Multi-Criteria Decision Analysis of Concentrated Solar Power with

Nov 18, 2013 - Sustainability evaluation of Concentrated Solar Power (CSP) projects under Clean Development Mechanism (CDM) by using Multi Criteria ...
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Multi-Criteria Decision Analysis of Concentrated Solar Power with Thermal Energy Storage and Dry Cooling Sharon J. W. Klein* School of Economics, University of Maine, 5782 Winslow Hall, Room 206, Orono, Maine 04469-5782, United States of America S Supporting Information *

ABSTRACT: Decisions about energy backup and cooling options for parabolic trough (PT) concentrated solar power have technical, economic, and environmental implications. Although PT development has increased rapidly in recent years, energy policies do not address backup or cooling option requirements, and very few studies directly compare the diverse implications of these options. This is the first study to compare the annual capacity factor, levelized cost of energy (LCOE), water consumption, land use, and life cycle greenhouse gas (GHG) emissions of PT with different backup options (minimal backup (MB), thermal energy storage (TES), and fossil fuel backup (FF)) and different cooling options (wet (WC) and dry (DC). Multicriteria decision analysis was used with five preference scenarios to identify the highest-scoring energy backup-cooling combination for each preference scenario. MB-WC had the highest score in the Economic and Climate Change-Economy scenarios, while FF-DC and FF-WC had the highest scores in the Equal and Availability scenarios, respectively. TES-DC had the highest score for the Environmental scenario. DC was ranked 1−3 in all preference scenarios. Direct comparisons between GHG emissions and LCOE and between GHG emissions and land use suggest a preference for TES if backup is require for PT plants to compete with baseload generators.

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INTRODUCTION Climate change, energy security, and economic concerns have motivated a recent resurgence in the deployment of concentrated solar power (CSP) plants, primarily in the United States and Spain, but increasingly in other areas of the world as well. Parabolic trough (PT) with fossil fuel-fired energy backup currently dominates the global CSP market, with an installed capacity of 2.7 GW; 24% of world capacity is in the U.S. and 70% in Spain.1 Most of these plants use a fossil fuel-fired heater or boiler (FF backup) to provide energy during cloudy periods and nighttime. Approximately 52% of currently operating PT plants use molten salt thermal energy storage (TES) as an alternative or complement to FF backup.2 Only one plant uses “dry” cooling (DC) instead of conventional “wet” cooling in the power cycle (Hassi-R′mel in Algeria3). TES and FF backup can increase the availability of a PT plant (i.e., the amount of time per year it is operational). Significant reductions in life cycle greenhouse gas emissions and water use also can be achieved by incorporating TES and DC, respectively; yet these technical and environmental benefits come at a cost. Although cost is often the main driver in energy decision-making, if future CSP deployment is to proceed in an environmentally, socially, and economically sustainable way, renewable energy decisionmakers must consider the trade-offs associated with the selection of different backup systems and cooling technologies for PT power plants. This paper examines these trade-offs through a multicriteria decision analysis (MCDA) of the technical, economic, and environmental implications associated © 2013 American Chemical Society

with different energy backup and cooling options for PT power plants. Parabolic trough CSP plants use curved mirrors to concentrate direct normal solar radiation (DNR) onto a receiver tube filled with a heat transfer fluid (HTF), typically synthetic oil called Therminol VP-1 (Figure 1). The DNR heats the HTF to approximately 390 °C, and thermal energy is transferred from oil to water through a heat exchanger to make steam for electricity generation through a conventional Rankine power cycle. The cooled HTF (around 290 °C) returns to the receiver tube in the solar f ield to be reheated. Since DNR is not available during cloudy periods and nighttime, all CSP plants must have an energy backup system. At the most basic level, the backup system may consist of a fossil fuel-fired HTF heater for freeze protection (since the Therminol VP-1 freezes at around 12 °C4) and electricity for plant startup/shutdown and for the operation of nighttime loads (computers, pumps, etc.). For the purposes of the analysis presented in this paper, the term minimal backup (MB) will be used to refer to this basic level of energy backup. A PT plant may also use the HTF heater to maintain the HTF at the power cycle operating temperature during cloudy periods and nighttime for a certain number of hours per day. Received: Revised: Accepted: Published: 13925

