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Home My WebLink About20061107Said-Youngblood rebuttal.pdf-:- DAHO POWE R (8) RECEIVED BARTON L. KLINE Senior Attorney 100& NOY -6 PM 4: 36 An IDACORP Company IDAHD fo;UbUC UTILITIES COf\M,HSSIO; November 6, 2006 Jean D. Jewell, Secretary Idaho Public Utilities Commission 472 West Washington Street P. O. Box 83720 Boise , Idaho 83720-0074 Re:Case No. IPC-06- In the Matter of Idaho Power Company s Application for a Certificate of Convenience and Necessity for the Evander Andrews Power Plant Dear Ms. Jewell: Please find enclosed for filing an original and two (9) copies of the following documents: Direct Rebuttal Testimony of M. Mark Stokes; and Direct Rebuttal Testimony of Gregory W. Said and Michael J. Youngblood. Also enclosed is a disk for use by the Court Reporter. I would appreciate it if you would return a stamped copy of this transmittal letter to me in the enclosed self-addressed stamped envelope. Barton L. Kline BLK:sh Enclosures Telephone (208) 388-2692 Fax (208) 388-2682 E-mail bkline(g)idahopower.com THIS DOCUMENT CONTAINS CONFIDENTIAL INFQ~~N 200& tmV -6 PM 4: 31 ,-- - 'J' ," i 1D;\hU !-,ubU\~ UTILITIES COMMISSION BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION IN THE MATTER OF IDAHO POWER COMPANY'S APPLICATION FOR A CERTIFICATE OF CONVENIENCE AND NECESSITY FOR THE EVANDER ANDREWS POWER PLANT Case No. IPC-O6- IDAHO POWER COMPANY DIRECT REBUTTAL TESTIMONY GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD Please state your names and positions with Idaho Power Company ("Idaho Power " or the "Company My name is Gregory W. Said and I am the Manager of Revenue Requirement at Idaho Power.My name is Michael Youngblood and I am a Senior pricing Analyst at Idaho Power. Are you the same Gregory W. Said who previously submi tted direct testimony in this proceeding? Yes , I am. Mr. Youngblood, have you previously submi tted direct testimony in this proceeding? No. Mr. Youngblood, please describe your educational background and work experience with Idaho Power Company. In May of 1977 , I received a Bachelor of Science Degree in Mathematics and Computer Science from the University of Idaho.From 1994 through 1996, I was a graduate student in the MBA program at Colorado State uni versi ty. I became employed by Idaho Power Company in 1977. During my career, I have worked in several departments and subsidiaries of the Company, including Systems Development, Demand Planning, Strategic Planning and IDACORP Solutions. Most relevant to this testimony, is my experience wi thin the Pricing and Regulatory Services Department.From 1981 to 1988,I worked' as a Rate Analyst in the Rates and Planning SAID /YOUNGBLOOD Di-Reb Idaho Power Company Department where I was responsible for the preparation of electric rate design studies and bill frequency analyses. was also responsible for the validation and analysis of the load research data used for cost of service allocations. From 1988 through 1991, I worked in Demand Planning and was responsible for load research and load forecasting functions including sample design, implementation, data retrieval, analysis and reporting.I was responsible for the preparation of the five-year and twenty-year load forecasts used in revenue proj ections and resource plans as well as the presentation of these forecasts to the public and regulatory commissions. In 2001, I returned to the Pricing and Regulatory Services Department and have worked on special proj ects related to deregulation , the Company s Integrated Resource Plan, and filings with this Commission and the Oregon Public utili ty Commission.In 2005, I was a member of the Peaking Resource RFP Bid Evaluation Team ("Evaluation Te~m that selected the Evander Andrews plant which is the subj ect matter of these proceedings. What is the purpose of your direct rebuttal testimony in this case? The purpose of our direct rebuttal testimony in this case is to address, among other things,(1) Commission Staff witness Sterling s testimony regarding the evaluation SAID / YOUNGBLOOD Di-Reb Idaho Power Company CASE NO. IPC-O6- IDAHO POWERCO. DIRECT REBUTTAL TES TIM 0 NY OF GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD, PAGES 3 THROUGH 19, ARE CONFIDENTIAL AND WERE NOT SCANNED Evander Andrews plant.What is the Company s response to that recommendation? The Company is willing to provide the Commission with a Transmission Commitment Estimate not to exceed a certain sum.At this time, however, that figure cannot be provided as sufficient studies have not been conducted to provide a reliable estimate.Once those studies are completed, a Transmission Commitment Estimate can be provided. Why is the Company 'unable ' at this time to provide a Commitment Estimate for the transmission and substation facili ties that would be required for the Evander Andrews si te? Federal Energy Regulatory Commission ("FERC" orders and rulings define the manner in which developers of generation projects can interact with transmission providers that are subj ect to FERC jurisdiction.Whether the developer is Idaho Power Company or an unaffiliated party, the Company s delivery department is obligated to treat all interconnection requests consistently and in a non- discriminatory manner. FERC's orders define three distinct study phases to assess what system modifications may be required to integrate a generation project into an electrical system. The three studies determine whether the system can accept SAID / YOUNGBLOOD Di-Reb Idaho Power Company the generation project output and, if not, what facility modifications will be required , and, finally, performance of the engineering and design work needed to construct the required facilities.This procedure is outlined in Idaho Power s Open Access Transmission Tariff ("OATT" In what phase of study are the transmission requirements for the Evander Andrews facility? This transmission project is currently in the last phase of study, that is, engineering design work is underway but not yet completed. What degree of cost accuracy will the Company delivery department provide? An interconnection customer may opt for one of two study options.Option one provides for cost estimates with an accuracy of +/- 20% to be completed within 90 calendar days.Option two, which requires 180 calendar days, provides a cost estimate with +/- 10% accuracy.In order to expedi te receipt of the transmission cost information, the Company s Power Supply department has requested that the cost estimate be determined with an accuracy level of 20%. Until a cost estimate within the selected accuracy level is obtained, only a non-binding good faith estimate is available to the party requesting interconnection to the Company s transmission system.Idaho Power s Power Supply department expects to receive a cost estimate with a 20% SAID/YOUNGBLOOD Di-Reb Idaho Power Company CASE NO. IPC-O6- IDAHO POWERCO. DIRECT REBUTTAL TESTIMONY OF GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD, PAGES 22 AND 23, ARE CONFIDENTIAL AND WERE NOT SCANNED resort, load curtailments. Longer-term alternatives include:(1) transmission system expansions to increase import capacity,(2 ) construction of base-load type resources and the associated transmission to enable the resources ' output to be delivered to the Treasure Valley load center, and (3) development of addi tional DSM programs requiring longer lead times to implement.The Company believes that these alternatives would be more co'stly to Idaho Power customers than constructing the proposed peaking resource. How long would these alternative resources be able to reliably provide electrical energy to Idaho Power cus tomers ? Theoretically, the temporary generation units might be a solution for quite a while if the Company added enough of them.However, Idaho Power s summertime peak-hour loads are forecast to grow at about 80 MW per year.Wi thout the new unit at the Evander Andrews Complex, under the 90 percentile water and 70~ percentile load and 95 ~ percentile peak-hour load planning scenario, the July 2007 peak-hour defici t is forecast to be 111 MW. Assuming all other resources identified in the Company s 2006 IRP's preferred portfolio are implemented as planned, in July of 2008, 2009 and 2010 the summertime peak- hour deficits are forecast to reach 147 MW and 154 MW and SAID /YOUNGBLOOD Di-Reb Idaho Power Company 268 MW, respectively.The 268 MW deficit forecast for 2010 incorporated an expected DSM contribution of almost 71 MW. If for some reason this reduction did not materialize as planned, the 2010 peak-hour deficit would grow to nearly 339 MW. Even if the forecast DSM contributions materialized as expected, 268 MW of temporary generation resources is excessive, expensive and logistically complex.A more practical solution is to have permanently installed generation capacity, such as the proposed Evander Andrews uni t, to reliably serve Idaho Power s peak-hour loads. IDAHO POWER DSM EFFORTS On page 36 of his testimony, lines 4-7, Dr. Reading asserts that between 1995 and 2001 , Idaho Power slashed its spending on DSM programs from $ 6.2 Million to $1.6 Million.What attributed to this funding cut? You may recall , during that timeframe, deregulation of the electric industry was an issue on the forefront.Even in states where full retail deregulation was not expected, wholesale markets were expecting to provide future resources.As recognized by the Commission in its acknowledgement of the Company s 2000 IRP, Idaho Power, along other electric utili ties in the region, began to wind down their DSM programs in the late 1990s in response to changing market expectations.In place of SAID / YOUNGBLOOD Di-Reb Idaho Power Company utili ty direct acquisition DSM programs, Idaho Power moved to a regional approach to conservation during that period through its participation in the Northwest Energy Efficiency Alliance. What DSM program spehding commitments has the Company made more recently? Following the 2000-2001 western energy crisis, utili ties once again turned to more traditional concepts for supplying future resources.There was also a reemergence of integrated resource planning with a renewed emphasis on utility-based DSM programs.Idaho Power now funds DSM acti vi ties through the Energy Efficiency Rider, Schedule 91. This removes the threat of stranded investment. In 2005, the Company spent $6.7 Million on DSM activi ties, an increase of approximately 80% over the previous year.At the end of the third quarter of 2006, Idaho Power spent $6.62 Million to fund DSM activities. By the end of 2006, the Company expects to, once again, make a significant increase in DSM spending over the previous year. Dr. Reading c laims th~ t "Idaho Power s DSM and conservation achievements have been relatively poor. Reading Direct at 35, 11 18-19.On what basis does he make that claim? Dr. Reading s assessment concentrates on enerGY savings as an indicator that the Company s DSM efforts have SAID / YOUNGBLOOD Di-Reb Idaho Power Company been poor.Idaho Power, on the other hand, has strategically focused its DSM efforts in recent years on programs that reduce summer peak demands Targeting summer peak demands is consistent with the recent resource acquisitions of peaking units. Summertime loads drive Idaho Power s capacity needs. Therefore, many of the Company s DSM programs are intentionally designed to provide significant load reductions during summertime peak-hour needs.It's for this reason that the Company has focused its efforts on peak reductions instead of overall energy reductions. In 2005, Idaho Power achieved a total peak load reduction of 47 MW 13.with 43 MW resulting from its two demand response programs. Idaho Power has also focused its DSM development efforts on programs that target lost-opportunity energy savings. Since the 2004 IRP , the Company has implemented the Energy Star~ Homes Northwest and Building Efficiency 1.8 programs that achieve energy savings in the commercial and residential sectors that would otherwise be lost as new construction occurs. In the 2006 IRP, the Company identified additional non- lost-opportuni ty DSM resources, often referred to as retrofi t programs, in the residential and commercial sectors.These programs will greatly broaden Idaho Power DSM acti vi ties beyond the initial focus on summer peak SAID / YOUNGBLOOD Di-Reb Idaho Power Company reduction and lost-opportunity energy savings. In fact, as Dr. Reading acknowledges in his testimony, the Company plans to significantly increase its spending on DSM programs as it implements the new and expanded programs identified in the 2006 IRP.The addi tional DSM resources are expected to reduce loads by approximately 88 aMW (on an annual basis) and reduce the system peak-hour load by approximately 187 MW during the summertime. On page 39, lines 11-12 of his testimony, Dr. Reading states that, according to the Quantum Consulting study completed on behalf of Idaho Power,Idaho Power may be underestimating the amount of peak demand savings through DSM that are available to it.How do you respond to that statement? Dr. Reading s testimony mischaracterizes the conclusions drawn by Quantum Consulting.In November 2004 at the request of Idaho Power , Quantum Consulting conducted a study to determine the potential for DSM resources through 2013 for the Company s commercial and residential sectors. The study identified a total economic potential of 384 MW of 21'peak demand reduction, or nearly 23% of the combined residential and commercial peak demand forecast in 2013. However , there is a distinct difference between energy savings potential that is determined to be "economic " and savings potential that is determined to be "achievable SAID /YOUNGBLOOD Di-Reb Idaho Power Company through utility-operated programs. Economic potential, Quantum Consulting explains,represents the savings possible if all cost-effective measures were installed in every application deemed physically feasible.See Exhibi t 3 at ES-(emphasis added) Quantum Consulting further describes economic potential as "a theoretical quantity that will exceed the amount of potential we estimate to be achievable through even the most aggressive voluntary program activities.See Exhibit 3 at 19. On the other hand, achievable potential , according to Quantum Consulting, can be viewed as a subset of economic potential which ranges from "maximum achievable " or "the amount of economic potential that could be achieved over time under the most aggressive program scenario possible " to naturally occurring " or the amount of savings estimated to occur "in the absence of any utility or governmental intervention. See Exhibit 3 at 2-2 and 2- To develop the estimates of achievable potential Quantum Consulting modeled energy savings potential based on four different funding ratios for the incremental cost of implementing the various measures.The cost share ratios used in the assessment ranged from 100% for the maximum achievable scenario to 33% for the low funding scenario. For year 10 of the analysis, estimates of peak demand SAID /YOUNGBLOOD Di-Reb Idaho Power Company reductions corresponded directly to cost share. Peak reduction estimates ranged from 190 MW (around 11% of 2013 peak demand) for the maximum achievable scenario to 42 MW (less than 3% of 2013 peak demand) for the low funding scenario.See Exhibit 3 at ES-By utilizing the estimate of economic potential rather than achievable potential , Dr. Reading is misstating the DSM conclusions drawn by Quantum Consulting in its 2004 report. To help put the Quantum Consulting results in perspective , what is Idaho Power s goal for peak reduction by 2013? Idaho Power has a target of 123 MW of peak reduction from the residential and commercial sectors by 2013, which assumes a 75% cost share. All the DSM resources identified in the 2004 IRP and 2006 IRP combined are expected to achieve 251 MW of peak reduction by 2013. Dr. Reading asserts on pages 40 and 41 of his testimony that "allowing Idaho Power to construct a 170 MW gas-fired unit will discourage Idaho Power from making (any further DSM commitments) .Do you agree wi th Dr. Reading assessment? No, I do not agree with the conclusion drawn by Dr. Reading.In developing the 2006 IRP, the Company worked with the Integrated Resource plan Advisory Council ("IRPAC" which was comprised of major stakeholders representing the SAID / YOUNGBLOOD Di-Reb Idaho Power Company environmental community, major industrial customers, irrigation customers, state legislators, public utility commission representatives, the Governor s office and others.Input from the IRPAC, including maj or industrial customers, was considered and incorporated into the 2006 IRP. The 2006 IRP assumes that the proposed Evander Andrews peaking resource that is the subject matter of this proceeding will be constructed and placed in service. Despite that assumption, the 2006 IRP sets forth various DSM programs that will be implemented along with other measures in order to meet the Company s load requirements.Thus, Idaho Power has already demonstrated through its 2006 IRP that allowing construction of the Evander Andrews peaking facility will not have a dampening effect on the Company incenti ve to implement new DSM programs.Dr. Reading testimony on this matter is unwarranted and unjustified. Wi tness Sterling testifies on page 41, lines 4- that he believes "that no matter how carefully crafted and well-intended an RFP evaluation methodology must be, the reasonableness of the outcome must be reevaluated at the end of the process, especially when the result comes down to a tradeoff between the price and non-price factors. Ultimately, he observes,the final result must make sense and be justifiable.Do you agree with this testimony? SAID /YOUNGBLOOD Di-Reb Idaho Power Company CASE NO. IPC-O6- IDAHO POWER CO. DIRECT REBUTTAL TESTIMONY OF GREGO R Y W. SAID AND MI HAE L J. YOUNGBLOOD , PAGE 32, IS 0 NFID E NTIAL AND WAS NOT SCANNED Yes, it does. SAID / YOUNGBLOOD Di-Reb Idaho Power Company CERTIFICATE OF SERVICE I HEREBY CERTIFY that on this 6th day of November, 2006, I served a true and correct copy of the within and foregoing IDAHO POWER COMPANY DIRECT REBUTTAL TESTIMONY OF GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD upon the following named parties by the method indicated below, and addressed to the following: Commission Staff Hand Delivered Donovan Walker US. Mail Deputy Attorney General Overnight Mail Idaho Public Utilities Commission FAX 472 W. Washington (83702)Email: Donovan. walker(Q)puc.idaho.gov O. Box 83720 Boise, Idaho 83720-0074 Industrial Customers of Idaho Power Hand Delivered Peter J. Richardson, Esq.US. Mail Richardson & O'Leary Overnight Mail 515 N. 27th Street FAX O. Box 7218 Email: peter(Q)richardsonandoleary.com Boise, Idaho 83702 Don Reading Ben Johnson Associates Hand Delivered 6070 Hill Road US. Mail Boise, Idaho 83702 Overnight Mail FAX Email: dreading(Q)mindspring.com ($. Monica B. Moen CERTIFICATE OF SERVICE, Page IDAHO POWER COMPANY CASE NO.IPC-O 6- DIRECT REBUTTAL TESTIMONY GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD EXHIBIT RECEIVED 2006 Nay -6 PM 4= IDAHO PUbLIC UTILITIES COMMISSION ' T(;j~~ ' j,.~,:) ;;, n:.~D!:Ji~.!tti REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES FOR PERMITTING ONE OF Two SIMPLE CYCLE COMBUSTION TURBINES AT ALTERNATIVE SITES IN ADA, CANYON, AND ELMORE COUNTIES TETRA TECH EM INC. , ,, ' .' 1325 AlRMOTIVE WAY, SUITE 200 RENO, NEVADA 89502 November 2005 CONFIDENTIAL . .. . IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO CONTENTS SECTION PAGE EXECUTIVE SlJMMARY ........................................................................................................ mTRODUCTION .......................................................................................................... IDAHO POWER SCREENING-LEVEL TURBINE DISPERSION MODELmG RESULTS .... ....... ............ .... ................... ........ ......... .... ........................................ ...... ..... LAND DEVELOPMENT POLICIES FOR ADA, CANYON, AND ELMORE COUNTIES, IDAHO...................................................................................................... ADA COUNTY............... ............... ............... ......... ....... ............. ........ ................ ........ 9 CANYON COUNTY................................................................................................ ELMORE COUNTY........ ............... ............... ......... .......... ................................ ........ 16 REFERENCES.......................................................................................................................... TABLES TABLE PAGE Idaho Power Screening Modeling Natural Gas Turbines ................................................. 3 Idaho Power Screening Turbine Modeling Results, Class II Modeling Results NAAQS Impacts Using 7EA Turbine ............................................................................. Idaho Power Screening Turbine Modeling Results, Class II Modeling Results NAAQS Impacts Using 501F Turbine............................................................................. Canyon County 2003 Summary of Pollutant Concentrations .......................................... 1999 Emissions Summary of Criteria Air Pollutants....................................................... FIGURES FIGURE PAGE NOx Spatial Representatives Canyon County Site........................................................... 6 NOx Spatial Representatives Ada County Site................................................................. 7 NOx Spatial Representatives Elmore County Site............................................................ Ada County Site............................................................................................................. 9 Canyon County Site....................................................................................................... Elmore County Site........................................................................................................ PAGE i IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO EXECUTIVE SUMMARY Idaho Power Company is developing a plan for constructing a natural gas-fIred simple cycle combustion turbine to provide additional peaking power to the Idaho electric power grid. Three sites were chosen as alternatives for installing the gas-tired simple cycle combustion turbine. The purpose of this study is to review state and local requirements related to permitting issues for air quality, noise, and land use at three alternative sites in Ada, Canyon, and Elmore Counties in southern Idaho. The units selected as representing the range of options, included a Westinghouse 501 F and a General Electric 7EA. Manufacturer s specifications were obtained ITom similar projects. The scope of the project includes potential environmental impacts ITom the additional natural gas and electric transmission systems (if applicable). One of the tasks was to perform a screening level air quality impact analysis to ascertain the likelihood of complying with national and state ambient air quality standards. Tetra Tech EM Inc. (Tetra Tech) reviewed these sites using publicly available information. The Idaho Department of Environmental Quality was contacted about potential air quality permitting issues in each of the three counties. No environmental or special interest groups were contacted. Without specific knowledge of any proposals, Tetra Tech prepared a review of potential constraints to permitting that could preclude or significantly delay the project at any of the proposed sites. Results of this investigation did not uncover any critical environmental issues that would significantly delay or prohibit the construction of a natural gas-fIred simple cycle combustion turbine at any of the three sites. Results of the air quality impact analysis shows that there should not be any exceedences of the ambient air quality standards PAGE 1 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL IsSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO INTRODUCTION Idaho Power Company is developing a plan for constructing a natural gas-fired simple cycle combustion turbine to provide additional peaking power to the Idaho electric power grid. Three sites were chosen as alternatives for installing the gas-fired simple cycle combustion turbine. The purpose of this study is to review state and local requirements related to permitting issues for air quality, noise, and land use at three alternative sites in Ada, Canyon, and Elmore Counties in southern Idaho. The units selected as representing the range of options, included a Westinghouse 501 F and a General Electric 7EA Manufacturer s specifications were obtained from similar projects. The scope of the projects included potential environmental impacts from the additional natural gas and electric transmission systems (as applicable). Without specific knowledge of any proposals, Tetra Tech EM Inc. (Tetra Tech) prepared a review of potential constraints to pennitting that could preclude or significantly delay the project at one ofthe proposed sites. Tetra Tech reviewed these sites using publicly available information. The Idaho Department of Environmental Quality was contacted about potential air quality permitting issues in each of the three counties (Idaho Department of Environmental Quality, 2005). IDAHO POWER SCREENING-LEVEL TURBINE DISPERSION MODELING RESUL TS Tetra Tech completed the screening-level dispersion modeling for Idaho Power to evaluate potential criteria pollutant impacts from two turbine models at three different sites in Idaho. The modeling was completed using the ISCST3 model and included one year of meteorological data collected at the Boise, Idaho National Weather Service station during 1991. The modeling included effects from building downwash. Since specific source parameter and emission information were not available, comparable values were taken from other projects that used the same turbine models. Table 1 shows the stack parameter information used for each turbine model. Table 2 shows the model results for the 7EA at the three potential sites evaluated in the modeling. Table 3 shows the model results for the 501F at the three potential sites. The values in green represent the lowest modeled value for each pollutant and averaging period at each of the three sites. The values shown in Tables 2 and 3 represent the highest modeled values from the model output and do not take into account whether the impacts would fall on or off the facility property. All modeled impacts from the 7EA and 501F are low and any of the three sites would be acceptable should it be selected. The impacts from the 501F are slightly higher than the 7EA Nitrogen oxide (NOz) is the only pollutant that had a modeled impact that exceeded the significant impact level, and only for the PAGE 2 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO 50 IF at the Elmore County site where the value was slightly above the annual significant impact level. (The "significant impact" level, also known as a de minimus, is the level at which no review is required under Environmental Protection Agency s (EPA) New Source Review or the Idaho Department of Environmental Quality) In the case of the Elmore County site with the 501F, the air quality impact for nitrogen oxides is 2.3 micrograms per cubic meter and the national ambient air quality standard is 100 micrograms per cubic meter. A spatial representation ofthe nitrogen oxide impacts for the three sites with the larger Westinghouse 501 F are shown in Figures 1 through 3. TABLE 1 IDAHO POWER SCREENJNG MODELING NATURAL GAS TURD INE Hourlv Emission Rate (els) Stack Stack Stack Stack Source UTM UTM Hei~ht Temp Velocity Diameter Name X(m)Y(m)(m)(K)(mls)(m)PM1n S02 NOx VOC 7EA Site Specific 18.810.49.907 069 284 12.220 378 501F Site Specific 18.722.75.1.88 690 193 135 032 0.378 Notes: 7EA 501F gls m/s NOx PMIO S02 VOC UTM Y(m) X(m) General Electric 7EA Westinghouse 501 F Caroon monoxide Gram per second Meter Meter per second Nitrous oxides Particulate matter w/aerodynamic diameter less than 10 microns Sulfur dioxide Volatile organic compound Universal Transverse Mercator Coordinates Coordinates PAGE 3 -- - - ID A H O P O W E R C O M P A N Y RE V I E W O F P O T E N T I A L L Y C R I T I C A L E N V I R O N M E N T A L I S S U E S AD A , C AN Y O N ~ AN D E L M O R E . 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No t e s : 7E A p. g / m N/ A NA A Q S NO 2 PM 1 O S0 2 Ge n e r a l E l e c t r i c 7 E A Mi c r o g r a m p e r c u b i c m e t e r Ca r b o n m o n o x i d e No t a p p l i c a b l e Ne v a d a A m b i e n t A i r Q u a l i t y S t a n d a r d s Nit r o u s d i o x i d e Pa r t i c u l a t e m a t t e r w / a e r o d y n a m i c d i a m e t e r l e s s t h a n 1 0 m i c r o n s Su l f u r d i o x i d e PA G E 4 I" ' ID A H O P O W E R C O M P A N Y RE V I E W O F P O T E N T I A L L Y C R I T I C A L E N V I R O N M E N T A L i s S U E S AD A , C A N Y O N , A N D E L M O R E C O U N T I E S , I D A H O , , , " . : ~ \ j " !" c L l: " , " . !c l i Ei ~ :; i TA B L E 3 ID A H O P O W E R S C R E E N I N G T U R B I N E M O D E L I N G R E S U L T S CL A S S n M O D E L I N G R E S U L T S , N A A Q S I M P A C T S U S I N G 5 0 1 F TU R B I N E 50 1 F M o d e l e d C o n c e n t r a t i o n (l ) (a l ! ! m NA A Q S A v e r a w . n ~ Si ~ n i f i c a n t I m p a c t Co n c e n t r a t i o n Po l l u t a n t Pe r i o d AD A C o u n t Y S i t e El m o r e C o u n t y S i t e Ca n y o n C o u n t y S i t e Le v e l (a 2 l m (a l ! ! m N0 2 An n u a l 10 0 PM I O 24 H o u r s 1. 7 15 0 An n u a l 1 H o u r 25 . 21 7 . 20 0 0 00 0 8 H o u r s 10 . 4 27 . 50 0 00 0 80 2 3 H o u r s 1. 0 30 0 24 H o u r s 36 5 An n u a l Oz o n e 1 H o u r 2. 4 20 . 4 nl a 23 5 (1 ) M o d e l e d v a l u e s d o n o t i n c l u d e b a c k g r o u n d c o n c e n t r a t i o n s GR E E N v a l u e s r e p r e s e n t t h e l o w e s t m o d e l e d v a l u e o f t h e t h r e e s i t e s e v a l u a t e d No t e s : 50 1 F fJ . . g i m N/ A NA A Q S N0 2 PM I O S0 2 We s t i n g h o u s e 5 0 1 F Mi c r o g r a m p e r c u b i c m e t e r Ca r b o n m o n o x i d e No t a p p l i c a b l e Ne v a d a A m b i e n t A i r Q u a l i t y S t a n d a r d s Nit r o u s d i o x i d e Pa r t i c u l a t e m a t t e r w / a e r o d y n a m i c d i a m e t e r l e s s t h a n 1 0 m i c r o n s Su l f u r d i o x i d e PA G E 5 '.. IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA~ CANYONL AND I:!-MORE COUNTIES,- IDAHO FIGURE 1 IDAHO POWER, CANYON COUNTY SITE SPATIAL REPRESENTATION PLOT ESTIMATED NI1ROGEN DIOXIDE CONCEN1RATION 4844000 4842000 4838000 4836000 Canyon County t\rbine location 4834000 4832000 4830000 524000 526000 528000 530000 532000 534000 536000 U1M Basting (meters) 0.3 uglm3 25 uglm3 02 uglm3 PAGE 6 : V ' . . .' . I , . IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO 4826000 4824000 4822000 4820000 4818000 4816000 4814000 4812000 FIGURE 2 IDAHO POWER, ADA COUNTY SITE SPATIAL REPRESENT A 11 ON PLOT ESTIMATED NI1ROGEN DIOXIDE CONCENTRATION Ada COtmty turbine location 562000 564000 566000 568000 570000 572000 574000 6 uglm3 0.5 uglm3 4uglm3 0.3 uglm3 0.2 uglm3 UIM Basting (meters) PAGE 7 1188 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENT AL ISSUES ADA, CANYON, AND ELMORE COUNTffiS, IDAHO 4788000 4786000 4784000 ';; 'bi; 4782000 4780000 4778000 4776000 4774000 FIGURE 3 IDAHO POWER, ELMORE COUNTY SITE SPATIAL REPRESENTATION PLOT ESTIMATED NI1ROGENDIOXIDE CONCENTRATION Elmore County~~bine location ""' iii ::, 596000 598000 600000 602000 604000 606000 608000 610000 i'" 2 uglm3 2uglm3 1.8 uglm3 1.6 uglm3 1.4 uglm3 1.2 uglm3 1 uglm3 8 uglm3 6 uglm3 0.4 uglm3 2 uglm3 DIM Basting (meters) PAGE 8 ..' 181-IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO LAND DEVELOPMENT POLICIES FOR ADA, CANYON, AND ELMORE COUNTIES, IDAHO The following information regarding land development issues was prepared for the development of future sites. This information is based on potential areas of concern that may be applicable to the construction of a new facility for Ada, Canyon, and Elmore Counties, Idaho. This section focuses on land use, noise, air quality, natural resources, cultural and historical resources, and utility issues as they may apply to the future project(s) for each specific county. ADA COUNTY The ADA County site (Figure 4) is located at the base of a hill and adjacent to commercial and industrial development. There is a single residence within 1 mile of the facility that did not appear to be occupied. There is 240 kilovolt electric transmission line adjacent to the site that will significantly reduce additional environmental impacts ITom construction if there is sufficient capacity on that line to accommodate the new unit. FIGURE 4 ADA COUNTY SITE PAGE 9 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL IsSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO Land Use The Ada County Comprehensive Plan (or Comprehensive Plan) addresses a number of land use issues (Ada County Comprehensive Plan Update, 2005. htqJ://www.adaweb.netidepartmentsi developmentservices/AdaCountvComprehensive). The applicable policies and goals that may affect potential development are summarized below. Support public facilities, utilities, and transmission lines to serve all areas of Ada County. Require that new development be designed for compatibility with the natural environment taking into consideration the topography, drainage, and other natural systems. Applications for industrial development must confonn to the adopted local, state and federal standards for air emissions; drainage systems; effects on neighboring land uses; employment characteristics; environmental impacts; fire and public safety; nature and volume of industrial activity; noise pollution; odor emissions; sewage collection and treatment; solid waste disposal; streets/roads/transportation; visual impacts; water quality; and utility services Lands designated for industrial use may be rezoned when a significant need for the land use change can be shown that will advance other goals of the Comprehensive Plan. Encourage commercial facilities at locations where they complement the existing transportation facilities and adjacent land uses. Noise The noise ordnance is principally aimed at air traffic and does not target any other specific projects. However, this noise limitation may set policy for other industrial activities including power generation facilities. The limits for airport noise range from 65 to 75 day-night weighted average noise level. Air Quality According to Mr. Bill Rogers at the Idaho Department of Environmental Quality, Ada County is maintenance area for particulate matter w/aerodynamic diameter less than 10 microns (PMlO) and carbon monoxide (CO). A maintenance plan indicates the air quality ambient monitoring data shows a trend toward non-attainment. However, there are currently no additional control requirements or emission offsets that would preclude development of power generations at the Ada County site. Idaho air quality is in attainment for CO, Ozone (1 hour), Ozone (8 hours), particulate matter w/aerodynamic diameter less than 2.5 microns (PM2.s), PMlO, sulfur dioxide (S02), and lead (Pb) levels. Although the U.S. Environmental Protection Agency (EPA) Region 10 web site shows some non-attainment areas in the eastern Idaho area for CO and PMlO dated 2002, EP A did a review and assessment for attainment and just published the results this year. Apparently the PAGE 10 I".IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO Region 10 web site is not fully updated to reflect this change. No air quality summary table was available in electronic format for ADA County. Ada County had the second highest number of reported releases of toxic pollutants. The EP data show chemicals and electrical equipment had the highest number of releases in Ada County. Due to the size of the units in this investigation and the limitation of only natural gas as a fuel these units should not be subject to EP A Toxics Release Index (TRI) reporting. Natural Resources The following summarizes the goal statement and policies related to natural resources from the Ada County Comprehensive Plan: Retain the existing living, working, and natural environment by ensuring that land, air water, and wildlife resources are properly managed. Support infill development with a variety of land uses and appropriate zoning designations to minimize development encroaching into natural resource areas. Buffer designated natural resource areas from more intensive urban uses with compatible transitional land uses. Establish density and development standards designed to protect existing terrain, steep slopes, benches, floodways, habitat areas, and ridge lines. Protect and preserve the natural beauty and habitat of the Snake River and land abutting the river and canyon. Protect and preserve the natural beauty and habitat of the Boise River and the black cottonwood forest and land abutting the river. Locate development away from designated wildlife habitat areas. Connect wildlife habitat areas by migration/movement corridors. Encourage preservation of existing healthy trees and rare plants throughout the County. Require all development to comply with applicable air quality standards. Cultural and Historic Resources The following summarizes the goal statement and policies related to cultural and historic resources from the Ada County Comprehensive Plan (Ada County Comprehensive Plan Update, 2005). To identify, protect, enhance, and perpetuate sites and structures that are significant components of the County's cultural, archeological, historical, agricultural, and architectural resources. Encourage the rehabilitation and retention of existing historic structures in Ada County. PAGE 11 ... " 0 . ' . .. IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO Establish a historic overlay zoning district with flexible development standards to allow convenient rehabilitation and multiple use of historic buildings and special sites within the County. Participate in the Idaho State Historical Society s Certified Local Government Program for historic preservation and improve interagency communication with all cities in the County and other community organizations regarding historic preservation. Support the Ada County Historic Preservation Council's role in identifying and inventorying all areas and sites that should be recognized and preserved. Require review of the exterior modifications to designated historic structures by the Historic Preservation Council to retain the historic character of such structures. Review proposed developments to determine if they would destroy or impact any unique geological or historical site and what steps may be needed to avoid or reduce negative impacts to the site. Consider incentives such as clustering and density bonuses for development that preserves historically or culturally significant sites or buildings. Encourage, enhance, and celebrate Ada County s ethnic and cultural diversity and heritage. Encourage activities and events that will celebrate the cultural heritage of Ada County. Encourage international cultural exchanges among individuals, organizations, and communities. Encourage cultural awareness through the creation and public exhibition of visual and performing arts. Assist community organizations in developing a sufficient variety of cultural facilities that meet the needs of all age groups and interests. Energy Services and Public Utility Facilities The following summarizes the goal statement and policies for energy services from the Ada County Comprehensive Plan. Coordinate with providers to develop plans for energy services and public utility facilities for the long-term energy and utility needs of Ada County. Promote the development of energy services and public utility facilities to meet public needs. Encourage the enhancement of the capacity and reliability of regional energy resources. Encourage the multiple-use of utility corridors by utility providers. Develop a future acquisitions map for inclusion into the Comprehensive Plan that identifies existing and future utility facilities and coITidors. PAGE 12 ...IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL IsSUES ADA, CANYON, AND ELMORE COUNTffiS, IDAHO CANYON COUNTY The Canyon County site (Figure 5) is located at an existing site approximately 20 miles west of Boise adjacent to a sand and gravel operation and the Boise River. There is a new housing development (approximately 20 homes) within 1 mile of the site. This site has a good buffer ITom trees and there were not any obvious land use issues. The adjacent gravel operation may have an adverse affect on the modeling result when combined with the impact ITom a future generating unit. However, the site was previously pennitted for a larger combined cycle unit and presumably would have included the gravel operation at that time. The emissions for the adjacent gravel operation were not included in the modeling contained in this report. There is an existing electric transmission line, and therefore visual impacts and potential natural resource impacts will be significantly less. FIGURE 5 CANYON COUNTY SITE Land Use Upon reviewing the Canyon County Comprehensive Plan (Canyon County 2010 Comprehensive Plan. October 20, 2005. http://www.canyoncounty.org/dsd/CompPlan.htm) and zoning ordinances there does not appear to be any specific prohibition against power generation. While we did not review the floodplain limits for the Boise River, a preliminary survey may be required to confirm that site is not within the floodplain. PAGE 13 1188,IDAllo POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL IsSUES ADA, CANYON, AND ELMORE COUNTIES, IDAIIO Noise The Canyon County Zoning ordinance states that "unreasonable dust, smoke, gas, fumes, noise vibration, or odor beyond the boundaries" of the facility are not acceptable. The only other requirement for noise control would appear to be in Idaho Code 52-101 where an evaluation of the existing and projected noise pollution in the immediate and surrounding area is required. Air Quality Canyon County air quality is in attainment for all criteria pollutants. Canyon County has substantial residential growth as well as growth in the small to large industrial sector in the county. According to Mr. Bill Rogers at the Idaho Department of Environmental Quality, Canyon County might have to be considered for an air quality maintenance plan as a result of industrial growth. An air quality maintenance plan is required by the US EP A when an area that was previously designated as non-attainment for a particular criteria air pollutant and has been re-designated as attainment. The state can also develop a maintenance plan for an area to prevent it from becoming non-attainment as a result of growth. There are no current requirements that would preclude power generation at the Canyon County site. Table 4 summarizes the highest concentration of criteria pollutants and the number of exceedences for 2003. Table 5 summarizes emissions in Canyon County taken in 1999. Idaho air quality is in attainment for CO, Ozone (1 hour), Ozone (8 hours), PMz.5, PMlO, SOz, and Pb levels (EP A 2005L Although the EP A Region 10 web site shows some non attainment areas in the eastern Idaho area for CO and PMlO dated 2002, EP A did a review and assessment for attainment and just published the results this year. Apparently the Region 10 site is not fully updated. TABLE 4 CANYON COUNTY 2003 SUMMARY OF POLLUTANT CONCENTRATIONS Pollutant Carbon monoxide hour average hour average Ozone hour average hour average PM- 24-hour average Annual arithmetic mean PM., 24-hour average 150 uglm 176 uglm 87 uglmAnnual arithmetic mean 50 uglm 27 uglm 0 uglm Source: Canyon County. zoos. http://www.canyoncounty.orgfdsd/CompPlan/ZOlO%ZOComp%ZOPlan%ZO-%ZOOct%ZOZO05.pdf. NAAQS Standard Highest Recorded Concentration Second Highest Recorded Concentration Number Stations ofNAAQS Monitoring Exceedances Pollutant 35 ppm 8 ppm 6ppm 9ppm 7ppm 5 ppm 12 ppm 08 ppm 08 ppm 08 ppm 07 ppm 07ppm 65 uglm 33 uglm 27 uglm 15 uglm 9 ug/m 5 ug/m PAGE 14 .........'.'..,--IDAHO PO~R COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO TABLE 4 (Continued) CANYON COUNTY 2003 SUMMARY OF POLLUTANT CONCENTRATIONS Notes: ugjm NAAQS PM1O PM2, ppm microgram per cubic meter Nevada Ambient Nr Quality Standards Particulate matter w/aerodynamic diameter less than 10 microns Particulate matter w/aerodynamic diameter less than 25 microns Part per million TABLE 5 1999 EMISSIONS SUMMARY OF CRITERIA AIR POLLUTANTS (Expressed in tons of pollutant emitted) Carbon monoxide Volatile anic com ounds Nitro oxides PM- Sulfur dioxidePM- obile Sources 36.365 6.820 4.862 29.618 513 ea Sources 304 1.434 2.274 8.361 619 oint Sources 32 1.133 1.037 3,575 9461 sources 42 702 9 388 8 173 41 554 2 078 Source: Canyon County. 