INCORPORATING DSM AND NEW TECHNOLOGIES INTO THE FORECAST

Transcription

1 INCORPORATING DSM AND NEW TECHNOLOGIES INTO THE FORECAST

2 CAPTURING EE PROGRAM SAVINGS» Customer usage has been trending down (use per customer) for the last ten years New appliance and construction efficiency standards Increasing real electricity rates Increasing multi-family home market share (smaller square footage) Economic downturn (higher vacancy rates) State and utility sponsored EE programs» Models estimated with historical sales data already have significant efficiency embedded in the model» May even be worse with an SAE model as end-use intensity inputs are calibrated to saturation survey information and shipments data that reflect the appliance stock. The appliance stock in part is impacted by EE program activity

3 Load DSM ISSUE How much DSM is already included in the forecast? Regression model assume that historical relationship continue in the future. History Before DSM DSM Begins Measured Load Using Measured Load implies that past DSM pattern continue in the future Historical Period Forecast Period

4 ADJUSTING FOR EE SAVINGS METHODS» Add-Back Approach Add historical EE savings back to actual sales data Forecast the new reconstituted sale data No DSM Forecast Subtract out all cumulative EE savings (past and forecast) Used by New England ISO» Incremental Approach Assume all past EE program savings are embedded in the baseline forecast Subtract only future cumulative EE program savings Used by majority of electric and gas utilities» Integrate through SAE model end-use intensity projections

5 Load ADD BACK METHOD (1) Load + DSM history = f(x) Forecast = f(x) DSM history DSM future Measured Load with DSM added back Forecast No DSM, but includes National Trends Forecast with Past and Future DSM History Before DSM DSM Begins Measured Load Forecast With Past DSM, but without Future DSM Historical Period Forecast Period

6 VERMONT RECONSTITUTED SALES Add-Back Past Efficiency savings Reconstituted Sales Actual Sales

7 GENERALIZES ECONOMETRIC MODEL Reconstituted sales (Add Back) No EE Model Variable Coefficient StdErr T-Stat CONST (1,815,390) 355, HDD LagHDD (3.902) CDD LagCDD Real Personal Income AR(1) EE Savings No EE Forecast With EE Forecast

8 ISSUES WITH THE ADD-BACK METHOD» Can be difficult to construct a reasonable reconstituted data series. Need a high level of confidence in historical EE savings data series May not have EE historical savings that goes back far enough How do you adjust for EE degradation (measure persistency)? How do you translate annualized historical EE savings estimates to monthly rate class sales adjustments?» Can be difficult to develop a reasonable forecast model How would income or GDP relate to energy if we never had any EE programs? - Difficult to find a right-hand drivers to explain adjusted sales growth Tend to be strong auto-regressive models You can t validate model performance against actual sales data

9 INCREMENTAL APPROACH» Develop baseline forecast with actual sales data and adjust for only future EE program savings. Need a cumulative incremental monthly EE program savings projection (starting with the first forecast month)» Generally starting with annualized program savings forecast Annualized estimates assume that all measures are installed in the first month of the year Meaningful for developing EE programs, not so meaningful for forecasting load impacts.» The challenge is to turn annualized savings estimates into meaningful monthly sales impact series. DSM accounting is really hard. Need to address the double-counting issue Need to capture seasonal impacts (e.g., lighting programs have a larger impact in the winter months. cooling programs only impact summer months)

10 HOW MUCH FUTURE EE IS 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 Vermont residential average use model: 2008 to 2013 Variable Coefficient StdErr T-Stat P-Value XHeat % XCool % XOther % DSM_perCust % Indicates 80% of EE program savings is captured by the baseline model

11 SAE APPROACH DSM Programs Impact End-Use Saturation, Efficiency, or Both XVar y, m EnergyIntensity EI ) y, m ( Utilizatio n y, m EI y, m EI Type 09 Sat Sat Type y Type 09 Eff Eff Type y Type 09 MoMult Type m Incentives to remove second refrigerators reduces saturation Lighting program improves efficiency Promotional heat pump program increases saturation and efficiency Utilizatio n y, m Price Price y, m Income Income y, m HHSizey HHSize, m BDays 31 y, m

