Co-optimizing the value of storage in energy and regulation service markets

Abstract

We develop a methodology for maximizing the present value of an independently operated electric energy storage (EES) unit co-optimized to perform both energy arbitrage (EA) and regulation service (RS). While our methodology applies to all types of EES, it is particularly suitable for EES units with a finite cycle life and a high power-to-capacity ratio (e.g., grid-scale batteries). We first state the constraints of the EES that limit its ability to simultaneously provide both EA and RS and formulate a deterministic linear program to find the perfect price foresight value (PPFV) of co-optimized storage. Next, we develop the receding horizon with multi-stage forecasting (RHMF) controller, which is able to efficiently co-optimize storage across EA and RS in an online real world setting with price uncertainty. We apply this controller to two battery technologies using market data from the independent service operator of New England (ISONE). We find that RHMF significantly outperforms several simpler feasible benchmark controllers and comes close to the PPFV upper bound. We also find that co-optimizing these technologies across both EA and RS provides increased profits relative to pursuing these strategies independently. The case study demonstrates the utility of our method in understanding how storage technology parameters impact financial returns.