Long-Term Modeling of Solar Energy: Analysis of Concentrating Solar Power (CSP) and PV Technologies

Transcription

1 PNNL Long-Term Modelng of Solar Energy: Analyss of Concentratng Solar Power (CSP) and PV Technologes Y Zhang SJ Smth August 2008

2 DISCLAIMER Ths report was prepared as an account of work sponsored by an agency of the Unted States Government. Nether the Unted States Government nor any agency thereof, nor Battelle Memoral Insttute, nor any of ther employees, makes any warranty, express or mpled, or assumes any legal lablty or responsblty for the accuracy, completeness, or usefulness of any nformaton, apparatus, product, or process dsclosed, or represents that ts use would not nfrnge prvately owned rghts. Reference heren to any specfc commercal product, process, or servce by trade name, trademark, manufacturer, or otherwse does not necessarly consttute or mply ts endorsement, recommendaton, or favorng by the Unted States Government or any agency thereof, or Battelle Memoral Insttute. The vews and opnons of authors expressed heren do not necessarly state or reflect those of the Unted States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830 Prnted n the Unted States of Amerca Avalable to DOE and DOE contractors from the Offce of Scentfc and Techncal Informaton, P.O. Box 62, Oak Rdge, TN ; ph: (865) fax: (865) emal: reports@adons.ost.gov Avalable to the publc from the Natonal Techncal Informaton Servce, U.S. Department of Commerce, 5285 Port Royal Rd., Sprngfeld, VA ph: (800) fax: (703) emal: orders@nts.fedworld.gov onlne orderng: Ths document was prnted on recycled paper. (9/2003)

3 PNNL Long-Term Modelng of Solar Energy: Analyss of Concentratng Solar Power (CSP) and PV Technologes Y Zhang SJ Smth August 2008 Prepared for the U.S. Department of Energy under Contract DE-AC05-76RL01830 Pacfc Northwest Natonal Laboratory Rchland, Washngton 99352

4 Long-Term Modelng of Solar Energy: Analyss of Concentratng Solar Power (CSP) and PV Technologes Yabe Zhang Steve Smth Jont Global Change Research Insttute, College Park, MD January, 2008 Abstract Solar technologes have unque characterstcs that requre detaled study to develop a sutable representaton for modelng purposes. Solar technologes generally have low operatng costs and carbon emssons, but hgh captal cost. Thus fnancng assumptons are partcularly mportant for ths type of captal-ntensve technology. Intermttency s also a major characterstc of solar energy. In partcular, when modelng solar energy, the nteractons between solar generators and other generators n the electrc system are crtcal n determnng the long-term market potental for solar energy. Ths report ncludes three separate analyses developed to study these characterstcs and gude the mplementaton of solar energy under JGCRI s ObjECTS MnCAM framework: (1) a revew of the senstvty of solar energy cost to dfferent fnancal assumptons, (2) the development of a new approach to modelng CSP market potental consderng ntermttency, and (3) an analyss of the mpact of ntermttency of solar energy on system relablty. The current mplementaton of solar energy n the ObjECTS Framework s dscussed at the end of the report along wth prelmnary model analyss results.

5 Table of Contents 1 Introducton Development of Solar Energy ObjECTS MnCAM Model Chapter Hghlghts... 3 Acknowledgements... 4 References Levelzed Energy Cost: Senstvty Study Introducton LEC Defnton Publc vs. Prvate Perspectve CSP LEC Calculaton Usng Prvate Fnancal Analyss Senstvty Analyss CSP LEC Calculaton Usng the Publc Sector Economc Analyss Concluson References Methodology for Estmatng the CSP Electrcty Cost: A New Approach for Modelng CSP Market Potental Introducton Methodology and Assumptons Results Senstvty Analyss Concluson References Appendx 1 Lst of Varables Impact of Intermttency of PV on System Reserve Margns Introducton Assumptons and Methodology Numercal Examples Concluson References The Role of Solar Energy Technologes: Prelmnary Modelng n the ObjECTS Framework Improved Representaton of Solar Technologes Prelmnary Calculatons of the Role of CSP power Next Steps Summary References... 76

6 Lst Of Fgures Fgure Fuel Shares of World Total Prmary Energy Supply (TPES)*... 5 Fgure 1-3. Annual Growth of Renewables Supply from 1971 to Fgure 2-1. LEC s Senstvty to Type of Fnancng Fgure 2-3. Equty Share Senstvty Analyss Fgure 2-5. LEC s Senstvty to Interest Rate Fgure 2-7. LEC s Senstvty to ITC Incentves Fgure 2-8. LEC s Senstvty to Deprecaton Method Fgure LEC s Senstvty to Drect Normal Irradance Fgure LEC s Senstvty to Dscount Rate: Publc Perspectve Fgure 3-1. Daggett Barstow Hourly-Mean DNI for non-cloudy Days by Season, Fgure 3-3. Solar Irradance Curve by Tme of the Day (Not to Scale) Fgure 3-5. Approxmaton of an sosceles trapezod on the daly solar rradance curve by season Fgure 3-7. Shares of Backup Operaton and Solar Output Loss vs. CSP Market Penetraton Fgure 3-9. Levelzed CSP Electrcty Cost vs. CSP Market Penetraton Fgure Levelzed CSP Electrcty Cost vs. CSP Market Penetraton- Senstvty Analyss on Natural Gas Prce/Carbon Tax Fgure Levelzed CSP Electrcty Cost vs. CSP Market Penetraton by Scenaros of System Load Curves Fgure Percentage of CSP Output Loss vs. CSP Market Penetraton by Scenaros of System Load Curves Fgure Comparson of Capacty Factors for CSP and Non-CSP I&P Technologes 53 Fgure Levelzed CSP Electrcty Cost vs. CSP Market Penetraton: the Case of Constant non-csp I&P Capacty Factor Fgure 4-1. Impact of No/Low Sun Days on Addtonal System Reserve Margn by Dfferent Scenaros Fgure 4-3. Impact of Number of No/Low Sun Days on Addtonal System Reserve Margn n Scenaro D Fgure 4-5. Correspondence Between Solar Irradance and Temperature Fgure 5-1. Thermal CSP penetraton for reference and advanced case technologes. The penetraton of CSP power s overestmated because geographc concentraton of the solar resource was not taken nto account (see text)

