Sampling/stochastic dynamic programming for optimal operation of multi-purpose reservoirs using artificial neural network-based ensemble streamflow predictions

Due to limited water resources and the increasing demand for agricultural products, it is significantly important to operate surface water reservoirs optimally, especially those located in arid and semi-arid regions. This paper investigates uncertainty-based optimal operation of a multi-purpose water reservoir system by using four optimization models. The models include dynamic programming (DP), stochastic DP (SDP) with inflow classification (SDP/Class), SDP with inflow scenarios (SDP/Scenario), and sampling SDP (SSDP) with historical scenarios (SSDP/Hist). The performance of the models was tested in Zayandeh-Rud Reservoir system in Iran by evaluating how their release policies perform in a simulation phase. While the SDP approaches were better than the DP approach, the SSDP/Hist model outperformed the other SDP models. We also assessed the effect of ensemble streamflow predictions (ESPs) that were generated by artificial neural networks on the performance of SSDP/Hist. Application of the models to the Zayandeh-Rud case study demonstrated that SSDP in combination with ESPs and the K-means technique, which was used to cluster a large number of ESPs, could be a promising approach for real-time reservoir operation.

[1]  G. Weatherill,et al.  Delineation of shallow seismic source zones using K-means cluster analysis, with application to the Aegean region , 2009 .

[2]  Marcello Restelli,et al.  Tree-based fi tted Q-iteration for multi-objective Markov decision processes in water resource management , 2013 .

[3]  Marcello Restelli,et al.  Tree‐based reinforcement learning for optimal water reservoir operation , 2010 .

[4]  Gerald N. Day,et al.  Extended Streamflow Forecasting Using NWSRFS , 1985 .

[5]  Walter D. Fisher.  On Grouping for Maximum Homogeneity , 1958 .

[6]  A. Burak Göktepe,et al.  Soil clustering by fuzzy c-means algorithm , 2005, Adv. Eng. Softw..

[7]  Haralambos V. Vasiliadis,et al.  Bayesian stochastic optimization of reservoir operation using uncertain forecasts , 1992 .

[8]  R. Bras,et al.  Real time adaptive closed loop control of reservoirs with the High Aswan Dam as a case study , 1983 .

[9]  S. Yue,et al.  The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series , 2004 .

[10]  D. Cox.  Note on Grouping , 1957 .

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

[12]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[13]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[14]  Richard N. Palmer,et al.  Optimal Drought Management Using Sampling Stochastic Dynamic Programming with a Hedging Rule , 2011 .

[15]  B. F. Sule,et al.  Stochastic dynamic programming models for reservoir operation optimization , 1984 .

[16]  Young-Oh Kim,et al.  Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction , 2007 .

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

[18]  S. Yakowitz.  Dynamic programming applications in water resources , 1982 .

[19]  D. G. Doran.  An efficient transition definition for discrete state reservoir analysis: The divided interval technique , 1975 .

[20]  Seyed Jamshid Mousavi,et al.  A stochastic dynamic programming model with fuzzy storage states for reservoir operations , 2004 .

[21]  Jin-Hee Lee,et al.  Stochastic optimization of multireservoir systems via reinforcement learning , 2007 .

[22]  Marcello Restelli,et al.  A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run , 2013 .

[23]  Mohammad Karamouz,et al.  Fuzzy-State Stochastic Dynamic Programming for Reservoir Operation , 2004 .

[24]  Jery R. Stedinger,et al.  Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts , 2001 .

[25]  J. Stedinger,et al.  Sampling stochastic dynamic programming applied to reservoir operation , 1990 .