SVM-based Implicit Stochastic Scheduling Mode for Cascade Hydropower Stations

¹ College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University, Ya’an 625014, China ;
² College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, China ;
³ Business School, Sichuan University, Chengdu 610065, China

^a Corresponding author: Li Jidong (1990-), male, Ph.D., research direction is the optimization of water and electricity management, E-mail: sdjpz@sicau.edu.cn.

Abstract

In view of the uncertainty of inflow of cascade reservoirs, an implicit stochastic joint scheduling function model for cascade hydropower stations based on support vector machine (SVM) was established. Taking the cascade hydropower station on the lower reaches of the Yalong River as an example, according to the long series of optimized scheduling simulation operation data, the Gauss radial basis (BRF) kernel function is utilized by LIBSVM for the scheduling function fitting of the cascade reservoirs in the lower reaches of the Yalong River. Besides, combined with particle swarm optimization algorithm, the support vector machine (SVM) model parameters c (penalty coefficient) and g (relaxation coefficient) were optimized. Eventually, the optimized scheduling function model was used for the cascade hydropower station simulation operation. The results show that compared with the existing scheduling technology, the nonlinear SVM scheduling function is better than the linear regression model, and the effect of the nonlinear SVM scheduling function is equivalent to that of the threshold regression model. Therefore, the SVM-based Implicit Stochastic Scheduling method can provide references for the actual operation of the cascade hydropower station.