8.10 Summary and Final Tasks

Summary

As a recap, in Lesson 8 we established that transdisciplinary research is a core part of integrative design, and while each of us doesn't need to be an expert in every field, we do need to stretch ourselves to understand and integrate our approach towards a common framework. In solar energy, that framework is tied to the goal of solar energy design and the foundations of sustainability systems. We then explored the role of risk and uncertainty in delivering solar resource units to the grid or managing the energy outputs on site, and defined risk as the dispersion of outcomes around an expected value, using the statistical variance of the data as a metric. Another way to explore dispersion from a banking and finance perspective has also been developed for renewables, using exceedance probabilities.

We then described the time-space relationship in both the grid, markets, and meteorological assessment. While there is still a lot to learn about the complex systems of weather, energy markets, and people demanding energy from the grid, we at least established a map to explore the scales of relevant time and distances using a general Earthly atmospheric advection speed of $17m/s$ , which we dubbed the FRYB relation.

We explored the early methods to forecast solar energy metrics, and we found that some of the current limitations of solar resource forecasting are tied to the early nature of the field, while others are based in the general diminishing skill to forecast further into the future due to the chaotic nature of the Earth's atmosphere. We discovered that resolution limitations can be found in either spatial or temporal scales, too.

The relevant meteorological metrics for many common SECS technologies are tied into DNI, but we see that the field currently has very few methods to evaluate DNI to a high accuracy without directly measuring the parameter with expensive equipment. Finally, we note that most solar forecasts are linked to GHI only, which has limited value given our prior knowledge of the inherent error bound to Liu and Jordan-style transformations of GHI to $I_b$ and $I_d$ , and later POA for oriented arrays. These transformations also hold very little value to extract a meaningful DNI measure. This should be a reminder that the field of solar forecasting is still young, and we should be on the lookout for new progress that will enable us to minimize risk for our clients, hopefully increasing their solar utility in the respective locale!