Water utilities are demonstrating increasing concern about the growing threat of climate

Transcription

1 emerging issues Water utilities are demonstrating increasing concern about the growing threat of climate change. The vast majority of energy consumed by these utilities is used to pump water and is a significant source of greenhouse emissions. Moreover, a large percentage of that energy is then lost in the distribution system through pipe friction, across control valves, and at customer taps. Energy loss is wasteful in systems requiring pumping. In gravity systems, it is a potential free energy source squandered. This article describes a simple approach for calculating the carbon footprint of the energy use in distribution systems and introduces a method for assessing distribution system efficiency. The new measure will enable the water industry to conceive and formulate optimized decisions for potential energy savings to reduce its carbon footprint, thus contributing to healthier air quality, minimizing environmental impacts, and improving water efficiency and infrastructure sustainability. Assessing the carbon footprint of water supply and distribution systems Strong evidence shows that human-induced emissions of greenhouse gases are changing the world s environment. The main greenhouse gas, carbon dioxide (CO 2 ), is produced when fossil fuels such as coal, oil, and gas are burned for energy. A carbon footprint measures energy use in terms of the mass of carbon dioxide emissions. Because water is heavy at 1 kg/l, supplying it to municipal and industrial customers can consume large amounts of energy, which generally accounts for a significant portion of a water provider s annual operating budget. Energy use is the primary source of greenhouse emissions for nearly all water utilities worldwide. In the United States, electricity represents about 75% of the cost of municipal water processing and distribution (USDOE, 2006). According to a recent study, between 80 and 90% of the average water utility s carbon footprint is attributable to electricity use and approximately 99 and 91% of the electricity use at groundwater and surface water plants, respectively, is for pumping (Carlson & Walburger, 2007). American Water, the largest investor-owned water and wastewater company in the United States, reported that 97% of its electricity consumption and 90% of its greenhouse gas emissions are the products of the water delivery process (Young, 2010). The US Environmental Protection Agency estimates that 3% of national energy consumption is used for drinking water and wastewater services, emitting approximately 45 million PAUL F. BOULOS AND CHRISTOPHER M. BROS BOULOS & BROS 102:11 JOURNAL AWWA PEER-REVIEWED NOVEMBER

2 tons of greenhouse gases into the atmosphere each year (Wallis et al, 2008). The agency is currently working closely with water and power agencies to develop programs that foster more efficient operations (Grumbles, 2008; Strutt et al, 2008). In the United Kingdom, the new asset management funding cycle for the first time now requires assessment of the carbon footprint of any capital improvements (Strutt et al, 2008). DISTRIBUTION SYSTEM ENERGY USE AND POTENTIAL SAVINGS Approaches to optimizing energy use. Energy-saving in water distribution systems can be realized in many ways, from field-testing and proper maintenance of equipment to use of optimal computer control. Energy use can be better managed by decreasing the volume of water pumped (e.g., adjusting pressure zone boundaries), lowering the head against which water is pumped (e.g., optimizing supply pressures), recognizing energy tariff incentives (e.g., avoiding peak-hour pumping and making effective use of storage tanks by filling them during off-peak periods and draining them during peak periods), increasing the wire-to-water efficiency of pumps through periodic efficiency testing (e.g., ensuring that pumps are operating near their best efficiency point and replacing inefficient pumps and/or motors), and ensuring proper application of variable-speed drives. Utilities can further reduce energy costs by implementing online telemetry and supervisory control and data acquisition systems and by managing their energy consumption more effectively and improving overall operations through the use of optimized pumping configurations and reservoir control. Other researchers Energy use is the primary source of greenhouse emissions for nearly all water utilities worldwide. have proposed strategies to help water systems optimize their energy use to help reduce facility operating costs and improve long-term sustainability of energy resources (Biehl & Inman, 2010). In recent years, several attempts have been made to develop numerical algorithms to assist in the optimal operation of water distribution systems. These algorithms were oriented toward determining optimal pump-scheduling policies (proper on off pump operation) to