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; 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 [410]
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AI in Renewable Energy Education
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Exploiting OLTC and BESS Operation Coordinated with Active Network Management in LV Networks
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Review of Learning-Assisted Power System OptimizationPreprint
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A Physics-Informed Graph Convolution Network for AC Optimal Power Flow via Refining DC Solution
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Deep Learning for Reactive Power Control of Smart Inverters under Communication Constraints
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Decentralized control in active distribution grids via supervised and reinforcement learning
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Data Driven Approach for Optimal Power Flow in Distribution Network
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Review of Learning-Assisted Power System OptimizationPreprint
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Introduction to machine learning in the power generation industry
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Optimal Adaptive Droop Design via a Modified Relaxation of the OPF
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Clustering Data-Driven Local Control Schemes in Active Distribution Grids
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Machine learning-accelerated distributed optimisation methods for optimal power flow: A review
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Feedback Power Cost Optimization in Power Distribution Networks With Prosumers
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A Linearized Optimal Power Flow Framework for a Balanced Active Distribution Network
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Multi-Agent Deep Reinforcement Learning-based Volt-VAR Control in Active Distribution Grids
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Deep Learning for Scalable Optimal Design of Incremental Volt/VAR Control Rules
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Distributed Optimal Power Flow with Data-Driven Sensitivity Computation
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Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids
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Self-citationLearning-Accelerated ADMM for Distributed DC Optimal Power Flow
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- 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 · 2018: 1
- 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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