Co-Optimizing Battery Storage for the Frequency Regulation and Energy Arbitrage Using Multi-Scale Dynamic Programming

We are interested in optimizing the use of battery storage for multiple applications, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Solving the problem for even a single-day operation would be computationally intractable due to the large state space and the number of time steps. We propose a dynamic programming approach that takes advantage of the nested structure of the problem by solving smaller subproblems with reduced state spaces, over different time scales.