Evaluation of optimization operation models for cascaded hydropower reservoirs to utilize medium range forecasting inflow

This paper evaluates the performances of the models that incorporate forecasting inflow for cascaded hydropower reservoirs operation. These models are constructed separately on the concepts of explicit stochastic optimization (ESO) and implicit stochastic optimization (ISO) as well as parameterization-simulation-optimization (PSO). Firstly, the aggregation-disaggregation method is implemented in ESO models to reduce the complexity of stochastic dynamic programming (SDP). And the aggregate flow SDP (AF-SDP) and aggregation-disaggregation SDP (AD-SDP) are constructed respectively. Secondly, in ISO model, decision tree is the well-known and widespread algorithm. The algorithm C 5.0 is selected to extract the if-then-else rules for reservoir operation. Thirdly, based on the PSO model, the hedging rule curves (HRCs) are pre-defined by fusing the storage and inflow as state variable. The parameters of the HRCs are determined by using the simulation-optimization model. Finally, China’s Hun River cascade hydropower reservoirs system is taken as an example to illustrate the efficiency and reliability of the models. In addition, the values of quantitative precipitation forecasts of the global forecast system (10 days lead-time) are implemented to forecast the 10 days inflow.

[1]  Adriano Rolim da Paz,et al.  Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting , 2009 .

[2]  M. Karamouz,et al.  Annual and monthly reservoir operating rules generated by deterministic optimization , 1982 .

[3]  Nien-Sheng Hsu,et al.  Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees , 2008 .

[4]  Slobodan P. Simonovic,et al.  Reservoir Systems Analysis: Closing Gap between Theory and Practice , 1992 .

[5]  Slobodan P. Simonovic,et al.  Optimal operation of water resource systems: trade-offs between modelling and practical solutions , 2001 .

[6]  Paresh Deka,et al.  Neural Network Based Decision Support Model for Optimal Reservoir Operation , 2005 .

[7]  P. P. Mujumdar,et al.  A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System , 2007 .

[8]  Dragan Savic,et al.  Water Reservoir Control with Data Mining , 2003 .

[9]  Daniel P. Loucks,et al.  Operating rules for multi-reservoir systems , 1994 .

[10]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[11]  Ximing Cai,et al.  Hedging rule for reservoir operations: 1. A theoretical analysis , 2008 .

[12]  BenDe Wang,et al.  Inter-basin water transfer-supply model and risk analysis with consideration of rainfall forecast information , 2010 .

[13]  XI Shu-feng.  Study on GFS Forecasted Rainfall Statistic and Its Feasibility in Real-Time Inter-Basin Water Transfer System Operation Decision Making , 2010 .

[14]  W. Collischonn,et al.  Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model , 2005 .

[15]  Huicheng Zhou,et al.  Value of Medium-range Precipitation Forecasts in Inflow Prediction and Hydropower Optimization , 2010 .

[16]  Paulo Chaves,et al.  Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks , 2007 .

[17]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .

[18]  Robin T. Clarke,et al.  Medium-range reservoir inflow predictions based on quantitative precipitation forecasts , 2007 .

[19]  Alcigeimes B. Celeste,et al.  Evaluation of stochastic reservoir operation optimization models , 2009 .

[20]  Ralph A. Wurbs.  Reservoir‐System Simulation and Optimization Models , 1993 .

[21]  Pan Liu,et al.  Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs , 2011 .

[22]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[23]  Nien-Sheng Hsu,et al.  Optimization of Reservoir Management and Operation with Hedging Rules , 2003 .

[24]  Florence Habets,et al.  On the utility of operational precipitation forecasts to served as input for streamflow forecasting , 2004 .

[25]  B. Golding.  Quantitative precipitation forecasting in the UK , 2000 .

[26]  T. R. Neelakantan,et al.  NEURAL NETWORK-BASED SIMULATION-OPTIMIZATION MODEL FOR RESERVOIR OPERATION , 2000 .

[27]  A. Turgeon,et al.  Learning disaggregation technique for the operation of long‐term hydroelectric power systems , 1994 .

[28]  Nien-Sheng Hsu,et al.  Real‐Time Operation of Tanshui River Reservoirs , 1990 .

[29]  Li Chen,et al.  Optimizing the reservoir operating rule curves by genetic algorithms , 2005 .

[30]  Demetris Koutsoyiannis,et al.  A parametric rule for planning and management of multiple‐reservoir systems , 1997 .

[31]  D. K. Srivastava,et al.  Study of a complex water resources system with screening and simulation models , 1981 .

[32]  Demetris Koutsoyiannis,et al.  Evaluation of the parameterization‐simulation‐optimization approach for the control of reservoir systems , 2003 .

[33]  Deepti Rani,et al.  Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation , 2010 .

[34]  Xiao-Lin Wang,et al.  A new approach of obtaining reservoir operation rules: Artificial immune recognition system , 2011, Expert Syst. Appl..

[35]  Jr-Shin Li,et al.  APPLICATION OF THE GENETIC ALGORITHM FOR OPTIMIZING OPERATION RULES OF THE LiYuTan RESERVOIR IN TAIWAN 1 , 2003 .

[36]  Daniel P. Loucks,et al.  Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation , 1982 .

[37]  Li Chen,et al.  REAL CODED GENETIC ALGORITHM OPTIMIZATION OF LONG TERM RESERVOIR OPERATION 1 , 2003 .

[38]  Juan B. Valdés,et al.  Aggregation‐Disaggregation Approach to multireservoir operation , 1992 .

[39]  A. Turgeon,et al.  An aggregation‐disaggregation approach to long‐term reservoir management , 1998 .

[40]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[41]  Danxun Li,et al.  Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC , 2012 .

[42]  G. K. Young.  Finding Reservoir Operating Rules , 1967 .

[43]  Abbas Afshar,et al.  Fuzzy rule-based model for hydropower reservoirs operation , 2011 .

[44]  Sh. Momtahen,et al.  Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies , 2007 .

[45]  Tao Zhang,et al.  Bilevel model for multi-reservoir operating policy in inter-basin water transfer-supply project , 2012 .

[46]  Guolei Tang,et al.  Study on reservoir operation optimization of Ertan Hydropower Station considering GFS forecasted precipitation , 2011 .

[47]  Salvatore Greco,et al.  Discovering Reservoir Operating Rules by a Rough Set Approach , 2006 .

[48]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .