Modelling and optimization of microgrid with combined genetic algorithm and model predictive control of PV/Wind/FC/battery energy systems

• Published in: Energy reports Vol. 13; pp. 238 - 255
• Main Authors: Agoundedemba, Maklewa, Kim, Chang Ki, Kim, Hyun-Goo, Nyenge, Raphael, Musila, Nicholas
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2025
• Subjects: Cost of energy; Genetic algorithm; Microgrid system; Model predictive control; Net present cost; PV/Wind/FC/Battery; Model predictive control; Microgrid system; Cost of energy; PV/Wind/FC/Battery; Genetic algorithm; Net present cost
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2024.12.008

Microgrid systems with hybrid renewable energy resources, such as PV, wind, have been widely used with storage devices to supply power to certain load demands. However, technical issues and fewer benefits can occur due to their intermittent nature and the high investment costs associated. So, an accurate model, sizing, and management approach are required to maximize the operational benefits of the microgrid with battery energy storage systems and fuel cells. This study used the combined genetic algorithm (GA) and model predictive control (MPC) to size and optimize the hybrid renewable energy PV/Wind/FC/Battery subject to certain constraints on the power flow and battery state of charge. The data used to validate the model of the system was from the University of California San Diago of 13.5 GWh a year. The main objective was to minimize the cost of energy (COE), power supply probability (LPSP) and the net present cost, by GA. Another goal was to minimize the cost of power imported from the main grid over the time horizon. This was done using MPC based on forecasted data. The results showed a total energy generation of 17.29 GWh in a year. A microgrid produced a cheap cost of energy of $0.19/kWh. A LPSP was 0 % indicating that technically the system is viable. The optimized power flow maintained the battery’s state of charge within the safe range of 20–95 %, significantly enhancing battery longevity by reducing degradation from frequent charging cycles. The total proposed system relies on the main grid only 5.80 % compared to the current real installed where 15 % relies on the main grid. Additionally, the proposed system resulted in a carbon dioxide reduction of 4412.108 tCO₂ annually, demonstrating the environmental benefits of the optimized microgrid. [Display omitted] •Hybrid renewable microgrid system optimized using a combined Genetic Algorithm and Model Predictive Control.•Effective integration of PV, Wind, Fuel Cell, and Battery systems to enhance energy reliability.•Reduced reliance on the main grid, improving system sustainability and efficiency.•Optimization approach avoid battery degradation and led to significant cost savings and operational benefits.•Accurate power flow and battery management ensuring longevity and performance of the system.