VELCO LONG-TERM ENERGY AND DEMAND FORECAST FORECAST DRIVERS AND ASSUMPTIONS. May 22, 2014 Eric Fox and Oleg Moskatov

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1 VELCO LONG-TERM ENERGY AND DEMAND FORECAST FORECAST DRIVERS AND ASSUMPTIONS May 22, 2014 Eric Fox and Oleg Moskatov

2 AGENDA» Review customer and system usage trends in Vermont» Review and discuss the forecast drivers Economic data and forecasts End-use intensity trends - EIA s updated projections (changes in the AEO 2014) - Calibration to Vermont reported end-use saturation data Defining normal weather conditions DSM savings projections and forecast integration PV market penetration and impacts Other technologies High efficiency heat pumps, electric vehicles

3 2015 FORECAST SCHEDULE» Preliminary System and State-Level Forecast June 30 th Monthly and annual system energy and peak-demand» Preliminary Zonal Level Demand Forecasts August 31 Summer and winter coincident and non-coincident peak demand» Final Forecasts October 31» Forecast Documentation November 28

4 FORECASTING FRAMEWORK Economic Drivers Structural Changes Weather Conditions Electric Prices End-Use and Customer Class Energy Forecast VELCO System Hourly Load Data Normal Peak- Producing Weather End-Use Coincident Peak Factors System Peak Forecast Model System Peak And Energy Forecast

5 INTEGRATE END-USE EFFICIENCY THROUGH SAE MODEL SPECIFICATION AC Saturation Central Room AC AC Efficiency Thermal Efficiency Home Size Income Household Size Price Heating Saturation Resistance Heat Pump Heating Efficiency Thermal Efficiency Home Size Income Household Size Price Saturation Levels Water Heat Appliances Lighting Densities Plug Loads Appliance Efficiency Income Household Size Price Cooling Degree Days Heating Degree Days Billing Days XCool XHeat XOther Sales m a b c XCool m b h XHeat m b o XOther m e m

6 VERMONT LOAD CHARACTERISTICS» Relatively flat system load profile Large base load relative to heating and cooling loads» Shift from winter to summer peaking» Summer peak demand has been growing faster than energy Strong growth in cooling load - Strong room air conditioning saturation growth Decline in heating load Large statewide efficiency program that has been focused on lighting - 70% of residential program savings from lighting - 50% of commercial program savings from lighting

7 VELCO DAILY PEAK DEMAND (MW) Each point represents one day from 2004 to 2013 MW Winter Spring Summer Fall Average Daily Temperature

8 VELCO DAILY PEAK DEMAND (MW) Each point represents one day from 2004 to 2013 MW Winter Spring Summer Fall Average Daily Temperature

9 VELCO SYSTEM ENERGY System Load moving average AAGR : - 0.1%» System delivered energy has been flat over the last ten years despite adding nearly 24,000 customers ( a 7% increase over 2013 customers)» The recession had a big impact. System load growth averaged 1.0% growth before the recession and 0.4% growth after the recession.

10 RESIDENTIAL AVERAGE USE (KWH) Average Use moving average AAGR : - 1.0%» Average use has been declining fairly consistently at 1.0% annual rate» Since 2012 usage has been falling a slightly faster as the new lighting standards phase in

11 COMMERCIAL MONTHLY AVERAGE USE Average Use moving average AAGR : - 0.9%» A large part of the decline in average use is due to the recession. Excluding 2008 and 2009 average used declined 0.2% annually

12 INDUSTRIAL SALES Industrial (excluding IBM) (AAGR: -0.9%) moving average IBM (AAGR: -1.0%)» Industrial sales fell 11.5% in 2009, since then industrial sales excluding IBM have been averaging 1.0% annual growth

13 SO WHAT DOES THIS ALL MEAN» With efficiency improvements (both from new standards and expected efficiency program activity) we expect to see continuing decline in residential and commercial usage» Trend level population and economic growth will balance the decline in average usage» Based on the past, our expectation is that total energy will be flat to declining going forward» But the factors that can change the path include:» Stronger than expected economic growth Faster decline in baseline energy intensity forecasts Higher (or lower) state-level efficiency expenditures - Targeting CFLs with LEDs Strong PV market penetration New programs that encourage adoption of heat pumps Meaningful market penetration of electric vehicles

