Real‐time modeling of water distribution systems: A case study

Abstract

Water utilities worldwide face increasing challenges to preserve the hydraulic and water quality integrity of their water distribution networks. These challenges stem from burgeoning populations and migration to urban cities that continue to increase the load on aging, inefficient, and already strained infrastructures. This has created a pressing need for integrating supervisory control and acquisition systems with network simulation models for proactive management of these networks. Such an integrated platform is the basis for the real‐time smart water network decision support system (SWNDSS) described here. The proposed system has the power to transform a water utility's routine network modeling functions from planning and design to full‐spectrum engagement that drives more efficient operations—including managing water quality and energy, developing daily operating plans, addressing planned and emergency outages, and diagnosing and resolving field issues. Aspects of the SWNDSS ar described in a case study from the Las Vegas Valley Water District in Las Vegas, Nev.

Rethinking the Framework of Smart Water System: A Review

“…Even if the SCADA system and so-called real-time simulation tools have been integrated into the water distribution system, it is still necessary to further understand what makes the water system smart. Real-time modeling tool applications like EPANET-RTX [90], LVVWD [91], and EPANET-CPS [92] actually need two steps to finish work: pausing execution and waiting for the new SCADA measurements to reload and to update the boundary condition [93]. The Pausing and Waiting takes typically 10 seconds and 14 minutes, respectively, which makes smart modeling is close to being near real-time process [94].…”

Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

“…For the planning, management, and development of water supply systems, the characteristics of water demand, together with uncertainties in natural phenomena, have caught the attention of many water resource researchers [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Kenney et al [7] described that water consumption is mainly composed of three main factors: (1) socioeconomic factors, such as population distribution and sizes of homes and lots; (2) climate factors, such as air temperature, precipitation, and percent of humidity; and (3) policy factors, such as the cost of water utilities, regulations, and conservation programs.…”

“…There are a few previous studies on water demand modeling undertaken in Las Vegas [13][14][15][16][17]. Trabia et al [13] used multiple linear regression to assess water use by several water demand related components (e.g., landscaping, showers, pools, etc.).…”

“…Exogenous factors include precipitation, temperature, wind speed, household characteristics, and economic trends. Boulos et al [16] used a hydraulic-based model and a water network decision support system to forecast real-time water demand in Las Vegas. The real-time integration of supervisory control and data acquisition (SCADA) data was used as a boundary condition (e.g., tank water levels) and operational status (e.g., pump speeds or on/off status, valve settings) in the network model [16].…”

“…Boulos et al [16] used a hydraulic-based model and a water network decision support system to forecast real-time water demand in Las Vegas. The real-time integration of supervisory control and data acquisition (SCADA) data was used as a boundary condition (e.g., tank water levels) and operational status (e.g., pump speeds or on/off status, valve settings) in the network model [16]. Lott et al [17] used a mix-effected model (fix and random model), which includes independent variables such as the number of bedrooms, age of the house, yard size, monthly income, water price, a one-month lag of average temperature, days of precipitation per billing cycle, and average wind speed per billing cycle.…”