DAILY OPTIMAL OPERATION OF HYDROPOWER PLANT BASED ON MARGINAL PRICE FORECASTING
Wu Shiyong, Ma Guangwen, Guo Xiaming (Sichuan University, Chengdu 610065, China)
This paper is concerned with the daily optimal operation of hydropower plant on a competitive power market. A liner moving mean auto-regression model is developed to forecast the system marginal price. Based on it, a daily optimal operation model with the genetic algorithm is presented to generate the daily generation schedule for the plant. The methodology has been tested at a typical plant in Sichuan, and the results show that the daily optimal operation schedule of the hydropower plant based on the forecasted marginal price can produce considerable economic benefits on a competitive electricity market.