14 RESIDENTIAL ECONOMIC DRIVERS

15 NON-RESIDENTIAL ECONOMIC DRIVERS

16 IS THE NEAR-TERM ECONOMIC FORECAST TOO OPTIMISTIC? Forecast Date Recovery GDP Growth (year) Real GDP Growth March % (2011) 1.5% March % (2012) 1.2% March % (2013) 1.1% October % (2015)?» With every new forecast, the recovery period is moved further out in time.» Do we get the jump in economic growth, or do we continue to trudge along?

17 Billions of 2005 Dollars REAL GDP COMPARED WITH POTENTIAL GDP 18,000 U.S. Real and Potential GDP 17 16,000 14,000 12,000 Potential GDP Real GDP Forecast 10,000 8,000 The economic models require us to get back to potential GDP 6, UNLV Center for Business & Economic Research

18 REAL GROSS STATE PRODUCT(CHANGE) 4.5% 4.0% 3.5% 3.0% Moody Analytics 2.5% 2.0% 1.5% 1.0% Capped 0.5% 0.0% 2012-Q Q Q Q Q1» One possible solution is to cap the economic recovery at a more reasonable level 2.7% peak growth vs. 3.8% peak growth

19 OTHER ECONOMIC DRIVERS Personal Income Employment 3.5% 2.5% 3.0% 2.5% Moody Analytics 2.0% Moody Analytics 2.0% 1.5% 1.5% 1.0% 0.5% Capped 0.0% 2012-Q Q Q Q Q1 1.0% Capped 0.5% 0.0% 2012-Q Q Q Q Q1» And then tie the other economic drivers the capped GSP forecast

20 DECISION ON ECONOMIC DRIVERS» We have been using Moody Analytics for the last ten years. It may be worth evaluating Global Insight but would probably have the same problems.» Woods and Poole provides an inexpensive annual state and county level forecast. Their model is driven off of population and demographic trends. The problem is its an annual forecast and we need quarterly. We could purchase the Woods and Poole forecast to get a second outlook.» Review and suggestions by state economist or state NEEP (New England Economic Project) forecaster?

21 END-USE INTENSITY TRENDS» End-use intensity estimates (historical and forecasted) are the primary long-term drivers of residential and commercial use in an SAE specification Energy Intensity (use per household) = Saturation * Efficiency * Use» You can t explain historical usage trends without accounting for end-use efficiency improvements» The updated forecast will be based on the AEO 2014 forecast for New England that is calibrated (to the extent possible) to Vermont specific saturation data

22 AVERAGE RESIDENTIAL ENERGY INTENSITY AEO 2014 VS. AEO ,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 NewEngland13 NewEngland : -0.3% : -0.5% Every year EIA s forecast is lower than the year before, but the AEO 14 is significantly lower.

23 U.S. RESIDENTIAL INTENSITY FORECAST AEO 2014 VS. AEO 2013 US13 US14 14,000 12,000 10,000 8,000 6, : -0.1% : -0.4% 4,000 2,

24 TV AND MISCELLANEOUS (KWH/HOUSEHOLD) 1, TV13 TV : 0.6% : -0.7% ,000» TV category now includes all home entertainment equipment.» This results in a higher starting use and declining intensity in the forecast as equipment efficiency improves. Misc13 Misc14» Calibration to 2009 RECS results in slightly higher miscellaneous use, but slightly lower growth. 2,500 2,000 1,500 1, : 0.4% : 0.3%

25 BASE USE INTENSITY (KWH/HOUSEHOLD) 8,000 7,000 6,000 Base13 Base14 5,000 4,000 3, : -0.3% : -0.5% 2,000 1, » Slower miscellaneous and declining TV intensity drives total base intensity (non-weather sensitive) lower.

26 LIGHTING (KWH PER HOUSEHOLD) Lighting13 Lighting14 1,800 1,600 1,400 1,200 1, : -2.9% : -3.2% » Projected change in lighting intensity is still the big driver.» Changes in long-term lighting intensity trend reflects revised technology assumptions.

