2.6 electric energy and demand forecasts ......2.6 electric energy and demand forecasts introduction...

Exhibit No. JM-1 Updated Vol.II, pages 2-98 through 2-148

2011 RESOURCE PLAN UPDATE VOLUME 2 - TECHNICAL APPENDIX PUBLIC SERVICE COMPANY OF COLORADO PAGE 2-98

2.6 ELECTRIC ENERGY AND DEMAND FORECASTS 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 2.6-1 and 2.6-2 graphically show the base, high, and low forecasts of native load peak demand and energy sales. Tables 2.6-1 and 2.6-2 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 50th percentile, or it is higher than 50% of the estimates and lower

than 50% of the estimates.



Figure 2.6-1 Native Load Peak Demand Forecasts

Peak Demand Forecast ComparisonNative Load

5,000

5,500

6,000

6,500

7,000

7,500

2001 2003 2005 2007 2009 2011 2013 2015 2017

MW

BaseLowHigh

Figure 2.6-2 Native Load Energy Sales Forecasts

Energy Sales Forecast Scenario Comparison

20,000

24,000

28,000

32,000

36,000

40,000

2001 2003 2005 2007 2009 2011 2013 2015 2017

GWh

BaseLowHigh



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 2008. 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 2011. Colorado population has increased 1.7% per year since 2007. 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 2018. Colorado real personal income will increase at a similar pace of 2.3% annually through 2018. 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 2018. 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.



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 2.6-1 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 2011. The bold line across the table delineates historical from projected information. Table 2.6-1 Actual and Forecasted Summer Native Load Peak Demand3

MW Annual Growth Compound Growth

to/from 2011 Base Low High Base Low High Base Low High

2002 6,057 5.3% 4.5% 2003 6,442 6.4% 2.4% 2004 6,445 0.0% 2.3% 2005 6,912 7.2% 0.0% 2006 6,656 -3.7% 1.2% 2007 6,940 4.3% -0.2% 2008 6,692 -3.6% 1.1% 2009 6,160 -7.9% 3.9% 2010 6,322 2.6% 3.0% 2011 6,908 9.3% 0.0% 2012 6,428 6,409 6,454 -6.9% -7.2% -6.6% 2.4% -0.9% -0.8%2013 6,532 6,418 6,653 1.6% 0.1% 3.1% 1.9% -0.9% -0.5%2014 6,589 6,409 6,773 0.9% -0.1% 1.8% 1.6% -0.9% -0.2%2015 6,670 6,434 6,910 1.2% 0.4% 2.0% 1.2% -0.9% 0.0%2016 6,759 6,477 7,048 1.3% 0.7% 2.0% 0.7% -0.8% 0.3%2017 6,829 6,485 7,174 1.0% 0.1% 1.8% 0.4% -0.8% 0.5%2018 6,897 6,517 7,286 1.0% 0.5% 1.6% 0.1% -0.7% 0.7%

Annual Energy Discussion

3 1 megawatt (MW) = 1,000 kilowatts (kW)



Table 2.6-2 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 2011. 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 2.6-2 Actual and Forecasted Annual Native Load Energy Sales4

GWh Annual Growth Compound Growth

to/from 2006 Base Low High Base Low High Base Low High

2002 31,432 2.0% 1.3% 2003 31,710 0.9% 1.0% 2004 32,275 1.8% 0.4% 2005 33,921 5.1% -1.2% 2006 34,082 0.5% -1.4% 2007 35,544 4.3% -2.8% 2008 34,764 -2.2% -2.0% 2009 33,213 -4.5% -0.5% 2010 33,146 -0.2% -0.5% 2011 32,672 -1.4% 0.0% 2012 30,884 30,881 30,886 -5.5% -5.5% -5.5% 1.9% -0.7% -0.7%2013 31,122 30,761 31,460 0.8% -0.4% 1.9% 1.6% -0.8% -0.5%2014 31,316 30,653 31,979 0.6% -0.4% 1.7% 1.4% -0.8% -0.3%2015 31,563 30,624 32,482 0.8% -0.1% 1.6% 1.2% -0.8% -0.1%2016 31,899 30,731 33,058 1.1% 0.3% 1.8% 0.8% -0.8% 0.1%2017 32,177 30,787 33,543 0.9% 0.2% 1.5% 0.5% -0.7% 0.3%2018 32,455 30,877 34,019 0.9% 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 2011. 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).



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 Forecasts 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 2.6-1 and 2.6-2 summarize the base, low and high energy sales and peak demand forecasts. Actual and Forecasted Demand and Energy Table 2.6-3 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.



Table 2.6-3 Actual and Forecasted Summer Peak Demand and Annual Energy

Summer Peak Demand (MW)

Annual Increase (MW)

Energy Sales(GWh)

Annual Increase (GWh)

2002 6,057 303 31,432 622 2003 6,442 385 31,710 278 2004 6,445 3 32,275 565 2005 6,912 467 33,921 1,646 2006 6,656 -257 34,082 161 History 2007 6,940 284 35,544 1,462 2008 6,692 -248 34,764 -781 2009 6,160 -532 33,213 -1,550 2010 6,322 162 33,146 -68 2011 6,908 586 32,672 -474 2012 6,428 -480 30,884 -1,788 2013 6,532 104 31,122 238 2014 6,589 57 31,316 194 2015 6,670 80 31,563 247 2016 6,759 89 31,899 336 2017 6,829 70 32,177 278 2018 6,897 68 32,455 278 2019 6,961 64 32,769 314 2020 7,018 57 33,151 382 2021 7,069 51 33,398 247 2022 7,124 55 33,704 306 2023 7,175 51 34,024 319 2024 7,243 68 34,454 430 2025 7,308 65 34,748 295 2026 7,386 77 35,132 384 2027 7,464 79 35,502 370 2028 7,550 86 35,957 455 2029 7,626 76 36,252 295 2030 7,708 82 36,645 393 2031 7,786 78 37,034 389 Forecast 2032 7,863 77 37,480 446 2033 7,929 66 37,775 295 2034 8,002 73 38,177 402 2035 8,080 78 38,588 411 2036 8,147 67 39,068 479 2037 8,212 65 39,416 348 2038 8,275 63 39,872 456 2039 8,335 60 40,342 470 2040 8,393 57 40,912 571 2041 8,447 55 41,316 404 2042 8,499 52 41,780 464 2043 8,548 49 42,250 470 2044 8,594 46 42,733 484 2045 8,637 43 43,229 495 2046 8,677 40 43,731 502 2047 8,704 27 44,240 509 2048 8,728 24 44,756 516 2049 8,747 20 45,275 520 2050 8,764 16 45,799 524

Energy and Demand Forecasts, 2012-2050 Below are tables presenting the base case energy and demand forecasts for each year within the planning period, 2012-2050:5

5 Public Service did not forecast any sales subject to the jurisdiction of other states.



Table 2.6-4 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 2012 28,354 2,529 0 5,977 451 02013 28,687 2,435 0 6,063 470 02014 28,883 2,433 0 6,121 468 02015 29,091 2,472 0 6,193 477 02016 29,416 2,483 0 6,271 488 02017 29,620 2,557 0 6,330 499 02018 29,866 2,589 0 6,388 509 02019 30,126 2,643 0 6,441 520 02020 30,452 2,699 0 6,487 531 02021 30,642 2,756 0 6,526 542 02022 30,890 2,815 0 6,570 554 02023 31,153 2,870 0 6,611 564 02024 31,524 2,930 0 6,669 574 02025 31,758 2,990 0 6,724 584 02026 32,081 3,051 0 6,791 594 02027 32,389 3,113 0 6,859 605 02028 32,782 3,175 0 6,935 615 02029 33,014 3,238 0 7,000 626 02030 33,343 3,302 0 7,071 637 02031 33,667 3,366 0 7,138 648 02032 34,048 3,431 0 7,204 659 02033 34,277 3,497 0 7,258 671 02034 34,613 3,564 0 7,320 682 02035 34,957 3,632 0 7,386 694 02036 35,368 3,700 0 7,441 706 02037 35,647 3,769 0 7,494 718 02038 36,032 3,839 0 7,544 731 02039 36,431 3,910 0 7,592 743 02040 36,930 3,982 0 7,637 756 02041 37,262 4,055 0 7,678 769 02042 37,652 4,128 0 7,717 782 02043 38,047 4,203 0 7,753 795 02044 38,455 4,279 0 7,786 809 02045 38,874 4,355 0 7,815 822 02046 39,298 4,433 0 7,841 836 02047 39,728 4,512 0 7,854 850 02048 40,164 4,591 0 7,863 864 02049 40,603 4,672 0 7,869 879 02050 41,044 4,754 0 7,870 893 0



