# Optimization of Water Pressure of a Distribution Network within the Water-Energy Nexus
## 3 Case Study

Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pipe** & **Length (m)** & **Diameter (mm)** & **Pipe** & **Length (m)** & **Diameter (mm)** & **Pipe** & **Length (m)** & **Diameter (mm)** \\ \hline
1 & 675 & 180 & 37 & 407 & 140 & 73 & 132 & 280 \\
2 & 657 & 500 & 38 & 400 & 180 & 74 & 130 & 140 \\
3 & 657 & 280 & 39 & 393 & 100 & 75 & 113 & 450  
calibration were gathered through a long-term monitoring of flowmeters, water pressure loggers, and basic operating and hydraulic information of the entire WDN. The supplied water for 79023 inhabitants in the Baharestan city is distributed through the network to each house. The total length of pipelines is about 511 km and the total number of subscribers is 23,254 (75.2% are residential). The per capita water consumption was 278 L per day in 2020. Based on the location of each subscriber, its basic demand is attributed to the closest node in the EPANET software. The data relating to the water pipe bursts were collected from 2016 to 2020. These data include the number of pipe bursts, and the average time and cost for repairing bursts for main, sub-main, and branch pipes. The water consumption data of five years (2016-2020), which were collected using 23,254 flowmeters, were used to calculate water loss. In addition, the data of inflow to the network (flow from reservoir node) were gathered at the same time. Pressure monitoring was conducted using 20 water pressure loggers which are distributed in the WDN. The collected data were used to calibrate the model and calculate parameters in the FAVAD and burst frequency equations. Considering the basic state in which L\({}_{0}\) and BF\({}_{0}\) were measured for this situation, the average and standard deviation of daily water pressure and water flow for each node were 45 \(\pm\) 17 mH\({}_{2}\)O and 5.4 \(\pm\) 4.2 L/s, respectively. The lowest and highest water pressure were 5.6 mH\({}_{2}\)O and 77.7 mH\({}_{2}\)O related to node11 (at 7 p.m.) and node 18 (at 4 a.m.), respectively. Moreover, the lowest and highest water flows were 0.25 L/s and 21.17 L/s related to nodes 5, 41, 59, 61, 67 (at 4 a.m.), and node 14 (at 7 p.m.), respectively. The model calculates the optimum average pressure of the network based on hourly intervals. The consumption coefficient for each hour was obtained, according to Figure 4[63]. Fluctuations in demand throughout a day significantly affect h\({}_{\text{avl}}\) for each node and, in turn, the power consumed by the WPBSs. Several types of information were considered by the model when calculating the amount of energy consumed by the WPBSs. This information included the number of buildings, the corresponding floor numbers, and the number of people living on each floor. Table 4 represents the values of the parameters related to running the energy section of the proposed model. Figure 5 illustrates the share of people that live on different floors of the buildings in Baharestan city. To collect the required data for energy sector, Iran's population and housing census data that were collected by Statistical Centre of Iran were used [64]. Moreover, the WPBSs data were collected by field survey. Figure 3: Schematic of the network of Baharestan. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Value** & **Unit** \\ \hline \multicolumn{3}{c}{Input data for calculating power consumed by WPBS} \\ Population & 79,023 & - \\ Housing census & 25,118 & - \\ Number of buildings & 11,081 & - \\ floor to floor height of buildings & 3.2 & m \\ \(\eta\) & 20 & \% \\ \multicolumn{3}{c}{Input data for calculating the energy cost} \\ \multicolumn{3}{c}{0.0064 (23 p.m.–8 a.m.)} & \$/KWh \\ \(c_{\text{E}}\) & 0.0080 (8 a.m.–16 p.m.) & \$/KWh \\ \multicolumn{3}{c}{0.1040 (16 p.m.–23 p.m.)} & \$/KWh \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic information on the energy sector model. Figure 4: The consumption coefficients.