27 LIGHTING STANDARDS AND TECHNOLOGY SHARES 70.0% Effective Date Lumen Output Current Wattage New Standard Jan Jan Jan Jan % 50.0% 40.0% 30.0%» Incandescent lighting is halogen based technology after % 10.0% 0.0% Incand Fluor CFL Hal HPS LED» LEDs take off beginning in 2019 as a result of the second wave of lighting standards.

28 COMMERCIAL INTENSITY (KWH/SQFT) NewEngland13 NewEngland : -0.1% : -0.4% » Much stronger projected decline in commercial energy intensities.» Largely as a result of stronger declines in lighting, miscellaneous and office-pc intensities.

29 LIGHTING AND MISCELLANEOUS (KWH/SQFT) I.Light13 I.Light14» Faster adoption of LED lighting : -1.3% : -1.8% » Slower miscellaneous intensity growth Misc13 Misc : 1.7% : 1.5%

30 OFFICE-PC (KWH/SQFT) Office13 Office : -0.8% : -6.9% » A number of factors drive PC usage down, including:» Replacement of desktop computers with laptops.» Changing dynamics of laptop usage.» Rapidly increasing monitor efficiency.

31 AEO14 VS. AEO13 ENERGY IMPACTS» AEO 14 Efficiency Assumptions reduces long-term sales growth by 0.2%.

32 AEO14 VS. AEO13 PEAK IMPACTS» Similarly, using 2014 indices shaves 0.2% off of the peak demand forecast.

33 END-USE SATURATION ESTIMATES 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% CWash_VELCo13 CWash_State13 CWash_KEMA05 CWash_EIA % 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% EDry_VELCo13 EDry_KEMA05 EDry_State13 EDry_EIA % Ref2_VELCo13 Ref2_KEMA05 Ref2_State13 Ref2_EIA % 15.0% 10.0% 5.0% 0.0%

34 ELECTRIC WATER HEATING» We need more information to determine the path of water heating saturation in Vermont.

35 AIR CONDITIONING» Relatively strong air conditioning growth has been driving system peak.

36 ELECTRIC HEAT SATURATION» Both the KEMA 05 and State 13 Survey indicate that there is little primary electric heat.» GMP billing data says otherwise.

37 RESIDENTIAL ELECTRIC HEAT CUSTOMERS Electric Heat GMP North Electric Water Heat Electric Heat GMP South» Customer counts in both GMP North and South suggest significant but declining numbers of heating customers.

38 MONTHLY AVERAGE USE (KWH) EH Customers Water Heat Customers Non-Electric Heat Customers Total Residential» EH Customers use significantly more energy in the winter months than the non-electric heating customers: If it s not heating load than what is it? If it s heating then how come it doesn t show up in the surveys?

39 RESIDENTIAL AVERAGE USE (BY END-USE)» SAE model specification allows us to break-out end-use loads: If you assume a small heating saturation you get a big heating coefficient.

40 DECISIONS ON INTENSITY DRIVERS» Where possible we benchmarked into the most current saturation survey information 2013 State Survey and the AEO 2014 estimates (based on the 2009 RECS).» Assume that for the most part saturation paths follow that for New England.» Water heating is it increasing (like New England) or flat?» Heating go with the survey data or saturation implied by the consumption data: We recommend going with the consumption data.» AEO 2014 vs. AEO 2013 Big Change, Go with 2014?

41 NORMAL WEATHER ASSUMPTIONS» The forecast is based on a concept of expected or normal weather conditions The most common approach is to use historical monthly averages of actual degree-days In past forecasts, we have used 10-year averages, 15 year averages, 20-year averages, and 30-year averages that includes the most recent full year - We need to decide which way to go.

42 VERMONT WINTER TEMPERATURE TREND» All three moving averages capture the winter warming trend» The ten-year moving average will have more volatility

43 VERMONT SUMMER TEMPERATURE TREND» CDD also show definite warming trend» We would recommend using a twenty or thirty year trend» There is no reason to assume that this trend won t continue into the future. Could use projected degree-day trends.

