Robust Optimal Operation of Active Distribution Network Based on Minimum Confidence Interval of Distributed Energy Beta Distribution

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Automated summary

With the increasing penetration of distributed energy, a new robust optimal operation method based on the minimum confidence interval of distributed energy Beta distribution is proposed in this paper to address the challenges faced by traditional distribution network optimization models in ensuring stable and efficient operation. The method includes establishing an ADN model with second-order cone, analyzing historical data of distributed energy, obtaining the minimum confidence interval, and solving a two-stage robust optimization model for ADN, resulting in more stable and efficient operation compared to traditional methods.