Leakage control of multi-source water distribution system by network partition and optimal pump schedule in China

Due to the rapid urbanization in China, water demand has significantly increased. As a result, the water supply system has developed gradually from a single water source to multiple water sources, which causes the problems of mismatched water supply capacity, unbalanced water supply pressure and unreasonable pump schedules among water sources. These not only lead to wastage of water and energy resources but also pollute the surrounding areas when pipe leakage occurs. This paper presents an economical method to reduce the water leakage and save energy through water network partitioning, by using optimal states of valves’ opening and closing, and reconstructing several connecting pipes and network partition.

Empirical methods are commonly used for partitioning the water supply network by adjusting the valve opening and closing status in the area, factitiously designating the water supply boundary and testing the partition using a hydraulic model. This partitioning method is time-consuming and suffers from a lack of theoretical basis, therefore, it is rarely used in practical projects (Giugni et al. 2008). The UK Water Industry Research (UKWIR) (1999) and Farley (2001) have successively published the principal guidance for district metering area (DMA) design. Morrison (2004) and Thornton (2004) summarized the essential role of using pressure metering area and DMA to control leakage. Izquierdo et al. (2009) proposed an agent-based division partition method for DMA design, and Herrera et al. (2011) improved Izquierdo's method through a semi-supervised learning method of using all existing information. However, all the research that has been conducted so far was based on small-scale water distribution networks. The partition methods for multi-source water supply networks cannot be applied to practical projects in the current state because there is still the unsolved problem of determining the boundary of water supply caused by constantly changing water consumption.

The ongoing research proved that the leakage in water distribution systems can be minimized through optimal valve actions (Vairavamoorthy & Lumbers 1998; Creaco & Pezzinga 2014). Di Nardo et al. (2015) developed a genetic algorithm for demand pattern and leakage estimation in a water distribution network. Ali (2014) presented a knowledge-based optimization model for leakage minimization in a water supply network by finding an effective location to set the control valves. Islam et al. (2012) evaluated leakage in water distribution systems using a fuzzy-based method. Burrows (2011) proposed an intelligent technology for continuous optimal adjustment of pressure at the outlet of the water pump and pressure reducing valve (PRV) to reduce the network leakage, decrease pipe bursting, and power consumption. Giustolisi et al. (2008) described a new hydraulic simulation model which is capable of quantifying the pressure-driven demand and leakage simultaneously. Real-time control of valves for minimizing the leakage in water distribution networks was reported, and head-driven simulation of a water network was considered under successive steady conditions in their work (Campisano et al. 2012; Creaco & Franchini 2013). A fast and efficient way to calculate the optimal time schedules and flow modulation curves for the boundary and internal PRVs to reduce the leakage in water supply networks was presented (Ulanicki et al. 2009). An optimal pump scheduling model, taking into account pressure control aspects in complex and large-scale water distribution networks, was studied by Skworcow et al. (2009, 2014).

In China, the generic water supply systems have pump stations. Each pump station has its own control for pump speed, no water tanks that are necessary to regulate water amount due to a large amount of water supply and centralized water users.

This research addresses two issues simultaneously: (i) saving energy by scheduling the pump optimally and (ii) reducing leakage by partitioning the water network. Water supply network partition and water pump schedule after partitioning are interdependent; it is difficult to achieve optimization by only considering one aspect. In this paper, it is assumed that the existing water supply boundary of a multi-water source system is acceptable but not optimal and the boundary is fluctuated with time to form a water supply boundary zone. The valves within the zone are taken as a feasible solution set of partitioning the network. The method of closing valves within a water supply boundary zone to secure the water supply boundary does not make a significant change to the original water supply conditions, which can be readily accepted by water enterprises and users.

From the point of scheduling and deployment of multi-source water, the minimization of network leakage and power consumption as the optimization goal is taken to be a measurement standard of the water supply network partition (Li et al. 2015), that is, to guide the network partition through an optimal schedule objective function of the water supply network and use the partition scheme to determine the optimal schedule for pump set. These two aspects complement each other.

Partitioning of water networks is essential for a multi-source water network optimal schedule. By modifying a small length of pipeline and partitioning the network appropriately, the reduced pressure during normal operation and flexibility to switch the water source of the main pipeline during accidents can be ensured. Primary partitions can be formed simply by closing certain valves in the partition zone. On the basis of the primary partitions, each primary partition can be subdivided into multiple secondary partitions; smart PRVs are installed at the entrance of secondary partitions. There are many ways to close the valves in the primary partition, however, in the practical water distribution network, there are only a handful of schemes that are feasible. According to different locations of water sources, terrain elevation of secondary partitions and bulk line distribution, the feasible alternative solution set for closing the valve is established to reduce the search space of closing valve solutions, thereby increasing the search speed to perform the next optimal schedule.

