Reactive Power Optimization of Distribution Network Based on Random Matrix and Historical Scenario Matching

Abstract

Reactive power optimization and voltage management of distribution networks is very important for the optimal operation of distribution networks. By introducing big data theory into reactive power optimization of distribution networks, a reactive power optimization method is proposed based on random matrix and historical scenario matching, which does not need the model and parameters of the distribution network, and can directly use the data generated during the operation of the distribution network to construct seven high-dimensional random matrices and extract 57 characteristic indicators. The extracted characteristic indicators are reduced in dimension to match the existing scenarios in the historical database, and the scenarios closest to the statistical characteristics of the current system are found. The control strategy under the matching scenarios is adopted as the reactive power optimization control strategy in the current period to reduce the active power loss and node voltage deviation. Finally, the method is verified on the modified IEEE-37 node distribution network model, where the model of random loads, including the distributed generations such as photovoltaic/wind power, and electric vehicles are added. The results show that the proposed method can effectively optimize the reactive power of the distribution network without the need of its model and parameters, and the online decision-making speed is fast.

Keywords

• distribution network reactive power optimization
• random matrix
• scenario matching
• big data