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)

Residential Small & Large

C&I Other Resale Total ResidentialSmall & Large

C&I Other Resale Total ResidentialSmall &

Large C&I Other Resale Total 2012 9,009 19,115 230 2,529 30,884 2,375 3,590 12 451 6,428 1,982 2,504 71 549 5,106 2013 9,040 19,405 241 2,435 31,122 2,394 3,651 18 470 6,532 2,004 2,491 76 568 5,140 2014 9,105 19,528 250 2,433 31,316 2,416 3,687 18 468 6,589 2,031 2,508 77 559 5,176 2015 9,183 19,655 253 2,472 31,563 2,441 3,733 19 477 6,670 2,063 2,538 78 570 5,249 2016 9,310 19,823 283 2,483 31,899 2,469 3,768 34 488 6,759 2,100 2,550 96 581 5,327 2017 9,370 19,935 314 2,557 32,177 2,495 3,801 34 499 6,829 2,134 2,562 103 594 5,393 2018 9,470 20,077 319 2,589 32,455 2,523 3,824 40 509 6,897 2,170 2,566 105 606 5,448 2019 9,570 20,218 338 2,643 32,769 2,551 3,837 53 520 6,961 2,206 2,562 119 619 5,507 2020 9,705 20,381 366 2,699 33,151 2,580 3,849 58 531 7,018 2,244 2,557 126 633 5,559 2021 9,759 20,499 384 2,756 33,398 2,606 3,861 58 542 7,069 2,277 2,551 131 646 5,606 2022 9,859 20,641 390 2,815 33,704 2,637 3,871 62 554 7,124 2,314 2,542 133 660 5,648 2023 9,961 20,796 396 2,870 34,024 2,670 3,879 63 564 7,175 2,350 2,538 135 673 5,696 2024 10,121 21,001 402 2,930 34,454 2,706 3,899 64 574 7,243 2,392 2,545 137 687 5,761 2025 10,202 21,148 409 2,990 34,748 2,739 3,920 66 584 7,308 2,430 2,550 138 701 5,820 2026 10,335 21,331 416 3,051 35,132 2,778 3,946 67 594 7,386 2,473 2,584 118 716 5,890 2027 10,466 21,501 422 3,113 35,502 2,817 3,974 68 605 7,464 2,515 2,594 119 731 5,959 2028 10,648 21,705 429 3,175 35,957 2,862 4,003 70 615 7,550 2,561 2,606 121 746 6,034 2029 10,731 21,847 436 3,238 36,252 2,899 4,030 71 626 7,626 2,600 2,613 125 762 6,099 2030 10,868 22,032 442 3,302 36,645 2,941 4,057 72 637 7,708 2,643 2,624 127 777 6,171



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)

Residential Small & Large



C&I Other Resale Total 2031 11,005 22,213 449 3,366 37,034 2,983 4,082 74 648 7,786 2,686 2,630 129 793 6,238 2032 11,181 22,411 456 3,431 37,480 3,026 4,103 75 659 7,863 2,729 2,634 131 810 6,304 2033 11,261 22,554 463 3,497 37,775 3,062 4,120 76 671 7,929 2,768 2,636 133 827 6,362 2034 11,401 22,743 470 3,564 38,177 3,104 4,139 78 682 8,002 2,811 2,639 135 844 6,428 2035 11,542 22,938 477 3,632 38,588 3,147 4,160 79 694 8,080 2,855 2,643 136 861 6,495 2036 11,718 23,166 484 3,700 39,068 3,189 4,172 80 706 8,147 2,897 2,645 138 879 6,559 2037 11,805 23,350 491 3,769 39,416 3,232 4,182 81 718 8,212 2,940 2,646 139 897 6,621 2038 11,955 23,579 499 3,839 39,872 3,274 4,189 82 731 8,275 2,982 2,644 141 915 6,682 2039 12,104 23,821 507 3,910 40,342 3,316 4,193 83 743 8,335 3,024 2,641 142 934 6,741 2040 12,319 24,097 514 3,982 40,912 3,358 4,195 83 756 8,393 3,066 2,635 144 953 6,798 2041 12,412 24,327 522 4,055 41,316 3,400 4,194 84 769 8,447 3,107 2,628 146 973 6,854 2042 12,563 24,559 530 4,128 41,780 3,442 4,190 85 782 8,499 3,148 2,619 147 992 6,907 2043 12,714 24,795 538 4,203 42,250 3,484 4,183 86 795 8,548 3,189 2,608 149 1,013 6,959 2044 12,866 25,044 545 4,279 42,733 3,525 4,173 87 809 8,594 3,229 2,595 151 1,033 7,008 2045 13,018 25,302 553 4,355 43,229 3,566 4,160 88 822 8,637 3,269 2,580 152 1,054 7,056 2046 13,168 25,569 561 4,433 43,731 3,607 4,144 89 836 8,677 3,309 2,563 154 1,076 7,101 2047 13,316 25,843 569 4,512 44,240 3,638 4,125 90 850 8,704 3,348 2,543 156 1,097 7,144 2048 13,462 26,125 577 4,591 44,756 3,668 4,103 91 864 8,728 3,386 2,522 157 1,119 7,185 2049 13,605 26,413 585 4,672 45,275 3,698 4,078 92 879 8,747 3,424 2,498 159 1,142 7,223 2050 13,746 26,706 593 4,754 45,799 3,727 4,050 94 893 8,764 3,461 2,473 161 1,165 7,259



Table 2.6-6 Base Case: Energy and Capacity Sales to Other Utilities (At the Time of Coincident Summer and Winter Peak Demand)

Energy Sales

(GWh)

Coincident Summer Demand

(MW)

Coincident Winter Demand

(MW) 2012 2,529 451 549 2013 2,435 470 568 2014 2,433 468 559 2015 2,472 477 570 2016 2,483 488 581 2017 2,557 499 594 2018 2,589 509 606 2019 2,643 520 619 2020 2,699 531 633 2021 2,756 542 646 2022 2,815 554 660 2023 2,870 564 673 2024 2,930 574 687 2025 2,990 584 701 2026 3,051 594 716 2027 3,113 605 731 2028 3,175 615 746 2029 3,238 626 762 2030 3,302 637 777 2031 3,366 648 793 2032 3,431 659 810 2033 3,497 671 827 2034 3,564 682 844 2035 3,632 694 861 2036 3,700 706 879 2037 3,769 718 897 2038 3,839 731 915 2039 3,910 743 934 2040 3,982 756 953 2041 4,055 769 973 2042 4,128 782 992 2043 4,203 795 1,013 2044 4,279 809 1,033 2045 4,355 822 1,054 2046 4,433 836 1,076 2047 4,512 850 1,097 2048 4,591 864 1,119 2049 4,672 879 1,142 2050 4,754 893 1,165



Table 2.6-7 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)

Interdpt Company

Use Interdpt Company

Use Interdpt Company

Use 2012 3 34 1 4 2 4 2013 3 34 1 4 2 4 2014 3 34 1 4 2 4 2015 3 34 1 4 2 4 2016 3 34 1 4 2 4 2017 3 34 1 4 2 4 2018 3 34 1 4 2 4 2019 3 34 1 4 2 4 2020 3 34 1 4 2 4 2021 3 34 1 4 2 4 2022 3 34 1 4 2 4 2023 3 34 1 4 2 4 2024 3 34 1 4 2 4 2025 3 34 1 4 2 4 2026 3 34 1 4 2 4 2027 3 34 1 4 2 4 2028 3 34 1 4 2 4 2029 3 34 1 4 2 4 2030 3 34 1 4 2 4 2031 3 34 1 4 2 4 2032 3 34 1 4 2 4 2033 3 34 1 4 2 4 2034 3 34 1 4 2 4 2035 3 34 1 4 2 4 2036 3 34 1 4 2 4 2037 3 34 1 4 2 4 2038 3 34 1 4 2 4 2039 3 34 1 4 2 4 2040 3 34 1 4 2 4 2041 3 34 1 4 2 4 2042 3 34 1 4 2 4 2043 3 34 1 4 2 4 2044 3 34 1 4 2 4 2045 3 34 1 4 2 4 2046 3 34 1 4 2 4 2047 3 34 1 4 2 4 2048 3 34 1 4 2 4 2049 3 34 1 4 2 4 2050 3 34 1 4 2 4