2005. http://www.canyoncounty.orgjdsdlCompPlan/2010%20Comp%20Plan%20-%200ct%202005.pdf 623 264 889 Notes: PMu PMlO Particulate matter w/aerodynamic diameter less than 2.5 microns Particulate matter w/aerodynamic diameter less than 10 microns Canyon COlUlty had tile Iilghest number of reported releases of toxic pollutants, followed by Ada County. The EP A data show food ( agriculture) had the highest releases in Canyon County. The dominant chemicals were ammonia and nitrates. Mr. Rogers indicated that housing and commercial development are replacing agriculture. This change would reduce the levels of ammonia and nitrates. Due to the size of the units in this investigation and the limitation of only natural gas as a fuel, these units should not be subject to TRI reporting. Natural Resources Specific information on natural resources was not available. However, due to the proximity of the Boise River and riparian habitat there may be issues related to nesting for the birds in the area that will need to be addressed prior to and during construction. Cultural and Historic Resources Goal Statement and Policies No specific information available on cultural or historic issues were available on the Canyon COlUlty web site Energy Services and Public Utility Facilities Goal Statement and Policies No specific information available on the Canyon COlUlty web site PAGE 15 --,IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO ELMORE COUNTY The Elmore County site (Figure 6) is located at the existing Evander Andrews Complex that contains two smaller Westinghouse simple cycle units. The area has a good buffer and does not appear to have any current conflicting land use. Surrounding land use includes a gravel operation, a highway maintenance facility, and agriculture. If the existing natural gas and electric transmission are adequate then the issues related to environmental clearances will be dramatically less. Under existing Idaho Administrative Procedures Act (IDAP A) and the federal New Source- Review programs, an additional simple cycle unit can be added as a "synthetic minor" source if the emission of any criteria pollutant is less than 250 tons per year. FIGURE 6 ELMORE COUNTY SITE Land Use The proposed site in Elmore County is outside the city limits of Mountain Home and therefore would only be subject to Elmore County requirements. A variance for structures over 75 feet will be required. PAGE 16 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL ISSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO Noise The only requirement for noise control is found in Idaho, Code 52-101. An evaluation of the existing and projected noise pollution in the immediate and surrounding area is required. Air Quality Elmore County air quality is in attainment for all criteria pollutants and there are no significant permitting issues according to Mr. Bill Rogers at the Idaho Department of Environmental Quality. Idaho air quality is in attainment for CO, Ozone (1 hour), Ozone (8 hours), PMz.5, PMlO, SOz, and Pb levels (EP A 2005). Although the EP A Region 10 web site shows some non-attainment areas in the eastern Idaho area for CO and PMlO dated 2002, EP A did a review and assessment for attainment and just published the results this year. Apparently the Region 10 site is not fully updated. Elmore County did not have any reported data for pollutants reported under TRI reporting requirements. Natural Resources No specific is information available Cultural and Historic Resources Goal Statement and Policies No specific information available Energy Services and Public Utility Facilities Goal Statement and Policies No specific information is available. Information was obtained from public comments on construction of a coal-fired energy plant in the Glenns Ferry Area (September 2004). A number of questions were raised about the project at that time. 1. What is the number of acre- feet of water that will be required to run the plant was asked during the legislative session. 2. A question was asked regarding the discussions with citizens and businesses that will be down wind from the coal burning power plant. 3. A doctor in Jerome was consulted by the legislature regarding clean coal technologies and down wind emissions. 4. A couple of questions were asked about the location of the plant in proximity to the coal load stations and if the coal that was going to be used would be local or brought in from other surrounding areas. PAGE 17 IDAHO POWER COMPANY REVIEW OF POTENTIALLY CRITICAL ENVIRONMENTAL IsSUES ADA, CANYON, AND ELMORE COUNTIES, IDAHO REFERENCES Ada County Comprehensive Plan. 2005. Ada County Comprehensive Plan Update. On-Line Address: http://www.adaweb. net/departments/ developmentseryices/ AdaCountyComprehensive Canyon County Comprehensive Plan. 2005. "2010 Comprehensive Plan." October 20 2005. On-Line Address: http://www.canyoncounty.orgldsdlCompPlan/20 1 0%20Comp%20Plan%20- %200ct%202005.pdf Idaho Department of Environmental Quality (IDEQ). 2005. On-Line Address: http://www.deq.state.id. us/air/prog issues.cfm IDEQ. 2005. Personal communication with Mr. Bill Rogers at the IDEQ regarding Ada County policy on particulate matter data collection. October 17 2005. S Environmental Protection Agency. 2005. Tetra Tech checked the Toxic Release Inventory releases for the three counties. On-line Address: http://www.epa.gov/triexplorer or http:!/www.epa.gov/cgi- bin/broker?view=USS T &trilib=TRI Q 1 &sort= VIEW &sort fmt= &state= All+states&county= All+counties&chemical= ALL &industry=ALL&yem=2003&tab rpt=l&fld=RE TOLBY &ma pit=l& service=oiaa& program=xp tri.sasmacr.tristmi.macro S. Environmental Protection Agency Region 10. 2005. "Air Quality Attainment." On-Line Address: http://yosemite.epa.gov/R10/ AIRP AGE.NSF/webpage/Boise+PM1 0+ Attainment (http://www.epa.goy/ebtpages/airairqualityattainment. html PAGE 18 CASE NO. IPC-O6- IDAHO POWERCO. DIRECT REBUTTAL TESTIMONY OF GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD , EXHIBIT 2 , IS CONFIDENTIAL AND WAS NOT SCANNED IDAHO POWER COMPANY RECEIVED , 200& NOY -6 PM 4: CASE NO. IPC-O6- ID,i""'J" f\ \ l Ub,.~'J UTILITIES COMMISSION DIRECT REBUTTAL TESTIMONY GREGORY W. SAID AND MICHAEL J. YOUNGBLOOD EXHIBIT QUANTUM CONSULTING IDAHO POWER DEMAND-SIDE MANAGEMENT POTENTIAL STUDY FINAL Prepared for Darlene Nemnich Project Leader Customer Relations and Research Department Idaho Power 1221 West Idaho Street Boise, Idaho 83702 Prepared by QUANTUM CONSULTING INc. 2001 Addison Street, Suite 300 Berkeley, CA 94704 510-540-7200 with assistance from KEMA-XENERGY, Inc. P1992 November 2004 - 0 - 0 ,""! ' Section l., TABLE OF CONTENTS EXECUTIVE SUMMARY INTRODUCTION ENERGY EFFICIENCY METHODS Characterizing the Energy-Efficiency Resource Overview of Energy Efficiency Forecasting Method Baseline and Measure Data Development 2.4 Estimation of Technical Potential and Development Energy- Efficiency Supply Curves Estimation of Economic Potential 3.4 Estimation of Maximum Achievable, Program, and Naturally Occurring Potentials DEMAND RESPONSE POTENTIAL METHODS Overview of Demand Response Forecasting Methods DR Data Development Estimation of "Economic" Potential for Demand Response Forecasting Program Impacts ENERGY EFFICIENCY PEAK DEMAND AND ENERGY SAVINGS POTENTIAL RESULTS Technical and Economic Potential 4.3 Energy Efficiency Supply Curves Forecasts of Achievable Program Potential Scenarios Page ES- DEMAND RESPONSE POTENTIAL RESULTS Economic Potential Forecast Scenarios DISCUSSION OF UNCERTAINTY Quantum Consulting Inc.Table of Contents ("1 APPENDICES ENERGY EFFICIENCY MEASURE DESCRIPTIONS MEASURE INPUTS ECONOMIC INPUTS BUILDING STOCK & LOAD SHAPES NON-ADDITIVE MEASURE RESULTS ACHIEVABLE POTENTIAL SCENARIOS ENERGY EFFICIENCY POTENTIAL RESULTS - FIGURES FOR PHASE Quantum Consulting Inc,Table of Contents l \L-I 1- n l,u EXECUTIVE SUMMARY The Idaho Public Utilities Commission (IPUC) directed the Idaho Power Company (IPCo) consult with their Energy Efficiency Advisory Group regarding the need to initiate a comprehensive DSM study of the Idaho Power service territory. In July 2002, the Energy Efficiency Group at Idaho Power received recommendations from the Idaho Power Energy Efficiency Advisory Group and from Idaho Power management to proceed with a study DSM opportunities. This study characterizes the potential for DSM resources through 2013 for the commercial and residential sectors. This study was carried out in two phases. In the study s initial phase, the focus was on the potential for summer capacity reduction from demand-response (DR) programs and energy- efficiency (EE) opportunities based on assessment of measures that maximize peak reduction. For a second phase of the study, additional measures were added to the original EE portion the analysis to produce estimates of DSM potential that include an emphasis on overall energy savings. Based on IPCo s resource planning needs, the potential for capacity reduction was the most important component of the study. As such, the results from the initial phase of the study were provided to IPCo s resource-planning department in late 2003 and early 2004 for incorporation into its 2004 Integrated Resource Plan (IRP). The scope of this study also includes review and analysis of Idaho Power s summer peak load characteristics and identification of residential and commercial end-uses that have potential for demand reduction during the summer peak time. In addition, significant effort went into the development of baselines for residential and commercial customers in Idaho Power s service territory. This included estimation of end use energy and peak demand contribution; development of parameters such as electric equipment saturation, current efficiency measure saturation; incorporation of the impact of current codes and standards; analysis of Idaho Power forecasts and rate schedules; and review of Idaho Power s current DSM programs. Inherent differences between EE and DR - with respect to both technologies and program types - called for distinct methodologies in assessing their respective potentials. The analysis of EE potential followed a measure-based methodology in . which technology and market characteristics were combined to produce an estimate of the total technical potential of all measmes under consideration. Using a forecast of avoided costs to remove all measures that were not cost effective from a total resource cost (TRC) perspective, the technical potential was reduced to produce an estimate of economic potentiaL Finally, the influence of market constraints given different program funding levels was modeled to reduce the economic potential to various estimates of achievable potentiaL A detailed description of these concepts and methodologies is presented in Chapter 2 of this report. The DR portion of the study was based on an approach that merged professional judgment about DR participation levels with available Idaho Power data to assess potential peak demand reduction: for a specific set of program offerings. Following an approach similar to that of the analysis of EE measures, the DR analysis first assessed the maximum amount of load to which DR programs could feasibly apply. This "applicable load" was then partitioned into "low partial " and "high" capability segments, which reflected the extent to which load Quantum Consulting Inc.ES-Executive Summary '-. automated and/ or centrally controlled. From this initial breakout of applicable load, achievable potential was estimated by modeling shifts in capability based on IPCo s program efforts and customer motivation given different incentive levels. The end result is a set of potential estimates by program concept and funding levels. . Chapter 3 provides a comprehensive description of the methodology. Finally, the results of the two analyses must stand on their own. Although EE and DR programs are not mutually exclusive, without accounting for the complex interactivity of the two, the individual results cannot be added to each other to produce a figure for the combined potential of both types of programs. BASELINE ESTIMATES In Exhibit ES-l we show estimated summer peak demand and actual energy sales for Idaho Power for 2002. The residential sector is the largest contributor to both summer peak demand and annual energy representing roughly 30 percent of each. The commercial sector is relatively small, representing roughly 20 percent of energy and 18 percent of peak demand. Seasonal irrigation contributes a very large and disproportionate amount to summer peak demand (representing 24 percent of summer peak demand but only 12 percent of annual energy). The industrial sector makes up 18 percent of annual energy usages but only 13 percent of summer peak, due to its higher than average load factor. Exhibit ES- Estimated Breakdown of Summer Peak Demand by Sector for Idaho Power, 2002 Residential 28% Commercial 18% Off System Sales Other Irrigation 24% Quantum Consulting Inc.ES-Executive Summary - , l d .. . ENERGY EFFICIENCY POTENTIAL, The study resulted in a total economic potential of 384 MW of peak demand reduction and 107 GWh of annual energy savings. These are displayed in Exhibit ES-2, broken out into residential and commercial sectors. This peak demand reduction represents nearly 23 percent of the combined residential and commercial peak demand forecast in 2013. For annual energy savings, the economic potential is about 12 percent of IPCo s 2013 energy forecast. Comprehensive results of technical and economic potential by sector, home or building type and end use are presented in Chapter 4. Exhibit ES- Economic Potential (2013) Peak Demand (MW) and Energy (GWh) Savings 200 ---------------------- 000 . Commercial III Residential 800 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - --------- 600 - - - - ------ - -------- - - - -- - -- - -----,-------- 400 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - --------- 200 ------------------------------ GWH Economic potential, which represents the savings possible if all cost-effective measures were installed in every application deemed physically feasible, is the point of departure from which more realistic assessments of the value of energy-efficiency programs are derived. To develop the estimates of achievable potential, the study modeled market penetration based on four different funding scenarios. These sc~narios consisted of the following: A Low efficiency funding scenario with rebates covering 33% of incremental measure costs and base marketing funding levels; A Moderate efficiency funding scenario with rebates covering 50% of incremental measure costs and slightly higher marketing expenditures; A High efficiency funding scenario with rebates ramping up over time to 75% of incremental measure costs and significantly increased marketing expenditures; and Quantum Consulting Inc.ES-Executive Summanj A Maximum Achievable scenario with rebates ramping up over time to cover 100% of incremental measure costs and marketing expenditures sufficient to create maximum market awareness. The achievable potential peak demand for the four scenarios as well as. the estimated naturally - occurring. energy efficiency (which represent efficiency adoption in the absence of any programs) is displayed in Exhibit ES-3. For year 10 of the analysis, peak demand reductions range from 190 MW (around 11 percent of 2013 peak demand) for the maximum achievable scenario to 42 MW (less than 3% of 2013 peak demand) for the low funding scenario. As shown in Exhibit ES-, the achievable potential energy savings in 2013 were 681 GWh for the maximum achievable scenario, roughly 7.5 percent of IPCo s energy forecast for that year. The low-funding scenario showed 195 GWh for the same year, just over 2 percent of the forecast. Based on the methodology used for this study, all of the measures that go into the assessment of achievable potential are estimated to be cost effective based on their incremental costs and incremental savings. For the achievable potential, however, marketing and administrative costs are added into the equation. After incorporating these costs, all four scenarios were still cost effective from the TRC perspective. In Exhibit ES-5, the present value of benefits is presented along with a breakout of the various costs components included in the TRC for all four scenarios. Exhibit ES- Peak Demand Reduction Potential by Funding Scenario,10-Year Forecast 200 180 lEI Nat. Occurring ~Low 0 Moderate .High III Max. Achievable 160 140 , .. 120 MW 100 Year Quantum Consulting Inc,ES-Executive SWnmal1j Exhibit ES- Energy Savings Potential by Funding Scenario, 10- Year Forecast 700 600 500 400 GWH 300 200 100 ~ Nat. Occurring rnilLow 0 Moderate .High I!II Max. Achievable I~. Year Exhibit ES- Present Value Costs and Benefits Achievable Potential Scenarios $450 $50 ~ Net Benefits ~ Total Benefits IIiiI Program Incentives . Non-Incentive Participant Costs 0 Marketing . Administration ------------ $400 $350 0 .~ $300 s $250 ::) ~ $200 ~ $150 a:: $100 ------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - Low Moderate High Max. Achievable Quantum Consulting Inc.ES-Executive Summary DEMAND RESPONSE POTENTIAL As displayed in Exhibit ES-6, of IPCo s total peak demand in 2004, 469 MW (32 percent) were deemed to be applicable for peak demand reduction programs. Of this applicable load, 105 MW of potential savings were estimated to be economic. Of the total economic potential, AC load control programs for the residential sector accounted for nearly 60 MW, around 57 percent. The next largest contributors were the small- and large-commercial back-up generation programs, which combined for around 43 percent of the total economic potential. Exhibit ES- Economic Potential for Residential and Commercial DR Programs % of Total Peak MW in 2004 Demand Estimated Applicable Demand for DR 469 32% Economic Potential for DR 105 The assessment of achievable DR potential was based on analysis of four program concepts - AC Load Control (DLC), Critical Peak Pricing (CPP), Voluntary Demand Response Incentives (DRP), and Back-up Generator Incentives (BUG) - bundled into four program strategies: DLC and BUG - Low Incentive Levels All 4 Concepts - Low Incentive Levels All 4 Concepts - High Incentive Levels Maximum Achievable The forecast of annual estimated MW reduction that would occur during system peak conditions is shown in Exhibit ES-7 for each of the four strategies. The growth in the various scenarios represents a forecasted successful effort of IPCo to shift applicable load into higher capability segments as well as customer response to incentives. The maximum achievable scenario s 129 MW in 2013 amounts to more than 7.5 percent of the peak demand. The lowest potential is associated with the DLC and BUG program concepts with low funding, which has a potential of 25 MW in 2013, approximately 1.5 percent of peak demand. Chapter 5 presents complete results for the assessment of DR potential. Quantum Consulting Inc.ES-Executive Summary Exhibit ES- Comparison of Load Reduction Forecasts Residential and Commercial Sectors 140 120 100 ~DLC & BUG. Low $ 1114 Concepts - Low $ - - - - - - - - - - - - - - - - - - - .4 Concepts - High $ 0 Max. Achievable !~: Year l,. I~~ L,_, Quantum Consulting Inc.ES-Executive Summary l n , f' 1. INTRODUCTION The Idaho Public Utilities Commission (IPUC) directed the Idaho Power Company (IPCo) toconsult with their Energy Efficiency Advisory Group regarding the need to initiate acomprehensive DSM study of the Idaho Power service territory. In July 2002, the Energy Efficiency Group at Idaho Power received recommendations from the Idaho Power Energy Efficiency Advisory Group and from Idaho Power management to proceed with a study of DSM opportunities with the primary focus being peak demand reduction opportunities in its service territory. The Energy Efficiency Advisory Group noted that since the information was to be used primarily as an Idaho Power management tool for DSM, that the focus of the study should be driven by the needs of Idaho Power DSM resource planning. In August 2003, Idaho Power selected the team of Quantum Consulting Inc. and KEMA-XENERGY Inc. to conduct this DSM potential study. The information needed most by Idaho Power for future DSM planning is summer peak end- use inf?rmation and summer demand reduction and demand response program research. Because Idaho Power s original focus in this study was on summer peak demand reduction potential, the consultant team originally focused on those end uses within the residential and commercial sectors that would contribute most to summer peak demand savings. The results of this initial project scope - or, phase - were provided to IPCo by the consultant team in December 2003 and January 2004 for use by IPCo in its 2004 Integrated Resource Plan. These initial results were also presented to the Energy Efficiency Advisory Group in January 2004. In late spring 2004 IPCo requested an expansion of the study scope to address additional measures and end uses that may produce cost effective energy efficiency savings even though' , they may not contribute significantly to summer peak demand reductions. Specific tasks included in this study were: . . Review of Idaho Power s summer peak load characteristics and identification of residential and commercial end-uses that have potential for demand reduction during the summer peak time. Development of a DSM measures database for end-uses identified above. Assessment of the measures, technologies and equipment practices that could reduce peak demand and annual energy consumption. Identification of savings, costs, measure lives, load shapes, non-energy benefits and other factors influencing cost-effectiveness. Establishment of current baselines for residential and commercial customers in Idaho Power s service territory. Development of parameters such as equipment type,saturation, building size, fuel type, efficiency and age~ Collection of existing data on current customers in residential and commercial sectors. Incorporation of the impact of current codes and standards. Examination of Idaho Power forecasts and rate schedules. Review of current DSM programs in Idaho Power service territory. Quantum Consulting Inc.Introduction Development of estimates of technical, economic, and achievable potential and performance of cost-effective analysis on programs options. Review of anomalies in Idaho markets that may affect program success as well as Idaho specific issues, trends, barriers and opportunities. Incorporation of potential barriers to the adoption of suggested technologies or practices. In the 1980s and early 1990s, DSM potential studies were conducted routinely by many utilities and other organizations throughout the United States. These studies were largely abandoned however, with the advent of electric restructuring. Recently, a number of factors-western u.S. supply shortages and price increases related to the California energy crisis, future price and supply uncertainty, and the environmental impacts of traditional power plants-have combined to warrant a detailed analysis of DSM potential. This study estimates potential electricity and peak demand savings from DSM measures in the Idaho Power territory. Analyses were carried out separately for demand response (DR) and energy efficiency program options. For DR, four program concepts were modeled with some slight variations either over time or . across segments. The four concepts included: AC Load Control (DLC):these programs provide lower energy rates for customers who are willing to have cycling equipment installed that can be directly controlled by the utility. Critical Peak Pricing (CPP):this program offers dynamic rates that change based on demand versus supply available. This program generally provides consistently lower off-peak rates. However, during a CPP event, rates may increase dramatically (e.g. 5 times the average for that period). Voluntary Demand Response Incentive (DRP):this program offers a credit to customers over a certain demand, who voluntarily commit to reduce their electricity usage by a significant percentage (such as 10%) during a DRP event. Back-up Generator Incentives (BUG):this program offers financial incentives to customers who run their back-up generation during program events. In contrast to energy conservation, which often involves short-term behavioral changes, energy-efficiency opportunities are typically physical, long-lasting changes to buildings and equipment that result in decreased energy use while maintaining constant levels of energy service. Examples of energy efficiency include: Compact fluorescent lighting systems that deliver equivalent light using 70 percent less electricity than incandescent light bulbs. New variable-speed drive chillers that deliver cooling to buildings using 40 percent less energy than typical systems in today s buildings. Energy management control systems that eliminate energy waste and optimize building operation. Quantum Consulting Inc.Introduction l.. Identification and repair of leaks in industrial compressed air systems that otherwise result in wasteful increases in product costs. These types of improvements, and hundreds of others, reduce electricity consumption without affecting the end-use services (e.g., light, heat , " coolth " drivepower, and the like) that - consumers and businesses require for comfort, pro~uctivity, and leisure. This report provides both detailed and aggregated estimates of the costs and savings potential of DSM measures in Idaho. In addition, forecasts are developed of savings and costs associated with different levels of program funding over a -la-year period. Consistent with our lO-year focus, the study is restricted to DSM measures and practices that are presently commercially available. These are the measures that are of most immediate interest to DSM program and resource planners. Quantum Consulting Inc.Introduction ( , r:' 2. BASELINE ESTIMATES AND ENERGY EFFICIENCY ASSESSMENT METHODOLOGY In this chapter, we give a brief overview of the concepts, methods, and scenarios used to develop the baseline and energy efficiency estimates for this study. Methods used to develop our estimates of dem~d response potential are presented in Section CHARACTERIZING THE ENERGY-EFFICIENCY RESOURCE Energy efficiency has been characterized for some time now as an alternative to energy supply options such as conventional power plants that produce electricity from fossil or nuclear fuels. In the early 1980s, researchers developed and popularized the use of a conservation supply curve paradigm to characterize the potential costs and benefits of energy conservation and efficiency. Under this framework, technologies or practices that reduced energy use through efficiency were characterized as "liberating 'supply' for other energy demands " and could therefore be thought of as a resource and plotted on an energy supply curve. The energy- efficiency resource paradigm argued simply that the more energy efficiency, or "mega-watts produced, the fewer new plants would be needed to meet end users' power demands. Defining Energy-Efficiency Potential Energy-efficiency potential studies were popular throughout the utility industry from the late 1980s through the mid-1990s. This period coincided with the advent of what was called least- cost or integrated repource planning (IRP). Energy-efficiency potential studies became one of the primary means of characterizing the resource availability and value of energy efficiency within the overall resource planning process. Like any resource, there are a number of ways in which the energy-efficiency resource can be estimated and characterized. Definitions of energy-efficiency potential are similar to definitions of potential developed for finite fossil fuel resources like coal, oil, and natural gas. For example, fossil fuel resources are typically characterized along two primary dimensions: the degree of geologic certainty with which resources may be found and the likelihood that extraction of the resource will be economic. This relationship is shown conceptually in Exhibit 2- Somewhat analogously, this energy-efficiency potential study defines several different types energy-efficiency potential, namely: technical, economic, achievable, program, and naturally occurring. These potentials are shown conceptually in Exhibit 2-2 and described below. Technical potential is defined in this study as the complete penetration of all measures analyzed in applications where they were deemed technically feasible from an engineering perspective. Economic potential refers to the technical potential of those energy conservation measures that are cost-effective when compared to supply-side alternatives. Maximum achievable potential is defined as the amount of economic potential that could be achieved over time under the most aggressive program scenario possible. Achievable program potential refers to the amount of savings that would occur in response to specific program funding and measure incentive levels. Savings associated with program potential are savings that are projected beyond those that would occur naturally in the absence of any market intervention. Naturally occurring potential Quantum Consulting Inc.Baseline Estimates and EE Methods refers to the amount of savings estimated to occur as a result of normal market forces, that is, in the absence of any utility or governmental intervention. Exhibit Conceptual Framework for Estimates of Fossil Fuel Resources Possible Possible and but not Economically Feasible Economically Feasible Known Known and but not Economically Feasible Economically Feasible II) II) (/) ::0. II) It!II) ... II) Decreasing Economic Feasibility Exhibit Conceptual Relationship Among Energy-Efficiency Potential Definitions Technical Economic Maximum Achievable Program Naturally Occurring Quantum Consulting Inc.Baseline Estimates and EE Methods l~' OVERVIEW OF ENERGY EFFICIENCY FORECASTING METHOD The crux of any forecasting process involves carrying out a number of systematic analytical steps that are necessary to produce accurate estimates of energy efficiency (EE) effects on system load. A simplified overview of these basic analytical steps used in this study is shown in Exhibit 2- Exhibit Simplified Conceptual Overview of Modeling Proc~ss ECONOMIC DATA MEASURE DATA I BUILDING DATA MODEL INPUTS TECHNICAL POTENTIAL ..... ECONOMIC POTENTIAL (J) (')):- (J) MAXIMUM ACHIEVABLE POTENTIAL NATURALLY OCCURRING EFFICIENCY PROGRAM DATA AND ADOPTION INPUTS PROGRAM POTENTIAL (Inputs to IRP model Quantum Consulting Inc.Baseline Estimates and EE Methods The approach to developing an energy efficiency forecast used for this study involves a five- step process. The steps include: Step 1: Develop Initial Input Data Develqp list of energy efficiency measure opportunities to include. Gather and develop technical data (costs and savings) on efficient measure opportunities. Gather, analyze, and develop information on building characteristics, including total square footage and households, electricity consumption and intensity by end use, end- use consumption load patterns by time of day and year (i.e., load shapes), market shares of key electric consuming equipment, and market shares of energy efficiency technologies and practices. Gather economic input data such as current and forecasted retail electric prices and current and forecasted costs of electricity generation, along with estimates of other potential benefits of reducing supply, such as the value of reducing environmental impacts associated with electricity production. Step 2: Estimate Technical Potential and Develop Supply Curves Match and integrate data on efficient measures to data on existing building characteristics to produce estimates of technical potential and energy efficiency supply curves. Step 3: Estimate Economic Potential Match and integrate measure and building data with economic assumptions to produce indicators of costs from different viewpoints (e.g., utility, societal, and consumer). Estimate total economic potential using supply curve approach. Step 4: Estimate Maximum Achievable, Program, and Naturally Occurring Potentials Gather and develop estimates of program costs (e.g., for administration and marketing) and historic program savings. Develop estimates of customer adoption of energy efficiency measures as a function of the economic attractiveness of the measures, barriers to their adoption, and the effects of program intervention. Estimate maximum achievable, program, and naturally occurring potentials; calibrate achievable and naturally occurring potential to recent program and market data. Quantum Consulting Inc.Baseline Estimates and EE Methods ! ' Develop alternative economic estimates associated with alternative future scenarios. Step 5: Scenario Analyses and Resource Planning Inputs Recalculate potentials under alternate economic scenarios and deliver data in format required for resource planning, Provided below is additional discussion of data development and the modeling approaches for technical, economic, and achievable DSM forecasts. The analysis was carried using KEMA- XENERGY's DSM ASSYSTTM (Demand-Side Management Technology Assessment System). BASELINE AND MEASURE DATA DEVELOPMENT Measure Data Measure level data was developed and obtained from a variety of sources for this study. The study authors had previously developed much of the measure information on recent previous studies, including the following: Northwest Power Planning Council, The Fifth Plan s Draft Conservation Resources Assessment,! April 8, 2004 (Presentation on NWPPC web site and associated spreadsheets) Regional Technical Forum Energy Trust of Oregon Energy Efficiency and Conservation Measure Resource Assessment, January 2003 Puget Sound Energy Least Cost Plan, 2003 Pacific Northwest Energy Star New Construction Specification for Site-built, Single-Family Dwellings, 20043 The California Statewide Commercial Sector Energy Efficiency Potential Study, 2002 (covering the commercial existing construction market) The California Statewide Residential Sector Energy Efficiency Potential Study, 2003 (covering the residential existing construction market) California s Secret Energy Surplus: The Potential for Energy Efficiency, 2002 (covering the industrial and new construction markets) 2001 Database on Energy-Efficient Resources (DEER) Update http:/ Iwww.nwppc.org/energy/rtf/presentations/ResourceAssess2003 04081 and personal communication with Tom Eckman. l~- http:/ Iwww.nwppc.org/energy/rtf/abouthtm 3 Prepared by Ecotope Inc. http:/ Iwww.energy.ca.gov/deer/ Quantum Consulting Inc.Baseline Estil1~ates and EE Methods Following is a description of the measure data used in the study. Refer to the above-referenced reports for a more complete discussion. Much of the measure cost and savings data for this study were developed as part of the DEER 2001 Update study. Part of that study involved collection and analysis of residential and commercial measure cost data. A second part of the study focused on development of savings fractions for residential measures. Regional sources, in particular the NWPPC's Fifth Conservation Assessment, were used to compare to cost and savings estimates developed on previous studies. In several cases, adjustments were made based on this comparison and discussions with the NWPPC's primary author. In order to assess the amount of energy efficiency savings available, estimates of the current saturation of energy efficient measures were developed from available data sources. Key sources for this study include: Baseline Characteristics of the Residential Sector (Idaho, Montana, Oregon, and Washington),20015 Baseline Characteristics of the Non-Residential Sector (Idaho, Montana, Oregon, and Washington),20016 Assessment of the Commercial Building Stock in the Pacific Northwest 20047 Development of Building and Base Energy Forecast Data Key building data necessary for this study include: units of consumption (number of households and square feet of building space), end use energy consumption (kWh/unit), electric end use saturations, and load shapes. The primary sources for these data were obtained and developed from Idaho Power internal data and models. Idaho Power currently utilizes econometric rather than end-use forecasting models. In the mid-1990s, however, Idaho Power implemented residential and commercial end-use forecasts using the REEPS and COMMEND models. These model inputs were developed from residential and commercial saturation surveys (mail based) carried out in the late 1980s and mid-1990s. Although dated, these model runs represented the only sources of Idaho Power specific end use data available. QC staff working with Idaho Power staff, re-ran these models to obtain an initial set of estimates of residential and commercial end use consumption, saturation, and units (households and square feet). These estimates were then compared to Idaho Power s latest system energy consumption and peak load data and adjusted so that the bottom-up end use estimates were reconciled with the known system totals. This process is described below. Initial Energy End Use Breakdowns and Calibration to Idaho Power Sales. Idaho Power provided QC staff with REEPS and COMMEND files from the mid-1990s, the last time the models were run for Idaho Power. Idaho Power reviewed what was necessary to rerun the output to produce the type of detailed end use and building type data needed for this study. 5 Prepared by Ecotope Inc. for the Northwest Energy Efficiency Alliance. 6 Prepared by Ecotope Inc. for the Northwest Energy Efficiency Alliance. 7 Prepared by Kema-Xenergy Inc. for the Northwest Energy Efficiency Alliance. Quantum Consulting Inc.Baseline Estimates and EE Methods r 1 I II , /"I r ' ! ' QC re-ran the models, generated numerous individual output files, and re-aggregated the files into more useful summaries. As it turned out, both the REEPS and COMMEND forecasts were quite good forecasts out to 2002 from a total sales perspective. The REEPS estimates of households, end use UECs (kWh/household) and electric end use saturations were also found to be reasonable starting points for this study. However, the COMMEND estimates of square footage, end use EUls (kWh/ square foot), and electric end use saturations could not be reconciled with the 2002 sales data. In particular, the EUls (kWh/ft2) by building type did not appear to be reasonable in many cases. As a result, we used whole-building EUls by building type from the recent Pacific Northwest Building Stock Assessment, with adjustments for Idaho Power electric end use saturation levels, to back into estimates of square footage. We also adjusted end use EUls to ensure that they were consistent with reasonable estimates of installed capacity (kW / square foot) and full load hours of operation.t ' Peak Load Development and Calibration. The peak calibration process was driven by the whole-building load research and census data provided by Idaho Power as well as end use load shapes from secondary sources. The Idaho Power load research data proved invaluable to the process. In particular, the breakout of true commercial from true industrial business types in the load research sample and census data was extremely useful. For the residential sector, we calculated a peak-to-energy ratio from the load research data and then adjusted our end-use peak-to-energy factors slightly to get close to the overall ratio of 0.21 MW per GWh (i.e., a load factor of 55 percent). For the commercial sector, we multiplied the calibrated energy by building type by the building type-specific peak-to-energy factors obtained from the Idaho Power load research data. This produced building type-specific estimates of peak demand. Wethen calibrated the end use peak demand estimates to sum to these control totals within eachbuilding type. I : To investigate the reasonableness of the estimates developed from the bottom up baseline peak demand estimates described above for the residential and commercial, estimates of peak demand were developed for the remaining Idaho Power customers (e.g., industrial, irrigation, and special customers). Peak demand estimates for these sectors were based on load factors from Idaho Power load research data. The combined results were very close to actual total Idaho Power peak demand in 2002. r - Housing and Building Stock Forecasts. After calibrating the baseline end use data to Idaho Power s 2002 sales and peak load QC used Idaho Power s forecasts of residential and commercial load growth to develop baseline data for the lO-year period used for this study. Existing and new construction loads were developed by decaying the existing stock and taking the difference between the forecasted loads and decayed existing stock loads as new construction. Baseline Results. The results of our baseline development work are presented in Exhibits 2- through 2-10, In Exhibit 2-4 and 2-5 we show the distribution by sector (including losses and off-system sales) of the estimated summer peak demand and actual energy sales for Idaho Power for 2002. In Exhibits 2-6 through 2-11 we present our estimates of residential and l- , 8 Residential stock was decayed at a rate of 1 percent per year, commercial stock was decayed at a rate of 2 percent per year. \-;:J ~." Quantum Consulting Inc.Baseline Estimates and EE Methods commercial loads by end use and building type for only Idaho (Le., excluding Idaho Power non-Idaho loads). Key characteristics of Idaho Power s customer base relevant to the findings in this study include the following: Total summer peak load in 2002, including line losses, was approximately 2 900 MW. Total energy consumption, including losses, was rougWy 14,500 GWh. Seasonal irrigation contributes a very large and disproportionate amount to summer peak demand (representing 24 percent of summer peak demand but only 12 percent of annual energy). The industrial sector makes up 18 percent of annual energy usages but only percent of summer peak, due to its higher than average load factor. The residential sector is the largest contributor to both summer peak demand and annual energy representing roughly 30 percent of each. Summer peak demand is dominated by air conditioning loads, which represent percent of residential peak demand. - A much wider variety of loads contribute significantly to annual energy consumption, particularly electric heating, water heating (including water loads for clothes and dish washers), air conditioning, and lighting. Single family homes dominate the residential sector, multi-family and mobile homes are relatively small contributors to peak demand. The commercial sector is relatively small, representing rougWy 20 percent of energy and 18 percent of peak demand. Summer peak demand is dominated by air conditioning and lighting loads, which represent 34 percent and 28 percent, respectively of commercial peak demand. With respect to annual energy usage, lighting is the largest contributor followed by electric heating, cooling, miscellaneous loads, refrigeration, and ventilation. ' Small Offices and Non-Food Retail are both individually at least twice as large as any other building type. Cooling and lighting dominate peak demand for both of these segments. Refrigeration loads are relatively small overall but are significant in both the grocery and warehouse segments. Quantum Consulting Inc.Baseline Estimates and EE Methods Exhibit Estimated Breakdown of Summer Peak Demand by Sector for Idaho Power, 2002 Residential 28% - t ' Other Commercial 18% Off System Sales Irrigation 24% Exhibit Estimated Breakdown of Energy Sales by Sector for Idaho Power, 2002 Residential 30% Other Commercial 20% Losses Off System Sales Irrigation 12%Industrial 18% 1'-" Quantum Consulting Inc.Baseline Estimates and EE Methods Exhibit Estimated Residential Summer Peak Demand by End Use, 2002 Estimated Peak MW for Idaho = 800 Cooling 57% Lighting - Dryer , 4% !"j Dishwasher TV1% Freezer 01 - e ngera Ion/0. Water Heating I \ \, Clothes Washer " \ l Exhibit Estimated Residential GWh by End Use, 2002 Clothes Washerlig ling 9% l Estimated GWh for Idaho = 4 300 Other 20% - ------.: Heating 21% Cooling 10% Heating (Sec, 0.4% Freezer i 3% Refrigeration Quantum Consulting Inc.Baseline Estimates and EE Methods Exhibit Estimated Residential Summer Peak Demand by Home Type, 2002 Mobile Home r.' Large Multi-Family Small Multi-Family Single-Family r ' 100 200 300 400 500 Estimated Peak MW 600 700 800 r J t,u Exhibit Estimated Commercial Summer Peak Demand by End Use, 2002 Lighting 28% . t, Lighting (Ex!.)1% . Refrigeration7% ~ Office Equipment Cooking Water Heating Miscellaneous 11% Cooling 34% Estimated Peak Demand for Idaho = 490 Quantum Consulting Inc.Baseline Estimates and EE Methods Exhibit 2- Estimated Commercial GWh by End Use, 2002 Miscellaneous 11% Office Equipment2% Heating 20% Cooling 12% ~ Ventilation - Water Heating l Cooking ...... Lighting 28% Estimated GWh for Idaho = 2 700 Exhibit Estimated Commercial Summer Peak Demand by Business Type and End Use, 2002 Miscellaneous Lodging Health College School Warehouse Grocery Retail Restaurant Large Office Small Office I!!!I Heating . Cooling 0 Ventilation I!I Water Heating . Cooking II Refrigeration . Lighting (Ext. I!!I Lighting . Office Equipment . Miscellaneous 100 120 Peak MW Quantum Consulting Inc.Baseline Estimates and EE Methods '-' f' ' 'i. t., ' , . I-, - , I Economic Data The key economic inputs utilized in the forecasting process are avoided costs, electricity rates, discount rates and inflation rates. Electricity rates were obtained from Idaho Power tariffs. Idaho Power rates are very low, roughly 4 cents per kWh for commercial customers and 6 cents per kWh for residential customers. Avoided cost forecasts were developed by Idaho Power as part of the current Integrated Resource Plan. The avoided costs used for this potential study ranged from 3 cents per kWh for off-peak periods to 5 cents per kWh for the summer on-peak period. In addition, a capacity avoided cost value of $50 per kW-year was also included in the calculation of total avoided costs. A nominal utility discount rate of 8 percent was used in the analysis. The inflation rate used was 3 percent per armum. ESTIMATION OF TECHNICAL POTENTIAL AND DEVELOPMENT ENERGY-EFFICIENCY SUPPL Y CURVES Technical potential refers to the amount of energy savings or peak demand reduction that would occur with the complete penetration of all measures analyzed in applications where they were deemed technically feasible from an engineering perspective. Total technical potential is developed from estimates of the technical potential of individual measures as they are applied to discrete market segments (commercial building types, residential dwelling types, etc. Core Equation The core equation