Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks Article Swipe
Jian-Dong Yao
,
Wenbin Hao
,
Zhigao Meng
,
Bo Xie
,
Jianhua Chen
,
J. Wei
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jnlest.2024.100290
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jnlest.2024.100290
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jnlest.2024.100290
- OA Status
- diamond
- Cited By
- 3
- References
- 79
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404298256Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jnlest.2024.100290Digital Object Identifier
- Title
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Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-11-12Full publication date if available
- Authors
-
Jian-Dong Yao, Wenbin Hao, Zhigao Meng, Bo Xie, Jianhua Chen, J. WeiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jnlest.2024.100290Publisher landing page
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jnlest.2024.100290Direct OA link when available
- Concepts
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Reinforcement learning, Virtual power plant, Reinforcement, Computer science, Dynamic pricing, Distributed computing, Power (physics), Distributed generation, Engineering, Artificial intelligence, Microeconomics, Economics, Structural engineering, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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79Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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