Performance of water supply operations measured by reliability and marginal cost

This study develops and applies a concept of combining marginal cost and reliability in an operational water supply model. This concept can be used to gauge the performance of various water supply strategies. Traditional methods of water supply planning typically capture capital costs of new supplies but do not necessarily capture measurements of reliability and operational efficiencies. Reliability and efficiency can significantly impact performance of producing and delivering water. Rapid population growth, climate change, extended droughts, and increasing public scrutiny are all reasons why it is becoming more important for water supply planners to develop strategies that provide reliable and cost-efficient solutions to the public. Based on a concept previously developed, this study uses an approach of assessing reliability of water supply and marginal costs by incorporating both supply and demand-side management options. Risk-based reliability of the system is estimated as a function of shortages in flow rate and system storage volumes. The new approach is applied to a water supply planning model for the Washington County Water Conservancy District (District), a regional water wholesaler located in St. George, Utah. The results of this study show that increased operational efficiencies can be found while maintaining higher reliability in the system. The results also show that this approach can provide better insight into timing of large future supply acquisitions.

PERFORMANCE OF WATER SUPPLY OPERATIONS MEASURED BY RELIABILITY AND MARGINAL COST by Jason Russell Lillywhite A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Department of Civil and Environmental Engineering The University of Utah May 2008 III Copyright © Jason Russell Lillywhite 2008 All Rights Reserved THE U N I V E R S I T Y OF UTAH G R A D U A T E S C H O OL SUPERVISORY COMMITTEE APPROVAL of a thesis submitted by Jason Russell Lillywhite This thesis has been read by each member of the following supervisory committee and by majority vote has been found to be satisfactory. * h Ion Chair: Steve Burian Christine Pomero^U V Dave Kiefer UNIVERSITY GRADUATE SCHOOL 3- ( 0 - d.-OO'8 THE U N I V E R S I T Y OF U T A H G R A D U A T E S C H O OL FINAL READING APPROVAL To the Graduate Council of the University of Utah: I have read the thesis of Jason Russell Lillywhite in i t s f m a l f o r m and have found that (1) its format, citations, and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the supervisory committee and is ready for submission to The Graduate School. 3 /l3/ofr ^h^^fL^ Date Steve Burian Chair: Supervisory Committee Approved for the Major Department *--y Paul Tikalsky 1 Approved for the Graduate Council David S. Chapmaani Dean of The Graduate School UNIVERSITY UTAH GRADUATE SCHOOL APPROVAL in its final form G~.;hk~ Chair/Dean Chapma ABSTRACT This study develops and applies a concept of combining marginal cost and reliability in an operational water supply model. This concept can be used to gauge the performance of various water supply strategies. Traditional methods of water supply planning typically capture capital costs of new supplies but do not necessarily capture measurements of reliability and operational efficiencies. Reliability and efficiency can significantly impact performance of producing and delivering water. Rapid population growth, climate change, extended droughts, and increasing public scrutiny are all reasons why it is becoming more important for water supply planners to develop strategies that provide reliable and cost-efficient solutions to the public. Based on a concept previously developed, this study uses an approach of assessing reliability of water supply and marginal costs by incorporating both supply and demand-side management options. Risk-based reliability of the system is estimated as a function of shortages in flow rate and system storage volumes. The new approach is applied to a water supply planning model for the Washington County Water Conservancy District (District), a regional water wholesaler located in St. George, Utah. The results of this study show that increased operational efficiencies can be found while maintaining higher reliability in the system. The results also show that this approach can provide better insight into timing of large future supply acquisitions. TABLE OF CONTENTS ABSTRACT iv LIST OF FIGURES vii LIST OF TABLES ix ACKNOWLEDGEMENTS x 1 INTRODUCTION 1 Background , 1 Cost 5 2 METHOD 8 Typical Water Supply Planning Strategies 8 Efficiency-Reliability Modeling Approach 11 Model Framework 12 3 CASE STUDY APPLICATION 26 District Background 26 Model Development 33 Simulation Scenarios 37 4 CASE STUDY ANALYSIS RESULTS 44 5 CONCLUSIONS 53 Model Limitations 54 .. ............... ... ... .......... ..... ... .... .... ....... .. .... ... .. .. ................... ... .... ....... ....... ... ...... . .......... .... .... ..... ..... .. ............. ... ... ... ... .. ......... ... .... ... .... .. .............. ..... ...... ... .... ... .... ......... ... .. ... .......... .......... .... .. .... .................. .... ...... .... .. ... .... ....... .................. ... ................. .. .. ....... .... .. .... .......... .. ... ..... ... ..... ........... .... ......... ...... ...... .. ..... ... ....... ................ .. ....... .. .. ...... ... .... .. ..... .. ........... .. .... .... .. ....... .. ... .... ......... ... ........ ....... .... ..... ...... .. ........... ... .... .. .. ........ .... ... Marginal Cost. ............... .. ........... ..... .. .............................. .... .... ....... ... ......... ... ......... .. 3 Reliability .... ..... ....... .. ..... .. ...... .. ....... .. .... ...... .. ..... ... ... ............. ..... ....... ..... ... .. .... ..... .... 3 Uncertainty and Variability ..... ... : ........... .... ......... ..... .... ..... .......... ............ ................ .5 Effects of Water Conservation .................... ... ..... .. ...... .... ....................... ..... ... .. .. ...... 5 Introduction to the Method ............ ....... ......... ........ ........... .. .. .... ..... ........ ... ..... .. ... .. ... 6 ...... ..... .... .. ..... ... .. .. ... .. ... .. ... ............ ... ... ... ...... ............... .. ... .... .................... .... ... .. .... .... .. .. .... .......... .... ... ........ ................ ............. ... ... ........... .......................... ... ... ... ....... ..... ... .. .. .. .. .... ... ... .. .. ........ .. ....... .... ..... ... .. .... .......... ........ .. APPLICA TION ..... .... ........ .... .... .. ...... .... .. ...... .... ......... ... ..... ........... .... ........ .. ............ ... .. ............ ...... .. .... .. ............. ..... .. .. ... ..... ... ......... Development. ...... .. ... .. .. .. .. .................... ... ....... .... .... ... .. .... ... ...... ........ .. .. ...... .33 ...... ... ... .. ........... .... .. .. ..... ................................. .. ..... ... ..... ... ... . RESUL TS ......... ............ ...... ....................................... .. .44 ......... ... ... .... ... ......... .... .......... ... .. ......................... .. .... ... .... ......... ... ..... 53 ..... .. ......... ............... ........ .................... ..... ..... ..... .. ..................... .54 A MODEL CALIBRATION 55 B DETAILED COST CURVES 64 REFERENCES 67 vi A MODEL CALIBRA TION .. ...... ... .. .. ..... .. ..... .... ..... ..... ....... ................. ...... .... ...... .... .. . .55 B DETAILED COST CURVES ....... ....... ...... ........................ .................. .... .... .. .... .... .... 64 REFERENCES ..... ................... ... .. ........... ... ............... ........ .. ...... .... ........... ... .... ... .. .. .... .. ..... 67 vi LIST OF FIGURES Figure Page 1. Hypothetical Water Supply Plan 10 2. Flow of Logic in a Hypothetical Supply Model 15 3. Shortage Management Actions 19 Reliability Framework District's 32 Model 11. Model Model 43 45 Reliability 15. Single Trace of Annual Additions to Supply Capacity from the Efficiency-Reliability Method 47 16. Annual Additions to Supply Capacity from the Traditional Method 48 17. Average Marginal Cost Results for the Efficiency-Reliability Method and the Traditional Method 48 ......... ........... ... ......... .... ...... .. ... ........ .... .......... ........ ... . ModeL .. .... ..... ....... ... ... .. ...... ........ ... .. ...... ... ............. ........ ... ... .... ..... .............. .... ... .. .... ....... .... .. ....... 4. Relationship Between Water Conservation and Nonessential Demands ... ..... .... .. ... ... 22 5. Visual Representation of the Efficiency-Reliability Modeling Framework. ...... .... .... 25 6. Plot of Historic Streamflow in the Virgin River at the Virgin Gauge ... ...... ....... .... ... 30 7. Exceedence Probability Plot for Annual Virgin River Flow Volumes ...... ..... ..... .. ... .. 30 8. Schematic Representation of the District 's Water Supply System .. ..... ... .... .. .. .. ....... . .32 9. Exceedence Probability of Annual Precipitation in St. George .... .... .. ... ..... ... ... ... ...... . 34 10. Mass Balance Check Plot for the ModeL ..... ... ............ ... ........ ............... ..... ...... .... .. .. 38 11 . Screen Capture of the Model. .... ... ...... .. .... .... ... ....... ... .. .... ..... ..... ... ... ..... ....... .......... .. .. 39 12. Population Growth Used in the Supply ModeL ... .. ... .... .... .. ... ... ......... .. .. .. ... ..... .... ... .43 13. Supply Reliability Using WTP Peaking Compared to Well Peaking ....... ..... ... .. ...... .45 14. Average Annual Additions to Supply Capacity from the Efficiency-Reliability Method ....... .. .... ... .......... ....... .. .. .. ... ........... ...... .... .. .... .... ... ... ... . 47 from the Efficiency-Reliability Method .. ... ...... ... ... .... .... .... .... .. .... ..... ..... .. .... ... ... .... ... 47 16. Annual Additions to Supply Capacity fro in the Traditional Method .......... ..... ...... .. .48 17. Average Marginal Cost Results for the Efficiency-Reliability Method and the Traditional Method .... ... ...... ................. .... .. .... ..... ... ...... .... .......... ... ....... ... .. ... .48 18. Reliability Plot for the Efficiency-Reliability Method Verses Traditional Method 50 19. Plot of Average Marginal Cost Compared to Reliability for the Efficiency-Reliability Method 50 20. Plot of Marginal Cost Compared to Reliability for the Traditional Method 51 21. Comparison Plot for the Virgin River Diversion 56 22. Comparison Plot for the Sand Hollow Transfer Pump Station 58 23. Comparison Plot for the Sand Hollow Transfer Pump Station Pumping Costs 58 24. Comparison Plot for the St. George City Well Pumps 59 25. Comparison Plot for the Cumulative Flow at the St. George City Well Pumps 59 26. Comparison Plot for the Quail Creek WTP 61 27. Comparison Plot for the Quail Creek Reservoir 61 28. Comparison Plot for the Sand Hollow Reservoir 63 29. Development of the Marginal Cost Curve for the Efficiency-Reliability Method... .64 30. Development of the Marginal Cost Curve for the Traditional Method 65 31. Additional SMAs in the Efficiency-Reliability Method 65 32. Additional SMAs in the Traditional Method 66 viii Reliability .......... .... .... ... ... ....... ... ...................................... ... ..... .... .... ...... .. ... .50 Reliability .. ...... ..... ...................................... .... .... ...... .... .. ... ........ .50 ....... ..... .51 ......... ..................... ............ ... ...... .. . .56 ................................. .58 ... ... . .58 .. ... .. .... ... ... ........ .... .... ... .. .. ..... .59 ... ... . .59 ............. .... .. ......... ... ... ...... ........... .... ...... .. Reservoir.. ...................................................... Reservoir.. .. ...... ...... ....... .... .... ... .......... ......... . Method .... 64 ............ ......... ..... ........ ..... ............ .... .. .. ...... ............................................................. OF TABLES Tables Page 1. Estimated Costs to Implement Demand-Side Shortage Management Actions 20 2. Summary of Planned Projects 32 3. Summary of Existing Conditions Model Input 35 4. Proposed Costs to Implement Water Conservation Measures and Estimated Capacities at Build-Out 41 5. Comparison of Traditional Approach vs. New Approach 52 LIST ...... ..... ... ..... .......... ......... ..... ........ ... .... .. .. ... .... ... .. .. ............ .... .... Input.. .................. ... ..... ...... ..... ... ... ... ..... ... ... Out.. ..... .... ........... .. .. ... ... .. ..... ..... .............. .. ......... ... ....... ........ .. .. ... .. .. ... ......... ........ ... .. ..... ..... .. .52 ACKNOWLEDGMENTS I acknowledge the generous contributions of Steve Burian, Christine Pomeroy, David Kiefer, and Muzaffar Eusuff and for their guidance in bringing this thesis to completion. I also acknowledge Corey Cram, hydrologic coordinator and Melodie Sorenson with the Washington County Water Conservancy District, and Scott Taylor, City Engineer with St. George City for their assistance. CHAPTER 1 INTRODUCTION Background increasingly challenge decisions to build new large water supply projects either in fear of this causing more population growth, or detriment to the environment (Barakat, 1994). As population grows and the natural resources around us become more of a shared asset, efficiency of use and reliability of supplies become more important. Often, however, reliability and efficiency are found to be in conflict (Mondal, 2007). For the purposes of this study, reliability is defined as the probability that a system will operate successfully for a specified period of time, under specified conditions, when used for the manner and purpose for which it was intended (Ramakumar, 1996). For the purposes of this study, efficiency is defined as a function of marginal costs, which is defined as the incremental cost of an additional unit of output or the cost of the additional inputs needed to produce that output. A very reliable water supply system always costs more to operate than a less reliable system, so it is important to maintain a balance between keeping costs down and being reliable. Populations in arid regions of the world are still growing at a rapid pace while water supplies are limited, creating significant water scarcity issues. In the last century, the world's water use rate has been growing at more than twice the population growth (UN-Water, 2007). Water supply sources will become less obtainable and people 2 The mission of water suppliers generally is multi-faceted; well stated by Contra Costa Water District of California as: [To] strategically provide a reliable supply of high quality water at the lowest cost possible, in an environmentally responsible manner. (CCWD, 2007) In order to continue serving the public with reliable, high quality water at low cost in the face of increasing challenges, it will become more important to try and find what is considered a balanced combination of reliability and efficiency. The typical method of water supply planning tends to be less focused on efficiency and reliability of a water supply portfolio over the long term and more focused on the technical design aspects of given supply options. Examples of some typical methods of supply planning can be found in many water master planning documents developed for various water districts, cities, and water supply wholesalers (Water Resources Master Plans: CDM, 2007, SNWA, 2006, and TC&B, 2004). Focusing on constructability and engineering soundness is vital for any proposed water supply infrastructure, but this thesis suggests that more effort should be placed on finding ways to increase efficiency and reliability of the long-range future of the system. Typical water resources master plans tend to base future reliable supplies on perpetual, historic low flows and drought periods. These historic low flows are used as a recurring annual supply for the future. This is a conservative approach, but it is possible that it may not be the most economic or realistic approach because hydrology periodically cycles between wet and dry. Water suppliers can make use of the water available in normal to wet years if they have storage facilities or other means to do so. efficiency. Resources Master Plans: CDM, 2007, SNWA, 2006, and TC&B, 2004). Focusing on constructability and engineering soundness is vital for any proposed water supply infrastructure, but this thesis suggests that more effort should be placed on finding ways to increase efficiency and reliability of the long-range future of the system. ifthey 3 Marginal Cost Economics also plays a major role in U.S. water supply decision making policies and standards, which were formally established in the 1965 Water Resources Planning Act (Sander, 1983). This act instituted a multi-objective planning approach that incorporates cost-benefit analysis in the evaluation of future water resources projects. Historically, water utilities have relied on historic accounting data to develop economic strategies rather than forward-looking marginal costs (Turvey, 1976). Incorporating marginal costs in the decision making and rate design of water utilities is a unique and cost-effective method of increasing efficiencies (Hall, 2006). As a function of variable operating (or production) costs, marginal costs provide a measure of economic performance. Marginal cost quantifies the rate at which these variable costs increase, which allows the planner to find ways to decrease the rate of variable cost increases and thus increase efficiency of the system. Marginal cost shows the impact of changes in operations as a function of cost, which is directly related to increases in efficiencies. Reliability Reliability is an important measure of water supply performance because it shows the behavior of proposed supply management strategies under multiple uncertain scenarios (Mondal, 2007). Within the scope of this thesis, the word reliability is used as a general term that encompasses three aspects of measuring reliability: reliability, resiliency, and vulnerability. These are defined by Mondal (2007) as follows: • Reliability: the frequency of occurrence of water shortage • Resiliency: the length of time needed to recover from water shortage Historically, water utilities have relied on historic accounting data to develop economic strategies rather than forward-looking marginal costs (Turvey, 1976). Incorporating marginal costs in the decision making and rate design of water utilities is a unique and cost-effective method of increasing efficiencies (Hall, 2006). As a function of variable operating (or production) costs, marginal costs provide a measure of economic performance. Marginal cost quantifies the rate at which these variable costs increase, which allows the planner to find ways to decrease the rate of variable cost increases and thus increase efficiency of the system. Marginal cost shows the impact of changes in operations as a function of cost, which is directly related to increases in efficiencies. 4 • Vulnerability: the maximum severity of the water shortage These terms are clarified in the following example: In a given city, water demands are not completely met once for a day in July and then again for a whole week in August. The shortage in July was found to be equal to 5 cfs and the shortage in August peaked at 1 cfs. The reliability for the year would be 2 because water shortage occurred twice. The Resiliency would be 7 days because it is assumed that the time-span of the longest occurrence is equal to the time required to get the system back into conformance. The vulnerability would be 5 cfs because this is the highest peak shortage flow that occurred. The value of water supply reliability was estimated by Barakat et al. (1994) as a measure of public acceptance using a survey conducted by the California Urban Water Agencies in 1994. The results of the survey show that people are willing to pay substantial amounts of money in addition to regular water bills in order to avoid even minor water shortages (Barakat, 1994). Since reliability is very important to the public, this should be an integral part of the water supply planning process. It has been found that enhancing and enlarging water supply portfolios even when not the most economical option provides a strong argument to developing water supplies before the need completely develops rather than deferring projects because these new supplies strengthen reliability of the supply (Geriani, 1998). longest occurrence is equal to the time required to get the system back into conformance. The vulnerability would be 5 cfs because this is the highest peak shortage flow that that enhancing and enlarging water supply portfolios even when not the most economical option provides a strong argument to developing water supplies before the need completely develops rather than deferring projects because these new supplies strengthen reliability of the supply (Geriani, 1998). 5 Uncertainty and Variability water source supplies through time. The reason to use variability in water supply planning is because decisions should be made in a way that would allow the water supplier to take advantage of the wetter to more normal periods in a way to hedge against shortage in the dry years (UDWR, 2001). Uncertainty parameters can be used to estimate reliability of the system. Uncertainty can be applied to water demands, hydrology, and system operational parameters. Effects of Water Conservation Under pressure of changing policies due to environmental concerns and risk of drought, decision makers often turn to water conservation as a means to show compliance and to reduce scarcity. Before forming planning strategies around a particular water conservation goal, however, the effects of water conservation on reliability and finances should be considered along with ways that conservation can interact with supply acquisition. Water conservation is becoming commonplace in a utility's water supply portfolio, but implications of water conservation are far from understood (Chesnutt, 1995). Utilities have faced reductions in revenues as conservation programs are put into place. Supply reliability can also be affected by water conservation (Thompson, 2007). Demand hardening is defined as decreasing ability to reduce demands as more un­necessary uses are eliminated and people begin to lose interest. Research has found evidence that in many cases, the effectiveness of some water conservation measures may decay somewhat over time after implementation (Little, 2002). Thus, conservation cannot Variability in supplies is defined here as the recorded or synthesized change in tum portfolio, but implications of water conservation are far from understood (Chesnutt, 1995). Utilities have faced reductions in revenues as conservation programs are put into place. Supply reliability can also be affected by water conservation (Thompson, 2007). Demand hardening is defined as decreasing ability to reduce demands as more un­necessary uses are eliminated and people begin to lose interest. Research has found evidence that in many cases, the effectiveness of some water conservation measures may decay somewhat over time after implementation (Little, 2002). Thus, conservation cannot 6 be treated as a one-time investment. Another complication in managing water conservation stems from the public's perception of the necessity to conserve, induced by nightly news reports on TV stating wetter than normal conditions or reports on high snowpack levels (Ellis, 2007). Introduction to the Method The method introduced in this study will combine measures of efficiency (marginal cost) and reliability to help better understand the performance of given water supply strategies. The basic concept of the proposed method is to simulate a water supply system, capturing marginal costs associated with water production and delivery and calculating shortages of water supplies. This method is referred to as the "efficiency-reliability" modeling approach. Marginal cost analysis will help the water supply planner visualize the financial impacts of multiple long-term strategies. Reliability analysis can be used as a tool to justify acquisition of new supplies or increased conservation efforts and will also help the decision maker understand the risk associated with their planning strategy. Variability of supplies provides a way to estimate reliability without designing a strategy that is overly conservative by using only supply data from the driest year on record. This study aims at determining whether using marginal cost analysis combined with reliability analysis will be an effective tool to assist in the water supply planning process. It is anticipated that the results of this study would provide a way to make an existing strategy more reliable and more efficient by suggesting a change to the timing of new projects, current operational schemes, and conservation plans. This concept is "efficiency­reliability" conservative by using only supply data from the driest year on record. 7 intended to show usefulness based on the performance of the system as a function of marginal cost and reliability. These performance measures can be used to help the decision makers in choosing the right time to increase capacity and aid them in proving to the public why the time was chosen. CHAPTER 2 METHOD Typical Water Supply Planning Strategies Water supply planning involves carefully choosing the timing and magnitude of current and future water supply options to provide reliable and cost effective water supply to the public. The planning process is usually implemented in an organized planning framework like those developed by King County Department of Natural Resources and Parks (DNRP, 2005), South Florida Water Management District (SFWMD, 2006), and Irvine Ranch Water District (IRWD, 2005). Typically, the planning framework includes some form of the following steps: 1. Conduct a regional demand forecast 2. Assess current supply portfolio 3. Evaluate new water source options (i.e., reclaimed water, conservation) 4. Evaluate the reliability of supplies 5. Consider climate change and risk of drought 6. Consider needs to protect the environment 7. Consider legal aspects such as acquisition of water rights, understand regulatory framework in which development decisions will take place 8. Identify funding opportunities Water supply planning involves carefully choosing the timing and magnitude of current and future water supply options to provide reliable and cost effective water 2. Assess current supply portfolio 3. Evaluate new water source options (Le., reclaimed water, conservation) 4. Evaluate the reliability of supplies 5. Consider climate change and risk of drought 6. Consider needs to protect the environment 7. Consider legal aspects such as acquisition of water rights, understand regulatory framework in which development decisions will take place 8. Identify funding opportunities Future supplies are based on a need calculated using population forecasts with associated estimates of per capita water demands. Population growth forecasts are often associated with significant uncertainty and are therefore viewed as sets of growth scenarios. Typically, a single trace of conservative demand growth is chosen to develop a single-line population projection. Population growth estimates are typically developed 9 by governmental agencies or planning groups such as a state Governer's Office of Planning and Budget (UDWR, 2001). In addition to population growth estimates, historic water use data are also used to project future demand growth using extrapolation techniques. Once future demand growth scenarios are selected, current supply sources are analyzed by the water supplier to determine their adequacy in terms of annual volume available. Often times, the driest year on record or the 25% probability supply volume is chosen as the adequate water supply to use for projections (Thompson, 2007). The 25th percentile probability refers to the lowest 25% in annual flow volume if all the years of data were ranked from wettest to driest. This is done to ensure conservative estimates of supply need in the future. Potential new sources are decided on and also evaluated for adequacy to be included in the supply portfolio. The demand projection is overlaid on a time plot showing existing and proposed new supplies as shown in Figure 1, which is a plot showing a hypothetical supply and demand projection. The new supplies are staggered to represent time of implementation in a fashion that follows the increase in demands. Additionally, a projection of demand with and without conservation measures is shown to provide a range of possible demand scenarios. The plot shown in Figure 1 is a gross simplification of a typical planning procedure, and is not intended to describe the methodology of any one entity, but it is patterned after that used in the Drought Year Water Supply Plan by Jordan Valley Water Conservancy District (JVWCD, 2007). This methodology captures the timing of additional supplies under what is considered a conservative scenario based on historic hydrologic and demand information. Additionally, a projection of demand with and without conservation measures is shown to provide a range of possible demand scenarios. The plot shown in Figure 1 is a gross simplification of a typical planning procedure, and is not intended to describe the methodology of anyone entity, but it is patterned after that used in the Drought Year Water Supply Plan by Jordan Valley Water Conservancy District (NWCD, 2007). 