used to calculate the energy technical potential for each individual efficiency measure, by market segment, is shown below (using a commercial example): Technical Potential of Efficient Measure Base Case Equipment EUI ' (kWh/ft') Total Square Feet Not Complete x FeasibilityFactor Factor Applicability Factor Savings Factor where: Square feet is the total floor space for all buildings in the market segment. For the residential analysis, the number of dwelling units is substituted for square feet. Base-case equipment Eur is the energy used per square foot by each base-case technology in each market segment. This is the consumption of the energy-using equipment that the efficient technology replaces or affects. For example, if the efficient measure were a CFL, the base EUI would be the armual kWh per square foot of an equivalent incandescent lamp. For the residential analysis, unit energy consumption (UECs), energy used per dwelling, are substituted for EUIs. 9 Note that stock turnover is not accOlmted for in our estimates of technical and economic potential, stock turnover is accounted for in our estimates of achievable potential. Our definition of technical potential asswnes instantaneous replacement of standard-efficiency with high-efficiency measures. Quantum Consulting Inc.Baseline Estimates and EE Methods Applicability factor is the fraction of the floor space (or dwelling units) that is applicable for the efficient technology in a given market segment, for the example above, the percentage of floor space lit by incandescent bulbs. Not complete factor is the fraction of applicable floor space (or dwelling units) that has not yet been converted to the efficient measure; that is, (1 minus the fraction of floor space that already has the ENERGY EFFICIENCY measure installed). Feasibility factor is the fraction of the applicable floor space (or dwelling units) that is technically feasible for conversion to the efficient technology from an engineering perspective. Savings factor is the reduction in energy consumption resulting from application of the efficient technology. Technical potential for peak demand reduction is calculated analogously substituting kW for kWh per household or square foot of commercial floorspace. An example of the core equation is shown in Exhibit 2-12 for the case of a perimeter-based daylight dimming system. Exhibit Example of Technical Potential Calculation-Peak Period Commercial Perimeter Zone Dimming (Generic Data for Example Purposes Only) Technical Total Base Case Not Potential of =Square x Equipment x Complete x Feasibility x Savings Measure Feet Demand Applicability Factor Factor Factor (kW If!')Factor 20.13 MW 214 1.5 0.4 million Technical potential is calculated in two steps. In the first step, all measures are treated independently; that is, the savings of each measure are not adjusted for overlap between competing or interactive measures. By treating measures independently, their relative cost- effectiveness is analyzed without making assumptions about the order or combinations in which they might be implemented in customer buildings. However, the total technical potential across measures cannot be estimated by summing the individual measure potentials directly. The cumulative savings cannot be estimated by adding the savings from the individual savings estimates because some savings would be double counted. For example, the savings from a measure that reduces heat gain into a building, such as window film, are partially dependent on other measures that affect the efficiency of the system being used to cool the building, such as a high-efficiency chiller - the more efficient the chiller, the less energy saved from the application of the window film. Quantum Consulting Inc.BaselineEstimates and EE Methods . ..1 Use of Supply Curves In the second step, cumulative technical potential is estimated using an energy efficiency supply curve, approach. This method eliminates the double-counting problem. A supply curve typically consists of two axes-one that captures the cost per unit of saving a resource or mitigating an impact (e.g., $/kWh saved or $/ton of carbon avoided) and the other that shows the amount of savings or mitigation that could be achieved at each level of cost. The curve is typically built up across individual measures that are applied to specific base-case practices or technologies by market segment. Savings or mitigation measures are sorted on a least-costbasis, and total savings or impacts mitigated are calculated incrementally with respect to measures that precede them. Supply curves typically, but not always, end up reflecting diminishing returns, i.e., as costs increase rapidly and savings decrease significantly at the end of the curve. f , , t The cost dimension of most energy efficiency supply curves is usually represented in dollars per unit of energy savings. Costs are usually annualized (often referred to as IIlevelized") in supply curves. For example, energy efficiency supply curves usually present levelized costs per kWh or kW saved by multiplying the initial investment in an efficient technology or program by the "capital recovery rate" (CRR): CRR - 1- (1 + drn where is the real discount rate and, is the number of years over which the investment is written off (i., amortized). Thus, Levelized Cost per kWh Saved = Initial Cost x CRR/ Annual Energy Savings Levelized Cost per kW Saved = Initial Cost x CRR/Peak Demand Savings , " The levelized cost per kWh and kW saved are useful because they allow simple comparison of the characteristics of energy efficiency with the characteristics of energy supply technologies. However, the levelized cost per kW or kWh saved are biased indicators of cost-effectiveness because all of the efficiency measure costs are allocated to either peak savings or annual energy savings. As a result, energy efficiency supply curves do not reflect the integrated value of both peak and energy savings. The integrated value of both peak and energy savings is captured in the methodology used in this study by calculation of the total resource cost test for each measure as described in the section on Economic Potential below. \ . Exhibit 2-13 shows a simplified numeric example of a supply curve calculation for several energy efficiency measures applied to commercial lighting for a hypothetical population of buildings. What is important to note is that in an energy efficiency supply curve, the measures are sorted by relative cost-from least to most expensive. In addition, the energy consumption of the system being affected by the efficiency measures goes down as each measure is applied. As a result, the savings attributable to each subsequent measure decrease if the measures are interactive. For example, the occupancy sensor measure shown in Exhibit 2-13 would save more at less cost per unit saved if it were applied to the base-case consumption before the T8 lamp and electronic ballast combination. Because the T8 electronic ballast combination is more Quantum Consulting Inc.Baseline Estimates and EE Methods cost-effective, however, it is applied first, reducing the energy savings potential for the occupancy sensor. Thus, in a typical energy efficiency supply curve, the base-case end-use consumption is reduced with each unit of energy efficiency that is acquired. Notice in Exhibit 13 that the total end-use GWh consumption is recalculated after each measure is implemented, thus reducing the base energy available to be saved by the next measure. Exhibit 2-13 shows an example that would represent measures for one base-case technology in one market segment. These calculations 'are performed for all of the base-case technologies market segments, and measure combinations in the scope of a study. The results are then ordered by levelized cost and the individual measure savings are summed to produce the energy efficiency potential for the entire sector. In the next subsection, we discuss how economic potential is estimated as a subset of the technical potential. Exhibit Sample Technical Potential Supply Curve Calculation for Commercial Lighting (Note: Data are illustrative only) Total End Use Applicable, Not Consumption Complete and Average Levelized of Population Feasible kWh/fe of Savings GWh Cost ($/kWh Measure (GWh)(1 OOOs of ft'population Savin~s saved) Base Case: T12 lamps with 425 100 000 4.3 N/A N/A N/AMagnetic Ballast 1. T8 w. Elec. Ballast 425 100 000 4.3 21%$0. . Occupancy Sensors 336 000 3.4 10%$0. 3. Perimeter Dimming 322 000 45%$0. With all measures 309 27%116 ESTIMATION OF ECONOMIC POTENTIAL Economic potential is typically used to refer to the technical potential of those energy conservation measures that are cost effective when compared to either supply-side alternatives or the price of energy. Economic potential takes into account the fact that many energy efficiency measures cost more to purchase initially than. do their standard-efficiency counterparts. The incremental costs of each efficiency measure are compared to the savings delivered by the measure to produce estimates of energy savings per unit of additional cost. These estimates of energy efficiency resource costs can then be compared to estimates of other resources such as building and operating new power plants. Quantum Consulting Inc.Baseline Estimates and EE Methods ! ' . r- Cost Effectiveness Tests To estimate economic potential, it is necessary to develop a method by which it can be determined that a measure or program is economic. We used the total resource cost (TRe) test to assess cost effectiveness. The TRC is a form of societal benefit-cost test. Other tests that are sometimes used in analyses of program cost-effectiveness include the utility cost, ratepayer impact measure (RIM), and participant tests. Before discussing the TRC test and how it is often used in our DSM forecasts, we present below a brief introduction to the common tests: ~ : Total Resource Cost Test-The TRC test measures the net costs of a demand-side management program as a resource option based on the total costs of the program, including both the participants' and the utility'costs. The test is applicable to conservation, load management, and fuel substitution programs. For fuel substitution programs, the test measures the net effect of the impacts from the fuel not chosen versus the impacts from the fuel that is chosen as a result of the program. TRC test results for fuel substitution programs should be viewed as a measure of the economic efficiency implications of the total energy supply system (gas and electric). A variant on the TRC test is the societal test. The societal test differs from the TRC test in that it includes the effects of externalities (e.g. environmental, national security), excludes tax credit benefits, and uses a different (societal) discount rate. Participant Test-The participant test is the measure of the quantifiable benefits and costs to the customer due to participation in a program. Since many customers do not base their decision to participate in a program entirely on quantifiable variables, this test cannot be a complete measure of the benefits and costs of a program to a customer. Utility (Program Administrator) Test-The program administrator cost test measures the net costs of a demand-side management program as a resource option based on the costs incurred by the program administrator (including incentive costs) and excluding any net costs incurred by the participant. The benefits are similar to the TRC benefits. Costs are defined more narrowly. Ratepayer Impact Measure Test-The ratepayer impact measure (RIM) test measures what happens to customer bills or rates due to changes in utility revenues and operating costs caused by the program. Rates will go down if the change in revenues from the program is greater than the change in utility costs. Conversely, rates or bills will go up if revenues collected after program implementation are less than the total costs incurred by the utility in implementing the program. This test indicates the direction and magnitude of the expected change in customer bills or rate levels. The key benefits and costs of the various cost-effectiveness tests are summarized below Exhibit 2-14. 10 California Standard Practice Manual, October 2001. Baseline Estimates and EE MethodsQuantum Consulting Inc. Exhibit Summary of Benefits and Costs of Common Benefit-Cost Tests Test Benefits Costs TRC Test . Generation, transmission and . Generation costs distribution savings . Program costs paid by the . Participants avoided equipment costs administrator (fuel switching only) . Participant measure costs Participant Test . Bill reduc::;tions . Bill increases . Incentives . Participant measure costs . Participants avoided equipment costs (fuel switching only) Utility (Program . Generation, transmission and . Generation costs Administrator) Test distribution savings . Program costs paid by the administrator . Incentives Ratepayer Impact . Generation, transmission and . Generation costs Measure Test distribution savings . Revenue loss . Revenue gain . Program costs paid by the administrator . Incentives Generation, transmission and distribution savings (hereafter, energy benefits) are defined as the economic value of the energy and demand savings stimulated by the interventions being assessed. These benefits are typically measured as induced changes in energy consumption valued using some mix of avoided costs. Electricity benefits are valued using three types of avoided electricity costs: avoided distribution costs, avoided transmission costs, and avoided electricity generation costs. Participant costs are comprised primarily of incremental measure costs. Incremental measure costs are essentially the costs of obtaining energy efficiency. In the case of an add-on device (say, an adjustable-speed drive or ceiling insulation), the incremental cost is simply the installed cost of the measure itself. In the case of equipment that is available in various levels of efficiency (e.g., a central air conditioner), the incremental cost is the excess of the cost of the high-efficiency unit over the cost of the base (reference) unit. Administrative costs encompass the real resource costs of program administration, including the costs of administrative personnel, program promotions, overhead, measurement and evaluation, and shareholder incentives. In this context, administrative costs are not defined to include the costs of various incentives (e.g., customer rebates and salesperson incentives) that may be offered to encourage certain types of behavior. The exclusion of these incentive costs reflects the fact that they are essentially transfer payments. That is, from a societal perspective they involve offsetting costs (to the program administrator) and benefits (to the recipient). Quantum Consulting Inc.Baseline Estimates and EE Methods I' . Use of the Total Resource Cost to Estimate Economic Potential The TRC test is used in two ways in this study. First, we develop an estimate of economic potential by calculating the TRC of individual measures and applying the methodology described below. Second, we develop estimates of whether different program scenarios are cost effective. Economic potential can be defined either inclusively or exclusively of the costs of programs that are designed to increase the adoption rate of energy efficiency measures. At this stage of the analysis, we define economic potential to exclude program costs. We do so primarily because program costs are dependent on a number of factors that vary significantly as a function of program delivery strategy, There is no single estimate of program costs that would accurately represent such costs across the wide range of program types and funding levels possible. Once an assumption is made about program costs, one must also link those assumptions to expectations about market response to the types of interventions assumed. Because of this, we believe it is more appropriate to factor program costs into our analysis of maximum achievable and program potential. Thus, our definition of economic potential is that portion of the technical potential that passes our economic screening test (using the TRC test) exclusive of program costs. Economic potential, like technical potential, is a theoretical quantity that will exceed the amount of potential we estimate to be achievable through even the most aggressive voluntary program activities. As discussed previously, the TRC focuses on resource savings and counts benefits as utility- avoided supply costs and costs as participant costs and utility program costs. It ignores any impact on rates. It also treats financial incentives and rebates as transfer payments; i.e., the TRC is not affected by incentives. The somewhat simplified benefit and cost formulas for the TRC are presented in Equations 2-1 and 2-2 below. . ~ Avoided Costs of Supply p e Benefits = L.. e=1 (l+d)e-Eqn. Program Coste + Participant Cost Costs = L.. e 1t=1 (l+d)-Eqn. 2- where: d = the discount rate p = the costing period t = time (in years) n = 20 years Quantum Consulting Inc.Baseline Estimates and EE Methods A nominal discount rate of 8 percent is used.11 We use a normalized measure life of 20 years to capture the benefit of long-lived measures. Measures with measure lives shorter than 20 years are "re-installed" in our analysis as many times as necessary to reach the normalized 20-year life of the analysis. This assumption is reasonable given that most measures are eventually replaced with more, not less, efficient alternatives. The avoided costs of supply are calculated by multiplying measure energy savings and peak demand impacts by per-unit avoided costs by costing period. Energy savings are allocated to costing periods and peak impacts estimated using load shape factors. As noted previously, in the measure-level TRC calculation used to estimate economic potential program costs are excluded from Equation 2-2. Using the supply curve methodology discussed previously, measures are ordered by TRC (highest to lowest) and then the economic potential is calculated by summing the energy savings for all of the technologies for which the marginal TRC test is greater than 1.0. In the example Exhibit 2-, the economic potential would include the savings for measures 1 and 2, but exclude savings for measure 3 because the TRC is less than 1.0 for measure 3. The supply curve methodology, when combined with estimates of the TRC for individual measures, produces estimates of the economic potential of efficiency improvements. Again, by definition and intent, this estimate of economic potential is a theoretical quantity that will exceed the amount of potential we estimate to be achievable through program activities in the final steps of our analyses. Exhibit Sample Use of Supply Curve Framework to Estimate Economic Potential (Note: Data are illustrative only) Total End Use Applicable, Not Savings Consumption Complete and Average Total Included in of Population Feasible kWh/ft' of Savings GWh Resource Economic Measure (GWh)Sq. Feet (OOOs)population Savings Cost Test Potential? Base Case: T12 lamps 425 100 000 4.3 N/A N/A N/A N/A with Magnetic Ballast 1. T8 w. Elec. Ballast 425 100 000 4.3 21%Yes 2. Occupancy Sensors 336 000 3.4 10%1.1 Yes 3. Perimeter Dimming 322 000 45% Technical Potential w. all measures 27%116 Economic Potential w. measures for which TRC )0 1.24%102 11 We recognize that the 8-percent discount is much lower than the implicit discount rates at which customers are observed to adopt efficiency improvements. This is by intent since we seek at this stage of the analysis to estimate the potential that is cost-effective from primarily a societal perspective. The effect of implicit discount rates is incorporated into our estimates of program and naturally occurring potential. Quantum Consulting lnG,Baseline Estimates and EE Methods ESTIMATION OF MAXIMUM OCCURRING POTENTIALS ACHIEVABLE PROGRAM,AND NATURALL Y In this section we present the method we employ to estimate the fraction of the market that adopts each energy efficiency measure in the presence and absence of energy efficiency - programs. We define: Maximum achievable potential is a forecast of the amount of economic potential that could be achieved over time under the most aggressive program scenario possible Program potential is a forecast of the amount of savings that would occur in response to one or more specific market interventions . Naturally occurring potential is a forecast of the amount of savings estimated to occur as a result of normal market forces, that is, in the absence of any utility or governmental intervention. Forecasts of program potential are the most important results of the modeling process. Estimating technical, economic, and maximum achievable poten.tials are necessary steps in the process from which important information can be obtained; however, the end goal of the process is better understanding how much of the remaining potential can be captured in programs, whether it would be cost-effective to increase program spending, and how program costs may be expected to change in response to measure adoption over time. According to our definitions and the method described in this section, the maximum achievable potential forecast is really a type of program potential forecast that defines the upper limit of savings from market interventions. Therefore, in the remainder of this section, we will often discuss our general method using the term "program potential" to represent both program and maximum achievable potential. Adoption Method Overview We use a method of estimating adoption of energy efficiency measures that applies equally to be our program and naturally occurring analyses. Whether as a result of natural market forces or aided by a program intervention, the rate at which measures are adopted is modeled in our method as a function of the following factors: The availability of the adoption opportunity as a function of capital equipment turnover rates and changes in building stock over time f..Customer awareness of the efficiency measure The cost-effectiveness of the efficiency measure Market barriers associated with the efficiency measure. l_, The method employed is executed in the measure penetration module of KEMA-XENERGY's DSM ASSYST model. Only measures that pass the measure-level TRC test are put into the penetration module for estimation of customer adoption. Quantum Consulting Inc.Baseline Estimates and EE Methods Availability In most cases, the model uses a stock accounting algorithm that handles capital turnover and stock decay over a period of up to 20 years. In the first step of our achievable potential method, we calculate the number of customers for whom each measure will apply. The input to this calculation is the total floor space available for the measure from the teclmical potential analysis, Le., the total floor space multiplied by the applicability, not complete, and feasibility factors described previously. We call this the eligible stock. The stock algorithm keeps track of the amount of floor space available for each efficiency measure in each year based on the total eligible stock and ",:hether the application is new construction, retrofit, or replace-on-burnout.12 Retrofit measures are available for implementation by the entire eligible stock. The eligible stock is reduced over time as a function of adoptions13 and building decay.14 Replace-on- burnout measures are available only on an annual basis, approximated as equal to the inverse of the service life.15 The "annual portion of the eligible market that does not accept the replace- on-burnout measure does not have an opportunity again until the end of the service life. New construction applications are available for implementation in the first year. Those customers that do not accept the measure are given subsequent opportunities corresponding to whether the measure is a replacement or retrofit-type measure. ' Awareness In our modeling framework, customers cannot adopt an efficient measure merely because there is stock available for conversion. Before they can make the adoption choice, they must be aware and informed about the efficiency measure. Thus, in the second stage of the process, the model calculates the portion of the available market that is informed, An initial user-specified parameter sets the initial level of awareness for all measures. Incremental awareness occurs in the model as a function of the amount of money spent on awareness/information building and how well those information-building resources are directed to target markets. User-defined program characteristics determine how well information-building money is targeted. Well- targeted programs are those for which most of the money is spent informing only those customers that are in a position to implement a particular group of measures. Untargeted 12 Replace-on-burnout measures are defined as the efficiency opportunities that are available only when the base equipment turns over at the end of its service life. For example, a high-efficiency chiller measure is usually only considered at the end of the life of an existing chiller. By contrast, retrofit measures are defined to be constantly available, for example, application of a window film to existing glazing. 