An Energy Management System for Multi-Microgrid system considering uncertainties using multi-objective multi-verse optimization

Abstract

A Multi-Microgrid (MMG) system fosters cooperative interaction among various energy sources, reducing operating costs and carbon emissions while enhancing reliability and supporting the integration of renewable energy. This paper proposes the development of a multi-objective Energy Management System (EMS) for an MMG system comprising four microgrids connected to the main grid. The EMS aims to minimize the cost of energy (COE) and the loss of power supply probability (LPSP) within the MMG system, utilizing a 24-h time horizon for day-ahead scheduling. To address the daily mismatch between DER generation output and load demand, the uncertainties related to distributed energy resources (DER) meteorological data, load demand, and energy price data have been considered. After detailed mathematical modeling and techno-economic analysis, multi-objective multi-verse optimizer (MOMVO) has been proposed which demonstrated speed and robustness, outperforming its counterparts in achieving superior Pareto front solutions. It achieved an optimum COE value of approximately $0.11\$/kWh$ and an LPSP of 0.16%, with an overall execution time that further supports the algorithm’s superiority. The daily operating cost has been reduced by 40.32% from the base case system. The results of the proposed system EMS have been compared to those of multi-objective grey wolf optimization (MOGWO) and multi-objective salp swarm algorithm (MSSA) algorithms. Hence, The MOMVO-based EMS outperforms the others by delivering greater cost savings, lower power consumption, optimal utilization of DERs, and achieving zero emissions. The simulation is performed on MATLAB 2022b environment.