10 2015 2020 2025 2030 2035 2040 Year Figure 1. Hypothetical Water Supply Plan Graphs like these provide a good overall view of possibilities and is a well-known visual tool often used by water supply planners (see SNWA, 2006, JVWCD, 2007, TC&B, 2004, LYR&B, 2005, or IRWD, 2005). However, some of the issues that may not be adequately captured in such a representation include supply reliability, economic efficiency, resilience of reservoirs pulling out of a drought cycle, and variations in timing of demand reductions. In short, this representation does not show adequately how the system will perform under multiple uncertain and controlled scenarios. An example of this typical approach was used by LYR&B (2005) for a capital facilities plan for Washington County Water Conservancy District. This traditional approach uses the driest year on record to establish adequate and available supplies and incrementally adds new supplies as demands increase. New supplies are added in order to maintain a supply excess of at least 10% above demands at any given time. A linearly interpolated conservation reduction factor is imposed on demands starting at 0% in 2007 80 o New Supply 3 70 • New Supply 2 ~ 60 IT] New Supply 1 L(L.J) o Existing Suppry e 50 0 <: 8 0_ 40 ~ 30 0.. 0- ::J (f) 20 10 0 2005 2010 Demand - no Conservation 2045 2050 L NWCD, 2005)_ reductions_ system will perform under multiple uncertain and controlled scenarios_ District increase_ 11 and ending with 20% in 2025. It is not known why the proposed supplies need to exceed demands by 10% except to provide added security. This approach does not provide any quantifiable measure of security in the supply. Also, there is no measure of costs on the basis of incremental production so that efficiencies can be found. The new proposed methodology of incorporating efficiency and reliability will be used to see if the strategy developed using the LYR&B approach is adequate based on reliability and efficiency. By just using the LYR&B approach to develop a strategy, some important information is missing. It is unknown what effects conservation will have on marginal costs and reliability and it is unknown whether using worst-case driest year on record will provide an adequate future supply outlook. It is also not understood how acquisition of new supplies will affect operational efficiencies. Efficiency-Reliability Modeling Approach A water supply system can be characterized by many cause and effect relationships, many allocation priority schedules, storage targets that change through time, and capacities dependent on flows calculated on feedback loops. Capturing all of this detail, along with volumes of input data by hand (or in real-time), could become complicated and unmanageable. Computer simulation models are often developed to represent water supply systems and facilities to provide insight into the planning and decision making process. Typically, logical rules, decision parameters, theoretical simplifications, and assumptions are implemented into models to approximate reality (Drobak, 1972). A computer model can aid decision making by integrating the complex components of the water supply planning process. Computer models can be used to developed using the L YR&B approach is adequate based on reliability and efficiency. L YR&A water supply system can be characterized by many cause and effect relationships, many allocation priority schedules, storage targets that change through time, and capacities dependent on flows calculated on feedback loops. Capturing all of this detail, along with volumes of input data by hand (or in real-time), could become complicated and unmanageable. Computer simulation models are often developed to represent water supply systems and facilities to provide insight into the planning and decision making process. Typically, logical rules, decision parameters, theoretical 12 simulate real world functions that would take months or years to observe and can also be used to run repeated trial and error experimentation with controlled variables (Drobak, 1972). Distinction should be made between the types of decision support models referred to in this study and more specific models targeting physically based hydraulic and hydrologic phenomena. The decision based models used for this study are best for simulation of water flows in the context of mass balance and decision process on time scales while running time steps no smaller than daily or monthly. Typical software used for this kind of model needs to be fundamentally flexible enough to allow for development of custom made rules and operating logic that can incorporate nuances that operators deal with on a regular basis. Tools such as pipe network flow models or open channel flow simulators will present difficulties in developing the decision science that goes into these kinds of models because they lack the flexibility. Some examples of software that may be appropriate for the application presented in this study may include: AnyLogic, GoldSim, Aescl Xtreme, Extend, PowerSim, Stella, and custom applications built in computer scripting code. Model Framework The efficiency-reliability model framework consists of a combination of some of the traditional elements discussed above (i.e., water demand growth, supply portfolio) along with the added elements of marginal cost and risk based performance measures. The framework of the proposed methodology consists of four major components, which include a Supply Model, Reliability Forecast Model, Shortage Management Actions, and 1972). development of custom made rules and operating logic that can incorporate nuances that operators deal with on a regular basis. Tools such as pipe network flow models or open channel flow simulators will present difficulties in developing the decision science that goes into these kinds of models because they lack the flexibility. Some examples of software that may be appropriate for the application presented in this study may include: AnyLogic, GoldSim, Aescl Xtreme, Extend, PowerSim, Stella, and custom applications built in computer scripting code. Le., 13 a System Performance module. The following paragraphs describe the components of this framework. Uncertainty and Variability in Water Supply Planning Uncertainty and variability can be incorporated in the modeling framework using Monte Carlo analysis, which is a method of propagating uncertainties from model inputs to the model results. Monte Carlo simulation produces random fluctuations to a given variable within an identified probability distribution. For the purposes of this study, uncertainty is based on random variables within a probability distribution that are applied to a given parameter to represent unknown outcomes. Variability is a term used to describe changes in historic patterns and recorded data. Both of these can be applied to the inputs of a model to show probabilities of model outputs. When water managers are faced with uncertainty of supplies while experiencing booming population growth, they typically will make decisions based on scarce water supply scenarios such as drought conditions within the 25t h percentile (Thompson, 2007) of probability or driest year on record. However, using uncertainty or variability provides a wider range of outputs that can be plotted on a probability distribution curve to assess probabilities of performance rather than performance of an isolated scenario. Some of the most common variables that are characterized by uncertainty in water supply models are stream flow, population growth, demands, and power costs. Increasing supply capacity for growing populations is necessary, but it may be possible more economical solutions can be found before capacity is increased significantly. Money may be saved when large capital improvements are deferred to a Perfonnance tenn 25th ofperfonnance perfonnance 14 later time. The problem is uncertainty in supplies and demands makes it difficult to quantify length of deferment. Decisions are usually made by weighing chances of success or failure. If there is a 10% chance of failure to meet water demands in 5 years, the decision to expand the water supply system will most likely happen now. What if the chance of failure could be reduced to 1% in 5 years by managing shortage? Do we decide rather than spend money now, wait and rely on chance to get us through? How long are we willing to spend money on implementation of water conservation measures to reduce risk? What risk level are we comfortable with? Including uncertainty in the modeling framework will assist in weighing the chances of success or failure. The Supply Model The supply model is intended to simulate long-term projected operations of water supply being delivered to customers and is similar to the approach developed by Lund (2000) and others. The proposed model is demand based, so demands are entered into the model as inputs and this drives all the flows throughout the model using decisions and controls to confine the simulation to represent reality. It is vital that this supply model be calibrated to some period of actual time-based data. Once the model is calibrated and operational parameters are fully understood, the model can then be expanded to run for an extended period and again tested for continuity over time. Much of the input data for the supply model will also be used for the forecast model. With a demand driven model, the sequence of calculations, will in general follow the path summarized in the hypothetical example shown in Figure 2. Note the arrows shown in this table are 1 % weighing the chances of success or failure. It calibrated to some period of actual time-based data. Once the model is calibrated and operational parameters are fully understood, the model can then be expanded to run for an extended period and again tested for continuity over time. Much of the input data for the supply model will also be used for the forecast model. With a demand driven model, the sequence of calculations, will in general follow the path summarized in the hypothetical example shown in Figure 2. Note the arrows shown in this table are 15 Shortage A/ell pump costs Well pump rates Population Total Growth w Demand r .Allocation of Supplies Total Water rights Deliveries w Priority 1. Supply Aquifer Constraints Priority 2 Supply r WTP Constraints Reservoir Operations i Diversions Booster pump rates Pump costs WTP costs Stream Flov Figure 2. Flow of Logic in a Hypothetical Supply Model intended to show flow of logic (causes and effect) and not necessarily flow of water. In many cases, feedback loops are caused by information driving a decision, but being constrained on the other end by some criteria that needs to feed back information. Input Data for the Supply Model Input data used for of the efficiency-reliability modeling approach would typically include, but may not be limited to the following items: Existing Supply Portfolio New Source Options Demand Management Hydrology System Constraints Operating Logic Water right priorities Demand Forecast oflogic • • • • • • • • 16 These input parameters can be characterized with or without uncertainty. If there is not enough information available to use uncertainty, then use of multiple traces or a range a values could be considered as input. The existing supply portfolio is a list of all existing supply sources that the water supplier may rely on. New source options might be completely new sources of water such as groundwater or new transfers in, but it can also include wastewater reuse. A companion to new source options is demand management, which is aimed at reducing demands (e.g., water conservation). Hydrology is usually one of the driving factors in water supply, and should therefore be carefully implemented. Hydrology, in the form of streamflow, can be either synthetically reproduced or indexed from historic data in order to represent uncertainty and/or variability. System constraints usually include controls in the model such as pipe and pump capacity, and reservoir stage-storage curves. Operating logic is critical for the development stage of the model since the operating logic drives the decisions made in the model. Usually, this kind of information must be obtained from system operators who are very familiar with operations of the system. Examples of operating logic would be typical reservoir fill rates and timing, modes of base-loading verses peaking at a water treatment plant, and operation of a diversion gate on the river. Water right priorities are very important as these could significantly impact the amount of water that can be diverted for supply to a utilities customer. Instream flows for environmental benefit also fit into this category. The demand forecast can be laden with considerable uncertainty, and it is also usually the biggest driver in the decision process of supply planning. range a values could be considered as input. The existing supply portfolio is a list of all existing supply sources that the water supplier may rely on. New source options might be completely new sources of water such as groundwater or new transfers in, but it can also include wastewater reuse. A companion to new source options is demand management, which is aimed at reducing demands (e.g., water conservation). and/or variability. System constraints usually include controls in the model such as pipe and pump capacity, and reservoir stage-storage curves. Operating logic is critical for the development stage of the model since the operating logic drives the decisions made in the model. Usually, this kind of information must be obtained from system operators who are very familiar with operations of the system. Examples of operating logic would be typical reservoir fill rates and timing, modes of base-loading verses peaking at a water treatment plant, and operation of a diversion gate on the river. Water right priorities are very important as these could significantly impact the amount of water that can be diverted for supply to a utilities customer. Instream flows for environmental benefit also fit into this category. The demand forecast can be laden with considerable uncertainty, and it is also usually the biggest driver in the decision process of supply planning. 