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Figure 1. Diagram of a parabolic trough concentrated solar power plant with “wet” cooling.5 Direct normal radiation (DNR) from the sun heats a fluid in the solar field. The heat is transferred to a conventional steam power cycle to generate electricity. When the sun is not shining, any combination of three energy backup systems may be used: molten salt thermal energy storage, fossil fuel-fired heat transfer fluid heater, and fossil fuel-fired boiler. Reprinted with permission from ref 5. Copyright 2013, Elsevier.

by monetized estimates of costs and benefits.9 MCDA has been used in national energy policy development and analysis in the U.K., U.S., and other regions, for electricity expansion and planning and in the evaluation of impacts from new technologies.9 However, only three studies apply it to decisions involving CSP,10−12 and none of these include all energy backup and cooling options listed above. Cavallaro, 2009,10 evaluates 12 CSP alternatives (including PT with MB, but not FF or TES) across technical, economic, and environmental criteria and ranks integrated solar combined cycle as the most preferable option. Cavallaro, 2010,11 investigates the feasibility of a molten salt HTF in a PT solar field based on technical, economic, and environmental criteria. Molten salt TES is ranked higher than MB; however, all TES configurations involve using molten salt directly in the solar field as the HTF, whereas most currently operating plants use synthetic oil as the HTF. Nixon et al., 2010,12 compare a variety of existing CSP collection technologies, including PT with MB, based on technical, economic, and environmental criteria to select the optimum CSP technology for India (with comparisons to Spain, California, and the Sahara Desert). Fresnel lens in combination with compound parabolic concentrators was selected as the optimum choice. Jacobson13 provides rankings for twelve energy options for transportation (including electric vehicles powered PT) across 11 technical and environmental criteria. Rankings, based on a combination of quantitative and qualitative comparisons, and author-selected criteria weights indicate that vehicles powered by wind, PT, geothermal, and tidal energy are preferable to other options. Economic criteria were not included.

This system will be referred to as FF backup. A third option is to use a TES system, which uses a heat exchanger to transfer thermal energy from the HTF to molten salt during periods of high DNR and from salt to HTF during periods of low/no DNR. Nearly all currently operating PT plants use a “wet” cooling system, which consists of a condenser and cooling tower for reducing the temperature of the feedwater at the turbine outlet. A “dry” cooling system can reduce annual water consumption by around 90% by sending the feedwater directly from the turbine outlet to a series of fans (instead of the condenser and cooling towers in the wet system) that cool the steam to condensate before returning it to the power cycle.6 Although many studies have examined individual criteria for PT plants related to technical, economic, and environmental impacts (for a full literature review see Supporting Information Table S1, pp S1−S5), no study has conducted an MCDA to compare different energy backup systems and different cooling systems based on criteria in each of these impact areas. MCDA is a tool that allows decision-makers to incorporate a diverse set of criteria in the evaluation of a set of alternative choices. There are many different approaches to MCDA,7 but almost all involve creating a decision matrix, synthesizing the information in the matrix, and ranking alternatives accordingly. Each approach completes the last two steps in different ways and incorporates an algorithm to select an optimal solution. Some MCDA approaches rank options; some provide a partial ranking; some select a single optimal choice; and some differentiate between acceptable and unacceptable choices.8 MCDA can be an ideal tool for energy policy development because of the ability to identify trade-offs, cobenefits, and solutions to complex policy and planning problemswhile representing attributes by their physical quantities rather than 13926

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Figure 2. MCDA decision matrix and equations. xij refers to the performance of the j-th criterion of the i-th alternative; wj represents the weight of criterion j; n is the number of criteria and m is the number of alternatives;14 vij is the nominal value of the j-th criterion of the i-th alternative in measured criteria units per energy unit (i.e., gCO2eq/kWh for life cycle GHG emissions); vj‑opt is the optimal value for the j-th criterion in the set of m alternatives. For criterion #1, the optimum value is the maximum value (vj‑max). For criterion #2−5, the optimum value is the minimum value (vj‑min).

