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

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