Modelling and Optimization of Microgrid with Combined Genetic Algorithm and Model Predictive Control of PV/Wind/FC/Battery Energy Systems
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Date
2024-12
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Researchgate
Abstract
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 envi ronmental benefits of the optimized microgrid
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Agoundedemba, M., Kim, C. K., Kim, H. G., Nyenge, R., & Musila, N. (2025). Modelling and optimization of microgrid with combined genetic algorithm and model predictive control of PV/Wind/FC/battery energy systems. Energy Reports, 13, 238-255.