Abstract
To make full use of inflow forecasts with different lead times, a new reservoir operation model that considers the long-, medium- and short-term inflow forecasts (LMS-BSDP) for the real-time operation of hydropower stations is presented in this paper. First, a hybrid model, including a multiple linear regression model and the Xinanjiang model, is developed to obtain the 10-day inflow forecasts, and ANN models with the circulation indexes as inputs are developed to obtain the seasonal inflow forecasts. Then, the 10-day inflow forecast is divided into two segments, the first 5 days and the second 5 days, and the seasonal inflow forecast is deemed as the long-term forecast. Next, the three inflow forecasts are coupled using the Bayesian theory to develop LMS-BSDP model and the operation policies are obtained. Finally, the decision processes for the first 5 days and the entire 10 days are made according to their operation policies and the three inflow forecasts, respectively. The newly developed model is tested with the Huanren hydropower station located in China and compared with three other stochastic dynamic programming models. The simulation results demonstrate that LMS-BSDP performs best with higher power generation due to its employment of the long-term runoff forecast. The novelties of the present study lies in that it develops a new reservoir operation model that can use the long-, medium- and short-term inflow forecasts, which is a further study about the combined use of the inflow forecasts with different lead times based on the existed achievements.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Eum H, Kim Y, Palmer RN (2011) Optimal drought management using sampling stochastic dynamic programming with a hedging rule. J Water Resour Plan Manag 137(1):113–122. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000095
Hamlet AF, Huppert D, Lettenmaier DP (2002) Economic value of long-lead streamflow forecasts for Columbia River hydropower. J Water Resour Plan Manag 128(2):91–101. https://doi.org/10.1061/(ASCE)0733-9496(2001)128:2(91)
Karamouz M, Vasiliadis HV (1992) Bayesian stochastic optimization of reservoir operation using uncertain forecasts. Water Resour Res 28(5):1221–1232. https://doi.org/10.1029/92WR00103
Kim YO, Palmer RN (1997) Value of seasonal flow forecasts in Bayesian stochastic programming. J Water Resour Plan Manag 123(6):327–335. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:6(327)
Li X, Guo S, Liu P, Chen G (2010) Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty. J Hydrol 391(1–2):124–132. https://doi.org/10.1016/j.jhydrol.2010.07.011
Maurer EP, Lettenmaier DP (2004) Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs. J Clim 17(1):174–186. https://doi.org/10.1175/1520-0442(2004)017<0174:PEOLHP>2.0.CO;2
McCollor D, Stull R (2008) Hydrometeorological short-range ensemble forecasts in complex terrain. Part i: meteorological evaluation. Weather Forecast 23(4):533–556. https://doi.org/10.1175/2008WAF2007063.1
Mujumdar PP, Nirmala B (2007) A bayesian stochastic optimization model for a multi-reservoir hydropower system. Water Resour Manag 21(9):1465–1485. https://doi.org/10.1007/s11269-006-9094-3
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330. https://doi.org/10.1002/hyp.554
Tang G, Zhou H, Li N (2010a) Reservoir optimization model incorporating inflow forecasts with various lead times as hydrologic state variables. J Hydroinf 12(3):292–302. https://doi.org/10.2166/hydro.2009.088
Tang G, Zhou H, Li N, Wang F, Wang Y, Jian D (2010b) Value of medium-range precipitation forecasts in inflow prediction and hydropower optimization. Water Resour Manag 24(11):2721–2742. https://doi.org/10.1007/s11269-010-9576-1
Tejada-Guibert JA, Johnson SA, Stedinger JR (1995) The value of hydrologic information in stochastic dynamic programming models of a multireservoir system. Water Resour Res 31(10):2571–2579. https://doi.org/10.1029/95WR02172
Xu W, Peng Y, Wang B (2013) Evaluation of optimization operation models for cascaded hydropower reservoirs to utilize medium range forecasting inflow. SCIENCE CHINA Technol Sci 56(10):2540–2552. https://doi.org/10.1007/s11431-013-5346-7
Xu W, Zhang C, Peng Y, Fu G, Zhou H (2014) A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts. Water Resour Res 50(12):9267–9286. https://doi.org/10.1002/2013WR015181
Yao H, Georgakakos A (2001) Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management. J Hydrol 249(1–4):176–196. https://doi.org/10.1016/S0022-1694(01)00418-8
Zhao R (1992) The Xinanjiang model applied in China. J Hydrol 135(1):371–381
Acknowledgements
This work is supported by the National Key Research and Development Program of China (Grant No. 2017YFC0406005), the National Natural Science Foundation of China (Grant No. 91547111, 51609025, 51709108), Scientific Research Foundation for High-level Talents of North China University of Water Resources and Electric Power (Grant No. 201702013), the National Natural Science Foundation of Chongqing (Grant No. cstc2015jcyjA90015), Open Fund Approval (Grant No. SKHL1427), and Scientific Research Foundation of Chongqing Jiaotong University (Grant No. 15JDKJC-B019).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
None.
Rights and permissions
About this article
Cite this article
Zhang, X., Peng, Y., Xu, W. et al. An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times. Water Resour Manage 33, 173–188 (2019). https://doi.org/10.1007/s11269-018-2095-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-018-2095-1