A Novel Parallel Cellular Automata Algorithm for Multi-Objective Reservoir Operation Optimization

摘要

In this paper, a novel Parallel Cellular Automata (PCA) approach is presented for multi-objective reservoir operation optimization. The problem considers the multi-objective operation of a single reservoir with the two conflicting objectives of water supply and energy production. The water supply objective is defined as the squared deviation of the monthly release from the downstream demand while the hydropower objective is defined as the squared deficit of the monthly power production from the installed capacity. The proposed method uses two parallel cellular automata methods each searching for the solution of a single objective problem starting from an initial random solution. Each CA, however, is randomly seeded with the solution provided by the other CA method at each CA iteration. Two different version of the proposed PCA is considered based on the way the CAs are seeded. In the first method referred to as PCA1, a fixed value of 0.5 is used for the probability of exchange while in the second method, referred to as PCA2, a temperature-based variable probability of exchange is used for seeding the CAs. The proposed methods are used for bi-objective operation of Dez reservoir in Iran. Various operation periods of 60, 120, 240 and 480 months are considered to illustrate the efficiency and effectiveness of the proposed PCA methods for problems of different scales. In addition, Non-dominated Sorting Genetic Algorithm (NSGAII), is also used to solve the problems and the results are presented and compared. The results indicate that Pareto solutions obtained by the proposed temperature based method PCA2 are well-scattered over the front and in particular toward the end points compared to those of NSGAII requiring much less computational time. The superiority of the proposed method to that of NSGAII is shown to increase with increasing scale of the problem.