Abstract
Electric power utilities face a wide range of risks that cause financial uncertainty, with potential impacts on prices for customers. Among these, weather variability and retail load defection are perhaps two of the most studied. Whereas weather extremes expose utilities to unpredictable swings in electricity supply and demand, retail load defection, in which customers adopt distributed energy resources or switch to alternative providers, can alter utility business models fundamentally. We showed for the first time that these two phenomena can interact dynamically, with potential negative consequences for electricity ratepayers. We found that retail load defection could alter utilities’ financial exposure to weather risk in a matter of years. Using open-source power system simulation software coupled with a utility financial model, we simulated outcomes for a major hydropower-producing California electric utility under stationary hydrometeorological uncertainty, and under three different load defection scenarios ranging from 0% to 90%. We found that as load defection increases, the utility’s three main businesses (wholesale generation, transmission, and retail distribution) shift in relative importance. As a consequence, the impacts of interannual variability in hydropower production and demand in the utility’s system (a function of streamflow and air temperatures, respectively) become significantly altered. Air temperatures (a proxy for demand) become more predictive of utility financial performance, whereas the utility’s exposure to hydrology is poised to shift in complex ways. Drought will remain a major risk, but extremely wet years (which historically are beneficial to the utility’s significant hydropower holdings) may become damaging due to their association with low market prices. Our results also suggest that load defection could put much more pressure on utilities to make major annual rate adjustments or quickly adapt current strategies for managing weather risk.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Received: Mar 26, 2021
Accepted: Nov 19, 2021
Published online: Dec 28, 2021
Published in print: Mar 1, 2022
Discussion open until: May 28, 2022
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