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

2012 689 1,218 16 64 181 242 1 13 152 166 5 15 2013 690 1,224 17 65 182 244 1 12 154 164 5 15 2014 693 1,228 17 65 183 246 1 12 156 165 5 14 2015 698 1,235 18 66 185 248 1 12 159 167 6 15 2016 706 1,244 19 66 186 250 2 13 161 168 6 15 2017 710 1,251 21 68 188 251 2 13 164 169 7 15 2018 716 1,258 21 69 190 252 2 13 167 169 7 16 2019 722 1,266 22 71 192 252 3 14 170 169 8 16 2020 731 1,274 24 72 193 252 3 14 173 169 8 16 2021 734 1,282 25 74 195 253 3 14 175 168 8 17 2022 741 1,290 25 75 197 253 3 14 178 168 8 17 2023 747 1,298 25 77 199 253 3 15 181 167 9 17 2024 757 1,310 26 78 201 253 3 15 184 168 9 18 2025 763 1,319 26 80 204 254 3 15 187 168 9 18 2026 772 1,330 27 81 206 255 3 15 190 171 7 18 2027 781 1,340 27 83 209 256 4 16 193 171 7 19 2028 793 1,351 27 85 212 258 4 16 197 172 7 19 2029 799 1,360 28 86 214 259 4 16 200 172 8 20 2030 808 1,371 28 88 217 260 4 17 203 173 8 20

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.



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

2031 817 1,381 29 90 220 261 4 17 207 174 8 20 2032 828 1,392 29 92 223 262 4 17 210 174 8 21 2033 834 1,400 29 93 225 262 4 17 213 174 8 21 2034 843 1,411 30 95 228 263 4 18 216 174 8 22 2035 853 1,421 30 97 231 264 4 18 220 174 8 22 2036 864 1,433 31 99 234 264 4 18 223 175 8 22 2037 871 1,444 31 100 237 264 4 19 226 175 8 23 2038 881 1,456 32 102 240 264 4 19 229 174 8 23 2039 891 1,470 32 104 242 263 4 19 233 174 9 24 2040 905 1,484 32 106 245 263 4 20 236 174 9 24 2041 912 1,498 33 108 248 262 4 20 239 173 9 25 2042 922 1,511 33 110 251 261 4 20 242 173 9 25 2043 933 1,524 34 112 254 260 4 21 245 172 9 26 2044 943 1,537 34 114 256 258 4 21 248 171 9 26 2045 954 1,553 35 116 259 257 5 21 251 170 9 27 2046 964 1,568 35 118 262 255 5 22 254 169 9 27 2047 974 1,584 36 120 264 253 5 22 257 168 9 28 2048 983 1,598 36 122 266 251 5 23 260 166 9 29 2049 993 1,617 37 124 268 249 5 23 263 165 9 29 2050 1,003 1,634 37 126 270 246 5 23 266 163 10 30

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.



Table 2.6-9 Base Case: Energy and Peak Demand DSM Savings

Energy Savings

(million kWh)

Coincident Summer Demand

Savings (MW)

Coincident Winter Demand Savings

(MW) 2012 108 18 9 2013 241 44 21 2014 401 76 39 2015 587 117 62 2016 773 159 86 2017 960 204 111 2018 1,146 252 138 2019 1,333 303 166 2020 1,519 357 196 2021 1,706 411 227 2022 1,893 465 257 2023 2,079 519 287 2024 2,266 573 317 2025 2,452 627 348 2026 2,639 681 378 2027 2,825 735 408 2028 3,012 789 438 2029 3,199 843 469 2030 3,385 897 499 2031 3,572 951 529 2032 3,758 1,005 559 2033 3,945 1,059 590 2034 4,131 1,113 620 2035 4,318 1,167 650 2036 4,505 1,221 680 2037 4,691 1,275 711 2038 4,878 1,330 741 2039 5,064 1,384 771 2040 5,251 1,438 801 2041 5,437 1,492 832 2042 5,624 1,546 862 2043 5,811 1,600 892 2044 5,997 1,654 922 2045 6,184 1,708 953 2046 6,373 1,762 983 2047 6,564 1,816 1,013 2048 6,758 1,870 1,043 2049 6,955 1,924 1,074 2050 7,154 1,978 1,104

Exhibit No. JM-1 Updated Vol. II, pages 2-98 through 2-148


Forecast Overview Table 2.6-10 presents the base case forecast of native summer peak demand through the resource acquisition period ending in 2018. 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 2.6-10 Actual and Forecasted 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)

2007 6,515 281 424 3 6,940 284 2008 6,413 -102 278 -146 6,692 -248 2009 5,871 -542 288 10 6,160 -532 2010 6,014 143 308 20 6,322 162 2011 6,608 594 300 -8 6,908 586

Actual Data

2012 6,428 -180 0 -300 6,428 -480 2013 6,532 104 0 0 6,532 104 2014 6,589 57 0 0 6,589 57 2015 6,670 80 0 0 6,670 80 2016 6,759 89 0 0 6,759 89 2017 6,829 70 0 0 6,829 70 2018 6,897 68 0 0 6,897 68

Forecast

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 2.6-11. 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



contracts. Native energy sales without the DTR customers are expected to increase by 0.5% annually through 2018. 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 2.6-11 Actual and Forecasted Annual Energy Sales

Annual Energy Sales

without Defined Term Resale (GWh)


Annual Defined

Term Resale Energy Sales (GWh)


Total Annual Energy Sales (GWh)


2007 32,362 1,630 3,182 -168 35,544 1,462 2008 32,551 189 2,213 -969 34,764 -781 2009 31,439 -1,112 1,774 -438 33,213 -1,550 2010 31,401 -38 1,745 -29 33,146 -68 2011 31,354 -47 1,318 -427 32,672 -474

Actual Data

2012 30,803 -551 81 -1,237 30,884 -1,788 2013 31,122 319 0 -81 31,122 238 2014 31,316 194 0 0 31,316 194 2015 31,563 247 0 0 31,563 247 2016 31,899 336 0 0 31,899 336 2017 32,177 278 0 0 32,177 278 2018 32,455 278 0 0 32,455 278

Forecast

Forecast 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. Forecasted economic and demographic data are obtained from IHS Global Insight, Inc.

Energy Sales Forecast 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



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 2006. Thus, the index has a value of 1.0 in 2006. 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



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 2000. Increasing saturation levels drive an index higher,



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 2007. 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 2006. 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.



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 2000. Forecasts for sales to resale customers are received from Public Service’s wholesale customers.

Figure 2.6-3 Native Electric Sales (GWh)

Navtive Electric Sales (GWh)excluding short term wholesale sales

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

2002 2004 2006 2008 2010 2012 2014 2016 2018

GWh

Residential Comm & Ind Other ITR DTR

Demand Forecast 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:



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 commercial-industrial 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. Forecasts of peak demand for each REA and municipality are received from the respective wholesale customers. Forecasts 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 non-coincident 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,



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 2.6-4 Native Peak Demand (MW)

PSCo System Summer Peak Demand (MW)Native Load Obligation

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2002 2004 2006 2008 2010 2012 2014 2016 2018

M W

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 2.6-12 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 2.6-13 provides the confidence bands above and below the annual native



energy sales forecast. Graphs of the peak demand and sales confidence bands are presented in Figure 2.6-4 and Figure 2.6-5.

