Assessing Portfolio Variability

As the industry grows and picks up speed, deals involving portfolios of projects are more common. A key measure that investors look at is how much energy does it produce. In energy assessment, we commonly call this value the P50 of generation, usually in kWh. Then we assign a measure of risk or uncertainty. From this, we derive various risk scenarios that feed into financial modeling. We call these scenarios probabilities of exceedance. For example, P75 is the scenario that has 75% chance of being exceeded.

When dealing with portfolios, it is natural to expect that there is some risk diversification that happens as individual project risks offset each other. These risks might be natural climate variability, or they might be technical in nature, such as exposure to different grid conditions. We expect this when buying stocks. But how do we handle this when buying portfolios of wind or solar projects? When looking at groups of projects, we have to understand and quantify each of the possible categories of uncertainty. We need to know not only the risks at each individual project, but also how these risks might vary between projects.

Understanding resource variability is critical.For example, two projects with dissimilar resource profiles, say one which is reliably summer-peaking and another that is reliably winter-peaking, would likely offset each other over the year; helping to contribute to a portfolio’s risk diversification. Another example, if a windfarm in the Midwest is having a low wind month, it could still be business as usual at a solar plant in Southern California. Combined these two projects would still be producing less than expected, but the solar plant would be buffering the impacts from the windfarm. Properly assessing each project’s resource profile and their variance, not only year-to-year but also month-to-month, helps quantify how different projects within a portfolio might offset each other.

Outside of climate variability, there are a number of other risk considerations. Maybe a windfarm’s shear assumption is wrong. Maybe a solar plant’s electrical loss is wrong. These can all be quantified, but most risks may impact only a single project. There are other risks that are not so independent. Windfarms that use the same turbine type might all meet their power curve warranties. But they could also suffer from the same blade defects. Projects sharing a similar point of interconnection are likely to be impacted by the same grid system upgrades. These correlated risks need to be considered in a portfolio. How these risks correlate, or how independent the component projects are, helps to reduce risk in the portfolio.

The more diverse a portfolio, the greater its risk diversification. By accounting for natural climate variability and the correlation of risks across the portfolio, we can quantify these portfolio effects and in turn calculate new probabilities of exceedances. Incorporating these portfolio effects into financial models directly benefits investors by allowing for better financing terms in portfolio deals.

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