Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
Abstract
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit intertemporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.
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One strategy is to explicitly design local control algorithms; recent work has controlled energy storage and solar inverters using supervised learning techniques trained on historical optimization data.
“One strategy is to explicitly design local control algorithms; for instance, recent work has controlled energy storage and solar inverters using supervised learning techniques trained on historical optimization data [191,192,410,411].”
From Tackling climate change with machine learning (2023) · cites this paper as [192]
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“The work in [10] considers training DERs to learn how to shape their nodal behaviors based on local information to collectively be independent from power flow coming from the substation.”
BackgroundMethodInfluentialSelf-citation
- Authors
- Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D., & Tomlin, C.
- Affiliations
- New York University (US)
- Boston Consulting Group (United States) (US)
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2020-03
- Volume
- 11
- Issue
- 2
- Pages
- 1296-1306
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2968135077
- Cited by
- 120
- References
- 44
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 13 · 2023: 35 · 2022: 18 · 2021: 16 · 2020: 13 · 2019: 1
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Funders
- NSF CPS FORCES Grant (CNS-1239166)
- UC–Philippine–California Advanced Research Institute (IIID-2015-10)
- National Science Foundation (1351900)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Keywords
- optimal power flow, distributed energy resources, decentralized control, rate-distortion analysis, distribution systems, communication topology, data-driven control, distributed generators
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