A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

Abstract

In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS) is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM) is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network.

Keywords

• random matrix theory
• reactive power optimization
• distribution network analysis
• big data