reduce energy costs and were based on the use of linear programming, nonlinear programming, dynamic programming, enumeration techniques, and general heuristics. The success of these procedures has been limited, however, and very few have actually been applied to real water distribution systems (Boulos et al, 2006). Pumping and energy use. A significant percentage of energy input to a water distribution system is lost through pipe friction, via pressure and flow control, and at customer taps. Energy loss is particularly wasteful in systems requiring pumping. In gravity systems, it is a potential free energy source squandered. From treatment through delivery to the customer, utilities commonly wipe out a large proportion of this energy through pressure management that is generally practiced as part of leakage control measures. Little consideration is given to managing or accounting for the significant potential energy embodied in the water present in the network infrastructure at high elevations. Pumped systems normally will attempt to optimize energy input by ensuring that pressure is not excessive. However, many pumped systems are also pressure-reduced because of the need to maintain a minimum level of service pressure to customers at the highest points in the system while also managing pressure to reduce leakage in lower regions. FIGURE 1 Water distribution system energy loss occurs during transmission, across valves, and at customer taps Source Static head Hydraulic grade line Friction Customer taps Pressure reduction 48 NOVEMBER 2010 JOURNAL AWWA 102:11 PEER-REVIEWED BOULOS & BROS

3 Today, many water utilities use hydraulic network simulation models (e.g., EPANET) to plan improvements and operate better systems (Boulos et al, 2006; NRC, 2006; AWWA, 2005; Walski et al, 2003; Cesario, 1995). These models make use of the laws of conservation of mass and energy to determine the spatial and temporal distribution of flows, pressures, energy losses, and other parameters throughout the distribution system for specified system characteristics, demand patterns, and operating conditions. These predictive capabilities are useful for evaluating system response to various management alternatives and for developing sound capital improvement programs. They can be further ex panded to determine the potential environmental impacts of proposed system design and operational changes. The ability to compute the carbon footprint from energy loss attributable to pipe friction and pressure/flow control can greatly assist water utilities with decisions about sustainable operations to improve the energy efficiency of their water distribution systems and reduce their greenhouse gas emissions and energy costs. Current research s approach to assessing distribution system energy use. This article offers a simple approach for incorporating carbon emissions calculations into water distribution network models. In addition, the authors introduce a new water network efficiency measure that assesses the energy efficiency of the entire network in conveying water from the point of entry to the distribution system to the customer and represents this efficiency in terms of potential energy converted to friction, discrete loss, and loss at the customer tap. With this information, water industry professionals can better understand the potential for energy savings or small-scale energy generation and incorporate these factors in planning and operational decisions. They can also better assess the consequences of harnessing unused energy embodied in the network, from energy recovery initiatives through the installation of energy recovery turbines, when excess water pressure is present and the network topography is favorable (Lemon & Dekker, 2010). The efficiency measure also allows for direct incorporation into the objective function of existing optimization algorithms for improved results. Studies of eight actual water distribution systems showed that significant cost savings and reductions in carbon footprint can be achieved while preserving system hydraulic integrity and reliability. A significant percentage of energy input to a water distribution system is lost through pipe friction, via pressure and flow control, and at customer taps. METHODOLOGY A water distribution system consists of a complex network of pipes, pumps, ground-level and elevated storage facilities, wells, treatment plants, and a complement of hydraulic components such as bends, meters, and valves. Energy is input to the system at sources and pumps. Energy is lost during transmission, across valves, and at customer taps. Although some energy losses are unavoidable, others are not (Figure 1). Some losses are diffuse, such as friction and minor (local) losses at bends and fittings in the water distribution network. Without network reconfiguration or pipe upsizing, these losses are largely unavoidable. However, some losses are induced