Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

Abstract

This paper introduces an optimization method (SCE-SR) that combines shuffled complex evolution (SCE) and stochastic ranking (SR) to solve constrained reservoir scheduling problems, ranking individuals with both objectives and constrains considered. A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals. This method is suitable for handling multiple conflicting constraints, and is easy to implement, requiring little parameter tuning. The search properties of the method are ensured through the combination of deterministic and probabilistic approaches. The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system, and its performance is compared with that of two classical methods (the dynamic programming and genetic algorithm). The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly, with sufficient global convergence properties and robustness. The operation schedules obtained satisfy the basic scheduling requirements of reservoirs. Keywords: Reservoir scheduling, Optimization method, Constraint handling, Shuffled complex evolution, Stochastic ranking