Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges
Open accessAbstract
Today, system operators rely on local control of distributed energy resources (DERs), such as photovoltaic units, wind turbines and batteries, to increase operational flexibility. These schemes offer a communication-free, robust, cheap, but rather sub-optimal solution and do not fully exploit the DER capabilities. The operational flexibility of active distribution networks can be greatly enhanced by the optimal control of DERs. However, it usually requires remote monitoring and communication infrastructure, which current distribution networks lack due to the high cost and complexity. In this paper, we investigate data-driven control algorithms that use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We elaborate on the suitability of various schemes based on different local features, we investigate safety challenges arising from data-driven control schemes, and we show the performance of the optimized local controls on a three-phase, unbalanced, low-voltage, distribution network.
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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 [411]
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Cites
YALMIP : a toolbox for modeling and optimization in MATLAB
Löfberg, J. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/cacsd.2004.1393890 · Cited by 9,733 · see citing works
Estimating regression models with unknown break‐points
Muggeo, V. M. R. (2003) · Statistics in Medicine · doi:10.1002/sim.1545 · Cited by 2,408 · see citing works
Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314 · see citing works
Distribution System Modeling and Analysis
Kersting, W. (2012) · Electric Power Engineering Series · doi:10.1201/b12056-31 · Cited by 1,241 · see citing works
Zero Duality Gap in Optimal Power Flow Problem
Lavaei, J., & Low, S. H. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2160974 · Cited by 1,197 · see citing works
A direct approach for distribution system load flow solutions
Teng, J. (2003) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2003.813818 · Cited by 1,042 · see citing works
Distribution System Modeling and Analysis
Denny, F., & Dismukes, D. (2001) · The electric power engineering series/Electric power engineering series · doi:10.1201/9781420058451 · Cited by 900 · see citing works
Benchmark systems for network integration of renewable and distributed energy resources
Barsali, S., & Tf, C. C. (2014) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 494 · see citing works
A Distributed Control Strategy for Reactive Power Compensation in Smart Microgrids
Bolognani, S., & Zampieri, S. (2013) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2013.2270317 · Cited by 282 · see citing works
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2905348 · Cited by 162 · see citing works
Self-citationHatziargyriou, N. D., Cutsem, T. V., Milanović, J. V., Pourbeik, P., Vournas, C. D., Vlachokyriakou, O., Kotsampopoulos, P. C., Hong, Ramos, R. A., Boemer, J., et al. (2017) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 85 · see citing works
Self-citationOptimal sizing and placement of distributed storage in low voltage networks
Fortenbacher, P., Zellner, M., & Andersson, G. N. (2016) · Power Systems Computation Conference · doi:10.1109/pscc.2016.7540850 · Cited by 79 · see citing works
Hybrid approach for planning and operating active distribution grids
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2016) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2016.0642 · Cited by 52 · see citing works
Self-citationKotsampopoulos, P. C., Hatziargyriou, N. D., Bletterie, B., & Lauss, G. F. (2013) · IEEE International Workshop on Intelligent Energy Systems (IWIES) · doi:10.1109/iwies.2013.6698583 · Cited by 41 · see citing works
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Sondermeijer, O., Dobbe, R., Arnold, D., Tomlin, C. J., & Keviczky, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08594 · Cited by 34 · see citing works
Self-citationKaragiannopoulos, S., Aristidou, P., Ulbig, A., Koch, S., & Hug, G. (2016) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2016.7741538 · Cited by 28 · see citing works
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Bellizio, F., Karagiannopoulos, S., Aristidou, P., & Hug, G. (2018) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2018.8586079 · Cited by 28 · see citing works
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Garg, A., Jalali, M., Kekatos, V., & Gatsis, N. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646638 · Cited by 28 · see citing works
Operational Planning of Active Distribution Grids under Uncertainty
Karagiannopoulos, S., Roald, L., Aristidou, P., & Hug, G. (2017) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 26 · see citing works
Self-citationA Centralised Control Method for Tackling Unbalances in Active Distribution Grids
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8442493 · Cited by 26 · see citing works
Self-citationCo-optimisation of planning and operation for active distribution grids
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2017) · doi:10.1109/ptc.2017.7981112 · Cited by 20 · see citing works
Self-citationData-Driven Decentralized Optimal Power Flow.Preprint
Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D. S., & Tomlin, C. J. (2018) · arXiv (Cornell University) · Cited by 16 · see citing works
Self-citationTowards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine LearningPreprint
Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D. S., & Tomlin, C. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06790 · Cited by 10 · see citing works
Self-citationKernel-Based Learning for Smart Inverter ControlPreprint
Garg, A., Jalali, M., Kekatos, V., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.03769 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [20][20] IEEE 1547, 'IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems,' Jul 2003. -
- [21][21] V. M. R. Muggeo, 'Estimating regression models with unknown breakpoints,' Statistics in Medicine , vol. 22, no. 19, pp. 3055-3071, Oct 2003. -
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- Authors
- Karagiannopoulos, S., Dobbe, R., Aristidou, P., Callaway, D., & Hug, G.
- Affiliations
- ETH Zurich (CH)
- University of California, Berkeley (US)
- University of Leeds (GB)
- Venue
- 2019 IEEE Milan PowerTech
- Publisher
- IEEE
- Published
- 2019-06
- OpenAlex
- W2970175253
- Cited by
- 24
- References
- 22
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 1 · 2018: 1
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- green
- api.elsevier.com
- Licence
- Locations
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- data-driven control, active distribution networks, distributed energy resources, local control, communication-free control, machine learning control, offline optimization, operational flexibility, safety challenges, photovoltaic units, wind turbines, battery energy storage
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