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1 Attachment RML-RD-13 Page 201 of 207 Docket o =---_== i o 5 xs x Z a 5 F a p v cc 0 a a p 3 Z F 0 E4i F a Z n a Wg Y Z SL a - p G F E W 5 Z s E - - F? C s' S W af z s C oy.apf S F S -} yi G F } < W 4 Mi l. ;evxze_«e RD1-405 of

2 Attachment RML-RD-13 Page 202 of 207 Docket o V <... ' a_o Z f.. Z C C Q - I Z Z F ya a h Y 3 z Fx. 3-5c. '>.e= ' e [ F -- 'c ac F 3` - a S`V _ =-cr.=e r 's Z Z, e C W V F z z_t. og =_ Jz 3-3 o C W Va Z F i o z a F o F a:.. a a zc tas- an'a z c a JJL~'JL <1 RD of

3 Attachment RML-RD-13 Page 203 of 207 Docket o g g = C e = o u =_ > z z u o = r s Q a = c F 3 Z, Q -_ = o - Z - Z r - - z >, - Z r = L c - "r 4.c _ 0o? z z z -' M r - = - 0 z >e a Z 5 4 Cj a5; ii Q w C F w zm = C z " 'o i, a _ RD of

4 Attachment RML-RD- 13 Page 204 of 207 Docket o Z > F v z y x i z v S Z rg 0 E Q n a a a 3 a F 3 0 z` P, 7, ' 4 G 3 QPM.M.7_ :> - rg a 3-4 r = ra+ >, -... _ 67 RD of

5 Attachment RML-RD-1 3 Page 205 of 207 Docket o STHWESTER PBLIC SERVICE CMPAY WRKPAPERS F RICHARD M. LTH ALLCATI MDEL ADJSTED BASE PERID, ADJSTED FR WEATHER, RETAIL AD WHLESALE ADJSTMETS TTAL SYSTEM STATE ALLCATI F EERGY AD DEMAD FR THE TEST PERID EDED: JE 30, 2013 METER ALLCATI Line o State Energy (kwh) Allocation 1 Texas 13,831,086, % 2 ew Mexico 4,701,165, % 3 Total Wholesale 7,515,082, % 4 System 26,047,334, % SRCE ALLCATI (IPT KWH) 8 9 State Energy (kwh) Allocation Texas 14,856,398, % 12 ew Mexico 5,109,705, % 13 Total Wholesale 7,658,339, % 14 System 27,624,443, % CP PRDCTI SRCE ALLCATI State Demand (kw) Allocation Texas 2,051, % 22 ew Mexico 739, % 23 Total Wholesale 1,144, % 24 System 3,934, % CP TRASMISSI DEMAD ALLCATI State Demand (kw) Allocation Texas 2,062, % 32 ew Mexico 744, % 33 Total Wholesale 1,703, % 34 System 4,509, % RD of

6 Attachment RML-RD- 13 Page 206 of 207 Docket o o o s sssss so s o y p r D M y.7 W c Q F 7 d' T W n V 7 - M M oo a0 00 V M V h id p Q C.., n` E o M D D r G G M r V co r o0 0o oo v ao V oo C 00 r 00 1 r vt n 7 r r 7 V r oo - - rn v 00 G o o M r -.o M, v- rn - h o0 v: o, rn D G ag -, v D r V vl -- - n r v. - T - Aa G n a M o r 00 id v co r M vml r v) o - ' `3 C r in - vn --- C' + " c n - id n og `9 Q W P. V - LL'i r G ig r V- cu rnv oilc ll.-rv'i - ` co 7 ; +S I r Q = Ic Q '3 G a G W 10 v. T W r a0 - \p - V o V t I w.. Q ao Q a 00-1 t+` G vl M, C1 I Cl M 7IC a Q T G (n W, M 00 v1 D a0 00 C 00 M V -- p vl - op D o r0 lc o lc s r h M V (V - -D 01 M V M M 00 W ~ - Vi M-. W V 00 _ r - oo W K V o0 0o 7 t zq T G C' til G' M -- D l0-00 vi M < G V Q D M M r - [ -+ r p r h - V n 00 r o0 r -- r v: r Z Q z,z w., o,z 'yc FW E'" ow.l aw [ FF E p, 4 y G G c = C > ` V j cg G?C ` r" (n c3 4 G C (n G Q G 3 n o? c V c L a on ou -cs c E E' F q t rn,, vv p: v a1 v Q = F q c az - M hd r 00 _ M'cf M l0 r G RD of

7 Attachment RML-RD-13 Page 207 of 207 Docket o Vt G C1 h h f DL C C v Y 10 4 W `n v a oo' M D t7 M h v. +,D Y 7 b.i V 00 t7 14 C c v Cn q c z cco a C c 0 L E 0 C M 00 ID cfa 7 C r b 7 M M V E r co.. o0 a A qz A u -'T A C V1 p1 f M V1 r M M Qy f+t V o l vi C 1 l0 f\ v y E q C3qWd v -_ -- M r p,.0 pr V oc oo n c+; = c0 ' u CC 7 q ' d' 1 V V V M 1 M C Yy p M o p M Y Q y [ Lc? a W ti q = s u C u uco'" co y y v 'wa3 C V] (_ C bl V] y t J p C 1 _ (n Cj a 73 ( G t+ a n GJ ` G R < Ca T C7 4S F zy c'c7 v v c y y c no =t au y K a a Cl) '52 a.a ` cl a a. z -- cm h M 7 Vl p 1 RD of

8 DCKET APPLICATI F STHWESTER PBLIC SERVICE CMPAY FR PBLIC TILITY CMMISSI ATHRITY T CHAGE RATES AD T RECCILE FEL AD PRCHASED PWER CSTS FR THE PERID JLY 1, 2012 F TEXAS THRGH JE 30, 2013 DIRECT TESTIMY of LKE JARAMILL on behalf of STHWESTER PBLIC SERVICE CMPAY (filename: JaramilloRDDirect.doc) Table of Contents GLSSARY F ACRYMS AD DEFIED TERMS... 2 LIST F ATTACHMETS WITESS IDETIFICATI AD QALIFICATIS II. ASSIGMET AD SMMARY F TESTIMY AD RECMMEDATIS... 7 III. FRECAST METHDLGY IV. WEATHER'S EFFECT TEST YEAR SALES V. WEATHER'S EFFECT TEST YEAR PEAK DEMAD VI. CCLSI AFFIDAVIT Jaramillo Direct - Rate Design Page I RD of

