Incorporating Global Warming Into Avista s Electricity Forecasts Presented by Randy Barcus Avista Chief Economist Itron s Energy Forecaster s Group Meeting May 1, 009 Las Vegas, Nevada
Presentation Outline The Computations Weather and Sales Relationship Climate Change Theory Global Warming Data Driven Results Regression Analysis Actual Results Presentation Tips
Utility Business Characteristics My Company Investor-Owned We W sell electricity it in Eastern Washington and Northern Idaho 6,000 square miles 700,000 people 355,000 retail customers We distribute natural gas in Southwest Oregon 3,000 square miles 1,100,000 people 315,000 retail customers Fuel Sources Hydro, coal, wood-waste, gas and wind Gas purchased in Rockies and Canada 3
Management, Bankers and Stock Price Utility Senior Management high risk aversion, engineers and accountants, budget driven decision making process Bankers and Stock Analysts benchmarks to similar companies, management stock options, peer performance Financial Considerations Global Warming impact on revenue heretofore unknown; calming the jitters of the stakeholders 4
The Math Weather and Sales Relationship Electric residential in Washington Electric commercial in Idaho (Constant) NOSUMHDD QualityHDD CDD 788.35 0.553 0.95 0.16 (Constant) NOSUMHDD QualityHDD CDD 1353.07 0.19 0.475 0.606 Electric Washington 5 monthly data series Electric Idaho 5 monthly data series 5
Climate Change Theory Preponderance of evidence on climate warming Various contributions, carbon dioxide most prominent Even with stringent reductions, warming likely to continue Consistent application of information to balance Supply and Demand Expect 1 degree warming every 40 years, both summer and winter 6
35 30 5 0 15 10 5 0 Average January Spokane Temperature 1890-008 1/31/1890 1/31/1894 1/31/1898 1/31/190 1/31/1906 1/31/1910 1/31/1914 1/31/1918 1/31/19 1/31/196 1/31/1930 1/31/1934 1/31/1938 1/31/194 1/31/1946 1/31/1950 1/31/1954 1/31/1958 1/31/196 1/31/1966 1/31/1970 1/31/1974 1/31/1978 1/31/198 1/31/1986 1/31/1990 1/31/1994 1/31/1998 1/31/00 1/30/006 7-5 -10-15 y = 5E-05x 3-0.0087x + 0.3769x + 6.3138
8,500 8,000 7,500 7,000 6,500 6,000 5,500 1971-007 Spokane HDD Trend y = -18.596x + 7164.5 R = 0.1963 1971-000 NOAA Normal is 6,80 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 001 003 005 007 009 011 013 015 017 019 01 03 05 07 09 031 033 035 037 8
80 75 70 65 60 55 Average July Spokane Temperature 1890-008 y = 4E-05x 3-0.007x + 0.355x + 66.141 7/31/1890 7/31/1894 7/31/1898 7/31/190 7/31/1906 7/31/1910 7/31/1914 7/31/1918 7/31/19 7/31/196 7/31/1930 7/31/1934 7/31/1938 7/31/194 7/31/1946 7/31/1950 7/31/1954 7/31/1958 7/31/196 7/31/1966 7/31/1970 7/31/1974 7/31/1978 7/31/198 7/31/1986 7/31/1990 7/31/1994 7/31/1998 7/31/00 7/31/006 9
800 700 600 500 400 300 00 100 0 1976-007 Cooling Degree Day Trends 1971-000 Normal = 394 y = 4.9635x + 34. R = 0.1571 1976 1977 1978 1979 1980 1981 198 1983 1984 1985 1986 1987 1988 1989 1990 1991 199 1993 1994 1995 1996 1997 1998 1999 000 001 00 003 004 005 006 007 10
Intermountain Annual Weather Summary November 008 to October 009 Winter will be much colder and drier than normal, on average, with snowfall above normal in the north and below normal in the south. The coldest temperatures will occur in late December; early, mid-, and late January; and early February. The snowiest periods will be in mid- November, early and mid-december, mid- and late January, and late February. April and May will be cooler than normal, with slightly above-normal precipitation. Summer will be cooler than normal, with slightly above-normal rainfall. The hottest periods will be in mid- and late June and early and mid- to late July. September and October will be warmer and drier than normal. http://www.almanac.com/weatherforecast/us/13 11
Spokane NWS Global Warming Degree Day Trends 007-038 % % 5%.8% 6.1% 47.3% 48.6% 160% % % 4% 6% 5.9% 7.% 8.4% 19.7% 130.9% 13.% 133.5% 134.7% 136.0% 137.% 138.5% 139.8% 141.0% 14.3% 143.5 144. 146 14 1 140% 109.5% 110.8% 11.0% 113.3% 114.6% 115.8% 117.1% 118.3% 119.6% 10.9% 1.1% 13.4 14. 15 1 1 1 10% % % % 1 100% 95.% 95.0% 94.7% 94.4% 94.1% 93.9% 93.6% 93.3% 93.1% 9.8% 9.5% 9.% 9.0% 91.7% 91.4% 91.1% 90.9% 90.6% 90.3% 90.1% 89.8% 89.5% 89.% 89.0% 88.7% 88.4% 88.1% 87.9% 87.6% 87.3% 87.1% 86.8% 80% 007 008 009 010 011 01 013 014 015 016 017 018 019 00 01 0 03 04 05 06 07 08 09 030 031 03 033 034 035 036 037 038 1 Heating Degree Days Cooling Degree Days
15,000 14,000 13,000 Electric Average Use per Average Customer 100,000 y = 183x + 79577 90,000 80,000 Annual kwh Residential Annual kwh Commercial 1,000 11,000 10,000 9,000 70,000 y = -4x + 1175 60,000 50,000 40,000 1997 1998 1999 000 001 00 003 004 005 006 007 008 009 010 011 01 013 014 015 016 017 018 019 00 01 0 03 04 05 06 07 08 09 030 13 Residential Commercial Linear (Residential) Linear (Commercial)
1,500,000 500,000 (500,000) Global Warming Impact Normal minus Warming HDD and CDD Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 (1,500,000) (,500,000) (3,500,000) (4,500,000) Residential Commercial Industrial System Total kwh 14
0.0 0.0 MW Difference Normal minus Warming HDD & CDD -.0 009-0.6 011-1.9 19 013 015 017 019 01 03 05 07 09 -.7-4.0-3.5-4.3 Av erage MW -6.0-8.0-10.0-1.0-14.0-5. -6.1-7.0-7.9-8.9-9.3-9.8-10.3-10.7-11. -11.7-1.3-1.8-13.3-13.8-14.4-16.0 15
Electric Sales Forecast Base w/ GW vs. Normal Weather 14,000,000,000 13,000,000,000 Compound Growth Rates 009-09 Base 1.68% 009-09 Normal 1.73% 1,000,000,000 h annual kw 11,000,000,000 10,000,000,000 Reduction in avg MW 019 9 09 14 9,000,000,000 8,000,000,000 7,000,000,000 1997 1998 1999 000 001 00 003 004 005 006 007 008 009 010 011 01 013 014 015 016 017 018 019 00 01 0 03 04 05 06 07 08 09 030 Electric Base Electric Normal 16
Process Timeline Concept presented to senior management January 007 Rough estimate June 007 Presented to Investment Bankers July 007 NOAA/CPS Climate Change Conference November 007 Industry Forecaster s Conference May 008 Completed July 008 Presented to Technical Advisory Committee September 008 17
Lessons Learned It s important to be consistent It helps to have the data and a model The first time is the worst time 18