battery, enabling the generation of electricity even when the sun is not shining.

Credit: Gemasolar2012 [7], by Ion Tichy [8] (CC BY-SA [9]) from Wikimedia Commons [10]

The following video explains how CSP works. The basic idea is that a collection of mirrors reflects the sun’s light (and heat) onto a large vessel of water or some other fluid in a metal container. With enough mirrors reflecting all of that sunlight, the fluid in the metal container will get hot enough to turn water into steam. The steam is then used to power a turbine just like in almost any other power plant technology

Solar Energy Potential and Utilization

In addition to being free as a source of energy (it does cost money to harness it and turn it into electricity), energy from the sun is practically limitless. The surface of the Earth receives solar energy at an average of 343 W/m². If we multiply this times the surface area of the Earth, about 5x10¹⁴ m², we get 1715x10¹⁴ W. But, 30% of this is reflected, and only 30% of the Earth is above sea level, so the usable solar energy we receive on the land surface is about 360x10¹⁴ W. We need to reduce this further because not all of the land surface is suited to installation of solar PV panels — we don't want to cut down forests, and ice-covered areas are not suitable, so we reduce the area by about one half. Over the course of a year, this amount of solar energy adds up to 66x10²² Joules. In 2018, we used about 600x10¹⁸ Joules of energy, which is just a shade less than 0.1% of the harvestable solar energy we receive on the land. This means that even if we got all of our energy from the Sun, we would not make a dent in the total! The potential is vast — 10,000 times what we need!

Let’s consider what it would mean for us to get all of our energy from Solar PV — how much of the Earth’s surface would we need to cover with panels? The black dots (radii of 100 km) in the figure below represent areas that could generate enough energy from sunlight to completely power the planet for an entire year. Practically, there are barriers to running the planet entirely on sunlight (everything would need to be electrified, we would need very large quantities of battery energy storage, and so forth), but the dots are useful as a demonstration of just how vast the energy production potential from solar is.

Global average solar irradiance. Areas in red have a higher-strength solar energy resource on average. The black dots indicate areas with sufficient solar energy potential to supply the entire world’s energy demands.

Click here for a description of the Global average solar irradiance image.

This image is a world map illustrating the annual mean solar potential across the globe, measured in watts per square meter (W/m²). The map is sourced from Matthias Loster, 2006, and includes a total solar potential estimate of 18 terawatt-equivalents (TWe) indicated in the bottom right corner with the symbol "Σ⦿ = 18 TWe."

Map Overview:
The map shows a global view with continents outlined in black, including North and South America on the left, Africa in the center, Europe and Asia above, and Australia on the right. The background is a gradient of colors representing solar potential, with a color scale at the bottom ranging from purple (lowest) to red (highest)

• Color Scale:
  The color bar at the bottom ranges from 0 to 350 W/m². Purple represents the lowest solar potential (0–50 W/m²), transitioning through blue (50–100 W/m²), green (100–150 W/m²), yellow (150–200 W/m²), orange (200–300 W/m²), and red (300–350 W/m²) for the highest solar potential.
• Solar Potential Distribution:
  • High Solar Potential (Red/Orange): Areas with the highest solar potential (250–350 W/m²) are concentrated near the equator, including Central Africa, the Middle East, northern Australia, and parts of the central Andes in South America. These regions appear in shades of red and orange.
  • Moderate Solar Potential (Yellow/Green): Moderate solar potential (150–250 W/m²) spans much of South America, Southern Africa, India, Southeast Asia, and northern Australia, shown in yellow and green shades.
  • Low Solar Potential (Blue/Purple): Areas with the lowest solar potential (0–150 W/m²) include the polar regions, northern North America, Northern Europe, and parts of Russia, depicted in blue and purple shades
• Black Dots:
  Several black dots are scattered across the map, likely representing specific locations or solar energy projects. These dots are located in the southwestern United States, central South America, central and Southern Africa, the Middle East, India, and northern Australia.
• Additional Details:
  The map uses a gradient color scheme to visually represent the intensity of solar potential, with equatorial regions showing the highest potential due to their proximity to the sun’s direct rays. The source, "Matthias Loster, 2006," is noted in the bottom right corner.

It’s kind of amazing to see how little area of the Earth would need to be covered to achieve this.

Source: Total Primary Energy Supply [13]

One of the important differences between Solar PV and CSP is that CSP requires more intense sunlight, and as such, it is not a viable option in many places. In contrast, Solar PV works just about everywhere — it is more versatile. Another important difference is in scale — CSP is really suited to utility-scale power plants, whereas Solar PV works at both the utility-scale and the very small scale.