9 GLSSARY F ACRYMS AD DEFIED TERMS Acronym/Defined Term Commission CRMWA DW test statistic Golden Spread MW MWh AA PSCo RFP R2 SPS Meaning Public tility Commission of Texas Canadian River Municipal Water Authority Durbin-Watson test statistic Golden Spread Electric Cooperative, Inc. Megawatt Megawatt-hour ational ceanic and Atmospheric Administration Public Service Company of Colorado, a Colorado corporation Rate Filing Package R-squared test, a coefficient of determination Southwestern Public Service Company, a ew Mexico corporation Test Year July 1, 2012 through June 30, 2013 Xcel Energy XES Xcel Energy Inc. Xcel Energy Services Inc. Jaramillo Direct - Rate Design Page 2 RD of

10 LIST F ATTACHMETS Attachment LJ-RD-1 LJ-RD-2 Description Comparison of Test Year Sales and Weather ormalized Sales (Filename: LJ-RD-l.xls) Comparison of Test Year Peak Demand and Weather ormalized Peak Demand (Filename: LJ-RD-2.xls) Jaramillo Direct - Rate Design Page 3 RD of

11 DIRECT TESTIMY F LKE JARAMILL 1 I. WITESS IDETIFICATI AD QALIFICATIS 2 Q. Please state your name and business address. 3 A. My name is Luke Jaramillo. My business address is 1800 Larimer Street, Denver, 4 Colorado Q. n whose behalf are you testifying in this proceeding? 6 A. I am filing testimony on behalf of Southwestern Public Service Company, a ew 7 Mexico corporation ("SPS"), an electric utility subsidiary of Xcel Energy Inc. 8 ("Xcel Energy"). Xcel Energy is a registered holding company that owns several 9 electric and natural gas utility operating companies and a regulated natural gas 10 pipeline company.' 11 Q. By whom are you employed and in what position? 12 A. I am employed by Xcel Energy Services Inc. ("XES"), the service company 13 subsidiary of Xcel Energy, as Senior Energy Forecasting Analyst. 14 Q. Please briefly outline your responsibilities as Senior Energy Forecasting 15 Analyst. 16 A. I am responsible for the development and presentation of forecasted customer 17 counts, sales, and peak demand data for SPS and PSCo. I am also responsible for ` Xcel Energy is the parent company of the following four wholly owned utility operating companies: orthern States Power Company, a Minnesota corporation; orthern States Power Company, a Wisconsin corporation; Public Service Company of Colorado, a Colorado corporation ("PSCo"); and SPS, a ew Mexico corporation. Xcel Energy's natural gas pipeline subsidiary is WestGas Interstate, Inc. Jaramillo Direct - Rate Design Page 4 RD of

12 1 preparing historical and forecasted information regarding customer counts, sales, 2 and peak demands for reporting to various regulatory agencies and others. 3 Q. Please describe your educational background. 4 A. I graduated from the niversity of Colorado at Denver with Bachelor of Arts and 5 Master of Arts degrees in Economics. 6 Q. Have you attended any special courses or seminars involving the area in 7 which you will be testifying? 8 A. Yes. I have attended the Institute for Professional Education's Economic 9 Modeling and Forecasting class, Energy Insights' Load Forecasting Workshop, 10 and the Edison Electric Institute's Load Forecasting Meeting. 11 Q. Please describe your professional experience. 12 A. While attending graduate school from 1993 to 1995, I was employed as a 1.3 Forecasting Analyst at the Center for Business and Economic Forecasting, where 14 I was responsible for forecasting regional economics. In 1996, I was employed 15 by the State of Colorado as an economist charged with forecasting general fund 16 and cash revenues for the state budget. In 1998, I began employment for Global 17 Insight, Inc. (formerly Standard and Poor's DRI Inc.) as a Senior Associate in its 18.S. Regional Service, forecasting economics. In 2001, I began my employment 19 with XES as a Principal Forecasting Analyst in the Energy and Economics 20 department, forecasting customer counts, sales, and peak demand for SPS. I have 21 been promoted several times since then, with the latest promotion in May of to the position of Senior Energy Forecasting Analyst. My duties as a Senior Jaramillo Direct - Rate Design Page 5 RD of

13 1 Energy Forecasting Analyst include the development of customer counts, sales, 2 and peak demand forecasts not only for SPS, but also for PSCo's electric and gas 3 utilities. 4 Q. Have you previously filed testimony at any regulatory commission? 5 A. Yes. I have filed testimony before the Public tility Commission of Texas 6 ("Commission") on behalf of SPS is its last three base rate cases, Docket os , 38147, and 35763, each time regarding load forecasting. I also filed 8 testimony in two fuel factor revision cases, Docket os and 39925, 9 regarding load forecasting. Jaramillo Direct - Rate Design Page 6 RD of

14 1 II. ASSIGMET AD SMMARY F TESTIMY AD 2 RECMMEDATIS 3 Q. What is your assignment in this docket? 4 A. In my testimony, I discuss three topics: 5 (1) the forecasting methodology and statistical data used by SPS to 6 develop the forecasted monthly megawatt-hour ("MWh") sales by 7 customer class and the forecasted monthly system peak megawatt 8 ("MW") demand; 9 (2) the effect of weather variances on SPS's MWh sales in the Test 10 Year, which is the period from July 1, 2012, through June 30, ("Test Year"); and 12 (3) the effect of weather variances on peak demand by customer class 13 in the Test Year. 14 In addition, I sponsor or co-sponsor Schedules 0-7.1, 0-8.1, 0-8.2, 0-8.3, 0-8.4, , 0-9.2, 0-9.3, , and of SPS's Rate Filing Package ("RFP"). 16 Q. Please summarize your testimony and recommendations. 17 A. I recommend that the Commission approve the sales and demand forecasts that I 18 discuss in this testimony. I developed the sales and demand forecasts based on 19 well-established econometric models, and the results of those models tested 20 satisfactorily against quantitative and qualitative standards used to evaluate the 21 validity of models and projections. 22 I also recommend that the Commission approve the weather-normalization 23 adjustments discussed in this testimony. During the Test Year, deviations from 24 the 10-year average weather caused SPS's Texas retail customers to consume ,694 more MWh than they would have consumed in a year with normal 26 weather, and that variance needs to be reflected in the sales data used to determine Jaramillo Direct - Rate Design Page 7 RD of

