Multi-Objective Reservoir Operation with Sediment Flushing; Case Study of Sefidrud Reservoir

In this study, the non-dominated sorting genetic algorithm (NSGA-II) is used for the multi-objective optimization of the Sefidrud reservoir in Northern Iran. The main objectives include water supply, hydropower generation and sediment flushing. In addition to some physical constraints such as the reservoir storage and the outlet flow, maximum flushing outflow and non-flushing in irrigation seasons, the environmental constraints as fish migration and spawning are taken into an account. After obtaining the Pareto optimal solutions by means of the weighted objective functions and non-symmetric Nash bargaining, various scenarios are defined. Then these scenarios are analyzed by introducing a new sustainability index. Furthermore, the different percentages of downstream water demand are considered in order to achieve a better evaluation of the scenarios. The results of study indicate that the optimal solutions are more sustainable than those of the current operation of Sefidrud reservoir, which increase the sediment flushing by 37 million tons compared to the current operation, with the same hydroelectric energy and downstream water supply. The proposed methodology could be used successfully in other reservoir operations including the sediment flushing.

[1]  Mohammed Atiquzzaman,et al.  Optimal design of water distribution network using shu2ed complex evolution , 2004 .

[2]  Li-Chiu Chang,et al.  Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation , 2011 .

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

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

[5]  Ximing Cai,et al.  Building more realistic reservoir optimization models using data mining – A case study of Shelbyville Reservoir , 2011 .

[6]  George Kourakos,et al.  Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. , 2013 .

[7]  Omid Bozorg Haddad,et al.  Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems , 2013, Water Resources Management.

[8]  Reza Kerachian,et al.  Multi-objective genetic local search algorithm using Kohonen's neural map , 2009, Comput. Ind. Eng..

[9]  Omid Bozorg Haddad,et al.  Reservoir Operation for Simultaneously Meeting Water Demand and Sediment Flushing: Stochastic Dynamic Programming Approach with Two Uncertainties , 2013 .

[10]  E. Atkinson,et al.  The feasibility of flushing sediment from reservoirs , 1996 .

[11]  R. Selten,et al.  A Generalized Nash Solution for Two-Person Bargaining Games with Incomplete Information , 1972 .

[12]  Noor Khan,et al.  Reservoir Optimization-Simulation with a Sediment Evacuation Model to Minimize Irrigation Deficits , 2012, Water Resources Management.

[13]  Hiroshi Ishidaira,et al.  Sustainability Analysis for Yellow River Water Resources Using the System Dynamics Approach , 2002 .

[14]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[15]  D. Loucks.  Quantifying trends in system sustainability , 1997 .

[16]  Jian-xia Chang,et al.  Optimization of Water Resources Utilization by PSO-GA , 2013, Water resources management.

[17]  Mahdi Zarghami,et al.  Urban water resources planning by using a modified particle swarm optimization algorithm , 2013 .

[18]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[19]  Brian E. Skahill,et al.  More Efficient Bayesian-based Optimization and Uncertainty Assessment of Hydrologic Model Parameters , 2012 .

[20]  Akira Kawamura,et al.  Performance Risk Analysis for Fukuoka Water Supply System , 1998 .

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

[22]  MalekmohammadiBahram,et al.  Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis , 2011 .

[23]  O. Bozorg Haddad,et al.  MOPSO algorithm and its application in multipurpose multireservoir operations E. Fallah-Mehdipour, O. Bozorg Haddad and M. A. Marino , 2011 .

[24]  Hisao Ishibuchi,et al.  Multi-objective genetic local search algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[25]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[26]  Luigi Berardi,et al.  Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms , 2009, Environ. Model. Softw..

[27]  Daniel P. Loucks,et al.  Sustainability Index for Water Resources Planning and Management , 2011 .

[28]  Reza Ardakanian,et al.  Extended OWA Operator for Group Decision Making on Water Resources Projects , 2008 .

[29]  D. Tarboton,et al.  A sediment transport model for incision of gullies on steep topography , 2003 .

[30]  Reza Kerachian,et al.  Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[31]  Yang Liu,et al.  Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm , 2009, Expert Syst. Appl..

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

[33]  Idel Montalvo,et al.  Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems , 2010, Eng. Appl. Artif. Intell..

[34]  Wenyan Wu,et al.  Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions , 2013 .

[35]  William W.-G. Yeh,et al.  A diversified multiobjective GA for optimizing reservoir rule curves , 2007 .

[36]  H. Chaves,et al.  An Integrated Indicator Based on Basin Hydrology, Environment, Life, and Policy: The Watershed Sustainability Index , 2007 .

[37]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

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

[39]  Reza Kerachian,et al.  Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis , 2011, Expert Syst. Appl..

[40]  Yong Tang,et al.  Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications , 2007 .

[41]  S. Raquel,et al.  Application of game theory for a groundwater conflict in Mexico. , 2007, Journal of environmental management.

[42]  Hamid R. Safavi,et al.  Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems , 2013, Water Resources Management.

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

[44]  R. Rajabpour,et al.  Application of local and global particle swarm optimization algorithms to optimal design and operation of irrigation pumping systems , 2009 .

[45]  Soroosh Sorooshian,et al.  A shuffled Complex Evolution Metropolis algorithm for confronting parameter uncertainty in hydrologic modeling , 2004 .

[46]  Li-Chiu Chang,et al.  Constrained genetic algorithms for optimizing multi-use reservoir operation , 2010 .

[47]  Charles S. Revelle,et al.  Optimizing Reservoir Resources: Including a New Model for Reservoir Reliability , 1999 .