Abstract: This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes.
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In this paper, we propose an optimal scheduling method for microgrids based on the distributed economic model predictive control (DEMPC) model. The method uses a DEMPC
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The control algorithms inside the microgrid controller are what enables the microgrid operation objectives to be achieved. Popular control techniques include rule-based (RB) and optimal dispatch
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It explores the integration of hybrid renewable energy sources into a microgrid (MG) and proposes an energy dispatch strategy for MGs operating in both grid-connected and standalone modes.
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1 Introduction In order to solve the increasingly prominent energy crisis and environmental problems, a variety of renewable energy (including photovoltaic, wind power) has been gradually applied to the
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The simulated and physical microgrid characteristics are described and the hourly dispatch results for generation, storage and load devices are presented, standing out as a reliable
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Different control problems in a MG system such as frequency and voltage stability, load balancing, bidirectional power flow with EV integration, power quality improvement, energy
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Microgrids can include distributed energy resources such as generators, storage devices, and controllable loads. Microgrids generally must also include a control strategy to maintain, on an
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In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative
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This study investigates the economic dispatch and optimal power flow (OPF) for microgrids, focusing on two configurations: a single-bus islanded microgrid and a three-bus grid-tied
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