The Brazilian Labeling Program is a forceful instrument for the conservation of energy. In addition to allowing consumers to evaluate the best products from an energy point of view, it sets targets to make the products increasingly more efficient. In this context, buildings that can currently obtain the energy efficiency label are also included, demonstrating to customers the concern for sustainability. However, there are still many products and systems that can be labeled but for which legislation has not yet been enacted, including water supply systems. Owing to the different characteristics of the systems and the different possibilities for efficiency improvement, it is necessary to develop indicators that actually represent the reality of each system and make it possible to compare them. Thus, this study proposes the use of certain indicators for classification of the systems. A classification by sector benchmarking is also proposed in Brazil, and an example is presented for the classification of Brazilian systems based on four indicators obtained by the National Sanitation Information System.

INTRODUCTION

The rational use of water and energy has become increasingly important with respect to both environmental issues and supply shortages. Some European countries and the United States offer incentives to industries in the form of tax breaks and subsidies for the purchase of equipment that adheres to energy efficiency programs and micro-generation (Fracaro 2012). In Brazil, the National Program for Energy Conservation encourages energy efficiency measures, particularly through equipment labeling by the Brazilian Labeling Program. The law 9911/2000 established that 0.5% of net operating revenues of public utility companies in the electricity distribution sector should be invested in actions to combat electricity wastage as part of the Energy Efficiency Program of Distribution Companies coordinated by the National Electric Energy Agency (ANEEL 2008). One system that appears favorable for energy efficiency studies is water supply pumping stations. According to Eletrobrás (2009), the equivalent of 2.3% of the total energy consumed in Brazil is used in the sanitation sector, and pumping stations account for 90% of this consumption.

There are various ways to reduce energy costs in water supply systems. The first is to reduce water loss, which is on average 40% of the water collected and treated in Brazil, according to the National Sanitation Information System (SNIS 2013). This water loss may occur due to leaks in the supply pipelines, reservoir overflow, illegal connections, or unbilled usage (Bezerra & Cheung 2013).

Another way to reduce energy costs and leakage is to perform a sectorization of consumers (Xu et al. 2014). Thus, only the neighborhoods located at the highest elevations use pumping systems, while neighborhoods at lower elevations are supplied by gravity or by a pumping station of lower power. Moreover, it is necessary to analyze the existing systems because, according to Europump & The Hydraulic Institute (2004), approximately 75% of systems are oversized in relation to the actual demand. In addition to the motor-pump sets, it is fundamentally important to assess the operating conditions of water supply networks, in which fouling and wear can occur that increase the head losses, thus increasing the power required by the sets (Kaya et al. 2008).

In addition to energy consumption, energy efficiency programs seek to reduce demand during peak hours. In water supply systems, it is possible to accomplish this goal through the use of reservoirs that supply the population during the period while the pumping station remains off. Batchabani & Fuamba (2014) show that the optimal operation of the system should be based on the use of reservoirs at peak hours.

All these practices can be adopted in any water supply system. However, each has specific characteristics, including topography, network extension, population served, and seasonality. This means that an apparent increase in energy consumption is not necessarily represented by decreased system efficiency, which makes comparison among systems difficult. Thus, the use of indicators is fundamentally important in assessing their effectiveness.

The International Water Association (IWA) published the document ‘Performance Indicators for Water Supply Services’ (Alegre et al. 2004), which has become the main reference with regard to the performance of water supply systems. In this document, six different groups of indicators were proposed: water resources, human resources, infrastructural resources, operational resources, quality of service, and economic resources.

In Brazil, in 1996, the SNIS was established and contains information on the same sets of indicators proposed by the IWA. The data are updated annually based on data submitted by the utility companies. The SNIS was created with the goal of becoming an important tool for the planning and implementation of public sanitation policies and the assessment of the performance of service providers. However, operational information is often false, either due to the lack of measurement parameters or the lack of knowledge of managers.

The use of indicators facilitates benchmarking the sector. According to Kingdom (1998), benchmarking is a way of identifying improvements to the system through comparison with industry-leading systems. For benchmarking to be established with reliable values, Berg & Padowski (2010) proposed five steps for the study:

  • (a) Identifying objectives.

  • (b) Establishing a methodology and collecting data.

  • (c) Using specific techniques for data analysis.

