article · Energy Reports
Hybrid renewable microgrids combining solar photovoltaic, wind, fuel cells, and batteries face challenges from intermittency and high investment costs. An optimisation framework combining a genetic algorithm and model predictive control addresses these issues by optimising system sizing, power flow, and battery state of charge. Validated using institutional load data of 13.5 gigawatt-hours annually, the configuration achieved a yearly generation of 17.29 gigawatt-hours with a cost of energy of 0.19 dollars per kilowatt-hour and zero loss of power supply probability. The predictive control maintained battery storage within safe operating limits of 20 to 95 percent, helping to limit battery degradation. The optimised system reduced reliance on the main electrical grid to 5.80 percent, down from 15 percent in the existing setup, and achieved an annual carbon dioxide reduction of over 4,412 tonnes.
Hybrid microgrids offer a path toward cleaner, decentralised electricity, but balancing multiple renewable sources and storage can be technically complex and expensive. Demonstrating that predictive software control can eliminate power deficits, cut electricity costs, and preserve battery life makes low-carbon microgrid investments more viable and attractive for operators seeking energy security and emissions reductions.
This management approach is relevant to microgrid operators, industrial estates, and campus utility managers seeking to integrate solar, wind, and storage systems while lowering operational costs. The method is an applied computational model validated against real-world campus demand data. Realising commercial deployment would require integrating these predictive control algorithms into commercial energy management software and physical microgrid controllers.
AI-generated from the published abstract. Always read the original work before citing.
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. • 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.egyr.2024.12.008
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
