Smart Microgrid Management and Optimization: A Systematic
The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids.
Smart microgrids are defined as scalable and autonomous energy systems that can operate independently or in coordination with the main grid, integrating seamlessly into larger energy networks to enhance reliability and adaptability while providing resilience against disasters and fluctuations in energy demand. How useful is this definition?
In this context, smart microgrids have become a foundational element for future power systems, enabling the efficient integration of distributed energy resources (DERs) and renewable energy sources (RES) while strengthening system resilience and operational flexibility [1, 2].
The ongoing evolution of smart microgrid systems is marked by significant advancements in the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL), addressing the increasing complexities of modern power systems while enhancing efficiency, scalability, and reliability.
In parallel, the application of artificial intelligence (AI) and digital twin technologies is transforming the operational management of smart microgrids. AI-based models have shown high accuracy in forecasting renewable generation and load demand, optimizing energy dispatch strategies, and facilitating predictive maintenance [6, 7].
The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids.
It is a small-scale power grid, with its distributed local energy sources, loads and energy storage systems that can operate connected or disconnected to the main grid.
Smart microgrids are emerging as a pivotal solution within this framework, offering localized energy management that aligns with sustainability goals. These systems leverage diverse
Smart MicroGrids (SMGs) can be seen as a promising option when it comes to addressing the urgent need for sustainable transition in electric systems from the current fossil fuel-based centralised
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
Microgrid (MG) is the technical blessing that takes the advantages of renewable energy (RE) sources such as wind, solar, biogas, and tidal energy to produce electricity and overcome the aforementioned
Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation
This review critically examines the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) into smart microgrid platforms,
The AI intelligent inspection solution launched by SCIYON integrates AI technology through smart devices such as drones and robots, achieving automatic inspection and precise fault
A smart grid is an advanced electrical grid that uses digital technology and two-way communication to optimize energy production, distribution, and consumption, while a microgrid is a localized grid that
Microgrids are power distribution systems that can operate either in a grid-connected configuration or in an islanded manner, depending on the availability of decentralized power
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