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 e...
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To realize the distributed generation potential, adopting a system where the associated loads and generation are considered as a subsystem or a microgrid is essential. In this article, a literature
This study would help researchers, scientists, and policymakers to get in-depth and systematic knowledge on microgrid. It will also contribute to identify the key factors for mobilizing this
This review makes several key contributions to smart microgrid technologies and urban energy planning. While existing studies provide fragmented insights into smart microgrid
Microgrids represent a transformative paradigm in modern energy systems, enabling localized, efficient, and resilient energy management.
Artificial intelligence (AI) has recently demonstrated immense potential for optimizing energy management in microgrids, providing efficient and reliable solutions.
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
Leveraging renewable energy sources, smart technologies, and efficient operational strategies, microgrids address challenges such as energy reliability, decarbonization, and economic...
This paper reviews the rising, applications, challenges, and future prospects in energy smart grids and smart cities systems. Several key components, such as IoT, renewable energy
Smart grid, an advanced evolution of traditional grid, leverages cutting edge technologies, like digital communication, sensors, and mechanization technologies to improve
The concept of microgrids (MGs) as compact power systems, incorporating distributed energy resources, generating units, storage systems, and loads, is widely acknowledged in the
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