An Online Control Method of Reactive Power and Voltage Based on Mechanism–Data Hybrid Drive Model Considering Source–Load Uncertainty

Xu Huang, Guoqiang Zu, Qi Ding, Ran Wei, Yudong Wang () and Wei Wei ()
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Xu Huang: Chengdong Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin 300250, China
Guoqiang Zu: Chengdong Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin 300250, China
Qi Ding: Chengdong Power Supply Branch, State Grid Tianjin Electric Power Company, Tianjin 300250, China
Ran Wei: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Yudong Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Wei Wei: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2023, vol. 16, issue 8, 1-15

Abstract: The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive (MDHD) model. Based on the concept of a mechanism and data hybrid drive, the mechanism-driven deterministic reactive power optimization strategy and the stochastic reactive power optimization strategy are used as training data. By training the data-driven CNN–GRU network model offline, the influence of source–load uncertainty on reactive power optimization can be effectively assessed. On this basis, according to the online source and load predicted data, the proposed hybrid-driven model can be applied to quickly obtain the reactive power optimization strategy to enable fast control of voltage. As observed in the case studies, compared with the traditional deterministic and stochastic reactive power optimization models, the hybrid-driven model not only satisfies the real-time requirement of online voltage control, but also has stronger adaptability to source–load uncertainty.

Keywords: source–load uncertainty; data-mechanical hybrid drive; reactive power optimization; CNN–GRU (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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