A distributed optimal reactive power flow for global transmission and distribution network. (January 2019) - Zhao, Jinquan; Zhang, Zhenwei; Yao, Jianguo; Yang, Shengchun; Wang, Ke
Highlights: A HGD based distributed reactive power OPF for integrated TN and DNs is proposed. Global ROPF problem is decomposed into a TN-ROPF problem and some DN-ROPF problems. The coordination are achieved by transferring boundary variables and sensitivities. A penalty function discretization scheme is used to handle the discrete variables. Abstract: With the large-scale integration of distributed generators (DGs), the relationships between transmission network (TN) and distribution networks (DNs) are becoming more closely, especially in the aspects of reactive power and voltage. Optimal reactive power flow (ORPF) problems for TN and DNs are not suitable to be solved independently. Due to the fact that TN and DNs are operated by different control centers, a heterogeneous decomposition (HGD) based distributed ORPF method for global transmission and distribution (T&D) networks is presented in this paper. Considering the characteristics of the master–slave structure, the global T&D-ORPF problem can be decomposed into the TN-ORPF master sub-problem, the DN-ORPF slave sub-problems and the boundary consistency coordination sub-problem. Any of those optimization algorithms based on duality and gradient theory can be adopted to solve the TN-ORPF or DN-ORPF sub-problems. By incorporating a penalty function into optimization algorithm, the discrete control variables can successively discretized and therefore the augmented Lagrangian functions are differentiable. Boundary sensitivities constructed by the boundary dual multipliers are used to decouple the global ORPF problem of T&D networks. The same solution as the centralized optimization is achieved by transferring the boundary variables and sensitivities between the TN control center and DN control centers. The simulation results of IEEE 30-bus (TN) and modified IEEE 33-bus (DN) test systems containing multiple DGs show that the HGD algorithm is effective.