Exhibit No. JM-1 Updated Vol.II, pages 2-98 through ELECTRIC ENERGY AND DEMAND FORECASTS

Transcription

1 2.6 ELECTRIC ENERGY AND DEMAND FORECASTS Updated Vol.II, pages 2-98 through Introduction Projections of future energy and peak demand are fundamental inputs into Public Service s resource need assessment. As required by ERP Rule 3606(b), Public Service prepared a base forecast and high and low forecast sensitivities. Public Service projects base or median native load peak demand (retail and firm wholesale requirements customers) to decline at a compounded annual rate of -0.02% or an average of decrease of -2 MW per year through the Resource Acquisition Period (RAP). This is less than the 0.7% annual growth rate over the last five years. The higher historical growth and subsequent lower future growth of native load is due in large part to a high 2011 peak, which included backup generation for the partners in the Comanche 3 generator. Under normal operating conditions, the Comanche 3 generator would be on line and Public Service would not be providing backup generation to the partners of that power plant. If Comanche 3 was on line at the time of the 2011 peak, the historical load growth over the last five years would have been flat, with annual gains less than a tenth of a percent. Likewise, under normal conditions, the expected native load peak demand growth over the RAP would be 0.5% annually. These lackluster growth rates (excluding the anomalous 2011) are impacted by the loss of wholesale customers, high levels of DSM, and on-site solar during both the historical time period and during the RAP. Public Service s low-growth sensitivity for peak demand decreases at a compounded annual growth rate of -0.8% through 2018, and the high-growth sensitivity for peak demand increases at a compounded growth rate of 0.0% per year over the same period of time. If the 2011 native load peak demand is adjusted to remove the backup generation provided to the partners of Comanche 3, the low-growth compounded annual growth rate through 2018 is projected to be -0.3%, while the high-growth scenario would be 1.3% per year for the same period of time. Public Service projects base or median annual energy sales to decrease at a compounded annual growth rate of -0.1% or an average of -31 GWh per year through the RAP. Public Service s low growth sensitivity for the forecast of annual energy sales decreases at a compounded annual growth rate of -0.8% through 2018, and the high growth sensitivity for the forecast of annual energy sales grows at a compounded rate of 0.6% per year. Figures and graphically show the base, high, and low forecasts of native load peak demand and energy sales. Tables and show the data supporting the charts. The base peak demand forecast assumes economic growth based on projections from IHS Global Insight, Inc., and median summer peak weather conditions. 1 Public Service estimates that there is a 70% chance that the actual peak demands will fall between the high and the low forecast scenarios. 1 Median is synonymous with the 50 th percentile, or it is higher than 50% of the estimates and lower than 50% of the estimates. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-98

2 Updated Vol.II, pages 2-98 through Figure Native Load Peak Demand s MW 7,500 Peak Demand Comparison Native Load 7,000 6,500 6,000 Base 5,500 Low High 5, Figure Native Load Energy Sales s GWh 40,000 Energy Sales Scenario Comparison 36,000 32,000 28,000 24,000 Base Low High 20, PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-99

3 Updated Vol.II, pages 2-98 through Peak Demand Discussion Native load peak demand in Public Service s service territory has historically demonstrated anemic growth, except in 2011 when the Comanche 3 generator was off-line at the time of the peak and Public Service provided approximately 255 MW of backup generation to the partners of that power plant. The expiration of wholesale contracts and the participation of wholesale customers in the Comanche 3 power plant have contributed to this weak load growth. 2 Since 2007 and accounting for backup generation in 2011, Public Service s firm wholesale load has decreased by 287 MW. The loss of wholesale load was offset by load growth within the retail sector, which has averaged gains of 0.7% or 41 MW annually during the past five years. Colorado s economy was not immune to the prolonged downturn in the housing market and the financial sector crisis that started in The national recession impacted the Colorado economy, with declines in real personal income, real gross state product ( GSP ), non-farm employment, and home construction. In the five years ending in 2011, Colorado real GSP has averaged gains of 0.9% annually and real personal income advanced at the same pace of 0.9% annually. Large job losses in 2008 and 2009 resulted in a decline in non-farm employment since 2007, with annual decreases averaging -0.3% annually with the 5 year period ending in Colorado population has increased 1.7% per year since During the same period, Public Service s residential sector added 53,300 customers, an increase of 4.8% over the 2006 customer count. The economic outlook for Public Service s service territory through the RAP indicates that Colorado will experience stronger growth compared with the previous five years. Growth in Colorado real GSP is expected to advance 2.1% per year from 2011 to Colorado real personal income will increase at a similar pace of 2.3% annually through Nonfarm employment should advance by 1.8% annually over the same period. Population growth will continue at its recent historical pace of 1.7% annually. Public Service s residential customer counts are expected to increase by 99,700 over the next 7 years with average gains of 1.7% per year through Native load peak demand growth has been flat over the past 5 years with gains in the retail sector being offset by declines from wholesale load as contracts expired. Growth in Public Service s residential air conditioning load has stabilized over the last few years. The 2010 Residential Energy Use Survey conducted by Xcel Energy s Market Research Department indicates that 75% of Public Service s customers had some form of air condition/cooling system in 2010, which has 2 Public Services wholesale customers Intermountain Rural Electric Association and Holy Cross Energy reduced their wholesale load on Public Service s system by using a portion of the Comanche 3 coal-fired generation resource to serve their load. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-100