15 1 revenues. During the Test Year, deviations in peak-day weather conditions from 2 the 10-year average peak day weather for the summer months (i.e., June, July, 3 August, and September) caused SPS's retail customer peak demand to average 90 4 MW more on the peak day per month than it would have if weather conditions 5 had been normal, as defined by the 10-year average. Likewise, weather variation 6 on the peak days for the summer months in the Test Year caused SPS's full 7 requirements wholesale peak demand to average 42 MW of additional peak 8 demand per month compared to normal weather conditions. And weather 9 variation on the peak days of the summer months caused the Golden Spread 10 Electric Cooperative, Inc. ("Golden Spread") full load peak demand to average MW of additional peak demand per month, compared to normal weather. 12 SPS witness Richard M. Luth explains (in the Rate Design phase) how 13 SPS uses weather-normalized sales to determine revenues. He also explains how 14 SPS uses weather-normalized peak demands to determine the class allocation of 15 production and transmission capacity costs. Jaramillo Direct - Rate Design Page 8 RD of

16 1 III. FRECAST METHDLGY 2 Q. Please briefly describe SPS's forecasting methodology. 3 A. SPS forecasts monthly customer counts, retail sales, retail peak demand, and full 4 requirements wholesale sales using econometric forecasting models. 5 Q. What is an econometric model? 6 A. An econometric model is a widely accepted modeling approach in which a linear 7 regression equation relates a dependent variable, such as sales, to a set of 8 explanatory variables, such as economic and demographic concepts, customers, 9 price, and weather. After the relationships are identified, forecasts of the 10 explanatory variables can be used to predict future sales. 11 Q. What inputs does SPS use in its econometric models? 12 A. The inputs used to arrive at the dependent variable in the econometric models are: 13 (1) SPS's historical customer counts and billing month retail MWh sales by 14 jurisdiction and class, and (2) SPS's historical calendar month MWh sales for 15 each full requirements wholesale customer. For the explanatory variables, SPS 16 obtains historical and forecasted economic and demographic variables for the 17 nation, state, and SPS service territory from Global Insight, Inc., a source of data 18 typically relied on by forecasting professionals. Those explanatory variables are: 19 population, 20 number of households, 21 Gross Domestic Product, 22 Gross State Product, 23 employment, 24 personal income, Jaramillo Direct - Rate Design Page 9 RD of

17 1 Consumer Price Index, 2 crude oil spot prices, and 3 Gross Domestic Product deflator. 4 SPS also obtains weather information to use as explanatory variables in the 5 models. That weather information, which comes from ational ceanic and 6 Atmospheric Administration ("AA") weather stations at the Amarillo 7 International Airport located in Amarillo, Texas at the Lubbock Regional Airport 8 located in Lubbock, Texas and at the Roswell Industrial Air Center located in 9 Roswell, ew Mexico, is composed of 65-degree based heating-degree days, degree based cooling-degree days, precipitation, and temperature. I discuss 11 weather information in more detail in Section IV of my testimony. ther 12 variables used by SPS include autoregressive and moving average correction 13 terms, seasonal binary variables, trend variables and various other binary 14 variables. 15 Q. What are the outputs from SPS's econometric models? 16 A. The outputs from the econometric models are forecasts of customer counts, retail 17 peak demand, billing month retail MWh sales by jurisdiction and class, and 18 forecasts of calendar month MWh sales for each full-requirements wholesale 19 customer. 20 Q. Please describe the techniques SPS uses to evaluate the validity of its 21 quantitative forecasting models and sales projections? 22 A. SPS uses a number of quantitative and qualitative tests to evaluate the validity of 23 its models and projections. ne of those tests, the coefficient of determination, is Jaramillo Direct - Rate Design Page 10 RD of

18 a measure of the quality of the model's fit to historical data. The R-squared test statistic ("R2") that results from that test represents the proportion of the variation of historical sales that can be attributed to the functional relationship between the historical sales and the explanatory variables included in the model. The possible values for the R2 statistic range from 0.00 to The closer the R2 statistic is to a value of 1.00, the higher the degree to which the explanatory variables account for the variability in the dependent variable. The linear regression models used to develop SPS's sales forecasts produce high R2 statistics, ranging between 0.84 and A second test involving the model coefficient t-statistics of the explanatory variables indicates the degree of correlation between the variables' data series and the sales data series being modeled. The t-statistic is a measure of the statistical significance of each variable's individual contribution to the 14 prediction model. Generally, the absolute value of each t-statistic should be 15 greater than 1.96 to be considered statistically significant at the 95 percent 16 confidence level. SPS applied this criterion in the development of the linear 17 regression models used to develop the sales forecast, and the final linear regression models used to develop the sales forecast tested satisfactorily by this standard. SPS also inspected each model for the presence of first-order autocorrelation, as measured by the Durbin-Watson ("DW") test statistic. Autocorrelation refers to the correlation of the model's error terms for different Jaramillo Direct - Rate Design Page 11 RD of

19 1 time periods. For example, an overestimate in one period is likely to lead to an 2 overestimate in the succeeding period and vice versa, under the presence of first- 3 order autocorrelation. Thus, when forecasting with a linear regression model, 4 absence of autocorrelation between the residual errors is important. The DW test 5 statistic, which ranges between zero and four, provides a measure to test for 6 autocorrelation. In the absence of first-order autocorrelation, the DW test statistic 7 equals two. The final models used to develop the sales forecast tested 8 satisfactorily for the absence of first-order autocorrelation, as measured by the 9 DW test statistic. 10 ext, SPS used graphical inspection of each model's error terms (i.e. 11 actual less predicted) to verify that the models were not mis-specified and that 12 there was no violation of the statistical assumptions pertaining to constant 13 variance among the residual terms and their random distribution with respect to 14 the predictor variables. Analysis of each model's residuals indicated that the 15 residuals were homoscedastic (of constant variance) and randomly distributed, 16 which demonstrated that the linear regression modeling technique was an 17 appropriate selection for each customer class's sales that were statistically 18 modeled. 19 Finally, SPS reviewed the statistically modeled sales forecasts for each 20 customer class for reasonableness as compared to the respective monthly sales 21 history for that class. Graphical inspection reveals that the forecast patterns fit 22 well with the respective historical patterns for each customer class. The annual Jaramillo Direct - Rate Design Page 12 RD1-423 of