Table 1. Range of Literature Estimates for Individual Criteria Valuesa capacity factor

water consumption (L/kWh)

LCOE ($/kWh)

GHG emissions (g CO2eq/kWh)

life cycle direct land transformation (m2/GWh)

plant type

low

avg.

high

low

avg.

high

low

avg.

high

low

avg.

high

low

avg.

high

MB, wet MB, dry TES, wet, 3 hb TES, wet, 6 h TES, wet, 12 h TES, dry, 3 h TES, dry, 6 h TES, dry, 12 h FF, wet FF, dry TES and FF, wet TES and FF, dry

25% 26% 32% 38% 54% 35% 38% 53% 34% 32% 36% NS

28% 27% 35% 42% 55% 35% 44% 53% 45% 44% 36% NS

29% 28% 37% 47% 55% 35% 49% 53% 55% 52% 36% NS

$0.10 $0.14 $0.09 $0.09 $0.10 $0.20 $0.09 $0.24 $0.10 $0.18 $0.17 NS

$0.14 $0.15 $0.14 $0.15 $0.17 $0.20 $0.14 $0.24 $0.16 $0.18 $0.17 NS

$0.17 $0.17 $0.18 $0.20 $0.23 $0.20 $0.21 $0.24 $0.18 $0.19 $0.17 NS

3.3 0.20 4.7 3.7 3.1 1.9 0.20 1.7 3.1 1.5 NSc NS

4.4 0.81 5.8 5.0 5.1 1.9 0.73 1.7 6.7 1.7 NS NS

6.9 1.9 7.0 7.0 7.0 1.9 1.8 1.7 7.1 2.0 NS NS

35 37 73 26 64 73 28 67 127 205 NS NS

35 37 73 49 64 73 52 67 241 274 NS NS

35 37 73 72 64 73 75 67 317 332 NS NS

250 250 240 250 269 240 250 260 200 230 NS NS

436 250 240 286 270 240 281 260 233 234 NS NS

625 250 240 322 270 240 312 260 240 250 NS NS

a

The average values (avg.) were calculated as the average of all data points listed in Supporting Information Table S1, pp S1−S5, for each Plant type and Criteria combination. They do not necessarily equate to the average of the low and high values presented in this table. bh = hour of storage. cNS = not specified.

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METHODS

represent a decision-maker’s relative preference for each criterion (Figure 2). The average annual capacity factor (CF) is a common metric used to compare the potential availability of power plants to supply baseload electricity generation. Most baseload generators in the U.S. have a CF between 75% and 90%.15 CF is calculated as the ratio of annual electricity generation to maximum potential electricity generation

The MCDA presented here uses the weighted sum method, which is the most commonly used approach in MCDA applied to sustainable energy systems.14 This method involves creating a decision matrix with numeric scores for a set of alternatives across a set of criteria and then applying preference weights that 13927

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Table 2. Criteria Input Values to MCDAa capacity factor

5

LCOE ($/kWh)

2,5

life cycle GHG emissions (gCO2eq/kWh)5

on-site water consumption (L/kWh)5

life cycle on-site direct land use (m2/MWh)5

plant configuration

backup system capacity (h)

WC

DC

WC

DC

WC

DC

WC

DC

WC

DC

MB TES

0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

29% 34% 36% 37% 42% 45% 45% 49% 51% 52% 53% 53% 55% 34% 36% 37% 42% 44% 45% 48% 50% 52% 52% 53% 55%

27% 32% 34% 35% 40% 42% 43% 46% 48% 50% 50% 50% 53% 32% 34% 35% 40% 42% 43% 46% 48% 50% 50% 50% 52%

$0.16 $0.17 $0.18 $0.19 $0.19 $0.19 $0.20 $0.20 $0.21 $0.21 $0.21 $0.22 $0.23 $0.18 $0.18 $0.18 $0.17 $0.18 $0.17 $0.18 $0.17 $0.17 $0.18 $0.18 $0.17

$0.17 $0.18 $0.19 $0.20 $0.20 $0.20 $0.21 $0.21 $0.22 $0.22 $0.23 $0.23 $0.24 $0.19 $0.19 $0.19 $0.18 $0.19 $0.18 $0.18 $0.18 $0.18 $0.18 $0.18 $0.18

35 60 67 73 69 69 72 69 66 65 66 66 64 127 148 162 219 230 235 273 284 301 297 299 317

37 64 72 77 73 73 75 73 70 68 69 70 67 267 218 205 245 242 248 287 298 315 312 314 332

6.85 6.93 6.95 6.97 6.93 6.94 6.96 6.96 6.97 6.97 6.98 7.00 7.02 7.07 7.06 7.06 6.96 6.97 6.91 6.93 6.90 6.88 6.88 6.88 6.85