deliberately, through pressure management at discrete locations, e.g., via pressure-reducing valves, pressure-sustaining valves, or flow-control valves. Water utilities have the potential to better manage these losses. By far the greatest energy loss is at the customer tap. This loss is inevitable and is necessary to meet a minimum level of service pressure. However, a high residual pressure indicates that there is good potential for optimizing supply pressures in the associated zone. The pie chart in Figure 2 shows the energy distribution for an actual medium-size system. For this system, 7% of energy is lost in friction, 8% is lost through pressure management, and 85% is lost at the customer tap. In the United States, the minimum level of service pressure is 20 psi or 14.1 m (under fire flow conditions). In the United Kingdom, the minimum level is 10 m (at a flow of 9 L/min at the kitchen tap). Normally, utilities use a surrogate target of 20 m at the closest point in the water distribution network. Therefore, a potential energy of 20 m times customer consumption can be considered an unavoidable energy loss. Ideally, all customers would FIGURE 2 Pressure reduction Friction (and minor losses) Customer tap 85% Energy loss distribution for an example medium-size system BOULOS & BROS 102:11 JOURNAL AWWA PEER-REVIEWED NOVEMBER % 8%

4 receive the water they need at exactly the right pressure. Of course, it is not practical to maintain that pressure at every single customer tap without losing energy. Computer-based network simulation models provide the most simple and effective means of predicting carbon footprints in water distribution systems. Engineers today use network simulation models to solve a variety of hydraulic problems. These models can determine pressure and flow distribution throughout the network. Carbon footprint analyses are modeling applications based on the predicted network flow and pressure (head) results. A properly calibrated network model can be used to analyze a system s operational carbon footprint, evaluate the adequacy of the overall water system, and determine necessary improvements. Network model. The water distribution network is usually represented by the node link system. It is an assemblage of a finite number of links interconnected by nodes in some particular branched or looped configuration. Links are pipes, pumps, regulators, and valves with specified characteristics. The endpoints of each link are nodes of known energy grade (e.g., constant-pressure regions, elevated storage facilities, lakes, rivers, treatment plants, and well fields) or external water consumption (junction node). Nodes and links are uniquely identified by labels, allowing the network topology to be defined. Regardless of the network topologic configuration, the hydraulic state of the water system is described mathematically by the following equations for each link (between nodes i and j) and each node k (Rossman, 2000): The energy lost in the network during transmission from source to customer includes diffuse energy loss, discrete energy loss, and tap energy loss. H i H j = f(q i,j ) (1) i Q i,k j Q k,j q k = 0 (2) in which Q i,j is the volumetric flow rate in link {i,j} from node i to node j; H i and H j are the heads at nodes i and j, respectively; f(.) is a nonlinear functional relation between head loss (or gain) and flow rate; and q k is the external demand and represents the flow consumed (+) or supplied ( ) at node k. Eq 1 represents the energy loss or gain (pump) attributable to flow within a link. Eq 2 expresses node flow continuity, which asserts that at each node the algebraic sum of inflows ( ) or outflows (+) must equal the external demand. The external demand comprises domestic and nondomestic consumption and leakage. These equations constitute a set of quasilinear algebraic equations over all links and nodes in the network. The simultaneous solution of these equations gives the volumetric flow rate in each link, Q (m 3 /s), and the head, H (m), at each node and may be obtained using methods as described elsewhere (Boulos et al, 2006). Carbon footprint calculation. The energy lost in the network during transmission from source to customer includes diffuse energy loss, discrete energy loss, and tap energy loss. Diffuse energy losses are losses attributable to pipe friction and minor (local) losses. Discrete energy losses are primarily the result of head losses at pressure-regulating and flow-control valves and throttle valves. For these two types of energy loss, the water power P (W) in each link i,j from node i to node j can be computed as TABLE 1 Energy loss in water networks Total Energy Loss kw h/d Unit Energy Unit CO 2 Impact Demand Diffuse Discrete Tap (Customer Total per m 3 Supplied per m 3 Supplied Network ML/d (Friction) (Valves) Connection) Energy Loss kw h/m 3 g/m 3 CO 2 A ,483 5, B ,069 7,789 76,921 86, C , ,518 24, D ,388 5, E ,790 1,630 5,953 10, F ,495 4,587 24,770 37, G , ,450 24, H ,103 2,070 15,974 20, CO 2 carbon dioxide 50 NOVEMBER 2010 JOURNAL AWWA 102:11 PEER-REVIEWED BOULOS & BROS

5 P = gq i,j (H i H j ) (3) in which is the water density (kg/m 3 ) and g is the acceleration of gravity (m/s 2 ). Tap losses at junction nodes include domestic and nondomestic consumption and leakage losses. As water leaves the network, it is discharged to atmospheric pressure and loses all potential energy relative to discharge elevation. Thus, for each junction node k (with a positive demand), the water power P (W) can be computed as P = gq k H k (4) The carbon footprint from energy lost during transmission can then be computed as CO 2 = PC F (5) in which C F is the carbon emission factor (kg/w). Water network energy efficiency (WNEE). In a perfect water distribution system, the power needed to convey water to each customer is based on the required minimum level-of-service pressure, which is normally determined by statutory requirements or level-of-service agreements. The difference between the actual energy used at the customer tap and that needed to maintain the minimum level-of-service pressure requirement constitutes the measure of inefficiency of the system. For each junction node k with a positive demand q k, the minimum power P min required to meet the minimum level of service pressure H min can be computed from Eq 4 as P min = gq k H min (6) For the entire network, the minimum water power can then be computed as P min = gq T H min (7) in which q T is total system demand. This contributes to the excess carbon footprint (energy use), which could be eliminated through the achievement of more-efficient system design and operation. In order to assess the efficiency of the network, a WNEE measure Network can be defined as the ratio of the power required to meet the minimum level of service pressure to the total (cumulative) actual power used (P T ), or Network P min PT (8) Network efficiency is comparable to measures such as pump or turbine efficiency in that there is always an inevitable energy loss and 100% efficiency can never be achieved. In a pump or turbine, energy is lost in heat, friction, and noise. In a distribution network, energy is lost in conveyance (friction), control (valves), and customer pipe work (e.g., domestic plumbing). The WNEE measure can be effectively used to pinpoint those areas (zones) in the system with the greatest potential for improvement. These areas are represented by a relatively low WNEE measure. The main objective is to reduce the excess hydraulic energy lost in the system. Improvement alternatives can include reconfiguration of networks (e.g., rezoning of high-elevation properties onto adjoining pressure zones), upsizing of mains, valving versus pumping tradeoffs, and recovery of excess hydraulic energy (head). However, energy is only one of the chief drivers to be considered by a water utility when optimizing its distribution system. A holistic assessment will include carbon footprint, cost, criticality, reliability, water quality, and leakage, among other operational factors. TABLE 2 Water network energy efficiency Network Total Energy Minimum Energy Excess Energy Current Network Demand ML/d Loss kw h/d Loss at Tap kw h/d Loss at Tap kw h/d WNEE % A ,709 3,368 2, B ,778 31,561 45, C ,650 8,238 9, D ,864 3,176 2, E ,373 3,217 2, F ,852 14,232 10, G ,832 14,622 5, H ,146 9,792 6, WNEE water network energy efficiency BOULOS & BROS 102:11 JOURNAL AWWA PEER-REVIEWED NOVEMBER

6 APPLICATIONS WNEE measure in use. The WNEE measure was used to perform an energy audit of eight water distribution systems of a water utility serving a large city in Europe. (The identity of the corresponding water utility is withheld because of security concerns.) Calibrated all-pipes network models for these eight systems range in size from 10,000 pipes to more than 70,000 pipes. These represent discrete supply zones that are pumped from the city s water treatment works at river level, with localized balancing storage provided at high elevation. Zonal demand varies from 60 to 580 ML/d (15 to 148 mgd). The zones provide approximately half of the city s total water demand. For some zones, the existing storage at high elevation dictates the minimum pumping pressure. For other zones, which are boosted directly into supply, there is potential for pump-pressure reduction. Using the principles outlined previously, the total network energy loss at diffuse, discrete, and tap locations was calculated from the network model hydraulic results (Table 1). This can be expressed as total daily energy loss (kw h/d) or unit volumetric energy consumption (kw h/m 3 ). The corresponding carbon footprint (g/d CO 2 ) and unit carbon impact (g/m 3 CO 2 ) are Increased awareness of climate change has now become a major design and operational factor for many utilities, with the added objective of minimizing their environmental impacts. inferred using published carbon emission factors from the UK government (UKDEFRA, 2009). Energy is expressed as water power, neglecting wire-to-water losses involved in pumping before entering distribution. The network model results allow calculation of the minimum energy needed to supply the same volume of water while satisfying desired levels of service at the customer tap. This provides an indication of the relative efficiency of each network, or WNEE measure, as shown in Table 2. For the eight zones studied, the efficiency varied from 31 to 59%. Four networks (A, D, G, and H) had a computed efficiency approaching 50% or better. The remaining four networks (networks B, C, E, and F) had significantly lower efficiencies of 31 to 38%. Four networks (B, E, F, and H) lost between 9 and 16% of energy through discrete losses. These networks were both pumped and pressure-reduced. Thus, the energy efficiency audit showed that these networks offered the opportunity for energy savings through optimization. Potential for operational optimization and energy savings. A further exercise was conducted to determine the potential for optimization of pumping, pressurereduced, and/or boosted pressure in the eight zones. FIGURE 3 Schematic of pump-pressure optimization Original hydraulic grade line Reduced input pump head Supply zone boundary Input energy saved Optimized boosted subzone Optimized hydraulic grade line Optimized boosted subzone Pumping station Target level-of-service pressure 20 m Pumping station Treated water pumping station City River Treated water pumping station 52 NOVEMBER 2010 JOURNAL AWWA 102:11 PEER-REVIEWED BOULOS & BROS

7 This exercise assessed the current pumped energy in - put, the savings achievable by reducing pump head, and the corresponding cost of providing local booster stations to supply customers at higher elevations. The principle behind this exercise is shown schematically in Figure 3. Table 3 compares the estimated savings in operational power, effect on customers, and benefit cost ratio (ratio of operational expenditure saving to whole life cost of new booster schemes). Three of the zones (networks D, E, and F) showed potential to reduce pump inlet pressure by up to 20 m, resulting in savings in both operational power and carbon. This can be achieved by adjusting pressure-reducing valve settings (to reduce discrete energy losses) and boosting pressures locally by creating new, small pressure zones within the network. As the audit exercise demonstrated, the water networks that were more efficient offered less potential for energy optimization; this group included networks A, D, G, and H with efficiencies of 49 to 59%. Topography was an important factor in determining whether savings could be made by zonal reconfiguration. This factor was not considered when calculating efficiency, and it can be seen that the efficiency measure was only a guide to potential savings. Detailed examination showed that it was possible to improve the efficiency of network D (from 54 to 77%), whereas network restrictions prevented further optimization of networks B and C (with efficiencies of 36 and 33%, respectively). Conversely, two of the lowest-efficiency networks (E and F, with efficiencies of 31 and 38%, respectively) provided the greatest opportunity for energy savings The design of energy-efficient and carbonefficient systems is the principal benefit of the proposed approach. through optimization of pumping, pressure-reducing valves, and boosted pressure. After optimization, the efficiency of the two networks E and F increased to 38 and 47%, respectively. The potential savings to the utility through optimization of networks D, E, and F totaled approximately $4 million in operating costs (as present value, discounted over 10 years), with an estimated cost benefit ratio for scheme implementation of greater than 2 (Table 3). The potential energy saving was 754 kw and a reduction in operational carbon footprint of 3, kg CO 2 per year. CONCLUSION Water utilities are an indirect source of carbon emissions because of the use of energy in distribution systems (Wallis et al, 2008). These systems have generally been designed and operated to consistently supply water in sufficient quantity, at appropriate pressure, and of acceptable quality as economically as possible. Increased awareness of climate change has now become a major design and operational factor for many utilities, with the added objective of minimizing their environmental impacts. This effort requires effective management of the overall carbon emissions of water supply and distribution systems. Water utilities use network simulation models to solve a variety of hydraulic problems. They can also benefit by using network modeling to help them improve the energy efficiency of their distribution systems, thereby reducing their overall carbon emissions. These systems can use a significant amount of energy to move water from one location to another via pumps, but they TABLE 3 Potential future energy savings with optimization Inlet Pressure Average Power Properties Network Reduction m Reduction kw Requiring Boosting n Cost Benefit Ratio Optimized WNEE A NA 59% B NA 36% C NA 33% D , % E , % F ,672 > 2 47% G NA 59% H NA 49% Total 754 > 2 n number, NA not applicable, WNEE water network energy efficiency BOULOS & BROS 102:11 JOURNAL AWWA PEER-REVIEWED NOVEMBER

