Hierarchical Distributed Chance-Constrained Voltage Control for HV and MV DNs Based on Nonlinearity-Adaptive Data-Driven Method
Mingtong Yang, Yixin Liu, Li Guo, Zhongguan Wang, Jiebei Zhu, Yuxuan Zhang, Bing Cheng, Chengshan Wang
Abstract
With large-scale distributed energy resources (DERs) being integrated into distribution networks (DNs), the coordinated optimal power flow (OPF) of high voltage (HV) and medium voltage (MV) DNs becomes challenging due to enhanced uncertainties and nonlinearity. This paper presents a hierarchical distributed chance-constrained OPF model for HV and MV DNs based on a nonlinear adaptive data-driven method. The HV master problem aims to minimize the overall operational costs, and is transformed into mixed-integer convex problem via second order cone programming. A model-free chance-constrained optimization sub-problem is established to tackle inaccurate model parameters issues and DER uncertainties in MV DNs and realize rapid response to control schemes from HV DN. The nonlinearity of power flow is approximately depicted by a data-driven incomplete dimension lifting power flow (IDL-PF) constraint, which is integrated into the MV DNs optimization model. A novel multi-parameter programming (MPP)-based distributed algorithm is proposed to solve the coordinated OPF model, which can significantly accelerate the iterative speed between the master and sub-problems. Finally, the convergence, efficiency and optimality of the proposed method are validated by numerous simulations in a practical DNs in China.