Voltage regulation enhanced hierarchical coordinated volt/var and volt/watt control for active distribution networks with soft open points
Abstract
The integration of renewable energy sources (RESs) into active distribution networks (ADNs) is essential for reducing carbon emissions but often results in voltage fluctuations and violations. This paper proposes a hierarchical voltage control framework that effectively coordinates diverse controllable devices with various response times in an ADN. The framework comprises three stages: day-ahead scheduling of on-load tap changer (OLTC), intra-day optimization for droop slopes and references for droop controllers in Soft Open Points (SOPs) and distributed generators (DGs), and real-time local voltage regulation. Unlike existing approaches, the proposed approach analytically establishes voltage stability constraints and incorporates them into droop slope optimization for local controllers, mitigating voltage oscillation risks. Additionally, a novel deviation-aware optimization method is developed to calculate optimal voltage references. This method treats the deviations between fixed-point voltages and their references as uncertainties and accounts for their impacts on voltage security through chance-constrained programming. Simulation results demonstrate the effectiveness of the proposed framework in improving voltage regulation performance with guaranteed stability.
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Centralized optimization-based VVC formulations can incorporate detailed models of device capabilities and network constraints, and optimize for objectives such as loss minimization, voltage profile improvement, or conservation voltage reduction.
“Such formulations can incorporate detailed models of device capabilities and network constraints, and optimize for objectives such as loss minimization, voltage profile improvement, or conservation voltage reduction.”
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 [9]
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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
- Authors
- Hou, Q., Dai, N., & Huang, Y.
- Affiliations
- University of Macau (MO)
- Zhejiang University (CN)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2024-07
- Volume
- 15
- Issue
- 3
- Pages
- 2021-2037
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W4395686259
- Cited by
- 42
- References
- 0
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 8
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- Funders
- National Key Research and Development Program of China (2022YFE0205100)
- Science and Technology Development Fund (0029/2022/AMJ, 001/2024/SKL)
- 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
- voltage regulation, soft open points, active distribution networks, hierarchical control, volt/var control, Volt-Watt control, droop control, on-load tap changer, distributed generation, renewable energy integration, voltage stability constraints, chance-constrained programming, voltage oscillations
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