  • (d) Performing consistency and sensitivity tests of the data.

  • (e) Creating improvement goals.

As stated above, the use of indicators is the best alternative for reliable comparison. In the case of water supply systems, the use of benchmarking is primarily designed for infrastructure indicators and quality of service, including supply coverage, and continuity of service. This is due to the major existing concern regarding health problems caused by lack of sanitation, which is directly linked to these indices. Thus, many studies, including those of the Pacific Water and Wastes Association (2011) and Van den Berg & Danilenko (2011), seek benchmarking with the objective of establishing minimum levels so there is an improvement in the quality of life. Regarding the energy performance of the systems, the studies by Carlson & Walburger (2007) and Cabrera et al. (2010) are noteworthy. However, the first represents the situation in the United States and the second uses a complex method and allows only an overall evaluation.

Thus, this study develops indicators that can identify the water supply systems where there is the greatest potential to improve efficiency. These indicators should be able to portray the various characteristics of the water supply systems, such as reservoir capacity, efficiency of the motor-pump sets, pipeline efficiency, and leakage control. In addition, this study presents an example of the classification to be made based on the indicators available in the SNIS.

METHODOLOGY

Indicators

Each water supply system has its own characteristics with regard to topography, network extension, population supplied, seasonality, etc. Thus, an apparent increase in energy consumption is not necessarily the result of declining system efficiency, which makes it difficult to compare systems. For this reason, the use of indicators is fundamentally important for making a comparison and, therefore, obtaining the benchmarking of the sector. Four indicators are proposed, some of which are already in the SNIS and IWA. It is emphasized that field measurements are necessary to obtain some indicators.

Efficiency index (EI [%]) demonstrates the efficiency of the existing motor-pump set. In this case, it is ideal to obtain the set efficiency based on a field test. The efficiency is then compared with the maximum efficiency for sets with the same operating conditions. 
formula
1
where EI [%]: efficiency index; ηcurrent [%]: current efficiency of the motor-pump set; ηmax [%] maximum possible efficiency for the motor-pump set under the same operating conditions; Pc [kW]: power of the motor-pump set; and Ptotal [kW]: total power installed in the system.
Reservoir capacity (RC [%]) represents the capacity that pumping station reservoirs have to meet the water supply during peak hours. Although some systems have this technical reserve, many continue to operate during peak hours for fear of a possible shortage. For this reason, this indicator is calculated using the number of operational hours during peak hours, as shown in Equation (2). In this equation, the number 782 represents the peak hours during 1 year (discounting weekends). A more accurate number can be achieved for a specific year by also discounting the holidays. 
formula
2
where RC [%]: reservoir capacity; t [h]: number of operational hours during the peak hours for 1 year; Pc [kW]: average power consumed by the pumping station during the peak hours; Pi [kW]: power installed at the pumping station; and ne [1]: number of pumping stations in the system.
Roughness index (RI [%]) evaluates the increase in the roughness of pipelines due to aging and deposits. It relates the current roughness to values for new pipes. 
formula
3
where RI [%]: roughness index; Ccurrent [1]: current Hazen–Williams roughness coefficient; Cnew [1]: Hazen–Williams roughness coefficient of a new pipe of the same material; and na [1]: number of pipelines in the system.
Connection loss index (CLI [l/con/day]) determines the volume loss from the system, both physical and apparent. Although there is not a consensus among experts, it provides better information on the leakage situation of the system. 
formula
4
where CLI [l/con/day]: connection loss index; Vp [m3]: volume of water produced annually; Vi [m3]: volume of water imported annually (water obtained from other systems, e.g. water trucks); Vs [m3]: volume of water consumed for service annually; Vc [m3]: volume of water effectively consumed annually (water exported, measured, and estimated); and nl [con]: number of service connections in the system.

Evaluation of the SNIS

Among the indicators presented, only the CLI is provided by the SNIS. Thus, to exemplify the classification methodology, three other indicators are selected to perform the case study.

