徐炜,彭勇,张弛,王本德.基于降雨预报信息的梯级水电站不确定优化调度研究Ⅱ:耦合短、中期预报信息[J].水利学报,2013,44(10): |
基于降雨预报信息的梯级水电站不确定优化调度研究Ⅱ:耦合短、中期预报信息 |
Stochastic optimization operation for cascade hydropower reservoirsby using precipitation forecastsⅡ. Coupling short and medium-term information |
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DOI: |
中文关键词: 水库群 数值降雨预报 预报信息套接 聚合分解 贝叶斯随机动态规划 |
英文关键词: bayesian stochastic dynamic programming quantitative precipitation forecasts inflow forecasting hydropower operation decision processes |
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中文摘要: |
基于聚合分解思想将梯级水库群聚合为“虚拟单库”,降低了随机动态规划(SDP)模型的计算量,从而使SDP模型可以考虑更多预报信息。为了更近一步充分利用预报信息和降低预报信息不确定性对调度的影响,将降雨预报和径流预报的不确定性考虑为随预见期延长而不断增加的过程。首先将浑江流域的10d预报径流划分为前5d和后5d,并将前5d预报径流视为较准确的部分,而后5d预报径流视为不确定性较大部分;然后建立短、中期径流预报信息相套接的分段聚合分解贝叶斯随机动态规划(Two-Step-BSDP,TS-BSDP),为浑江梯级水库群制定前5d、后5d和10d的预报调度图;最后以5d为调度时段分别决策未来5d发电和10d发电计划。模拟调度结果表明,该模型中决策长度为5d的滚动决策具有更好的效果,该决策方式充分地利用了预报信息并有效地提高了发电效益。 |
英文摘要: |
This paper presents the Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model to use the forecasting inflow from Quantitative Precipitation Forecasts of Global Forecast System (QPF-GFS). Firstly, this model divides the forecasting inflow (e.g., 10 days) into two parts. The near part (e.g., 1~5 days) is assumed as accurate one. While the rest (e.g., 6~10 days) needs to account the
uncertainty, which addressed by Bayesian Decision Theory. Secondly, the TS-BSDP model runs iteratinely to derive the hydropower operation policies, which used to make operation decision for near part by rolling forward or the entire forecasting horizon. In this paper, China’s Hun River cascade hydropower reservoirs system is taken as an example, and the values of QPF-GFS (10 days lead time) are used to forecast the 10 days inflow. The operation policies for 1~5 days and entire 10 days incorporate with forecasting inflow to simulate the decision processes by 5 days rolling forward and entire 10 days, respectively. Finally, the simulation results demonstrate that the performance of rolling forward by 5 days is better than the other de?
cision processes. |
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