7 Tables Table 2-1. Estmaton of LEC: Baselne Assumptons and Results Table 2-3. LEC s Senstvty to Type of Fnancng Table 2-5. LEC s Senstvty to Interest Rate Table LEC s Senstvty to ITC Incentves Table 2-9. LEC s Senstvty to Deprecaton Method Table LEC s Senstvty to Drect Normal Irradance Table LEC s Senstvty to Dscount Rate: Publc Perspectve Table 3-1. Classfcaton of Tme Slces and System Load Table 3-3 Lst of Varables Related to CSP Solar Output Table 3-5 Example of Calculatng the CSP Operatonal Tme: Daggett Barstow Table 3-7. CSP Solar Output Capablty and Operatonal Hours by Tme Slce: Daggett Barstow wth a Solar Multple of Table 3-9. Baselne Assumptons for Calculatng CSP LEC (n 2004$) Table 3-11 Scenaros of System Load Curves Table 5-1. Assumed thermal CSP captal costs. No thermal storage was assumed v

8 1 Introducton Renewable energy s ncreasng as a component of the energy supply portfolo, contrbutng to energy supply securty and provdng opportuntes for mtgatng greenhouse gases. As a part of the renewable famly, solar energy, defned as solar radaton exploted for hot water producton and electrcty generaton (IEA, 2007), has developed rapdly n recent years. In ths chapter, we brefly revew the current status of solar technologes. We also descrbe the general clmate-change modelng framework that motvates ths study. Fnally, we provde bref prevews of the report s chapters. 1.1 Development of Solar Energy Solar s the world s most abundant, renewable source of energy. Every year, the sun rradates the earth's land masses wth the equvalent of 19 trllon tonnes of ol equvalent (toe). A small fracton of ths energy could satsfy the world's energy requrements, around 9 bllon toe per year (WEC, 2001). The challenge s harnessng solar energy n a cost-effectve way. Technology advances and polcy supports are major drvers for the development of solar energy. Accordng to the Internatonal Energy Agency (IEA) energy statstcs, although solar energy only provdes 0.039% of the world s total prmary energy supply (TPES) (Fgure 1-1), t had the second hghest annual growth rate (28.1%) from 1971 to 2004 (Fgure 1-2). Based on hstorcal technology progress and cost reducton, some have predcted that over the next two decades solar energy wll ncreasngly become a compettve choce for electrcty and energy applcatons. There are three major ways to use solar energy: photovoltac (PV) systems that convert lght drectly nto electrcty, solar water heatng systems that use sunlght to heat water, and solar thermal systems that concentrate solar radaton nto a small space and produce hgh temperatures, whch use ths heat to operate a conventonal power cycle. We focus our study here on grd-connected electrcty generated usng concentratng thermal solar power (CSP) and grd-connected photovoltacs (PVs). What s the role of solar energy n the long term? Accordng to the Offce of Energy Effcency and Renewable Energy (EERE) of the US Department of Energy (2006), to be compettve n the long term (10 15-year horzon), the cost of utlty grd-connected PV 1

9 and CSP needs to be reduced to $ /kWh and $ /kWh 1, respectvely. What wll be the market share of these energy technologes f such goals are acheved? How wll solar energy contrbute to greenhouse gas reductons? These questons can be analyzed usng the Mn Clmate Assessment Model (MnCAM) developed by the Jont Global Change Research Insttute (JGCRI). 1.2 ObjECTS MnCAM Model The Object-orented Energy, Clmate, and Technology Systems (ObjECTS) framework uses a flexble, object-orented modelng structure to mplement an enhanced verson of the partal-equlbrum model MnCAM (Km et al. 2006). The ObjECTS MnCAM s an ntegrated model of the economy, energy supply and demand technologes, agrculture, land-use, carbon-cycle, and clmate. Ths framework s ntended to brdge the gap between bottom-up technology models and top-down macro-economc models. By allowng a greater level of detal where needed, whle stll enablng nteracton between all model components, the ObjECTS framework allows a hgh degree of technologcal detal whle retanng system-level feedbacks and nteractons. By usng object-orented programmng technques (Km et al. 2006), the model s structured to be data-drven, whch means that new model confguratons can be created by changng only nput data wthout changng the underlyng model code. The MnCAM s a partal-equlbrum model structure that s desgned to examne longterm, large-scale changes n global and regonal energy systems. The MnCAM has a strong focus on energy supply technologes and has been recently expanded to nclude a comprehensve sute of end-use technologes. The MnCAM was one of the models used to generate the IPCC SRES scenaros (Nakcenovc and Swart 2000). Ths model has been used n a number of natonal and nternatonal assessment and modelng actvtes such as the Energy Modelng Forum (EMF; Edmonds, et al. 2004, Smth and Wgley 2006), the U.S. Clmate Change Technology Program (CCTP; Clarke et al. 2006), and the U.S. Clmate Change Scence Program (CCSP; Clarke et al. 2007) and IPCC assessment reports. The MnCAM model s calbrated to 1990 and 2005 and operates n 15-year tme steps to the year It takes nputs such as labor productvty growth, populaton, fossl and non-fossl fuel resources, energy technology characterstcs, and productvty growth rates and generates outputs of energy supples and demands by fuel (such as ol and gas) and energy carrers (such as electrcty), agrcultural supples and demands, emssons of 1 The reason that PVs can compete at hgher costs than CSPs s that PVs are less resource constraned and can usually be closer to transmsson grds. 2

