Fuzzy constrained optimization of eco-friendly reservoir operation using self-adaptive genetic algorithm: a case study of a cascade reservoir system in the Yalong River, China

With the increasing demands for hydropower development in China, it is particularly important to implement eco-friendly reservoir operation in consideration of the vulnerability of downstream aquatic environment. In this paper, a fuzzy constrained nonlinear programming model is presented. Fuzzy programming is used to deal with the inherent imprecision and vagueness in constraints, and a self-adaptive genetic algorithm with simulated binary crossover is proposed for searching for the optimal reservoir operating rules. First, the fuzzy reservoir operation model is transformed into two deterministic sub-models. Self-adaptive genetic algorithm is then used to generate optimal solutions by applying penalty functions to integrate constraints into the objective function to form the fitness function. To achieve a final compromise between the fuzzy objective and the constraints, a max-min decision principle is incorporated into the optimization process to obtain satisfactory operating schemes. The methodology is demonstrated through a cascade system of reservoirs in the Yalong River, southwest China. A monthly reservoir operation model, which considered downstream ecological flow requirements as fuzzy constraints, is developed to optimize eco-friendly reservoir operation with the objective of maximizing total hydropower generation. Monthly operating rules are generated for two cascade reservoirs, and the optimal hydropower generation is also obtained. Results indicate that the proposed approach can effectively improve the operation of the cascade reservoir system and fulfill the flow requirements of the downstream ecosystem that are expressed as fuzzy sets. It is a useful tool for generating optimal strategy to achieve an eco-friendly reservoir operation under uncertainty. Copyright (c) 2011 John Wiley & Sons, Ltd.