Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives
Open accessAbstract
In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across different time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).
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Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.
“Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”
From Tackling climate change with machine learning (2023) · cites this paper as [292]
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“The work in [2] provides an overview on how reinforcement learning (RL) has been used in power systems, discussing general trends, common algorithms used, and specific power system applications.”
Background
- Authors
- Glavic, M., Fonteneau, R., & Ernst, D.
- Affiliations
- University of Liège (BE)
- ORCID
- Ernst, Damien
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 50
- Issue
- 1
- Pages
- 6918-6927
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2591980212
- Cited by
- 241
- References
- 81
- Citations by year
- 2026: 27 · 2025: 26 · 2024: 27 · 2023: 27 · 2022: 36 · 2021: 32 · 2020: 36 · 2019: 19 · 2018: 9 · 2017: 2
- FWCI
- 7.50
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- ORBi (University of Liège) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- reinforcement learning, power system control, safe reinforcement learning, deep reinforcement learning, path integral control, emergency control, robust control
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