10 greenhouse gases (carbon doxde, CO 2 ; methane, CH 4 ; ntrous oxde,n 2 O), and emssons of other radatvely mportant compounds (sulfur doxde, SO 2 ; ntrogen oxdes, NO X ; carbon monoxde, CO; volatle organc compounds, VOC; organc carbon aerosols, OC; black carbon arosols, BC). The model has ts roots n Edmonds and Relly (1985), and has been contnuously updated (Edmonds et al. 1996; Km et al. 2006). MnCAM also ncorporates MAGICC, a model of the carbon cycle, atmospherc processes, and global clmate change (Raper et al. 1996; Wgley and Raper 1992). 1.3 Chapter Hghlghts Ths report ncludes three separate analyses developed to gude mplementaton of solar energy under the ObjECTS framework. However, because these analyses focus on methodologcal development, they may have applcatons n other settngs. Solar technologes have some unque economc characterstcs that need detaled study to develop representaton and parameters for modelng. The three separate analyses focus on these unque characterstcs. In Chapter 2, we dscuss how the levelzed energy cost (LEC) s calculated and how dfferent methodologes and assumptons can change the LEC substantally. LEC s a wdely used ndcator to compare the compettveness of dfferent energy sources. One feature of solar energy s ts low operatng cost, wth a relatvely hgh captal cost. Thus fnancng assumptons are partcularly mportant for ths type of captal-ntensve technology. Usng a 100-MW CSP plant as an example, we calculate LEC from both the prvate perspectve and the publc perspectve. We fnd that the results from the two methodologes are farly comparable under certan assumptons. However, the LEC from the prvate perspectve s very senstve to fnancng assumptons and polcy ncentves towards CSPs (e.g. tax credts and favorable deprecaton schedules). Thus, specal attenton should be gven to these assumptons when comparng LECs from dfferent sources. Intermttency s a major characterstc of solar energy and also a major challenge when modelng solar energy. Because we model grd-connected solar electrcty, the nteractons between solar generators and other generators n the electrc system become partcularly mportant. Chapters 3 and 4 deal wth ntermttency for CSP and PV systems, respectvely. In Chapter 3, we develop a methodology to calculate CSP electrcty costs consderng ntermttency. We fnd a strong dependency of the CSP electrcty cost on CSP market penetraton when the CSP market penetraton s hgh. Ths s partly due to the ncreasng 3

11 need for the backup output when the rradance s low or unavalable, and partly due the loss of CSP output from the solar component when there s excess supply. Because the CSP backup component s powered by fossl fuel, ths means that the effectveness of usng CSP to reduce carbon emssons decreases as the CSP market penetraton level passes a certan threshold. Usng the examples of San Dego and Phoenx, we fnd that ths threshold can be qute hgh, more than 40% of the total ntermedate and peak electrcty supply. Therefore, CSP has the potental to supply a sgnfcant share of electrc demands wthout a sgnfcant penalty due to ntermttency. In Chapter 4, we analyze the mpact of ntermttency of solar energy on system relablty plannng. We consder the mpact of no/low sun days on system reserve margns. Usng a stylzed analyss, we fnd that when the market penetraton of PV s low, the number of no/low sun days plays an mportant role n determnng addtonal system reserve margn and therefore t should be a consderaton n addton to average rradance level when selectng locatons for PV systems. When the market penetraton of PV s hgh, the requrement for addtonal system reserve margn can converge to one-to-one backup, whch wll sgnfcantly ncrease PV electrcty cost. Chapter 5 descrbes the current mplementaton of solar energy n the ObjECTS Framework. The results presented n the prevous sectons have been used to gude both a general mplementaton of solar energy and a specfc ncorporaton of CSP solar technology. Acknowledgements The authors would lke to thank Marshall Wse, Katherne Calvn, and Aprl Volke for comments on draft versons of the report and G. Page Kyle for nvaluable assstance n the constructon of ObjECTS model nput fles. Ths work was funded by the U.S. Department of Energy s Offce of Energy Effcency and Renewable Energy wth addtonal support from the Calforna Energy Commsson and the Global Energy Technology Strategy Program (GTSP). 4

12 Fgure Fuel Shares of World Total Prmary Energy Supply (TPES)* Fgure 1-2. Annual Growth of Renewables Supply from 1971 to

13 References Bradford, Travs (2006). Solar Revoluton: The Economc Transformaton of the Global Energy Industry. MIT Press. Clarke, L. E., M. A. Wse, J. P. Lurz, M. Placet, S. J. Smth, R. C. Izaurralde, A. M. Thomson, and S. H. Km Technology and Clmate Change Mtgaton: A Scenaro Analyss. PNNL Clarke, L., J. Edmonds, J. Jacoby, H. Ptcher, J. Relly, R. Rchels Scenaros of Greenhouse Gas Emssons and Atmospherc Concentratons. Report by the U.S. Clmate Change Scence Program and approved by the Clmate Change Scence Program Product Development Advsory Commttee (Unted States Global Change Research Program, Washngton, D.C.). Edmonds J. A., J. F. Clarke, J. J. Dooley, S. H. Km, and S. J. Smth Modelng Greenhouse Gas Energy Technology Responses to Clmate Change. Energy 29 (9-10): Edmonds, J., and J. Relly Global Energy: Assessng the Future. Oxford, Unted Kngdom: Oxford Unversty Press. Edmonds, J. A., M. Wse, H. Ptcher, R. Rchels, T. Wgley, and C. MacCracken An ntegrated assessment of clmate change and the accelerated ntroducton of advanced energy technologes: An applcaton of MnCAM 1.0. Mtgaton and Adaptaton Strateges for Global Change 1 (4):

14 EERE (2006). Solar Energy Technologes Program, Mult-Year Program Plan Energy Effcency and Renewable Energy, Department of Energy. IEA (2007). Renewables n Global Energy Supply- An IEA Fact Sheet Km, Edmonds, Lurz, Smth, and Wse (2006). Hybrd Modelng of Energy-Envronment Polces: Reconclng Bottom-up and Top-down. The Energy Journal, Nakcenovc, N., and R. Swart, eds Specal Report on Emssons Scenaros. Cambrdge, U.K.: Cambrdge Unversty Press. Raper, S. C. B., T. M. L. Wgley, and R. A. Warrck Global sea level rse: Past and future. In Sea-Level Rse and Coastal Subsdence: Causes, Consequences and Strateges. (Mllman, J. D., and B. U. Haq, eds.). Kluwer Academc Publshers Smth, S. J., and T. M. L. Wgley Mult-Gas Forcng Stablzaton wth the MnCAM. The Energy Journal Specal Issue #3. Wgley, T. M. L., and S. C. B. Raper Implcatons for Clmate and Sea-Level of Revsed IPCC Emssons Scenaros. Nature 357 (6376): World Energy Councl (2001). Survey of Energy Resources

