Data-driven stochastic service restoration in unbalanced active distribution networks with multi-terminal soft open points. (October 2020)

• Record Type: Journal Article
• Authors: Wang, Jian; Zhou, Niancheng; Wang, Qianggang
• Is Part Of: International journal of electrical power & energy systems. Volume 121(2020)
• Publication Date: 2020-10
• Subjects: Unbalanced active distribution networks -- Service restoration -- Multi-terminal soft open points -- Data-driven stochastic optimization; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106069
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A service restoration model for MTSOP-based UADN is proposed. A modified spanning tree method is used to meet the requirement of radical structure constraints. The MINLP model is transformed to a MISOCP problem for a three-phase distribution system. Abstract: Service restoration is critical in ensuring the reliability of distribution networks after the isolation of faults. Multi-terminal soft open points (MTSOPs) can realize power flow among multiple feeders and provide voltage support for power outage areas. This mechanism can help improve the restoration ability of distribution systems. A distribution network is typically unbalanced when the load, distributed generation, and line parameters are unbalanced. This study proposes accordingly a service restoration model for unbalanced active distribution networks (UADNs) with MTSOP. The power control principles of MTSOP in the fault condition and a service restoration strategy in MTSOP-based UADN are first analyzed. Then, a deterministic service restoration model is established. The maximized weighting restored load and the minimized voltage unbalance are used as the objective functions. The original optimization model is converted into a mixed integer second-order cone programming problem by linearizing the three-phase power flow equation, the big-M method, and the second-order cone relaxation, which can be solved by commercial solvers. A two-stage data-driven stochastic optimization model for service restoration in UADN is established by considering the uncertainty of load demand and the generation of renewable energy. The IEEE 33-node, Taiwan Power Company, and modified IEEE 123-node systems are used to verify the proposed model.