Abstract
Optimal operation of multi-reservoir systems is one the most challenging problems in water resource management due to their multi-objective nature and time-consuming solving process. In this paper, Multi-Reservoir Parallel Cellular Automata-Simulated Annealing (MPCA-SA), a hybrid method based on cellular automata and simulated annealing is presented for solving bi-objective operations of multi-reservoir systems problems. The problem considers the bi-objective operation of a multi-reservoir system with the two conflicting objectives of water supply and hydropower generation. The MPCA-SA method uses two single-objective cellular automata acting in parallel to explore the problem search space and find the optimal solutions based on the probabilistic interaction with each other. Bi-objective operation of the Dez-Gotvand-Masjed Soleyman three-reservoir system, as a real-world system in southwestern Iran for a period of 60 months, is considered in order to evaluate the ability of the proposed method. In addition, a Non-dominated Sorting Genetic Algorithm (NSGAII) is also used to solve the problems and the results are compared with those of MPCA-SA, indicating the capabilities of the proposed MPCA-SA method. The results show that the MPCA-SA method is able to produce solutions comparable to those of NSGAII with a much-reduced computational cost equal to 1.2% of that required by the NSGAII, emphasizing the efficiency and practicality of the proposed method.
Highlights
Most of the water resource problems, such as the operation of reservoir systems, are multi-purposes and commonly considered as multi-objective optimization problems
Non-linear programming, and dynamic programming can be mentioned as the most popular classical methods used in the past to solve the multi-objective operation of reservoir systems
Water supply and hydropower generation were considered as the objective functions and the results showed a better
Summary
Most of the water resource problems, such as the operation of reservoir systems, are multi-purposes and commonly considered as multi-objective optimization problems. Afshar et al [19] developed a multi-colony ant algorithm for the bi-objective operation of the multi-purpose Des reservoir in Iran with water supply, hydropower generation and flood control as the objective functions Their results indicated that the proposed algorithm was superior to the weighting approach in finding the Pareto optimal set. Hajiabadi and Zarghami [24] solved the optimal operation of the Sefidrud reservoir in Iran for water supply, hydropower generation and sediment evacuation objectives They used the non-dominated sorting genetic algorithm (NSGAII) for finding the optimal Pareto set of the problem and defined various scenarios based on the weighting approach. The results indicate that the proposed methodology is highly efficient and capable of producing comparable solution to those of NSGAII while requiring much less computational efforts
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