Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach
For power system operators, it offers a parallelizable method for network reconfiguration that integrates load forecasting, but the results are incremental and lack concrete performance numbers.
This paper proposes a distributed data-driven approach for distribution system network reconfiguration using SVR-based load forecasting and ADMM-based distributed optimal power flow, demonstrating feasibility and effectiveness through numerical results.
In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.