Specific consumption of electricity (SC [kWh/m3]) shows the amount of energy consumed to produce and distribute 1 m3 of water. However, when comparing different systems, those with more rugged topography are at a disadvantage. Nevertheless, it can serve as an initial reference for the study. 
formula
5
where SC [kWh/m3]: specific consumption of electricity; EC [kWh]: energy consumed annually; and Vp [m3]: volume of water produced annually.
Macrometering index (MMI [%]), although not directly related to energy consumption, macrometering plays an important role in system management, which is reflected in energy consumption. 
formula
6
where MMI [%]: macrometering index; Vp [m3]: volume of water produced annually; Vi [m3]: volume of water imported annually; VM [m3]: volume of water macrometered at the exit of the water treatment station, simplified treatment units and wells, and at the inlet of the imported water; and Ve [m3]: volume of water exported annually.
Water metering index (WMI) [%] is also unrelated to energy consumption but has great value in the economic evaluation of the system. A metering service makes the consumer give more value to water, thus making its use more rational and reducing costs throughout the supply chain. 
formula
7
where WMI [%]: water metering index; nlH [lig]: number of active connections with a metering service; and nl [lig]: number of active connections in the system.

RESULTS

Sample

Using the indicators proposed in the section ‘Indicators’, the results of 21 pumping stations were calculated, as shown in Table 1. The CLI values were taken from the SNIS; therefore, the same index was considered for pumping stations in the same city. It can be observed that five pumping stations operate with a difference of 10% or less for the maximum possible efficiency, five pumping stations operate during 10% or less at peak hours, three operate with roughness indices showing a difference less than 10% from new pipes, and all operate with high loss indices. It is also observed that the pumping stations with greater installed power showed better results due to their greater impact on operating costs. Despite this, the number of small pumping stations is much higher, which indicates great potential for improvement in the sector in Brazil. To perform a classification of systems based on these indicators, a larger sample covering all regions of the country is necessary.

Table 1

Performance indicators of the 21 pumping stations

Installed power [hp] EI [%] RC [%] RI [%] CLI [l/con/day] 
30 63 53 58 765 
50 56 94 48 248 
80 84 50 – 203 
80 94 32 – 203 
90 72 42 82 765 
120 85 87 92 248 
120 65 68 90 248 
120 76 10 48 765 
120 89 21 – 248 
150 82 84 73 248 
150 55 10 – 549 
225 66 29 – 549 
225 72 30 – 549 
450 100 72 248 
495 84 – – 549 
775 94 23 77 320 
1,200 88 49 56 203 
1,500 84 – – 342 
1,950 93 – 84 342 
2,550 91 81 342 
3100 89 91 342 
Installed power [hp] EI [%] RC [%] RI [%] CLI [l/con/day] 
30 63 53 58 765 
50 56 94 48 248 
80 84 50 – 203 
80 94 32 – 203 
90 72 42 82 765 
120 85 87 92 248 
120 65 68 90 248 
120 76 10 48 765 
120 89 21 – 248 
150 82 84 73 248 
150 55 10 – 549 
225 66 29 – 549 
225 72 30 – 549 
450 100 72 248 
495 84 – – 549 
775 94 23 77 320 
1,200 88 49 56 203 
1,500 84 – – 342 
1,950 93 – 84 342 
2,550 91 81 342 
3100 89 91 342 

Brazil

Statistical analysis

The search performed in the SNIS resulted in 4,941 samples. However, some do not have information for all indicators. In addition, some erroneous data are easily identified, such as negative values for loss indices and values exceeding 100% for macrometering and service metering. Thus, a statistical analysis was performed by removing the outlier values that were more than one standard deviation from the mean, thereby yielding a confidence level of 68.27%. Furthermore, in the case of the loss index, values below 50 l/con/day, which are considered to represent inevitable losses, were removed.

Next, correlation between the indicators and the size of the systems, represented by the population supplied, the number of active connections, the network size, and the amount of water produced, was evaluated. The results for linear, polynomial and exponential correlations showed that there were no cases of correlation, which indicates that, regardless of the system size, the service quality can be good or bad.

Classification

Based on the obtained information, limits for each classification range were established for each of the indicators, as presented in Table 2.