17 Reliability Forecast Model The reliability forecast model is used to first determine whether additional supplies or demand management options are necessary for the next year in the supply model, then to estimate the magnitude of the additional supply/demand management options. The forecast model is a variation to Lund's approach discussed in earlier and is a part of the overall efficiency-reliability modeling approach. The forecast model runs at the beginning of each year that the supply model runs while the supply model is paused. While the supply model is paused, information is fed from the supply model to the forecast model to be used as starting conditions. This information can include factors and variables such as reservoir volume, population, current levels of conservation and added supplies. This model then runs for a single year that is chosen by the user. The year chosen by the user could include historic stream flow data, precipitation, and known growth rates. The year that is chosen is important because if the year is associated with very low streamflow data, then the forecast model will predict lower than normal carryover storage. For a conservative model, dry years of record should be chosen as the forecast year, but different years should be tested for sensitivity. The forecast model calculates demands and facility operations the same way the supply model does this. At the end of one year, the carryover storage volume of reservoirs is converted to a shortage flow (if storage is lower than a target) and flow shortages are also calculated if supplies cannot meet demands. The combination of storage shortage and direct flow shortage are added together and sent out of the forecast model. This shortage number is very important as it drives the next year's estimated action to reduce potential water supply shortages. forecast model to be used as starting conditions. This information can include factors and variables such as reservoir volume, population, current levels of conservation and added supplies. This model then runs for a single year that is chosen by the user. The year chosen by the user could include historic stream flow data, precipitation, and known growth rates. The year that is chosen is important because if the year is associated with very low streamflow data, then the forecast model will predict lower than normal carryover storage. For a conservative model, dry years of record should be chosen as the forecast year, but different years should be tested for sensitivity. 18 Shortage Management Actions The term "Shortage Management Actions" refers to actions that can be taken by water managers to influence the magnitude and/or occurrence of supply shortages. These may include actions that either increase available supplies, or decrease the total demand. Figure 3 is a layout summarizing some possible components of SMAs that can be incorporated into a supply model based on Mays (2002). Depending on particular circumstances of a water supplier, priorities of SMA's will vary from one water supplier to another. In the SMA module, priorities are assigned to SMAs so that they will be used in the order desired by the water supplier. Cost efficiency is increased if lower unit cost SMA's are implemented first. The shortage values calculated in the Forecast Model are sent to the Shortage Management Actions module, where a level of action is determined. For example, on the first year of the simulation, it is determined that existing supplies can meet all the demands. If this is the case, the shortage flow is zero and no SMA is used. On the following year, the forecast model shows that there was a direct flow shortage of 3 cfs and a storage shortage flow equivalent of 2 cfs. The SMA module adds them up and applies 5 cfs to select from the highest priority SMAs until the 5 cfs is met. After establishing the SMA flows, these values are sent back to the supply model where the SMA flow value used to increase the supply capacity (in the case of supply side actions) or decrease demand (in the case of demand-side actions). The initial 5 cfs of SMA flow is remembered and sent into the forecast model the following year as the "new" capacity of the supply system. The forecast model repeats the process of incorporated into a supply model based on Mays (2002). applies 5 cfs to select from the highest priority SMAs until the 5 cfs is met. 19 Demand Reduction Measures - Public Education - Rebates and free distribution of water saving devices - Restrictions on non­essential uses - Prohibition of selected uses - Drought Emergency Pricing - Rationing Programs • Groundwater • Surface water • Enlarged storage • Wastewater Ruse • Desalinization • Cloud seeding • Import water by train or truck • Emergency interconnection System Improvements S • Reduce system pressure • Leak detection and repar • Discontinue hydrant and main flushing Reduction of reservoir releases for non-depletion uses Relax environmental requirements Emergency Water Supplies • Use dead storage in reservoirs Figure 3. Shortage Management Actions (based on Mays, 2002) estimating supply shortages and if shortage is found, additional SMAs will be produced on top of the previous 5 cfs determined earlier. This process is repeated through the duration of the supply model simulation. During the simulation, marginal costs of these actions are estimated. Costs to implement the demand-side SMAs were obtained from research by Little (2002) and are summarized in Table 1. There may be costs associated with maintaining water conservation, which would impact marginal costs. A study by JVWCD found that the average cost to maintain their conservation program was $39/AF (JVWCD, 2007). Cost information on the supply-side De mand Publ ic non-essential Prohibit ion • Groundwater • Surface water • Wastewater Ruse • Desalinization • Cloud seeding I mport • Emergency (I) c (I) as E ~ -a (I) 0 'E 1 (I) (I) ill E c (5 0 1 ~ 'S .Q 5as: Qj ~ 0:: l 0 • Leak detection and repar • Reduction of reservoir releases for • (I) (I) c ~ 0 .~ ~ ~ > '5 ti (I) -ti Q~ . '5 !:l Qj Q. C ~ e u • Use dead storage in G c 8. Qj E (I) >- ·~x ~ • Temporary river diversions NWCD NWCD, 20 Table 1. Estimated Costs to Implement Demand-Side Shortage Management Actions Conservation Measure Average Cost for Saved Water ($/AF)a Voluntary Cut back $0 Water Audits/Surveys $ 1,300 Washing Machine Rebate $2,000 Landscape conversion Program $650 Toilet Distributions $200 Rate Changes (conservation pricing) $0 Notes: a. Costs based on Little (2002) is usually site-specific and is typically obtained from the water agency or utility. Supply-side costs should include capital expenses annuitized (if money is borrowed) and additional operational costs added to this for the duration of the life of the SMA. Demand hardening is an important aspect of implementing SMAs. As defined in the Introduction, demand hardening can reduce the flexibility of a water supplier to manage supply shortages. Water conservation causes demand hardening because it reduces the amount of nonessential demands that can be temporarily cut back in order to reduce the risk of a shortage. Phoenix's Water Resources Plan 2005 Update reveals an unwillingness of the municipality to discourage outdoor water use except as a means to reduce drought derived financial losses. It views nonessential water use as a buffer that provides the utility flexibility in dealing with potential supply shortages (Bush, 2007). Nonessential water use is defined as "water uses that are not required for the protection of the public health, safety, and welfare" according to the City of Georgetown, Texas (2007). AF)" 1,300 ofa It 21 Nonessential uses may include uses such as: • Use of water to wash any motor vehicle, motorbike, boat, trailer, or airplane, or other vehicle. • Use of water to wash sidewalks, walkways, driveways, parking lots, or other hard-surfaced areas. • Flushing of gutters or permitting potable water to run or accumulate in any gutter, street, or drainage culvert. • Use of water to add to an indoor of outdoor swimming pool or hot-tub. • Use of water in a fountain or pond except where necessary to support aquatic life. • Use of water from fire hydrants for other than fire fighting and permitted use in conjunction with a hydrant meter. A simple relationship between nonessential demands and water conservation is proposed to be used in the methodology discussed here to simulate demand hardening. The basic concept is that there is a point when conservation measures have reduced non­essential demands to a point that they are no longer considered as part of total demands (Figure 4). The SMA module simulates demand hardening by allowing an initial amount of shortage leniency. After establishing the initial amount, if additional conservation measures are put in place, the leniency is reduced. The leniency is defined as 1- Ci / CC, where Ci = conservation at the current time-step and CC = conservation at the point of diminishing nonessential demands. Leniency is the ability of the public to reduce waste or unnecessary water use on a short term basis. This term was developed for the purposes of this study, but is based on the demand hardening concepts spoken of by TC&A (1994). hard­surfaced non-essential (Figure 4). ICC, = = diminishing nonessential demands. Leniency is the ability of the public to reduce waste or unnecessary water use on a short term basis. This term was developed for the purposes of this study, but is based on the demand hardening concepts spoken of by TC&A (1994). Figure 4. Relationship Between Water Conservation and Nonessential Demands Definition of Marginal Cost Marginal costs are defined as an increase in cost that accompanies a unit increase in production or supply, or the partial derivative of the total cost function with respect to production or supply. Marginal cost is calculated using a differential equation of the total cost function: TC = FC f(Q) = FC f(Ql, Q2, ..., Qn), where TC = total cost, Q = production increment, FC = fixed costs, Qn = "nth" number of supply components for a single set of alternatives, and f(Q) is the cost function for variable supply or production rate. Fixed costs do not change over time, so this parameter drops out of the differential equation. Differentiating the total cost equation yields this equation for marginal cost, MC = dTC/dQ - d(FT + f(Q))/dQ = df(Q)/dQ. The term f(Q) is the cost function to supply water, which can change depending on economies of scale, facility changes, varying operating parameters, time of year, and other economic drivers. The model of the new approach can be run for a number of times 22 - ConseNation - - Non-Essential Demand ..... ........ ..... ....... ....... ........ ..... ........ Time De:fmition = + = + Ql , .. . , = = dTC/dQ = d(FT f(Q) /dQ = df(Q)/dQ. equal to the desired steps in marginal cost analysis. Marginal costs are then provided as end-of-year values. Reliability-Based Performance For the purposes of this study, the term reliability will be used as a general term that encompasses reliability, resiliency, and vulnerability. These are defined by Mondal (2007) as discussed earlier. Each of these three measures can be estimated as an average value over the duration of the simulation to show comparisons between scenarios and also as probability distributions at a given point in time to compare the probabilities of a measure against other scenarios. Reliability is calculated in the supply model and then sent to the System Performance module. System Performance Module The system performance module is a store house for results of the marginal cost and reliability analysis after implementation of SMAs. These measures can be displayed for selected years in the future in the form of mean values, levels at a given probability, or in probability distributions. Understanding the reliability of a system can help in determining future changes in rates and rate structures. A contingency fund can be created to hedge against uncertainties in the risk of shortages. If drought measurements are put in place and therefore reducing revenue, a nest egg or reserve fund can be established to accommodate the change (Chesnutt, 1995). 23 Summary of Efficiency-Reliability Modeling Framework To simplify the approach for this initial development, testing, and demonstration, climate change, environmental management options, legal aspects, and funding mechanisms are excluded from this framework. The new decision support framework provides extensibility and flexibility to permit future incorporation of these factors as needed for a particular application. Figure 5 is a representation of the modeling framework to be used for this study. The efficiency-reliability modeling framework is similar to that used by Jenkins and Lund (2000), which also incorporated supply and shortage management under multiple uncertainties. The supply portion of their model estimates the probability of supply shortages based on uncertainty of input parameters. The shortages (of their model) are sent to the shortage management optimization model to determine appropriate supply or demand management action at a given time. The new approach introduced in this study differs in two ways. First, demand and supply management actions are automatically fed back into the supply model on an annual basis using a forecasting submodel. This is done to simulate long-term effects of operational decisions on storage reservoirs (captures propagation of reservoir shortages if necessary) and to calculate efficiency of on-going operational components of the system. This approach assumes a water supplier has a set of options in mind for managing supplies and demands, thus not requiring optimization to determine types of management options automatically. This approach takes actions from the set of actions available, and determines when they should be implemented. The forecast model also helps predict the most economical amount of a given supply (or demand) action needed for the next year 24 Reliability needed for a particular application. Figure 5 is a representation of the modeling framework to be used for this study. options automatically. This approach takes actions from the set of actions available, and determines when they should be implemented. The forecast model also helps predict the most economical amount of a given supply (or demand) action needed for the next year - Existing Supply Portfolio - New Source Options - Demand Management - River diversions - System Constraints - Operating Logic - Water right priorities - Demand Forecast Supply Model term flow) Variability in River Flow Uncertainty in population growth Shortages Shortage Management Actions (SMA) Supply Reliability Marginal Costs of Operations System Performance Reliability Forecast Model (run for one year at a tine) Figure 5. Visual Representation of the Efficiency-Reliability Modeling Framework that will still maintain reliability of the system overall. The forecast model will act similar to a water supply manager looking ahead to the next year to make decisions without knowing the future with certainty. This change to the Jenkins and Lund (2000) approach is necessary to provide a tool that more closely resembles day to day operational decisions and management strategies. The second way in which this method differs from the Jenkins and Lund (2000) approach is it incorporates reliability of the system as a function of shortages in the supply model. Reliability is measured in a manner similar to that used by Mondal and Wasimi (2007). For example, rather than saying, "our system may fall short of demands in 10 years," a reliability analysis will provide information to say, "our system has a 10% chance of not meeting demands in 10 years." 