8 also lose some of that energy during transport through pipe friction or controls such as pressure-reducing valves. Therefore, a measure for quantifying carbon footprint attributable to energy loss is needed. This article proposed a simple approach for incorporating carbon footprint calculations with water distribution network models. Carbon footprints are determined on the basis of hydraulic network flow and pressure calculations and can be implemented in any existing hydraulic network simulation model or through the use of a simple spreadsheet. A new water network efficiency measure was also introduced. It represents the efficiency of the entire network in conveying water from entry to the distribution system to the customer, in terms of potential energy converted to friction, discrete loss, and loss at the customer tap. Under this measure, the network (or pressure zone) with the lowest efficiency usually has the highest potential for energy savings. Using this information, water utilities can effectively use hydraulic network modeling to conduct an energy audit of their distribution systems. Where inefficient systems or pressure zones are identified, water utilities can then use the modeling results to examine ways to reduce their operating costs and carbon footprint, prioritize sustainable improvements, elevate their green credentials, and act as positive stewards of the environment. The design of energy-efficient and carbon-efficient systems is the principal benefit of the proposed approach. ABOUT THE AUTHORS Paul F. Boulos (to whom correspondence should be addressed) is the president and chief operating officer of MWH Soft, 380 Interlocken Crescent, Ste. 200, Broomfield, CO 80021; paul.boulos@mwhsoft.com. He has BS, MS, and PhD degrees in civil engineering from the University of Kentucky in Lexington and an MBA from Harvard University in Cambridge, Mass. Boulos has more than 24 years of experience in both the academic and corporate world, with extensive expertise in water resources engineering. He is the author of nine textbooks and more than 100 technical articles on water and wastewater engineering. Christopher M. Bros is a client service manager at MWH Soft Ltd. in Warrington, England. Date of submission: 06/15/10 Date of acceptance: 07/12/10 JOURNAL AWWA welcomes comments and feedback at journal@awwa.org. REFERENCES AWWA, AWWA Manual M32 Computer Modeling of Water Distribution Systems. AWWA, Denver. Biehl, W.H. & Inman, J.A., Energy Optimization for Water Systems. Jour. AWWA, 102:6:50. Boulos, P.F.; Lansey, K.; & Karney, B.W., 2006 (2nd ed.). Comprehensive Water Distribution Systems Analysis Handbook For Engineers and Planners. MWH Soft Press, Broomfield, Colo. Carlson, S. & Walburger, A., Energy Index Development for Benchmarking Water and Wastewater Utilities. AwwaRF, Denver. Cesario, L., Modeling, Analysis, and Design of Water Distribution Systems. AWWA, Denver. Grumbles, B.H., The Nexus Between Water and Energy: Promoting Energy Efficiency for the Water Sector. Memo, Ofce. of Water, US Environmental Protection Agency, Washington. nexus-between-water-energy_ pdf (accessed June 5, 2010). Lemon, M. & Dekker, J., Melbourne Harnesses Unused Energy in Water Supply Network. Water & Wastewater Intl., 25:2:44. NRC (National Research Council), Drinking Water Distribution Systems: Assessing and Reducing Risks. National Academies Press, Washington. Rossman, L.A., EPANET Version 2 Users Manual. US Environmental Protection Agency, Drinking Water Res. Div., Cincinnati. Strutt, J.; Wilson, S.; Shorney-Darby, H.; Shaw, A.; & Byers, A., Assessing the Carbon Footprint of Water Production. Jour. AWWA, 100:6:80. UKDEFRA (United Kingdom Department of Environment, Food and Rural Affairs), Greenhouse Gas (GHG) Conversion Factors. conversion-factors.htm (accessed June 5, 2010). USDOE (US Department of Energy), Energy Demands on Water Resources: Report to Congress on the Interdependencies of Energy and Water. USDOE, Washington. Wallis, M.J.; Ambrose, M.R.; & Chan, C.C., Climate Change: Charting a Water Course in an Uncertain Future. Jour. AWWA, 100:6:71. Walski, T.M.; Chase, D.V.; Savic, D.A.; Grayman, W.M.; Beckwith, S.; & Koelle, E., Advanced Water Distribution Modeling and Management. Haestad Press, Waterbury, Conn. Young, J.S., The Greening of Water: Taking Aim at Climate Change Through Reducing Greenhouse Gas Emissions, Increasing Efficiency. Jour. AWWA, 102:6: NOVEMBER 2010 JOURNAL AWWA 102:11 PEER-REVIEWED BOULOS & BROS