Research on optimal operation of cascade pumping stations based on an improved sparrow search algorithm

Abstract

Abstract For the low efficiency and large loss of cascade pumping stations, aiming to maximize system efficiency, an optimized scheduling model of cascade pumping stations is established with consideration of multiple constraints, and the optimal scheduling method based on the improved sparrow search algorithm (BSSA) is proposed. The BSSA is initialized by the Bernoulli chaotic map to solve the insufficient initial diversity of the sparrow search algorithm (SSA). The random boundary strategy is introduced to avoid local optimum when dealing with the scheduling problem of pumping stations. The performance and improvement strategy of BSSA are verified by eight benchmark functions. Results show that BSSA has better convergence accuracy and faster speed. BSSA is applied to a three-stage pumping station considering three flow conditions, and compared with the current scheme, particle swarm optimization and genetic algorithm optimization schemes, the operation efficiency of SSA can be increased by 0.72–0.96%, and operation cost can be reduced by ¥263,000/a–¥363,300/a. On this basis, the improvement of 0.04–0.30% and ¥14,800/a–¥109,900/a can be further achieved by the BSSA, which confirms the feasibility and effectiveness of BSSA to solve the pumping station optimal scheduling problem. The findings present useful reference for the optimized scheduling of pumping station system.