20 1 total forecasted sales have been compared to their respective historical trends for 2 consistency. The forecast models and model data are provided in Schedules through Q. Did SPS make any adjustments to the outputs? 5 A. Yes. SPS converted the billing-month sales data to calendar-month sales data. In 6 addition, SPS adjusted the model output for incremental Demand Side 7 Management savings, as well as load growth or load reductions that are identified 8 by commercial customer Account Managers and that would not be captured in the 9 historical modeling data. 10 Q. Please explain the terms "billing-month sales" and "calendar-month sales." 11 A. SPS reads electric meters each working day according to a meter-reading 12 schedule based on 21 billing cycles per billing month. Meters read early in the 13 calendar month reflect consumption that occurred mostly during the previous 14 calendar month. Meters read late in the calendar month reflect consumption that 15 occurred mostly during the current calendar month. Because "billing-month" 16 sales for the current calendar month reflect consumption that occurred in both the 17 previous calendar month and the current calendar month, billing-month sales lag 18 calendar-month sales. The "calendar-month" sales number is therefore an 19 estimate of electricity consumption that occurred during the current calendar 20 month. Jaramillo Direct - Rate Design Page 13 RD of

21 1 Q. What is the purpose of developing a calendar-month sales forecast? 2 A. The purpose is to align the projected sales with revenues and the relevant projected 3 expenses, both of which are estimated on a calendar-month basis. 4 Q. How did SPS determine the estimated monthly calendar-month sales for the 5 forecast period? 6 A. SPS calculated the calendar-month sales based on the projected billing-month sales 7 for the following rate classes: Residential, Residential With Space Heating, Small General Service, Secondary General Service, Primary General Service, Large-General Service Transmission, Municipal and Schools, Street Lighting, and Area Lighting. SPS calculated the calendar-month sales both in terms of the sales load component that is not associated with weather ("base load") and the sales load component that 19 is influenced by weather ("total weather load"). The weather was measured in terms of normal heating-degree days, cooling-degree days, and precipitation as described in Section IV. SPS calculated the base load sales and the total weather load sales components for each class, and the two components were then combined to provide the total calendar-month volumes. Jaramillo Direct - Rate Design Page 14 RDI of

22 1 Q. How did SPS calculate the calendar-month base load component? 2 A. SPS calculated the calendar-month base load component using four steps: 3 Step 1-SPS calculated the billing-month total weather load by 4 multiplying the billing-month sales weather normalization regression 5 coefficients (defined in terms of billing-month heating degree days, 6 cooling degree days, and number of customers), times billing-month 7 normal heating degree days and cooling degree days, and then multiplying 8 the product times the projected customers. 9 Step 2-SPS calculated the billing-month base-load by taking the 10 difference between the projected total billing-month sales and the billing- 11 month total weather load (as calculated in Step 1). 12 Step 3-SPS next determined the billing-month base-load sales per billing 13 day by dividing the billing-month base-load sales (from Step 2) by the 14 average number of billing days per billing month. 15 Step 4-SPS then calculated the calendar-month base-load sales by 16 multiplying the billing-month base-load sales per billing day (from Step 3) 17 times the number of days in the calendar month. 18 Q. How did SPS calculate the calendar-month total weather load component? 19 A. SPS calculated the calendar-month total weather load component in the same way it 20 calculated the billing-month total weather load (as described in Step 1 above). SPS 21 performed the calculation by substituting the calendar-month sales weather 22 normalization regression coefficient (defined in terms of calendar-month heating Jaramillo Direct - Rate Design Page 15 RD of

23 1 degree days, cooling degree days, and number of customers) and the calendar- 2 month normal heating-degree days and cooling-degree days. 3 Q. How did SPS calculate the calendar-month total sales? 4 A. SPS calculated the calendar-month total sales for the rate classes listed earlier by 5 combining the calendar-month base-load and calendar-month total weather load 6 components. For the Area Lighting, Primary General, and Large General Service 7 Transmission classes, SPS calculated the forecasted calendar-month sales based on 8 the projected billing-month sales in the same manner as detailed above. However, 9 for these classes, there are no total weather load sales. The calendar-month total 10 sales for these classes were calculated only in terms of their base load, where the 11 billing-month base load equaled the projected billing-month sales. 12 The Street Lighting class is billed on a calendar-month basis in the succeeding 13 month. Therefore, for this class, the calendar-month sales equal the billing-month 14 sales in the succeeding month. 15 Q. Please describe how the other portions of SPS's customer count and sales 16 forecasts are developed. 17 A. The sales forecasts for SPS's firm partial requirements and non-firm wholesale 18 loads are based on contract terms and analysis of historical trends. The ew 19 Mexico large commercial and industrial customer count forecast was developed 20 using an exponential smoothing model. Jaramillo Direct - Rate Design Page 16 RD of

24 1 Q. How are the Texas voltage level MWh sales estimates derived? 2 A. Texas retail sales by rate class are allocated to voltage levels using historical sales 3 proportions. After developing both the SPS system and Texas retail sales 4 estimates by voltage level, SPS derives the MWh sales estimates at the source by 5 applying the voltage level loss factors to the sales estimates. A loss factor 6 represents transmission and distribution losses, plus all unaccounted for energy. 7 Q. How is SPS's system peak demand forecast developed? 8 A. SPS develops the retail peak demand forecast using an econometric model, with 9 monthly historical system retail peak demand (MW) as the dependent variable, 10 and system retail sales, weather concepts, a linear trend, seasonal binary, and 11 month specific binary variables as explanatory variables. For full-requirements 12 wholesale peak demand forecasts at the delivery point, SPS uses historical 13 monthly load factors to develop monthly peak demands based on the projected 14 monthly sales. A load factor is the ratio of sales to the peak demand sustained 15 over a period of time. The formula to calculate the load factors is: Sales (MWh) 16 Load Factor (%) = Peak Demand (MW) x hours per month 17 Peaks at the delivery point are then grossed up to the source by applying loss 18 factors. 19 The projected load factors are based on historical load factors. The 20 monthly load factors and loss factors used for the forecast period are assumed to 21 be the same as the historical load factors and loss factors. Jaramillo Direct - Rate Design Page 17 RDl of

