Optimal operation framework of customer-premise battery storage for energy charge reduction and primary frequency regulation

Abstract

This paper focuses on the intra-day operation of customer-premise battery storage for multiple applications, in particular energy charge reduction and primary frequency regulation (PFR). The nature of this problem requires the battery to provide reliable PFR, as well as coordinate online optimization performance with computing time while accounting for multidimensional stochastic information. Therefore, we propose a novel optimal operation framework. In the day-ahead phase, the piecewise linear functions (PLFs) are trained offline to approximate the expected operating cost of the long-term future. During the intra-day operation, a two-stage approximate dynamic programming (ADP) model is formulated utilizing the updated short-term future information and the PLFs based long-term estimation. To ensure the reliability of frequency regulation, we develop a robust correction strategy of PFR signals. Combining the correction feedback, the two-stage ADP model is optimized through rolling horizon procedures to dynamically adjust the power basepoint. Numerical simulations demonstrate the performance of our proposed operation framework. The convergence rate of PLFs offline training is 97.3%. In addition, the intra-day operating cost of our proposed framework is merely 1.62% higher than the theoretical optimum, while the online calculation takes only 3.25 s for one rolling period.