15 2 Levelzed Energy Cost: Senstvty Study 2.1 Introducton Levelzed energy cost (LEC) s often used to compare competng energy sources. It s especally mportant for renewable energy due to ts captal ntensve nature. Experence has llustrated that the calculaton of LEC for renewable energy sources s both complex and often subject to debate. Moreover, results can be sgnfcantly nfluenced by the methodology and the assumptons employed. For example, the frst verson of Sargent and Lundy s report, Assessment of Concentratng Solar Power Cost and Performance Forecasts (2002), was crtczed for the use of some unrealstc fnancng assumptons and the absence of a senstvty study on fnancal parameters (BEES, 2002). Ths chapter documents a methodology for calculatng LEC usng a standard 100-MW concentraton solar power (CSP) plant as an example and focuses on senstvty analyss. 2.2 LEC Defnton A levelzed unt cost s a delvered product unt cost that, f charged for each year s producton over the analyss perod, would yeld the same net present value of revenues as f the actual annual cost for each alternatve were collected nstead over the perod. It s C n the followng equaton: (2-1) CE C E n n = = 1 (1 + r) = 1 (1 + r) where C s a constant $/kwh cost to be charged each th year over the analyss perod (n=30 years, for example), E s the kwh generated n each such year, and C s the actual annual $/kwh for each year, comprsed of a current expense for fuel, labor, etc. plus a component for recovery of the nvestment cost, whch may be a level seres or may vary through tme n some fashon. Equaton (2-1) can also be wrtten as (2-2) 8

16 C n = 1 = n = 1 C E (1 + r) E (1 + r) Snce the term C E s dollars for each year, and the 1/(1+r) s a dscount factor, the top of the rght sde can be nterpreted as the present value of revenue requrements. The bottom s denomnated n kwh and can be nterpreted as the present value of energy. Ths s why LEC s often stated as the present value of costs dvded by the present value of energy. 2.3 Publc vs. Prvate Perspectve The choce of analytcal perspectve s crtcal because there s an mportant dstncton n the calculatons from a publc perspectve as compared to a prvate perspectve. The bass for conductng prvate sector analyss ncludes market prces, taxes, deprecaton, prvate cost of captal, and applcable ncentves. The fnancal analyss for the prvate sector attempts to determne the actual costs and revenues that wll be realzed by the nvestor. Because solar projects are very captal ntensve, the LEC from the prvate perspectve s partcularly senstve to fnancng condtons and tax polces. The economc analyss for the publc sector s from the perspectve of socety as a whole. It gnores the effect of taxes and uses a socal dscount rate nstead of a dscount rate reflectng the cost of borrowng and desred returns (the latter s usually larger). In the followng example, we compute the LEC from both the publc and prvate perspectves for comparson. 2.4 CSP LEC Calculaton Usng Prvate Fnancal Analyss For ths analyss, we adopted the Independent Power Producer (IPP) Project Fnance Model ntally developed by Ryan Wser of LBL and revsed by Henry Prce of NREL. The technology we consder s a trough hybrd solar plant wth capacty of 100 MW. However, the general concluson s not technology specfc. The detaled baselne assumptons and results are presented n 9

17 Table 2-1. The bolded fgures are key assumptons for LEC calculaton usng prvate fnancal analyss. Except for solar rradance level, they are prmarly fnancng assumptons ncludng fnancng structure, tax ncentves, and the deprecaton method. In terms of fnancng structure, the baselne assumes IPP project fnance. There are two major fnancng structures: corporate fnance and project fnance. Corporate fnancng, also known as nternal or equty fnancng, s characterzed by the use of corporate credt and general assets of a corporaton, typcally a utlty, as the bass for credt and collateral. Because the overall credt ratng of the company s used to estmate debt and equty costs rather than project specfc captal costs, the cost of fnancng s low due to a better credt standng. However, because of hgh nvestment costs, most of the utltes are not able to generate suffcent corporate fnance resources for solar projects (Kstner and Prce, 1999). Thus, project fnance s often used n long-term captal-ntensve nfrastructure and ndustral projects such as solar power projects. Project fnance can be defned as the arrangement of debt, equty, and credt enhancement for the constructon of a partcular faclty n a captal-ntensve ndustry where lenders base credt apprasals on the estmated cash flows from the faclty rather than on the assets or credt of the promoter of the faclty (Short et al, 1995). It s more complcated and more expensve compared to corporate fnance. Project fnance s the prmary fnancng structure used by IPPs. The cost of rasng captal, whch can be measured as the nternal rate of return (IRR) for equty nvestors and nterest rate for lenders, depends on real and perceved technology rsk, type of fnance, and debt-equty rato. Our baselne assumes 60% debt and 40% equty, whch has a reasonable debt/equty rato for IPP projects. Because a nomnal IRR between 16%-20% s generally expected from IPP projects (Kstner and Prce, 1999), our baselne assumes a real IRR of 14%. Note that all our assumptons are n constant dollars wthout accountng for nflaton. If we consder 2-3% nflaton rate, ths IRR falls n the above range. In addton, we assume 20-year debt wth a 6% real nterest rate, whch s also reasonable for IPP projects n the US. As part of rsk management, lenders usually requre a certan debt servce coverage rato (DSCR). The DSCR s the amount of cash/operatng ncome avalable dvded by debt payments. Lenders want to assure that durng the entre project lfetme the cash generated always covers debt servce. One of the most mportant loan requrements s the mnmum annual debt servce coverage rato (MADSCR). Lenders normally requre that durng every stage of the project the annual DSCR never falls short of the MADSCR. 10

18 Many lenders requre a MADSCR between 1.2 and 1.5, dependng on specfc project rsks and contractual arrangements (Kstner and Prce, 1999). Our baselne assumes that the MADSCR s 1.3. In terms of tax ncentves, the baselne assumes no tax ncentves because the ObjECTS model s for long-term projectons so we expect the government tax ncentves would be phased out over tme as a technology becomes wdely used. However, we note that nvestment tax credt (ITC) currently serves as a major ncentve for CSP nvestment. For example, the Energy Polcy Act of 2005 offers 30% federal tax credts for solar projects begnnng n January 2006 tll 2007 and t was later extended untl the end of In addton, some states provde addtonal tax ncentves for solar energy nvestment. For example, Calforna offers 15% ITC for solar projects. In terms of the deprecaton method, the baselne assumes 5-year modfed accelerated cost recovery system (MACRS), whch means that the applcable captal cost s deprecated accordng to the 5-year MACRS schedule. The MACRS establshes a set of schedules for varous types of property, rangng from 3 to 50 years, over whch the property may be deprecated. We use the assumpton of a 5-year MACRS because the current polcy allows 5-year MACRS for solar, wnd, and geothermal property placed n servce after and most references also use ths assumpton. For comparson, the MACRS schedule for fossl fuel power plants s normally 15 or 20 years. Solar rradance level determnes the total electrcty output from the CSP, whch n turn determnes revenues from energy and the CSP LEC. The baselne assumes 7.65 kwh/m^2/day whch represents the San Dego regon. We use ths assumpton because ths regon s one of the most deal areas for solar energy n the US and a number of studes on solar energy have focused on ths regon. Usng the baselne assumptons as shown n 2 Source: 3 Source: aged=1 11

