Study on the Raw Water Allocation and Optimization in Shenzhen City, China

Abstract

In order to allocate the raw water of the complex water supply system in Shenzhen reasonably, this paper studied the complex network relationship of this large-scale urban water supply system, which consists of 46 reservoirs, 67 waterworks, 2 external diversion water sources, 14 pumping stations and 9 gates, and described each component of the system with the concepts of point, line and plane. Using the topological analysis technology and graph theory, a generalized model of the network topological structure of the urban water allocation system was established. On this basis, combined with the water demand prediction and allocation model of waterworks, a water resources allocation model was established, aiming at satisfying the guaranteed rate of the water supply. The decomposition and coordination principle of the large-scale system and the dynamic simulation technology of the supply-demand balance were adopted to solve the model. The forward calculation mode of controlling waterworks and pumps, and the reverse calculation mode of controlling reservoirs and waterworks were designed in solving the model, and a double-layer feedback mechanism was formed, which took the reverse calculation mode as outer feedback and the reservoir water level constraint or pipeline capacity constraint as inner feedback. Through the verification calculation of the case study, it was found that the proposed model can deal well with the raw water allocation of a large-scale complex water supply system, which had an important application value and a practical significance.

A scenario analysis-based optimal management of water resources supply and demand balance: A case study of Chengdu, China

“…Third, the achieved optimal scheme can meet the water supply and demand balance, but ignore the cost of saving water and unconventional water supply [ 55 ]. Therefore, the following research should include the economic cost in planning schemes selection [ 56 ].…”

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

“…From a mathematical point of view, the target problem was a typical multi-objective constrained optimization problem with a set of complex equality or inequality constraints [7], and many classical methods have been successfully developed by scholars, during the past decades [8][9][10][11][12][13], like linear programming [14], quadratic programming [15], dynamic programming [16][17][18], Lagrange relaxation, and network optimization [19][20][21]. However, the hydropower operation problem is usually modeled with nonlinear characteristic curves, physical constraints, or objective functions [22][23][24].…”