Optimal planning and sizing of BESS for wind energy ramp rate control through DES initiated large data clustering and PSO technique

Abstract

This paper presents optimal sizing and planning of Battery Energy Storage System (BESS) through Dynamic Exponential Smoothing (DES) initiated Particle Swarm Optimization (PSO) technique for wind energy ramp rate control. In a power system with high proportion of variable wind energy generation, power producers participate in a bid should meet the standards set by grid owners and/or transmission system operators, one of which is ramp rate limit. A onetime sizing and planning of BESS for ramp rate control requires analysis of long period recorded data. Application of dynamic smoothing process together with large data clustering technique and PSO based optimization algorithm is applied for the sizing of BESS to limit the ramp rate of Zhangjiakou wind power plant in China. Results indicate that the BESS improves the RR characteristics of the wind farm. Besides, the joint DES initiated clustering and PSO based optimization significantly reduced the computation time compared to the time required if only PSO is used.