Optimal sizing of autonomous hybrid microgrids with economic analysis using grey wolf optimizer technique

Integrating microgrids with existing distribution systems is a complex process that requires optimal design. This study seeks to develop a robust methodological framework to design optimal configurations of hybrid Microgrid systems (HMGs). Different configurations of hybrid Microgrids are proposed comprising various generating resources to meet the electrical load of small villages in Malaysia. Grey Wolf Optimizer (GWO) is employed to minimize the cost of energy COE ($/kWh) considering operation constraints. Four indicators are calculated to assess the reliability and performance of the hybrid system, which are loss of power supply probability (LPSP), renewable energy index (IRE), storage performance index (ISP), and excess energy index (IEE). These formations are subjected to two energy management strategies: load following (LFs) and cyclic charging (CCs). The results indicate that the energy cost of the optimal configuration was $0.24/kWh, whereas renewable resources contributed 75.3% of total energy production, and the percentage of unserved loads was 0.039. The results reveal that climatic conditions are essential in selecting generation resources. A genetic algorithm (GA) is applied to compare the results. This study provides essential information for electrical power designers.