Towards Pareto-optimal energy management in integrated energy systems: A multi-agent and multi-objective deep reinforcement learning approach

Towards Pareto-optimal energy management in integrated energy systems: A multi-agent and multi-objective deep reinforcement learning approach

Deep Reinforcement Learning (DRL) is effective in solving complex, non-linear optimization problems, which is particularly relevant in energy management within Integrated Energy Systems (IESs). However, DRL approaches conventionally focus on single-objective policy learning, which is inadequate for...

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Bibliographic Details
Main Authors: Jiaming Dou, Xiaojun Wang, Zhao Liu, Qingkai Sun, Xihao Wang, Jinghan He
Format: Article
Language:English
Published: Elsevier 2024-08-01
Series:International Journal of Electrical Power & Energy Systems
Subjects:
Integrated energy systems
Deep reinforcement learning
Multi-agent reinforcement learning
Multi-objective reinforcement learning
Energy management
Online Access:http://www.sciencedirect.com/science/article/pii/S0142061524002436

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http://www.sciencedirect.com/science/article/pii/S0142061524002436

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