Random clustering and dynamic recognition-based operation strategy for energy storage system in industrial park

Abstract

The high volatility and intermittency of power load pose significant challenges to achieving optimal operation of energy storage system (ESS), which ultimately affects the economic benefits of industrial parks. To address this issue, this paper proposes a random clustering and dynamic recognition-based operation strategy for ESS in industrial parks. Firstly, we propose an expected cost minimization-driven random clustering method to determine the optimal cluster number of load curves. We then develop optimal day-ahead self-operating strategies of ESS corresponding to each of the clustered load curves. The uncertainty of power load is described by linearized risk assessment indexes. Secondly, we present a dynamic recognition technology that consists of the characteristics of value range, slope similarity, power size similarity, and curve step similarity to recognize the cluster to which the intra-day load curve belongs. Finally, we select the optimal intra-day charging/discharging strategy of ESS according to the recognized cluster as the operation strategy of the industrial park. Numerical experiments on a real-life industrial park with photovoltaic and ESS validate that the proposed strategy can reduce operation costs as well as promote photovoltaic local accommodation.