Table 2.6-12 Native Peak Demand Weather Variability Coincident Summer Peak Demand (MW) Coincident Winter Peak Demand (MW)

+1.65 Std

Dev +1 Std

Dev Base -1 Std Dev

-1.65 Std Dev

+1.65 Std Dev

+1 Std Dev

Base Case

-1 Std Dev

-1.65 Std Dev

2012 6,833 6,686 6,428 6,168 6,013 5,459 5,327 5,106 4,882 4,749 2013 6,949 6,791 6,532 6,268 6,114 5,491 5,363 5,140 4,918 4,791 2014 7,014 6,852 6,589 6,325 6,168 5,525 5,395 5,176 4,953 4,823 2015 7,080 6,932 6,670 6,406 6,250 5,594 5,466 5,249 5,024 4,895 2016 7,165 7,024 6,759 6,491 6,339 5,676 5,548 5,327 5,111 4,971 2017 7,243 7,093 6,829 6,570 6,418 5,748 5,614 5,393 5,173 5,046 2018 7,315 7,156 6,897 6,637 6,484 5,802 5,665 5,448 5,224 5,086 2019 7,370 7,215 6,961 6,696 6,543 5,854 5,725 5,507 5,286 5,158 2020 7,434 7,277 7,018 6,757 6,606 5,906 5,778 5,559 5,331 5,202 2021 7,477 7,327 7,069 6,810 6,662 5,959 5,833 5,606 5,388 5,258 2022 7,537 7,385 7,124 6,868 6,717 5,996 5,868 5,648 5,428 5,307 2023 7,590 7,433 7,175 6,918 6,768 6,044 5,917 5,696 5,477 5,351 2024 7,654 7,498 7,243 6,991 6,838 6,103 5,982 5,761 5,540 5,412 2025 7,717 7,569 7,308 7,055 6,903 6,164 6,040 5,820 5,594 5,464 2026 7,793 7,640 7,386 7,124 6,977 6,247 6,109 5,890 5,669 5,537 2027 7,876 7,731 7,464 7,211 7,057 6,316 6,183 5,959 5,738 5,611 2028 7,959 7,809 7,550 7,294 7,143 6,387 6,255 6,034 5,812 5,682 2029 8,026 7,875 7,626 7,361 7,218 6,458 6,325 6,099 5,877 5,743 2030 8,118 7,968 7,708 7,446 7,295 6,532 6,400 6,171 5,945 5,817 2031 8,199 8,048 7,786 7,525 7,367 6,597 6,463 6,238 6,011 5,871 2032 8,270 8,125 7,863 7,601 7,452 6,669 6,534 6,304 6,078 5,945 2033 8,342 8,192 7,929 7,669 7,510 6,734 6,594 6,362 6,139 6,002 2034 8,419 8,266 8,002 7,741 7,581 6,793 6,660 6,428 6,196 6,067 2035 8,496 8,340 8,080 7,812 7,658 6,869 6,730 6,495 6,266 6,129 2036 8,563 8,406 8,147 7,878 7,724 6,935 6,795 6,559 6,330 6,190 2037 8,629 8,471 8,212 7,942 7,787 7,000 6,858 6,621 6,392 6,250 2038 8,691 8,532 8,275 8,004 7,849 7,062 6,919 6,682 6,452 6,309 2039 8,751 8,592 8,335 8,064 7,908 7,123 6,979 6,741 6,511 6,365 2040 8,809 8,648 8,393 8,121 7,964 7,182 7,037 6,798 6,568 6,421 2041 8,863 8,702 8,447 8,175 8,019 7,239 7,093 6,854 6,624 6,474 2042 8,914 8,752 8,499 8,227 8,070 7,294 7,147 6,907 6,677 6,526 2043 8,963 8,800 8,548 8,275 8,119 7,347 7,199 6,959 6,729 6,576 2044 9,008 8,845 8,594 8,321 8,165 7,398 7,248 7,008 6,779 6,623 2045 9,049 8,886 8,637 8,364 8,208 7,447 7,296 7,056 6,827 6,669 2046 9,088 8,924 8,677 8,404 8,248 7,493 7,341 7,101 6,873 6,713 2047 9,113 8,949 8,704 8,432 8,276 7,537 7,385 7,144 6,916 6,755 2048 9,135 8,970 8,728 8,456 8,300 7,578 7,425 7,185 6,958 6,795 2049 9,152 8,988 8,747 8,477 8,321 7,617 7,464 7,223 6,997 6,833 2050 9,166 9,002 8,764 8,494 8,339 7,654 7,500 7,259 7,034 6,868



Table 2.6-13 Annual Native Energy Sales Weather Variability

Energy Sales (million kWh)

+1.65 Std

Dev +1 Std Dev Base -1 Std Dev -1.65 Std Dev 2012 32,698 32,018 30,884 29,766 29,1202013 33,014 32,307 31,122 29,955 29,2722014 33,198 32,495 31,316 30,143 29,4652015 33,450 32,743 31,563 30,401 29,7322016 33,773 33,073 31,899 30,734 30,0572017 34,054 33,353 32,177 31,023 30,3392018 34,339 33,624 32,455 31,295 30,6242019 34,645 33,938 32,769 31,613 30,9432020 35,010 34,318 33,151 32,004 31,3362021 35,265 34,569 33,398 32,245 31,5782022 35,581 34,869 33,704 32,549 31,8782023 35,894 35,190 34,024 32,865 32,1932024 36,304 35,608 34,454 33,302 32,6302025 36,623 35,924 34,748 33,599 32,9322026 37,009 36,308 35,132 33,987 33,3052027 37,372 36,667 35,502 34,350 33,6742028 37,822 37,123 35,957 34,808 34,1342029 38,124 37,419 36,252 35,102 34,4292030 38,515 37,815 36,645 35,493 34,8082031 38,903 38,206 37,034 35,882 35,2082032 39,337 38,646 37,480 36,328 35,6562033 39,653 38,953 37,775 36,626 35,9542034 40,048 39,343 38,177 37,021 36,3472035 40,473 39,762 38,588 37,437 36,7602036 40,958 40,244 39,068 37,915 37,2382037 41,306 40,591 39,416 38,265 37,5912038 41,766 41,048 39,872 38,720 38,0472039 42,241 41,520 40,342 39,189 38,5182040 42,820 42,095 40,912 39,757 39,0852041 43,225 42,498 41,316 40,162 39,4932042 43,692 42,962 41,780 40,626 39,9592043 44,165 43,432 42,250 41,096 40,4322044 44,652 43,917 42,733 41,580 40,9182045 45,151 44,413 43,229 42,076 41,4162046 45,656 44,916 43,731 42,579 41,9212047 46,168 45,425 44,240 43,088 42,4332048 46,687 45,941 44,756 43,605 42,9522049 47,209 46,461 45,275 44,125 43,4752050 47,735 46,984 45,799 44,650 44,003



Figure 2.6-5 Native Peak Demand Weather Confidence Bands (MW)

Weather Confidence Bands:Native Peak Demand (MW)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038

+1.65 Std Dev +1 Std DevBase -1 Std Dev-1.65 Std Dev

Figure 2.6-6 Native Sales Weather Confidence Bands (GWH)

Weather Confidence Bands: Annual Native Energy Sales (GWh)

0

10,000

20,000

30,000

40,000

50,000

60,000

2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048

+1.65 Std Dev +1 Std DevBase -1 Std Dev-1.65 Std Dev

High Growth Forecast



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 2051. High energy sales growth over the next 7 years is anticipated to average 0.6% annually with sales of 34,019 GWh in 2018. 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%. Short-term 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 2018. The Base Case growth rate will increase with annual increases averaging 0.6% through 2051. The forecasted high peak demands and high sales are contained in Figures 2.6-7 and 2.6-8 and listed in Tables 2.6-14 and 2.6-15. Low Growth Forecast 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 2051. 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 2018.

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 2018. The forecasted low peak demands and low sales are illustrated in Figures 2.6-7 and 2.6-8 and listed in Tables 2.6-14 and 2.6-15.