4 Updated Vol.II, pages 2-98 through remained flat compared with the 2008 survey (75%) and the 2006 survey (76%), but is up from the 2003 survey which reported that 63% of Public Service s customers had some form of air condition/cooling system. We expect native load peak demand growth over the RAP, through 2018, to remain flat, advancing by less than one-tenth of one percent annually. Peak demand growth in 2012 will be negative with the expiration of the wholesale sales contract with Black Hills Colorado. During the period from 2013 to 2018, a period that is not influenced by the expiration of wholesale contracts, native load peak demand increases at a rate of 1.0%, or 78 MW per year. Table shows Public Service s native load summer peak demand forecasts along with ten years of history. It also shows annual growth and compounded growth to/from The bold line across the table delineates historical from projected information. Table Actual and ed Summer Native Load Peak Demand 3 MW Annual Growth Compound Growth to/from 2011 Base Low High Base Low High Base Low High , % 4.5% , % 2.4% , % 2.3% , % 0.0% , % 1.2% , % -0.2% , % 1.1% , % 3.9% , % 3.0% , % 0.0% ,428 6,409 6, % -7.2% -6.6% 2.4% -0.9% -0.8% ,532 6,418 6, % 0.1% 3.1% 1.9% -0.9% -0.5% ,589 6,409 6, % -0.1% 1.8% 1.6% -0.9% -0.2% ,670 6,434 6, % 0.4% 2.0% 1.2% -0.9% 0.0% ,759 6,477 7, % 0.7% 2.0% 0.7% -0.8% 0.3% ,829 6,485 7, % 0.1% 1.8% 0.4% -0.8% 0.5% ,897 6,517 7, % 0.5% 1.6% 0.1% -0.7% 0.7% Annual Energy Discussion 3 1 megawatt (MW) = 1,000 kilowatts (kw) PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-101

5 Updated Vol.II, pages 2-98 through Table shows Public Service s forecast for its total annual energy sales with ten years of history. It also shows annual growth and compounded growth to/from The bold line across the table delineates historical from projected information. The decrease in 2008 is caused by the termination of the firm wholesale contract with Cheyenne Light Fuel & Power Company. The decrease in 2010 and 2011 are due to the participation of Intermountain Rural Electric Association and Holy Cross Energy in the Comanche 3 project. The decrease in 2012 is attributable to the termination of the wholesale contract with Black Hills Colorado. Table Actual and ed Annual Native Load Energy Sales 4 GWh Annual Growth Compound Growth to/from 2006 Base Low High Base Low High Base Low High , % 1.3% , % 1.0% , % 0.4% , % -1.2% , % -1.4% , % -2.8% , % -2.0% , % -0.5% , % -0.5% , % 0.0% ,884 30,881 30, % -5.5% -5.5% 1.9% -0.7% -0.7% ,122 30,761 31, % -0.4% 1.9% 1.6% -0.8% -0.5% ,316 30,653 31, % -0.4% 1.7% 1.4% -0.8% -0.3% ,563 30,624 32, % -0.1% 1.6% 1.2% -0.8% -0.1% ,899 30,731 33, % 0.3% 1.8% 0.8% -0.8% 0.1% ,177 30,787 33, % 0.2% 1.5% 0.5% -0.7% 0.3% ,455 30,877 34, % 0.3% 1.4% 0.2% -0.7% 0.5% Due to the declines in wholesale sales, native load energy sales have decreased an average of -0.8% (-282 GWh) per year from 2007 to During the RAP ending in 2018, growth in native load energy sales will decrease on average by -0.1% per year. The forecasted growth rate from 2013 to 2018, which is no longer influenced by the expiration of wholesale contracts, is expected to average 0.8% or 262 GWh per year. Variability Due to Weather Weather has an impact on energy sales and an even greater impact on peak demand. The Public Service system usually experiences its annual peak demand during the month of July. The base forecast assumes normal weather based on a 30-year average of historical temperature data. Because Public Service is aware of the impact of weather on both energy sales and peak demand, Monte Carlo 4 1 gigawatt hour (GWh) = 1 million kilowatt hours (kwh). PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-102