25 1 Q. Have you provided SPS's forecasted monthly sales and system peak 2 demands? 3 A. Yes. SPS's sales forecast and system peak demands are provided in Schedule Jaramillo Direct - Rate Design Page 18 RD of

26 1 IV. WEATHER'S EFFECT TEST YEAR SALES 2 Q. Did SPS adjust its Test Year MWh sales to account for the effect of weather 3 on sales? 4 A. Yes. Because the twelve months that comprise the Test Year were warmer than 5 the 10-year average in SPS's service area, it was necessary to adjust SPS's Test 6 Year sales for the following rate classes to account for weather: 7 Residential 8 Small General Service 9 Secondary General Service 10 Small Municipal and School 11 Large Municipal 12 Large School 13 Canadian River Municipal Water Authority ("CRMWA") 14 SPS's research indicates that weather has little or no effect on the consumption of 15 the Primary General, Large General Service-Transmission, and Street and Area 16 Lighting classes. Therefore, SPS did not make weather adjustments for those 17 classes. 18 Q. How did SPS define the normal weather? 19 A. SPS used a 10-year average. SPS agrees with AA's definition that normal 20 weather is representative of long-term typical weather based on a 30-year period. 21 SPS believes that normal weather based on a 30-year average is reflective of 22 weather that will occur over the long-term. However, given the Commission's 23 ruling in favor of using a 10-year period to establish normal values in Docket o. Jaramillo Direct - Rate Design Page 19 RD1-430 of

27 ,2 SPS will calculate its weather adjustment based on a 10-year normal 2 period for this filing. 3 Q. How did SPS determine the normal weather? 4 A. ormal daily weather was based on the average of the last 10 years of historical 5 heating-degree days, cooling-degree days, and precipitation data used to develop 6 the weather adjustment coefficients for the Test Year period. The Test Year and 7 normal-weather cooling-degree days and heating-degree days are reflected on 8 page 1 of Attachment LJ-RD-1. 9 Q. What measure did SPS use to calculate heating-degree days and 10 cooling-degree days? 11 A. SPS used heating-degree days and cooling-degree days based on a 65 degree 12 Fahrenheit temperature base and rainfall equivalent precipitation. The weather 13 data is aggregated to the state level by weighting the individual weather station 14 data by the share of load in the Amarillo and Lubbock regions of the Texas 15 service area. 16 Q. Please explain how SPS calculated heating-degree days. 17 A. SPS calculated heating-degree days for each day by subtracting the average daily 18 temperature from 65 degrees Fahrenheit. For example, if the average daily 19 temperature was 45 degrees Fahrenheit, then 20 heating-degree days were 20 calculated for that day. If the average daily temperature was greater than 65 2 Application of Southwestern Electric Power Company for Authority to Change Rates and Reconcile Fuel Costs, Docket o , rder at (ct. 10, 2013) (rehearing pending). Jaramillo Direct - Rate Design Page 20 RD of

28 1 degrees Fahrenheit, then that day recorded zero heating-degree days. Daily 2 heating-degree days are aggregated to monthly totals. 3 Q. How did SPS calculate cooling-degree days? 4 A. SPS calculated cooling-degree days for each day by subtracting 65 degrees 5 Fahrenheit from the average daily temperature. For example, if the average daily 6 temperature was 75 degrees Fahrenheit, 10 cooling degree days were calculated 7 for that day. If the average daily temperature was less than 65 degrees Fahrenheit, 8 then that day recorded zero cooling degree days. Daily cooling-degree days are 9 aggregated to monthly totals. 10 Q. How did the Test Year weather compare to normal weather? 11 A. The Test Year heating-degree days were 4.4 percent below normal; the Test Year 12 cooling-degree days were 22.8 percent above normal; and the Test Year 13 precipitation was 30.2 percent below normal. As shown on pages 3 and 4 of 14 Attachment LJ-RD-1, taken together these weather deviations resulted in 136, more MWh being consumed in the Test Year than would have been consumed in 16 the Test Year with normal weather, which amounts to 1.0 percent of total Texas 17 retail sales. The calculation of the 1.0 percent appears on page 2 of Attachment 18 LJ-RD-1. SPS concluded that the weather deviation appreciably affected the level 19 of sales, and thus SPS adjusted the Test Year sales for deviations of the actual 20 Test Year weather from the 10-year average weather. Jaramillo Direct - Rate Design Page 21 RD1-432 of

29 1 Q. How was the Test Year weather adjustment calculated? 2 A. SPS developed weather normalization regression coefficients that quantify the 3 impact of a one-unit change in weather on sales per customer. SPS then 4 converted the coefficients to a calendar-month basis by prorating the sales model 5 weather coefficients based on the number of billing days in each billing month 6 that occur in a particular calendar month. Pages 6 and 7 of Attachment LJ-RD-1 7 reflect the conversion of modeled weather coefficients to a calendar month basis. 8 Q. Please explain the steps you went through to complete the 9 weather-normalization calculation. 10 A. After calculating the calendar-month coefficients, I undertook a six step process 11 to calculate the effect on sales of weather variance from normal conditions during 12 the Test Year. The numbers used as examples in the six steps recounted below 13 appear in pages 3-4 of Attachment LJ-RD-1: 14 Step 1-I calculated the difference between the 10-year average 15 heating-degree days in a particular month and the heating-degree days in 16 that month of the Test Year. For example, the 10-year average number of 17 heating-degree days in ctober is 208, whereas the number of 18 heating-degree days in ctober of the Test Year was 223, for a difference 19 of Step 2-I multiplied the difference calculated in Step 1 times the number 21 of customers in each class. For example, the Residential with Space Jarainillo Direct - Rate Design Page 22 RDl of

