Optimization planning of energy storage based on multi-objective improved particle swarm and droop control algorithm

Abstract

Based on the nonlinear loads of distributed power sources, large-scale deployment can easily lead to problems such as overvoltage, reverse power flow, and harmonic circulation. In this paper, we constructed a multi-objective optimization planning model for distributed energy storage in active distribution networks using an improved particle swarm optimization algorithm and an improved droop control algorithm. A splitting strategy was introduced in the improved particle swarm algorithm to increase population diversity and reduce the number of local optima. The optimization planning model was built with the minimum sum of voltage vulnerability multi-indicators as the main objective. Furthermore, to better eliminate harmonic circulation in the power grid system under the impact of a large amount of nonlinear loads, this paper also designed and improved the LCL filter at the output of the inverter and reintroduced a new droop quantity in the traditional droop control. Adjusting the two links proportionally, the effectiveness and applicability of the proposed distributed energy storage optimization planning method were verified through simulation examples.