19 Table 2-1, the calculated real CSP LEC s 2004$ /kWh. To ensure the project s fnancally feasble, the frst year electrcty prce needs to be 2004$ /kWh. In addton, the MADCSR s 1.53, whch meets the requrement of a 1.3 MADSCR. The key assumptons dscussed above can make a sgnfcant mpact on the CSP LEC. Thus, we wll conduct a senstvty analyss n the followng secton to evaluate how LEC s affected by changng these assumptons. 12

20 Table 2-1. Estmaton of LEC: Baselne Assumptons and Results Varables Value Notes Baselne Assumptons Reference Year Dollars 2004 Assumed Capacty (MW) 100 Assumed Drect Normal Irradance (kwh/m^2/day) 7.65 Assumed Annual Solar-to-Electrc Effcency 12.60% Assumed Capacty Factor w/o hybrd 0.28 Calculated Increased Capacty Factor due to the backup fuel 0.02 Assumed Capacty Factor w/ hybrd 0.30 Calculated Captal Cost w/ hybrd ($/kw) 3486 Assumed Solar Feld Sze (km^2) 0.69 Assumed Land Area (km^2) 2.30 Assumed Land cost ($/m^2) 0.49 Assumed Land Cost (M$s) 1.14 Calculated Allowance for Funds Used Durng Constructon 3.5% Assumed (AFUDC) Const. Perod/Frst Year of Op. 1 Assumed Fxed O&M Expense ($/kw-yr) Assumed Varable O&M Expense ($/MWh) 2.72 Assumed Share of Electrcty Produced by Gas 7% Calculated Gas Converson Effcency 0.46 Assumed Annual Fuel Usage (MMBtu) Calculated Insurance (% of nstalled cost) 0.5% Assumed Effectve Income Tax Rate 40.0% Assumed Investment Tax Credt/dep adj 0.0% Assumed Percentage of Captal Deprecaton at 5-yr 100% Assumed MACRS Percentage of Captal Deprecaton at 15-yr 0.0% Assumed MACRS Percentage of Captal Deprecaton at 20-yr 0.0% Assumed MACRS Energy Prce Escalaton Rate 1.3% Assumed Equty Fracton 40% Assumed Debt Fracton 60% Assumed Interest Rate 6% Assumed Mnmum Annual Debt Servce Coverage Rato 1.3 Assumed (MADSCR) Equty Internal Rate of Return (IRR) 14% Assumed 13

21 Dscount Rate 9% Assumed Baselne Results Average Annual DSCR 1.79 Calculated MADSCR 1.53 Calculated Frst Year Electrcty Prce (2004 $/kwh) Calculated Real LEC (2004 $/kwh) Calculated 2.5 Senstvty Analyss (1) Fnancng Structure Table 2-2 and Fgure 2-1 show the results of senstvty analyss of the dfferent fnancng structures. In addton to IPP fnancng, as we dscussed earler, the CSP plant may be owned and fnanced by utltes. If we use a typcal fnancng structure for an nvestorowned utlty (IOU) wth 50% debt, 30-year term, 4% nterest rate, and 12% IRR, the LEC wll decrease slghtly to $0.1567/kWh. Furthermore, rather than usng commercal fnancng, f we use muncpal fnancng wth 100% debt, 30-year term, and 3.5% nterest rate, the LEC can decrease to only 52% of the baselne cost. However, t should be noted that the MADSCR s only 0.6 n ths case, whch means that operatng ncome for certan perods s not hgh enough to pay for amortzed annual debt. Therefore, the muncpal fnancng structure has to have some specal payment schedule or other arrangements to make t feasble. If we allow the debt rato to change whle keep other assumptons the same and assure a 1.3 MADSCR s met, the lowest real LEC would be $0.1508/kWh, 94% of the baselne. In Fgure 2-1 and subsequent fgures, the shaded bar represents the case that the requrement of a 1.3 MADSCR s not met. Costs of rasng captal also depend on the debt-equty rato. If we assume LEC s constant at the baselne level and the debt term and nterest rate do not change, we can nvestgate how the IRR and the MADSCR change wth respect to the equty share. The results of ths senstvty analyss are depcted n Fgure 2-2. It shows that the IRR s negatvely correlated to the equty share whle the MADSCR s postvely correlated to the equty share. The MADSCR often bnds n the ntal years of operaton and restrcts the amount of low-cost debt that can be used by the project. If lenders requre restrctve MADSCR, front-loadng of contract payments and/or a back loadng of debt payment could help to acheve a hgher level of debt leverage (Kstner and Prce, 1999). 14

22 The nterest rate reflects lender s percepton of the project rsk and market condtons. It s also a major determnant of the real LEC. Assumng debt-equty rato and IRR do not vary, Table 2-3 and Fgure 2-3 show LEC s senstvty to dfferent nterest rates. If we use a more conservatve nterest rate 4%, the LEC decreases to 95% of the baselne. If we use a hgher nterest of 10%, the LEC ncreases to 114% of the baselne. Compared to other assumptons, the LEC s only moderately senstve to nterest rates. Table 2-2. LEC s Senstvty to Type of Fnancng IPP Debt: 60%, 20 yrs, =6% Equty: 40%, IRR=14% IOU Debt: 50%, 30 yrs, =4% Equty: 50%, IRR=12% Mun Debt: 100%, 30yrs, =3.5% Optmal Debt Rato Debt: 65.2%, 20 yrs, =6% Equty: 34.8%, IRR=14% Senstvty to Type of Fnancng Real LEC (2004 $/kwh) Relatve Cost Comparng to the Baselne 100% 97% 52% 94% MADSCR Fgure 2-1. LEC s Senstvty to Type of Fnancng 15

23 LEC's Senstvty to Type of Fnancng Real LEC (2004 $/kwh) IPP Debt: 60%, 20 yrs, =6% Equty: 40%, IRR=14% IOU Debt: 50%, 30 yrs, =4% Equty: 50%, IRR=12% Mun Debt: 100%, 30yrs, =3.5% Optmal Debt Rato Debt: 65.2%, 20 yrs, =6% Equty: 34.8%, IRR=14% Fgure 2-2. Equty Share Senstvty Analyss Equty Share Senstvty Analyss 120% % IRR MADSCR 9 8 IRR 80% 60% 40% Base Case MADSCR 20% 2 1 0% 0 0% 20% 40% 60% 80% 100% Equty Share Table 2-3. LEC s Senstvty to Interest Rate Senstvty to Interest Rate 6% 4% 8% 10% 16