6 Updated Vol.II, pages 2-98 through simulations were developed to establish confidence bands around the base forecast to determine the possible extent of these impacts. These confidence bands are provided in detail below. High and Low Case s Development and use of different energy sales and demand forecasts for planning future resource additions is an important aspect of the planning process. Low and high growth sensitivities to the base case were developed for the 2011 ERP. Monte Carlo simulations were developed to establish confidence bands around the base forecast to determine the possible extent of variation in Public Service s service territory s economic growth. Tables and summarize the base, low and high energy sales and peak demand forecasts. Actual and ed Demand and Energy Table depicts Public Service s base case demand and energy forecast in the context of the last ten years of history. The bold line across the table delineates historical from projected information with 2011 values reflecting actual sales through September. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-103

7 Updated Vol.II, pages 2-98 through Table Actual and ed Summer Peak Demand and Annual Energy Summer Peak Demand (MW) Annual Increase (MW) Energy Sales (GWh) Annual Increase (GWh) , , , , , , , ,921 1, , , History , ,544 1, , , , ,213-1, , , , , , ,884-1, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Energy and Demand s, Below are tables presenting the base case energy and demand forecasts for each year within the planning period, : 5 5 Public Service did not forecast any sales subject to the jurisdiction of other states. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-104

8 Updated Vol.II, pages 2-98 through Table Base Case: Energy/Coincident Summer and Winter Demand (Including Impacts of DSM Programs) Energy Sales (GWh) Coincident Summer Demand (MW) Coincident Winter Demand (MW) Retail Wholesale Retail Wholesale Retail Wholesale ,354 2, , ,687 2, , ,883 2, , ,091 2, , ,416 2, , ,620 2, , ,866 2, , ,126 2, , ,452 2, , ,642 2, , ,890 2, , ,153 2, , ,524 2, , ,758 2, , ,081 3, , ,389 3, , ,782 3, , ,014 3, , ,343 3, , ,667 3, , ,048 3, , ,277 3, , ,613 3, , ,957 3, , ,368 3, , ,647 3, , ,032 3, , ,431 3, , ,930 3, , ,262 4, , ,652 4, , ,047 4, , ,455 4, , ,874 4, , ,298 4, , ,728 4, , ,164 4, , ,603 4, , ,044 4, , PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-105

9 Updated Vol.II, pages 2-98 through Table 2.6-5A Base Case: Energy/Coincident Summer Demand/Winter Peak Demand by Major Customer Class (Including Impacts of DSM Programs) Energy Sales (GWh) Coincident Summer Peak Demand (MW) Coincident Winter Peak Demand (MW) Small & Large Small & Large Small & Residential C&I Other Resale Total Residential C&I Other Resale Total Residential Large C&I Other Resale Total ,009 19, ,529 30,884 2,375 3, ,428 1,982 2, , ,040 19, ,435 31,122 2,394 3, ,532 2,004 2, , ,105 19, ,433 31,316 2,416 3, ,589 2,031 2, , ,183 19, ,472 31,563 2,441 3, ,670 2,063 2, , ,310 19, ,483 31,899 2,469 3, ,759 2,100 2, , ,370 19, ,557 32,177 2,495 3, ,829 2,134 2, , ,470 20, ,589 32,455 2,523 3, ,897 2,170 2, , ,570 20, ,643 32,769 2,551 3, ,961 2,206 2, , ,705 20, ,699 33,151 2,580 3, ,018 2,244 2, , ,759 20, ,756 33,398 2,606 3, ,069 2,277 2, , ,859 20, ,815 33,704 2,637 3, ,124 2,314 2, , ,961 20, ,870 34,024 2,670 3, ,175 2,350 2, , ,121 21, ,930 34,454 2,706 3, ,243 2,392 2, , ,202 21, ,990 34,748 2,739 3, ,308 2,430 2, , ,335 21, ,051 35,132 2,778 3, ,386 2,473 2, , ,466 21, ,113 35,502 2,817 3, ,464 2,515 2, , ,648 21, ,175 35,957 2,862 4, ,550 2,561 2, , ,731 21, ,238 36,252 2,899 4, ,626 2,600 2, , ,868 22, ,302 36,645 2,941 4, ,708 2,643 2, ,171 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-106

