A novel robust Volt/Var optimization method based on worst‐case scenarios for distribution network operation

• Published in: IET generation, transmission & distribution transmission & distribution, 2023-04, Vol.17 (7), p.1663-1673
• Main Authors: Ahmadi, Mahdi, Salami, Abolfazl, Alavi, Mohammadhasan
• Format: Article
• Language: English
• Subjects: distribution networks; MISOCP; renewable energy sources; robust optimization; Volt/Var optimization
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/gtd2.12777

The Volt/Var optimization (VVO) problem is used for scheduling the voltage regulation and reactive power compensation equipment in distribution networks to minimize power loss and voltage violation. In order to solve the VVO problem for a forthcoming time horizon, it is necessary to predict some parameters such as load demand and renewable energy production. The prediction of these parameters is always accompanied by uncertainty that robust optimization can be used to solve this concern. This paper presents a scenario‐based robust Volt/Var optimization (RVO) method that significantly reduces the number of scenarios required for the worst‐case approach. Solving the VVO problem with the worst‐case scenarios reduces the computational burden and maintains the voltage security of the distribution network against the severe events. The proposed RVO is formulated based on a mixed‐integer second‐order cone programming (MISOCP) model in which Volt/Var control (VVC) equipment is scheduled over a two‐stage strategy. The proposed method is validated using modified 33‐bus and 69‐bus IEEE test systems. The results demonstrate that the proposed RVO method maintains the network's voltage profile within the acceptable range against uncertainties. This paper proposes a new RVO approach based on worst‐case scenarios that can coordinate the voltage and reactive power control equipment at different time stages to reduce voltage violation. Instead of using numerous scenarios to realize a specific robustness rate in the proposed method, only two scenarios (determined according to the worst‐case robust optimization approach) has been selected.