1.93 1.90 1.91 1.90 1.80 1.77 1.77 1.74 1.72 1.70 1.72 1.74 1.73 2.02 1.98 1.94 1.78 1.74 1.67 1.65 1.59 1.57 1.57 1.57 1.51

0.25 0.24 0.24 0.24 0.25 0.25 0.25 0.26 0.26 0.26 0.26 0.26 0.27 0.24 0.24 0.24 0.23 0.24 0.23 0.23 0.23 0.23 0.23 0.23 0.23

0.25 0.25 0.25 0.24 0.25 0.25 0.25 0.26 0.26 0.26 0.26 0.26 0.26 0.25 0.24 0.24 0.23 0.24 0.23 0.23 0.23 0.23 0.23 0.23 0.23

FF

a

Note: LCOE values are pretax, unsubsidized values derived from the engineering-economic model17 with updated auxiliary electricity and land use values as described in Supporting Information, p S9.

Table S1, pp S1−S5, for a more detailed list of criteria values by reference). However, the estimates in Table 1 are not directly comparable within or across criteria because each study uses different approaches, time periods, power plant characteristics, weather parameters, and/or financial assumptions. Wagner and Rubin, 2014,17 and Klein and Rubin, 2013,5 are the only studies that estimate all five criteria for a range of energy backup options and for wet and dry cooling. These two studies present results from an integrated assessment model (IAM), which incorporates consistent assumptions (Supporting Information Table S2, p S6) regarding plant size (100 MWnet), operation procedures, DNR (8 kWh/(m2·day) on average), technology and financial specifications (8% discount rate; 30-yr lifetime) in the calculation of technical, economic and environmental criteria values for each PT plant configuration. For this reason, the criteria values presented in Wagner and Rubin, 2014,17 and Klein and Rubin, 2013,5 were used as inputs to the MCDA calculation (Table 2), with updates to the auxiliary electricity and land use calculations as described in Supporting Information, p S9. The IAM is a complex model that incorporates hundreds of variables and equations. These variables and equations are described in detail in Wagner and Rubin, 201417 and Klein and Rubin, 2013,5 and it is not practical to reproduce all of them here. However, the main calculations that led to the technical and economic MCDA criteria input values are presented in Supporting Information, p S7. The specific technical and economic input values from the IAM that were used in the

calculated as the product of the net rated power capacity and the number of hours in a year.16 Levelized cost of energy (LCOE) is a common metric used to compare different electricity generation technologiesit is calculated as the ratio of the sum of the amortized capital and annual operation/ maintenance costs to the annual electricity generation.17 The amortization is based on several financial parameters including interest rate, debt, and equity fractions, investment lifetime, and cost of capital. The specific financial assumptions for the LCOE values presented in this analysis are described in the context of eqs 1−5 presented in Supporting Information, p. S7. Five sets of preference weights were selected to represent a variety of different stakeholders. Twenty-two studies5,17−37 (Supporting Information Table S1, pp S1−S5) have estimated at least two of the criteria used in this MCDA study, and 14 of these studies5,17−23,25,28,29,31−33 are based on U.S. locations (mostly California). Nine of the twenty two studies5,17−20,22,23,28,29 compare at least two energy backup options, and 10 studies5,17,20,21,24,25,28,31−33 examine differences in wet and dry cooling criteria. Only four studies compare multiple criteria for two or more energy backup options and for wet and dry cooling.5,17,20,28 Individual criterion estimates from the literature range 25−55% for capacity factor, $0.09−0.24 for LCOE, 0.2−7.0 L/kWh for water consumption, 26−332 g CO2eq/kWh for GHG emissions, and 200−625 m2/ GWh for life cycle direct land transformation. Table 1 presents the low, average, high values for each criterion for each plant type as reported in the literature (see Supporting Information 13928

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Figure 3. MCDA Results. Five hypothetical preference scenarios were modeled (equal, environmental, climate change-economy, availability, and economic for the six parabolic trough CSP energy backup-cooling system combinations shown in the legend.