13 That is, each square foot that adopts the retrofit measure is removed from the eligible stock for retrofit in the subsequent year. 14 An input to the model is the rate of decay of the existing floor space. Floor space typically decays at a very slow rate. 15 For example, a base-case technology with a service life of 15 years is only available for replacement to a high- efficiency alternative each year at the rate of 1/15 times the total eligible stock. For example, the fraction of the market that does not adopt the high-efficiency measure in year will not be available to adopt the efficient alternative again until year + 15. Quantum Consulting Inc.Baseline Estimates and EE Methods r-- f', r i !.. programs are those in which advertising cannot be well focused on the portion of the market that is available to implement particular measures" The penetration module in DSM ASSYST has a target effectiveness parameter that is used to adjust for differences in program advertising efficiency associated with alternative program types. The model also controls for information retention. An information decay parameter in the model is used to control for the percentage of customers that will retain program information from one year to the next. Information retention is based on the characteristics of the target audience and the temporal effectiveness of the marketing techniques employed. Adoption The portion of the total market that is available and informed can now face the choice of whether or not to adopt a particular measure. Only those customers for whom a measure is available for implementation (stage 1) and, of those customers, only those who have been informed about the program/measure (stage 2), are in a position to make the implementation decision. In the third stage of our penetration process, the model calculates the fraction of the market that adopts each efficiency measure as a function of the participant test. The participant test is a benefit-cost ratio that is generally calculated as follows: Customer Bill Savings ($) ene Its = L.J t=l (l+d)t-Eqn. 2- Participant Costs ($)t osts = L.J t=l (1+dt Eqn. 2- where: d = the discount rate t = time (in years) n = 20 years As noted previously, we use a normalized measure life of 20 years in order to capture the benefits associated with long-lived measures. Measures with lives shorter than 20 years are "re- installed" in our analysis as many times as necessary to reach the normalized 20-year life of the analysis. The bill reductions are calculated by multiplying measure energy savings and customer peak demand impacts by retail energy and demand rates. The model uses measure implementation curves to estimate the percentage of the informed market that will accept each measure based on the participant's benefit-cost ratio. The model provides enough flexibility so that each measure in each market segment can have a separate implementation rate curve. The functional form used for the implementation curves is: Quantum Consulting Inc.2c23 Baseline Estimates and EE Methods (l+e ln~) x(l+e d'(bX) Eqn. 2- where: the fraction of the market that installs a measure in a given year from the pool informed applicable customers; the customer s benefit-cost ratio for the measure; the maximum annual acceptance rate for the technology; the inflection point of the curve. It is generally 1 over the benefit-cost ratio that will give a value of 1/2 the maximum value; and the parameter that determines the general shape (slope) of the curve. The primary curves utilized in our model are shown in Exhibit 2-16. These curves produce base year program results that are calibrated to actual measure implementation results associated with major IOU commercial efficiency programs over the past several years. Different curves are used to reflect different levels of market barriers for different efficiency measures. A list market barriers is shown in Exhibit 2-18. It is the existence of these barriers that necessitates program interventions to increase the adoption of energy efficiency measures. (For more information on market barriers see Eto, PraW, ScWegel 1997, Golove and Eto 1996, DeCanio 2000, DeCanio 1998. Note that for the moderate, high, and extremely high barrier curves, the participant benefit-cost ratios have to be very high before significant adoption occurs. This is because the participant benefit-cost ratios are based on a IS-percent discount rate. This discount rate reflects likely adoption if there were no market barriers or market failures, as reflected in the no-barriers curve in the figure. Experience has shown, however, that actual adoption behavior correlates with implicit discount rates several times those that would be expected in a perfect market.16 The model estimates adoption under both naturally occurring and program intervention situations. There are only two differences between the naturally occurring and program analyses. First, in any program intervention case in which measure incentives are provided, the participant benefit-cost ratios are adjusted based on the incentives. Thus, if an incentive that pays 50 percent of the incremental measure cost is applied in the program analysis, the participant benefit-cost ratio for that measure will double (since the costs have been halved). The effect on the amount of adoption estimated depends on where the pre- and post-incentive benefit-cost ratios fall on the curve. This effect is illustrated in Exhibit 2-17. 16 For some, it is easier to consider adoption as a function of simple payback. However, the relationship between payback and the participant benefit-cost ratio varies depending on measure life and discount rate. For a long-lived measure of IS years with a IS-percent discount rate, the equivalent payback at which half of the market would adopt a measure is roughly 6 months, based on the high barrier curve in Exhibit 2-7. At a I-year payback one-quarter of the market would adopt the measure. Adoption reaches near its maximum at a 3-month payback. The curves reflect the real-world observation that implicit discount rates can well over 100 percent. Quantum Consulting Inc.Baseline Estimates and EE Methods Achievable potential energy efficiency forecasts are developed for several scenarios, from low levels of program intervention, through moderately increased levels, up to an aggressive energy efficiency acquisition scenario. The final results produced are forecasts of annual streams of achievable program impacts (energy and demand by time-of-use period) and all societal and participant costs (program costs plus end-user costs). Exhibit Primary Measure Implementation Curves Used in Adoption Model , r"100% 90% 80% 70% r:: 60% r::50% a.. 40% 30% 20% 10% No Barriers .. ' ...- - - - - - \.- ~ -; - - -,0- A,- - - - - - ~:d - ~a - - - - - - - - - - - - - .. - - --- - - - - - c - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- Low Barriers : . :. - . ,~~.:. ~:-~: :~ ~~;;- -.. -- - - - - +i~:~~:,~;t ~~I~ Hig~ ~~r~~r ~ - - - - - - - - -- - 7- - - - , - - - - - - - -:-/- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -..- - - - . - -.. - - - - - ~'- - - - - - .. - - - .. - - - - - - - - - - - - - .. - - .. - - .. .. - - - -. ".. - ~- - - - -;;'- - - - - - - - - - - - -.. - - -.... - - - - - - - - - - - - - - - - - - - - --...-""'- Participant Benefit-Cost Ratio , !- Exhibit Illustration of Effect of Incentives on Adoption Level as Characterized in Implementation Curves 80% 70% / ,.. , ~ 60% r::950% ~ 40% a.. E 30% itj220% I." 10% 0% ! "_.,_.._"-"-"_.,-"_.,_.. ! B-C Ratio: With 50% incentiveNet increase in adoption' '-"-"-"-"- Initial B-C Ratio: No incentive Participant Benefit-Cost Ratio Quantum Consulting Inc.Baseline Estimates and EE Methods Exhibit Summary Description of Market Barriers from Eto, Prahl, Schlegel 1997 Barrier Description Information or The costs of identifying energy-efficient products or services or of learning about energy-efficient Search Costs practices, including the value of time spent finding out about or locating a product or service or hiring someone else to do so. Performance The difficulties consumers face in evaluating claims about future benefits. Closely related to high search Uncertainties costs, in that acquiring the information needed to evaluate claims regarding future performance is rarely costless. Asymmetric The tendency of sellers of energy-efficient products or services to have more and better information Information and about their offerings than do consumers, which, combined with potential incentives to mislead, can lead Opportunism to sub-optimal purchasing behavior. Hassle or The indirect costs of acquiring energy efficiency, including the time, materials and labor involved in Transaction obtaining or contracting for an energy-efficient product or service. (Distinct from search costs in that it Costs refers to what happens once a product has been located. Hidden Costs Unexpected costs associated with reliance on or operation of energy-efficient products or services - for example, extra operating and maintenance costs. Access The difficulties associated with the lending industry s historic inability to account for the unique features Financing of loan~ for energy savings products (Le., that future reductions in utility bills increase the borrower ability to repay a loan) in underwriting procedures. Bounded The behavior of an individual during the decision-making process that either seems or actually is Rationality inconsistent with the individual's goals. Organization Organizational behavior or systems of practice that discourage or inhibit cost-effective energy efficiency Practices or decisions, for example, procurement rules that make it difficult to act on energy efficiency decisions Customs based on economic merit. Misplaced or Cases in which the incentives of an agent charged with purchasing energy efficiency are not aligned Split incentives with those of the persons who would benefit from the purchase. Product or The failure of manufacturers, distributors or vendors to make a product or service available in a given Service area or market. May result from collusion, bounded rationality, or supply constraints. Unavailability Externalities Costs that are associated with transactions, but which are not reflected in the price paid in the transaction. Non-externality Factors other than externalities that move prices away from marginal cost. An example arises when Pricing utility commodity prices are set using ratemaking practices based on average (rather than marginal) costs. Inseparability of The difficulties consumers sometimes face in acquiring desirable energy efficiency features in products Product Features without also acquiring (and paying for) additional undesired features that increase the total cost of the product beyond what the consumer is willing to pay. Irreversibility The difficulty of reversing a purchase decision in light of new information that may become available which may deter the initial purchase, for example, if energy prices decline, one cannot resell insulation that has been blown into a wall. Quantum Consulting Inc.Baseline Estimates and EE Methods o- ! 3. DEMAND RESPONSE POTENTIAL METHODS OVERVIEW OF DEMAND RESPONSE FORECASTING METHODS Similar to the energy efficiency forecast, the carrying out of a number of systemq.tic analytical steps was necessary to produce accurate estimates of demand response effects on system load. To conduct this analysis we utilized a model to forecast demand reduction from demand response (DR) programs. The supply curve method used to forecast DR impact is a simpler process than the measure- based models used to forecast energy efficiency. Information ,on the characteristics and penetration of potential DR measures does not exist in sufficient fashion to justify a measure- based modeling approach. We therefore relied on the professional judgment of a panel of experts to reach a consensus on key inputs to the supply curve models based on their experience in designing, managing, and evaluating DR programs. The forecast of demand reduction from potential demand response programs was produced using a series of DR supply curves that varied by program type and market segment. An overview of the DR modeling framework used is shown in Exhibit 3- DR DATA DEVELOPMENT This section describes the data used for the DR Forecasting Model. Although the DR forecasts produced for Idaho Power are largely the outcome of professional judgment, they rely on a modeling framework that provides the ability maximize the use of the limited amount of data available. The framework accounts for both the "capability" and motivational" aspects of DR programs. Capability is a somewhat abstract concept that reflects a combination of awareness, experience, and technology. Increases in DR capability will occur over time due to external market forces and possibly due to capability-building activities pursued by Idaho Power. In addition to capability, motivation is other key factor that determines the amount of load achievable from a DR program. A customer must have sufficient motivation to reduce electric demand for a period of time. Motivation usually takes the form of a financial incentive, although the ability to avoid a blackout can also be significant motivator to reduce a portion of load. Incentives can take the form of reduced rates or a performance payment. For modeling purposes, the motivation for all programs was expressed in terms of dollars per kWh reduced. The $/kWh concept allowed us to take into consideration that customers required additional motivation for each hour that they are asked to reduce their demand. Quantum Consulting Inc.DR Methods ELIGIBLE LOAD by Sector and End Use Exhibit DR Forecasting Model Framework LOW DR CAPABILITY PROJECTED IMPACTS & COST ESTIMATE Motivation Programs RESPONSE CURVE Capability Segment & Program Type Quantum Consulting Inc.DR Methods load Forecast Shares The first step of the DR modeling framework is to define market segments and the demand produced by each segment during the system peak. We elected to segment load using a combination of market sector, end use, and customer size based on maximum demand. The eight market segments were defined as shown in Exhibit 3-2. The industrial and irrigation sectors were excluded from the analysis. Exhibit DR Market Segment Sector End-use Size Residential Other All Residential Cooling All Small Commercial HVAC -::::l OOOkW Small Commercial Lighting -:::: 1 000 kW Small' Commercial Other -:::: 1 000 kW OOOkWLarge Commercial HVAC Lighting ~ 1 000 kWLarge Commercial Large Commercial Other OOOkW Back-up Generation All All The system peak load forecast by market segment was developed by market sector from Idaho Power s 2003 demand forecast. A table of Idaho Power electricity sales by market segment and customer demand group was available and was used to split the forecast into the various size categories. Segmenting the load by end use was based on data for the Idaho Power end use forecast database. Applicability Factors The issue of technical potential for DR is not as straightforward a concept. One could argue that the technical potential for DR is 100 percent of load. However, our expert panel felt that there was a significant portion of peak demand that would be unresponsive to standard DR programs at any reasonable level of motivation. We elected to apply an applicability factor to the load of each segment, reflecting that portion of load where response was feasible. The Eligible Load for each market segment is equal to the total peak period load for that market segment. The Applicable Load (or the technical potential) is a portion of Eligible Load where customers are willing and able to reduce demand at the highest conceivable motivation level. Quantum Consulting Inc.DR Methods The applicability factors were set using Delphi estimation. These factors were held constant throughout the time period addressed in the forecast. Exhibit 3-3 shows the estimates of applicable load by market segment. Exhibit 2004 Peak Load and DR Applicable Load 600 500 lID Other Load IITech Potential 400 3: 300 :a: 200 100 Res-oth Res-ac Small-hvac Small-oth Small-lgt Large-hvac Large-oth Large-Igt Back-up Gen Market Segments Capability Shares Once the applicable load was determined, this load was split into three capability segments for the base year: Low, Partial, and High. There are two primary reasons for splitting load into capability segment. This first reason is based on the theory that the portion of load that will respond at a given motivation level will vary by capability segment. Stated differently, each segment has a different motivation response curve. The second reason for segmentation involves the ability to assess the impact of DR programs that are designed to build capability in addition to providing motivation. We estimate the portion of the load that moves from one segment to another resulting from capability-building activities, such as an incentive program for enhanced automation. Low Capability is characterized as the loads that lack variable control and cannot be easily controlled from a centralized location. DR activities in the low capability segment would achieved through a labor-intensive process and often will have high transaction costs. The Partial Capability segment contains the load that has either variable control or centralized control but not both. The High Capability segment includes loads that involve an automated response process or centralized control of variable loads. This High Capability segment can implement DR actions with little or no transaction costs while minimizing the impact on Quantum Consulting Inc.DR Methods productivity and building comfort. Exhibit 3-4 provides examples of lighting loads for each of the three segments. Exhibit Lighting Examples of Capability Segments Se2ment load Description DR Option Low Building with individual wall switches in each Manually turn off lights selected area or floor. No bi-Ievellighting.areas. Partial or Lighting circuits are controlled by a central EMS Use EMS to turn off lights in selected Medium system. No bi-Ievel lighting areas Partial or Building with individual bi-Ievel wall switches Manually turn off portion of lamps in all Medium in each area or floor.areas. High Bi-Ievel or dimmable ballast lighting is Use EMS to reduce lighting levels in all controlled by EMS.areas. Our panel of experts concluded that the ma.jority of load would currently fall into the Low Capability segment because most customers have very little experience with DR programs and the penetration of DR-friendly technologies such as dimmable lighting ballasts is very low. Certain segments such as )01,000 kW customers and residential HV AC were felt to have a moderate portion of the market in the Partial or Medium Capability segment, based on their experience with existing cycling and interruptible rate programs. It was the conclusion of the panel that a very small portion of all markets would fall into the High Capability segment at this time, given the very limited experience with dynamic rates, demand bidding, real-time energy information systems, and DR automation technologies. The estimated peak load, DR applicable load, and the assumed portion of applicable load by capability segment for each market segment are shown in Exhibit 3- Exhibit 2004 Load Statistics by Market Segment Applicability Applicable low Medium High Sector End-use Size Peak MW Factor Capability Capability Capability Residential Other All 38C 10%100% Residential Cooling All 50~50%252 100% Small Commercial HVAC 0( 1 000 kW 19E 40%100% Small Commercial Lighting 0( 1 000 kW 141 10%100% Small Commercial Other 0( 1 000 kW 13~20%100% Large Commercial HVAC :;, 1 000 kW 50%98% Large Commercial Lighting :;, 1 000 kW 15%95% Large Commercial Other :;, 1 000 kW 30%100% Back-up Generation All All 75%80%20% Quantum Consulting Inc.DR Methods The portion of load in each capability segment was forecast to change over time based on two factors: Idaho Power capability-building. activities,. such as customer education, and external market forces. The effect from external market forces was addressed by assuming a very small portion of the load would move each year from the low to medium segment and from the medium to high segment. The effect from Idaho Power capability building was modeled by estimating, using the Delphi process, the cost per kW to increase the capability level and by specifying the amount of capability-building budget spent by market segment in each year, The cost to increase capability was set between $5/kW