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Cognitive Computation

A Data-Driven Energy Management Strategy Based on Deep Reinforcement Learning for Microgrid Systems

  • 05-01-2023
Published in:
Authors
Gang Bao
Rui Xu
Published in
Cognitive Computation | Issue 2/2023

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Abstract

The article discusses the challenges of managing microgrid systems due to the uncertainty of renewable energy and interactions among system equipment. It reviews existing methods, including mixed integer linear programming and dynamic programming, which have limitations in handling system uncertainty. The author introduces scenario-based and prediction-based methods but highlights their dependence on accurate predictions. To address these issues, the author proposes a data-driven energy management strategy based on deep reinforcement learning, specifically the PERSAC algorithm. This algorithm optimizes the use of high-value samples and improves the exploration efficiency of traditional methods. The author compares PERSAC with other algorithms, such as DQN and DDPG, demonstrating its superior performance in reducing operation costs and adapting to power fluctuations. The article concludes by emphasizing the advantages of PERSAC in continuous control, robustness, and optimization performance, and suggests future research directions in multiagent systems.
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previous article Distance from Unimodality for the Assessment of Opinion Polarization
next article A New Approach for JPEG Steganalysis with a Cognitive Evolving Ensembler and Robust Feature Selection
Title
A Data-Driven Energy Management Strategy Based on Deep Reinforcement Learning for Microgrid Systems
Authors
Gang Bao
Rui Xu
Publication date
05-01-2023
Publisher
Springer US
Published in
Cognitive Computation / Issue 2/2023
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10106-3
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