25 - ~ - De mand ~---- -~ (run for long 18m Reliabilly forecast 4 with variable stream ManagerneR ~ar ~ tHle) fJO\I'II) I Reliabi lity I I Svsrem in 10 years," a reliability analysis will provide information to say, "our system has a 10% chance of not meeting demands in 10 years." CHAPTER 3 CASE STUDY APPLICATION In testing the new approach against the traditional approach to water supply planning, marginal costs will first be used for comparisons. If a new strategy that is different from that used in the traditional approach is determined to have a lower marginal cost, then this will demonstrate that more efficient strategies can be identified using the new approach. Next, reliability of supplies will be compared against those of the traditional approach to see if this new approach can find a more reliable strategy. A model of Washington County Water Conservancy District's (District) system was developed using GoldSim Pro (GoldSim, 2008) software to help test the concept of measuring performance based on marginal cost and reliability. The results of various SMA implementation strategies will be compared against the strategy currently in place for the District (LYR&B, 2005). The model was built using the methodology described in Chapter 3. District Background The District is located in Washinton County, Utah. The largest city served by the District is St. George, Utah. The area is characterized by arid climate, rapid population growth, and limited and uncertain water supplies. The District began as a provider for agricultural irrigators, but has more recently evolved into a major municipal water supplier. In 1989, their water treatment plant (WTP) was upgraded to 10 MGD, followed L YR&The District is located in Washinton County, Utah. The largest city served by the District is St. George, Utah. The area is characterized by arid climate, rapid population growth, and limited and uncertain water supplies. The District began as a provider for agricultural irrigators, but has more recently evolved into a major municipal water 27 by two more upgrades to 20 MGD then 40 MGD in 1995 and 2001, respectively. It is proposed that the next upgrade to 80 MGD occur in 2012. Because of the fast growth this area has been experiencing, the District has determined that local supplies will be exhausted within the near future. The District has expressed interest in evaluating future operational and supply strategies on the basis of marginal cost so that efficiencies in the system can be found. These strategies are summarized in the Model Scenarios section of this report. The District has been questioning the idea of changing the current operations of their WTP and whether or not to increase conjunctive use of groundwater. They wanted to test their ideas using marginal costs as a measure of performance. This study will help determine whether the District's ideas will reduce costs. The District is currently working with the State of Utah Division of Water Resources to design a pipeline that will carry water from Lake Powell to St. George to add 70,000 AF/yr to the District's current supply portfolio. Currently, the price for this project is estimated to exceed $494 million and recent studies indicate that the pipeline will need to be in place around year 2018 (UDWR, 2007). The Lake Powell pipeline project has progressed through initial planning stages into preliminary design, bringing along with it support and criticism (Wilson, 2007 and Wilkinson, 2002). It is very important that the District can show a need for this large project and can show when the project should be built. This case study will assist the District in showing a need for the project through the use of reliability and efficiency performance measures. area has been experiencing, the District has determined that local supplies will be exhausted within the near future. whether the District's ideas will reduce costs. project has progressed through initial planning stages into preliminary design, bringing along with it support and criticism (Wilson, 2007 and Wilkinson, 2002). It is very important that the District can show a need for this large project and can show when the project should be built. This case study will assist the District in showing a need for the project through the use of reliability and efficiency performance measures. Water Demand in the District Current average water consumption in the District's service area is 259 gpcpd, with 61% of this being used for landscape irrigation (Thompson, 2007). Since 1990, only four of the last 17 years has the area experienced above average surface water runoff. Water conservation has become very important to this area because of fast growth and dry conditions. Since 1993, the District has played an active role in promoting conservation and was the first water district in Utah to develop a conservation plan. As stated in the District's "Long Term Framework for Water Resources Management, Development, and Protection Plan," created in 1993, states, The District shall develop a water conservation plan which promotes public education and information dissemination concerning water conservation; and promotes the adoption of technologies, practices, and devices which will yield improvements in the efficiency and management of water use. (WCWCD, 1993, p. 9) Historically, the District has estimated water supply needs by determining the 25th percentile annual streamflow magnitude. This has worked well because residents have been able to cut back on the 61% of nonessential demand being used for landscaping. As the 61% of nonessential water consumption is reduced, the risk of not meeting essential demands increases. This forces the District to carefully consider reducing the percentile of historical water supply probability to around 10% or less. By using the demand hardening approach in this study, the results of the model will also assist the District in determining the effects of conservation on reliability and marginal costs. Population growth and increasing growth in demands is a major concern to the District. In the past 20 years, the population has tripled and it is estimated that the current growth rate might stay around 6% for some time to come (WCWCD, 2002). If residents continue using water at the current rate, demands will exceed existing available supplies 28 61 % ofthis 61 % 61 % hardening approach in this study, the results of the model will also assist the District determining the effects of conservation on reliability and marginal costs. corne 29 within a few years. This is why conservation and demand management have been essential aspects to the District's water management philosophy. The District's goal is to undergo 25% demand reduction by 2015, from the starting point of year 2000. For this model, it is assumed that the District will be able to conserve an additional 20% from today. This assumption is based on the fact that conservation is currently at about the 15% level (year 2007) and 10% more will get them to year 2015. It is assumed that another 10% could be reduced after 2015, but that nonessential demands will be diminished substantially by the time a 10% reduction occurs from today. Current District Operations Approximately 75% of the District's water supply comes from the Virgin River and the remaining 25% is groundwater. Figure 6 is a plot of historic flows in the Virgin River from 1940 to 2003 and an exceedence plot of annual flow volumes is shown on Figure 7. Water that is diverted from the Virgin River flows through a pipeline to an off-stream storage reservoir called Quail Creek Reservoir (QCR). Before entering this reservoir, water has an opportunity to pass through two hydro power plants. One of the two hydro plants returns the flow back to the Virgin River. The Quail Creek WTP treats water from QCR for delivery to St. George City and parts of Washington City. Before water enters QCR, there is an opportunity to pump water up to a different off-stream storage reservoir called Sand Hollow Reservoir (SHR). The purpose of this reservoir is mainly for aquifer recharge. Many groundwater wells are located within the vicinity of SHR so that water stored in the aquifer can be pumped back into the delivery system for St. George City. 15% level (year 2007) and 10% more will get them to year 2015. It is assumed that another 10% could be reduced after 2015, but that nonessential demands will be diminished substantially by the time a 10% reduction occurs from today. off­stream SHR so that water stored in the aquifer can be pumped back into the delivery system for St. George City. 1955 Time Figure 6. Plot of Historic Streamflow in the Virgin River at the Virgin Gauge 350 400 AF) Figure 7. Exceedence Probability Plot for Annual Virgin River Flow Volumes 10 .- -- ~ .-.. - ---~---.--~ 9 ---Daily Flow Rate ~I - - - - Annual Flew Volume I I 8 I I I I , ' 0" 7 , I 't , --I " c 6 r I , ' '-j ,, '' , , , c '-j , I , , , I c. , I, , ... I , I ' , , J , , , I' - " , I , , , lei , ' C) " , I-I ~, I I I I , , , 1,1, , I 1e ~ , , , .-, I, I , I , ,I , , I c::: 4 I I I ' ' , , I I I I ,1.-, ' " I =: I I -, I I I II I I I I I ) I I II I I, II II 0 I I I I I I ~ - I I I I u::: - , 3 ~ I r I ,'"',", : : r I : I I_IJ I II I I - I I I l'l~ I L I..! Lf 1,-, [I I I .' I I , LI - ~I I~ I -- ~ I ~ -~ -, 2 I, 'I I~ I , - I -.~ 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 1 00% ........................................ .... .... ................ . - ... . . .. ·············································· .. ·1 90% - 80% 70% 60% 50% - 40% 30% 20% 10% 0% +----.----,,----r----.----,-~~====~=:~ o 50 100 150 200 250 300 Annual Flow Volume (1,000 30 400 350 300 u:- 250 ~ c c c. 200 ... C) E ::: 150 ~ 100 50 31 Water can also flow by gravity from SHR to QCR, but this is currently rarely done because this operation has not yet been needed. A regional pipeline connects these two reservoirs and WTP with many groundwater wells located throughout the City and plays an important role in providing essential conveyance capacity and flexibility to the system. A schematic representation of the system is shown in Figure 8. Future Water Development Plan A capital facilities plan was developed for the District in 2005 in which the timing of new projects was laid out (LYR&B, 2005). Table 2 is a summary of these planned projects. The Lake Powell Pipeline is by far the most expensive project on a per AF basis so delaying this project will likely help reduce costs to the District over the long term. Even though projects that are delayed will cost more in the future, money will always be saved if purchases are delayed. This is because money that is saved now can most likely be invested at a higher return than inflation and because the overall life of the project can be prolonged. The well development project consists of building new wells and a transmission pipe to pump water from below the Sand Hollow Reservoir and convey it to the District's regional pipeline. It has been estimated that up to 9,000 AF/yr of reliable yield can be obtained from these wells. Work has already begun on this project, but for the purposes of this study it is assumed that full implementation of this project will begin as a completely new project. The Ash Creek project would capture water from a watershed located to the north of Quail Creek Reservoir, but is intended to serve customers mostly located outside the scope of this model so this project has been ignored. The agricultural located to the north of Quail Creek Reservoir, but is intended to serve customers mostly located outside the scope ofthis model so this project has been ignored. The agricultural 32 Recharge Weis I Figure 8. Schematic Representation of the District's Water Supply System Table 2. Summary of Planned Projects Project Date Project is to be Completed Estimated Project Yield (1,000 AF/yr) Estimated Capital Cost3 (M$) Well Development (recharge) 2008 9 4.3 Ash Creek 2009 6 12.7 Agricultural Land Conversion 2015 15 11.1 Wastewater Reuse 2014 10 19.5 Quail Creek WTP upgrade 2015 See note b 42.5 Lake Powell PL WTP #1 2020 See note b 21.5 Lake Powell PL WTP #2 2025 See note b 21.5 Lake Powell Pipeline 2018 70 392.2 a. Cost shown in year 2007 dollars b. These projects increase conveyance capacity and are not considered an added source of supply C,tyWeUs WeNs Quail Creek \lVastewa ter Treatment Plant j5' To downstream Virgin RIVer I \ \\ 6 Virgin RIVer lust Up5tream of \ Washington Fields Diversion ~ (malnta lll minimum flow of 86 cis) •• --- _: Sand Hollow ___ • ____ ••• __ • __ ' •• -- -- -- ' .- -- Reservoir (SHR) Ash Creek & LaVerkm Creek Proj ect 1 ,000 Cost' ILl 33 land conversion project would allow water that is currently diverted away from the Districts main Virgin River supply system and allow this water to be directed to Quail Creek Reservoir. The land conversion will mostly occur in the Hurricane Irrigation company's land area, where it is proposed that some irrigated lands will be fallowed. The District has estimated that buying these water rights could cost up to $1,000/AF. Input Data Data were obtained from the District, St. George City, and Utah DWR for daily operations in 2006 of all supply facilities. The year 2006 was chosen because the District did not own the WTP prior to this year and data prior to this time is not available. This year is also considered by the District to be a relatively normal year for hydrology when compared to other recent years. Figure 9 is a graph of exceedence probability of annual precipitation for the period of record from 1893 thru 2006. Precipitation in 2006 totaled about 6.5 inches, which is just over the 60% probability level. Only one year was used for calibration because of time constraints on data collection. Much of the daily flow records were obtained from hand-written data sheets, with each day's record on individual sheets. Table 3 is a summary of the information obtained and used to develop the model for the case study. Model Development A model was developed to represent existing conditions and operations that occurred in year 2006. The development process required frequent conversations with District and City staff, including the City engineer, District hydrology coordinator, District O&M supervisor, WTP manager, and District accountant. The existing 1,OOO/AF. nomlal thm 34 T 3 inches} Figure 9. Exceedence Probability of Annual Precipitation in St. George (USU, 2007) conditions model was built using GoldSim software through an iterative process of on­going calibration and discussions with operations staff, including St. George City engineer and the District's O&M supervisor in order to capture a significant amount of the operations logic. The model was built in a way that would allow for future additions to the model in preparation for building the case study model. This was done by building the model in compartments representing individual facilities. These compartments can be edited, removed, or new compartments can be added. The hurricane hydro power plant flow rate is calculated as the flow required to achieve 86 cfs downstream in the river. Because of return flows downstream of the power plant and the recursive nature of this kind of operation, an iterative approach is used to calculate this flow. If the downstream flow is found to be less than 86 cfs in the river, then a flow increment is added to the power plant flow. This process is repeated until the minimum flow in the river is achieved. Sometimes the flow cannot be achieved 120% 100% ~ ..'QQ" 80% C> ~ 60% Q) ~ c Q) "C Q) 40% Q) .u. w 20% 0% 0 2 4 6 [jear 2006 // /~. 