Table 2

Classification ranges of the systems per indicator

Classification Connection loss index [l/con/day] Macrometering [%] Specific consumption [kWh/m3Water metering [%] 
x < 100 x = 100 x < 0.4 x = 100 
100 ≤ x < 200 80 ≤ x < 100 0.4 ≤ x < 0.6 95 ≤ x < 100 
200 ≤ x < 300 60 ≤ x < 80 0.6 ≤ x < 0.8 90 ≤ x < 95 
300 ≤ x < 400 40 ≤ x < 60 0.8 ≤ x < 1.0 85 ≤ x < 90 
x ≥ 400 x < 40 x ≥ 1.0 x < 85 
Classification Connection loss index [l/con/day] Macrometering [%] Specific consumption [kWh/m3Water metering [%] 
x < 100 x = 100 x < 0.4 x = 100 
100 ≤ x < 200 80 ≤ x < 100 0.4 ≤ x < 0.6 95 ≤ x < 100 
200 ≤ x < 300 60 ≤ x < 80 0.6 ≤ x < 0.8 90 ≤ x < 95 
300 ≤ x < 400 40 ≤ x < 60 0.8 ≤ x < 1.0 85 ≤ x < 90 
x ≥ 400 x < 40 x ≥ 1.0 x < 85 

These ranges were determined to limit the number of samples classified as A at approximately 20%. However, for macrometering and water metering, this was not possible, even when establishing maximum efficiency as the limit, as observed in Figure 1.
Figure 1

Distribution of the system classification by indicator.

Figure 1

Distribution of the system classification by indicator.

To construct an overall classification of the system, it is necessary to group the results of each of these indicators. However, due to the greater relevance of the specific consumption of electricity and the connection loss index on the energy efficiency of the system, and the difficult to measure impact of macrometering and service metering on the energy efficiency, weights of 1.5 and 2 were attributed to these indicators, respectively. For the score A, a value of 1 was attributed, for B a value of 2, and so on. Summing the values obtained for the four indicators and calculating the weighted average, the final score of the system was obtained. Then, ranges were established for each classification. In this case, the criterion of 20% of the samples being classified as A was not adopted because this would make the classification somewhat restrictive and defeat its purpose, which is to encourage the improvement of the systems.

Thus, the classification of each of the systems was obtained, as shown in Table 3. It is observed that only 11% of the systems have a score of A, whereas 37% are given scores of D and E, which is considered a bad rating. Table 3 also shows the distribution in each region of the country. It is observed that, in the north and northeast regions, over half of the systems are attributed scores of D and E.

Table 3

Distribution of the overall classification of the systems in each region of the country

Region 
Midwest 11.2 29.4 36.4 15.3 7.7 
Northeast 5.2 17.5 21.7 36.1 24.8 
North 1.5 16.6 19.5 34.3 28.2 
Southeast 20.3 39.1 19.4 12.0 9.2 
South 11.0 36.9 26.0 17.7 8.3 
Brazil 11.3 29.0 22.7 22.2 14.8 
Region 
Midwest 11.2 29.4 36.4 15.3 7.7 
Northeast 5.2 17.5 21.7 36.1 24.8 
North 1.5 16.6 19.5 34.3 28.2 
Southeast 20.3 39.1 19.4 12.0 9.2 
South 11.0 36.9 26.0 17.7 8.3 
Brazil 11.3 29.0 22.7 22.2 14.8 

These results indicate that there is great potential to improve the efficiency of the water supply systems in the country. It is emphasized that this classification procedure is based on the reality of the situation in Brazil. When global benchmarks are considered, the results would be quite different. For example, we could use the CLI value of 60 l/con/day of the city of Queensland, Australia (Thornton 2014). Given that, to receive a classification of A, the systems need to present losses lower than this value, the number of systems classified as A for this indicator would drop to 3.4%.

CONCLUSIONS

This study presented indicators that can be used for a possible classification of water supply systems from the perspective of energy efficiency. Some of these indicators are not available in the SNIS, thus requiring field measurements, which makes it difficult to obtain a representative sample for classification. Nevertheless, indicators available on the SNIS related to the good efficiency of the systems, such as the connection loss index, the specific consumption of electricity, and the percentage of macrometering and water metering, were utilized. Statistical analysis of the data showed that the efficiency of the system is independent of its size. Using this analysis, classification of the systems was performed, with limiting ranges so that approximately 20% of the sample received a score of A for each indicator. Nevertheless, in the general classification that encompassed all indicators, only 11% were classified with a score of A, indicating significant potential to improve the efficiency of the systems in Brazil. It should be noted that this classification was performed through benchmarking within Brazil. When considering global benchmarks, the potential would be even greater.

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