Abstract: This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes.
In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model
r focuses on ensuring reliable and economic dispatch of MG under strong uncertainties of RES. Early research mainly focused on the day-ahead dispatch of MG, and several approaches are commonly
First, the multi-timescale dispatch strategy of seasonal hydrogen storage, day-ahead economic dispatch and intraday dynamic optimization is proposed to balance the supply-and
Abstract and Figures This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system.
This study proposes an optimized day-ahead economic dispatch framework for wind-integrated microgrids, combining energy storage systems with a hybrid demand response (DR)
This article presents an economical and sustainable, stochastic, multi-objective energy management strategy for an interconnected multi-microgrid system with flexible multi-energy
In this study, the optimization of microgrids has been explored, with particular emphasis on the dispatch of thermostatically controllable loads (TCLs) commonly found in residential settings.
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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