Maximizing the Revenue of Energy Storage Participants in Day-Ahead and Real-Time Markets

Determining the expected revenues from services provided by energy storage in a market is very important for investment decisions. Arbitrage in day-ahead and real-time markets provides revenue streams that depend on the operational strategy, energy market prices, and uncertainty. This paper proposes optimization models to maximize the revenue of energy storage systems (ESS) that participate in both day-ahead and real-time energy markets. We proposed a strategy where the real-time market can increase the total revenue while keeping its risk decoupled from day-ahead market revenue. A quadratic programming model is used to determine the optimal day-ahead dispatch that maximizes the operator's revenue of a price-maker ESS. The optimal dispatch is then modified by the results of a dynamic mixed integer optimization problem, which models the real-time market participation. Uncertainties in the day-ahead and real-time price forecasts are handled by back-casting and by normal error models, respectively. PJM market historical prices are used in this paper. Price sensitivities with respect to the energy storage operation are modeled as a part of the method. Results show that both day-ahead and real-time markets can provide significant arbitrage opportunities. The economic viability of four ESS technologies is assessed.