Research on Power System Optimization Control System in Intelligent Dispatching of Mobile Energy Storage Equipment

This paper presents a novel Cycle Multi-objective Particle Swarm (CMOPS) algorithm, which combines the advantages of gated cycle unit (GRU), particle swarm optimization (PSO) and MOEA/D (decomposition based multi-objective evolutionary algorithm). This paper aims at the research of power system optimization control system applied to intelligent scheduling of mobile energy storage equipment. GRU is employed to capture both long-term and short-term dependencies within time series data in order to enhance forecasting precision. PSO can improve the efficiency of system optimization by simulating group cooperative behavior to search for the optimal solution. MOEA/D converts the multi-objective optimization problem into a series of single-objective optimization subproblems, achieving the global optimal solution through iterative solutions. In the experiment, we compared CMOPS with traditional scheduling systems. The results show that CMOPS can achieve the efficient utilization of mobile energy storage equipment through intelligent scheduling in multiple optimization goals of power system, such as improving energy utilization efficiency and reducing operating costs. This fully verifies the feasibility and effectiveness of CMOPS architecture in this paper, and provides a new solution for power system optimization control.