8 10 12 Total Annual Rainfall (inches) 14 16 S1. on-going S1. modeL Table 3. Summary of Existing Conditions Model Input Parameter Input Value Virgin River flows Varies Maximum Diversion Capacity 165 cfs Minimum instream flow requirement 3 cfs LaVerkin Irrigation Demand Avg flow: 5 cfs, Total: 1,200 AF/yr (demand pattern based on 2006 flow rates) Hurricane Hydro Power Plant Capacity 30 cfs, 590 kW Hurricane Hydro Plant operational Maintain minimum 86 cfs in downstream Virgin River for Washington objective Fields Canal Diversion Quail Creek Pipeline capacity 150 cfs downstream of Hurricane Hydro Plant Hurricane Irrigation Demand Avg flow: 25 cfs, Total: 12,000 AF/yr (demand pattern based on 2006 flow rates) Other Irrigation Demands on Quail Creek Avg. flow: 6 cfs in summer, 3 cfs otherwise, Total: 1,500 AF/yr Pipeline Quail Creek Hydro Power Plant Capacity 110 cfs when pumping to SHR, 150 cfs otherwise, 243 kW Quail Creek Hydro Plant operational Reduce power production at plant when maintaining high flows is objective critical Sand Hollow transfer pump station 110 cfs capacity Sand Hollow transfer pump station Pump water to SHR only when water diverted out of the Virgin River operational objective is clean. This typically occurs in the winter and in April. Water can flow by gravity from SHR to QCR when not pumping. Gravity flow to fill QCR when needed. Pump duration on monthly intervals to avoid demand charges. Sand Hollow Reservoir (SHR) Capacity: 50,000 AF, drought pool: 20,000 AF, avg. seepage rate to parameters aquifer: 10,000 AF/yr SHR operational objectives This reservoir takes second priority to fill behind QCR. Purpose of reservoir is to recharge aquifer. SHR groundwater recharge wells Ultimate sustainable capacity in the future is estimated to be 9,000 capacity AF/yr. The District water rights to pump <= 15,000 AF/yr. Capacity is limited by a conveyance pipe from the wells to the delivery point Quail Creek Reservoir (QCR) parameters Capacity: 40,000 AF, drought pool: 20,000 AF Quail Creek Reservoir operational Filling this reservoir takes priority over SHR. June 1 storage target is objectives 40 AF. Quail Creek Water Treatment Plant 40 MGD (WTP) capacity Quail Creek WTP operational objectives Currently, the District desires to base-load the WTP instead of peaking off the well supply base flows. Notes: Data source is WCWCD except for Virgin River Flows are from Utah DWR. Utah DWR = Utah Division of Water Resources (a division of the Utah Department of Natural Resources. WCWCD Washington County Water Conservancy District 35 La Verkin obj ective 40MGD = = 36 because of insufficient flows upstream in the river. If this is the case, flow into Quail Creek reservoir is reduced to zero so that the remaining flow can be conveyed through the power plant and back to the river. Variability of Streamflow and Uncertainty of Demands Streamflow variability was incorporated into this model in an effort to capture hydrologic uncertainty that appears to drive operations of much of the system. The streamflow variability was captured by indexing the historic streamflow data and running various years of data using Monte Carlo simulation. Monte Carlo typically involves numerous simulations performed with variables changed based on random sampling from assigned probability distributions. For this model, the Monte Carlo capabilities of GoldSim were used only to randomly sample from historic streamflow data rather than assigning probability distributions to random variables. Uncertainty in water demands was estimated using a stochastic parameter based on a log-normal statistical distribution with a geometric mean of 1 and a standard deviation of 1.06. The stochastic function provides a multiplier that is applied to the daily demand function to represent daily fluctuations in demand. The stochastic function was developed to produce a similar range of fluctuation as that which actually occurred in 2006. The Monte Carlo analysis was performed by running 100 realizations in order to capture uncertainty variations in demand in combination with variability in the Virgin River streamflow. capture uncertainty variations in demand in combination with variability in the Virgin 37 Model Calibration and Validation The existing conditions model was calibrated using observed data from five main areas of the system. These areas were chosen for calibration because of their overall effect on the system as a whole. The calibration elements are diversion from the Virgin River, pumping flows and costs, WTP production, reservoir levels, and hydro plant flows and revenues. The calibration was performed by adjusting parameters in the model until simulated values matched observed values within reason. Details of how the calibration was quantified and qualified are discussed in Appendix A of this report. The results of the calibration show that the existing conditions model simulates actual operations with a reasonable level of accuracy based on scatter plot line fit comparisons and qualitative measures based on visual plot comparisons. After calibration, the model was converted to be used as the Supply Model to run for multiple years as described in the next section. The model was validated by performing a daily mass balance check on the entire system. The mass balance was calculated using the equation MB = Qin - Qout - Astorage, where Qin is total inflow, Qout is total outflow, and Astorage is the change in volume of all storage elements in the model. Figure 10 is a graph showing the mass balance error value, which is very small. Simulation Scenarios Two modeling scenarios were developed and incorporated in the model: 1. New Method: This scenario is called the new method because it incorporates the methodology of using reliability and efficiency as discussed above. Shortage management actions are chosen based on forecasted shortages each year. simulated values matched observed values within reason. Details of how the calibration was quantified and qualified are discussed in Appendix A of this report. The results of the calibration show that the existing conditions model simulates actual operations with a reasonable level of accuracy based on scatter plot line fit comparisons and qualitative measures based on visual plot comparisons. After calibration, the model was converted to be used as the Supply Model to run for multiple years as described in the next section. = ~storage, ~storage volume of all storage elements in the model. Figure 10 is a graph showing the mass Two modeling scenarios were developed and incorporated in the model: 1. New Method: This scenario is called the new method because it incorporates the 0.0002 O c t Figure 10. Mass Balance Check Plot for the Model 2. Traditional Method: This scenario uses the supply and conservation strategy based on the District's capital facilities plan. This scenario is used to compare results against Scenario 1. Performance will be measured using reliability and marginal cost results for both scenarios. The Forecast Model - Case Study The forecast model was developed using the methodology discussed in Chapter 2 and is based on the calibration model. Figure 11 is a screen capture showing how the forecast model interacts with the rest of the model. The forecast model receives inputs from the supply model, such as reservoir volume, population, supply capacity with SMAs, and all the other duplicate input parameters that are used to control the supply model. The forecast model runs for 365 days at the beginning of each year the supply model is run and then results from the forecast model are fed back into the SMA module. e- o. S... 0.0001 .0.. W Q/ Uc: 0.0000 ~ IV CD IIII!! -0.0001 IV ~ -0.0002 +----t----t-----t-----+----t----l Oct Dec Feb Apr Time Jun Aug Oct 38 Model- l> p» «4i in |» • » • » • ' p* Shortage^Prod Reliability TotalCosts Score Figure 11. Screen Capture of the Model The purpose of the forecast model is to predict end of year storage carryover and flow shortages given some starting conditions from the supply model, specified hydrology, and scenario inputs based on the type of scenario that is run. This model runs under the same operations logic as the existing conditions model. The user can enter a hydrologic year in the forecast model so that it can select that year's historic streamflow for the forecast simulation. By selecting a dry year, the forecast model will project a more conservative shortage value to be used to calculate the SMA, which is then put in the supply model. For this model, the year 2002 was used as the hydrologic year of the forecast model. The year 2002 is the driest year on record (1940 to 2007) for the Virgin River. Note that using the driest year on record in the forecast model is not the same as using the driest year on record for all years run in the supply model. Using the driest year on record in the forecast model will force the next year's SMAs to be conservative, but will not propagate shortages in reservoirs of the supply model. Since the supply model is running on indexed historic record flows, it will 39 SMAS / .( / l 'm _ ~ ~. t ".,"' +' ,,_ ' ,.7J t', ~:,' , ... v./"•..- ... - ................ ....,. ,.. > '" ,"'" ... ""''o",/J1" . ".';c..;;;.: ... .:r." W.... , ,,>' ..1 ,".,. ,'>,;i._-:.;. -----i.. ... ... ~,. <y~' ::", '->....;..:«'"",........ """~~.. , .... ,A',...~ Data Relt.abdlty Score modeL modeL modeL (1940 to 2007) for the Virgin River. Note that using the driest year on record in the forecast model is not the same as using the driest year on record for all years run in the supply modeL Using the driest year on record in the forecast model will force the next year's SMAs to be conservative, but will not propagate shortages in reservoirs of the supply modeL Since the supply model is running on indexed historic record flows, it will 40 be able to rely on wet to normal years to assist in recovering from droughts by replenishing storage. The SMA Module - Case Study The SMA module receives supply shortage information from the Forecast model and uses the magnitude of the shortage to determine what level of additional SMA needs to take place for the following year in the supply model. For example, it is found in the forecast model that there is a direct flow shortage of 3 cfs and a carryover shortage of 2,000 AF. Carryover shortage is calculated for QCR, SHR, and the aquifer below SHR. The aquifer below SHR is simulated as a contained system with 10% outflow. The District has stated that if 10,000 AF are injected into the aquifer from SHR, then they are entitled to 9,000 AF to pump out. Knowing this, the aquifer can be modeled as a simple reservoir. The carryover shortage is converted to a flow by calculating what flow rate it would take to eliminate the 2,000 AF shortage and match the carryover target. It is assumed that if a constant flow rate over the year is added, it would be enough to allow the reservoirs to meet their targets. Depending on the scenario, the individual SMAs are allocated to the shortage that was calculated. Following the example, it was determined that a 3 cfs direct flow shortage and a 2,000 AF carryover shortage (found to be equal to an equivalent 2.8 cfs) is 5.8 cfs. If conservation measures are required to be used before supply-side actions, then up to 5.8 cfs of conservation will be applied to the supply model to reduce demand by that much. This process is repeated each year until SMA capacity is met, and then no more additions can be made to the supply model and shortages begin to increase. District has stated that if 10,000 AF are injected into the aquifer from SHR, then they are entitled to 9,000 AF to pump out. Knowing this, the aquifer can be modeled as a simple reservoir. The carryover shortage is converted to a flow by calculating what flow rate it would take to eliminate the 2,000 AF shortage and match the carryover target. It is assumed that if a constant flow rate over the year is added, it would be enough to allow the reservoirs to meet their targets. more additions can be made to the supply model and shortages begin to increase. 