Opportunities for Energy Storage in CAISO: Day-Ahead and Real-Time Market Arbitrage

摘要

Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage (day-ahead and real-time markets) in the California Independent System Operator (CAISO) market. The optimization algorithm was then applied to three years of historical market data (2014–2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. Two additional trading algorithms were tested that do not require perfect foresight. The first sets a buy price threshold and a sell price threshold (e.g., limit orders) for participation in the real time market, subject to the constraints of the energy storage system. The second uses the day-ahead prices as an estimate for the real time prices and performs an optimization on a rolling time horizon. The simple threshold algorithm performed the best, but both fell well short of the potential revenue identified by the optimization with perfect foresight.