Review of Smart Microgrid Platform Integrating AI and Deep Reinforcement Learning for Sustainable Energy Management

作者：Chijioke Paul Agupugo, Mezue Francis Canice Tochukwu, K. Ogunmoye, Asnath Sethiel Mosha, Frank Sabbih · 发表于：International Journal of Future Engineering Innovations · 年份：2025 · DOI：10.54660/ijfei.2025.2.3.01-17 · 被引用次数：7

The transition to sustainable and intelligent energy systems has intensified the development of smart microgrids, which offer decentralized, resilient, and efficient power solutions. This review critically examines the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) into smart microgrid platforms, focusing on their role in optimizing sustainable energy management. Traditional energy management systems often struggle to adapt to the dynamic nature of modern energy demands, renewable energy intermittency, and grid complexity. AI-driven solutions, particularly DRL, provide adaptive, autonomous, and data-driven mechanisms for real-time decision-making and predictive control within microgrids. DRL, by learning optimal policies through interaction with the environment, is capable of handling multi-objective problems, including demand-response optimization, energy storage control, load forecasting, and distributed generation scheduling. This paper synthesizes recent advancements and applications of DRL algorithms such as Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) in smart microgrids. It also explores hybrid models that combine DRL with other AI techniques, such as fuzzy logic and neural networks, to improve performance under uncertainty and nonlinearity. Furthermore, the review evaluates benchmark testbeds, simulation tools, and real-time platforms used to implement and validate these int...