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Optimal Localization of Pressure Reducing Valves in Water Distribution Systems by a Reformulation Approach

机译:通过重新配方方法优化配水系统中减压阀的定位

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摘要

Leakage reduction represents one of the most challenging tasks in managing water distribution systems (WDSs). An effective way to leakage reduction is to carry out network operational pressure management through optimizing locations and regulations for pressure reducing valves (PRVs) and system pressures. This leads to a mixed-integer nonlinear program (MINLP) with a large number of binary variables which make it difficult to solve by an available software package. In this study, instead of directly solving the MINLP problem, we reformulate it to a mathematical program with complementarity constraints which can be efficiently solved by available NLP algorithms. The binary variables are replaced by continuous ones with complementarity constraints to be satisfied by a penalization scheme. To improve the quality of the solution and also to accelerate the convergence, in each relaxed NLP the results of the binary variables are rounded to binary values with which the NLP problem is solved again to achieve a MINLP solution. The final solution will be determined by the best one among the MINLP solutions. The results from two case studies reveal new and better combinations of PRVs as compared with those given in the literature.
机译:减少泄漏是管理配水系统 (WDS) 中最具挑战性的任务之一。减少泄漏的有效方法是通过优化减压阀 (PRV) 和系统压力的位置和规定来执行管网运行压力管理。这导致了具有大量二进制变量的混合整数非线性规划 (MINLP),这使得可用的软件包难以求解。在这项研究中,我们没有直接求解 MINLP 问题,而是将其重新表述为具有互补约束的数学程序,该程序可以通过可用的 NLP 算法有效求解。二元变量被具有互补约束的连续变量所取代,以通过惩罚方案来满足。为了提高求解的质量并加速收敛,在每个松弛的 NLP 中,将二元变量的结果四舍五入为二进制值,用这些值再次求解 NLP 问题以实现 MINLP 解。最终解决方案将由 MINLP 解决方案中的最佳解决方案决定。两个案例研究的结果揭示了与文献中给出的减压阀相比,新的和更好的PRV组合。

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