An uncertainty microgrid system with the regulation potential of photovoltaic energy storage charging stations and its optimization algorithm

Abstract

This study addresses the multi-objective optimization problem of multi-microgrid systems (MMGS) considering the uncertainties of renewable energy sources (RES) with respect to economy, security, and operational stability. A coordinated participation model integrating microgrid units (MGU), photovoltaic-storage-charging stations (PESCS), and distribution networks (DN) is constructed. In the model, the PESCS not only meets the charging demands of electric vehicles (EVs) but also supports DN voltage stability through coordinated charging and discharging. To effectively balance multiple objectives under RES uncertainties, a data-driven multi-objective fuzzy aggregation spider wasp optimization algorithm (DD-MOFASWO) incorporating fuzzy logic reasoning is proposed. The algorithm leverages the advantages of data-driven learning from historical data to specifically handle RES uncertainties in MGUs, generating partial dimensions of the initial-stage Pareto solution set. By dynamically maintaining an external archive through fuzzy aggregation crowding distance (FACD) and non-dominated sorting (NDS), the algorithm ensures population uniformity and diversity. Simulation results under four typical scenarios on the IEEE 33-bus system demonstrate that the proposed method significantly outperforms several classical multi-objective evolutionary algorithms in terms of convergence speed, Pareto front quality, and overall system operational performance, effectively validating the superiority of both the model and the algorithm.