Optimal scenario generation algorithm for multi-objective optimization operation of active distribution network

Considering the uncertain and stochastic of intermittent distributed generations (DGS) in active distribution network (ADN), a scenario method using Wasserstein distance metric and K-means cluster scenes reduction technology to generate optimal scene is proposed in this paper. So the stochastic problem is transformed into a deterministic problem. The multi-scenario tree models of wind-photovoltaic-load are built. A multi-objective optimization mathematical model of active distribution network containing intermittent DGS is established, which includes objectives that are the annual profits, the total active power losses and the voltage deviations of the bus, and considering active management characteristic. The tabu search artificial bee colony algorithm is used to solve the optimization problems. The simulation results show that the optimal scenes based on the Wasserstein distance indicators and K-means cluster technique reflect the random feature of distributed generation active power output more accurately. Finally, the simulation analysis of IEEE33-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method.