The proposed two-stage microgrid optimization problem is where some of the decision variables are binary variables and the data is uncertain is a multi-stage mixed-integer stochastic
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Microgrids that incorporate renewable energy resources can have environmental benefits in terms of reduced greenhouse gas emissions and air pollutants. In some cases, microgrids can sell power
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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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Optimization in microgrid design focuses on maximizing efficiency, minimizing costs, and balancing supply-demand relationships, often achieved through advanced algorithms and real-time data...
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A microgrid is a group of interconnected loads and distributed energy resources that acts as a single controllable entity with respect to the grid. It can connect and disconnect from the grid to operate in
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This white paper focuses on tools that support design, planning and operation of microgrids (or aggregations of microgrids) for multiple needs and stakeholders (e.g., utilities, developers,
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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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Advanced microgrids enable local power generation assets—including traditional generators, renewables, and storage—to keep the local grid running even when the larger grid
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Microgrid optimization promotes resilience by reducing the reliance on centralized power grids, which are vulnerable to outages, cyberattacks, and natural disasters.
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First, we set the economic cost and environmental cost as two modeling objectives. Second, we introduce the backward learning method and nonlinear variable factor in the basic whale
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