A system optimization method for mitigating three-phase imbalance in distribution network. (December 2019)

• Record Type: Journal Article
• Authors: Zeng, X.J.; Zhai, H.F.; Wang, M.X.; Yang, M.; Wang, M.Q.
• Is Part Of: International journal of electrical power & energy systems. Volume 113(2019)
• Page Start: 618
• Page End: 633
• Publication Date: 2019-12
• Subjects: Three-phase imbalance -- Three-phase four-wire -- Joint optimization model -- Linearization techniques -- Mixed-integer linear programming; 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.2019.05.038
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A kind of device is utilized to mitigate three-phase imbalance in distribution system. A joint optimization model from the perspective of the whole system is proposed. The current imbalance and voltage imbalance are simultaneously considered. Some linearization techniques are applied to simplify the model. Abstract: Three-phase imbalance is a common phenomenon in three-phase four-wire distribution network systems (DNSs), which may cause power quality deterioration, increase power losses, and can even damage appliances as well. The situation is becoming even worse because of the increasing integration of the distributed generations (DGs) and storage-capable loads into DNSs. In this paper, a joint optimization model based on the Y-connected and Δ-connected static reactive power compensation devices are proposed to mitigate the three-phase imbalance and minimize the active power losses in DNSs. The model is formulated to optimize the operation of all the devices in order to mitigate the imbalance of the whole DNS rather than a single feeder or a particular area. Meanwhile, the current imbalance of the transformer and the voltage imbalance of all three-phase nodes are simultaneously considered in the model according to the practical requirements. Several linearization techniques are applied to simplify the computation and accelerate the solving speed. These techniques convert the non-linear and non-convex model into a mixed-integer linear programming model, which can be effectively solved by off-the-shelf solvers. The effectiveness and scalability of the proposed method are verified by the test results obtained from the modified IEEE 34 and IEEE 123 test systems.