This paper addresses the operational optimization challenges posed by the uncertainty, stochasticity, and complex power constraints of distributed power sources in microgrids.
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This paper presents a systematic literature review encompassing recent advancements in MG technology. It delves into MG architecture, diverse control objectives, associated
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A comparative analysis of diverse metaheuristic algorithms for microgrid optimization is provided in this paper, which emulates natural phenomena, such as evolutionary processes and
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This paper proposes an integrated framework to improve microgrid energy management through the integration of renewable energy sources, electric vehicles, and adaptive demand
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Obtaining a better understanding of the microgrid models and the type of optimization technique used by the energy management system (EMS) in microgrids (MGs) is considered as one
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These advancements underscore the critical role of AI-driven and optimization-based approaches in enhancing the efficiency, resilience, and cost-effectiveness of modern microgrid systems.
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Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based
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Battery energy storage systems (BESS) play an important role in a microgrid for efficient exploration and exploitation of renewable energy sources such as solar and wind energy sources with various
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This paper reviewed the role of meta-heuristic optimization techniques in improving the MG''s performance as well as ensuring their sustainability across various modes of operation.
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In this paper, we establish a stochastic multi-objective sizing optimization (SMOS) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions.
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