A multi-objective optimization solution for distributed generation energy management in microgrids with hybrid energy sources and battery storage system

This manuscript proposes an intelligent Golden Jackal Optimization (GJO) for distributed-generation energy management (EM) issues in battery storage systems (BSSs) and hybrid energy sources (HESs). The objectives of the proposed method are to minimize the operating cost, and solve the microgrid (MG) energy management problem. Numerous constraints, including power balance, generation capacity, consumer loads, and the charging-discharging and dynamic performance of energy storage units, have an impact on microgrid energy management system. The proposed approach is run in the MATLAB platform and is compared to existing approaches. Also, the simulation result concludes that the proposed approach has lower costs than the existing methods. The proposed approach provides 96 % high efficiency, and 2 x 106 $ lower cost compared with other existing Particle Swarm Algorithm (PSO), Artificial Bee Colony (ABC), and Tabu Search (TS) methods.