The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques
Open accessAbstract
The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on sub-optimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.
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“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 [410]
Citations
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Research works printed but not linked: 4
- roald, 2017, Power system optimization with uncertainty and AC power flow: Analysis of an iterative algorithm, Proc IREP Symp Bulk Power Syst Dyn Control VII
- bellizio, 2018, Optimized local control schemes for active distribution grids using machine learning techniques, Proc IEEE PES Gen Meeting
- 0, MeteoSwiss—Federal Office of Meteorology and Climatology
- gurobi optimization, 2016, Gurobi Optimizer Reference Manual
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Cao, D., Zhao, J., Hu, W., Ding, F., Huang, Q. P., Chen, Z., & Blaabjerg, F. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3072251 · Cited by 220
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
Pan, X., Chen, M., Zhao, T., & Low, S. H. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3201041 · Cited by 205
Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review
Zhang, Z., Liu, M., Sun, M., Deng, R., Cheng, P., Niyato, D. T., Chow, M., & Chen, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3349381 · Cited by 170
Trivedi, R., & Khadem, S. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100147 · Cited by 147
Metadata
- Authors
- Karagiannopoulos, S., Aristidou, P., & Hug, G.
- Affiliations
- ETH Zurich (CH)
- University of Leeds (GB)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-11
- Volume
- 10
- Issue
- 6
- Pages
- 6461-6471
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2922251527
- Cited by
- 162
- References
- 40
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 26 · 2023: 27 · 2022: 28 · 2021: 24 · 2020: 20 · 2019: 10
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, public-domain)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Keywords
- active distribution networks, optimal power flow, data-driven control, machine learning, communication-free control, three-phase distribution systems, distribution network control
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