10 Updated Vol.II, pages 2-98 through Table 2.6-5B Base Case: Energy/Coincident Summer Demand/Winter Peak Demand by Major Customer Class (Including Impacts of DSM Programs) Energy Sales (GWh) Coincident Summer Peak Demand (MW) Coincident Winter Peak Demand (MW) Small & Large Small & Large Small & Large Residential C&I Other Resale Total Residential C&I Other Resale Total Residential C&I Other Resale Total ,005 22, ,366 37,034 2,983 4, ,786 2,686 2, , ,181 22, ,431 37,480 3,026 4, ,863 2,729 2, , ,261 22, ,497 37,775 3,062 4, ,929 2,768 2, , ,401 22, ,564 38,177 3,104 4, ,002 2,811 2, , ,542 22, ,632 38,588 3,147 4, ,080 2,855 2, , ,718 23, ,700 39,068 3,189 4, ,147 2,897 2, , ,805 23, ,769 39,416 3,232 4, ,212 2,940 2, , ,955 23, ,839 39,872 3,274 4, ,275 2,982 2, , ,104 23, ,910 40,342 3,316 4, ,335 3,024 2, , ,319 24, ,982 40,912 3,358 4, ,393 3,066 2, , ,412 24, ,055 41,316 3,400 4, ,447 3,107 2, , ,563 24, ,128 41,780 3,442 4, ,499 3,148 2, , ,714 24, ,203 42,250 3,484 4, ,548 3,189 2, ,013 6, ,866 25, ,279 42,733 3,525 4, ,594 3,229 2, ,033 7, ,018 25, ,355 43,229 3,566 4, ,637 3,269 2, ,054 7, ,168 25, ,433 43,731 3,607 4, ,677 3,309 2, ,076 7, ,316 25, ,512 44,240 3,638 4, ,704 3,348 2, ,097 7, ,462 26, ,591 44,756 3,668 4, ,728 3,386 2, ,119 7, ,605 26, ,672 45,275 3,698 4, ,747 3,424 2, ,142 7, ,746 26, ,754 45,799 3,727 4, ,764 3,461 2, ,165 7,259 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-107

11 Updated Vol.II, pages 2-98 through Table Base Case: Energy and Capacity Sales to Other Utilities (At the Time of Coincident Summer and Winter Peak Demand) Coincident Summer Demand (MW) Coincident Winter Demand (MW) Energy Sales (GWh) , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,165 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-108

12 Updated Vol.II, pages 2-98 through Table Base Case: Intra-Utility Energy and Capacity Use (At the Time of Coincident Summer and Winter Peak Demand) Energy Sales (GWh) Coincident Summer Demand (MW) Coincident Winter Demand (MW) Company Company Company Interdpt Use Interdpt Use Interdpt Use PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-109

13 Updated Vol.II, pages 2-98 through Table 2.6-8A Base Case: Losses by Major Customer Class Energy Losses (million kwh) Coincident Summer Demand Losses (MW) Coincident Winter Demand Losses (MW) Residential C&I Other FERC Residential C&I Other FERC Residential C&I Other FERC , , , , , , , , , , , , , , , , , , , Note: System Loss estimates cannot be made for the transmission and distribution levels because the forecast was not developed at the transmission and distribution voltage level. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-110

14 Updated Vol.II, pages 2-98 through Table 2.6-8B Base Case: Losses by Major Customer Class Energy Losses (million kwh) Coincident Summer Demand Losses (MW) Coincident Winter Demand Losses (MW) Residential C&I Other FERC Residential C&I Other FERC Residential C&I Other FERC , , , , , , , , , , , , , , , , , , , ,003 1, Note: System Loss estimates cannot be made for the transmission and distribution levels because the forecast was not developed at the transmission and distribution voltage level. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-111

15 Updated Vol.II, pages 2-98 through Table Base Case: Energy and Peak Demand DSM Savings Coincident Summer Demand Savings (MW) Coincident Winter Demand Savings (MW) Energy Savings (million kwh) , , , , , , , , , , , , , , ,758 1, ,945 1, ,131 1, ,318 1, ,505 1, ,691 1, ,878 1, ,064 1, ,251 1, ,437 1, ,624 1, ,811 1, ,997 1, ,184 1, ,373 1, ,564 1,816 1, ,758 1,870 1, ,955 1,924 1, ,154 1,978 1,104 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-112

16 Overview Table presents the base case forecast of native summer peak demand through the resource acquisition period ending in The forecast is broken into two segments: 1) Retail plus indefinite term resale ( ITR - contracts that expire beyond the Planning Period) and without defined term resale ( DTR - contracts that expire within the forecast period) and 2) Retail with ITR and DTR which is the total summer native load peak demand. The bold line across the table delineates historical from projected information. Table Actual and ed Summer Peak Demand Native Peak Demand without Defined Term Resale (MW) Annual Increase (MW) Defined Term Resale Demand (MW) Annual Increase (MW) Total Summer Native Load Peak Demand (MW) Annual Increase (MW) , , , , , , , , , , , , , , , , , , , , , , , , Actual Data Growth in total native peak demand has been averaged 0.7% per year over the past five years, with annual gains averaging 50 MW. Native peak demand without defined term resale has grown 1.2% per year over this time period, averaging annual increases of 75 MW per year. The projected growth rates through 2018 are lower due to the expiration of wholesale contracts as well as the anomalously high 2011 peak. The average annual growth rate for total native load peak demand is expected to be 0.0% through the RAP ending in 2018, with an average decline of 2 MW per year. The growth rate for native peak demand without DTR is expected to be 0.6% the resource acquisition period. For consistency, native energy sales to the DTR customers were separated from total energy sales in Table The growth rates for sales are different in both history and forecast. Native sales including the DTR customers decreased by 0.8% annually over the past five years while native sales excluding DTR customers grew 0.4% per year. Native energy sales with DTR customers are expected to remain below the 2011 level through 2018 as growth from the retail sector and ITR wholesale customers is more than offset by the expiration of the DTR wholesale PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-113