The map below shows the PV potential for the world. The variability in the map is mainly a function of cloudiness and latitude. Many of the big, utility-scale solar PV plants are located in the red areas, but there is a surprising amount of Solar PV energy being harvested in places like Germany and Japan, both of which are fairly cloudy. But, even in a fairly cloudy place like Pennsylvania, you can see from the map that we could expect about 1460 kWh per year from a 1 kW PV array. From this, you can calculate how many square meters of PV panels you’d need to provide the electricity for a house that uses the typical 10,800 kWh per year. If you divide 10,800 kWh by 1460, you see that you’d need about 7kW of solar panels, which would fit on a typical house roof. The main point here is that Solar PV is a viable energy source in most parts of the world where people are living. In contrast to Solar PV, energy from CSP is only viable in places where the daily totals in the map above are higher than 6 kWh/day. Nevertheless, there are many regions where CSP viability and human population coincide, so it too can be an important energy resource in the future.

This world map from the World Bank Group’s Global Solar Atlas shows the estimated potential for Solar PV energy in terms of kWh energy produced from a solar PV array of 1 kW. It is important to understand that daily totals are an average value — the output each day will vary according to how cloudy it is and how high in the sky the Sun is. The yearly totals are just the daily total times 365.

Click for a text description of the Photovolitic power potential map.

The image is a world map titled "Solar Resource Map: Photovoltaic Power Potential," displaying the global distribution of photovoltaic power potential. The map uses a color gradient to indicate the long-term average of photovoltaic power potential (PVOUT), measured in kilowatt-hours per kilowatt-peak (kWh/kWp). Areas with higher potential are depicted in shades of red and orange, while lower potential areas are shown in blues and greens. Notably, regions such as North Africa, the Arabian Peninsula, and parts of Australia show high potential with intense red and orange hues. Northern Europe and parts of Canada show lower potential with blue and green shades. A scale beneath the map indicates the PVOUT values ranging from 2.0 to 6.4 kWh/kWp. Below the scale, yearly totals are listed from 730 to 2337 kWh/kWp. Logos of the World Bank Group, ESMAP, and Solargis are displayed at the top right.

Credit: Global Solar Atlas [15]

The Changing Economics of Solar Energy

The generation of solar energy – primarily through Solar PV – is a story of exponential growth. Since 2000, the global Solar PV industry has grown by around 25% per year on average, so installed capacity has been doubling every 2.7 years (see below). Even so, solar represents a very small sliver of total global power generation — for now.

This shows the history of global solar energy generation in Exajoules (EJ) of energy each year (1 EJ = 10¹⁸J). This growth history is following an exponential curve. In 2018, this represents a small fraction of global energy consumption, which is almost 600 EJ.

Click for a text description of the Global Solar Energy Generation History graph.

The image is a line graph illustrating the historical trend of global solar energy generation from 1985 to 2020. The x-axis represents the years, marked from 1985 to 2020, while the y-axis indicates the energy generated per year in exajoules (EJ), ranging from 0 to 6. The graph depicts a sharp upward trend starting around 2005, indicating significant increases in solar energy production. The blue line that represents the data forms a steep curve upward after 2010, emphasizing rapid growth. A text box on the graph annotates that the average growth rate is 25% per year. The title of the graph is "Global Solar Energy Generation History."

Source: David Bice, data from BP Statistical Review of Energy, 2018 [16]

The nice thing about exponential growth is that it is easy to project it into the future. Over the time period shown in the graph above, solar energy generation has grown by 25% per year; if we continue that into the future, we find that before long, we would have enough solar energy to make up a substantial portion of the global energy needs by 2030 (see figure below). By the year 2040, this growth would rise to 1360 EJ, more than twice the global energy consumption of the present. Of course, that makes no sense — we would not produce more energy than we need, and this reminds us of an important fact, which is that exponential growth cannot continue forever.

This shows the history of global solar energy generation in Exajoules (EJ) of energy each year (1 EJ = 10¹⁸J). This growth history is following an exponential curve. In 2018, this represents a small fraction of global energy consumption, which is almost 600 EJ.

Click for a text description of the Global Solar Energy Generation Projection graph.

The image is a line graph titled "Global Solar Energy Generation Projection," depicting projected solar energy generation from 1985 to 2030. The x-axis represents the year, ranging from 1985 to 2030, and the y-axis represents the energy generated per year in exajoules (EJ), ranging from 0 to 120. The graph starts with a nearly flat blue line from 1985 to around 2015, indicating little to no growth in solar energy production. After 2015, the line turns orange and begins to curve sharply upward, showing a significant increase, projecting exponential growth in solar energy generation up to 2030. A text box within the graph notes "Projection into the future assuming 25% per year growth."

Source: David Bice, data from BP Statistical Review of Energy, 2018 [16]

One reason to trust this projected future growth is that the price of solar energy has fallen dramatically over time as can be seen in the graph below. In fact, if the generation of solar PV energy has been growing exponentially, the price has been dropping exponentially.

The price history of solar PV cells in $/watt shows an incredible decline in price. This is due to technological advances and the economy of scale as more and more PV cells are manufactured.

Click for a text description of the graph: Price History of Silicon PV Cells.

This bar chart illustrates the decline in the cost of silicon photovoltaic (PV) cells from 1977 to 2015, measured in US dollars per watt ($/Watt).

