Optimization of Customer-Side Battery Storage for Multiple Service Provision: Arbitrage, Peak Shaving, and Regulation

Abstract

Battery storage management that involves multiple revenue streams would affect customers’ monthly electricity costs. In this article, a three-level model of battery storage management is proposed for achieving various functionalities, including energy arbitrage, peak shaving, and frequency regulation. The original joint optimization problem with the horizon of one month is decomposed into three subproblems according to time scales, covering the daily battery power baseline decision, hourly regulation capacity submission, and real-time regulation response in seconds. A dynamic programming framework is adopted to integrate three submodels, featuring reduced solving complexity and the overall optimality of the proposed model. Also, the unexpected regulation capacity of customers and the uncertainty of exogenous information are considered to accurately depict the characteristics of practical scenarios. Extensive case studies for two types of customers, i.e., the pure consumer and the prosumer, are conducted to validate the effectiveness of the proposed model, which achieves at least 6% (consumer) and 19% (prosumer) benefit improvement as compared with other strategies.