Optimal local volt/var control for photovoltaic inverters in active distribution networks
Abstract
The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method.
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Hybrid VVC approaches can substantially reduce computation and communication requirements compared with fully centralized OPF, while achieving better voltage regulation than fixed droop control.
“Overall, hybrid methods can substantially reduce computation and communication requirements compared with fully centralized OPF, while achieving better voltage regulation than fixed droop control”
From Physics-Informed AI-Based EV smart inverter control for two-stage Volt/Var coordination in renewable-rich active distribution networks (2026) · cites this paper as [21]
The performance of hybrid VVC methods depends on the update rate of the supervisory layer, the fidelity of network models/sensitivities, and the ability to maintain feasibility under forecast errors and fast operational changes.
“nevertheless, their performance depends on the update rate of the supervisory layer, the fidelity of network models/sensitivities, and the ability to maintain feasibility under forecast errors and fast operational changes.”
From Physics-Informed AI-Based EV smart inverter control for two-stage Volt/Var coordination in renewable-rich active distribution networks (2026) · cites this paper as [21]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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Zhong, Y., Qiu, J., Yang, Y., Zhang, C., Tao, Y., Sun, X., & Lai, S. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100869
Self-citation
- Authors
- Sun, X., Qiu, J., & Zhao, J.
- Affiliations
- The University of Sydney (AU)
- Chinese University of Hong Kong, Shenzhen (CN)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2021-11
- Volume
- 36
- Issue
- 6
- Pages
- 5756-5766
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W3161451446
- Cited by
- 111
- References
- 1
- Citations by year
- 2026: 22 · 2025: 19 · 2024: 30 · 2023: 26 · 2022: 13 · 2021: 1
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Funders
- ARC Research Hub (IH180100020)
- ARC Training Centre (IC200100023)
- ARC linkage project (LP200100056)
- Shenzhen Institute of Artificial Intelligence and Robotics for Society
- Training Program of the Major Research
- National Natural Science Foundation of China (91746118)
- Shenzhen Municipal Science and Technology Innovation Committee (ZDSYS20170725140921348, JCYJ20170410172224515, JCYJ20160510153103492)
- Foundations of Shenzhen Science and Technology Committee (JCYJ20170817100412438, JCYJ20190808141019317)
- NARI Technology research
- Comprehensive Energy System Simulation and Assessment Key Technology Research
- 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 › Power System Optimization and Stability
- Keywords
- volt/var control, photovoltaic inverter, active distribution networks, voltage regulation, reactive power support, voltage violation, optimal power flow, power flow analysis, deep convolutional neural networks, data-driven control, power loss reduction, distributed generation
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