Key Observations:

1977:

• • The price of silicon PV cells was $76.00 per watt, the highest recorded in the dataset.
  • Marked by a tall blue bar at the far left.

1980s:

• • Significant price reduction as PV technology improved.
  • By 1985, the price dropped to around $10 per watt.
  • The bars become progressively shorter, showing a steep decline.

1990s:

• • The price stabilized around $5–10 per watt.
  • The bars maintain similar heights, indicating slower reductions.

2000s:

• • Continued gradual decline, reaching around $2 per watt by mid-2000s.
  • More efficient manufacturing and increased adoption contributed to lower costs.

2015:

• • The price reached $0.30 per watt, as indicated by a highlighted blue label at the bottom right.
  • This marks a 99.6% reduction from 1977 prices.

Additional Details:

• A text box in the center of the graph reads:
  "Price history of silicon PV cells in US$ per watt"
• Data Source: Bloomberg New Energy Finance & pv.energytrend.com
• The trend follows Swanson’s Law, which states that solar PV prices drop by 20% for every doubling of cumulative shipped volume.

Source: Price history of silicon PV cells since 1977 [17] by Rfassbind [18]from Wikimedia Commons [19]

The price decrease is following a pattern that has been given a name: Swanson’s Law, which states that the price drops by about 20% for each doubling in the number of PV cells produced. This law suggests that the prices of solar PV energy will continue to decline in the future.

This brings us to an important question — how does the cost of solar energy compare to other sources of energy? Energy economists have come up with a good way of comparing these costs by adding up all of the costs related to producing energy at some utility-scale power plant (a big wind farm, a big solar PV array, a CSP plant, a nuclear plant, a gas or coal-burning power plant). This is called the levelized cost of energy, and you get it by taking the sum of construction costs, operation and maintenance costs, and fuel costs over the lifetime of a plant and then dividing that by the sum of all the energy produced by the plant over its lifetime. This cost provides us with a way of comparing the energy from different sources. Since the boom in natural gas production due to fracking, natural gas has been the lowest cost form of energy (which is why coal is being used less and less), but energy from solar and wind have been decreasing rapidly, as can be seen in the following graph. When a renewable electrical energy resource such as solar or wind becomes equal in cost to the cheapest fossil fuel source of electricity, we say that the renewable resource has reached "grid parity". Once grid parity is achieved, the renewable resource makes sense from a purely economic standpoint, and as it drops below the grid parity point, it is the smartest electrical energy resource.

The levelized cost of solar energy in the US has been dropping fast in recent years and has been slightly cheaper than electrical energy from natural gas since 2015. The point in time where the two are equal is when solar energy achieved what is called grid parity. Wind energy reached grid parity by 2012, so at this point in time, both of these renewable sources provide cheaper energy than the cheapest fossil fuel source.

Click for a text description of the graph: Levelized Costs of Solar, Wind, and Natural Gas (2008–2018).

This line graph displays the levelized cost of energy (LCOE) for solar, wind, and natural gas from 2008 to 2018 in 2018 dollars per megawatt-hour (MWh). The LCOE represents the total cost of generating electricity from each source, considering installation, maintenance, and fuel costs.

Key Observations:

Solar Energy (Red Line)

• • In 2008, the LCOE for solar was about $180 per MWh, the highest of the three energy sources.
  • The cost declined rapidly over the years, reaching approximately $40 per MWh by 2016.
  • By 2018, solar became the cheapest energy source at under $30 per MWh.
  • The decline reflects advances in solar panel technology, manufacturing efficiency, and increased adoption.

Wind Energy (Green Line)

• • The cost of wind energy fluctuated between $60–80 per MWh from 2008 to 2010.
  • After 2010, wind costs began to decline, reaching a low of about $20 per MWh in 2018.
  • The drop can be attributed to larger and more efficient wind turbines, better grid integration, and falling installation costs.

Natural Gas (Blue Line)

• • The LCOE for natural gas remained relatively stable, fluctuating between $40-60 per MWh over the period.
  • Natural gas was cheaper than solar for most of the timeline but was surpassed by both solar and wind by 2018.
  • The stability of natural gas prices is due to fuel costs, infrastructure maintenance, and policy factors.

Overall Trends:

• Solar energy saw the most dramatic cost reduction, making it one of the cheapest electricity sources by 2018.
• Wind energy also experienced a significant drop, becoming the least expensive energy source by 2018.
• Natural gas remained relatively stable, but was no longer the most cost-effective option by the end of the period.

Source: David Bice, data from Lawrence Berkeley Labs [20].

