Tanks have always played a crucial role in urban water distribution systems. With well known advantages (as to guarantee reserves for emergencies) they are widely used. But they present, as well, inconveniences such as their contribution to a higher water age and are, as will be seen, energy-hungry. This paper introduces an indicator to assess energy savings of direct water supply (without head tanks) with regard to the traditional indirect one (throughout head tanks). Furthermore a case study is presented showing the reliability of this indicator. The paper concludes with an economic comparison of both strategies

To achieve maximum efficiency in water pressurized transport, it is necessary to perform a global analysis, whenever possible starting from the system’s conception. The first stage of the process is the network layout, the main topic of this paper. The optimum topology from an energy point of view (or eco-layout) is the one that, insofar as is feasible, allows equalizing the network’s pressure to the set pressure standards. Eco-layouts can be easily designed in new systems but are difficult to implement, mainly in the short term, in operating networks. Nevertheless, because no system is eternal, the required actions can be gradually implemented. Therefore, the main goal of this paper is to identify and discuss these guidelines and actions, some of which are apparently contradictory to current design criteria, whereas others endorse modern management trends. These strategies can be summarized in two points: (1) providing lower pressure to consumers saves energy, and (2) setting up smaller pressure zones in terms of the elevation steps between zones will enable water companies to supply water at lower pressure in hilly areas. In the end, networks with more-efficient layouts can achieve important energy savings.

Energy intensity, I_e (kWh/m³) is the most popular indicator when characterizing the energy requirements of the water cycle, due to its direct and easy interpretation. In pressurized water transport systems, when referred to an appropriate physical framework (such as a single water transport pipeline), it assesses the efficiency of the process. However, in complex urban water transport networks, I_e only provides a basic notion of the energy needs of the system. The aim of this paper is to define a standard physical framework for assessing the energy intensity in water transport and distribution systems. To that purpose, an analytic expression that estimates I_e is proposed, based on system data and its operating conditions. The results allow for a realistic approximation of the energy needs of water transport. This energy assessment is completed with two context indicators: energy origin (C₁) and topographic energy (θ_t), both essential when the energy efficiency of different systems is to be compared.

In this paper, an algebraic expression is presented to determine the optimum hydraulic gradient (J₀) in a pressurized water system. J₀ represents the economic level of friction losses (ELF), which is dependent on the network’s behavior as well as other parameters, including energy and the pipe costs. As these have prices changed over time, so has the value of J₀. The network-related parameter was obtained from the total costs function and the sum of the operational and capital expenditures. Because these costs exhibited an opposite trend from J, a minimum total cost exists, specifically, J₀. The algebraic expression, which was derived from the mathematical model of the network, was first calculated for the network’s steady state flow and was later generalized for application to a dynamic one. For a network operating in a given context, J₀ was fairly stable in terms of dynamic flow variations, providing valuable information. The first piece of information was the ELF itself, which indicated the energy efficiency of the system from the perspective of friction loss. The second indicated which pipes required renewal from a similar perspective. Thirdly, it provided a simple criterion to calculate the diameter of new pipes. Finally, as J₀ can be easily updated, when predictions are performed at the network’s designed time fail (e.g., growing urban trends, demand evolution, etc.), decisions can also be updated.