Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy

In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.

[1]  Donald L. Tennant.  Instream Flow Regimens for Fish, Wildlife, Recreation and Related Environmental Resources , 1976 .

[2]  Daniel Rivero,et al.  Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks , 2007 .

[3]  Hamed Assaf,et al.  Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system , 2008, Environ. Model. Softw..

[4]  Hilmy Sally,et al.  Testing water demand management scenarios in a water-stressed basin in South Africa: application of the WEAP model , 2003 .

[5]  Avi Ostfeld,et al.  State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management , 2010 .

[6]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[7]  Reza Kerachian,et al.  Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm , 2010, Appl. Soft Comput..

[8]  J. Stedinger,et al.  Water resource systems planning and analysis , 1981 .

[9]  Alireza B. Dariane,et al.  Optimization of multireservoir systems operation using modified direct search genetic algorithm. , 2009 .

[10]  Hossein Orouji,et al.  Modeling of Water Quality Parameters Using Data-Driven Models , 2013 .

[11]  Mahdi Zarghami,et al.  Simulating hedging rules for effective reservoir operation by using system dynamics: a case study of Dez Reservoir, Iran , 2013 .

[12]  Mustafa Gunal,et al.  Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures , 2008 .

[13]  Andrea Sulis,et al.  Comparison of generic simulation models for water resource systems , 2013, Environ. Model. Softw..

[14]  T. R. Neelakantan,et al.  Hedging Rule Optimisation for Water Supply Reservoirs System , 1999 .

[15]  Aytac Guven,et al.  Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming , 2011 .

[16]  Chunli Shi,et al.  Application of Water Evaluation and Planning (WEAP) model for water resources management strategy estimation in coastal Binhai New Area, China , 2015 .

[17]  Patricia Gober,et al.  Managing water in complex systems: An integrated water resources model for Saskatchewan, Canada , 2014, Environ. Model. Softw..

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  J. Lund,et al.  Optimal Hedging and Carryover Storage Value , 2004 .

[20]  Jonathan L. Goodall,et al.  Design and implementation of a general software library for using NSGA-II with SWAT for multi-objective model calibration , 2016, Environ. Model. Softw..

[21]  Vladan Babovic,et al.  GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING 1 , 2001 .

[22]  Li-Chiu Chang,et al.  Multi-objective evolutionary algorithm for operating parallel reservoir system , 2009 .

[23]  Guangtao Fu,et al.  Multiple objective optimal control of integrated urban wastewater systems , 2008, Environ. Model. Softw..

[24]  Zejun Li,et al.  Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis , 2017 .

[25]  Soroosh Sorooshian,et al.  Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration , 1999 .

[26]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[27]  Rebecca Smith,et al.  Many Objective Analysis to Optimize Pumping and Releases in a Multi-Reservoir Water Supply Network , 2016 .

[28]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[29]  Henrik Madsen,et al.  Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation , 2014 .

[30]  Kalyanmoy Deb,et al.  MOMM: Multi-objective model merging , 2015, J. Syst. Softw..

[31]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .