Flexible Decision Frameworks for Long-term Water Resources Planning. Melanie Wong ESD.71 Application Portfolio 12/13/11

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1 Flexible Decision Frameworks for Long-term Water Resources Planning Melanie Wong ESD.71 Application Portfolio 12/13/11

2 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 2 of 37 Table of Contents 1. Introduction Model Development Deterministic Scenarios Flexible Scenarios Single Iteration Model Results Sensitivity Analysis Simulation Target Curves and Comparisons Conclusions and Lessons Learned References Appendix: Reports... 29

3 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 3 of Introduction Rapid urbanization and growth requires city planners, engineers, and other decision makers to develop long-term, forward-thinking strategies for natural resources. Water is one resource that must be intelligently managed. However, the standard process of forecasting and building for an assumed future water demand is inadequate. In reality, uncertainty looms in future technical and economic conditions. There is also uncertainty in the future value of infrastructure investments. The standard design process does not explore how future benefits and costs could change under new circumstances. An intelligent design recognizes that system owners and operators can respond actively to future changes. Flexibility is one component of an intelligent design, where the managers and operators anticipate a range of future circumstances and have proactive plans to reduce downside risks and maximize upside opportunities (de Neufville and Scholtes, 2011, p. 147). This would ultimately create an intelligent design and management system, with continuous feedback loops between the design and management elements. This paper provides a framework for intelligent resource system design by recognizing the uncertainties in a complex water resources system, identifying desirable kinds of flexibility, and understanding how flexibility can add value to the system. With an analysis inspired by Singapore, this study may provide insight on flexible resource management for rapidly growing cities in India, China, and elsewhere. System definition Cities with high population densities and limited natural catchment systems can benefit from flexible natural resource management frameworks in the face of an uncertain future. For this study, the country s four water sources commonly referred to as national taps serve the needs of the industrial, commercial, and domestic sectors. These four sources include imported water, water captured by reservoirs, water treated in NEWater plants, and water treated in desalination plants. Engineers and planners must make many decisions on what technology to invest in, how big to build facilities, and when to build them. This study investigated the different decision frameworks that these engineers and planners could use.

4 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 4 of 37 Scenarios This study examined how decision makers could utilize different decision frameworks and how flexible frameworks could add value to the system by reducing the cost to fulfill future water demand. More specifically, this study examined two deterministic and six flexible scenarios. The deterministic scenarios considered small and large facilities, and made construction decisions with several assumed predictions and goals. The modeler determined the staggered build schedules from these two scenarios. The six flexible scenarios considered small and large facilities, and also tested a decision rule that forced at least one desalination plant to be built every ten years. Instead of userdetermined build schedules, the model made construction decisions within the model. The build schedules were also automated within Excel for compatibility with simulation.

5 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 5 of Model Development All scenarios considered economies of scale, learning effects, reduced desalination operating costs, and increased electricity costs. The following section outlines various assumptions and formulations for the model inputs. Fixed and operating costs The fixed costs and operating costs for the four technologies were assumed to be the following, with fixed costs directly impacting the cost of new construction: Tap Fixed cost (per cubic meter) Operating cost (per cubic meter) Imported water - $0.003 Reservoir $1,329 $0.25 NEWater plant $1,351 $0.30 Desalination plant $1,818 $0.48 Table 1: Current fixed and operating costs Imported water Assuming that the infrastructure is set in place, there are no additional fixed costs. Imported water is priced at less than 1 cent per 1000 gallons (Tortajada, 2006, p.1). With a conversion of Imperial gallons to one cubic meter, the operating cost is approximately $ per cubic meter. The model rounded this value up to $0.003 per cubic meter. Reservoir Two-thirds of the country s land is presently covered by water catchment area. Reclaimed land, such as the Marina Barrage, is more recently being used to create reservoirs. This model assumed that future reservoirs would be completed on reclaimed land. Thus, the cost (CNN World, 2011) and capacity of the Marina Barrage project were used. The operating cost for reservoirs was estimated considering the relative costs of NEWater plants and desalination plants. NEWater plant The fixed costs were based on the recently built Ulu Pandan NEWater plant, which consisted of a $200 million contract (Keppel Corporation, 2004) for a plant with a daily capacity of 148,000 cubic meters (Keppel Corporation Infrastructure. n.d.). The operating cost was retrieved from a 2004 press release. Desalination plant The fixed cost was based on the Tuas desalination plant, which consisted of a $200 million contract for a plant with a daily capacity of 110,000 cubic meters (Water Technology Net

6 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 6 of 37 Tuas, 2006). The operating cost was retrieved from a 2008 report (Water Technology Net Singapore s Self Sufficiency, 2008). The model assumed that the operating costs included labor, maintenance, and the cost of electricity. This study also considered the operating costs for infrastructure currently in place. Model setup Timespan The timespan was 50 years, starting with the first build decision in 2015 and continuing in five-year time increments until Discount rate For all scenarios, the discount rate was assumed to be 10%. Current facility assumptions This model assumed that the current facilities would continue to fulfill the current demand of 1.7 MCM (million cubic meters of water) per day. This model also assumed that none of the current facilities would shut down in the future. In addition, the operating costs, although they evolve over time, are the same for each respective facility, with no regard to its age. Considering the current daily demand of 1.7 MCM of water, the current daily demand fulfilled by imported water, reservoirs, NEWater plants, and desalination plants was considered: Tap Current load Currently fulfilled (cubic meters of water per day) Imported water 40% 680,000 Reservoir 20% 510,000 NEWater plant 30% 340,000 Desalination plant 10% 170,000 Table 2: Current fulfillment rates

7 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 7 of 37 Imported water assumptions This study assumed a 0% draw of imported water by In the model, this decrease occurred linearly. Small versus large facilities To examine the value of building different facility sizes, this model assumed that the building strategy would utilize either small or large facilities with certain daily capacities. The small capacities were modeled from the current daily capacities, while the large capacities were double the current daily capacities. All capacities are in units of cubic meters of water per day. Small capacity (cubic meters) Large capacity (cubic meters) Reservoir 170, ,000 NEWater plant 148, ,000 Desalination plant 110, ,000 Table 3: Small and large capacities Economies of scale and learning rates Economies of scale addressed the concept that it is cheaper per unit to build facilities in larger sizes. Each facility type assumed a specific economies of scale factor, A. These values were based on the realistic range of 0.6 to 0.7 (de Neufville and Scholtes, 2011, p. 211) and the relative nascent nature of each technology. As a budding technology with the potential to become more efficient through further research and development, desalination was assigned the largest economies of scale factor. Economies of scale are defined by the equation (de Neufville and Scholtes, 2011, p. 210):!"#$%&#!"#$!"!"#"!$%& =!!"#"$%&'!!! The coefficient K was determined for each facility from historical data (de Neufville and Scholtes, 2011, p. 211):! =!"#$%&#!"#$!"!"#"!$%& (!"#"$%&')!!! Learning rates referred to the common observation that as you produce more items, the cost to build subsequent items becomes cheaper. In addition to becoming more efficient over time, a company may alter design elements, introduce new technologies, or find ways to eliminate waste (de Neufville and Scholtes, 2011, p. 213). These cost reductions counteract the advantages of economies of scale.

8 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 8 of 37 Learning rates are often modeled by reducing costs by a certain percentage after total capacity doubles. The slope B of the learning rate is defined by the following equation (de Neufville and Scholtes, 2011, p. 214): log (100!"#$"%&!!"#$"%&)! = log (2) The slope was then used in the functional form (de Neufville and Scholtes, 2011, p. 213):!! =!!!! where!! was the production cost per cubic meter of the ith unit,!! was the fixed cost per cubic meter, and B was the slope of the learning rate. The learning rate considered the nascent nature of each technology, similar to the judgments made for the economies of scale factors. The learning rate for desalination plants assumed that desalination will get much cheaper with further development and refinement. Tap Economies of Coefficient k Learning Slope B scale factor rate Reservoir ,376 5% NEWater plant ,169 10% Desalination plant ,164 20% Table 4: Economies of scale, learning rates, and associated coefficients The economies of scale factors and learning rates were applied to the fixed costs of each facility. Economies of scale were considered first, then the learning rates were applied. Operating costs considering desalination and electricity prices Operating costs over time were affected by an assumed percent increase in the cost of electricity. Each technology s operating cost had a certain percentage due to electricity costs: Tap Percentage of operating cost due to electricity Imported water 60% Reservoir 60% NEWater plant 70% Desalination plant 80% Table 5: Percentage of operating cost due to electricity The model assumed that there would be a 60% increase in the cost of electricity by 2060, and this increase was considered to happen in a linear fashion.

9 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 9 of 37 Operating cost of desalinated water In addition, the operating cost of desalinated water was assumed to be $0.40 in 2060, which was a $0.08 decrease from the current $0.48 price. This change was assumed to occur linearly. Operating costs for remaining three technologies The operating costs for imported water, reservoirs, and NEWater plants remained the same.

10 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 10 of Deterministic Scenarios The first two deterministic scenarios used assumptions on the daily water demand and spread out the required additional demand according to goal loads. Water demand One assumed prediction was that the daily water demand would double from now until 2060, from 1.7 MCM to 3.4 MCM. This increase was projected linearly throughout the timespan. Facility choice driver The deterministic scenarios assumed water fulfillment goals that influence construction decisions of additional reservoirs, NEWater plants, and desalination plants. The current loads and 2060 goal loads were as follows: Tap Current load 2060 goal load Imported water 40% 0% Reservoir 20% 20% NEWater plant 30% 50% Desalination plant 10% 30% Table 6: Current and 2060 "goal loads" The 2060 daily fulfillment requirement of 2.38 MCM was spread among the 2060 goal loads. The number of facilities requested was calculated by dividing the additional capacity required by the small or large capacity sizes for each technology. The Excel function, ROUNDUP was used to round each facility count value to the next highest integer. Tap 2060 goal load Additional capacity required (MCM) Facilities requested for build small Imported water 0% Reservoir 20% NEWater plant 50% Desalination 30% plant Total 100% Table 7: Deterministic scenarios facility choices Facilities requested for build large

11 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 11 of 37 The user determined the build schedule, using personal judgment to spread out the timing and type of facility built. Construction began in 2015 and continued in 5-year increments until Up to three facilities were built in each time period. The build schedule also confirmed that the total water demand fulfilled was equal to or higher than the required water demand for that time increment. The build schedules for the deterministic scenarios were as follows: Year Facility 1 Facility Desalination Reservoir 2020 NEWater NEWater 2025 NEWater 2030 NEWater Desalination 2035 Reservoir Desalination 2040 NEWater Desalination 2045 NEWater Desalination 2050 NEWater Reservoir 2055 Desalination NEWater 2060 NEWater Desalination Table 8: Build schedule for "deterministic, build small" Year Facility Reservoir 2020 NEWater 2025 Desalination 2030 Reservoir 2035 NEWater 2040 Desalination 2045 NEWater 2050 Desalination 2055 NEWater 2060 Desalination Table 9: Build schedule for "deterministic, build large" These build schedules were later tested under uncertainty in Monte Carlo simulations.

12 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 12 of Flexible Scenarios The remaining scenarios, consisting of six flexible scenarios, considered further uncertainty in population growth and future water demand. The flexible scenarios allowed one to study the effects of various decision rules. Population growth The annual population growth was estimated to be 2%. This estimate was based off of UN data: Year Range Average annual population growth rate % % % Table 10: Average annual population growth rate (UN Data, 2011) Water demand The current daily water demand is 1.7 MCM. This includes domestic, industrial, and commercial uses for water. An assumed prediction is that by 2030, the domestic per capita water usage can be reduced from cubic meters to 0.14 cubic meters per day. This 0.14 value was used for the 2060 projection. Considering the current population (5.077 million people) and current water demand (1.7 million cubic meters per day), the current total per capita water usage was calculated: 1.7!"#"$%!"#$!!"#"$%!"#!"# 5.077!"##"$%!"#!$" = 0.33!"#$!!"#"$%!"#!"#$%",!"#!"# The current domestic per capita water usage rate is cubic meters per day. The domestic percentage of the total consumption was calculated: 0.156!"#$!!"#"$%!"#!"# 0.33!"#$!!"#"$%!"#!"# = 47% Assuming that the domestic percentage remained constant at 47% through the timespan, the total 2060 per capita water usage was calculated: 47% 0.14!"#$!!"#"$%!"#!"# = 100%! = 0.30!"#$!!"#"$%!"#!"#!!"#$!!"#"$%!"#!"# A safety factor of 20% was added to this value: 0.30!"#$!!"#"$%!"#!"! 1.20 = 0.36!"#$!!"#"$%!"#!"#

13 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 13 of 37 Flexible scenarios 1 and 2: assumed goals Two scenarios tested both small and large facility sizes. They relied on the same assumed projections and goals from the deterministic scenarios, however the build schedule was automated in Excel. Facility choice driver Similar to the deterministic scenarios, the number of facilities to build through the entire timespan was determined by the final 2060 additional capacity required. This capacity load was spread among the technologies according to the 2060 goal loads. For the assumed average annual population growth rate of 2%, the final fulfillment requirement was 3.95 MCM. The final daily fulfillment requirement of 3.95 MCM was spread among the 2060 goal loads. Tap 2060 goal load Additional capacity required (MCM) Facilities requested for build small Imported water 0% Reservoir 20% NEWater plant 50% Desalination 30% plant Total 100% Table 11: Flexible (assumed goals) scenarios facility choices Facilities requested for build large The build schedules were automated within Excel to spread out the types of facilities built over the timespan. Flexible scenarios 3 and 4: cheapest The next two flexible scenarios did not rely on the assumed projections and goals. Facility choice driver The additional amount of water demand that needed to be fulfilled at each time step was addressed from the cheapest to the most expensive: building two additional reservoirs, then one NEWater plant, then one desalination plant. Flexible scenarios 5 and 6: cheapest with a forced desalination rule Facility choice driver The final two flexible scenarios followed the same facility choice driver from the previous two scenarios.

14 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 14 of 37 Forced desalination rule In addition, these scenarios forced at least one desalination plant to be built every ten years. If the facility choice driver called for a desalination plant to be built during the same timespan of a forced desalination plant, then two desalination plants were built. By investigating the effect of forcing desalination plants to be built, one can consider the long-term gains by investing in desalination technology. Though desalination is currently the most expensive option, choosing to build these plants can drive future fixed and operating costs down. Furthermore, it allows the learning rate of desalination to play a larger role.

15 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 15 of Single Iteration Model Results Six scenarios were run in the single-iteration Excel-based models. A summary table of each scenario s setup as well as the output the present cost to fulfill the demand is as follows. All scenarios were also compatible with Monte Carlo simulation, which we investigate later in this paper. Type Driver Capacity size Present cost to fulfill demand (in billions of dollars) Deterministic Assumed goals Small $5.14 Deterministic Assumed goals Large $4.74 Flexible Assumed goals Small $6.07 Flexible Assumed goals Large $5.57 Flexible Cheapest Small $5.41 Flexible Cheapest Large $5.23 Flexible Flexible Cheapest with forced desalination Small $5.59 Cheapest with forced Large $5.37 desalination Table 12: Scenario Development Table The present cost to fulfill demand was found to be consistently cheaper when building large facilities versus small facilities.

16 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 16 of Sensitivity Analysis The sensitivity analysis inspected the effects of how changing singular inputs can change the present value to fulfill future water demand. This analysis identified several key inputs. However, one should note that two or more variables might have simultaneous effects that are not clear in a traditional sensitivity analysis, such as this (de Neufville and Scholtes, 2011, p. 225). The sensitivity analysis focused on two flexible cases, the build small and build large with the assumed goals driver. Low and high values were assigned to four inputs. The baseline values used in the previous sections, the low extreme values, and the high extreme values are below: Average annual population growth rate Per capita water usage (in cubic meters per day) Operating cost for desalination plants Baseline value Low value High value 2% 0.5% 3.5% $0.40 $0.20 $0.48 Increase in electricity costs 60% 20% 100% Table 13: Sensitivity analysis on low and high extreme values The table below highlights the flexible scenarios outputs from testing extreme low and high values. All values are in billions of dollars. Flexible, build small, assumed goals Baseline value Average annual population growth rate Per capita water usage (in cubic meters per day) Operating cost for desalination plants Increase in electricity costs Low extreme High extreme Flexible, build large, assumed goals Low extreme Table 14: Sensitivity analysis results High extreme

17 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 17 of 37 The following tornado diagrams compared the relative effects of the extreme low and high inputs on the two flexible scenarios: <8/.0=,;%-614%>6.%?/1032,0=6,% C2;B% 7/.%-08240%904/.%:10;/% D69% +,-./01/%2,%/3/ %-6141%!"#$%%!"#&%%!"#'%%!&#(%%!&#)%%!&#$%%!&#&%%!&#'%%!*#(%%!"#$#%&'()$&'*%'+*,,*)%$')-'.),,/"$' Figure 1: Tornado diagram for flexible, build small, assumed goals 0,#1*+,#2'+3*,.',/"4#2'/$$35#.'4)/,$' A.-%,/:13/%</3.-%90/8.% B18=% 67.-/8.%/++9/2%:5:92/;5+%8-5<3=% C5<% *+,-./0.%1+%.2.,3-1,134%,5030%!"#$%%!&#'%%!&#(%%!&#"%%!&#)%%!&#$%%!)#'%%!)#(%%!"#$#%&'()$&'*%'+*,,*)%$')-'.),,/"$' Figure 2: Tornado diagram for flexible, build large, assumed goals

18 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 18 of 37 The general shapes of the tornado diagrams for both scenarios were quite different. For the build small scenario, the range of outputs was much more dramatic compared to the build large scenario. This suggested that a decision scenario with larger facilities is less sensitive to uncertainty than one with smaller facilities. For all scenarios, the three most impactful inputs were per capita water usage, operating cost for desalination, and average annual population growth. These were tested in the following simulation section.

19 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 19 of Simulation Simulation was run on the eight scenarios (two deterministic and six flexible) for average annual population growth rate, per capita water usage, and the operating cost of desalination plants. The Monte Carlo simulations were run with 5000 iterations each. Prior to running the simulation, it is predicted that the deterministic scenario outputs would skew toward higher values. Since the deterministic cases used the build schedules from Section 3, it locks the model into using the schedules as a minimum. The scenario was forced to overbuild even when there was a water demand lower than the build schedules requested for. If there was a high demand, then the model must build extra facilities. The average annual population growth rate was modeled as a normal distribution, centered on 2% and approaching close to 0% probability for the low extreme (0.5%) and high extreme (3.5%) values. Figure 3: Probability distribution for average annual population growth rate

20 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 20 of 37 The 2060 per capita water usage was modeled as a normal distribution, centered on 0.30 cubic meters per day and approaching close to 0% probability for the low extreme (0.20) and high extreme (0.40) values. Figure 4: Probability distribution for 2060 per capita water usage

21 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 21 of 37 The operating cost of desalination plants was modeled as a Weibull distribution, with the extreme low value at approximately $0.10 and extremely high value at approximately $0.50. The distribution was skewed to the left, with the bulk of the values between $0.20 and $0.40. The skew was based on the fact that there are increasingly strong efforts towards the research and development of desalination technology. Figure 5: Probability distribution for operating cost of desalination plants

22 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 22 of Target Curves and Comparisons Figure 6: Target Curves for Present Costs to Fulfill Demand The table below lists the scenarios from the Target Curve in the same order as the legend. Name Type Driver Capacity size Det small Deterministic Assumed goals Small Det large Deterministic Assumed goals Large Flex goals small Flexible Assumed goals Small Flex goals large Flexible Assumed goals Large Flex small Flexible Cheapest Small Flex large Flexible Cheapest Large Flex small desal Flexible Cheapest with forced desalination Small Flex large desal Flexible Cheapest with forced desalination Large Table 6: Scenario Development Legend for Target Curve Analysis

23 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 23 of 37 None of the scenarios showed stochastic dominance, however the most promising may be the flex large (flexible, build large, cheapest) scenario. 70% of the time, it resulted in the lowest cost to fulfill the water demand. Det large (deterministic, build large, assumed goals) was the best choice for approximately 30% of the time. The next best choices were flex small, flex small desal, and flex goals large. Risk-seeking individuals may value det large because of the possibility of the very low cost. Risk-averse individuals are more likely to value flex large because it is the cheapest option for the majority of the simulated iterations. As mentioned earlier, learning rates favor building many facilities with lower capacities, while economies of scale favor building fewer facilities with high capacities. Due to the lower costs of the build large scenarios, this analysis suggested that the assumed economies of scale factors had a stronger effect on the model output than assumed learning rates. Multiple Criteria Analysis Mean, P5, P95, and cost-effectiveness ratio The mean, P5, and P95 values were calculated from results. See the Appendix for output reports. Another ranking criterion, the cost-effectiveness ratio, was also calculated. The amount of water that needed to be fulfilled by additional facilities was simulated 5000 times. The cost-effectiveness ratio was then calculated for each scenario as follows:!"#$#%&!"#$!"#$!""!#$%&!'!((!"#$% =!"#$%&!"!"#$%!""#"#!"!"#!$##!"#$%! The following is the water fulfillment output from 5000 iterations: Figure 7: Output for 2060 daily water demand

24 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 24 of 37 Figure 8: Target Curves for Cost-Effectiveness Ratios The target curves for the cost-effectiveness ratio provided further insight. The first and second most stochastically dominant scenarios were flex large (flexible, build large, cheapest) and flex large desal (flexible, build large, cheapest with forced desalination rule), respectively. Individuals of all risk and tolerance levels should choose flex large because of its dominance. This further suggested that with the model s assumptions, building larger facilities was consistently more cost-effective than building smaller facilities. The value of flexibility was computed for the six flexible scenarios by calculating the cost differences of the mean values to that of the corresponding deterministic cases. Positive values of flexibility are in green, while negative values are in red. The following table highlights the mean, P5, P95, value of flexibility, and cost-effectiveness ratio for each scenario. The most favorable scenario, flex large, is italicized.

25 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 25 of 37 Name Mean (billions of dollars) P5 (billions of dollars) P95 (billions of dollars) Mean value of flexibility (billions of dollars) Det small $6.23 $5.42 $ $1,697 Det large $5.22 $4.34 $ $1,404 Flex goals small $5.88 $5.30 $6.58 $0.35 $1,612 Flex goals large $5.60 $5.03 $6.24 $0.38 $1,544 Flex small $5.23 $4.85 $5.76 $1.00 $1,300 Flex large $4.95 $4.65 $5.33 $0.27 $724 Flex small desal $5.41 $5.03 $5.92 $0.82 $1,345 Flex large desal $5.10 $4.80 $5.48 $0.12 $746 Table 15: Multiple criteria analysis values Mean costeffectiveness ratio (dollars per cubic meter) The simulation results found positive values of flexibility for all flexible scenarios, except for flex goals large. The greatest value of flexibility was found in flex small, with a mean value of flexibility of $1 billion. The value of flexibility for the suggested scenario flex large, was $0.27 billion.

26 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 26 of Conclusions and Lessons Learned Conclusions This project suggested that substantial system insight could be gained through sensitivity analyses, simulations, and target curve analyses. The project found that the most suitable scenario under the model s assumptions was the flex large (flexible, build large, cheapest), which had the best performance in the cost target curve and cost-effectiveness target curve analyses. The results also suggested that building larger facilities is more cost-effective than building smaller facilities. Though this model makes many simplified assumptions, it provides a broad look into the interactions of the water resources system. Further work can surely be done to refine the input data and to incorporate more model interactions. In addition, one can further investigate costs related to water flows, facility lifetimes, and how operating costs can change over time. One could also look into the effects of different discount rates. This project investigates the optimal development of water infrastructure in the face of uncertainty. This framework is particularly applicable to regions undergoing rapid urbanization, such as cities in China and India. The analysis creates a preliminary framework for decision makers to better understand the impacts of uncertainty on the system, ultimately presenting a valuable approach for long-term resource planning. Model choice I chose to use simulation because it allowed me to incorporate many different inputs and to see the effects of a range of uncertain values. software also allowed me to run multiple iterations in a short amount of time, which provided representative values that would be difficult to retrieve otherwise. Also, using the Excel software makes it possible for those without limited programming or technical experience to understand how the model works. Lessons learned One important lesson was that my approach for the project is very likely to change and I should be prepared for that. In the early stages of this project, my model made decisions in fixed time increments and chose three facilities to fulfill the future water demand. However, since the future water demand rose linearly, this meant that the demand fulfilled at each time increment was very small at the beginning and consequently very large at the end of the timespan. The facility capacities started small and became very large towards the end, which may not be practical from a construction standpoint. By triggering the facility constructions by the rise in water demand and establishing two fixed sizes for each facility, the revised setup produced much more reasonable results. I also learned that in the early stages of a research project, understanding how to build a simulation model and being able to make conclusions is the first priority. Data can always

27 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 27 of 37 be refined later, and if the simulation model is easy to understand and implement, then designers can easily refine the data for their own application. I thought that this was a very good opportunity to delve into my area of research and learn more about the system that I am investigating. The research that I have done for this project has strengthened my understanding of the different interacting components. This was also a helpful exercise to apply the tools and techniques that I learned in class. I feel much more confident using Excel and software to model resource systems.

28 Melanie Wong ESD.71 Application Portfolio, 12/13/11 Page 28 of References CNN World. (2011). How Singapore is making sure it doesn t run out of water. Retrieved from De Neufville, R. and Scholtes, S. (2011). Flexibility in Engineering Design. Cambridge, Massachusetts: MIT Press. Department of Statistics, Singapore. (2010). Statistics. Retrieved from Keppel Corporation. (N.D.). Infrastructure. Retreived from Keppel Corporation. (2004). Keppel Engineering wins contract to build Singapore s largest NEWater plant at Ulu Pandan. Retreived from Tortajada, C. (2006). Water Management in Singapore. Water Resources, 22(2), doi: / UN Data. (2011). Average annual population growth rate. Retrieved from Water Technology Net. (2006). Tuas Seawater Desalination Plant Singapore. Retrieved from Water Technology Net. (2008). Singapore s Self Sufficiency. Retrieved from World Bank, World Development Indicators. Google Public Data Explorer. Retrieved from dim=country:sgp&dl=en&hl=en&q=singapore%27s+current+population Cover photo retrieved from:

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