Wind storage combined optimization configuration method based on extension distance K-mean clustering

Abstract

The invention relates to the technical field of power distribution networks, in particular to a wind storage combined optimization configuration method based on extension distance K-mean clustering. The method comprises the following steps: combining an extension distance theory with a K-mean clustering algorithm to perform multi-scene analysis on wind storage combined optimization configuration;establishing a wind storage combined system optimal configuration model based on multi-scene analysis, source network load collaborative optimization and differentiated demand response; and introducing the idea of a sine function and a parallel computing technology into a differential evolution algorithm, and constructing a parallel multi-objective sine differential evolution algorithm to solve the wind storage combined system optimal configuration model. The extension distance K-mean clustering algorithm provided by the invention can effectively improve the accuracy and balance of the clustering result; a scene generated on the basis of an extension distance K-mean clustering multi-scene analysis method can effectively process uncertainty of distributed wind power output and load requirements; according to the method, the PMOSDE algorithm is adopted to solve the model, and the optimization speed and the optimization depth are effectively improved through the PMOSDE algorithm.

Description

technical field [0001] The invention relates to the technical field of distribution network, in particular to a wind-storage joint optimization configuration method based on extension distance K-means clustering. Background technique [0002] In recent years, distributed wind power has developed rapidly in the distribution network. However, due to the strong fluctuation and intermittency of the output power of distributed wind turbines, the ability of distribution network to accept distributed wind power is limited. The energy storage system has the characteristics of fast charging and discharging, flexible configuration, etc. Combining the energy storage system with distributed wind turbines to form a wind-storage joint system can effectively stabilize the volatility of wind power and improve the rate of wind power consumption. In view of this, if the optimal configuration of the wind-storage combined system can be realized in the power supply planning of the distribution ...

AI Technical Summary

Problems solved by technology

However, the conventional K-means clustering algorithm often leads to unstable clustering results and low clustering accuracy due to the randomness of the initial clustering center point selection.

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