24 Real LEC (2004 $/kwh) Relatve Cost Comparng to the Baselne 100% 95% 107% 114% MADSCR Fgure 2-3. LEC s Senstvty to Interest Rate LEC's Senstvty to Interest Rate Real LEC (2004$/kWh) % 4% 8% 10% Interest Rate (2) Tax Incentves Table 2-4 and Fgure 2-3 show LEC s senstvty to ITC ncentves. If we assume 10% ITC, the LEC can decrease to $ 0.131/kWh. It can further decrease to $ /kWh wth 17

25 the assumpton of 30% ITC whch s only 44% of the baselne cost. However, we need to note that the MADSCR s 1.18 for the assumpton of 10% ITC and only 0.46 for the assumpton of 30% ITC. Wthout changng the fnancng structure or havng other specal payment arrangements, these LECs are dffcult to realze n practce. If we requre a MADSCR of 1.3 and allow the fnancng structure to change (equty share ncreases to 58.8%) we can get a LEC of $0.1077/kWh n the case of 30% ITC. Table 2-4. LEC s Senstvty to ITC Incentves Senstvty to ITC Incentves 0% ITC 10% ITC 30% ITC 30% ITC* Real LEC (2004 $/kwh) Relatve Cost Comparng to the Baselne 100% 81% 44% 67% MADSCR * equty share ncreased to 58.8%, other assumptons reman. Fgure 2-4. LEC s Senstvty to ITC Incentves LEC's Senstvty to ITC Incentves Real LEC (2004 $/kwh) % ITC 10% ITC 30% ITC 30% ITC* (3) Deprecaton Method 18

26 Snce solar projects are very captal ntensve, the LEC can be very senstve to the deprecaton method used. If we use 15-year MACRS, the LEC wll ncrease to $0.2015/kWh. It wll ncrease further to $0.2616/kWh and 163% of the baselne f we assume 20-year MACRS. Table 2-5 and Fgure 2-5 llustrate LEC s senstvty to dfferent MACRS schedules. We can see that the assumpton of the MACRS schedule sgnfcantly changes the LEC result. Table 2-5. LEC s Senstvty to Deprecaton Method Senstvty to Deprecaton Method 5-yr MACRS 15-yr MACRS 20-yr MACRS Real LEC (2004 $/kwh) Relatve Cost Comparng to the Baselne 100% 125% 163% MADSCR Fgure 2-5. LEC s Senstvty to Deprecaton Method LEC's Senstvty to MACRS Real LEC (2004 $/kwh) yr MACRS 15-yr MACRS 20-yr MACRS 19

27 (4) Drect Normal Irradance DNI, dependng on locaton and collecton effcency, can also sgnfcantly affect the CSP LEC. As shown n Table 2-6 and Fgure 2-6, f DNI goes down to 6.05 kwh/m^2/day (e.g. Albuquerque, New Mexco), the LEC goes up to $0.1992/kWh and further up to $ /kWh f DNI s 5.14 kwh/m^2/day (e.g. Austn, Texas). Table 2-6. LEC s Senstvty to Drect Normal Irradance Senstvty to Drect Normal Irradance (kwh/m^2/day) 7.65 (San Dego/CA) 6.05 (Albuquerque/NM) Real LEC (2004 $/kwh) Relatve Cost Compared to San Dego (Baselne) 100% 124% 143% MADSCR * Drect Normal Irradance s mputed based on NASA data. Fgure 2-6. LEC s Senstvty to Drect Normal Irradance 5.14 (Austn/TX) LEC's Senstvty to Drect Normal Irradance Real LEC (2004 $/kwh) (San Dego/CA) 6.05 (Albuquerque/NM) 5.14 (Austn/TX) Drect Normal Irradance (kwh/m^2/day) 20

28 2.6 CSP LEC Calculaton Usng the Publc Sector Economc Analyss LEC from the publc perspectve can be calculated usng the followng formula (2-3) LEC = FCR * I + OM E + F Where FCR = Fxed charge rate, a constant dscount factor can be calculated usng PMT(dscount rate, lfe tme of the plant, 1)+ nsurance rate. I = Installed captal cost OM = Annual operaton and mantenance costs F = Annual expenses for fuel E = Annual energy producton Ths method s sgnfcantly smpler compared to the prvate fnancng cash flow model. The basc assumptons are the same as the ones n 21

29 Table 2-1 except for the dscount rate. Because the publc sector economc analyss gnores fnancng and tax effects, the major assumpton that determnes the LEC s the dscount rate. As we dscussed earler, the socal dscount rate s usually smaller than the one used n the prvate fnancal analyss, so we assume 8% dscount rate for the baselne. The calculated baselne LEC from the publc perspectve s $0.1535/kWh, s qute comparable to the baselne LEC from the prvate perspectve. Table 2-7 and Fgure 2-7 present the LEC s senstvty to the dscount rate. We can see that LEC s moderately senstve to the dscount rate. Table 2-7. LEC s Senstvty to Dscount Rate: Publc Perspectve Dscount Rate LEC's Senstvty to Dscount Rate 8% 6% 7% 9% Fxed charge rate (FCR) 9.38% 7.76% 8.56% 10.23% LEC from publc perspectve (2004$/KWh) Relatve Cost Comparng to the Baselne 100% 85% 93% 108% Fgure 2-7. LEC s Senstvty to Dscount Rate: Publc Perspectve LEC's Senstvty to Dscount Rate (Publc Perspectve) Real LEC (2004$/kWh) % 6% 7% 9% Dscount Rate 22