The ultimate goal of optimal scheduling of a traditional water supply system is to minimize the power consumption, which does not guarantee the minimum amount of leakage in a water supply system. The leakage in the pipe network is a non-linear function of pressure in the pipe network. To ensure the minimum amount of leakage in the water supply network, the pressure at all nodes of the network should be kept at a minimum value under the premise that the head of the minimum water supply services could be met (Thornton 2004; Nicolini et al. 2011). This ideal pressure state cannot be achieved through optimal scheduling of the pump. Different pump set scheduling schemes will produce different water supply area boundaries, and the pressure at each node will vary with different water supply area boundaries and scheduling schemes.

To meet the requirements of water demand from users, pressure and quality, a reasonable optimal schedule of pump set can balance the pressure in the partitioned water pipe network during day and night. Moreover, it can reduce the leakage, save water and fundamentally improve the efficiency of water supply. After identifying the valves that need to be closed, assuming no significant changes to the pipes in the water supply system (such as laying new bulk pipes, building a new pump station), each pump station serves their partition independently, i.e. water networks among pump stations are no longer connected. Therefore, water network partitioning and pump scheduling based on partition need to be considered simultaneously.

The optimal leakage control model is to design a feasible set of valve closing in a multi-source water supply network. A multi-source water supply partition model is actually the optimization process of the water supply boundaries at different times with a combination of urban water demand forecast model and the leakage hydraulic model of water supply network to calculate the node demand and pipe flow (Giustolisi et al. 2008).

The method of obtaining a main water supply route and water supply crossing area mentioned above verified the feasibility and effectiveness of engineering rather than theory. If all the water valves in the network are put into the search scope, the resultant search space is too large.

The goal of an optimal leakage control model in a multi-source water distribution system is to minimize the sum of power consumption and leakage. That is to say, water supply enterprises can gain economic benefits only by adjusting the opening or closing of valves and pumps (Tucciarelli et al. 1999).

The solution of optimal leakage control model in multi-source water distribution system is a solution of water supply network partition model and pump optimal schedule model. This model is a multi-dimensional and multi-objective optimization problem, which contains both non-linear and linear constraints. It is difficult to work it out by any simple mathematical methods. Hence, NSGA-II algorithm is adopted for choosing the state of all valves and pumps in the network–opening or closing to be the solution domain. Further, the partition scheme is determined for solving the optimal off valve and the optimal pump scheduling programs (Giugni et al. 2014).

According to optimal pump scheduling at peak hours of the above six partition solutions, the total supply amount and outpressure of each water plant are compared with the current situation and the results are shown in Table 4.

Saving electricity cost = electricity unit price × annual energy saved = electricity unit price × annual energy consumption × percentage of energy saved. Where the electricity unit price is 0.8 CNY/KW h, the results are shown in Table 5, column 4.

Saving water purification cost = water purification unit price × annual leakage reduced amount = water purification unit price × current leakage amount × percentage of leakage reduced × 365 days. Where the water purification unit price is 0.23 CNY/m³, the results are shown in Table 5, column 7.

Saving water resource cost = water resource unit price × annual leakage reduced amount = water resource unit price × current leakage amount × percentage of leakage reduced × 365 days. Where the water resource unit price is 0.33 CNY/m³, the results are shown in Table 5, column 8.

Saving total cost = saving electricity cost + saving water purification cost + saving water resource cost. The results are shown in Table 5, column 9.

Considering the cost of pump start/stop and the stability of pump operation, the scheduled time is divided into three periods in this paper. The first period is 6:00–13:00 (average), the second period is 14:00–21:00 (peak), the third period is 22:00–5:00 (low), the optimal pump schedule solutions of each period of each water plant are shown in Tables 6 and 7.

This research simultaneously addressed two optimal aspects: saving energy by optimal pump scheduling and reducing leakage by water network partitioning. A leakage control model of a multi-source water distribution system is established by using optimal pump scheduling. The outcome of this research is to work out water supply network partitioning and pump set scheduling schemes. This method has been proved to be efficient for reducing energy consumption and network leakage simultaneously. Furthermore, it improves the water supply enterprises’ services and economic profit. This part of the profit is net income, which can be gained by water supply enterprises only by switching valves, scheduling pumps and rehabilitating a few pipelines, and without the need of any extra capital investment.

This research was supported by the National Water Pollution Control and Treatment Science and Technology Major Project (2014ZX07405002C), National Natural Science Foundation of China (51278148) and The Union Project of Industry–Study–Research of Guangdong Provincial Department of Education (2011A090200040).