Optimizing Sustainable Multi-Microgrid System Considering Uncertainties and Seasonal Factor

Abstract

This study proposes the multi-microgrid (MMG) system as a solution to address the challenges posed by the intermittent nature of renewable energy sources and demand uncertainty. The objective is to find the optimal choices regarding the quantity, site, and size of renewable distributed generation sources, as well as the battery charging state in each microgrid. Additionally, the model aims to manage the electricity flows between microgrids, demand areas, the main grid, and nearby microgrids within the MMG system. The proposed model also considers energy trading within the peer-to-peer (P2P) intra-trading framework, considering the simultaneous connection of microgrids to both the main grid and neighboring microgrids within the MMG under various types of uncertainty and impacted factors. The objective function is to maximize the overall financial gain, minimize the cumulative cost related to the environment while meeting customer demand. A combination of Genetic algorithm and CPLEX is developed to solve the proposed model. The experimental findings demonstrate the effectiveness and fulfillment of the proposed model and algorithm.