Part of the reason that solar and wind have expanded in recent years has to do with government policies — a number of countries have instituted subsidy and incentive programs that offset a large portion of the construction/installation costs of solar and wind technologies or devise rules that otherwise give advantages to electricity generation from renewables. Subsidies enacted in various countries have included feed-in tariffs (which guarantee an above-market sales price for solar power); rebates (which directly offset capital and installation costs); and favorable tax treatment (which is like an indirect feed-in tariff). Germany has one of the world’s largest Solar PV markets not because it has the best solar resource on earth but because it has been willing to support a generous feed-in tariff on solar power. (For many years the tariff was over 30 cents per kilowatt-hour, or more than five times the average power price in the United States; in recent years the tariff has been reduced.) These government policies have effectively stimulated the growth of these renewable energy resources, which has, in turn, resulted in lower prices.

How Wind Turbines Work

In a conventional power plant (fueled by coal or natural gas), combustion heats water to steam and the steam pressure is used to spin the blades of a turbine. The turbine is then connected to a generator, which is a giant coil of wire turning in a magnetic field. This action induces electric current to flow in the wire. The workings of a wind turbine are much different, except that instead of using a fossil fuel heat to boil water and generate steam, the wind is used to directly spin the turbine blades to get the generator turning and to get electricity produced.

The inner workings of a wind turbine consist of three basic parts, seen in the figure below. The tower is the tall pole on which the wind turbine sits. The nacelle is the box at the top of the tower that contains the important mechanical pieces – the gearbox and generator. The blades are what actually capture the power of the wind and get the gears turning, delivering power to the generator. The direction that the blades are facing can be rotated so that the turbine always faces into the wind, and the pitch of the blades (the angle at which the blades face into the wind) can also be adjusted. Pitch control is important, especially in very windy conditions, to keep the gearbox from getting overloaded.

Modern wind turbines sit upon towers that are typically 80 meters high or taller, with rotating blades that are 50 meters or longer.

Source: Wikipedia: Lamma wind turbine [22] CC BY-SA 3.0 (Creative Commons [23])

The amount of power (in Watts) collected by a wind turbine is explained in the following equations:

The physics of wind power

Click here for a text description of the physics of wind power equation.

This figure explains the physics of wind power. So we begin with this notion of the moving wind having some kinetic energy which is 1/2 m v2. So that is kinetic energy. Power is related to energy in the following way: It is energy per unit of time. So if we want the power that we can get from that moving wind, we have to take the kinetic energy, and then the mass flux rate instead of just the mass. Mass flux rate is how much mass is moving per unit of time. That is dm over dt, change in mass over change in time. And that is equal to the air density times the area swept out by the windmill blades times the velocity, so that velocity and area multiplied together gives you something with the units of measure are cubic meters per second, and then you multiply that by the density and that gives you kilograms per second and that’s the mass flux rate. If you put all that together, you see the wind power is equal to one half times the air density times the area swept by the blade times the velocity cubed. So you see, the velocity is super important in this. Now then it turns out that there is an efficiency limit, something called the “ Betts Limit” that means that the power you can actually collect is 0.3 times the air density times the velocity cubed.

Source: David Bice

The Kinetic Energy (KE) of the wind is:

Where m = mass, and v = velocity of wind.

Power (P) in the wind is the KE per unit time, so we replace the mass(m) with the mass flux rate dm/dt:

Where p = air density, and A = swept area of blades.

So the wind Power(P) is:

If the wind turbine collected all of this power, the wind would have to stop and the blades would stop spinning. If you want the blades to keep spinning, it turns out that you can collect about 60% of the power (called the Betz limit).

So, collectible Power(P) is:

How much power could we get with a turbine whose blades are 100m long, with a wind speed of 10m/s (about 22mpg>, with an air density of 1.2kg/m2?

This is clearly a lot of power! But, mechanical inefficiencies related to the gears and the generator mean that we might only get 30% of this figure, but that is still a lot of power from one turbine.

All wind turbines have a minimum wind speed that differs depending on the size but is typically about 4-5 m/s (10 mph) and maximum wind speed above which they shut down to avoid damage, usually around 20-25 m/s (about 50 mph). Most wind turbines have a maximum spinning rate, reached a bit above the minimum velocity, and when the wind speeds up, the pitch of the blades is adjusted so that the rate of spinning remains more or less constant. The figure below shows a typical "power curve" for a small wind turbine.

Power curve for a 1.5 MW wind turbine

Click here for a text description of the power curve for 1.5 MW wind turbine.

This figure shows the power curve for a 1.5 megawatt wind turbine. So on the Y-axis is the power and the X-axis is the wind speed in miles per hour. And what you can see is that there is sort of a threshold speed that is something like 6 miles per hour wind speed you start to get some power. And as the wind speed increases, the power output rises rapidly until you get to about 30 miles per hour. At that point the power sort of saturates and flattens out and with more wind you don’t get any more power. So it reaches its capacity at 1.5 megawatts and it generates that up until 50 miles per hour and above that the power drops off rapidly because the wind turbine has a shut off mechanism will turn off if the wind gets going to fast because of the turbulence that can cause damage to the wind turbine. So they just shut down if the winds get to great.