MCDA are presented in Supporting Information Table S3, p S8. The economic portion of the IAM calculates the total capital cost of the plant and the annual operation and maintenance (O&M) costs using a slightly adapted version of the National Renewable Energy Laboratory’s Solar Advisor Model’s cost model.38 Taxes and subsidies were not included in the LCOE calculation as these policies are subject to frequent and unexpected changes. The IAM does not include transmission and distribution losses because it was designed to be a plant-level model. The following changes were made to the engineering− economic portion of the IAM in order to calculate the LCOE of dry-cooled configurations: 1. The unit cost of the dry cooling system was obtained by multiplying the unit cost of the wet cooling system ($94/ kWnet) from Wagner and Rubin, 2014,17 by two, based on a similar trend calculated in the cost estimation for dry cooling for the previously proposed Beacon CSP project in California.25 2. The unit cost of the power block was adjusted by subtracting the cost of the components associated with the wet cooling system (blowdown, cooling, condensate, auxiliary cooling water, and water treatment systems) and adding the cost of the dry cooling system, resulting in a power cycle unit cost of $1130/kWnet for a plant with a dry cooling system, compared to $1035/kWnet for the power cycle of a plant with a wet cooling system (based on updated calculations in the engineering-economic model17). 3. The service contract O&M costs related to water treatment were adjusted to be 40% cheaper than the treatment costs for wet cooling,21 and the O&M costs for

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utilities were adjusted to reflect the reduced annual water purchase with the dry cooling system.5 4. Land requirements for the plants with dry cooling were calculated based on the equations presented in Klein and Rubin, 2013,5 and used in the equations from Wagner and Rubin, 2014,17 to calculate land cost.

RESULTS AND DISCUSSION Although it is not possible to characterize actual stakeholder preferences related to the MCDA conducted in this study without formal preference elicitation procedures, the equal preference weighting method could serve as a proxy for aggregated “society” preferencesin fact, this weighting method has been applied to many decision-making problems due to evidence that it produces nearly as good results as other weighting methods.14 In the equal weighting scenario (Figure 3), the MCDA results show FF-DC at 12 h of backup capacity to be the highest ranked option overall (where an “hour” of backup capacity refers to the amount of time the power cycle can operate using solely energy from the backup systemwith no solar input). Three main factors contribute to this result: (1) high backup capacities are valued in this scenario for the higher capacity factor they achieve; (2) FF is preferred to TES because of lower LCOE and land use values; and (3) DC is preferred to wet cooling due to the preference for reduced water consumption. FF and FF-DC scores are lower in the environmental weighting scenario than in the equal weighting scenario, resulting in MB-DC and 3 h TES-DC as the highest scoring options. This is due to the fact that this scenario values GHG emissions, land use, and water consumption equally and includes very little relative preference for cost savings. Similar to the equal weighting scenario, the environmental weighting 13929

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scenario shows a clear (and even more pronounced) preference for dry cooling over wet cooling. MB-WC has the highest score overall in the economic weighting scenario because MB-WC has the lowest LCOE of all of the plant configurations due to the fact that it has the lowest capital cost. No other plant configuration is able to generate enough additional electricity to compensate sufficiently for the increase in capital cost compared to MB-WC. It is not surprising that MB-WC scores highest in the economic scenario since most PT plants in the U.S. are currently being built in this configuration, and cost is probably the primary driver for energy backup and cooling technology decision-making under current policies. Scores decrease with increasing backup capacity for TES and TES-DC because the marginal increase in capital cost for each incremental TES capacity is too large to be compensated by the marginal increase in electricity generation, so the LCOE increases with increasing TES capacity. Scores generally increase with increasing backup capacity for FF and FF-DC because the marginal increase in electricity generation at each higher capacity is achieved at a relatively low marginal increase in capital cost (compared to TES), so LCOE stays relatively the same and even decreases in some cases with increasing capacity. Wet cooling is clearly preferred over dry cooling in this weighting scenario because dry cooling incurs an efficiency penalty, which reduces the annual electricity generation, while at the same time incurring a higher capital cost than WC, thereby increasing the LCOE. In the scenario in which climate change mitigation and the economy are the main priorities, there is a clear rank-order preference from most to least preferred at each backup system capacity 1−12: TES, TES-DC, FF, FF-DC, with scores decreasing with increasing backup system capacity. This is because the 5−20% decrease in LCOE obtained by using FF backup at each capacity is relatively small compared to the 2- to 4-fold increase in life cycle GHG emissions incurred by FF backup compared to TES. MB and MB-DC have the highest scores of all other backup system capacities, and wet cooling is slightly preferred to dry cooling due to its lower cost and higher efficiency (lower GHG emissions). The picture is quite different when plant availability is the main priority: MB-DC has the lowest score, and scores increase with increasing backup capacity. This scenario reflects one of the main attractions of CSP versus other renewable technologiesincreased TES capacity allows for more flexibility and security in meeting peak and variable loads without decreasing the “renewable” nature of the power plant. Under this preference scenario, energy backup is a priority in order to increase the capacity factor. TES-WC and FF-WC at the same capacity have similar scores because the IAM was designed for these plant configurations to have the same capacity factor. For the same reason, TES-DC and FF-DC have similar scores at each capacity. Wet cooling is preferable to dry cooling at all backup capacities because of the efficiency penalty associated with dry cooling, which results in a lower capacity factor. MCDA results vary significantly based on selected preferences and backup capacity. Table 3 summarizes the overall rank order of the alternatives for each preference weight scenario. Dry cooling is ranked 1−3 in all preference weighting scenarios. Although DC only increases LCOE by 5−7% while decreasing water use by 72−78% at each backup system capacity (Table 2), very few CSP plants currently operating or under construction use dry cooling.2 It appears that under current policies, the relatively small economic disincentive is