and $30/kW, depending on the market segment. Exhibit Capability Building Cost Assumptions per kW (Shift from Low to Medium Capability) Segment All Others Max. Ach. Res-$15 $20 Small-HV AC $10 $15 Small-Other $20 $30 Small-Lighting $15 $20 Large-HV AC $10 Large-Other $15 $20 Large-Lighting $10 $15 Back-up Gen Program Definitions Once the amount of load in each capability segment was estimated, we developed a set of motivation-response curves for various types of DR programs. It is our theory that the motivation response curve for an emergency program is different than that for an economic or rate program. Customers tend to be more willing to take actions when a rotating blackout is possible. The motivation response curve relates the portion of applicable load that will be reduced at a given $/kW of motivation. Given that the goal of this forecast was the support of resource planning and that the forecast was largely developed based on expert opinion, it was not feasible to forecast every possible DR program. Instead, four program concepts were modeled with some slight variations either over time or across segments. The four concepts included: AC Load Control (DLC):these programs provide lower energy rates for customers who . are willing to have cycling equipment installed that can be directly controlled by the Quantum Consulting Inc.DR Methods , u \:. utility. There are usually a maximum number of events and/or hours that may be called in a year. Critical Peak Pricing (CPP):this program offers dynamic rates that change based on demand versus supply available. This program generally provides consistently lower off-peak rates. However, during a CPP event, rates may increase dramatically (e.g. 5 times the average for that period). Customers may choose to voluntarily reduce load during a CPP event or pay the substantially higher charges for maintaining their peak load. There are usually a maximum number of events and/or hours that may be called in a year. Voluntary Demand Response Incentive (DRP):this program offers a credit to customers over a certain demand, who voluntarily commit to reduce their electricity usage by a significant percentage (such as 10%) during a DRP event. Customers can generally chose whether to participate when an event is called, as long as they meet the program minimum requirements. Back-up Generator Incentives (BUG):this program offers financial incentives to customers who run their back-up generation during program events. Since, in many cases, two DR programs will compete for the same load, it was necessary to account for this competition in the forecast model. An overlap factor was specified for each program that reflected the amount of load that a program would lose to the other programs that were offered to the same segment. We recognize that program types listed above may not represent every possible DR program; however, they provide reasonable program prototypes for the purposes of IRP. There is little justification for specifying a large number of well-defined DR programs given there is considerable uncertainty in the response and impacts of anyone DR program. The forecasts produced in this project are designed to support strategic resource planning rather than tactical program design. Thus, the program concepts for the DR forecasts only need to representative of the program activities that could be pursued. Where feasible, we based our assumptions on information provided directly by Idaho Power regarding their current or intended future offerings. For example, we varied the programs addressed in each scenario by market segment, as indicated by Idaho Power tariff structures. ESTIMAT/ON OF ECONOMJC" POTENTIAL FOR DEMAND RESPONSE, The concept of economic potential for a DR program is not as straight forward as the economic potential for energy efficiency measures. The economic potential for an energy efficiency measure involves the comparison of the measure cost to the avoided supply cost that is obtained from installing the measure. Most DR programs involve encouraging customers to , make behavioral changes on the use of appliances or equipment and do not often involve the purchase of a measure. Thus, the standard concept of economic potential of energy efficiency measures does not readily apply. I.n DR MethodsQuantum Consulting Inc. An estimate of economic potential is useful because it provides a measure of the maximum amount of load reduction that could be obtained within some economic constraint. In order to achieve this information need, a definition of economic potential was developed for both DR and TOU programs. Economic potential for DR programs was defined as the load reduction that could be obtained if the entire applicable market was in the high capability segment and if a minimum of 50 cents per kWh was offered for all programs. The economic potential results provided in Section 5 are based on the peak demand load 2004. The economic potential for future years would increase in proportion to the increase in total peak demand. FORECASTING PROGRAM IMPACTS A supply curve or response curve was developed for each program concept, market segment, and capability segment using the Delphi process. The response curve provides an estimate the portion of applicable load in each capability segment that will be reduced at a given $/kWh of motivation. Although the ability existed in the model to specify a different curve for each program and market segment, the experts felt that their collective knowledge and experience did not justify the development of a large number of unique curves. Curves were developed that vary significantly across capability segment but tended to vary only slightly across programs and market segments. Exhibit 3-7 shows an example of the curves used for CPP program and market/ capability segments. 60% 50% 40%CtI ..J 30% :I.. 20%c.. 10% Exhibit 3- 7 CPP Supply Curves ---tr- High ---- Medium ~Low 100 Cents per kWh Quantum Consulting Inc.DR Methods r.' l:. l., Scenario Definitions Achievable potential forecasts can be developed for multiple scenarios. For example, program savings can be modeled under low levels of program intervention, through moderate levels, up to an aggressive DSM acquisition scenario. As discussed above, four program concepts were modeled: AC Load Control (DLC), Critical Peak Pricing (CPP), Voluntary Demand Response Incentives (DRP), and Back-up Generator Incentives (BUG). Using these concepts, four bundled program strategies were developed: 1. DLC and BUG - Low Incentive Levels 2. All 4 Concepts - Low Incentive Levels 3. All 4 Concepts - High Incentive Levels 4. "Maximum Achievable The primary drivers between the lower intervention bundles and the more aggressive intervention bundles are the amount of capability building or marketing that is pursued and the amount of customer incentives offered for demand reductions. The Maximum Achievable scenario is designed to forecast the maximum achievable DR that is obtainable by large-scale capability building and high incentive payments. In the Maximum Achievable scenario, incentive payments were set at 50 cents per kWh, the highest level that was determined to be cost effective. Exhibit 3-8 summarizes the incentive payments utilized for each program concept and program bundling strategy. Exhibit 3-9 shows the assumed capability budget for each program strategy. Exhibit Customer Incentive Assumptions Cents per kWh Program Concepts Program Bundle Strategy AC DLC Back-up Gen CPP DRP OLC & BUG - Low $ 4 Concept - Low $ 4 Concepts - High $ Maximum Achievable ..,. Quantum Consulting Inc.DR Methods ~Max. Achievable -.tr-4 Concepts - High $ -8-4 Concept - Low $ -+-DLC & BUG - Low $ 600 $1,400 200 000 $800 ,... $600 $400 $200 2004 2005 Exhibit Capability Builditig Budgets 2006 2007 2008 2009 2010 2011 2012 2013 Year Quantum Consulting Inc.DR Methods I.~ 4. ENERGY EFFICIENCY PEAK DEMAND AND ENERGY SA VINes POTENTIAL RESULTS In this section we present summary results of the Idaho Power energy efficiency potential analysis for the residential and commercial sectors. First, economic and technical potential are discussed. Next, we present summary energy efficiency supply curves, which are an alternative method of presenting forecasted potentials. Finally, we present scenario forecasts for achievable energy efficiency potentiaL Definitions of the different types of energy efficiency potential andmethods used to develop them are provided in Section 2 of this report. Section 2 also presents the baseline estimates used in our analyses. At the outset of this study, the primary focus was on peak demand reduction and the scope was limited to measures with impacts on summer peak. In a later, second phase, the scope was expanded to look at all measures with the potential to provide cost-effective energy savings. Where possible, the figures in this section delineate the peak demand and energy savings associated with the two phases. In cases where there is no distinction, the figures represent the results of the second phase. Because the results of the first phase were provided to the resource- planning group at IPCo, identical graphs based only on the results of the initial phase are provided separately in Appendix G. TECHNICAL AND ECONOMIC POTENTIAL In Exhibits 4-1 and 4-2 we present our overall estimates of total technical and economic potential for peak demand and eleCtrical energy in the residential and commercial sectors in the Idaho Power territory. Technical potential represents the sum of all savings achieved if all measures analyzed in this study were implemented in applications where they are deemed applicable and physically feasible. As described in Section 2, economic potential is based on efficiency measures that are cost-effective based on the total resource cost (TRC) test, a benefit- cost test used to compare the value of avoided energy production and power plant construction to the costs of energy-efficiency measures and program activities necessary to deliver them. The value of both energy savings and peak demand reductions are incorporated into the TRC test. Overall and by Sector If all measures analyzed in this study were implemented where technically feasible, we estimate that overall technical demand savings would be roughly 551 MW, about 33 percent of projected combIned residential and commercial peak demand in 2013. If all measures that pass the TRC test were implemented"economic potential savings would be 384 MW, about 23 percent of total residential and commercial demand in 2013. Technical energy savings potential is estimated to be roughly 1 917 GWh, about 21 percent of total residential and commercial energy usage projected in 2013. Economic energy savings are estimated at 1,107 GWh, about 12 percent of base residential and commercial usage. The technical and economic potential estimates are shown by sector and vintage (existing stock versus new construction) in Exhibits 3 through 4-5, The largest share of both technical and economic savings is in the residential existing stock. Quantum Consulting Inc.Efficiency Potential Results Exhibit Technical and Economic Potential (2013) Peak Demand Savings- ::;E-'" 300 0.. 600 500 400 200 100 . Phase - - - - - - - - - - - - III Phase I Technical Economic Exhibit Technical and Economic Potential (2013) Energy Savings-G Wh per Year 500 000 - - - - - - - - - - - - - - - - - - - - - - - - - ~ 1 500 --.. ~ 1 000 - - - - - - - - - - - - - - - - - - - - - 500 Technical Economic Exhibit Technical and Economic Potential by Sector and Vintage, Peak Demand Savings (2013) 3: 200 rf 150 350 300 . phase II Phase I - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 250 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- 100 Tech. Econ. Residential Existing Tech. Econ. Residential New Tech. Econ. Commercial Existing Tech. Econ. Commercial New Quantum Consulting Inc.Efficiency Potential Results Exhibit Technical and Economic Potential by Sector and Vintage, Energy Savings (2013) 1200 800 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - . Phase III Phase I1000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ..c:: :s: (.!J (ij 600::J -:( I U 400 200 ----------------- Tech. Ecan. Residential Existing Tech. Ecan. Residential New Tech. Ecan. Commercial Existing Tech. Ecan. Commercial New Exhibit 4- Phase II Technical and Economic Potential Estimates GWh Sector and Vintage Technical Economic Technical Economic Residential - Existing 299 201 102 554 Residential - New 139 102 373 235 Commercial - Existing 373 252 Commercial - New Total 551 384 917 107 Quantum Consulting Inc.Efficiency Potential Results Exhibit 4- Phase Technical and Economic Potential Estimates GWh Sector and Vintage Technical Economic Technical Economic Residential - Existing 237 189 520 444 Residential - New 117 216 173 Commercial - Existing 265 179 Commercial - New Total 442 337 060 851 End Use Potential Residential economic potential is presented by key end use in Exhibit 4-6. Lighting, cooling, and clothes washing dominate economic energy savings, while cooling makes up the vast majority of peak demand impacts. Exhibit 4-7 shows commercial sector economic potential estimates by end use. Lighting is the largest contributor in terms of both energy savings potential and peak demand savings potential, cooling is the second largest contributor to commercial economic peak demand savings. Potential by Building Type Exhibit 4-8 displays residential economic potential by building type. Single-family homes account for the vast majority of potential. Commercial sector economic potential is displayed by building type in Exhibit 4-9. The largest contributors to both GWh and peak MW potential are small offices, food stores, retail establishments, hospital/health care facilities, and miscellaneous" buildings. ENERGY EFFICIENCY SUPPL Y CURVES Energy efficiency supply curves for energy and peak demand savings are shown in Exhibits 4- 10 and 4-11, respectively. The supply curves show the distribution of measure-level potentials by relative cost. Energy supply curve sununary data are presented Exhibits 4-12 through 4- for the residential existing, residential new construction, commercial existing and commercial new construction vintages. Note that these values are aggregated across market segments and that individual segment results can vary significantly from the average values shown. addition, it is important to recognize that cost-effectiveness, as defined by the TRC test, cannot be determined exclusively from these curves because the value of both energy and demand savings must be integrated when comparing to supply side alternatives. Measure-level TRC estimates are provided in Appendix E. Quantum Consulting Inc.Efficiency Potential Results Exhibit Residential Economic Potential by End Use (2013) 300 250 . Phase II Phase I - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 200 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 150 - - - - - - - - - - - - - - - - - - - - - -------------- 100 - - - - - - - - - - - - - - - - - - - - - -------------- 0 . GWH MW Space Cooling GWH MW GWH MW Dish Washer GWH MW GWH MW Water Heating GWH MW Clothes WasherLightingRefrigeration Exhibit 4- 7 Commercial Economic Potential by End Use (2013) 200 180 . Phase . Phase - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 160 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 140 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 120 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 100 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0 . GWH MW Lighting GWH MW Cooling GWH MW Heating GWH MW Water Heat GWH MW Ventilation GWH MW Refrigeration Quantum Consulting Inc.Efficiency Potential Results Exhibit Residential Econo1Jtic Potential by Building Type (2013) 700 100 . Phase 600 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - III! Phase I 500 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 400 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 300 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 200 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - GWH MW Single-Family GWH MW GWH MW Small Multi-Family Large Multi-Family GWH MW Mobile Home Exhibit Commercial Economic Potential by Building Type (2013) GWHSchool MW College G Small Office G Large Office G GWHRestaurant MW GWHetal MW GWHFood Store MW GWHWarehouse MW Hospital G Hotel GWH III Phase I Miscellaneous . Phase Quantum Consulting Inc.Efficien~j Potential Results Exhibit 4- Residential and Commercial Energy Efficiency Supply Curve Energy - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -~- - - - - - - - - - - - .r" $0.05 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -~-~-...- $0. $0. $0. ;:. :s:: $0. ... c.. -g $0. ;:. ..J - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - J - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - L - - - - - - - - 10%15%20%25% Percent Savings Exhibit Residential and Commercial Energy Efficiency Supply Curve Peak Demand $1,000 $900 $800 :::I $700 $600 ... $500c.. $400 ;:. ..J l_, l_. l.., - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - $300 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - $200 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - $100 --- ------ -- -------------------------- 25%10%15%20%30%35% Percent Savings Quantum Consulting Inc.Efficiency Potential Results Exhibit Residential-Existing Energy Efficiency Supply Curve Data Measure Cumulative MW Savings Levelized Capacity Cosl $/kW $35 $51 $74 $75 $94 $98 $98 $139 GWH Savings Measure MW Savings Double Pane, Med Low-E Coaling Duct Insulation (.4) Basic HVAC Diagnostic Testing And Repair HE Room Air Conditioner- EER 10, Duct Repair (0,32) 10 to 12 SEER Split-System Air Condilloner Wall2x4 R-O 10 Blow-In R-13 Insulation (0.14) Direct Eva rallve Cooler Wal12x4 R-O to Blow-In R-13 Insulation (0,14) Direct Evaporative Cooler Whole House Fans 101013 SEER Spll~Syslem Air Conditioner Ceiling R-19 to R-38 Insulation (27) Ceiling Fans Infillration Reduction (0.4) 101014SEERS 111- slemAlrConditioner $0,577 $0.758 $1.094 $1.759 1204 1208 1209 1219 Infiltration Reduction (0, Ceiling Fans 10 to 14 SEER Spill-System Air Conditioner 313 314 325 $870 $1,445 $1,648 *Measures incremental to Phase II are highlighted. Exhibit 13* Residential-New Construction Energy Efficiency Supply Curve Data -10 Years Measure Cumulative Levellzed GWH Savings GWH Savings En~;~~~oSI MW Savings Cumulative MW Savings Levelized Capacity Cosl $/kW $18 $70 $92 $98 $116 , $117 Measure Double Pane, Med Low-E Coating Basic HVAC Diagnostic Testing And Repair uct Repair (0.32) Irect Evaporative Cooler HE Room Air Conditioner- EER 10, 10 to 12 SEER Split-S tem Air Conditioner Duct Repair (0,32) Direct Evaporative Cooler HE Room Air Conditloner- EER 10, 10 to 12 SEER Splil.Syslem Air Conditioner Wall2x4 R-13 10 2x6 R-13 Insulation (0.14) Whole House Fans 10 to 13 SEER Split-System AIr Conditioner 309 330 333 342 350 Ceiling Fans 10t014SEERS lit- stem Air Conditioner 367 $0,542 10t014SEERSplit-SystemAirCondltioner373 $1.616 .. .. Measures incremental to Phase II are highlighted. Quantum Consulting Inc.Efficiency Potential Results c-- Exhibit Commercial-Existing Energy Efficiency Supply Curve Data Measure GWH Savings Cumulative GWH Savings 10.49. 56.106, 79.185, 16,202. 11.213. 19.240.0 13.253, Measure CumulativeMW Savings MW Savings Prog. Thermostat - DX T8iEB Replacement CFL Screw-in, Modular 18W Ventilation Occupan Sensor $0,028 $0,031 $0.040 $0.047 $0.059 DX Packaged System, EER-10,9, 10 tons T81EB Replacement Prog. Thermostat - DX CFLScrew-ln, Modular 18W Window Film . Standard) 12, 15, 20. 12, 27, 30, 50.4 55, Leveiized Capacity Cost $/kW $88 $116 $136 $155 $180 Continuous Dimming Eva orative Pre-Cooler 32. *Measures incremental to Phase II are highlighted. $0.057 $0,068 DX Packaged System, EER-10,, 10 tons DX Tune Upl Advanced Diagnostics Window Film (Standard) Exhibit 15* Commercial-New Construction Energy Efficiency Supply Curve Data -10 Years Cumulative Levellzed Cumulative Leveiized Measure GWH Savings GWH Savings Energy Cost Measure MW Savings MW Savings Capacity Cost $IkWh $IkW Low-e Windows $0.022 Low-e Windows 3.4 $33 10 % More Efficient Uohtino Deslon 14.4 19,$0.023 10 % More Efficient LIghting Design $87 OX Packaged System 11,$9220 % More Efficient LIghting Design 15.46.$0,034 20 % More Efficient LIghting Design 16,$109Ventilation51.$0,047 OX Tune Upl Advanced Diagnostics 20.$318OX Packaged System 59.$0:060 OX Tune Val Advanced Dlaonostics 69.$0.070 Ventilation 20,$481 *Measures incremental to Phase II are highlighted. FORECASTS OF ACHIEVABLE PROGRAM POTENTIAL SCENARIOS In this section we present our overall achievable potential forecasts. In contrast to technical and economic potential estimates, achievable potential estimates take into account market and other factors that affect adoption of efficiency measures. Our method of estimating measure adoption takes into account market barriers and reflects actual consumer and business implicit discount rates (see Section 2 for this methodology). Achievable potential refers to the amount of savings that would occur in response to one or more specific program interventions. Net savings associated with program potential are savings that are projected beyond those that would occur naturally in the absence of any market intervention. Because achievable potential will vary significantly as a function of the specific type and degree of intervention applied, we develop estimates for multiple scenarios. Peak demand and energy savings forecasts were developed for four possible program-funding scenarios. These scenarios were designed to address market changes to increasing incentive levels (as a percent of incremental measure cost) and marketing levels. The scenarios include: A Low efficiency funding scenario with rebates covering 33% of incremental measure costs and base marketing levels; Quantum Consulting Inc.Efficiency Potential Results 2. A Moderate efficiency funding scenario with rebates covering 50% of incremental measure costs and slightly higher marketing expenditures; 3. A High efficiency funding scenario with rebates ramping up over time to 75% of incremental measure costs and significantly increased marketing expenditures; and 4. A Maximum Achievable scenario with rebates ramping up over time to cover 100% of incremental measure costs and marketing expenditures sufficient to create maximum market awareness. Maximum achievable efficiency potential is the amount of economic potential that could be achieved over time under the most aggressive program scenario possible.17 We forecasted program energy and peak demand savings under each achievable potential scenario for a 10-year period beginning in 2004. Our estimates of achievable potentials and their effect on forecasted demand and energy consumption are shown in Exhibits 4-15 through 4- for both Phase II and Phase As shown in Exhibit 4-15a, by 2013 net18 peak demand savings are projected to be roughly 42 MW under Low, 72 MW under Moderate, 116 MW under High, and 190 MW under Maximum efficiency spending scenarios. In Exhibit 4-16a, we show projected net annual energy savings of 195 GWh under Low, 298 GWh under Moderate, 489 under High, and 681 GWh under Maximum efficiency futures. Exhibit 4-17 provides a breakdown of Year-10 peak demand reduction potential by scenario, sector and vintage for both Phase II and Phase I results. As shown, the residential and commercial existing construction market segments account for most of the potential for the Low and Moderate scenarios. The residential existing segment accounts for an increasing share of potential impacts for the higher funding scenarios. Exhibits 4-18 and 4-19 summarize the total ten-year results for all funding scenarios for both phases of results. Exhibit 4-18 juxtaposes the total program benefits - based on the cumulative avoided costs associated with each , scenario - with a breakout of the various cost components. Exhibit 4-19 provides the total ten- year program spending and forecasted achievable potential estimates by program scenario, sector and vintage. All of the funding scenarios are cost effective based on the TRC test. The TRC benefit-cost ratios are 1.7, 1.6, 1.5, and 1.4 for the Low, Moderate, High, Maximum Achievable scenarios, respectively. 