30 I Heating class had 45,315 customers in ctober 2012, so I multiplied 15 2 times 45,315, for a total of 679, Step 3-I then multiplied the result from Step 2 times the heating-degree 4 day coefficient for that class to determine the number of MWh resulting 5 from the abnormal weather. Multiplying 679,725 times the ctober coefficient for the Residential with Space Heating class, which is , yields MWh. 8 Step 4-I then performed Steps 1-3 using the cooling-degree data. For 9 ctober 2012, that calculation results in MWh (-7 x 45,315 x = ). 11 Step 5-I netted the heating-degree MWh against the cooling-degree 12 MWh for each class by month. That produces a total of MWh for 13 the Residential with Space Heating class for ctober 2012 (2.685 MWh MWh = MWh). 15 Step 6-Finally, I totaled the number of MWh of all classes in each 16 month, and then I added the monthly amounts to arrive at the 12-month 17 total of 136,694 MWh attributable to abnormal weather. 18 Q. How did SPS use the weather-adjusted sales figures? 19 A. After calculating the weather-adjusted sales by class, I supplied those sales figures 20 to Mr. Luth, who used them to calculate revenues. The numbers that I provided to 21 Mr. Luth are on page 8 of Attachment LJ-RD-1. Jaramillo Direct - Rate Design Page 23 RDI of

31 1 V. WEATHER'S EFFECT TEST YEAR PEAK DEMAD 2 Q. Did SPS adjust its Test Year system peak demand to account for the effect of 3 weather on peak demand? 4 A. Yes. Because the twelve months that comprise the Test Year were warmer than 5 the 10-year average in SPS's service area, it was necessary to adjust the Test Year 6 peak demand to account for weather for the following customer classes: 7 Total retail; and 8 Aggregated full requirement wholesale. 9 Q. What source of weather did SPS use to measure the adjustment? 10 A. SPS uses a combination of peak day average daily temperature, peak day heating 11 degree days, precipitation one week prior to the peak day, and the number of days 12 with a maximum temperature of at least 95 degrees Fahrenheit for the week 13 ending on the peak day to measure weather adjustments for peak demand. These 14 weather concepts were calculated using weather data reported from the AA 15 weather stations in Amarillo, Texas, Lubbock, Texas, and Roswell, ew Mexico. 16 The Texas Panhandle weather is an average of the Amarillo and Lubbock weather 17 station data weighted by sales associated with the respective regions of the SPS 18 service area located in Texas. The total SPS weather is an average of the 19 Amarillo, Lubbock, and Roswell weather station data weighted by sales 20 associated with the respective regions of the SPS service area. 21 Q. How did SPS calculate average peak day temperature? 22 A. The peak day average temperature was calculated by adding the peak day 23 maximum daily temperature and peak day minimum daily temperature, then Jaramillo Direct - Rate Design Page 24 RD1-435 of

32 1 dividing that amount by 2. For example, if the peak day maximum temperature 2 was 55 degrees Fahrenheit and the peak day minimum temperature was 35 3 degrees Fahrenheit, the average peak day temperature would be 45 degrees 4 Fahrenheit. 5 Q. Please explain how SPS calculated the peak day heating-degree days. 6 A. SPS calculated peak day heating-degree days by subtracting the peak day average 7 temperature from 65 degrees Fahrenheit. For example, if the peak day average 8 daily temperature was 45 degrees Fahrenheit, then 20 heating-degree days were 9 calculated for that day. If the average peak day temperature was greater than degrees Fahrenheit, then that peak day recorded zero heating-degree days. 11 Q. How did SPS calculate precipitation? 12 A. SPS calculated the accumulation of water equivalent precipitation for the seven 13 days prior to the peak day. 14 Q. What is the "days with a maximum temperature of at least 95 degrees 15 Fahrenheit" weather concept and how is it calculated? 16 A. This variable is designed to measure the impact of an accumulation of hot weather 17 on the system peak demand. SPS tabulated the number of days with a maximum 18 temperature of at least 95 degrees Fahrenheit for the week (7 days) ending on the 19 peak day. 20 Q. How did SPS define the normal weather? 21 A. As noted earlier, SPS agrees with AA's definition that normal weather is 22 representative of typical weather based on a 30-year period. SPS believes that Jaramillo Direct - Rate Design Page 25 RDl of

33 1 normal weather based on a 30-year average reflects the weather that will occur 2 over the long-term. However, given the Commission's ruling in favor of using a 3 10-year period to measure normal weather in Docket o , SPS will 4 calculate its weather adjustment based on a 10-year normal period for this filing. 5 Q. How did SPS determine the normal weather? 6 A. ormal peak day weather was based on the average of the 10-year period used to 7 develop the weather adjustment coefficients for the peak day of each month for 8 historical average daily temperature, heating-degree days, precipitation, and days 9 with a maximum daily temperature of at least 95 degrees Fahrenheit data. The 10 Test Year and normal-weather for maximum temperatures, heating degree days, 11 precipitation, and days with a maximum temperature of at least 95 degrees 12 Fahrenheit are summarized on page 1 of Attachment LJ-RD Q. How did the Test Year peak day weather for the June through September 14 period compare to normal weather? 15 A. The Test Year summer months (June through September) peak day average daily 16 temperature was 6.7 percent above normal; accumulated precipitation was percent below normal; and the number of days with a maximum temperature of at 18 least 95 degrees Fahrenheit (for the week ending on the peak day) was percent above normal. As shown on Page 2 of Attachment LJ-RD-2, taken 20 together these weather deviations resulted in an average of 90 MW, or percent, more retail peak demand per month and an average of 42 MW, or percent, more full requirement wholesale peak demand per month from June Jaramillo Direct - Rate Design Page 26 RD of