17 contracts. Native energy sales without the DTR customers are expected to increase by 0.5% annually through For both native load peak demand and native energy sales, the forecast without the DTR customers presents a clearer view of the expected patterns of growth for the retail and resale customers that will be served throughout the resource acquisition period. Table Actual and ed Annual Energy Sales Annual Energy Sales without Defined Term Resale (GWh) Annual Increase (GWh) Annual Defined Term Resale Energy Sales (GWh) Annual Increase (GWh) Total Annual Energy Sales (GWh) Annual Increase (GWh) ,362 1,630 3, ,544 1, , , , ,439-1,112 1, ,213-1, , , , , , , , ,237 30,884-1, , , , , , , , , , , , , Actual Data Methodologies The following discussion describes the methods Public Service uses to forecast each of the various customer classes that make up the total Public Service energy and demand forecasts. Public Service uses monthly historical customer, sales and peak demand data by rate class to develop its forecasts. ed economic and demographic data are obtained from IHS Global Insight, Inc. Energy Sales Public Service s residential sales and commercial and industrial sales forecasts are developed using a Statistically-Adjusted End-Use ( SAE ) modeling approach. The SAE method entails specifying energy use as a PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-114

18 function of the primary end-use variables (heating, cooling, and base use) and the factors that affect these end-use energy requirements. The SAE residential sales forecast is calculated as the product of average use and customer forecasts. The SAE modeling approach consists of regressions for average use per customer and number of customers. The use per customer regression model is estimated using monthly historical sales per customer, weather, economics, price, and appliance saturation and efficiency trend data. Customer growth is strongly correlated with growth in state housing stock. Therefore, the number of customers is forecasted as a function of housing stock projections. End-use concepts are incorporated in the average use per customer model. Average use is defined as a function of heating, cooling, and base use requirements, as shown below. The term e is the model error term. Average Use = Heating + Cooling + Base + e Each of these elements of average use is defined in terms of both an appliance index variable, which indicates relative saturation and efficiency of the stock of appliances, and a utilization variable, which reflects how the stock is utilized. The end-use variables are defined as: Heating = HeatIndex * HeatUse Cooling = CoolIndex * CoolUse Base = BaseIndex * BaseUse The indices are calculated as the ratio of the appliance saturation and average efficiency of the existing stock. To generate a relative index, the ratio is divided by the estimated value for Thus, the index has a value of 1.0 in The indices reflect both changes in saturation resulting from end-use competition and improvements in appliance efficiency standards. For example, if gas heating gains market share, the electric heating saturation will decline, resulting in a decline in the heating index variable. Similarly, improvements in electric heating efficiency will also contribute to a lower heating index. The trend towards greater saturation of central air conditioning has the opposite effect, contributing to an increasing cooling index over time. Air conditioning efficiency gains mitigate this increase. Appliance trends in other end-uses such as water heating, cooking, refrigeration, and miscellaneous loads are captured in the base index. The utilization variables (CoolUse, HeatUse, and BaseUse) are designed to capture energy demand driven by the use of the appliance stock. For the residential sector, the primary factors that impact appliance use are weather PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-115

19 conditions (as measured by heating and cooling degree days), electricity prices, household income, household size, and hours of daylight. The utilization variables are defined as: COOLUSE = (PRICE^(-0.2)) * (INCOME_PER_HOUSEHOLD^0.2) *(HOUSEHOLD_SIZE^0.01) * COOLING_DEGREE_DAYS HEATUSE = (PRICE^(-0.2)) * (INCOME_PER_HOUSEHOLD^0.2) *(HOUSEHOLD_SIZE^0.01) * HEATING_DEGREE_DAYS BASEUSE = (PRICE^(-0.2)) * (INCOME_PER_HOUSEHOLD^0.2) *(HOUSEHOLD_SIZE^0.01) * (HOURS_OF_LIGHT^(-0.2)) In this functional form, the values shown in the specifications are, in effect, elasticities. The elasticities give the percent change in the utilization variables (CoolUse, HeatUse, and BaseUse) given a 1% change in the economic variables (Price, Income per Household, and Household Size). The elasticities are inferred from the Electric Power Research Institute ( EPRI ) residential end-use model REEPS. The forecast model is estimated by regressing monthly average residential usage on Cooling Use, Heating Use, Base Use, and monthly seasonal variables for all months except January, July, and August. The regression model effectively calibrates the end-use concepts to actual residential average use. Monthly seasonal variables for each month are included to account for non-weather-related seasonal factors. The forecast model results are adjusted to reflect the expected incremental impact of residential DSM programs, reductions in sales that can be attributed to distributed solar generation, and the expected impacts from the residential tiered rate structure that is effective from June through September each year. The same general approach is used to construct the commercial and industrial sales forecast model. For this model, sales can again be decomposed into heating, cooling and base use. The end-use variables Heating, Cooling and Base are structured in a manner similar to those used in the residential model and are defined as the product of a variable that reflects technology stock and efficiency (Index) and a variable that captures stock utilization (Use). For the commercial and industrial sector, saturation and efficiency trends can be captured by the change in annual energy intensities (kwh per square foot). These intensity trends are estimated using the EPRI commercial end-use model COMMEND. The Heating Index, Cooling Index, and Base Index have values of 1.0 in Increasing saturation levels drive an index higher, PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-116

