Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error. (15th November 2015)

• Record Type: Journal Article
• Authors: Xie, Mengfei; Zhou, Jianzhong; Li, Chunlong; Zhu, Shuang
• Is Part Of: Energy conversion and management. Volume 105(2016)
• Page Start: 368
• Page End: 376
• Publication Date: 2015-11-15
• Subjects: Long-term hydro generation scheduling -- Monthly streamflow forecasting error -- Monte Carlo simulation -- Improved parallel progressive optimality algorithm -- Forecasting dispatching chart; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.08.009
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Monthly streamflow forecasting error is considered. An improved parallel progressive optimality algorithm is proposed. Forecasting dispatching chart is manufactured accompanying with a set of rules. Applications in Xiluodu and Xiangjiaba cascade hydro plants. Abstract: Reliable streamflow forecasts are very significant for reservoir operation and hydropower generation. But for monthly streamflow forecasting, the forecasting result is unreliable and it is hard to be utilized, although it has a certain reference value for long-term hydro generation scheduling. Current researches mainly focus on deterministic scheduling, and few of them consider the uncertainties. So this paper considers the forecasting error which exists in monthly streamflow forecasting and proposes a new long-term hydro generation scheduling method called forecasting dispatching chart for Xiluodu and Xiangjiaba cascade hydro plants. First, in order to consider the uncertainties of inflow, Monte Carlo simulation is employed to generate streamflow data according to the forecasting value and error distribution curves. Then the large amount of data obtained by Monte Carlo simulation is used as inputs for long-term hydro generation scheduling model. Because of the large amount of streamflow data, the computation speed of conventional algorithm cannot meet the demand. So an improved parallel progressive optimality algorithm is proposed to solve the long-term hydro generation scheduling problem and a series of solutions are obtained. These solutions constitute an interval set, unlike the unique solution in the traditional deterministic long-term hydro generation scheduling. At last, the confidence intervals of the solutions are calculated and forecasting dispatching chart is proposed as a new method for long-term hydro generation scheduling. A set of rules are proposed corresponding to forecasting dispatching chart. The chart is tested for practical operations and achieves good performance.