Parameter Optimization of Energy Storage Virtual Synchronous Machine Based on Particle Swarm Optimization Algorithm

Abstract

Energy transformation is a severe challenge and major demand faced by China’s sustainable development, and new energy development has become a key driving force for energy transformation. The issue of system stability is brought to light by the steadily rising share of renewable energy sources like wind and solar, which in turn places greater demands on the inertia support capacity of power plants. In order to replicate the dynamic equations of a conventional synchronous machine and add inertia and damping to the system, a new technology known as virtual synchronous machine (VSM) control has arisen. Therefore, improving the system’s absorption capacity of new energy generation and ensuring its safe and stable operation are both greatly aided by the study of energy storage VSM control technology’s grid stability and parameter design methods. Although VSM control adds inertia to a system, which is good for stability, it also complicates the system’s ability to withstand tiny disturbances. This paper investigates the small disturbance stability of VSM-controlled power generating and storage systems. The stability analysis method of storage VSM when subjected to random power sources is investigated further, and a method for optimising the system’s parameters is proposed. VSM with optimised parameters can provide sufficient inertia and damping under complex operating conditions, as shown by simulation results in real-world systems. Finally, the proposed method’s efficacy was assessed with the help of an enhanced binary particle swarm optimisation (EBPSO) algorithm, and the outcomes confirmed its superiority.