44 PROGRAM EFFICIENCY SAVINGS PROJECTION SCENARIO 2 Residential 2014 Sales (MWh) Program Savings (MWh) Reduction in Sales Annual Reduction ,088,217 41, % 2.0% ,088, , % 2.1% ,088, , % 2.1% Business 2014 Sales (MWh) Program Savings (MWh) Reduction in Sales Annual Reduction ,376,225 65, % 1.9% ,376, , % 1.7% ,376,225 1,080, % 1.6%» We would argue that a large share of the savings forecast is already embedded in the baseline forecast

45 CUMULATIVE SAVINGS SINCE , , , , , , , ,000 50,000 Residential Commercial Industrial » Cumulative savings forecasts for the next ten years are roughly the same as the cumulative savings over the past ten years

46 LOOKING AT IT ANOTHER WAY RESIDENTIAL AVERAGE USE Baseline Adjusted AAGR Baseline: -1.2% Adjusted: -3.0%» Given program impacts on average use over the last ten years, it s not likely we will see a 3.0% average annual decline over the next ten years

47 HOW MUCH IS CAPTURED IN THE BASELINE MODEL?» Add the cumulative historical savings as a model variable If nothing is captured > DSM coefficient = -1.0 If half is captured > DSM coefficient = -.5 If everything is captured > DSM coefficient = 0.0» Estimated models indicates that 80% of efficiency program savings are captured in the residential baseline forecast and 85% or commercial program savings are captured in the commercial baseline sales forecast

48 MODEL RESULTS Residential Average Use: 2008 to 2013 Variable Coefficient StdErr T-Stat P-Value XHeat % XCool % XOther % DSM_perCust % Commercial Sales: 2008 to 2013 Variable Coefficient StdErr T-Stat P-Value XHeat 31,700 24, % XCool 164,570 10, % XOther 114,356 28, % DSMSavings %

49 kwh RESIDENTIAL AVERAGE USE WITH 20% FACTOR 7,000 Baseline 6,000 5,000 4,000 Adjusted 3,000 2,000 AAGR Baseline: -1.2% Adjusted: -1.5% 1,000 -

50 DSM IMPACTS» How much of future efficiency impact is embedded in the baseline forecast?» Given that programs have been reducing energy use for over ten years, we would expect a large percentage of future DSM savings to embedded in the estimated baseline model

51 NET METERING LOAD FORECAST In the last GMP forecast, we incorporated a net metering (solar PV) load forecast Data: monthly number of installed systems, system size, total output, amount used by the customer Used the data to develop a solar market saturation model as a function of simple payback, and existing number of customers with solar systems Assumes 10% annual decline in system costs. Continued availability of Federal and State tax credits Increasing real energy costs and continued payment for excess generation Monthly savings impact generated as Impact = customers * system saturation * use

52 RESIDENTIAL SHARE SIMPLE PAYBACK MODEL Estimated April 2009 to Sept 2013 Variable Coefficient StdErr T-Stat P-Value CONST % Payback % LagShare % AR(1) % PV market saturation forecast Model Statistics Iterations 99 Adjusted Observations 42 Deg. of Freedom for Error 38 R-Squared Adjusted R-Squared F-Statistic Prob (F-Statistic) 0 Mean Abs. % Err. (MAPE) 2.14% Durbin-Watson Statistic 2.269

53 RESIDENTIAL SYSTEM GENERATION (KWH) System Generation Own Use Excess On annual basis, a typical residential system is expected to generate 6,600 kwh per year using 5,200 kwh for their own use and selling 1,400 kwh back to the grid.

54 GMP (NORTH) SOLAR LOAD FORECAST systems output monthly generation consumed output consumed excess

55 SOLAR LOAD FORECAST» Update GMP forecast database and models» Validate model performance how is the model tracking actual adoption» Develop similar model database using state level data» Explore alternative market share models Bass diffusion model Logistic share model

56 WRAP-UP» Other forecast elements Heat pump market forecast - State program to encourage adoption of efficient heat pumps - Base case or scenario? Electric vehicle load forecast?» Working with the Load Forecast Committee What is the best way to work with you through this process? What information would you like to see? What scenarios would you like to see? We have no monopoly on the right answer

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