With many staying home to prevent the spread of COVID-19, energy use in California is changing. Last Tuesday, the California Independent System Operator (CAISO) released a report showing reductions in energy demand and energy prices following stay at home orders from some California counties in mid-March and the March 20 statewide stay at home order. California saw more significant reductions in weekday energy use compared to weekend energy use.
CAISO oversees California’s energy market, and relies heavily on modeling to do so. This analysis is helping the CAISO “fine-tune” its models to reflect this new reality and produce more accurate energy market forecasts. No model is 100% perfect, and the difference between what the model predicts and what actually happens is called “model error.” Its analysis highlights any changes in energy demand or energy prices that exceed typical model error, assuming that those changes are due to COVID-19 stay at home orders.
Stay at home orders shrink weekday morning energy demand
As the graphic above indicates, the biggest changes in energy demand happened during the weekday morning peak. Weekday morning demand has declined by 7.5 percent across all sectors, possibly due to fewer people rising early to get ready and commute to work or school, a trend that has also been seen in New York City. But in California, are not limited to weekdays. Even weekends have seen slight reductions in demand, particularly during the morning peak.
Additional analysis is needed to definitively identify the reasons for these changes in demand. But understanding why demand is changing will allow CAISO and utilities to anticipate and prevent issues that may arise with any future changes in energy demand. For example, assume spikes in residential use are driving energy demand changes. If people are still staying home more often in the summer, heat waves might result in higher than normal residential energy demand on weekdays, particularly in hotter inland regions of the state. At least one study is exploring this possibility in New York City.
CAISO’s analysis does not parse reductions in energy demand by sector. However, the Energy Information Administration (EIA) has estimated a 4.7 percent reduction in commercial energy demand in 2020 as businesses close. EIA also predicts a 4.2 percent reduction in electricity sales to the industrial sector due to reduced production in factories. Hopefully future CAISO reports will provide additional information about how COVID-19 is impacting energy demand in the residential, commercial, and industrial sectors.
Lower energy prices in March and April
Stay at home orders have also reduced energy prices in the day-ahead and real time energy markets. Prices have declined steadily starting even before the March 20 order. The chart below indicates that prices began declining on March 16 in the real-time market, and on about March 19 in the day-ahead market. Prices in both markets then rose slightly during the weeks of April 3-9 and April 24-30.
CAISO’s analysis does not provide any possible explanations for the slight increases in energy prices during those two weeks in April. As the CAISO continues studying the impacts of the COVID-19 pandemic on energy demand and energy markets, it will be helpful to see an analysis of market forces that could be shaping these fluctuations in energy prices.
CAISO’s analysis provides a useful synopsis of how COVID-19 is impacting energy demand and energy markets across the state. It also raises some important questions about the drivers of these changes and how they might affect our everyday lives as seasons change and stay at home orders are revised:
- Will these shifts in energy demand impact future grid planning?
- Will these changes in energy demand persist even as California modifies its stay at home orders, due to the economic downturn or individuals’ reluctance to resume “normal life” before there’s a COVID-19 vaccine or treatment?
With so much uncertainty surrounding the next steps in battling this pandemic, it will be important to gather as much data as possible to ensure ongoing grid reliability.