Figure 2.6-7 Base Case, High and Low Peak Energy Sales Forecast Comparison – Base, High, Low

Native Energy Sales Forecast Comparison

20,000

25,000

30,000

35,000

40,000

45,000

50,000

55,000

2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048

GWh

Base

Low

High

Figure 2.6-8 Base Case, High and Low Peak Demand Forecast

Peak Demand Forecast ComparisonNative Load

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048

MW

Base

Low

High



Table 2.6-14 Base Case, High, and Low Sales of Energy (Including Impacts of DSM Programs)

Base Low High

2012 30,884 30,881 30,886 2013 31,122 30,761 31,460 2014 31,316 30,653 31,979 2015 31,563 30,624 32,482 2016 31,899 30,731 33,058 2017 32,177 30,787 33,543 2018 32,455 30,877 34,019 2019 32,769 31,009 34,515 2020 33,151 31,207 35,061 2021 33,398 31,312 35,475 2022 33,704 31,466 35,946 2023 34,024 31,635 36,406 2024 34,454 31,921 37,000 2025 34,748 32,070 37,400 2026 35,132 32,316 37,918 2027 35,502 32,576 38,442 2028 35,957 32,914 39,005 2029 36,252 33,078 39,410 2030 36,645 33,364 39,907 2031 37,034 33,632 40,401 2032 37,480 33,968 40,980 2033 37,775 34,147 41,383 2034 38,177 34,450 41,911 2035 38,588 34,751 42,410 2036 39,068 35,104 43,016 2037 39,416 35,340 43,477 2038 39,872 35,670 44,060 2039 40,342 36,011 44,660 2040 40,912 36,440 45,376 2041 41,316 36,719 45,906 2042 41,780 37,050 46,505 2043 42,250 37,384 47,113 2044 42,733 37,728 47,739 2045 43,229 38,082 48,380 2046 43,731 38,439 49,031 2047 44,240 38,801 49,691 2048 44,756 39,166 50,361 2049 45,275 39,534 51,038 2050 45,799 39,902 51,721



Table 2.6-15 Base Case, High and Low Coincident Summer and Winter Peak Demand

(Including Impacts of DSM Programs)

Coincident Summer Demand (MW) Coincident Winter Demand (MW) Base Low High Base Low High

2012 6,428 6,409 6,454 5,106 5,098 5,115 2013 6,532 6,418 6,653 5,140 5,047 5,216 2014 6,589 6,409 6,773 5,176 5,028 5,329 2015 6,670 6,434 6,910 5,249 5,047 5,449 2016 6,759 6,477 7,048 5,327 5,081 5,575 2017 6,829 6,485 7,174 5,393 5,108 5,685 2018 6,897 6,517 7,286 5,448 5,139 5,771 2019 6,961 6,543 7,396 5,507 5,146 5,874 2020 7,018 6,558 7,482 5,559 5,180 5,957 2021 7,069 6,544 7,571 5,606 5,181 6,016 2022 7,124 6,589 7,647 5,648 5,202 6,096 2023 7,175 6,613 7,740 5,696 5,227 6,176 2024 7,243 6,639 7,815 5,761 5,263 6,253 2025 7,308 6,694 7,914 5,820 5,302 6,351 2026 7,386 6,762 8,037 5,890 5,343 6,441 2027 7,464 6,775 8,127 5,959 5,370 6,537 2028 7,550 6,859 8,257 6,034 5,427 6,640 2029 7,626 6,908 8,351 6,099 5,457 6,722 2030 7,708 6,938 8,493 6,171 5,522 6,830 2031 7,786 6,989 8,559 6,238 5,567 6,908 2032 7,863 7,049 8,653 6,304 5,613 6,993 2033 7,929 7,109 8,757 6,362 5,633 7,047 2034 8,002 7,166 8,837 6,428 5,713 7,177 2035 8,080 7,237 8,962 6,495 5,763 7,254 2036 8,147 7,290 9,048 6,559 5,810 7,339 2037 8,212 7,340 9,132 6,621 5,855 7,422 2038 8,275 7,389 9,214 6,682 5,899 7,503 2039 8,335 7,435 9,293 6,741 5,941 7,583 2040 8,393 7,479 9,369 6,798 5,981 7,662 2041 8,447 7,520 9,443 6,854 6,019 7,738 2042 8,499 7,559 9,513 6,907 6,056 7,812 2043 8,548 7,595 9,581 6,959 6,090 7,885 2044 8,594 7,628 9,645 7,008 6,123 7,955 2045 8,637 7,658 9,706 7,056 6,154 8,023 2046 8,677 7,686 9,763 7,101 6,183 8,089 2047 8,704 7,702 9,807 7,144 6,210 8,153 2048 8,728 7,715 9,846 7,185 6,234 8,214 2049 8,747 7,725 9,881 7,223 6,257 8,273 2050 8,764 7,731 9,912 7,259 6,277 8,330



Forecast Accuracy Public Service reviews its demand and energy forecasts for accuracy annually. Overall, forecast accuracy is better in the short term than in the long term. Tables 2.6-16 through 2.6-24 on the following pages compare the actual energy sales and demand forecasts to the forecasted sales and system demands, as required by the Electric Resource Planning rules. Figures 2.6-9 through 2.6-13 contain a graphical description of the forecasts.



Table 2.6-16 Native Energy Sales Forecast Comparison (GWh)

Actual Energy Sales

2011 Forecast

2010 Forecast

2009 Forecast

2008 Forecast

2007 Forecast

2006 Forecast

2005 Forecast

2006 34,082 34,041 34,3102007 35,544 34,201 34,698 34,8722008 34,764 34,523 34,027 34,542 34,7272009 33,213 34,143 35,124 34,666 35,310 35,6072010 33,146 33,398 33,093 34,858 33,612 35,801 35,2672011 32,672 32,794 33,229 33,735 35,739 34,090 36,698 36,061

Table 2.6-17 Forecast Sales less Actual Sales (GWh) Actual less Forecast (GWh) Percent Difference 2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast 2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast 2006 41 0.12% 2007 1,343 846 3.93% 2.44% 2008 241 737 221 0.70% 2.17% 0.64% 2009 -929 -1,910 -1,453 -2,097 -2.72% -5.44% -4.19% -5.94% 2010 -252 53 -1,712 -466 -2,655 -0.75% 0.16% -4.91% -1.39% -7.42% 2011 -122 -556 -1,063 -3,066 -1,417 -4,026 -0.37% -1.67% -3.15% -8.58% -4.16% -10.97%



Table 2.6-18 Coincident Summer Demand Forecast Comparison (MW)

Actual

Demand 2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast 2006 6,656 6,7552007 6,940 6,869 6,9262008 6,692 6,910 6,861 6,9212009 6,160 6,863 7,066 7,039 7,1022010 6,322 6,490 6,711 7,067 6,925 7,1152011 6,908 6,604 6,542 6,846 7,244 7,083 7,193

Table 2.6-19 Forecast Demand less Actual Summer Native Peak Demand (MW) Actual less Forecast (MW) Percent Difference

2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast2006

Forecast2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast2006 -99 -1.46%2007 71 14 1.03% 0.20%2008 -218 -169 -229 -3.16% -2.47% -3.31%2009 -703 -906 -879 -942 -10.25% -12.82% -12.49% -13.27%2010 -168 -389 -745 -603 -793 -2.59% -5.80% -10.54% -8.71% -11.15%2011 304 366 62 -336 -175 -285 4.61% 5.59% 0.91% -4.64% -2.46% -3.97%



Table 2.6-20 Weather Normalized Coincident Summer Demand Forecast Comparison (MW)

Weather Normal Demand

2011 Forecast

2010 Forecast

2009 Forecast

2008 Forecast

2007 Forecast

2006 Forecast

2006 6,542 6,7552007 6,766 6,869 6,9262008 6,505 6,910 6,861 6,9212009 6,384 6,863 7,066 7,039 7,1022010 6,415 6,490 6,711 7,067 6,925 7,1152011 6,901 6,604 6,542 6,846 7,244 7,083 7,193

Table 2.6-21 Forecast Demand less Weather Normal Summer Demand (MW) Actual less Forecast (MW) Percent Difference

2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast2006

Forecast2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast2006 -213 -3.15%2007 -103 -160 -1.51% -2.31%2008 -405 -356 -416 -5.87% -5.20% -6.01%2009 -479 -682 -655 -718 -6.99% -9.65% -9.30% -10.12%2010 -75 -296 -652 -510 -700 -1.15% -4.42% -9.22% -7.36% -9.84%2011 297 359 55 -343 -182 -292 4.50% 5.49% 0.81% -4.74% -2.56% -4.07%