41 Table 4. Proposed Costs to Implement Water Conservation Measures and Estimated Capacities at Build-Out Conservation Measure Average Cost for Saved AF)b Assumed SMA Capacity at Build-out (AF/yr)c Voluntary Cut back $0 7,000a Water Audits/Surveys $1,300 7,000 Washing Machine Rebate $2,000 7,000 Landscape conversion Program $650 15,000 Toilet Distributions $200 3,000 Rate Changes (conservation pricing) $0 7,000 a. Voluntary cut backs are based on the public's leniency to supply shortage induced mandates to cut water use on a short-term basis. This SMA is reduced to zero as all demand based SMAs reach full capacity. b. Costs based on Little (2002) c. Total build-out capacity estimated as 20% of demand at growth rate documented in the District' CFP Proposed unit costs of SMAs are located inside the SMA module and are used to calculate the annuitized cost to implement the SMA. For all SMA costs, it is assumed that projects are paid for using a 30-yr loan with a 5.5% interest rate. Proposed costs and ultimate capacities of each SMA are summarized in Table 4. The SMA module also simulates demand hardening by allowing a small amount of shortage leniency on the first year of the simulation. After the first year, if additional conservation measures are put in place, the leniency is reduced. Conservation capacity (CC) is 20% of demand, which corresponds with the District's conservation plan. The Supply Model - Case Study The supply model is very similar to the forecast model except that it runs for 20 years and makes changes to supplies and demands based on results of the SMA module. The supply model is run as a Monte Carlo simulation for river indexing. River indexing is a method of applying a Monte Carlo type of approach to select various years of record Model- Water ($/AF)b yr)< 7,000' 42 to represent the uncertainty in the system. Daily river flow data were obtained from Utah Division of Water Recourses for the years 1941 thru 2006. Every time the supply model runs, it selects a different starting year to index the historic data. In order to capture all the years on record, the supply model jumps forward 5 years on every realization when it indexes the historic stream flow. Population projections drive supply forecasts, so it is important to be careful not to over or under estimate projections. For this model, the population projection used in the capital facilities plan (CFP) is the bases for future demands. Figure 12 shows a plot of the population growth rate used in the model based on the projected rate from the CFP. The supply model pauses at the beginning of each year while the forecast model and SMA module perform their tasks, then it continues until year 2025. While the supply model is running, it sends cost data and reliability data to a results module that categorizes the information into each year. When the simulation is complete, these data are exported to MS excel so that costs can be plotted against production for a marginal cost plot and reliability measures can be plotted against production as well. System Performance Module - Case Study The system performance module is used to synthesize results of the reliability and marginal cost analysis. Using Monte Carlo analysis, 100 realizations are run and the percent chance of not meeting demands is calculated. Marginal cost is calculated by plotting variable costs against increasing production levels, curve fitting the scatter points, and taking the derivative of this curve. Marginal cost shows the rate at which variable costs are increasing as production increases. Lower marginal costs show that the system is more efficient. efficient. 5.5 -i 3 0 H 1 1 1 1 r 1- 2006 Figure 12. Population Growth Used in the Supply Model 43 6.5 ~ 6 .. 0 :e:I:.e-. l.l>. 5.5 e'z":: .-c 5 .. 0 ;: .0. . C) 4.5 ;; :c: l 4 ..0 c 00( 3.5 3.0 2000 2009 2012 2015 2018 2021 2024 Year CHAPTER 4 CASE STUDY ANALYSIS RESULTS The model results are organized to demonstrate two main points: 1. To show that operational costs can be reduced while maintaining reliability 2. To show that this new approach can provide additional insight into the timing of large future supply acquisitions The District was planning on operating the WTP with a base flow and peaking off the groundwater wells. Using the new modeling approach, it was found that this operational scheme would pose greater risk of shortage as opposed to using the wells as base supply and peaking off the WTP. Figure 13 shows the reliability of the system with and without the peaking operation of the WTP using the New Method. On this graph, reliability is represented as the probability that no shortage occurs. If no shortage occurs for the entire Monte Carlo analysis (100 realizations), the system is assumed to be completely reliable for the purposes of this demonstration. The reason peaking wells is less reliable than WTP peaking is because base loading the WTP draws heavily on the reservoirs and prevents them from recharging in normal to wet years. It was found to be very important that the reservoirs stay as full as possible during the normal to wet years so that they can be relied upon during the dry years. This issue came to light using the new method of supply planning, which relies on operational details, hydrologic variability, and demand uncertainty. It was also found that l3 reliability is represented as the probability that no shortage occurs. If no shortage occurs completely reliable for the purposes of this demonstration. It 45 Figure 13. Supply Reliability Using WTP Peaking Compared to Well Peaking peaking off the WTP is less expensive because in this case the WTP is treating less volume of water and expensive chemical usage is reduced. Base loading makes operations of the WTP a little less complicated, but does not mean less man-power is required to run the plant in base-loading operations (Childers, 2007). It was also found that maximizing diversions to Quail Creek Reservoir (QCR) is useful even if the reservoir is full and the water needs to be spilled back to the Virgin River because of increased hydropower revenues. The traditional approach to water supply planning has no way of analyzing the financial and risk impacts of these operational modifications. The new supply planning method uses an optimal timing approach to adding new SMAs as described in the forecast and SMA model methodology. This approach assumes that SMA's can be built in phases so that capital costs can be minimized while maintaining reliability of the supplies. For example, the new approach will tell the supply planner just how many wells to build for each succeeding year rather than assuming that ~ :0 .~ Gi a: ti ~ .a.Q..../ o ~ :0 .'a" 2 a. 100% ~------:::;~;;;;;;;;;:=------------------ 90% 80% 70% 60% 50% 40% 30% '" 20% --Peaking WTP 10% - - Peaking Wells ....... , \ , ...... ,-- 0% .~--~--~--,---,---,---,---,---,--=~ 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 Year 46 the ultimate number of wells for maximum capacity would be built all at once. Figure 14 is a plot of average annual additions to system supply capacity using the New Method. This plot represents the average of 100 Monte Carlo realizations. As seen in Figure 14, SMAs are added gradually over time in phases. This is considered to be practical for the types of SMAs used in this case study. For example, wells can be added to a system one at a time rather than all at once as can conservation practices. It is also important to consider the more aggressive projections of SMA additions that may come out of the Monte Carlo analysis. Figure 15 is a plot of a single trace from the Monte Carlo analysis showing one of many possible scenarios of supply acquisition. This scenario shows aggressive SMA additions from 2011 to 2016 and again from 2021 to 2024, which occurred during drought years. SMAs did not increase from 2016 to 2021 because these were wetter than normal years and the reservoirs were full in 2016. Figure 16 is a plot showing annual additions to the system supply using the traditional approach. The traditional approach does not provide a way to differentiate between drought and nondrought years, so it will produce the same plot for all realizations of the Monte Carlo analysis. The supply acquisition plan shown in Figure 16 is not dynamic like it is using the efficiency-reliability method. Figure 17 is a graph showing the difference in average marginal cost between the new method as compared to the traditional method. The marginal cost curves shown in Figure 17 were developed based on variable costs as shown in plots in Appendix B. Note that using the traditional method, it was found that marginal costs rise sharply in the range of 35,000 to 45,000 AF of production and then drop off after 50,000 AF of types of SMAs used in this case study. For example, wells can be added to a system one at a time rather than all at once as can conservation practices. It is also important to consider the more aggressive projections of SMA additions that may come out of the Monte Carlo analysis. Figure 15 is a plot of a single trace from the Monte Carlo analysis showing one of many possible scenarios of supply acquisition. differentiate non drought is not dynamic like it is using the efficiency-reliability method. • Conservation • Reuse • Ag Conversion • New Wells Time Figure 14. Average Annual Additions to Supply Capacity from the Efficiency-Reliability Method Figure 15. Single Trace of Annual Additions to Supply Capacity from the Efficiency- Reliability Method 70 ~------------------------------------------------------~ iL 60 « o o o 50 QI § 40 '0 > ~ 30 VI .iUg. 20 '6 ~ 10 0 to Cl 0 0 0 0 N N o o C _New 0 N 0 0 0 N N N C"l V 0 0 N N 1I1 \0 "- to Cl 0 N N C"l V 1I1 0 0 0 0 0 N N N N N 0 0 0 0 0 0 N N N N N N N N N N N 47 70 < 60 o Conservation 00 o Reuse C!. 50 C c Ag Conversion QEI • New Wells :::l 40 '0 > < ~ 30 VI iU c .0. 20 :c '0 < 10 0 .......... - to o o N Cl 0 g 0 N N N a o N N lJ'l 10 ,.... to Cl o o 0 o a N N N N N Time o N o N ~ N o N N N o N C"l V N N o 0 N N lJ'l N o N Efficiency­Reliability 2.5 T- 1 s uo 1 1.0 I 0.5 New Method - - Traditional Method / / / / / / ---"-" \ \ \ r i ! , , 35 45 50 55 Production (1,000 AF) 60 65 Figure 17. Average Marginal Cost Results for the Efficiency-Reliability Method and the Traditional Method 70 , -·-·----··------·----··-··-------·--··----·-·-····---..... --.-.--................... -. ---............... ---- .. --.------- .. --------, 60 50 40 30 20 10 0 ex) en 0 0 0 0 N N o Conservation o Reuse c Ag Com.ersion • New Wells 0 .- N .- 0 .- 0 0 N N N I.0" -) 0'<t N N lI'l \0 ,.... .- .- .- .ex-) e.n- N0 .N- NN IN" ) N'< t NlI' l 0 0 0 0 0 0 0 0 0 0 0 N N N N N N N N N N N Time Figure 16. Annual Additions to Supply Capacity from the Traditional Method i' 2.0 'I"'" V-. . Vi 1.5 o U ~ 1.0 ~ lIS ~ 0.5 .--, --,; ~ '\ - - ,; \ \ \ / \ II " " , '--- 0.0 -t-----r------r---,----.-----,------i 40 /ItF) 48 17 . Reliability 49 production. This shape is due to a much more aggressive SMA implementation than is required for a 90-95% reliable supply system. The average marginal costs of the new method are much flatter and more stable through the range of future additional SMAs. A higher marginal cost means that more money is being spent to produce the same unit of water. The marginal curve for the new method follows closely to the growth rate of water demands. The District could save money by delaying future expenditures until they are found to be required based on the reliability analysis. Note that a flatter marginal cost curve does not necessarily mean less total money was spent by year 2025, but rather that there is a difference in the rate at which the money is being spent. It is also important to note that the reliabilities for these two options are very similar, as shown in Figure 18. The results of the model also demonstrate that the new approach can provide additional insight into the timing of large future supply acquisitions. The future Lake Powell Pipeline is considered a very large supply acquisition and the need for this project should be considered carefully. The model was run without including the Lake Powell pipeline to see when it would be needed based on a reliability standpoint. Figure 19 is a plot of marginal cost compared to reliability using the new modeling approach. Reliability begins to drop off around the production level of about 55,000 AF. At this point, the marginal cost of production is just over $1 million. Compare this result to that of the traditional approach shown in Figure 20. The traditional approach shows reliability starting to break down around the 50 to 55,000 AF production level, similar to the reliability of the new method. However, the marginal cost at this point is closer to $2 million. More money was spent to produce the water. The marginal curve for the new method follows closely to the growth rate of water demands. plot of marginal cost compared to reliability using the new modeling approach. 50 2007 2012 2017 2022 Year Figure 18. Reliability Plot for the Efficiency-Reliability Method Verses Traditional Method 30 35 40 45 50 55 60 65 70 Production (1,000 AF) Figure 19. Plot of Average Marginal Cost Compared to Reliability for the Efficiency- Reliability Method 100% 90% I C, BO% I .~0c 70% --New Method I (Jl I .... 60% - - Traditional Method 0 I :~c 50% I ta 40% I .a .0.. 30% I Q. 20% ,. 10% / \ 1.B . .-.- 100% 1.6 • • • • • • • • • 90% 1.4 • BO% i' 1.2 . 70% - Marginal Cost - ~ .... 60% ~~ I0II 1.0 • Reliability • ~ u 50% /ij O.B :c c: 40% .!!! .C.. I Qj 0.6 · a: ~ta 30% 0.4 20% 0.2 · 10% • 0.0 0% 51 30 35 40 45 50 55 60 65 70 Production (1,000 AF) Figure 20. Plot of Marginal Cost Compared to Reliability for the Traditional Method same amount of water for essentially the same reliability for both scenarios. These results also show that the SMAs noninclusive of Lake Powell can reliably supply up to 55,000 AF. After this point, it appears that the Lake Powell pipeline shouldbe put in use. Using the new modeling approach, it was found that 55,000 AF/yr of production would occur around year 2020 based on the assumed demand growth. Table 5 is a summary of results of a comparison made between the traditional approach as described in Chapter 2 and the new approach of incorporating marginal cost and reliability. Rather than just finding the point where annual demand crosses the supply line to mark a time to build a new project, a more rigorous approach to supply planning should be considered. Using this new approach, the limitations of the reservoirs, pipelines, pump stations, and WTP are incorporated for a more operational focus. The reliability of the combination of all these components can be different than the reliability of an isolated component when viewed independently of the system. This new method 2.5 - . ---• .•• - .•.• •..• " .-, .--• .. ---.. -.-.•. ---.. --------.-_._ .•• -_.- • • - Marginal Cost :::i! ~ "ti o U 2.0 1.5 iii t: 1.0 .~ IU :::i! 0.5 • • Reliability • • 100% 90% 80% 70% 60% 50% 40% 30% -- 20% 10% O . O +-----,----.-----r----~----,-----~---.