Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 8 No 11 (2024): Volume 8, Issue 11, November 2024 | Pages: 236-240
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 29-11-2024
The integration of renewable energy sources, particularly solar power, into the energy grid requires effective battery management systems (BMS) to optimize energy storage and usage. This research presents a multi-agent reinforcement learning (MARL) approach to distributed optimization of solar microgrids, focusing on enhancing energy efficiency and load satisfaction. The proposed method employs multiple agents to collaboratively manage battery charging and discharging cycles based on solar generation and load demand. Simulation results indicate that the MARL approach significantly outperforms conventional methods in terms of load satisfaction and energy efficiency, demonstrating its potential for enhancing solar microgrid operations.
Solar Microgrid Optimization, Multi-Agent Reinforcement Learning (MARL), Battery Management System (BMS), Load Satisfaction, Distributed Energy Storage
Ankita Fouzdar, & Dr. Ruchi Pandey. (2024). Design and Development of Autonomous Control for Solar Microgrids Using Multi-Agent Systems. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(11), 236-240. Article DOI: https://doi.org/10.47001/IRJIET/2024.811029
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