Risk-based optimization for facilitating the leasing services of shared energy storage among renewable energy stations

Due to the inherent power output correlation and uncertainty, renewable energy stations normally incur the deviation penalty in the day-ahead and real-time electricity market. Meanwhile, shared energy storage operators have been appearing to provide energy storage leasing services for neighboring renewable energy stations. In this context, this paper presents a novel optimization strategy to provide leasing services for renewable energy station clusters while improving the utilization rate and revenue of shared energy storage simultaneously. Especially, the proposed strategy utilizes a two-stage optimization model to incorporate the overselling risk. In the first stage, a matching index is defined to select a cluster of wind and solar power stations in the geographically-close region, when a set of highly complementary stations are selected by matching the typical output curve of the shared energy storage. In the second stage, an optimization strategy is determined to explore the benefit and risk of overselling for shared energy storage with the goal of maximizing the total revenue, when the correlation of wind and solar power output is realized in the scenario generation and sampling process. The results of numerical experiments have demonstrated that employing a moderate overselling method can provide an economical and efficient operational solution to improving the utilization of shared energy storage.