Multiobjective intelligent energy management optimization for grid-connected microgrids

Document Type

Journal Article




School of Engineering


Originally published as:

EI-Bidairi, K. S., Nguyen, H. D., Jayasinghe, S. D. G., & Mahmoud, T. S. (2018, June). Multiobjective intelligent energy management optimization for grid-connected microgrids. In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe).

Original article available here.


In the rapid growing of the green energy technology, microgrid systems with renewable energy sources (RESs) such as solar, wind and fuel cells are becoming a prevalent and efficient way to control and manage these renewable sources. Moreover, owing to the intermittency and the frequent irregular responses of the RESs, battery energy storages have become an integral part of microgrids. In such complex systems, optimal use of RESs heavily depend on the energy management strategy used. Besides, the reduction of conventional fuel utilization and the resultant drop in the emissions also depend on the energy management strategy. This paper presents a novel expert system Fuzzy Logic - Grey Wolf Optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management in grid-connected microgrids. The proposed method is tested on different scenarios, and the simulation results are compared with other existing approaches methods such as GA, PSO, BA, IBA and GWO. The simulation results show a significant improvement with the proposed method in terms of satisfying the demands and to minimizing the operating costs of the microgrid compared to other existing methods.