Table 3. Comparison of MCDA Results (Rank 1 = highest score) a

rank

equal

environmental

1 2 3 4 5 6

FF-DC MB-DC TES-DC FF-WC MB-WC TES-WC

TES-DC MB-DC FF-DC TES-WC MB-WC FF-WC

a

a

economic

a

MB-WC FF-WC MB-DC TES-WC FF-DC TES-DC

climate changeeconomy

availabilitya

MB-WC MB-DC TES-WC TES-DC FF-WC FF-DC

FF-WC TES-WC FF-DC TES-DC MB-WC MB-DC

Rank order varies with backup system capacity.

more important than the relatively large water savings incentive in current CSP cooling option decision-making. This disconnect between the overall benefit of dry cooling and the current actions of installers suggests that more research should be conducted on how well installers and other decision-makers understand/value these benefits versus costs of different cooling options and whether there may be some value in future incentives or regulations to incorporate dry or hybrid (mix of dry and wet) cooling in future PT plants. The climate change-economy weighting scenario clearly shows MB to be the preferred option over TES and FF. Directly comparing life cycle GHG emissions and LCOE shows a different picture (Figure 4a). The ratio of life cycle GHG emissions to LCOE is lowest at MB, but only by 30% compared to 12 h TES. A 12 h TES plant could reduce life cycle GHG emissions per dollar spent by 12−23% compared to 1−7 h TES, while providing a capacity factor greater than 50%. For comparison, the ratio of life cycle GHG emissions to LCOE for pulverized coal (PC), integrated gasification combined cycle (IGCC), natural gas combined cycle (NGCC), PC with carbon capture and storage (CCS), IGCC with CCS, and NGCC with CCS are 31, 19, 12, 3, 2, and 2 kgCO2eq/$, respectively (Supporting Information Table S4, p S10). This metric is useful for decision-makers that are concerned primarily with GHG emissions reduction versus cost and will have more importance if there is a policy in place that puts a price on GHG emissions. MB also has the lowest ratio of life cycle GHG emissions to land use (Figure 4b). This comparison is especially important because of recent pushback from environmental groups in constructing PT plants in desert areas due to the destruction of habitat.39 There is a trade-off between reducing GHG emissions and disturbing local ecosystems that must be considered as part of the overall environmental impact of energy options, especially given that increased GHG emissions may lead to even greater ecosystem disturbance through climate change.40 If backup is required to compete with baseload generators, increasing TES capacity beyond 3 h TES reduces life cycle GHG emissions per unit land use by 5−17%, and TES results in 2−5 times less GHG emissions per unit land use than FF at each backup capacity. These results are based on the IAM, which selected land use for MB and TES to minimize the LCOE, and for FF backup to match TES electricity generation. Different objectives would result in different ratios of GHG emissions to land use. For comparison, PC and NGCC plants emit approximately 2900 and 1300 kgCO2eq/m2 of direct land transformation (Supporting Information Table S4, p S10). In MCDA, the specific preferences of the decision-maker are highly influential on the results. While quantifying the specific 13930

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Figure 4. Two-criteria comparisons. (a, left) Ratio of life cycle greenhouse gas emissions to levelized cost of energy. (b, right) Ratio of life cycle greenhouse gas emissions to land use.

