The different optimization techniques used in energy management problems, particularly focusing on forecasting, demand management, economic dispatch, and unit commitment, are identified and critically
For the multi-objective scheduling problem of smart microgrids, a collaborative optimization framework based on deep reinforcement learning (DRL) and digital twins is proposed to achieve
The research on microgrid power scheduling is more in the direction of microgrid systems, distributed energy scheduling, etc. Dhiman G (2020) [12] proposed a novel hybrid multi
Our innovative approaches in forecasting and dispatch, coupled with addressing existing research gaps, provide a comprehensive framework that empowers microgrid operators to optimize energy resources
In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load,
This research addresses pressing environmental concerns by proposing a novel optimization framework for combined economic and emissions dispatch (CEED) in microgrids, aiming to enhance their
Abstract: This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes.
The concept of microgrids (MGs) as compact power systems, incorporating distributed energy resources, generating units, storage systems, and loads, is widely acknowledged in the research
The widespread integration of renewable energy resources (RES) brings significant challenges to microgrid on establishing a dispatch decision due to intrinsic r
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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