30 2.7 Concluson In ths chapter, we document the methodologes of calculatng LEC from both the prvate perspectve and the publc perspectve. We fnd that LEC from the prvate perspectve s very senstve to fnancng assumptons, polcy ncentves, and levels of drect normal rradance. The factors wth the largest effect on LEC are nvestment tax credts, deprecaton schedule, and drect normal rradance. In terms of fnancng assumptons, we examned how types of fnancng, debt-equty rato, and nterest rate affect LEC. We fnd that hgh debt-equty rato wthout an ncreased nterest rate can sgnfcantly decrease LEC. Holdng other fnancng assumptons unchanged, nterest rates only moderately affect LEC. In terms of polcy ncentves, we examned the effect of nvestment tax credts and deprecaton schedules. We fnd that ether polcy ncentve can reduce LEC tremendously. Our baselne assumes current deprecaton schedule (5-year MACRS) for solar energy. If ths favorable polcy s lfted, the estmated LEC can ncrease more than 60%. Another caveat s that many lenders requre certan mnmum annual debt servce coverage (MADSC), and we fnd that some lowest cost scenaros (e.g. muncpal fnancng, 10% ITC and 30% ITC) are not able to meet ths requrement wthout changng other assumptons such as specal payment structures or other arrangements. Therefore, specal attenton should be gven to these assumptons when comparng LECs between dfferent analyses. Alternatvely, the method of calculatng LEC from the publc perspectve s much smpler. Comparable results can be obtaned between the smple publc method and the more detaled calculaton gven approprate assumptons for the dscount rate. 23

31 References Board on Energy and Envronmental Systems (BEES) (2002). Letter Report: Crtque of the Sargent and Lundy Assessment of Concentratng Solar Power Cost and Performance Forecasts (2002). US 109 Congress (2005). Energy Polcy Act of Kearney, D and H. Prce (2004) Recent Advances n Parabolc Trough Solar Power Plant Technology. Manuscrpt from the authors. Kstner, R., and H. Prce, 1999, Fnancng Solar Thermal Power Plants. Proceedngs of the ASME Renewable and Advanced Energy Systems for the 21st Century Conference, Aprl 11-14, 1999, Mau, Hawa Natonal Renewable Energy Laboratory (2005) Potental for Renewable Energy n the San Dego Regon, Appendx E, August Prce, H and S. Carpenter (1999) The Potental for Low-Cost Concentratng Solar Power Systems. Conference Paper, NREL/CP Prce, H. (2006) Concentratng Solar Power, Presentaton to Global Energy Strategy Addressng Clmate Change Techncal Workshop. May Sargent & Lundy LLC Consultng Group (2003) Assessment of Parabolc Trough and Power Tower Solar Technology Cost and Performance Forecasts. NREL/SR October Short, W., Packey, D.J., Holt, T., 1995, A Manual for the Economc Evaluaton of Energy Effcency and Renewable Energy Technologes, NREL/TP , Golden, USA. World Bank (1999) Cost Reducton Study for Solar Thermal Power Plants. May

32 3 Methodology for Estmatng the CSP Electrcty Cost: A New Approach for Modelng CSP Market Potental 3.1 Introducton Wth hgher energy costs and new regulatory support, concentratng solar power (CSP) technology, usng the sun's thermal energy to generate electrcty from steam, has reemerged as a potentally compettve power generaton opton, partcularly n ard regons where power demand peaks durng the heat of the day. Currently over 45 CSP projects are n the plannng stages globally wth a combned capacty of 5,500 MW, accordng to Emergng Energy Research (2006), an advsory and consultng frm that tracks emergng technologes n global energy markets. How compettve s CSP electrcty and how much can CSP contrbute to carbon reducton by replacng the tradtonal thermal power plants? The answers to these questons depend on the cost of electrcty generated by CSP plants. Although a few studes (e.g. S&L, 2003, NREL, 2005) have projected future CSP costs based on certan assumptons such as technology advancement, economes of scale, and upward learnng curves, few studes have consdered the combned effects of ntermttency, solar rradance changes by season, system load changes over a year, and nteractons wth other generatng unts. Because the generaton of a solar plant vares over the day and year, the nteractons between CSP generators and other generators n the electrc system may play an mportant role n determnng costs. In effect, CSP electrcty generaton cost wll depend on the CSP market penetraton. Ths chapter examnes ths relatonshp. Three dfferent types of CSP technologes have been developed: (1) parabolc trough, (2) power tower, and (3) parabolc dsh. There s sgnfcant desgn and cost varatons among the three technologes. Because the parabolc trough s currently the most mature technology (Müller-Stenhagen and Treb, 2004a), we focus on ths technology and ts characterstcs n ths chapter, although many of our nsghts could also apply to power tower technologes. The methodology we develop here s customzed for the ObjECTS framework, but can also be adopted for other settngs. CSP plants ether need backup auxlary generaton or storage capacty to mantan electrcty supply when sunlght s low or not avalable. Therefore, the electrcty generaton cost for CSP plants has two components: costs arsng from the solar component and costs due to the backup and/or storage components. All exstng commercally operated CSP plants are hybrd plants. They ether have a backup natural- 25

33 gas-fred boler that can generate stream to run the turbne, or they have an auxlary natural-gas-fred heater for the solar feld flud that can be used to produce electrcty (NREL, 2005). Ths hybrd structure s an attractve feature of CSP compared to other solar technologes because the backup component has low captal cost and can mtgate ntermttency ssues to ensure system relablty. However, such hybrd CSPs are not cost effectve to provde base load electrcty. The addton of thermal storage would allow full use of avalable solar energy and would further reduce ntermttency ssues. A recent paper (Blar et al., 2006) that consders CSP s ntermttency ssue assumes sx hours of thermal storage. As the paper ndcates, ths storage assumpton greatly smplfes the treatment of resource varablty. Because such a plant s assumed to be dspatchable, the capacty value for the plant s assumed to be equal to the capacty factor durng the summer peak perod. In addton, surplus s assumed to be neglgble due to the general algnment of the solar resource and load. However, addng a 6-hour thermal storage to CSP plant can ncrease captal cost by more than 40% (NREL, 2005). Long-term cost effectve thermal storage technologes are stll under development. In ths chapter, we focus on hybrd CSPs wthout storage and we wll, therefore, deal wth the ntermttency ssue drectly. We wll nclude the case wth thermal storage n future research. Ths chapter s organzed as follows. Secton II presents the detaled methodology and assumptons. To better llustrate the methodologes, we provde some example calculatons. We present the results n Secton III and conduct senstvty analyss of key assumptons n Secton IV. Then we conclude n Secton V. For easy reference, Appendx 1 provdes a detaled lst of all varables used n ths chapter. 3.2 Methodology and Assumptons Three types of costs need to be consdered to calculate the electrcty generaton cost of CSP plants: captal costs of buldng the CSP hybrd plant, varable costs of runnng the solar component, and varable costs of runnng the backup component. The captal costs for buldng the CSP plant are a functon of plant capacty. To calculate varable costs, we need to know the electrcty output from the solar component and from the backup component, respectvely. Then the key questons are: When does the CSP backup mode need to run? How much electrcty does the CSP backup mode need to generate? How wll electrcty output from the backup component and from the solar component depend on the market penetraton level of CSPs? The followng analyss addresses these questons. Because the system load curve and the CSP electrcty output are correlated and both are senstve to tme of the day and seasons, we frst defne our classfcaton of tme slces 26

