According to the features of multi-source water supply systems in big cities,the hydraulic balance equations were replaced by a BP neural network model of nodal pressures,and a primary optimal scheduling model of the multi-source water supply system was built.The constrained optimization problem was changed to an unconstrained problem after the mixed penalty method was used to treat constraint conditions,and the particle swarm optimization (PSO) algorithm was used to solve the problem.To avoid the algorithm getting into a local optimum and improve the precision of the algorithm,the chaos search was brought into the standard PSO algorithm to form the chaotic PSO algorithm.The model was applied to the Tianjin water supply system.The optimized water supply scheme could reduce power consumption of pumping stations by 4 087 kW · h daily and 1.492 million kW · h annually,and the total cost was reduced by 2.13％.This clear economic benefit indicated the applicability of the model and the feasibility of the algorithm.
water supply systemoptimal schedulechaos searchparticle swarm optimization algorithm
NETL
NSTL
万方数据
维普
