Multi-agent deep reinforcement learning-based cooperative energy management for regional integrated energy system incorporating active demand-side management

Liu, Jiejie; Ma, Yanan; Chen, Ying; Zhao, Chunlu; Meng, Xianyang; Wu, Jiangtao

doi:10.1016/j.energy.2025.135056

Cited by 36Integrated Energy Systems OptimizationSmart Grid Energy ManagementOptimal Power Flow Distribution

Multi-agent deep reinforcement learning-based cooperative energy management for regional integrated energy system incorporating active demand-side management

Liu, Jiejie, Ma, Yanan, Chen, Ying, Zhao, Chunlu, Meng, Xianyang, Wu, Jiangtao·2025·Energy

DOI 10.1016/j.energy.2025.135056

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The Imitation-Augmented Actor-Critic (IAAC) method integrates expert trajectories with attention mechanisms, enabling the model to concentrate learning on relevant state-action subspaces and mitigating overestimation issues common in algorithms such as MADDPG when state-action spaces are large.
“The Imitation-Augmented Actor-Critic (IAAC) method integrates expert trajectories with attention mechanisms, enabling the model to concentrate its learning on the most relevant state-action subspaces, mitigating overestimation issues common in algorithms such as MADDPG when state-action spaces are large [20].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [20]
MADRL offers scalability and adaptability, allowing the system to learn complex coordination strategies from experience without rigid pre-programming.
“Multi-Agent Deep Reinforcement Learning (MADRL) offers the significant advantage of scalability and adaptability, allowing the system to learn complex coordination strategies from experience without rigid pre-programming [20].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [20]
RIES environments add horizontal interactions among peer hubs, where agents coordinate across electrical, thermal, and gas carriers.
“RIES environments add horizontal interactions among peer hubs, where agents coordinate across electrical, thermal, and gas carriers [20].”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [20]

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Authors: Liu, J., Ma, Y., Chen, Y., Zhao, C., Meng, X., & Wu, J.
Affiliations: Xi'an Jiaotong University (CN)
Corresponding author: Meng, Xianyang
ORCID: Liu, Jiejie
Ma, Yanan
Chen, Ying
Meng, Xianyang
Wu, Jiangtao
Venue: Energy
Publisher: Elsevier BV
Published: 2025-03
Volume: 319
Pages: 135056
ISSN: 0360-5442 (ISSN-L)
DOI: 10.1016/j.energy.2025.135056
OpenAlex: W4407596824
Cited by: 35
References: 59
Citations by year: 2026: 22 · 2025: 13
FWCI: 7.86
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Energy (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Natural Science Basic Research Program of Shaanxi Province (2024JC-YBMS-257)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
Keywords: regional integrated energy system, cooperative energy management, multi-agent deep reinforcement learning, demand response, shared energy storage, multi-energy flow, MATD3, user satisfaction, energy scheduling, energy sharing

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