34 and the system load curve. Secondly, because CSP electrcty output from the solar component drectly depends on solar rradance levels, solar feld sze, and system effcences, we frst dscuss ther quanttatve relatonshps and then present how we process solar rradance data n order to estmate CSP solar output. Fnally, we detal our approach to estmatng CSP output from the solar and the backup components separately for each tme slce Classfcaton of Tme Slces Snce defnton of seasons can vary by locaton, we defne seasons based on rradance levels as follows. Summer: the three months wth the hghest rradance level. Wnter: the three months wth the lowest rradance level. Sprng/Fall: other months. We then classfy peak and ntermedate load perods nto dfferent tme slces for each season. The classfcaton used s presented n Table 3-1. The exact defnton of the tme slces s for computatonal convenence and s not crtcal for the results other than a requrement that the summer peak should be dentfable as ths s a key tme perod. Table 3-1. Classfcaton of Tme Slces and System Load Slce Classfcaton of Tme Slces Average System Load as A Percentage of the Maxmum System Load 1 Summer mornng (5:00-5:30) 45.46% 2 Summer daytme 1 (5:30-9:00) 57.87% 3 Summer daytme 2 (9:00-14:00) 85.73% 4 Summer peak (14:00-17:00) 96.78% 5 Summer evenng (17:00-24:00) 76.70% 6 Wnter mornng (6:00-10:00) 70.14% 7 Wnter daytme (10:00-17:30) 63.85% 8 Wnter evenng (17:30-23:00) 67.69% 9 Sprng/Fall daytme (5:00-19:30) 61.99% 10 Sprng/Fall evenng (19:30-22:00) 59.36% System Load Curve 27

35 Electrc system load, usually measured n megawatts (MW), refers to the amount of electrc power delvered or requred at any specfc pont or ponts on a system. A system load curve shows the level of a load for each tme perod consdered. The assumed system load curve, denoted as AveSysLoad, s estmated usng Calforna electrcty data for 2003 (CEC 2005). The average system load n each tme slce as a fracton of the maxmum system load s shown n Table 3-1. The load of an electrc utlty system s affected by many factors such as customer mx (e.g. resdental, commercal and ndustral), temperature, and equpment type and effcency. For example, a hot summer can sgnfcantly ncrease the summer peak load due to the ncreased coolng demand. We wll examne how the shape of the load curve mpacts the results later n the chapter. Electrc system load can be classfed as base load, peak load, and ntermedate (I&P) load. Base load refers to the mnmum amount of power that a utlty must make avalable to ts customers and I&P load refers to the demand that exceeds base load. Thus base load power plants do not follow the load curve and generally run at all tmes except for repars or scheduled mantenance. I&P generaton vares wth the load curve. Power plants that provde I&P load, n aggregate, must follow the load curve. For ths analyss we consder the case where CSP plants serve I&P load Factors that Determne CSP Solar Output CSP solar output drectly depends on solar rradance levels (duraton and ntensty), solar feld sze, and system effcences. Ther quanttatve relatonshps can be expressed n the followng equaton 4. (3-1) Output_Net=Output_Gross*(1-Loss_Parastc) =(1-Loss_Parastc)*Eff_Turbne*Asf*(DNI*Eff_OPT-Loss_HCE- Loss_SFP)/1,000,000W/MW, where the meanngs of each varable and reference values are gven n Table 3-2. Once the CSP plant s bult, the solar feld area and system effcences are fxed, so usng equaton (3-1), we can calculate how CSP solar output vares wth solar rradance level. 4 Ths functonal form s from the Solar Advsor Model (SAM) developed by NREL, n conjuncton wth Sanda Natonal Laboratory and n partnershp wth the U.S. Department of Energy (reference: personal communcaton wth SAM support staff). 28

36 The assumed solar fled area s based on the optmal solar multple we calculate for our baselne case. The solar multple s the rato of the solar energy collected at the desgn pont to the amount of solar energy requred to generate the rated turbne gross power (NREL, 2005). Hgher solar multples ncrease CSP plant capacty factors but also ncrease captal cost. An optmzaton procedure s used to fnd out the solar multple that acheves the lowest CSP electrcty cost, whch s 1.07 n our baselne case. Table 3-2 Lst of Varables Related to CSP Solar Output Varable Meanng Value Source Output_Net Desgn Turbne Net Output (MW) -- Calculated Output_Gross Desgn Turbne Gross Output (MW) -- Calculated Loss_Parastc Electrc Parastc Loss (%) 11.1% Assumed* Eff_Turbne Desgn Turbne Gross Effcency (%) 36.4% Assumed* Asf Solar Feld Area (m 2 ) 685,666 Assumed*** DNI Drect Normal Irradance (W/m 2 ) Value vares Eff_OPT Optcal Effcency (%) 60.2% Assumed* Loss_HCE HCE Thermal Losses (W/m 2 ) Assumed** Loss_SFP Solar Feld Ppng Heat Losses (W/m 2 ) Assumed ** References: *Kearney and Prce (2004), ** SAM, *** Authors calculated optmal solar feld area for a 100-MW net capacty CSP plant n Daggett Barstow, CA Solar Irradance Data We use solar rradance data from NREL s Natonal Solar Radaton Data Base and Update 5. The Update contans annual drect normal rradance (DNI) hourly mean data and DNI threshold data (whch ndcate the number of subsequent days DNI s less than a certan threshold over the 15-year perod). CSP plants requre a mnmum rradance level to be operatonal. Currently, for plants wthout storage, the mnmum rradance level s assumed to be 300 W/m 2 (Kearney and Prce, 2004). We use the DNI threshold data to calculate NoSunDays, whch represents the number of days n a season durng whch there s not suffcent drect sunlght to operate the CSP plant. A threshold of 3000 Wh/m 2 /day s used n ths study. The threshold nformaton s used to adjust the NREL s annual DNI hourly mean data to obtan an estmate of the DNI hourly mean value for each month for non-cloudy days, defned as days wth rradance greater than 3000 Wh/m 2 /day. We use Daggett Barstow (Lat (N) 34.87, Long (W) ), Calforna, as an example to llustrate the adjustment procedure as follows. 5 Data source: 29