Table 2.6-22 Coincident Winter Demand Forecast Comparison (MW)

Actual

Demand 2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast 2006 5,747 5,5962007 5,822 5,731 5,7192008 5,818 5,767 5,738 5,7042009 5,961 5,499 5,684 5,886 5,8432010 5,312 5,607 5,588 5,918 5,742 5,8202011 5,816 5,472 5,762 5,740 6,076 5,869 5,865

Table 2.6-23 Forecast Demand less Actual Winter Demand (MW) Actual less Forecast (MW) Percent Difference

2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast2006

Forecast2011

Forecast 2010

Forecast 2009

Forecast 2008

Forecast 2007

Forecast 2006

Forecast2006 151 2.71%2007 91 103 1.59% 1.80%2008 51 80 114 0.89% 1.39% 2.00%2009 462 277 75 118 8.40% 4.87% 1.27% 2.03%2010 -295 -275 -606 -430 -508 -5.26% -4.93% -10.24% -7.48% -8.73%2011 344 54 76 -260 -53 -49 6.28% 0.94% 1.33% -4.28% -0.90% -0.83%



Figure 2.6-9 Forecast Comparison to Actual Native Energy Sales

Forecast Comparison to Actual Native Energy Sales

20,000

25,000

30,000

35,000

40,000

2003 2004 2005 2006 2007 2008 2009 2010 2011

GWh

Actual 2006 Forecast2007 Forecast 2008 Forecast2009 Forecast 2010 Forecast2011 Forecast

Figure 2.6-10 Forecast Comparison to Actual Summer Native Peak Demand

Forecast Comparison to Actual Summer Native Peak Demand

4,0004,5005,0005,5006,0006,5007,0007,500

2003 2004 2005 2006 2007 2008 2009 2010 2011

MW


Figure 2.6-11 Forecast Comparison to Actual Winter Native Peak Demand

Forecast comparison to Actual Winter Native Peak Demand

4,000

4,500

5,000

5,500

6,000

6,500

2003 2004 2005 2006 2007 2008 2009 2010 2011

MW




Table 2.6-24 2007 ERP Forecast vs. 2011 ERP Forecast

Summer Coincident Peak Demand (MW) Annual Energy Sales (GWh) Year 2007 RP

Forecast 2011 RP Forecast

2011 RP Forecast Update

2007 RP Forecast

2011 RP Forecast

2011 RP Forecast Update

2007 6,698 34,265 2008 6,650 34,027 2009 6,773 34,666 2010 6,603 33,613 2011 6,722 6,664 34,091 32,774 2012 6,545 6,391 6,428 32,850 31,046 30,884 2013 6,694 6,464 6,532 33,306 31,248 31,122 2014 6,854 6,521 6,589 34,054 31,550 31,316 2015 7,025 6,599 6,670 34,835 32,052 31,563 2016 7,206 6,682 6,759 35,736 32,270 31,899 2017 7,402 6,743 6,829 36,501 32,635 32,177 2018 7,593 6,797 6,897 37,332 32,849 32,455 2019 7,792 6,854 6,961 38,173 33,184 32,769 2020 7,997 6,905 7,018 39,082 33,652 33,151 2021 8,241 6,950 7,069 39,931 33,829 33,398 2022 8,489 6,918 7,124 40,849 33,742 33,704 2023 8,735 6,968 7,175 41,787 33,745 34,024 2024 8,995 7,026 7,243 42,824 34,096 34,454 2025 9,268 7,082 7,308 43,790 34,437 34,748 2026 9,536 7,149 7,386 44,821 34,900 35,132 2027 9,812 7,212 7,464 45,876 35,204 35,502 2028 10,095 7,280 7,550 46,973 35,610 35,957 2029 10,383 7,346 7,626 47,983 36,007 36,252 2030 10,665 7,412 7,708 49,065 36,314 36,645 2031 10,945 7,472 7,786 50,034 36,667 37,034 2032 11,209 7,531 7,863 51,070 37,109 37,480 2033 11,479 7,580 7,929 51,978 37,344 37,775 2034 11,736 7,636 8,002 52,911 37,692 38,177 2035 11,996 7,696 8,080 53,825 38,129 38,588 2036 12,256 7,747 8,147 54,742 38,434 39,068 2037 12,517 7,797 8,212 55,666 38,802 39,416 2038 12,770 7,843 8,275 56,541 39,260 39,872 2039 13,026 7,887 8,335 57,424 39,588 40,342 2040 13,282 7,928 8,393 58,315 39,981 40,912 2041 13,540 7,966 8,447 59,214 40,457 41,316 2042 13,797 8,000 8,499 60,103 40,801 41,780 2043 14,055 8,032 8,548 60,996 41,207 42,250 2044 14,314 8,060 8,594 61,892 41,692 42,733 2045 14,574 8,085 8,637 62,793 41,851 43,229 2046 14,835 8,107 8,677 63,697 42,154 43,731 2047 8,118 8,704 42,537 44,240 2048 8,125 8,728 42,781 44,756 2049 8,132 8,747 43,086 45,275 2050 8,156 8,764 43,472 45,799



Figure 2.6-12 Energy Sales Forecast Comparison – 2007 ERP & 2011 ERP

Native Energy Sales Forecast Comparison

0

10,000

20,000

30,000

40,000

50,000

60,000

70,00020

07

2010

2013

2016

2019

2022

2025

2028

2031

2034

2037

2040

2043

2046

2049

GWh

2007 RP2011 RP2011 RP Update

Figure 2.6-13 Summer Native Load Peak Demand Forecast Comparison – 2007 ERP & 2011 ERP

Summer Peak Demand Forecast ComparisonNative Load Peak Demand

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2007

2010

2013

2016

2019

2022

2025

2028

2031

2034

2037

2040

2043

2046

2049

MW

2007 RP2011 RP2011 RP Update



Description and Justification The following tables show the parameters associated with Public Service’s econometric forecasting models.

Table 2.6-25 Number of Residential Electric Customers

REGRESSION PERIOD: Jan 2000- Dec 2011 NUMBER OF OBSERVATIONS: 143 LINEAR LEAST SQUARES MODEL WITH ARMA ERRORS Residential Customers = C1*HousingStock + C2*CRSPh1 + C3*CRSPh2

ARMA(1,0) process applied to errors Variable Coefficient Std Err T-Stat P-

Value C1 529.479 4.822 109.815 0.00%C2 -4216.575 1681.750 -2.507 1.33%C3 -10420.226 1677.688 -6.211 0.00%

AR(1) 0.981 0.009 114.322 0.00%

Table 2.6-26 Residential Electric Customers – Regression Statistics

Regression Statistics

Iterations 8Adjusted Observations 143Deg. of Freedom for Error 139

R-Squared 0.999Adjusted R-Squared 0.999Durbin-Watson Statistic 1.763Durbin-H Statistic #NAAIC 14.863BIC 14.946F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -1,261.63Model Sum of Squares 329,687,471,482.75Sum of Squared Errors 385,511,679.91Mean Squared Error 2,773,465.32Std. Error of Regression 1,665.37Mean Abs. Dev. (MAD) 1,076.49Mean Abs. % Err. (MAPE) 0.10%

Ljung-Box Statistic 36.91Prob (Ljung-Box) 0.0446Frequency of historical data is monthly



Table 2.6-27 Residential Electric Customers – Definitions and Sources

Variable Name

Definition/Source

Residential Customers

PSCo residential electric customers / PSCo

Housing Stock

Colorado housing Stock / IHS Global Insight Inc.