20 while improvements in stock efficiency or decreasing saturation levels lower the value of the index. Stock utilization is a function of electricity prices, business activity (as measured by Colorado Gross State Product), heating degree days, cooling degree days, and hours of light. The utilization variables are specified as: COOLUSE = (PRICE^(-0.2)) * (CO_GROSS_STATE_PRODUCT^0.3) * COOLING_DEGREE_DAYS HEATUSE = (PRICE^(-0.2)) * (CO_GROSS_STATE_PRODUCT^0.3) * HEATING_DEGREE_DAYS BASEUSE = (PRICE^(-0.2)) * (CO_GROSS_STATE_PRODUCT^0.6) * (HOURS_OF_LIGHT^(-0.2)) The forecast model is then estimated by regressing monthly commercial and industrial sales on Cooling, Heating, Base, monthly billing cycle days, a variable that quantifies identified new large customer load (MW), a monthly seasonal variable for each month, a variable to account for the implementation of the new billing system in 2004, and a binary variable for July and August 2004, and January The regression model effectively calibrates the end-use concepts to actual commercial and industrial sales. In this case, the Heating variable is excluded from the regression because it did not provide significant explanatory value. A variable for identified new large customer loads was added to explain growth in Public Service s service territory that was greater than the state-wide growth documented in the historical Colorado Gross State Product. The monthly seasonal variables for each month are included to account for non-weather-related seasonal factors. Binary variables for July and August 2004, and January 2007, are included to account for unusual billing activity. The model results are adjusted to reflect the expected incremental impact of commercial and industrial DSM programs, distributed solar generation, and new load additions as identified by the large commercial and industrial customer account managers. Public authority sales are forecasted using a regression model that is based on the same Base variable developed for the commercial and industrial sector and various monthly binary variables. The public authority model includes a binary variable for the latest extension of light rail service for the Regional Transportation District in 2002 and The forecast of street lighting sales for the test year is based on trend forecasts of light counts or customer counts by rate and wattage. The light counts and/or customer counts are then used to develop the sales forecast by rate and wattage based on watts per light and monthly hours of usage. PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-117

21 The interdepartmental sales forecast is developed using a regression model with seasonal binary variables, a binary variable to account for the implementation of the new billing system in 2004, and a binary variable for December 2008 and September s for sales to resale customers are received from Public Service s wholesale customers. Figure Native Electric Sales (GWh) GWh Navtive Electric Sales (GWh) excluding short term wholesale sales Residential Comm & Ind Other ITR DTR Demand Residential coincident peak demand is expected to increase in response to changes to residential energy requirements. For the residential demand regression model, residential energy requirements are defined as a 12-month moving average of monthly residential sales. The moving average calculation removes the monthly sales cyclical pattern. Efficiency improvements captured in the residential sales model are assumed to have the same impact on residential peak demand. Since peak demand does not necessarily grow at the same rate as the underlying sales, an end-use saturation term interacting with peak-day weather conditions and customer counts is also included in the model. This variable is defined as: PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-118

22 Peak_Day_Cooling_Degree_Days *Customer Counts* CoolIndex The cooling index is the same index used in the residential average use per customer model. With the cooling index variable the sensitivity to peak-day weather changes as residential cooling saturation and efficiency changes. Also included in the residential peak model are peak day heating degree days and binary variables to remove months with data anomalies (October 2005, April 2006, April 2007, May 2007, October 2007, September 2008, and October 2010). The commercial and industrial (nonresidential) coincident peak demand forecast is developed using a regression model similar to the residential peak model. Historical commercial and industrial coincident peaks are regressed against commercial and industrial energy requirements defined as the 12- month moving average of commercial and industrial sales. Also included in the model is a variable that allows peak demand to change at a different rate than sales. This variable, which interacts peak day weather with commercialindustrial customers, reflects increasing cooling usage as customer counts increase. In addition, the model contains non-farm employment and a binary variable to remove September 2008 from the regression. Information from the Xcel Managed Accounts group regarding Public Service s largest commercial and industrial customers may be used to make adjustments to the modeled peak demand forecasts. s of peak demand for each REA and municipality are received from the respective wholesale customers. s of the capacity required by these customers coincident with the system peak are developed from following sources of information. 1. Historical loads for Public Service sales to these customers coincident with the Public Service system peak are provided by Xcel Energy s Load Research Department. 2. Monthly billing reports provide historical data of energy and capacity sales itemized by the utility providing the power, the total noncoincident peak demand for the month, and the portion of that peak demand allocated to WAPA. A forecast of the capacity required by each of these customers coincident with the Public Service system peak is developed using the trends present in the non-coincident peak demand forecasts, the historical coincident loads, PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-119