12 SAE MODEL APPROACH 1. Develop baseline end-use sales forecasts from SAE model 2. Subtract out end-use EE program savings from baseline enduse sales forecasts adjust future impacts to reflect savings captured by baseline forecast (apply 0.20 to future EE savings forecast) 3. Calculate new end-use energy intensity forecasts that incorporate the EE program impacts 4. Execute estimated SAE model with EE program adjusted enduse intensity forecasts

13 MWh MWh RESIDENTIAL REFRIDGERATION FORECAST 205, , , , , , , ,000 Baseline Adjusted 165,000 30, ,000 20,000 Program Savings 15,000 10,000 5,

14 kwh / Hse Hld REFRIDGERATION END-USE INTENSITY Baseline Adjusted Average annual change: 2015 to 2020 Baseline: - 1.0% Adjusted: -1.2%

15 kwh per Hse Hld kwh per Hse Hld kwh per Hse Hld kwh per Hse Hld EE ADJUSTING END-USE INTENSITIES 600 Baseline Adjusted Water Heating Space Heating , , , ,900 1,800 1,700 1,600 1,500 Miscellaneous Kitchen and Laundry

16 kwh/household kwh/sqft EFFICIENCY PROGRAMS IMPACTS Residential Commercial 8,000 Baseline Adjusted 12.0 Baseline Adjusted 7,000 6, ,000 4,000 3,000 2,000 AAGR : -0.4% : -0.8% AAGR : -0.2% : -0.5% 1, Estimate that Vermont EE programs will reduce average use growth by half

17 TECHNOLOGIES THAT DRIVE THE FORECAST LED Program Cold Climate Heat Pump Program Net Metering Program Electric Vehicles

18 LED LIGHTING PROGRAM» In Vermont, lighting accounts for 15% of household electric use CFLs now account for roughly half of residential lighting» Efficiency Vermont is initiating a new lighting program promoting LEDs as the new go-to lighting technology. LEDs are roughly 1/3 more efficient than CFLs (as measure on a lumen per watt basis)» Lighting program impacts are captured through the SAE model A new lighting intensity forecast is derived using a stock accounting model» Program is expected to reduce residential sales by 54,000 MWh by 2020 (nearly as much as solar net metering).

19 VERMONT STATE FORECASTING FRAMEWORK Monthly sales and customer models are estimated for: Residential Commercial Industrial Street Lighting Economic Drivers Structural Changes Weather Conditions Electric Prices End-Use and Customer Class Energy Forecast LED lighting program modeled at the technology level detail VELCO System Hourly Load Data Normal Peak- Producing Weather End-Use Coincident Peak Factors System Peak Forecast Model Other technologies could not be modeled through an econometric model. Needed to take into account impact on system load shape Net metering impact Cold-weather heat pump impact Electric vehicle impact System Peak And Energy Forecast

20 LIGHT BULB ECONOMICS Home Depot Bulb Comparison Bulb Type Bulb Cost Life Hours Annl Life (Yrs) Watts kwh/yr First Yr Cost Five Yr Cost Incandescent $1.25 1, $19.19 $71.71 CFL $2.00 6, $6.09 $22.88 LED $ , $7.48 $17.41 * 4 hours per day; electricity price of $0.20 kwh

21 BASELINE LIGHTING TECHNOLOGY Saturation Forecast

22 LED TECHNOLOGY SHARE Lighting Program Baseline Projection Utility lighting program results in faster market penetration of LEDs

23 LIGHTING STOCK ACCOUNTING MODEL» Annual bulb estimates by technology - Incandescent - CFL - LED» Factors Average bulb life expectancy Average bulb purchase and operating cost Lighting standards Customer growth Fixture per household growth

24 LIGHTING INTENSITY CALCULATIONS Customers Fixtures per HH (EIA) = Total Fixtures Technology Shares (Adjusted for higher LED penetration) stock accounting model HH Bulbs by technology type Annual burn hours by technology type Average wattage by technology type Lighting sales by technology type (Lighting intensity = lighting sales / HH)