CRSPh1 Binary variable for the timing of CRS Phase 1 CRSPh2 Binary variable for the timing of CRS Phase 2

Table 2.6-28 Residential Electric Sales per Customer

SAMPLE PERIOD: Jan 2000 through Dec 2011

NUMBER OF OBSERVATIONS: 143

LINEAR LEAST SQUARES MODEL WITH ARMA ERRORS AvgRes_Use = C1*Cooling + C2*Heating +C3*Base + C4*Feb + C5*Mar + C76*Apr + C7*May + C8*Jun + C9*Sep +C10*Oct + C11*Nov + C12*Dec

ARMA(1,0) process applied to errors Variable Coefficient StdErr T-Stat P-

Value C1 0.083 0.005 16.912 0.00% C2 0.018 0.001 13.037 0.00% C3 262.313 4.657 56.331 0.00% C4 -94.719 5.643 -16.784 0.00% C5 -85.233 6.421 -13.273 0.00% C6 -100.076 7.151 -13.995 0.00% C7 -89.229 7.726 -11.550 0.00% C8 -50.029 7.243 -6.907 0.00% C9 -27.692 6.528 -4.242 0.00% C10 -99.638 8.797 -11.327 0.00% C11 -123.092 8.094 -15.208 0.00% C12 -41.431 5.800 -7.143 0.00% AR(1) 0.372 0.081 4.572 0.00%



Table 2.6-29 Residential Electric Sales per Customer – Regression Statistics

Regression Statistics Iterations 8Adjusted Observations 143Deg. of Freedom for Error 130

R-Squared 0.964Adjusted R-Squared 0.961Durbin-Watson Statistic 1.993

Durbin-H Statistic #NAAIC 5.693BIC 5.963F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -596.98Model Sum of Squares 947,135.53Sum of Squared Errors 35,393.02Mean Squared Error 272.25Std. Error of Regression 16.50

Mean Abs. Dev. (MAD) 12.03Mean Abs. % Err. (MAPE) 1.89%

Ljung-Box Statistic 22.60Prob (Ljung-Box) 0.5433

Table 2.6-30 Residential Electric Sales per Customer – Definition and

Sources Variable

Name Definition/Source AvgRes_Use Residential kWh sales per customer/PSCo

Cooling CoolIndex * CoolUse

CoolUse = (Price^(-0.2))*(Income per Household^0.2)*(Household Size^0.01)*Cooling Degree Days/ PSCo, IHS Global Insight Inc.

Heating HeatIndex * HeatUse HeatUse = (Price^(-0.2))*(Income per Household^0.2)*(Household Size^0.01)*Heating Degree Days/ PSCo, IHS Global Insight Inc.

Base BaseIndex*BaseUse BaseUse = (Price^(-0.2))*(Income per Household^0.2)*(Household Size^0.01)*(Hours of Light^(-0.2))/ PSCo, IHS Global Insight Inc.

Feb-Dec Binary variables for each month except January, July, and August



Table 2.6-31 Commercial / Industrial Electric Sales

SAMPLE PERIOD: Jan 1999 through Dec 2011 NUMBER OF OBSERVATIONS: 155 LINEAR LEAST SQUARES MODEL WITH ARMA ERRORS GS_MWh = C1*GS_Cool + C2*GS_Base + C3*BillCycleDays + C4*Jan + C5*Feb + C6*Mar + C7*Apr + C8*May + C9*Jun + C10*Jul + C11*Aug + C12*Sep + C13*Oct + C14*Nov + C15*Dec + C16*Jul04 + C17*Aug04 + C18*Jan07 + C19*CRSPh2 + C20*WS_Add_MW

ARMA(1,0) process applied to errors Variable Coefficient StdErr T-Stat P-

Value C1 15.057 3.841 3.920 0.01%C2 3106 286 10.873 0.00%C3 27851 3039 9.163 0.00%C4 -383302 136360 -2.811 0.57%C5 -425064 127730 -3.328 0.11%C6 -374507 128886 -2.906 0.43%C7 -406531 125665 -3.235 0.15%C8 -381699 125360 -3.045 0.28%C9 -346749 127509 -2.719 0.74%C10 -284848 124659 -2.285 2.39%C11 -266461 127000 -2.098 3.78%C12 -295959 125905 -2.351 2.02%C13 -385496 128499 -3.000 0.32%C14 -424325 126998 -3.341 0.11%C15 -413866 133762 -3.094 0.24%C16 -318695 43629 -7.305 0.00%C17 204441 46051 4.439 0.00%C18 112965 40976 2.757 0.66%C19 38283 7706 4.968 0.00%C20 1198.811 134.110 8.939 0.00%

AR(1) -0.304 0.082 -3.694 0.03%



Table 2.6-32 Commercial/Industrial Electric Sales – Regression Statistics



R-Squared 0.921Adjusted R-Squared 0.909Durbin-Watson Statistic 1.892Durbin-H Statistic #NAAIC 21.351BIC 21.763F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -1,854Model Sum of Squares 2,576,886,021,530Sum of Squared Errors 221,388,196,696Mean Squared Error 1,652,150,722Std. Error of Regression 40,647Mean Abs. Dev. (MAD) 28,117Mean Abs. % Err. (MAPE) 1.90%


Frequency of historical data is monthly



Table 2.6-33 Commercial & Industrial Electric Sales – Definitions and Sources

Variable

Name Definition/Source GS_MWh Commercial/Industrial electric sales/PSCo GS_Cool Commercial Cooling Index * (Price^(-0.2)) * (United States Gross

Domestic Product^0.3) * Cooling Degree Days (base 65)/Itron, Inc., PSCo, IHS Global Insight Inc., the National Weather Service, Denver, Colorado.

GS_Base “Other” Commercial Index * (Price^(-0.2)) * (united States Gross Domestic Product^0.6) * (Hours of Light^(-0.2))/Itron, Inc., PSCo, IHS Global Insight Inc.

BillCycleDays Average number of days in the monthly billing period Jan-Dec Binary variables for each month Jul04 Binary variable = 0 for all months except July 2004 = 1 Aug04 Binary variable = 0 for all months except August 2004 = 1 Jan07 Binary variable = 0 for all months except January 2007 = 1 CRSPh2 Binary variable for the timing of CRS Phase 2 WS_Add_MW New load additions from large industry expansion

Table 2.6-34 Electric Sales to Other Public Authorities

SAMPLE PERIOD: Jan 2000 through Dec 2011 NUMBER OF OBSERVATIONS: 144 LINEAR LEAST SQUARES MODEL Public Authority = C1 + C2*GS_Base + C3*LightRail2 + C4*LightRail3 Variable Coefficient Std Err T-Stat P-

Value C1 -2900.33 728.846 -3.979 0.01%C2 10.708 2.239 4.782 0.00%C3 2083.599 66.513 31.326 0.00%C4 1014.407 75.706 13.399 0.00%



Table 2.6-35 Electric Sales to Other Public Authorities – Regression

Statistics

Regression Statistics Iterations 1Adjusted Observations 144Deg. of Freedom for Error 140R-Squared 0.947Adjusted R-Squared 0.946Durbin-Watson Statistic 2.078Durbin-H Statistic #NAAIC 11.611BIC 11.694F-Statistic 837.687Prob (F-Statistic) 0.0000Log-Likelihood -1,036.34Model Sum of Squares 269,804,433.32Sum of Squared Errors 15,030,530.18Mean Squared Error 107,360.93Std. Error of Regression 327.66Mean Abs. Dev. (MAD) 200.51Mean Abs. % Err. (MAPE) 11.43%Ljung-Box Statistic 21.10Prob (Ljung-Box) 0.6329Frequency of historical data is monthly

Table 2.6-36 Electric Sales to Other Public Authorities – Definitions and Sources

Variable

Name Definition/Source Public Authority

Public Authority electric sales /PSCo

GS_Base “Other” Commercial Index * (Price^(-0.2)) * (US Gross Domestic Product^0.6) * (Hours of Light^(-0.2))/Itron, Inc., PSCo, IHS Global Insight Inc.

