Optimal solution of large-scale reservoir-operation problems: Cellular-automata versus heuristic-search methods

A novel cellular-automata approach is developed in this article for the optimal solution of large-scale reservoir-operation problems. The aim of this article is to show how cellular automata can be used for the solution of reservoir-operation problems, and, more importantly, to demonstrate that the method is extraordinarily more efficient and effective than heuristic-search methods. Both penalized and non-penalized versions of the method are proposed and formulated for the solution of water-supply and hydropower reservoir-operation problems. The cells are defined as the discrete points chosen on the operation horizon of the problem and storage volumes are taken as the cell states. The optimization objective functions of the problems are used to derive the updating rule of the problems. In the non-penalized method, the problems constraints are satisfied explicitly by limiting the change in the cell states between one iteration and the next. In the penalized version, however, a penalty method is used to modify the updating rules so that the constraints are automatically satisfied. The proposed methods are used to optimally solve the problem of water supply and hydropower operation of the Dez reservoir in Iran over short, medium, and long operation periods, and the results are presented and compared with those obtained using three heuristic-search methods (genetic algorithms, Ant Colony Optimization algorithms, and Particle Swarm Optimization algorithms). The results show that the cellular-automata method is much more efficient and effective than most powerful search methods for both of the problems considered in this work. Application of the method to multi-reservoir systems is underway, with encouraging early results.