34 1 through September in the Test Year compared to normal weather. SPS concluded 2 that the weather deviation appreciably affected the level of peak demand, and thus 3 SPS adjusted the Test Year peak demand for deviations of the actual Test Year 4 weather from the 10-year average weather. 5 Q. How was the Test Year weather adjustment calculated? 6 A. SPS developed weather normalization regression coefficients that quantify the 7 impact of a one-unit change in weather on retail and full requirement wholesale 8 peak demand. 9 Q. Please explain the steps you went through to complete the peak demand 10 weather-normalization calculation. 11 A. I undertook a four step process to calculate the effect on peak demand of weather 12 variance from the 10-year normal conditions during the Test Year. The numbers 13 used as examples in the four steps recounted below are for the retail peak demand 14 and appear in page 3 of Attachment LJ-RD-2: 15 Step 1-1 calculated the difference between: (i) the 10-year average 16 weather concepts (as measured in average peak day temperature, peak day 17 heating degree days, days above 95 degrees Fahrenheit, and precipitation) 18 in a particular month, and (ii) the actual weather concept in that month of 19 the Test Year. For example, for the retail peak demand the 10-year 20 average peak day temperature in July is 83.3 degrees Fahrenheit, whereas 21 the actual average peak day temperature in July of the Test Year was degrees Fahrenheit, a difference of -3.5 degrees Fahrenheit. This step is Jaramillo Direct - Rate Design Page 27 RD1-438 of

35 1 repeated for each weather concept. The 10-year average of days with a 2 maximum temperature of at least 95 degrees Fahrenheit in July is 4.1 days, 3 whereas the actual number of days with a maximum temperature of at 4 least 95 degrees Fahrenheit the week before the peak day in July of the 5 Test Year was 7 days, for a difference of -2.9 days. The 10-year average 6 of precipitation for the week preceding the peak day in July is 0.39 of an 7 inch of water equivalent precipitation, whereas the actual water equivalent 8 precipitation for the week preceding the peak day in July of the Test Year 9 was 0.06 of an inch, for a difference of 0.34 of an inch of precipitation. 10 The 10-year average of heating degree days on the peak day in July is 0, 11 and the actual peak day heating degree days in July of the Test Year was 12 also 0, resulting in no difference from normal. 13 Step 2-The variance in weather from the 10-year average from Step I for 14 each weather concept is multiplied by the respective weather adjustment 15 coefficient to determine the number of MW resulting from the variance in 16 actual weather from the 10-year average weather. Weather adjustment 17 coefficients are developed with econometric models using the same 18 methodology described earlier, and they include only weather concepts for 19 months where the coefficients are statistically significant. To continue 20 with the retail peak demand example from Step 1, multiplying the variance 21 in average peak day temperature of -3.5 degrees Fahrenheit times the July coefficient for average peak day temperature, which is , Jaramillo Direct - Rate Design Page 28 RD1-439 of

36 1 yields MW. This step is repeated for each weather concept. 2 Multiplying the variance in days with a maximum temperature of at least 3 95 degrees Fahrenheit for the week ending on the peak day of -2.9 days 4 times the July 2012 coefficient for days with a maximum temperature of at 5 least 95 degrees for the week ending on the peak day, which is , 6 yields MW. Because there is no weather adjustment coefficient in 7 July for either heating degree days or precipitation the week prior to the 8 peak day, these weather concepts do not have a weather adjustment in 9 July. 10 Step 3-For each month I summed the weather adjustments calculated in 11 Step 2 from each weather concept. Continuing with the example from 12 Step I and Step 2, this step produces a total weather adjustment of MW for the Total Retail peak demand for July 2012 ( MW MW = MW). 15 Step 4-Finally, I averaged the weather adjusted MW for the summer 16 months of the Test Year for June 2013, July 2012, August 2012, and 17 September 2012 to arrive at a 4-month average of weather's impact on the 18 peak demand. Continuing with the example from the previous steps, the 19 average weather adjustment for the retail peak demand for the 4-months of 20 June, July, August, and September of the Test Year was MW per 21 month. sing the same methodology described in Step 1 through Step 4, 22 the average weather adjustment for the full requirement wholesale peak Jaramillo Direct - Rate Design Page 29 RD of

37 I demand for the 4-months of June, July, August, and September of the Test 2 Year was MW per month. Pages 3 and 4 of Attachment LJ-RD-2 3 contain the weather adjustment calculations for the retail peak demand and 4 the full requirement wholesale peak demand, respectively. 5 Q. Did SPS adjust the Golden Spread full load peak demand coincident with the 6 SPS system peak demand for the effect of weather on its peak demand? 7 A. Yes. I adjusted the Golden Spread full load peak demand coincident with the SPS 8 system peak demand for the effect of weather on the peak demand using the same 9 methodology previously described for weather adjusting the retail and full 10 requirement wholesale peak demand. 11 Q. What is the weather adjustment applied to the Golden Spread full load peak 12 demand coincident with the SPS system peak demand for the Test Year? 13 A. As shown on Page 2 of Attachment LJ-RD-2, the average weather adjustment for 14 the Golden Spread full load peak demand coincident with the SPS system peak 15 demand for the four months of June, July, August, and September of the Test 16 Year was MW per month. Page 5 of Attachment -RD-2 provides the 17 weather adjustment calculation for the Golden Spread full load peak demand. 18 Q. How did SPS use the weather-adjusted peak demand figures? 19 A. After calculating the weather-adjusted peak demand by customer class, I supplied 20 those peak demand figures to Mr. Luth, who used them to calculate class 21 allocation of production and transmission capacity costs. The numbers that I 22 provided to Mr. Luth are on page 2 of Attachment LJ-RD Jaramillo Direct - Rate Design Page 30 RD of

38 1 VI. CCLSI 2 Q. Were Attachments LJ-RD-1 and LJ-RD-2 prepared by you or under your 3 direct supervision and control? 4 A. Yes. 5 Q. Were the portions of the RFP schedules that you sponsor or co-sponsor 6 prepared by you or under your direct supervision and control? 7 A. Yes. 8 Q. Do you incorporate the RFP schedules that you sponsored or co-sponsored 9 into your testimony? 10 A. Yes. 11 Q. Does this conclude your pre-filed direct testimony? 12 A. Yes. Jaramillo Direct - Rate Design Page 31 RD of