Source: US Department of Energy, Idaho Office

The wind, as you may have noticed, is highly variable in any given place, but as a general rule, it is stronger and steadier as you rise up above the ground. This is because friction between the wind and the land surface slows the wind. But there is also a lot of regional variation in the wind velocity. Both of these factors (elevation above the ground and location) can be seen in the maps below, showing the average wind speed in the US at two different heights.

United States - Annual Average Wind Speed at 30 m

Click here for a text description of the U.S. Annual Average Wind Speed at 30 m.

These two maps of the United States show the average annual wind speed at two different heights above the surface. The upper map shows the wind speed at 30 meters height and the one below shows it at about a hundred meters. You can see a couple thing right away. One is that there is just a lot more wind at greater velocities at this higher elevation above the lands surface. You get to 100 meters and there are a lot of places in the central part of the US where you get wind speed from 8 to 10 meters per second, which is really moving along quit fast. And you also see this lower map of 100 meter of wind speeds of the offshore regions everywhere on the west coast and the east coast and around the Gulf of Mexico there are very high wind speeds. Also the Great Lakes are like this. The primary reasons these offshore regions have such high wind speeds and also why higher up you have such wind speeds are because there are less friction in those settings. So you go higher up from the surface there is less friction from the air and all the trees and the roughness of the land surface. That roughness slows the wind down and as you rise above that to 100 meters you get away from that disturbance and have higher wind velocities. You can also see that in the mid-continent region, both the 30 meter and the 100 meter heights, that’s the area with the greatest wind potential. You have these annual average wind speeds that are quit high and this is primarily because this is flat part of the country. There are not a whole lot of topography in those areas so the winds can really get going and be maintained. They do not encounter mountains and valleys and the sort of complexity that you see in other areas where further out west the wind speeds are not that high. So you can look at this and see right away that if you wanted to develop wind power, the best places are in the middle of the continent and at a high elevation 100 meters above the surface. That is why you see so many tall wind turbines to get up that high.

Credit: National Renewable Energy Laboratory (NREL) [24]

United States - Land-based and Offshore Annual Average Wind Speed at 100 m

Click here for a text description of the U.S. Land-based and Offshore Annual Average Wind Speed at 100 m.

These two maps of the United States show the average annual wind speed at two different heights above the surface. The upper map shows the wind speed at 30 meters height, and the one below shows it at about a hundred meters. You can see a couple thing right away. One is that there is just a lot more wind at greater velocities at this higher elevation above the land's surface. You get to 100 meters and there are a lot of places in the central part of the US where you get wind speed from 8 to 10 meters per second, which is really moving along quite fast. And you also see this lower map of 100 meter of wind speeds of the offshore regions everywhere on the west coast and the east coast and around the Gulf of Mexico there are very high wind speeds. Also, the Great Lakes are like this. The primary reasons these offshore regions have such high wind speeds and also why higher up you have such wind speeds are because there are less friction in those settings. So you go higher up from the surface, there is less friction from the air and all the trees and the roughness of the land surface. That roughness slows the wind down and as you rise above that to 100 meters you get away from that disturbance and have higher wind velocities. You can also see that in the mid-continent region, both the 30 meter and the 100-meter heights, that’s the area with the greatest wind potential. You have these annual average wind speeds that are quite high, and this is primarily because this is a flat part of the country. There are not a whole lot of topography in those areas, so the winds can really get going and be maintained. They do not encounter mountains and valleys and the sort of complexity that you see in other areas, where further out west the wind speeds are not that high. So you can look at this and see right away that if you wanted to develop wind power, the best places are in the middle of the continent and at a high elevation 100 meters above the surface. That is why you see so many tall wind turbines to get up that high.

Credit: National Renewable Energy Laboratory (NREL) [24]

The graphs above show annual average wind speeds in the US at 2 different heights above the ground surface. For reference, 10 m/s is 22.3 mph. You can see that the wind speeds at 100 m are far greater than at 30 m — this is the friction effect of the land surface (which is minimal above large water bodies). As you can see, the Great Plains have great wind potential, as do the Great Lakes and offshore areas on both coasts.

The area covered by the turbine’s blades is another important factor in determining power output. While wind turbines are available in a wide variety of capacities, from a few kilowatts to many thousands of kilowatts, it’s the larger turbine sizes that are being deployed most rapidly in wind farms. Several years ago the image on the right side of the figure below of a Boeing 747 superimposed on a wind turbine gave an astonishing representation of the scale of the state-of-the-art wind technology. Now, turbine rotor diameters are approaching the size of the Washington Monument!

Over the past decade, wind turbine blades have grown from being about the size of the wingspan of a jumbo jet to being about as long as an American football field.

Click for a text description of the Wind Turbine graph.

The image is a graph that illustrates the progression of rotor diameters of wind turbines over time, comparing them to the wing span of an Airbus A380.