25 LIGHTING INTENSITY COMPARISON Baseline Projection Lighting Program The lighting program pushes lighting savings forward

26 PHOTOVOLTAIC (PV)

27 SOLAR TRENDS» The U.S. installed 6,201 MW of solar PV in 2014, up 30% over 2013.» The Solar Energy Industries Association (SEIA) is forecasting 8,100 MW of solar PV installations in 2015, with the most rapid growth in the residential market.» Federal ITC, currently a 30% tax credit, is set to expire for individual tax payers at the end of 2016 and will be reduced to 10% for corporate tax payers. The race to beat the expiring credit. Systems must be installed by December 31, 2016.» Continued decline in installation costs Residential national average $3.84/watt Non-residential national average $2.27/watt

28 THE DIFFERENCES BETWEEN STATES» Massachusetts installed over 300 MW of solar in 2014» Total capacity of over 750 MW» Approx. 111 watts per capita» New Hampshire installed just 3 MW of solar in 2014» Total capacity of around 8 MW» Approx. 6 watts per capita» Does the sun not shine in New Hampshire??? STATE REGULATION AND INCENTIVES MATTER

29 VERMONT NET METERING PROGRAM» Vermont currently has in place a net metering cap of 15% of peak demand. The GMP forecast cap is reached by the end of 2017.» Net metering allows customers to sell excess generation back to the utility. Excess generation is credited to the customers future bills.» Excess generation is purchased by the utility at the retail rate. There is also a solar adder credit that is applied to total system generation. The solar adder is calculated as the difference between 20 cents and the retail energy rate.

30 ROOF TOP PV SATURATION Driven by Simple Payback» System costs (typical 5 kw system)» Federal tax credit» State incentive» Retail electricity costs» Utility buyback rate» Generated power Own use Excess (sold back to the utility)» Federal tax credit expiration results in an out-of-pocket cost increase of $4,300, resulting in a 2-year increase in payback period reduces market penetration by 50%.

31 IMPACT ON RESIDENTIAL ROOFTOP ADOPTION

32 TOTAL SOLAR CAPACITY FORECAST Most of the capacity is associated with Group Net Metering Systems» Stand alone solar generation (up to 500 kw) that is sold to individual customers (treated as net metering)

33 SOLAR HOURLY LOAD PROFILE» Solar shape based on a 1 MW solar system for a typical meteorological year (TMY).» Weighted combined shape based on the assumption that 33% of systems are roof-mounted, 57% fixed-tilt, and 10% 2-axis tracker.

34 SOLAR GENERATION FORECAST STEPS (MWh) Solar Shape Capacity Forecast Month MonthlyLdFct Jan 0.08 Feb 0.11 Mar 0.14 Apr 0.19 May 0.20 Jun 0.21 Jul 0.20 Aug 0.20 Sep 0.16 Oct 0.12 Nov 0.08 Dec 0.06 Generation Forecast

35 BASELINE SYSTEM LOAD FORECAST Combine energy, peak, and profile forecasts 1. Energy forecast derived from monthly customer class sales forecast 2. Baseline peak based on heating, cooling, and other load requirements from sales forecast models and peak-day weather 3. System profile generated based on calendar days, months, normal daily degree-days, and hours-of-light

36 FORECAST SOLAR HOURLY LOAD» Combine solar generation forecast (adjusted for line losses) with solar load profile

37 IMPACT ON SYSTEM PEAK DEMAND Add Solar Load Shape to Baseline Shape Shifts peak from 2:00 to 5:00 Savings = peak at 2:00 peak at 5:00 Given the peak-day profile is relatively flat, the solar demand impact is relatively small. One MW of solar capacity reduces peak demand by 310 kw No capacity savings in the winter months

38 MW NET METERING SYSTEM PEAK IMPACT By 2023, net metering reduces peak demand by 3.7 percent.

39 COLD CLIMATE HEAT PUMPS

40 ELECTRIC HEAT PUMPS» Several kinds of heat pumps: Central air source Ductless air source Ground source» Heat pumps are common in moderate winter climates where temperatures generally do not drop below freezing.» New cold climate heat pumps can operate at temperatures as low as 0 F.» Mini-split unit consists of two components: External condenser; similar to a central AC condenser. Internal evaporator, from which the unit emits or extracts heat to or from the building.» $3,000-$4,500 per unit, before state and utility incentives.