23 and information from the billing reports regarding WAPA capacity allocations and the total load coincident with the Public Service system peak. Coincident peak demand forecasts for the interruptible load are provided by Xcel Energy s Load Research Department. The components of this forecast are the primary, secondary, and transmission voltage Interruptible contracted loads and the Residential Saver s Switch program. Figure Native Peak Demand (MW) MW PSCo System Summer Peak Demand (MW) Native Load Obligation Residential Non-Residential ITR DTR Variability Due to Weather Weather has an impact on energy sales and an even greater impact on peak demand. The Public Service system usually experiences its annual peak demand during the month of July. The base forecast assumes normal weather based on 30- year average of peak day weather in the future. In order to quantify the possible outcomes of weather variation from the 30-year average weather, Monte Carlo simulations have been developed to establish confidence bands around the base forecast. The probability distributions for the simulation runs for both sales and demand were based on 30 years of historical weather data for Denver. Table provides the resulting confidence bands at the level of 1.00 standard deviation or 70% probability bandwidth and 1.65 standard deviations or 90% probability bandwidth above and below the base case forecast of native load peak demand. Table provides the confidence bands above and below the annual native PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-120

24 energy sales forecast. Graphs of the peak demand and sales confidence bands are presented in Figure and Figure Table Native Peak Demand Weather Variability Coincident Summer Peak Demand (MW) Coincident Winter Peak Demand (MW) Std Dev +1 Std Dev Base -1 Std Dev Std Dev Std Dev +1 Std Dev Base Case -1 Std Dev Std Dev ,833 6,686 6,428 6,168 6,013 5,459 5,327 5,106 4,882 4, ,949 6,791 6,532 6,268 6,114 5,491 5,363 5,140 4,918 4, ,014 6,852 6,589 6,325 6,168 5,525 5,395 5,176 4,953 4, ,080 6,932 6,670 6,406 6,250 5,594 5,466 5,249 5,024 4, ,165 7,024 6,759 6,491 6,339 5,676 5,548 5,327 5,111 4, ,243 7,093 6,829 6,570 6,418 5,748 5,614 5,393 5,173 5, ,315 7,156 6,897 6,637 6,484 5,802 5,665 5,448 5,224 5, ,370 7,215 6,961 6,696 6,543 5,854 5,725 5,507 5,286 5, ,434 7,277 7,018 6,757 6,606 5,906 5,778 5,559 5,331 5, ,477 7,327 7,069 6,810 6,662 5,959 5,833 5,606 5,388 5, ,537 7,385 7,124 6,868 6,717 5,996 5,868 5,648 5,428 5, ,590 7,433 7,175 6,918 6,768 6,044 5,917 5,696 5,477 5, ,654 7,498 7,243 6,991 6,838 6,103 5,982 5,761 5,540 5, ,717 7,569 7,308 7,055 6,903 6,164 6,040 5,820 5,594 5, ,793 7,640 7,386 7,124 6,977 6,247 6,109 5,890 5,669 5, ,876 7,731 7,464 7,211 7,057 6,316 6,183 5,959 5,738 5, ,959 7,809 7,550 7,294 7,143 6,387 6,255 6,034 5,812 5, ,026 7,875 7,626 7,361 7,218 6,458 6,325 6,099 5,877 5, ,118 7,968 7,708 7,446 7,295 6,532 6,400 6,171 5,945 5, ,199 8,048 7,786 7,525 7,367 6,597 6,463 6,238 6,011 5, ,270 8,125 7,863 7,601 7,452 6,669 6,534 6,304 6,078 5, ,342 8,192 7,929 7,669 7,510 6,734 6,594 6,362 6,139 6, ,419 8,266 8,002 7,741 7,581 6,793 6,660 6,428 6,196 6, ,496 8,340 8,080 7,812 7,658 6,869 6,730 6,495 6,266 6, ,563 8,406 8,147 7,878 7,724 6,935 6,795 6,559 6,330 6, ,629 8,471 8,212 7,942 7,787 7,000 6,858 6,621 6,392 6, ,691 8,532 8,275 8,004 7,849 7,062 6,919 6,682 6,452 6, ,751 8,592 8,335 8,064 7,908 7,123 6,979 6,741 6,511 6, ,809 8,648 8,393 8,121 7,964 7,182 7,037 6,798 6,568 6, ,863 8,702 8,447 8,175 8,019 7,239 7,093 6,854 6,624 6, ,914 8,752 8,499 8,227 8,070 7,294 7,147 6,907 6,677 6, ,963 8,800 8,548 8,275 8,119 7,347 7,199 6,959 6,729 6, ,008 8,845 8,594 8,321 8,165 7,398 7,248 7,008 6,779 6, ,049 8,886 8,637 8,364 8,208 7,447 7,296 7,056 6,827 6, ,088 8,924 8,677 8,404 8,248 7,493 7,341 7,101 6,873 6, ,113 8,949 8,704 8,432 8,276 7,537 7,385 7,144 6,916 6, ,135 8,970 8,728 8,456 8,300 7,578 7,425 7,185 6,958 6, ,152 8,988 8,747 8,477 8,321 7,617 7,464 7,223 6,997 6, ,166 9,002 8,764 8,494 8,339 7,654 7,500 7,259 7,034 6,868 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-121

