In response to the increasing global energy demand and the need to reduce fossil fuel dependence, multi-energy microgrids (ME-MGs) have emerged as a sustainable and efficient
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These uncertainties pose substantial challenges to achieving reliable, economical, and flexible microgrid scheduling. This paper proposes a robust multi-time-scale scheduling framework
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NLR develops and evaluates microgrid controls at multiple time scales. Our researchers evaluate in-house-developed controls and partner-developed microgrid components using software modeling
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As traditional power grids are unable to meet growing demand, extensive research on multi-microgrid scheduling has begun to address the issues present in conventional power grids.
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For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation.
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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 uncertainty and multi
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Aiming at the problem of power fluctuation caused by power prediction error in microgrid dispatching process, this paper proposes a day-ahead and intra-day dual-time scale power
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The purpose of this paper is to review the progress of intelligent optimal scheduling in new microgrids, and to discuss the technical challenges in multi-energy integration, real-time optimization,
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Genetic Algorithm generates demand response strategies and optimizes battery dispatch, while LightGBM forecasts solar power generation and building load consumption. The approach aims
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