Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

[1]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[2]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[3]  Dragan Savic,et al.  Genetic Algorithms for Least-Cost Design of Water Distribution Networks , 1997 .

[4]  Peter K. Kitanidis,et al.  Limitations of Deterministic Optimization Applied to Reservoir Operations , 1999 .

[5]  Richard Bellman,et al.  On a Dynamic Programming Approach to the Caterer Problem---I , 1957 .

[6]  Bengt-Åke Lundvall,et al.  DANISH RESEARCH UNIT FOR INDUSTRIAL DYNAMICS DRUID Working Paper No 04-01 Why the New Economy is a Learning Economy by Bengt-Åke LundvallWhy the New Economy is a Learning Economy , 2022 .

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

[8]  J. Bejarano.  Ética y economía , 2000 .

[9]  K. Moslehi,et al.  Stochastic Long-Term Hydrothermal Optimization for a Multireservoir System , 1985, IEEE Power Engineering Review.

[10]  Robin Wardlaw,et al.  Multireservoir Systems Optimization Using Genetic Algorithms: Case Study , 2000 .

[11]  Ian C. Goulter,et al.  Practical implications in the use of stochastic dynamic programming for reservoir operation , 1985 .

[12]  Fereidoun Mobasheri,et al.  a Stochastic Dynamic Programming Model for the Optimum Operation of a Multi-Purpose Reservoir , 1973 .

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

[14]  R. Wardlaw,et al.  EVALUATION OF GENETIC ALGORITHMS FOR OPTIMAL RESERVOIR SYSTEM OPERATION , 1999 .

[15]  Lyn C. Thomas,et al.  An aggregate stochastic dynamic programming model of multireservoir systems , 1997 .

[16]  L Chen,et al.  Multiobjective water resources systems analysis using genetic algorithms--application to Chou-Shui River Basin, Taiwan. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

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

[18]  Keith W. Hipel,et al.  Interior-Point Method for Reservoir Operation with Stochastic Inflows , 2001 .

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

[20]  S. P. Neuman,et al.  Three‐dimensional steady state flow to a well in a randomly heterogeneous bounded aquifer , 2003 .

[21]  R. Bellman.  Dynamic programming. , 1957, Science.

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  J. Doorenbos,et al.  Guidelines for predicting crop water requirements , 1977 .

[24]  Sharon A. Johnson,et al.  The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System , 1995 .

[25]  Arup Kumar Sarma,et al.  Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir , 2005 .

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

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  John W. Labadie,et al.  Computerized Decision Support Systems for Water Managers , 1986 .

[29]  Т. Ozernikova..  Labor Enforcement in the Transitional Economy , 2003 .

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

[31]  Mario T. L. Barros,et al.  Stochastic optimization of multiple-reservoir-system operation , 1991 .

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

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

[34]  V. Jothiprakash,et al.  Single Reservoir Operating Policies Using Genetic Algorithm , 2006 .

[35]  Barry J. Adams,et al.  Stochastic Optimization of Multi Reservoir Systems Using a Heuristic Algorithm: Case Study From India , 1996 .