25 Table Annual Native Energy Sales Weather Variability Energy Sales (million kwh) Std Dev +1 Std Dev Base -1 Std Dev Std Dev ,698 32,018 30,884 29,766 29, ,014 32,307 31,122 29,955 29, ,198 32,495 31,316 30,143 29, ,450 32,743 31,563 30,401 29, ,773 33,073 31,899 30,734 30, ,054 33,353 32,177 31,023 30, ,339 33,624 32,455 31,295 30, ,645 33,938 32,769 31,613 30, ,010 34,318 33,151 32,004 31, ,265 34,569 33,398 32,245 31, ,581 34,869 33,704 32,549 31, ,894 35,190 34,024 32,865 32, ,304 35,608 34,454 33,302 32, ,623 35,924 34,748 33,599 32, ,009 36,308 35,132 33,987 33, ,372 36,667 35,502 34,350 33, ,822 37,123 35,957 34,808 34, ,124 37,419 36,252 35,102 34, ,515 37,815 36,645 35,493 34, ,903 38,206 37,034 35,882 35, ,337 38,646 37,480 36,328 35, ,653 38,953 37,775 36,626 35, ,048 39,343 38,177 37,021 36, ,473 39,762 38,588 37,437 36, ,958 40,244 39,068 37,915 37, ,306 40,591 39,416 38,265 37, ,766 41,048 39,872 38,720 38, ,241 41,520 40,342 39,189 38, ,820 42,095 40,912 39,757 39, ,225 42,498 41,316 40,162 39, ,692 42,962 41,780 40,626 39, ,165 43,432 42,250 41,096 40, ,652 43,917 42,733 41,580 40, ,151 44,413 43,229 42,076 41, ,656 44,916 43,731 42,579 41, ,168 45,425 44,240 43,088 42, ,687 45,941 44,756 43,605 42, ,209 46,461 45,275 44,125 43, ,735 46,984 45,799 44,650 44,003 PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-122

26 Figure Native Peak Demand Weather Confidence Bands (MW) Weather Confidence Bands:Native Peak Demand (MW) 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3, Std Dev +1 Std Dev 2,000 Base -1 Std Dev 1, Std Dev ,000 50,000 40,000 30,000 20,000 10,000 0 Figure Native Sales Weather Confidence Bands (GWH) Weather Confidence Bands: Annual Native Energy Sales (GWh) Std Dev +1 Std Dev Base -1 Std Dev Std Dev High Growth PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-123

27 Public Service s high energy sales forecast is based on a Monte Carlo simulation of the energy sales forecast with probabilistic inputs for the main economic drivers of the forecast model and for model error. The primary component of the high sales scenario is the forecast level from the simulation that represents the upper limit of a one standard deviation wide confidence band. The resulting high energy sales forecast grows 1.0% annually over the next 40 years, from 32,672 GWh in 2011, to 52,410 GWh in High energy sales growth over the next 7 years is anticipated to average 0.6% annually with sales of 34,019 GWh in Public Service s high summer native load peak demand forecast grows from 6,908 MW in 2011 to 9,939 MW in 2051, an average annual growth rate of 0.9%. Shortterm annual growth is expected to be 0.8% over the next 7 years. The Base Case forecast indicates in the short-term, native load growth will be flat with 0.0% annual gains through The Base Case growth rate will increase with annual increases averaging 0.6% through The forecasted high peak demands and high sales are contained in Figures and and listed in Tables and Low Growth Public Service s low energy sales forecast is based on a Monte Carlo simulation of the energy sales forecast with probabilistic inputs for the main economic drivers of the forecast model and for model error. The primary component of the low sales scenario is the forecast level from the simulation that represents the lower limit of a one standard deviation wide confidence band. The resulting low native energy sales forecast grows 0.5% annually over the next 40 years, from 32,672 GWh in 2011, to 40,272 GWh in The low scenario energy sales growth over the next 7 years is anticipated to average -0.8% annually with sales of 30,877 GWh in Public Service s low summer native load peak demand forecast grows from 6,908 MW in 2011 to 7,735 MW in 2051, an average annual growth rate of 0.3%. The low short-term annual growth is expected to average declines of -0.8% over the next 7 years, with peak demand of 6,517 in The forecasted low peak demands and low sales are illustrated in Figures and and listed in Tables and PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-124