Category: Finance and Asset Management
As the amount of renewable generation on the grid increases, and gas prices experience significant seasonal and daily volatility, it has become increasingly difficult to accurately forecast long-term power market dynamics and fully incorporate market risks in deterministic power price forecasts. At the same time, power price forecasts, as they impact energy revenues, are instrumental to the valuation of a renewable asset. This poster presents the value of incorporating these risks into project revenue forecasts by quantifying the impact of price volatility on the value of solar and paired solar and storage projects.
This poster will benefit any audience who uses power price forecasts in their work, or who selects and sites solar and storage projects based on these forecasts. The poster will encourage this audience to scrutinize the power price forecasts used in their project financial models, and help them to identify which markets have characteristics that contribute to price volatility, such as high levels of wind generation and volatile daily gas prices, so that they can incorporate these risks into project financial models. The poster will also offer solutions to mitigate the impact of price volatility on project economics through co-sited storage and sizing options for the paired storage resource.
At a high-level, the poster will present forecasts of revenue and operations for sample solar and paired solar and storage projects for hundreds of stochastic price paths, summarized as NPV confidence intervals and compared to NPV forecasts that do not incorporate volatility. To conduct this analysis, we combine our Monte Carlo engine and fundamental price forecasts from Aurora, a chronological hourly dispatch model of the electricity system of the entire United States. The Monte Carlo engine generates stochastic price paths implicitly based on historical data observations that include market load shocks, fuel price changes, plant outages, and renewable penetration. We define statistical parameters, including volatility levels, mean-reversion rate, and the correlation between power and natural gas prices. The result is a set of simulations that captures the risk of volatility in any given hour, while following the underlying power price forecast on an annual basis. The Monte Carlo simulations, along with hourly solar generation profiles and paired storage parameters, are then fed into Aurora to forecast revenue and operations for each simulation, and a financial model to forecast NPV. The results quantify the impact of price volatility on project value, and the mitigation capability of storage.
Clare Everts– Associate, Charles River Associates