41 HEAT PUMP SATURATION BY CENSUS DIVISION

42 HEAT PUMP EFFICIENCY (Coefficient of Performance)

43 VERMONT COLD CLIMATE HEAT PUMP PROGRAM» Vermont plans to aggressively promote cold-climate air-source heat pumps The target market are homes currently heating with propane and oil» Unit projections were developed by Efficiency Vermont and GMP» Unlike other efficiency programs, this programs increases electric load

44 SATURATION PROJECTION» Roughly one percent of Vermont homes have heat pumps in 2014» Increases to 5% in 10-years and 25% in 20-years

45 HEATING AND COOLING INTENSITY FORECASTS Saturation projection translates into strong heating and cooling use in the second-half of the forecast horizon ( ) Heating Cooling Heating Cooling % 1.20% % 5.00% 45

46 ESTIMATING HEAT PUMP DEMAND IMPACTS» Our initial forecast attempt was through the SAE Peak Model Peak m a b c CoolVar m b h HeatVar m b o BaseVar m e m CoolVar HeatVar m m CoolLoad HeatLoad Increase in cooling and heating load due to heat pump growth is captured in the CoolLoad m and HeatLoad m variables derived from the SAE residential sales model» The winter peak impact, however, was too low. The peak regression model was not sensitive enough to changes in HDD the elasticity on HeatVar is too small. m m PeakDay _ wtthi PeakDay _ HDD m m Little electric heating in the historical load data

47 LOAD BUILD-UP APPROACH 1. Estimate Heat Pump Hourly Load» Forecast heat pump heating and cooling energy Derived from residential SAE sales model» Estimate heating and cooling hourly profiles Based on daily normal weather, day of the week, season, and holidays» Combine energy and profiles to generate a heat pump hourly load forecast

48 LOAD BUILD-UP APPROACH 2. Add-up loads» Aggregate system, solar, and heat pump load profiles and find the monthly or seasonal peaks

49 HEAT PUMP DEMAND IMPACT By 2024, heat pumps add 14 MW to summer peak and 8 MW to the winter peak And 29,500 MWh in sales

50 ELECTRIC VEHICLES (EV)

51 ELECTRIC VEHICAL SALES TREND» Total EV sales for Q were only 3.0% higher than Q » Nissan Leaf and Chevy Volt Q sales were both down more than 20% from Q1 2014, likely a result of lower gasoline prices.» Tesla sales continue to grow; sales not likely tied to gasoline prices. They are just cool cars to own.

52 ELECTRIC VEHICLE FORECAST» In 2014, there were fewer than 1,000 registered electric vehicles in Vermont.» Started with existing number of electric vehicles in Vermont and assumed a 20% long-term annual vehicle growth through 2024 and constant growth (2,500 new vehicles after that, based on a recent Navigant study and other studies) represents 3.0% of all vehicles by 2024 (includes plug-in hybrids)

53 SYSTEM IMPACT Energy: 35,166 MWh by 2024 Peak Demand: 6.9 MW by 2024 Assume average annual vehicle use of 2,084 KWh. Reflects current mix of EVs and PHEVs (plug-in hybrid electric) in Vermont (data provided by VEIC). Assumes most of the charging is shifted to night via EV charging tariff (based on recent EV assessment study)

54 SUMMER DEMAND IMPACTS New Technologies Year Base Forecast Net Metering Heat Pumps Electric Vehicles Total , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,113» Base forecast includes EE program impacts» Net metering capacity cap is reached at the end of 2022

55 LESSONS LEARNED» For long-term forecasts, we need a flexible modeling framework. The problem does not fit into a nice econometric model Allows us to evaluate the impact of new technologies and standards that are not reflected in the historical load data Allows for evaluating EE program impacts» Difficult to generate a long-term peak demand forecast or isolate the demand impact of specific technologies without accounting for how new technology adoption changes the system load profile Need to incorporate shapes into the long-term forecasting framework