Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning

Domínguez-Barbero, David; García-González, Javier; Sanz-Bobi, Miguel Á.; García-Cerrada, Aurelio

doi:10.1016/j.apenergy.2024.123435

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123435

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Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning

David Domínguez-Barbero,

Javier García-González,

Miguel Á. Sanz-Bobi

et al.

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Cited by 3 publications

References 33 publications

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Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Liu,

Mao

2025

Electric Power Systems Research

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Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Liu,

Mao

2025

Electric Power Systems Research

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Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

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Process Safety and Environmental Protection

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Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Arévalo,

Ochoa-Correa,

Villa-Ávila

2024

Electronics

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Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.

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Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning

Cited by 3 publications

References 33 publications

Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

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