Oriza Candra, Oday A. Ahmed, Laith H. Alzubaidi, M.K. Sharma, Carlos Rodriguez-Benites, I.S. Mude
This study addresses the necessity of energy storage systems in microgrids due to the uncertainties in power generation from photovoltaic (PV) systems and wind turbines (WTs). The research focuses on designing and sizing hybrid energy resources, including PV, WT, hydrogen storage, and battery systems. The main objectives of the study involve minimizing installation costs, maximizing the penetration of PV and WT systems in supply–demand, and reducing load shedding. To achieve these goals, the study utilizes combined algorithms such as particle swarm optimization (PSO) and non-dominated sorting genetic algorithm II (NSGA II) to optimize multi-objective functions. The effectiveness of the proposed method is validated through comparative experiments, demonstrating its ability to optimize the number of resources efficiently. The results obtained from the combined algorithms indicate significant improvements in installation costs, PV and WT systems penetration, and load shedding compared to the NSGA II algorithm, with savings of $325,765.3, an increase of 29.6% in PV and WT systems penetration, and a decrease of 4.3% in load shedding. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.
Department Teknik Elektro, Universitas Negeri Padang, Padang, Indonesia; Department of Electrical Engineering, University of Technology-Iraq, Baghdad, 10066, Iraq; College of Technical Engineering, The Islamic University, Najaf, Iraq; College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq; Department of Mathematics, Chaudhary Charan Singh University, Uttar Pradesh, Meerut, India; Departamento Académico de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad Nacional de Trujillo, Trujillo, Peru; Universiti Tun Hussein Onn, Johor, Parit Raja, Malaysia
