Indeed, the hydraulic model is cast as demand-driven and insufficient pressure conditions do not affect water delivered to customers. (b) They do not allow WDN analysis under pressure deficient conditions (which might happen while testing alternative pressure control scenarios). (a) They do not allow the volumetric leakage model as pressure-dependent outflow distributed along pipes. based on EPANET2 вЂ“ Rossman (2000)) are not able to support such kinds of analyses because of some main limitations. Unfortunately, traditional WDN models (e.g. All such works used a traditional hydraulic simulator, e.g. Other works mainly proposed strategies for the optimal location of PRVs and relevant setting modulation (e.g. The feedback control is distinguished as centralized (the settings of each valve is computed as a function of flow measurements at all PRVs) or decentralized (the settings of each valve is computed as a function of flow measurements at the same PRV) in both cases the controller is based on off-line simulation of the WDN. The predictive control uses a hydraulic model to optimize the valve scheduling based on predicted/measured total demand. (2000) first investigated the possibility of using optimized predictive or feedback control strategies. Nonetheless, they did not account for any pressure-demand relationship in their problem formulation. Vairavamoorthy & Lumbers (1998) accounted for leakages and optimized time modulation of PRVs, relaxing the constraint of maintaining a target pressure in a few nodes, thus further reducing leakages. The early work by Sterling & Bargiela (1984) proposed an optimal valve control to minimize the network overpressure, although it did not explicitly include the leakage term as water outflow. A number of authors proposed alternative strategies to optimize the time modulation of classic PRVs. This, in turn, requires a time-pattern of target pressure values of the classic PRV in order to avoid insufficient pressure conditions or excessive pressure at peak demand and low demand hours, respectively. Nonetheless, the change of customers' water demands over time causes a change of head losses through the WDN. Thereafter, the model allows comparison of three strategies for the real-time electric regulation of PRVs in some of the planned scenarios, thus supporting real-time operation of RRTC-PRVs.Д¬lassic (local) control of PRVs consists of modulating the valve opening to maintain a target pressure at a control node just downstream of the devices. The same analysis unveils that advanced models provide reliable evaluation of leakage reduction efforts, overcoming the inconsistencies of lumped indexes like the Infrastructure Leakage Index (ILI). The advanced hydraulic model permits demonstration that RRTC-PRVs in place of existing c lassic PRVs might reduce unreported and background leakages by up to 40%. This paper studies how such models are relevant to support pressure control strategies at both planning and operation stages on the real WDN of OppegГҐrd (Norway). Nowadays, advanced hydraulic models are able to simulate both RRTC-PRVs and classic PRVs accounting for unreported and background leakages as diffused pressure-dependent outflows along pipes. with target pressure node just downstream of the device). Information and communication technologies allow driving pressure reducing valves (PRVs) in real-time based on pressure observed at remote control nodes ( remote real-time control вЂ“ RRTC), going beyond the limitations of classic PRV control (i.e. Technical best practices recommend pressure control as an effective countermeasure to reduce leakages in water distribution networks (WDNs).

Water hydraulic modeling