-----+ 0% ~ ~ ~ :c IU Qj c::: ofa of an isolated component when viewed independently of the system. This new method Table 5. Comparison of Traditional Approach vs. New Approach Description Traditional Supply Planning Approach New Approach What operational changes can be made in the system to increase efficiency? Unknown Maximize use of wells, maximize Virgin diversions, and peak off the WTP while base loading the wells Reason for building Lake Powell Pipeline in year 2018 Demands are projected to exceed supply somewhere between 2018 and 2024 The probability of shortage rises above 20% in year 2020 or at a production rate of 50,000 AF/yr Long-term effect of using driest year on record to forecast supplies Unknown Reservoirs cannot provide supply after a repeat of 3 of the driest years on record Effects of water conservation Decreases demands Decreases demands, increases marginal cost, and increases risk of supply shortages system reliability. Putting more pressure on the WTP puts more pressure on the reservoirs, which increases risk of shortages. Putting more pressure on Sand Hollow Reservoir and drawing it down decreases the supply available to the recharge wells. These kinds of cause and effect issues in the system cannot be visualized using the traditional approach of supply planning. The traditional method of supply planning does not consider the marginal costs of base-loading the WTP and therefore would assume a higher risk scenario. The new method not only provides a way to find operational efficiencies but also quantifies the level of efficiency as a function of marginal cost. 52 ties the operations of all the facilities into one whole, providing a quantitative view of Marginal cost provides a way to compare efficiencies of operations of the system. CHAPTER 5 CONCLUSIONS operational scenarios for comparison. The new method demonstrated in the case study shows that risk of not meeting demands increases at a known point in time, related to a given level of production. As shown in the results, this may not be exactly the same as the point at which a projected demand line crosses the supply limit. reliable supply strategies at the lower costs while fitting their specific operational needs. This approach can also help the water agency provide a basis for a decision to build a new water supply project that can be backed up quantitatively. The traditional method of water supply planning that many utilities rely on shows the need for future projects but fails to effectively quantify the need. In developing a strategy using this approach, the decision maker will be empowered to justify their decisions by these measures. CHAPTERS The results of this study show that increased operational efficiencies in a water supply system can be found using the new method while maintaining equal or higher reliability in the system. The framework of the model is set up to easily facilitate multiple The results also show that this approach can provide better insight into timing of large future supply acquisitions. Quantifying reliability provides useful decision support for water suppliers who are considering acquiring significant water projects in the future. The approach developed in this thesis could be applied to real-world water supply applications. Using this concept can help a water supply agency or utility develop more 54 Model Limitations The model limitations are summarized in the list below. Calibration of the existing conditions model is based on a single year. Operational logic in the model could be further refined by calibrating to multiple years under various hydrologic conditions. Shortage management actions may look good on the basis of economics and reliability, but this approach does not incorporate public perception issues, public policies, and political pressures to build new large projects This approach is data intensive and time consuming in the development phase of the model. Depending on the modeling software used, simulation run times can become very long, making it difficult to iterate through scenarios and tuning strategies through trial and error. The GoldSim Pro software is extremely visual and easy to use, The model limitations are summarized in the list below. • Unit costs of SMAs are based on a single study and should be verified using additional research • • • but the run time for 100 realizations is nearly 3 hours, so a more efficient tool might be a better choice for this application. APPENDIX A MODEL CALIBRATION results and thin, dashed lines to represent observed data. Calibration of Diversion from the Virgin River pumping up to SHR. In order to achieve some form of calibration, these other elements were adjusted to match historic data and operational strategies set forth by the District. The irrigation demands are calculated using an annual volume multiplied by a demand pattern. The Hurricane Hydro plant flows are calculated using a more complex algorithm as discussed in the section on calibrating hydro plant flows. Adjustments made to pumping rates to SHR are also discussed later. Figure 21 is a calibration plot showing a qualitative comparison. from most of the inflows occur. The model produced a diversion flow almost 20 cfs lower than the observed flows in February and March. This is because in 2006, more water was sent to QCR even though it was already filled to capacity. According to the District, flows into QCR are typically shut down if QCR fills to capacity. Therefore, this difference in All calibration plots in this appendix use thick solid lines to represent model Calibration of the diversion was accomplished by validating outflows along the Quail Creek Pipeline, which include irrigation diversions, Hurricane Hydro Plant, and Three areas of this plot need to be considered separately. The first portion is from January through June. This is a critical filling period for QCR and SHR and is where 56 I L w A v f lr f^fv* 1 I&J-- - - o^ 1 1 1 1 1 1 M 1 1 1 1 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Legend: QC_divert QCPobserved Figure 21. Comparison Plot for the Virgin River Diversion February and March is due to an operational anomaly. The area between July and October is a period when most of the water flows back to the river through the Hurricane Hydro plant and is diverted to irrigation users. The difference between the model results and the observed data can be explained as a result of flow fluctuations in canals that divert water to agricultural lands. The available flow records did not show all of these fluctuations because flow measurements were not taken with enough accuracy or enough frequency. The area between November and December is a time when turbidity in the Virgin River is highly variable (late summer also has this problem). When turbidity is high, the District avoids diverting water to the reservoirs because the higher turbidity causes problems at the WTP and could reduce the infiltration capacity at SHR due to siltation of the reservoir bottom. 200r---~--~--~--~--~--~----r---~--~--~--~--~ 150'---+---+---~r-~---+---4----~--+---~--~---+~~ O~--+---+---~--~---+---4--~~--+---~--~---+--~ Jan Calibration of Pump Stations to reduce demand charges induced by multiple occurrences of pump start-ups. It is also important that dirty water is not pumped into SHR so that siltation of the reservoir bottom can be minimized. The pump will typically be turned on during the months that turbidity in the river is low. Figure 22 is a plot showing a comparison of the model results and observed data. As shown in this plot, a significant discrepancy occurs in the months of April and May. It was determined that the pumps were likely run at a lower speed in April possibly in anticipation of filling the reservoir. Since the model cannot simulate with perfect foresight, this operation was not reproduced. Rather, the model uses a ramping-down equation that reduces pump flow as the reservoir fills to capacity. This is why the model shows higher flows in April and lower flows in May. If the April- May time period is disregarded, the RA 2 value for the comparison is 0.9. On the basis of annual flow volume, the two are only 3% different. The pumping cost comparison of the Sand Hollow Transfer pump station is shown in Figure 23. The annual pumping costs difference is 3%. The City well pumps were also compared against observed data as shown in Figure 24. The observed data were provided in monthly flow increments, but the comparison seems qualitatively pretty accurate on a monthly basis. On the basis of annual flow volume, the two are 4% different. occurs in July, where actual flows were reduced from the previous month even though 57 The Sand Hollow Reservoir Transfer pump station is operated on a monthly basis April­May R/\different. The main significant difference between the model results and the observed data demands increased. This is considered to be an anomaly of operations because the wells n r A 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Pump SHpumpObs Figure 22. Comparison Plot for the Sand Hollow Transfer Pump Station J a n Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Legend: 100 80 t;:;;:~ TT r--, II I...i..i ~ 60 I--,i .!! a'": r-- 0~ 40 u:: '-- 20 o Jan Legend: --- SHpumpObs so 40 30 ~ ~ 20 10 o /, ~ L ----- , ~ V :/' Jan Jan Legend : SHPCcostObs --- TCshp 58 Figure 23. Comparison Plot for the Sand Hollow Transfer Pump Station Pumping Costs 6* 1 1 1 1 1 1 1 1 1 1 1 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Legend: Qcw CWobs Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Legend: CumCW Cum CWobs 'C C> S 2 IX'": ~ 0 iI 20 18 16 14 12 10 8 JM~~ [I ' ~ MI' . JJ/\, tL I ~lJ 1'1 l nAJI .1 .1 1"\ 'T ~ , I !lV"f ¥ ~ r- I\. n III I~n ~ ~ ;1 ""1 '--- 6 ]lrt I Jan Figure 24. Comparison Plot for the St. George City Well Pumps 16 14 12 10 iL « 8 ~ 6 4 2 0 Tine Legend CumCI,Vobs 59 Figure 25. Comparison Plot for the Cumulative Flow at the St. George City Well Pumps 60 have capacity to maintain the flow observed in June for three months time. The pumping cost comparison of the City wells is shown in Figure 25. The annual pumping costs difference is 4%. Calibration of the Quail Creek WTP Production The WTP production is a function of total demand and flows of competing supplies, which consist of the City wells and the Sand Hollow recharge wells. The demand has a stochastic component that produces uncertainty on a daily basis in anattempt to represent reality. Figure 26 shows a comparison of the model results and the observed flows at the Quail Creek WTP. When these are plotted against each other, an RA 2 value of 0.84 is achieved. Calibration of the Reservoirs The Quail Creek Reservoir comparison plot is shown on Figure 27. Many discussions with the District occurred because of this plot. After many thoughts were relayed back and forth and multiple conjectures pointed out, no one reason can be considered as the source of the error in this comparison. The most significant error in this comparison is related to the difference in drawdown in the summer. It is not understood why the observed data shows such a fast drop in the first part of July because no large spill actually occurred. One thought is that the water level readings could have been made erroneously. But even if this were the case, the volume later in the summer would still be able to match very closely. It is concluded that something is amiss with the observed data in June and July. The majority of outflow from the reservoir is made up of WTP flow, which was found to calibrate. The only other variables are seepage and 4 % . R"The Quail Creek Reservoir comparison plot is shown on Figure 27. Many discussions with the District occurred because of this plot. After many thoughts were relayed back and forth and multiple conjectures pointed out, no one reason can be considered as the source of the error in this comparison. The most significant error in this comparison is related to the difference in drawdown in the summer. It is not understood why the observed data shows such a fast drop in the first part of July because no large spill actually occurred. One thought is that the water level readings could have been made erroneously. But even if this were the case, the volume later in the summer would still be able to match very closely. It is concluded that something is amiss with the observed data in June and July. The majority of outflow from the reservoir is made up of WTP flow, which was found to calibrate. The only other variables are seepage and Jan Feb Jvlar Apr M Ju! Legend Qwtp WTP2006 Figure 26. Comparison Plot for the Quail Creek WTP 40 38 i I g 30 o CO 28 26 24 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time Legend: QCR QCRVobs Figure 27. Comparison Plot for the Quail Creek Reservoir 30.---------------------------------------------------------, 20 10 o ~--~--_+----4_--_+----+_--_r----r_--_r--~----~~_+--__4 J an Mar May Jun Jul Aug Sep Oct Nov Dec Jan Time Qwlp v"JT P2006 e;::- ,r:n;. 36 QJ E 34 ~ 0> 32 QJ ~ 30 // ~ " Il tLf / ~ I -~ ~ '''-.,. ~ .9 "'" r-- (f) 28 26 ""'-h -.... 24 - LJ lr--- f Legend : 61 62 evaporation. The District noted that seepage from this reservoir is near zero, so this leaves evaporation. However, the SHR calibrated well using the same evaporation, so would just shift the error from one reservoir to the other. Figure 28 is a plot showing the comparison of model results and observed volume at SHR. This reservoir calibrated well, with an RA 2 value of 0.97. changing evaporation for this lake would require changing evaporation at SHR, which R/\Legend SHR SHRVobs Figure 28. Comparison Plot for the Sand Hollow Reservoir 52.--------------------------------------------------------. ~.. 50 o.!!.! . § 48 "0 > ~ 46 : o ~ 44 42~--~--~--~----+---~--------+---~--------+---;---~ Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Time 63 APPENDIX B DETAILED COST CURVES Efficiency-~ ,~.... ~.... 111 0 U cu :c .;II:I III > 9 8 7 6 5 - 4 - 3 2 - APPENDIXB y = 103814eo.067x R2 = 0.8479 .' Cost x Prod. - Marginal Cost --Variable Cost o +-----,-.-----,-----,------,-----,------,-----,------+ 30 35 40 45 50 Year 55 60 65 70 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Figure 29. Development of the Marginal Cost Curve for the Efficiency-Reliability Method i,..".. ~... 1/1 0 u iV c .~ III ~ 65 y = 41.047x4 - 8074.6X3 578350X2 - R2 0.8762 Cost x Prod. • Marginal Cost • Variable Cost 3.0 2.5 2.0 _ 1.5 1.0 0.5 0.0 65 70 Figure 30. Development of the Marginal Cost Curve for the Traditional Method Time Figure 31. Additional SMAs in the Efficiency-Reliability Method (Note: upper five SMAs are all conservation-based SMAs) 9 y= 41 .047x'· 8074.6'( + 578350,( · 2E+07x + 2E+08 8 R2 = 0.8762 7 ".. """"- 6 /f/1 ... ~.... / ~ Prod . .... ~ 5 ~ ~" .. ... \ ... - - III III 0 0 u.. 4 I \ --Variable Cost iua :J..S. \ '" .~ ..'".. 3 / ... > ::::E 2 / ..... .. \ " ..... ...... ..... 0 30 35 40 45 50 55 60 Year 70 o Conservation Rates 60 o Rebates L«L o Audits 00 50 Cl Landscape Conversiol q ~ • Toilet Distributions G> E::::l 40 o Reuse "0 o Ag Conversion > « Cl City Wells :CEI) 30 .ASRWp.lIs IU c: 0 .. 20 'C "« 10 0 OJ m 0 N ('') "<t 1.0 ({) l"- eo m 0 N (") "<t 1.0 0 0 ..- ..- ..- r- '''- ..- ..- ..- N N N N N N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N N N N N N N N N N N N N N N N N N Figure 32. Additional SMAs in the Traditional Method 70 T · .. ·························· 60 -~ 50 u.. ~ §. 40 ~ ~ 30 Q. lIS U -g 20 "0 "0 ~ 10 0 00 0 0 N o Conservation o Reuse [] Ag Conversion • New Wells Q) 0 N 0 ..-- ..-- ....... 0 0 0 0 N N N N (.."--) .".-f- <.....(..). i..l...l.. .r.--- o..o- 0 0 0 0 0 0 N N N N N N Year 66 · .. ·-.... · ....... ···i (J) 0 ..- N (") "f t!) ..- N N N N N N 0 0 0 0 0 0 0 N N N N N N N REFERENCES Barakat & Chamberlin, Inc. (1994). The Value of Water Supply Reliability: Results of a Contigent Valuation Survey of Residential Customers. California Urban Water Agencies. August, 1994. Bishop, D. B., Weber, J.A. (1996). Impacts of Demand Reduction on Water Utilities. AWWA Research Foundation. C , Statewide Market Survey: Landscape Water Use Efficiency, Final Report. California Urban Water Agencies. Bush, J.C. (2007). Wringing Water-Thrifty Urban Design from Southwestern Water Plans. Southwest Hydrology Journal, May/June 2007. 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