• The x-axis represents the years from 1985 to 2010 and beyond, with specific years marked: '85, '87, '90, '91, '93, '95, '97, '99, '01, '03, '05, '10, and an estimated future point labeled as "1ˢᵗ year of operation" with a capacity range of 8 to 10 MW.
• The y-axis represents the rotor diameter in meters (m), ranging from 15 meters to 250 meters.
• The graph shows a series of circles, each representing the rotor diameter of wind turbines at different points in time. These circles increase in size as the timeline progresses, indicating the growth in rotor diameter over the years.
• Each circle is labeled with its corresponding rotor diameter:
  • 1985: 15 m
  • 1987: 30 m
  • 1990: 40 m
  • 1991: 50 m
  • 1993: 60 m
  • 1995: 70 m
  • 1997: 80 m
  • 1999: 90 m
  • 2001: 100 m
  • 2003: 110 m
  • 2005: 120 m
  • 2010: 130 m
  • Future (8-10 MW): 250 m
• A red line runs through the centers of these circles, showing the trend of increasing rotor diameter over time.
• On the right side of the graph, there is an illustration of an Airbus A380 with a wing span of 80 meters for comparison. An arrow points from the Airbus A380 to the largest circle (250 m), suggesting a comparison between the wing span of the airplane and the rotor diameter of future wind turbines.
• The background of the graph is light blue, and the circles are shaded in orange with red outlines.

This graph visually demonstrates the significant increase in wind turbine rotor diameters over the years, projecting into the future with much larger sizes compared to current standards.

Credit: UpWind: Design Limits and Solutions for Very Large Wind Turbines [25], 2011. European Wind Energy Association

Activate Your Learning

Given that the area of wind captured by the turbine is proportional to the square of the radius (essentially the length of the blade), if you were to double the length of a wind turbine's blade, how much more power would that turbine generate? Assume that wind speed and all other variables remain the same.

Click for the answer.

ANSWER: The area of a circle is given by A=πr², where r is the radius of the circle. If the length of the blade, which defines the radius of the circular area over which wind is captured, was increased by a factor of 2, then the area would increase by a factor of 2². Therefore, if all other variables were held constant, then according to the wind power equation above, doubling the blade length will produce 4 times as much energy.

Market Deployment of Wind Energy

Over the last 20 years, growth in the total installed capacity of wind energy generation across the globe has been growing rapidly. Germany was the first country to lead the development of wind power, but the US and China have dominated the growth since 2010. China is especially impressive in terms of its recent growth.

The growth in wind power capacity over the last 20 years. Both China and the US are growing exponentially.

Click here for a text description of the growth in wind power capacity over the last 20 years graph.

The image is a line graph titled "Cumulative installed wind energy capacity, gigawatts," which shows the growth in installed wind energy capacity from 1997 to 2016 for several countries. The y-axis represents the capacity in gigawatts (GW), ranging from 0 to 140 GW. The x-axis represents the years from 1997 to 2016.

• China (green line) shows the most significant growth, starting from near 0 GW in 1997 and rising sharply to over 140 GW by 2016.
• United States (red line) starts with a low capacity in 1997 but increases steadily, reaching around 80 GW by 2016.
• Germany (blue line) also shows steady growth, starting from a low base and reaching approximately 45 GW by 2016.
• Spain (orange line) has a moderate increase, peaking at around 25 GW.
• India (purple line) starts from almost 0 GW and grows to about 30 GW by 2016.
• United Kingdom (light blue line), France (dark blue line), and Italy (yellow line) all show growth, but at a slower pace compared to the top countries, with capacities below 20 GW by 2016.

The graph is sourced from the BP Statistical Review of Global Energy and is provided by Our World in Data. Each country's line is color-coded for easy differentiation, with a legend on the right side of the graph identifying each color with its respective country. The overall trend indicates a global increase in wind energy capacity over the years, with China leading significantly.

Credit: BP Statistical Review of Global Energy, from Our World in Data [26] (CC BY 4.0 [27])

Part of the reason for this growth is the steady decline in the cost of wind energy, as discussed in the previous section on solar energy. But government policies are another important factor. The United States has one of the most volatile markets for wind energy in the world, while those in Europe and China have been among the most stable. This is due in part to differences in how governments in these countries treat wind energy. In many parts of Europe, wind energy (and other renewable generation technologies) enjoy subsidies and incentives known as feed-in tariffs. The feed-in tariff is essentially a long-term guarantee of the ability to sell output from a specific power generation resource to the grid at a specified price (typically higher than the prices received in the market by other generation resources). The United States, on the other hand, has favored a system of tax incentives called the “Production Tax Credit” (PTC) to encourage renewable energy deployment. In theory, a tax incentive should not work much differently than a feed-in tariff (both are just payments based on how many kilowatt-hours are generated). But the PTC has historically needed to be re-authorized frequently by the US Congress – this “on-off” policy strategy has been a major factor in the volatility of wind energy investment in the US as shown in the figure below. It is worth noting that the PTC was recently renewed for 2013, but will lapse again at the end of 2019, so it is difficult to say what impact it will have on wind investment going forward.

US wind energy annual installations, showing the relationship between lapses in the Production Tax Credit (PTC) and declines in new installations.