option, despite the location of CSP plants in desert areas. Future legislation should be considered to encourage and/or mandate dry cooling in PT plants, especially since the cost penalty is only a 5−7% reduction in LCOE, while the benefit is a 72−78% reduction in annual on-site (desert) water use. Energy options are often compared based on one criteria at a time and are often selected based on cost alone, yet there are a wide variety of impacts associated with energy use and generation that affect whether certain technologies are adopted as mainstream. Future energy analysis should focus on including multiple criteria in the assessment of different energy options. This study has presented an example of using MCDA to choose between CSP plants with different backup and cooling options. The same type of analysis could be extended to decisions between renewables and conventional electricity options, transportation fuels, energy policies, and a wide range of other energy decisions.

preferences of renewable energy policy-makers is beyond the scope of this analysis, the scenarios examined in this study give a sense of the range of variation in MCDA results based on differing preference weights. Future studies should elicit actual stakeholder preferences and should compare a wider range of CSP and other energy alternatives. The results of this analysis are also sensitive to criteria values used in the MCDA. This issue is examined through an uncertainty analysis for the equal weighting scenario (Supporting Information Table S5 and Figure S1, pp S10−S13). Policy Implications. As mentioned previously, most currently operating PT plants are located in Spain and the southwestern U.S. These countries have different financial incentives to encourage CSP development, which have resulted in different development trends. In Spain, a production-based feed-in-tariff (FIT) provides payment based on each unit of energy generated by the power plant and limits the size of the power plant to 50 MW with only 12−15% of annual electricity generation allowed to be generated via a FF backup system.41 With this incentive in place, Spain has become a world leader in PT with TES possibly because with the combination of the FIT and cap on plant size and FF backup, there is an incentive to generate as much electricity as possible (i.e., increase the capacity factor) with the maximum allowable plant size. The primary U.S. incentive for CSP is the federal investment tax credit, which is currently a 30% deduction on the total capital cost of all power plant components related to solar energy (i.e., FF backup is not included). However, only one PT plant (Solana) out of eight (SEGS I-II; SEGS III-VII; SEGS VIII-IX; Nevada Solar One; Martin, Solana; Mojave; Genesis) currently operating or under construction in the U.S. includes TES, while the majority of PT plants in Spain include TES.2 In addition, 39% of new (under construction or development) U.S. PT projects include TES plans, compared with 73% of new projects in Spain.2 Given the significant GHG emissions reductions that can be achieved with TES versus FF backup, future legislation should consider options for encouraging or mandating TES over FF backup in cases where GHG emissions reduction is a policy goal and backup is necessary or desired to provide more flexibility in meeting variable demand. Dry cooling was clearly preferred to wet cooling in all energy backup configurations in two of the five weighting scenarios and ranked second or third in the other three scenarios, yet neither national policy includes incentives for this cooling

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ASSOCIATED CONTENT

S Supporting Information *

Table S1: Literature Review of Parabolic Trough Technical, Economic, and Environmental Impacts; pp S1−S5. Table S2: Summary of Power Plant Characteristics; p. S6. Table S3: Updated Technical and Economic Criteria Input Values from the IAM; p. S8. Table S4: Life Cycle GHG Emissions, Land Use, LCOE, and Ratios for Fossil Fuel Generation Options; p. S10. Table S5: Criteria Value Ranges Used in Uncertainty Analysis; p. S11. Figure S1: Results of Uncertainty Analysis; p. S13. Pages S7−S9: Summary of IAM Equations and Results. Pages S10−S13: Uncertainty Analysis. Pages S14−S18: References for Associated Content. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 207-581-3174. Fax: 207-581-4278. E-mail: sharon. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Thank you to Drs. Edward Rubin, Jay Apt, and H. Scott Matthews (Carnegie Mellon University), Dr. Aimee Curtright (RAND Corporation), and Dr. Caroline Noblet (University of 13931

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Maine) for providing guidance at various stages of the development of this analysis.

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ABBREVIATIONS CCS carbon capture and storage CF capacity factor CSP concentrated solar power DC dry cooling DNR direct normal radiation FF fossil fuel FIT feed-in-tariff GHG greenhouse gas HTF heat transfer fluid IAM integrated assessment model IGCC integrated gasification combined cycle LCOE levelized cost of energy MB minimal backup MCDA multicriteria decision analysis MWhth megawatt-hour of thermal energy NGCC natural gas combined cycle O&M operation and maintenance PC pulverized coal PT parabolic trough TES thermal energy storage WC wet cooling

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