The expected revenue of energy storage from energy arbitrage service based on the statistics of realistic market data

The integration of intermittent renewable energy resources is increasing the volatility of electricity prices and is changing the way power systems are operated. Price volatility creates a unique business opportunity for energy storage owners to perform energy arbitrage: buying low cost energy and selling it back when the price is high. This paper provides a method to determine the expected revenue of energy arbitrage in the day-ahead energy market using the statistics of realistic market data. The proposed method uses an optimization model to calculate the maximum daily revenue from energy arbitrage. Clustering is used to differentiate among seasonal prices, and a regression model is used to fit the revenues to the price statistics for each cluster. The R-squared value for the goodness of fit is used to verify the observation. Results for the PJM market exhibit a linear correlation between the revenue and the price statistics of dispersion, mainly the price range and its standard deviation and hence the paper provides a straightforward method to estimate revenues. Winter prices provide more energy arbitrage opportunities due to their two-peak daily price data pattern with higher sensitivities to price statistics.