Energy Storage Optimization Strategy Considering Source-Load Bilateral Uncertainty

Energy Storage Configuration in New Energy Grid, there are too many scenarios where the output and load timing of wind and solar renewable units fluctuate, which will not only increase the difficulty of model solution, but also affect the optimal configuration results. To this end, Davies Bouldin index (DBI) is first introduced to combine K-means algorithm to reduce hourly scene clustering. Then, a two-layer optimal configuration model of active distribution network is established. The model considers the characteristics of wind and load timing. The goal of the upper model is that the total operating cost of the system throughout the year is the most reasonable. Decision variables are location and capacity of energy storage device connected to distribution network; The lower objective is to optimize the operation voltage level of the system at each time scale, and the decision variable is the operation cost under the active management measures of the distribution network; Considering the time sequence output of wind turbine generators and the time sequence response of load side, the distributed generation optimization included in the active distribution network is planned. Finally, adaptive genetic algorithm is used to solve the model. The IEEE 33 bus system example shows that the method adopted in this paper not only ensures the diversity and rationality of scenario selection, but also takes into account the output and load timing fluctuation characteristics of renewable energy units, improves the system operation level and reduces the system operation cost.