Jan-Dec Binary variables for each month LightRail1 Binary variable = 0 for all months until December 2001, = 1 after LightRail2 Binary variable = 0 for all months until October 2002, = 1 after



Table 2.6-37 Electric Street and Highway Lighting Sales

REGRESSION PERIOD: Jan 2000 - Dec 2011 NUMBER OF OBSERVATIONS: 143 LINEAR LEAST SQUARES MODEL WITH ARMA ERRORS StreetLight = C1*ResCustomers + C2*Jan + C3*Feb + C4*Mar + C5*Apr + C6*May + C7*Jun + C8*Aug + C9*Sep + C10*Oct + C11*Nov + C12*Dec + C13* CRSP2(-2 lag)

ARMA(1,0) process applied to errors Variable Coefficient Std Err T-Stat P-

Value C1 0.011 0.000 93.736 0.00% C2 5965.488 159.822 37.326 0.00% C3 5467.245 156.601 34.912 0.00% C4 3454.199 155.607 22.198 0.00% C5 3371.146 153.444 21.970 0.00% C6 1541.880 147.016 10.488 0.00% C7 436.403 126.795 3.442 0.08% C8 606.223 126.254 4.802 0.00% C9 1451.004 146.585 9.899 0.00% C10 2272.437 153.169 14.836 0.00% C11 3880.715 155.434 24.967 0.00% C12 4628.136 156.277 29.615 0.00% C13 -1001.385 100.604 -9.954 0.00%

AR(1) 0.348 0.083 4.169 0.01%



Table 2.6-38 Electric Street and Highway Lighting Sales – Regression

Statistics

Regression Statistics Iterations 8Adjusted Observations 143Deg. of Freedom for Error 129R-Squared 0.970Adjusted R-Squared 0.967Durbin-Watson Statistic 2.136Durbin-H Statistic #NAAIC 11.867BIC 12.157F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -1,037.40Model Sum of Squares 541,945,380Sum of Squared Errors 16,752,822Mean Squared Error 129,867Std. Error of Regression 360.37Mean Abs. Dev. (MAD) 232.92Mean Abs. % Err. (MAPE) 1.61%Ljung-Box Statistic 51.67Prob (Ljung-Box) 0.0009Frequency of historical data is monthly

Table 2.6-39 Electric Street and Highway Lighting Sales – Definitions and

Sources

Variable Name Definition/Source StreetLight PSCo street and highway lighting electric sales/ PSCo ResCustomers Historical and forecasted residential customers/PSCo Jan-Dec Binary variables for each month except July CRSPh2(-2 lag) Binary variable for the timing of CRS Phase 2 lagged 2 periods



Table 2.6-40 Residential Contribution to System Peak Demand

SAMPLE PERIOD: Jan 2002 through Dec 2011 NUMBER OF OBSERVATIONS: 120 LINEAR LEAST SQUARES MODEL Res_Coincident = C1*Res_SalesTrend + C2*ResCoolTrend_CDD_Cust_Jun + C3*ResCoolTrend_CDD_Cust_Jul + C5*ResCoolTrend_CDD_Cust_Aug + C8*Oct_HDD + C9*Nov_HDD + C10*Dec_HDD + C11*Jan_HDD + C12*Feb_HDD + C13*Mar_HDD + C14*Sep08 + C15*Oct10 + C16*Oct05 + C17*Apr06 + C18*Apr07 + C19*May07 + C20*Oct07 Variable Coefficient Std Err T-Stat P-

Value C1 2.003 0.033 61.041 0.00%C2 0.000 0.000 9.512 0.00%C3 0.000 0.000 14.262 0.00%C4 0.000 0.000 11.734 0.00%C5 0.000 0.000 2.170 3.23%C6 0.000 0.000 4.156 0.01%C7 0.000 0.000 10.286 0.00%C8 0.000 0.000 8.572 0.00%C9 0.000 0.000 7.318 0.00%C10 0.000 0.000 3.399 0.10%C11 645.852 136.709 4.724 0.00%C12 -576.563 136.801 -4.215 0.01%C13 -422.291 136.269 -3.099 0.25%C14 -546.240 136.460 -4.003 0.01%C15 -532.907 136.633 -3.900 0.02%C16 -437.179 136.651 -3.199 0.18%C17 -458.857 136.739 -3.356 0.11%



Table 2.6-41 Residential Contribution to System Peak Demand – Regression Statistics


Iterations 1Adjusted Observations 120Deg. of Freedom for Error 103R-Squared 0.853Adjusted R-Squared 0.830Durbin-Watson Statistic 1.756Durbin-H Statistic #NAAIC 9.934BIC 10.329F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -749.29Model Sum of Squares 10,772,911.89Sum of Squared Errors 1,863,280.52Mean Squared Error 18,090.10Std. Error of Regression 134.50Mean Abs. Dev. (MAD) 89.43Mean Abs. % Err. (MAPE) 5.39%Ljung-Box Statistic 33.25Prob (Ljung-Box) 0.0990Frequency of historical data is monthly



Table 2.6-42 Residential Contribution to System Peak Demand – Definition and Sources

Variable Name Definition/Source Res_Coincident Residential class contribution to system peak, MW/PSCo Res_SalesTrend 12 month moving average of actual and forecast Residential

kWh sales/ PSCo (calculated internally in the energy sales model)

ResCoolTrend_CDD_Cust

Cooling Degree Days (base 65) * Residential Cooling Index*Customer Counts for the months of June, July, and August/ the National Weather Service, Denver, Colorado, PSCo

HDD Heating Degree Days (base 55) for months October-March/ calculated from data from the National Weather Service, Denver, Colorado

Jan – Nov Binary variables for each month except December Oct97 Binary variable = 0 for all months except October 1997 = 1 Jan97 Binary variable = 0 for all months except January 1997 = 1 Oct01 Binary variable = 0 for all months except October 2001 = 1 May01 Binary variable = 0 for all months except May 2001 = 1 Aug02 Binary variable = 0 for all months except August 2002 = 1 Sep02 Binary variable = 0 for all months except September 2002 = 1 Oct05 Binary variable = 0 for all months except October 2005 = 1

Table 2.6-43 Non-residential Contribution to System Peak Demand

SAMPLE PERIOD: Jan 1999 through Jun 2011 NUMBER OF OBSERVATIONS: 144 LINEAR LEAST SQUARES MODEL NonRes_Coincident = C1*NonRes_SalesTrend + C2*May_PDMaxTemp_Cust + C3*Jun_PDMaxTemp_Cust+ C4*Jul_PDAvgTemp_Cust + C5*Aug_PDAvgTemp_Cust + C6*Sep_PDMaxTemp_Cust + C7*Oct_PDMaxTemp_Cust + C8*Employment + C9*Sep08 Seasonal ARMA(1,0) process applied to errors Variable Coefficient StdErr T-Stat P-

Value

C1 0.298 0.348 0.855 39.40%C2 0.000047 0.000 8.715 0.00%C3 0.000058 0.000 11.618 0.00%C4 0.000087 0.000 14.796 0.00%C5 0.000081 0.000 13.626 0.00%C6 0.000061 0.000 11.259 0.00%C7 0.000029 0.000 4.196 0.01%C8 -971.795 161.718 -6.009 0.00%C9 0.890 0.237 3.749 0.03%

SAR(1) 0.266 0.083 3.196 0.17%



Table 2.6-44 Non-residential Contribution to System Peak Demand – Regression Statistics



R-Squared 0.865Adjusted R-Squared 0.856Durbin-Watson Statistic 1.693

Durbin-H Statistic #NAAIC 10.346BIC 10.552F-Statistic #NAProb (F-Statistic) #NALog-Likelihood -939.21Model Sum of Squares 24,950,684.48Sum of Squared Errors 3,900,115.94Mean Squared Error 29,105.34Std. Error of Regression 170.60

Mean Abs. Dev. (MAD) 123.53Mean Abs. % Err. (MAPE) 4.56%


Frequency of historical data is monthly

Table 2.6-45 Non-residential Contribution to System Peak Demand – Definitions and Sources

Variable Name Definition/Source NonRes_Coincident Commercial and industrial class contribution to system peak,

MW/ PSCo NonRes_SalesTrend

12 month moving average of actual and forecast Non-Residential kWh sales/ PSCo (calculated internally in the energy sales model)

PDMaxTemp_Cust Peak day maximum temperature*Commercial-Industrial Customers for months May, June, September, and October/ the National Weather Service, Denver, Colorado, PSCo

PDAvgTemp_Cust Peak day average temperature*Commercial-Industrial Customers for months July and August/ the National Weather Service, Denver, Colorado, PSCo

Employment Colorado non-farm employment /IHS Global Insight Inc. Sep08 Binary variable = 0 for all months except September 2008 = 1

2.6 electric energy and demand forecasts ......2.6 electric energy and demand forecasts introduction...

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