Optimizing Energy Storage System Scheduling Using Intermediate Partitioning Method Based on Quadratic Programming

With the continuous increase in the proportion of new energy sources in the power system, the intermittency and randomness of renewable energy generation pose urgent need for the peak load shifting ability of energy storage systems. This paper proposes a novel computational approach for optimizing energy storage system scheduling which involves partitioning the equivalent load curve at an intermediate line to determine the charging and discharging states of the energy storage system. The intermediate line is obtained through the usage of the binary search method. The proposed method eliminates the need for binary 0-1 variables traditionally used to represent charging and discharging states, thereby transforming the mixed-integer programming problem into a quadratic programming one, which significantly reduces the complexity of model solving. The efficacy of the proposed method lies in its ability to enhance the efficiency of energy storage system operations while simplifying the computational aspects involved in the optimization process. Feasibility and effectiveness of the proposed approach are demonstrated in the case studies.