Cited by 162Optimal Power Flow DistributionSmart Grid and Power SystemsMicrogrid Control and Optimization

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Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

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Karagiannopoulos, Stavros, Aristidou, Petros, Hug, Gabriela·2019·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2019.2905348

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Abstract

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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Authors: Karagiannopoulos, S., Aristidou, P., & Hug, G.
Affiliations: ETH Zurich (CH)
University of Leeds (GB)
ORCID: Karagiannopoulos, Stavros
Aristidou, Petros
Hug, Gabriela
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-11
Volume: 10
Issue: 6
Pages: 6461-6471
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2019.2905348
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

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