39 AFFIDAVIT STATE F CLRAD CTY F DEVER LKE JARAMILL, first being sworn on his oath, states: I am the witness identified in the preceding testimony. I have read the testimony and am familiar with its contents. Based upon my personal knowledge, the facts stated in the testimony are true. In addition, in my judgment and based upon my professional experience, the opinions and conclusions stated in the testimony are true, valid, and accurate. tf < < LKE JAiZAIv1ILe Subscribed and sworn to before me this I -Z day of December, 2013 by LKE JARAMILL... n11, Q. t l t! otary Public, State of Colorado J My Commission Expires: 2/ Z3 /( 7 SHIREE M. VA DK TARY PBLIC STATE F CLRAD TARY ID MY CMMISSI EXPIRES FEBRARY 23, 2011 Jaramillo Direct - Rate Design Page 32 RD of

40 Attachment LJ-RD-I Page 1 of 11 Docket o > a) M pp [ p p 00 Q a - -,- r,,- -, M Q 00 tr) d Q Q M cv 00 C;,, o0 " M 00 (01, 00 1 vl M oo l' v ) in kn M I (1) y C13 M t kr) tf) 00 M C D Z p p p t ba oc V') oc -- n o0,-- M v -- M (L) kr) 3 00 C M M - M --I CC a) kr) 00 r- cd Q v M l l t M M 3 Q x M M M M M M CC CC z bj) p C D ci p. Ln o Z Q ti w. RD of

41 Attachment LJ-RD-1 Page 2 of 11 Docket o > z 10 CL M a0 7 7 cp 3 0r0 C'I W) -- '!1 M W ao M o0 M - n cd Q w -o 00 o o r M r V cd r '1 o0 M VJ l 0 M Vi p V'1 C) 7 w r- \0 C) aj -E z v' v d' V'1 M 7 C11 IC o/1 rt V'1 r M MD -- d--7 d v') 00 d M M== = L_ (n! z u v) oo C -- v M r -- M r M r 7k, V M. 4 v, o o v, - \o 00 M v n MD M d', r M L_ y p d p Vl y 00 r Cn M r M r!1 M z -c r 7 r T M d' M M M M M M M M M L V] c r, M10 - V col M l M M rd D r M V1 o0 Q M V 'D c0 7 M 00 t r M r M M M 7 qi - M-- v7 o vl + oo r d' M 'l W c0 +, o0 7 7 Q, d' r r V V o 00 M r/'1 n),c < 7D r r r-- cd M-- G M o0 V /'1 00 V1 M r M r M 7 v1 -- D V' 00 o r M C CT v' r V [ -- (V (V M M 7 V'I G [ r z L = X y' Fz ro u s,l o +z W R y C" u L_ q' -. M d' CI M M M M C y D o C M C Vl o0 - V'1 M V'1 rd p <f MD 00 r Cn M M oo --- C r l 01 r r V r ( n T c0 c0 o vl r o0 V e: 3 -C M r 00 M Ql rg '.T C d' J r 7 V1 M _ L_ -5 4 v _ F y ao 0o ao. v oo n V' r ao C r v', C r C C co r o f3 W, o0 V 7 Q -- 7 r Ir Vn 7 o V M r?'o 'o \o o c'o z v, \o tu Y ig W F o, o 7 c0 r CT ai 0, a, t l 0 0C1 o0 7 r IC M o r M r- 0,, r M E T a V l M 00 C, M V, r r r oo lc M o r t o F 10 M -- C1 T (Y+ 7! M M/1 00 rl1 Q V r 7 00 "J M vw n. d' V V'l M,-- r 00 Vl o!1 V'1 00 M r M c) 'D r- A M o r r o V <V.,_, o Mn V' AD pp r 7 ld M M o ± Lr V co,- [ o V 0o W oo,i _,, v-, 7 ni, d t co vi hi m oo oo F '; M v'; v 00 v1 y Q itl a) 'J+ 7 v'l cd co M C R M r 01 M, 'C ci, M M lp r r M r /'1 7 c0 C6 a0o ri M 7 00o d- h V 7 r r - rd -- 'p qs 7 M- o r V c M M M M M M M M C y X M M M V' Q n v \G Vl V a, t 00 Vl t It 7n o r co rl 00? ( 7 1 M Vl r 7 vi vl D d 00 r M oo rn n d o - cj ci M M vs v) o r 46 E Ln r M M r E - Vl o0 0 M o 00 D r 00 7 r- -- l Cl " cn r r o0 r 7 c0 D n M r oo r M M C E Q CS M M r M M C M 01 M a V1 t M 7D 00 M v, 7 r Cl ---- M M C/1 'c C, a M r M-- 7- r C) r- 'ct oo V o r oo Cl v e r r\ M C o0 D '- n co o \C r W M D Vl M 00 V - -t 7 o M /' o MD M V D M v' r M V'l r _, M M M M M M ly M M M. M M M q p > C A~ T C cy CL cd 'Q nzll, LQ., p, n0,'zll - CL >' C Q ti RD of

42 Attachment LJ-RD-1 Page 3 of 1 I Docket o > G \ M V 00 G r': l +: - p p... (y/ s l 01 M+: t i 'd Ll u >ai' aocrnoopooomv M --. > 00 o 't C1 M M M M ; v s o s s o o s _ oooo- a ' -- Cl Cl V > r- - v v, C C -» - ` > 00 V'1 d' D! f l l 00 Vl 7 7 M cv V > CD M f Vl d' V D o0 o o o vi vi ` > oo [.-. M 7,, 00 ( Vl M ( W - - M 'p f M d' - t 00 - Vl d' M V 7 >. [ oo l 00 7 l Q'w Q, \,, C, Q." - p ap p Z --- l >'r3 Q M 7 V d' v' 7 7 V V V T/l y:, -. V 7 V V 74 V d V h c D Q S C 'r, n o0 7'-' 'T _ 00 M M V l t+l d' 00 V'l -- M > 00 [ -- V M M M M M M M M[ - y.., ' M z `7 00 V'; '"'' 7 M l y Z, rm, 7 It -- d o0 7 > [- 6, o0 1 1 M 1 c3 00 t+1 - ft i 1 1 M l -- M cj' } cl (+1 M M 7 V' V1 ' (A M M M M M<y M M M M M M 7 aj n Z:1 - u ị 7 7 6, V cd. 3 Q v z A RD of