A Combing Data-Driven and Model-Driven Methods for Renewable Energy Acceptance Capacity Assessment in Distribution Networks

Abstract

To accurately improve the balanced new energy distribution network capacity assessment, this paper proposes a combined data-driven and model-driven renewable energy capacity assessment method for distribution networks. Firstly, the distribution generations (DG) output data are pre-processed to construct a scenario training sample set and extract seven types of DG daily output characteristics indicators. Secondly, a scenario generation model based on a conditional generation adversarial network is proposed to realize the coupling of scenario reduction and scenario generation based on K-means++ through daily output characteristics indicators. Finally, the optimization model based on hybrid positive linear programming is proposed for distributed power supply acceptance capacity evaluation of the distribution network with the objective of maximum DG access capacity and full consideration of voltage deviation, branch current, tidal current return, and other operational constraints. It is validated by IEEE 33-bus simulation. The simulation analysis shows that the proposed method can effectively quantify the DG capacity uncertainty compared with the traditional method, and the proposed method has higher computational accuracy and efficiency than the traditional method.