Federated reinforcement learning for decentralized voltage control in distribution networks
Abstract
Multi-agent reinforcement learning (MARL) with “centralized training & decentralized execution” framework has been widely investigated to implement decentralized voltage control for distribution networks (DNs). However, a centralized training solution encounters privacy and scalability issues for large-scale DNs with multiple virtual power plants. In this letter, a decomposition & coordination reinforcement learning algorithm is proposed based on a federated framework. This decentralized training algorithm not only enhances scalability and privacy but also has a similar learning convergence with centralized ones.
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Hybrid VVC approaches can substantially reduce computation and communication requirements compared with fully centralized OPF, while achieving better voltage regulation than fixed droop control.
“Overall, hybrid methods can substantially reduce computation and communication requirements compared with fully centralized OPF, while achieving better voltage regulation than fixed droop control”
From Physics-Informed AI-Based EV smart inverter control for two-stage Volt/Var coordination in renewable-rich active distribution networks (2026) · cites this paper as [23]
The performance of hybrid VVC methods depends on the update rate of the supervisory layer, the fidelity of network models/sensitivities, and the ability to maintain feasibility under forecast errors and fast operational changes.
“nevertheless, their performance depends on the update rate of the supervisory layer, the fidelity of network models/sensitivities, and the ability to maintain feasibility under forecast errors and fast operational changes.”
From Physics-Informed AI-Based EV smart inverter control for two-stage Volt/Var coordination in renewable-rich active distribution networks (2026) · cites this paper as [23]
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“However, data originating from a massive number of Internet of Things (IoT) devices are commonly stored in a distributed manner for a wide range of scenarios such as smart grids [1], remote health monitoring [2], or Internet of Vehicles (IoV) [3].”
BackgroundZhong, Y., Qiu, J., Yang, Y., Zhang, C., Tao, Y., Sun, X., & Lai, S. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100869
- Authors
- Liu, H., & Wu, W.
- Affiliations
- State Key Laboratory of Power System Operation and Control (CN)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2022-09
- Volume
- 13
- Issue
- 5
- Pages
- 3840-3843
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W4226025161
- Cited by
- 101
- References
- 0
- Citations by year
- 2026: 32 · 2025: 17 · 2024: 27 · 2023: 24 · 2022: 1
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Funders
- S&T Project of State Grid Corporation of China, “Learning Based-Renewable Cluster Control and Coordinated Dispatch,” (5100-202199512A-0-5-ZN)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- federated reinforcement learning, decentralized voltage control, distribution networks, multi-agent reinforcement learning, decomposition and coordination, privacy preservation, scalability, virtual power plants
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