17 Experience with efficiency programs shows that maximum achievable potential for voluntary programs will always be less than economic potential for two key reasons. First, even if 100 percent of the extra costs to customers of purchasing an energy-efficient product are paid for through program financial incentives such as rebates, not all customers will agree to install the efficient product. Second, delivering programs to customers requires additional expenditures for administration and marketing beyond the costs of the measures themselves. These added program costs reduce the amount of potential that it is economic to acquire. Policy makers should consider a combination standards that follow behind strong voluntary programs as a more optimal efficiency acquisition strategy than trying to achieve maximum potential through voluntary programs only. 18 Again, net refers throughout this chapter to savings beyond those estimated to be naturally occurring, that is, from customer adoptions that would occur in the absence of any programs or new standards. Quantum Consulting Inc.Efficiency Potential Results Exhibit 4-15a Phase II Net Peak Demand Reduction Potentiai by Funding Scenario, 10- Year Forecast I-+- Max Achievable -- High Moderate --- Low -Jib Nat. Occurring I 200 180 160 140 120 a: 100 :s::2 80 ...: ttS cf 60 Year Exhibit 4-15b Phase Net Peak Demand Reduction Potential by Funding Scenario,10-Year Forecast 200 180 160 ~ 140 120 a: 100 :s::2 80 ...: ttS cf 60 -+- Max. Achievable -- High Moderate --- Low Nat. ocCurring! ----- ---- -- Year Quantum Consulting Inc.Efficiency Potential Results Exhibit 4-16a Phase II Net Energy Savings Potential by Funding Scenario, 10- Year Forecast I-+- Max Achievable --- High Moderate ~ Low .....- Nat. Occurring I 800 700 600 CIJ ~ 500 (f) .s::. 3: 400 C!J gj 300 0::(200 100 Year Exhibit 4-16b Phase Net Energy Savings Potential by Funding Scenario,10-Year Forecast -+- Max. Achievable --- High Moderate ~ Low Nat. Occurring! 700 600 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - gJ, 500 :;; (f) 400 .s::. C!J 300 (ij::J 0::( 200 100 Year Quantum Consulting Inc.Efficiency Potential Results ,- 1 ! \ 1 \l...-I , I Exhibit 4- 1"7 Phase II Net Peak Demand Reduction Potential by Funding Scenario and Segment- Year 1111 Res Exist. Res NC 0 Com Exist ~ Com NC r I 200 , r 180 160 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - s: 140 ~ 120 .:.:: 100 (1j a... 80 ~ M ------------------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 40 . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Nat. Occurring Low Moderate Scenario High Max Achievable Exhibit4-17b Phase Net Peak Demand Reduction Potential by Funding Scenario and Segment Year . Res Exist . Res NC 0 Com Exist ~Com NC I 200 180 - - - - - - - - - - - - - - - - - - - - - - - .. - - - - - - - - - - - - - - - - - - - - - en 160 5 140CtI :s: 120 :2:.:.:: 100 CtI a... 80 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - .. - - - - - - - .. - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -, ' l__ ------------- Nat Occurring Low Medium High Max. Achievable Scenario l.. rho. Quantum Consulting Inc.Efficiency Potential Results (Jj ~ $300 S $250 , CD ~ $200 ~ $150 Exhibit 4-18a Phase II Cumulative Ten-Year Program Costs and Benefits $450 $400 IIIi Net Benefits ImTotal Benefits III Program Incentives . Non-Incentive Participant Costs 0 Marketing . Administration ------------ $100 ------------ $350 - - - - - - - - - - - - - - - - - - - - - - - - - - - $50 Low Moderate Max. AchievableHigh Avoided cost benefits and program costs discounted at nominal rate of 8 percent per year. (Jj ~ $250 S $200 ti3 ::::- $150 (Jj 0: $100 Exhibit 4-18b Phase Cumulative Ten-Year Program Costs and Benefits $350 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - $300 l\1li Net Benefits I!m Total Benefits iii Program Incentives . Non-Incentive Participant Costs 0 Marketing . Administration ------------- - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - ---------------- $50 $0 ' Low Moderate High Max. Achievable Avoided cost benefits and program: costs discounted at nominal rate of 8 percent per year. Quantum Consulting Inc.Efficiency Potential Results fl- l ! ( ( Exhibit 4-19a Summary of Phase II Net Achievable Energy Efficiency Potential Forecasts Year 10 (2013) Impacts Cumulative 10- Year Program Net MW Net Annual Costs ($Reductions by GWh Savings by Total Resource Sector/Vinta~e Scenario Millions)*2013 2013 Cost Ratio IResidential Low $16 1.6 Existing Moderate $31 126 1.5 High $78 249 1.4 Maximum $148 103 348 1.3 IResidential Low INew Moderate $12 1.8 IConstruction Hi~h $21 Maximum $38 ICommercial Low $15 1.7 Existing Moderate $24 126 1.7 High $37 159 Maximum $60 202 Commercial Low New Moderate Construction Hi~h $12 1.5 Maximum $28 1.2 otal Low $39 195 1.8 Moderate $73 298 1.7 High $149 116 488 1.6 Maximum $274 190 681 1.3 lo_ 1 ' \.. Program costs discounted for inflation at 3 percent per year. Quanfwn Consulting Inc.Efficiency Potential Results L, ' Exhibit 4-19b Summary of Phase Net Achievable Energy Efficiency Potential Forecasts Year 10 (2013) Impacts Cumulative 10- Year Program Net MW Net Annual Costs ($Reductions by GWh Savings by Total Resource Sedor/Vintage Scenario Millions)*2013 2013 Cost Ratio Residential Low $12 1.3 Existing Moderate $25 1.3 High $68 200 1.3 Maximum $139 295 1.2 Residential Low New Moderate 1.9 Construction High $14 1.7 Maximum $46 1.5 Commercial Low $12 1.4 Existing Moderate $18 1.4 High $36 144 1.4 Maximum $48 173 1.4 ::::ommercial Low 1.7 New Moderate Construction High $10 1.5 Maximum $21 1.4 otal Low $31 131 1.4 Moderate $54 201 1.4 High $128 395 1.4 Maximum $255 183 584 1.3 J J Program costs discounted for inflation at 3 percent per year. Quantum Consulting Inc.Efficiency Potential Results 5. DEMAND RESPONSE POTENTIAL RESULTS This section presents the economic potential and forecast results for Demand Response (DR) programs. Economic potential estimates are provided first. The forecast impacts DR programs are provided for three scenarios. The primary drivers in the scenarios are the effort directed at DR capability building (i.e. marketing, education and the promotion of DR enabling technologies) and the incentiye levels provided to customers who reduce demand. ECONOMIC POTENTIAL As stated in Section 3, an estimate of economic potential is useful because it provides an indication of the maximum amount of load reduction that could be obtained within an economic constraint. The difficulty in determining economic potential for demand response and rate programs is estimating the total resource cost associated with reducing load. Although it may be possible in the future to develop an economic potential definition for DR that is consistent with what is typically done with energy efficiency measures, it was decided to define and calculate a simplified measure of economic potential for DR programs at this time. The estimated economic potential for DR programs is shown in Exhibit 5-1. Economic potential was defined as the amount of peak load reduction that would occur if all customers had a high level of DR capability (i.e. awareness, experience, technology) and 50 cents per kWh was offered as the incentive for all DR programs. Since our definition of economic potential is dependent on the number and type of programs being offered, the economic potential estimates were based on the forecast loads and programs that would be in place in 2004 since this is the first year where the full set of potential programs are modeled to be offered to eachmarket segment. Exhibit Economic Potential for Residential and Commercial DR Programs % Of Total Peak MW in 2004 Demand Estimated Applicable Demand for DR 469 32% Economic Potential for DR 105 The residential sector AC Load Control program component contributes over half of the economic potential (57%). The economic potential is about 12% of the total residential AC load. The small commercial segment and the large commercial/back-up generation segment provide about 26% and 17% of the total economic potential, respectively. Overall cooling load reductions account for about 55% of the commercial economic potential and just over 80% of the total economic potential. Quantum Consulting Inc.DR Results FORECAST SCENARIOS As discussed in Section 3, four program concepts - AC Load Control (DLC), Critical Peak Pricing (CPP), Voluntary Demand Response Incentives (DRP), and Back-up Generator Incentives (BUG) were bundled into four program strategies: DLC and BUG - Low Incentive Levels All 4 Concepts - Low Incentive Levels All 4 Concepts - High Incentive Levels Maximum Achievable The forecast of annual estimated MW reduction that would occur during system peak conditions is shown in Exhibit 5-2 for each of the four strategies. Exhibit Comparison of Load Reduction Forecasts Residential and Commercial Sectors 150 135 - ~ Max. Achievable - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 120 --.- 4 Concepts - High $ -- 4 Concepts - Low $ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 105 - -+- OLC & BUG - Low $ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - s: 75 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year DR potential is compared against system peak demand in Exhibit 5-3. It is expected that Maximum Achievable" potential would approach economic potential after ten years of significant investment in building DR capability in the residential and commercial sectors. Quantum Consulting Inc.DR Results r.- Exhibit Peak Demand Load and DR Potential.:... Residential and Commercial Sectors -+-TotalPeakLoad -8-TechnlcalPotentiai ~EconomicPotential ---Max Achievable 000 800 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- -- ---- ---- ----- 600 --------------- - - 1,400 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 200 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 1 000 800 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 600 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~- - - - - - - - - - - - - - - - - - - - - - - - - - - - -- 400 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 200 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year A comparison of the estimated total annual cost for the three scenarios is provided in Exhibit 5- 4. These costs include program administration, capability building expenditures, and the equipment costs associated with direct load control and metering for the voluntary TaU program. The metering costs required for the dynamic rate programs were not included in these cost estimates. Quantum Consulting Inc.DR Results Exhibit Forecast of Estimated Costs by Scenario Residential and Commercial Sectors """*:-Max, Achievable --4 Concepts - High $ --4 Concepts - Low $ --+-DLC & BUG - Low $12 000 $10,000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - g $6 000T""tI7 000 000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year Exhibit 5-5 summarizes the net present value of la-year program costs and benefits for each program strategy. Exhibit Net Present Value ofl0 Year Costs and Benefits Avoided Costs Program Costs ($Utility Benefit-Cost Pro~ram Strate~y ($ Mil.)Mil.)Ratio Potential AC OLC and Back-up Gen - Low $$4.$7. All 4 Concepts - Low $$5.$9. All 4 Concepts - High $$12.$21. Maximum Achievable $19.$45.0.44 129 Quantum Consulting Inc.DR Results Exhibit 5-6 provides the MW impact and program cost forecast results for the DLC and BUG - Low Incentives scenario. The estimated load reductions grow from 3.5 MW in 2004 to 24.6 MW in 2013. Program costs (including incentives) increase from $0.84 million in 2004 to $1.59 million in 2013. Exhibit Forecast Results: DLC and BUG Low Incentive Levels Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Critical Peak Pricin2 (CPP) MW Impact Incentive Costs ($10005) Demand Response Incentives (ORP) MW Impact Incentive Costs ($10005) AC Load Control (OLC) MW Impact 2.3 10.12.14.16.18.20. Incentive Costs ($10005)123 162 203 245 287 329 372 417 Back-Up Generator Incentives (BUG) MW Impact 1.3 3.4 Incentive Costs ($10005) DR Total MW Impact B.4 10.13.15.17.20.22.3 24. Incentive Costs ($10005)101 145 190 237 279 322 365 409 455 Admin, equipment, and marketing costs 7B5 767 B15 864 917 941 9B3 02B 074 134($1000s) l, ' ".. l..,Quantum Consulting Inc.DR Results Exhibit 5-6 provides the MW impact and program cost forecast results for the 4 Concept - Low Incentives scenario. The OLC and BUG program concept results are similar to the OLC-BUG scenario presented in the previous table. Increases in impacts result from the addition of the CPP and ORP program concepts. The estimated load reductions grow from 4.2 MW in 2004 to 29.3 MW in 2013. Program costs (including incentives) increase from $1.02 million in 2004 to $1.88 million in 2013. Exhibit Forecast Results: Concepts Moderate Incentive Levels Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Critical Peak Pricin2 (CPP) MW Impact 0.4 1.1 1.4 1.6 1.9 2.4 Incentive Costs ($1 OOOs) Demand Response Incentives (DRP) MW Impact 0.3 0.4 1.1 Incentive Costs ($1 OOOs) AC load Control (Dle) MW Impact 6.3 8.4 10.12.14.17.19.21. Incentive Costs ($1 OOOs)127 168 210 253 297 341 385 432 Back-Up Generator Incentives (BUG) MW Impact 1.3 1.8 3.3 3.4 Incentive Costs ($1 OOOs) DR Total MW Impact 12.15.18.21.1 23.26.29. Incentive Costs ($1O00s)121 172 225 279 329 379 431 483 538 Admin, equipment, and marketing costs 952 938 991 044 102 130 177 227 278 345($1000s) Quantum Consulting Inc.DR Results - I I ' Exhibit 5-7 provides the MW impact and program cost forecast results for the 4 Concept - High Incentives scenario. Most of the increase over the 4 Concept - Low Incentives scenario are attributable to the DLC program concept. The estimated load reductions grow from 8.4 MW in 2004 to 69.6 MW in 2013. Program costs (including incentives) increase from $2.12 million in 2004 to $4.86 million in 2013. Exhibit 5- 7 DR Forecast Results: Concepts High Incentive Levels Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Critical Peak Pricin~ (CPP) MW Impact 0.7 1.0 1.6 1.8 2.4 Incentive Costs ($10005) Demand Response Incentives (ORP) MW Impact 0.3 0.4 1.0 1.1 1.2 1.4 Incentive Costs ($10005) AC load Control (OlC) MW Impact 11.17.23.29.35.41.47.54.60. Incentive Costs ($10005)172 332 496 663 834 002 171 342 515 698 Back-Up Generator Incentives (BUG) MW Impact 4.3 4.4 Incentive Costs ($10005) DR Total MW Impatt 8.4 15.22.29.36.42.49.55.62.69. Incentive Costs ($10005)203 377 555 737 923 098 276 1,455 637 829 Admin, equipment, and marketing 916 960 097 235 385 465 589 721 855 033costs ($1O00s) Quantum Consulting Inc.DR Results Finally, Exhibit 5-8 shows the maximum achievable forecast results. All foll program concepts show significant increases in impacts versus the 4 Concept - High Incentives scenario. The estimated load reductions grow from 14.5 MW in 2004 to 128.9 MW in 2013. Program costs (including incentives) increase from $4.08 million in 2004 to $10.52 million in 2013. Exhibit Forecast Results: Maximum Achievable Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Critical Peak Pricine (CPP) MW Impact 1.4 4.7 9.4 10.7 12. Incentive Costs ($10005)128 168 210 252 296 340 386 435 Demand Response Incentives (ORP) MW Impact 1.6 4.4 Incentive Costs ($10005)100 112 AC load Control (OlC) MW Impact 10.19.29.39.4 49.59.70.4 80.91.102. Incentive Costs ($10005)406 788 178 576 984 398 814 236 664 113 Back-Up Generator Incentives (BUG) MW Impact 8.4 Incentive Costs ($10005)110 124 1381 152 167 DR Total MW Impact 14.26.38.50.4 63.75.88.101.6 114.128. Incentive Costs ($10005)515 958 1 ,409 871 344 825 310 802 301 828 Admin, equipment, and marketing costs 565 668 905 143 402 641 871 117 364 688($1000s) Quantum Consulting Inc.DR Results \:.1 r ' 6. DISCUSSION OF UNCERTAINTY There are two principal classes of uncertainty underlying the results presented in this study. The first area is uncertainty associated with estimates of the current characteristics of end-use electricity consumption and DSM measure data (hereafter, "current market" uncertainty). The second area concerns estimates of the future potential for DSM, which is affected by the uncertainty in the first area, as well as additional uncertainty in future energy prices and electric load forecasts, changes in market and DSM measure characteristics over time, and forecasts of customer adoption of measures as a function of program interventions, among other factors (hereafter, "forecast" uncertainty). While there is considerable overlap in the underlying data associated with both types of uncertainty, it is useful to separate these classes of uncertainty for two reasons. First, the study attempts to reduce the effects of the two types of uncertainty through different approaches. Second, although both types of uncertainty could be reduced through further research, the types of research necessary are significantly different across the two classes. With respect to the first class of uncertainty noted above current market uncertainty, readers and users of this study should recognize that estimates of DSM potential involve a process of modeling the substitution of DSM equipment and systems in place of existing energy equipment and systems. As such, this process starts with estimates of current equipment characteristics and energy use by end use and market segment. These data typically are provided as inputs to DSM potential studies and are, in the best of cases, developed from up-to- date and statistically accurate studies that involve detailed collection of technology market shares and comprehensive modeling of end-use consumption and peak demand. When these data are absent, outdated, or inaccurate, the uncertainty in estimates of current equipment shares and associated consumption and peak demand directly impact estimates of DSM potential because DSM potential varies by equipment type and market segment. The principal sources of data used to develop estimates of current consumption by end use and market segment were data from the late 1980s and mid-1990s (see Section 2). These erid-use data were then analyzed with respect to Idaho Power s latest (2003) forecast of consumption at the sector level. Note that the most recent Idaho Power forecast did not provide any updated information for this potential study on the end use and market segment shares of energy consumption or peak demand. In addition, other sources of equipment saturation data were very limited for this study. DSM measure data are the second type of data associated with current market uncertainty. Examples of DSM measure data include the current incremental costs and savings of DSM measures, the useful lives of those measures, their current market saturation levels, and estimates of the fraction of the market for which DSM equipment and systems could substitute for existing equipment and systems. Fortunately, considerable data on the costs and savings associated with DSM measures were available for this study. This is attributable to the considerable number and quality of energy savings measurement and evaluation studies that have been conducted in the Pacific Northwest, as well as the rest of the United States. Nonetheless, uncertainties exist to varying degrees in estimates of costs and savings by individual technology. In general, new measures (e.g., those on the market for two years or Quantum Consulting Inc.Discussion of Uncertainty less) have samewhat greater uncertainty in casts and savings than measures that have been an the market far langer periads (e.g., 3 years or mare). The most significant uncertainty in the measure-level data is also. in the area af measure saturatian. Measure-level saturatian data typically came fram the same types af saurces discussed above far baseline equipment cansumptian and saturatian data. Turning naw to. the area af forecasting uncertainty, it shauld be samewhat obviaus that farecasts af DSM patential end electricity demand are also. affected by current market uncertainty. In any "farecasting pracess, o.ne wants to. begin with as accurate an assessment af current canditians as passible; errors in estimates af current canditians are otherwise carried farward and exacerbated. However, even with perfect data an current market canditians, forecasts are subject to. their awn uncertainties by their very nature. Far this study, the key areas af farecast uncertainty are future: end use cansumptian levels and equipment shares; incremental casts and savings far measures an the market taday; incremental casts and savings far measures nat an the market taday but likely to. be available aver the ten-year farecast periad (no. such measures are included in this study); DSM pragram funding levels; custamer adaption levels af DSM measures as a function of pragram interventian types and levels; and benefit-cast ratios far DSM measures, which, in addition to uncertainty in future measure casts and savings, are a functian af uncertainty in: energy and capacity prices, both retail and whalesale, including thase assaciated with canstrained areas, the value af any environmental externalities, and the level af the discaunt rate used in financial analyses af efficiency measures. As nated abave, there is also. uncertainty with future farecasts far Idaho. Pawer electricity sales and peak demand. If the future demand far electricity turns out to. be higher than currently forecast, then there will be mare patential far savings fram DSM measures. Likewise, if the future demand far electricity is lower than expected, the patential far savings fram DSM measures will be lawer than the figures pravided in this report. Quantum Consulting Inc.Discussion of Uncertainty 1'1