Click here for a text description of the US wind energy annual installations graph.

The image is a bar graph titled "Annual U.S. Wind Power Installation," which shows the amount of wind power installed in the United States each year from 1998 to 2018, measured in megawatts (MW).

• The y-axis represents the installed wind power capacity in MW, ranging from 0 to 14,000 MW.
• The x-axis represents the years from 1998 to 2018.

Key points from the graph:

• From 1998 to 2004, the installation of wind power was relatively low, with figures below 2000 MW each year. There is a notable annotation "PTC lapse" pointing to the years 1999, 2000, 2001, and 2003, indicating periods where the Production Tax Credit (PTC) lapsed, leading to lower installations.
• Starting in 2005, there is a noticeable increase, with installations generally above 2000 MW, peaking around 2008-2009 with over 10,000 MW.
• Another significant peak occurs in 2012, reaching over 12,000 MW, followed by a sharp decline in 2013, again marked with a "PTC lapse" annotation.
• From 2014 to 2018, the installations fluctuate but remain relatively high, with values generally between 6,000 MW and 8,000 MW, except for another peak in 2015.

The bars are colored in shades of red, with darker shades representing higher values. The graph visually represents the impact of the PTC lapses on the annual installation of wind power, showing significant drops in those years.

Credit: David Bice, data from American Wind Energy Association (AWEA) [28]

The above clarifies that government policies are important to the growth of renewable energy production (both wind and solar). In a very real way, you can think about these policies (feed-in tariffs or tax credits) as a form of investment. Governments can also provide investments in the form of funding for basic research related to these technologies. In general, these investments do not add up to a huge amount when seen in the context of a country's gross domestic product (GDP), which is a measure of the size of the economy, as seen in the figure below.

A comparison of some leading countries in terms of the percentage of their gross domestic product (GDP) is committed to the development of renewable energy technologies.

Click here for a text description of the graph of renewable energy investment (% of GDP) for 2015.

The image is a horizontal bar chart titled "Renewable Energy Investment (% of GDP), 2015," which shows the percentage of each nation's gross domestic product (GDP) invested in renewable energy in 2015. The data source is Bloomberg New Energy Finance and the World Bank, provided by Our World in Data.

• Chile has the highest investment at 1.4% of GDP.
• South Africa also invests 1.4% of its GDP in renewable energy.
• China follows with 0.9% of GDP.
• Japan, UK, and India each invest 0.8% of their GDP.
• Brazil invests 0.4% of its GDP.
• Germany and Mexico both invest 0.3% of their GDP.
• United States has the lowest investment at 0.2% of GDP.

The bars are colored in shades of blue, with the length of each bar corresponding to the percentage of GDP invested in renewable energy. The percentages are labeled at the end of each bar for clarity. The chart visually emphasizes the variation in investment levels across different countries.

Credit: Bloomberg New Energy Finance; World Bank, from Our World in Data [26] (CC BY 4.0 [27])

The Potential Wind Energy Resource

A quick look at an annually-averaged wind map of the world (below) shows the regions of the world that are best suited for the production of wind energy in colors ranging from yellows to red (where the average winds are at least 9.75 m/s or 20 mph). The offshore regions are clearly the best in terms of the energy potential, but not all of these offshore regions are close to where people live. Even for onshore portions of the world, the wind energy potential does not always coincide with where the people are concentrated. This points to the necessity of new transmission lines to deliver this wind energy to major population centers.

The global distribution of onshore and offshore wind resources, showing the annually-averaged wind speed at a height of 100 m. For reference, the blue regions have speeds of 2.5-4 m/s (too low for good wind energy), green regions have velocities in the range of 5-6 m/ (also a bit too low for big turbines), yellow regions have speeds of 6.5 m/s (about 15 mph), which is enough to get most large modern turbines spinning, orange areas range in speed from 7-8.5 m/s, and the red areas range from 8.5 to more than 9.75 m/s, which is plenty of speed to generate wind energy at the full capacity of big turbines.

Credit: Global Wind Atlas [29], CC BY 4.0 [30]

So, just how much energy could be produced by the wind? In 2009, a group of scientists makes some calculations to estimate the potential for the world and the US, using wind data and some assumptions about the size and spacing of the turbines. They assumed 2.5 MW turbines on land, and 3.5 MW turbines offshore, which were big for that time. They assumed that you could only place the turbines in unforested, ice-free, nonmountainous areas away from any towns and that the turbines had to be spaced by several hundred meters so they do not interfere with their neighbors. They further assumed that each turbine generated just 20% of its rated capacity to account for mechanical problems and intermittent winds. What they came up with is summarized in the table below, and it is pretty remarkable. The units here are exajoules (EJ = 1 x 10¹⁸ Joules) of energy over the course of a year. For reference, in 2018, the US total energy consumption (not just electrical energy) was 106 EJ and the global consumption was about 600 EJ. So, with just onshore wind energy, the potential is more than twice what we consume in the US, and more than 4 times the global consumption. But getting there is a matter of installing a lot of wind turbines!

