Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park
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Traditional centralized optimization methods struggle to handle the computational overhead and communication delays inherent in large-scale, geographically dispersed grids.
“Traditional centralized optimization methods struggle to handle the computational overhead and communication delays inherent in large-scale, geographically dispersed grids [10,11].”
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 [10]
MADRL faces limitations regarding convergence stability.
“However, MADRL faces limitations regarding convergence stability and the difficulty of explaining policies derived from”
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 [10]
MADRL faces difficulty explaining policies derived from black-box neural networks.
“the difficulty of explaining policies derived from”
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 [10]
Partial observability in realistic energy systems compounds explainability issues.
“Partial observability in realistic energy systems compounds these issues [10].”
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 [10]
Cites
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037 · see citing works
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Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109 · see citing works
Liu, J., Ma, Y., Chen, Y., Zhao, C., Meng, X., & Wu, J. (2025) · Energy · doi:10.1016/j.energy.2025.135056 · Cited by 35 · see citing works
Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23 · see citing works
A multi-agent deep reinforcement learning based energy management for behind-the-meter resources
Wilk, P., Wang, N., & Li, J. (2022) · The Electricity Journal · doi:10.1016/j.tej.2022.107129 · Cited by 9 · see citing works
- Authors
- Zhu, D., Yang, B., Liu, Y., Wang, Z., Ma, K., & Guan, X.
- Affiliations
- Ministry of Education of the People's Republic of China (CN)
- Yanshan University (CN)
- Corresponding author
- Yang, Bo
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 311
- Pages
- 118636
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W4211233998
- Cited by
- 94
- References
- 1
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 17 · 2023: 22 · 2022: 8
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Applied Energy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Basic Research Program of China (2018YFB1702300)
- Ministry of Science and Technology of the People's Republic of China
- National Natural Science Foundation of China (61731012, 92167205, 62103265, 62122065)
- 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 › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Keywords
- integrated energy systems, multi-agent deep reinforcement learning, partially observable Markov decision processes, soft actor-critic, counterfactual baseline, attention mechanism, renewable energy uncertainty, energy cost reduction, scalability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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