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

Publication Type

Journal Article

Name of Author

• Maklewa Agoundedemba, Department of Physics, Kenyatta University, Nairobi, KenyaFollow
• Chang Ki Kim, Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Republic of Korea
• Hyun-Goo Kim, Energy Engineering, University of Science and Technology, Republic of Korea
• Raphael Nyenge, Department of Physics, Kenyatta University, Nairobi, Kenya
• Nicholas Musila, Department of Physics, Kenyatta University, Nairobi, Kenya

Publication Date (Issue Year)

2025

Journal Name

Energy Reports

Abstract

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

Keywords

Microgrid system, Net present cost .Cost of energy, PV/Wind/FC/Battery, Genetic algorithm, Model predictive control

Rsif Scholar Name

Maklewa Agoundedemba

Rsif Scholar Nationality

Togo

Cohort

Cohort 3

Thematic Area

Minerals, Mining and Materials Engineering

Africa Host University (AHU)

Kenyatta University (KU), Kenya

Funding Statement

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143582). This work is conducted under the framework of the Partnership for Applied Sciences, Engineering, Technology (PASET) with Regional Scholarship, and Innovation Fund (RSIF) (B8501E30178).

Recommended Citation

Agoundedemba, M., Kim, C. K., Kim, H., 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 https://doi.org/doi.org/10.1016/j.egyr.2024.12.008