Abstract
Traditional techniques for hydraulic analysis of water distribution networks, which are referred to as demand-driven simulation method (DDSM), are normally analyzed under the assumption that nodal demands are known and satisfied. In many cases, such as pump outage or pipe burst, the demands at nodes affected by low pressures will decrease. Therefore, hydraulic analysis of pipe networks under deficient pressure conditions using conventional DDSM may cause large deviation from actual situations. In this paper, an optimization model is introduced for hydraulic analysis of water distribution networks using a meta-heuristic method called Differential Evolution (DE) algorithm. In this methodology, there is no need to solve linear systems of equations, there is a simple way to handle pressure-driven demand and leakage simulation, and it does not require an initial solution vector which is sometimes critical to the convergence. Also, the proposed model does not require any complicated mathematical expression and operation.
Highlights
In the recent past, several packages originally developed for steady state analysis of looped water distribution systems
The assumption simplifies the mathematical solution of the problem but is not always appropriate because it is clear that the amount of outflow at nodal outlets depends on network pressures
This study introduces Differential Evolution (DE) algorithm
Summary
Several packages originally developed for steady state analysis of looped water distribution systems. EPANET2 has been extended to include the possibility of “extended period simulations” (EPS), namely the possibility of simulating long periods of time by means of a succession of steady states, only accounting for the change in storage of reservoirs occurring from one time step to the [1] This model, which is used in current engineering practice, is based on the conventional Demand Driven Simulation Method (DDSM). Collins’s model can be minimized by application of differential evolution algorithm In this methodology, there is no need to solve linear systems of equations, there is a simple way to handle pressure-driven demand and leakage simulation, and it does not require an initial solution vector which is sometimes critical to the convergence.
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