Now let's consider a more practical question — how much wind energy have we managed to produce, and can we somehow project the past trends into the future? The figure below shows the global history of wind energy (solar is plotted too just for comparison), and you can see that it is growing fast.

The history of wind and solar energy production for the world have both grown dramatically over the past 20 years or so. Both curves are following an exponential growth trajectory, increasing by an average of 25% per year.

Click for a text description of the Global Solar and Wind Energy Generation History graph.

The image is a line graph titled "Global Solar and Wind Energy Generation History," depicting the growth in energy generation from solar and wind sources from 1985 to 2020, measured in exajoules (EJ) per year.

• The y-axis represents the energy generated per year in EJ, ranging from 0 to 14 EJ.
• The x-axis represents the years from 1985 to 2020.

Two lines are plotted on the graph:

• Solar energy generation is represented by an orange line. It shows a very gradual increase from 1985, remaining almost flat until around 2005. After 2005, there is a noticeable uptick, with a significant rise starting around 2010, reaching approximately 7 EJ by 2020.
• Wind energy generation is represented by a green line. Similar to solar, wind energy generation starts from a low base in 1985, with minimal growth until around 2000. From 2000 onwards, there is a steep increase, particularly sharp after 2010, reaching around 12 EJ by 2020.

The graph visually demonstrates the exponential growth in both solar and wind energy generation over the years, with wind energy showing a more pronounced increase compared to solar. The data points are marked with small dots along the lines, and a legend in the center of the graph identifies the colors associated with solar (orange) and wind (green).

Credit: David Bice, data from BP Statistical Review of Energy, 2018

Both of these curves are growing exponentially, and the history so far suggests a growth of about 25% per year on average. If we assume that they continue to grow in the further following this exponential growth, we can project where we'll be at any time in the future. Below, we see where we might be in the year 2030, just eleven years from now. What you see is that we end up with vast amount of wind energy by 2030 — if it grows at the same rate it has been growing at, we end up with almost 300 EJ per year, about half of the current global energy consumption, and if it grows at a smaller rate of 20% per year, we still end up being able to supply about 20% of the total global energy demand.

The history of wind energy production for the world projected into the future, increasing by an average of 25% per year and 20% per year.

Click here for a written description of the Global wind energy generation history and projection graph.

The image is a line graph titled "Global Wind Energy Generation History and Projection," which illustrates the historical data and projected growth of global wind energy generation from 1985 to 2030, measured in exajoules (EJ) per year.

• The y-axis represents the energy generated per year in EJ, ranging from 0 to 300 EJ.
• The x-axis represents the years from 1985 to 2030.

The graph features two lines:

1. Historical Data (blue line) - This line shows the actual wind energy generation from 1985 to around 2020. The generation starts from nearly 0 EJ in 1985 and shows a gradual increase over the years, with a noticeable acceleration starting around 2005, reaching approximately 15 EJ by 2020.
2. Projections - There are two projection lines for future growth:
   • 20% per year growth (yellow line) - This projection starts from the point where the historical data ends (around 2020) and shows a steep increase, reaching about 100 EJ by 2030.25% per year growth (orange line) - This projection also starts from the end of the historical data and shows an even steeper increase, reaching around 250 EJ by 2030 

The graph visually represents the exponential growth expected in wind energy generation if the growth rates continue at 20% or 25% per year. The lines are color-coded with labels indicating the growth rates, and the overall trend suggests a significant future increase in wind energy generation.

Credit: David Bice, data from BP Statistical Review of Energy, 2018

Barriers to Additional Wind Energy Development

It is worth noting that, as with solar, wind investments are not always happening in the windiest areas. The reality is that there are a large number of factors that influence the development of wind energy globally. As the technology for wind energy has improved, other factors have also come together to create market drivers for wind power. These drivers include:

• Declining Wind Costs
• Fuel Price Uncertainty
• Federal and State Policies
• Economic Development
• Public Support
• Green Power
• Energy Security
• Carbon Risk

We have already mentioned the US Production Tax Credit, which is responsible for a good amount of the trend in US wind energy investment – both up and down! A decline in wind investment in 2010 and 2011 was due in part to the global financial crisis. A drop in natural gas/wholesale electricity prices has made some planned projects less competitive than originally expected and halted development. There has also been a slump in the overall demand for energy. Another factor that limits the growth of wind power capacity is the constraint on the transmission infrastructure. As can be seen in the wind capacity map on the previous page, many of the locations that experience the windiest conditions are not close to coastal population centers. The cost of upgrading this infrastructure is significant — perhaps $30 to $90 billion in the US by the year 2030 according to some estimates. This seems like a huge amount, but consider that our government spends about \$20 billion each year in direct subsidies to the fossil fuel industry, which would sum up to \$200 billion by the year 2030. In light of that, the upgrade cost for better transmission lines is a bargain!