In this paper, we use the modified whale algorithm to solve the microgrid optimization problem. First, we set the economic cost and environmental cost as two modeling objectives.
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Regarding the limitations of the current microgrid demand response model, this study further optimizes the flexible load control strategy and proposes a two-objective optimization model
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More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy
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As an important part of smart grid optimization, microgrid optimal scheduling is of great significance to reduce energy consumption and environmental pollution.
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With the increasing penetration of renewable energy in power systems, the multi-objective optimal scheduling of microgrids has become increasingly complex. Traditional optimization
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To this end, this paper proposes an intelligent scheduling framework based on reinforcement learning and data-driven optimization to improve the adaptability of microgrids to
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This study evaluates the performance of the improved IMOPSO algorithm in comparison with three traditional multi-objective optimization methods, namely multi-objective gray wolf
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By incorporating the non—dominated sorting mechanism into the dung beetle optimization algorithm, the solution set is divided into different tiers. Solutions within each tier
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In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection
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Our specific contributions are as follows: Construct a microgrid model containing wind, photovoltaic, diesel generator, and energy storage, introduce a lost-load penalty as well as an over
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