Optimal Scheduling Strategy Studies for Energy Storage Stations Based on Improved Particle Swarm Optimization Algorithm

Energy storage technology is extensively applied in frequency regulation, peak shaving, smoothing renewable energy output fluctuations, demand-side response, and enhancing user reliability, thereby playing a crucial role in the development of the energy internet. An optimized dispatch strategy for energy storage stations based on an improved Particle This paper proposes an optimized dispatch strategy for energy storage stations based on an enhanced Particle Swarm Optimization (PSO) algorithm. Initially, the operational modes of energy storage stations are analyzed, with the objective functions and constraints established. The limitations of the standard PSO algorithm are subsequently scrutinized, prompting the introduction of an enhanced PSO variant characterized by a normally distributed decay of the inertia factor. A simulation study is conducted involving a representative industrial consumer incorporating an energy storage facility. The optimization dispatch model is formulated with the aim of minimizing operational expenses for the consumer using the refined PSO algorithm. The analysis encompasses the charging and discharging patterns, integration of renewable energy, and economic advantages for the industrial user employing the energy storage facility. The efficacy and precision of the proposed optimization dispatch approach are substantiated through these investigations.