Dynamic Reconstruction Strategy of Distribution Network Based on Uncertainty Modeling and Impact Analysis of Wind and Photovoltaic Power

Abstract

The distribution network with high penetration of renewable energy such as wind and photovoltaic power has higher flexibility and power supply efficiency, but it also faces more faults and uncertainties. Traditional dynamic reconfiguration under fault conditions are still limited by problems such as low load recovery rate and strong decision conservatism. To overcome these challenges, this article proposes a dynamic reconstruction strategy for distribution network under fault conditions that takes into account multivariate uncertainty. Firstly, in response to the uncertainty of distributed power generation output and load demand in the distribution network, an interval prediction method is adopted to construct a uncertainty model for source and load side. Then, the Latin hypercube sampling method is used to generate multiple operation scenarios, and computational efficiency is improved by reducing scenario samples using Cholesky sorting principle and synchronous backpropagation reduction method. Finally, a robust dynamic reconstruction model based on mixed-integer second-order cone programming (MISOCP) is constructed, and the feasibility and robustness of the proposed dynamic strategy are verified using the improved IEEE-33 node system. Through analysis, the proposed method effectively addresses the risk factors in the operation, thus improving the safety and reliability of the distribution network.

Keywords

• Distribution network
• dynamic reconstruction
• interval prediction method
• Latin hypercube sampling method
• mixed-integer second-order cone programming (MISOCP)