From Sales to Peak, Getting It Right Long-Term Demand Forecasting

Transcription

1 From Sales to Peak, Getting It Right Long-Term Demand Forecasting 12 th Annual Energy Forecasters Meeting Las Vegas, NV April 2 April 3, 2014 Terry Baxter, NV Energy Manager, Forecasting

2 Getting the Peak Right Over the long-term system peak demand is driven by underlying customer and end-use energy requirements As customer class and end-use energy requirements do not necessarily increase at the same rate, neither will peak demand Examples Strong increase in cooling saturation will translate into stronger summer peak demand but have no impact on winter peak demand New lighting standards impact winter peak more than summer peak Increasing refrigerator and freezer efficiency will reduce summer load more than winter load as will a DSM program targeted at removing second refrigerators To get the peak right, we need to capture the impact of changing customer class and end-use sales growth over time Hourly Load Build-Up Approach vs. SAE Peak Model

3 Hourly Load Build-Up Approach Using MetrixLT Forecast revenue class sales Res, Small C&I, Large C&I, StLight Combine class sales forecast with hourly revenue class profile forecasts Sales are allocated to hours based on the profile forecast Hourly profiles models are estimated with load research data that reflect the calendar, hours of light, and normal daily weather conditions Aggregate class hourly load forecasts to a system hourly load forecast forecast Adjust for Demand Response hourly load impacts Calculate the annual and monthly peaks from the adjusted system hourly load forecast

4 Import Monthly Sales Forecast (Calendarized) MetrixLT Data Table links to MetrixND sales forecast results Non-residential sales actual and forecast 4

5 Import Rate Class Hourly Load Profiles - Residential Interval Data Tables link to MetrixND hourly rate class load profiles forecasts 5

6 Import Rate Class Hourly Load Profiles Import class hourly load profiles for each rate class 6

7 Combine Sales and Profile Forecast Combine sales and profile using a MetrixLT Batch Transform Profile Forecast Sales Forecast Loss Factor Add up Name 7

8 Hourly System Load Forecast Add-up rate class hourly load forecasts using a MetrixLT Batch Transform Add up Name Add up revenue class hourly load forecasts 8

9 Comparison with Actual System Load Build up approach compares well with actual system load MetrixLT Report Object

10 Peak Forecast System and coincident peak demand are found using a Frequency Transform

11 Buildup Approach Advantages Captures the impact of differences in customer class sales growth on demand Can estimate class coincident peak demand as required for an NVE IRP filing Can incorporate other technologies such as DR and PV that impact the system load curve and resulting peak demand

12 Build-Up Approach - Issues Generally will under predict peak demand Profiles are based on hourly load regression models that effectively predict the average load (given temperature) for a given hour Uses the 8,760 hourly load profiles to allocate monthly or annual class sales Can get significant variation in year-to-year peak demand growth as a result of this process (e.g., picking up an additional weekend day in August impacts the energy allocation to days and hours in that month) Peak-day weather is not explicitly captured in the forecast Can t evaluate peak demand for more extreme weather conditions 12

13 SAE Peak Demand Modeling Approach Use the concept behind the hourly load build-up approach to construct end-use drivers for a monthly peak demand econometric model Explicitly incorporate peak-day weather conditions Derive end-use load estimates from the residential and commercial SAE models Combine heating and cooling load requirements with peak-day weather conditions Estimate end-use coincident peak loads for non-weather sensitive enduses Estimate monthly system peak demand regression model Develop seasonal peak demand forecasts for 50% and 90% probability weather conditions 13

14 Simulation Results from Sales Models Sales m a b b o c XCool XOther m m b e h m XHeat m Cooling Total Commercial Residential Heating Weather normal plus forecast Other

15 Monthly Residential Cooling Sales Derived from SAE Sales Models (MWh) 15

16 Cooling Variable Construction Index Cooling Sales to Base Year. Cool_Idx a = CoolSales a /CoolSales 2001 CoolVar m = Cool_Idx a * PkCDD m Interact Cooling with Peak-Day CDD Peak-day CDD Impact of peak- day CDD changes with Increasing cooling load requirements

17 Heating Variable Construction Heat_Idx a = HeatSales a /HeatSales 2001 HeatVar m = Heat_Idx a * PkHDD m Index Heating Sales to Base Year Peak-day HDD Interact Heating with Peak-Day HDD. Impact of peak- day HDD changes with heating load requirements

18 Find monthly end-use coincident peak demand Water Heating loads are lower in summer due to warmer inlet water temperatures Lighting Loads are larger in winter due to increased hours of darkness. Refrigerator and Freezer loads are larger in summer due to warmer ambient conditions inside the home.

19 Fraction of Annual End-Use Energy at Time of Peak 19

20 Base Use Variable Construction Allocate other use by end-use intensity estimates. CP Re s CPCom m, use Re s _ Other y EnergySAE u y, use EnergySAE EnergySAE y, u y, use m, use Com _ Othery EnergySAE y, u u BaseVar m = CPRes m + CPCom m + OtherAvgMW m PeakFrac PeakFrac m, use Fraction of annual energy at time of peak. m, use

21 Example of Transformations: Residential Lighting EnergySAE u y,use EnergySAE y,u 2 1 MWh 3 PeakFrac m,use Re s _ Other y 4 EnergySAE y, use CP m, use Re s _ Othery PeakFracm, use EnergySAE y, u u Lighting Standard Impacts. Res Light CP MW

22 22 End-Use CP Load Estimates Derived in a MetrixND Transform Table

23 Nevada Power SAE Peak Model Variable Coefficient StdErr T Stat BaseVar HeatVar CoolVar Jan Feb Apr May Oct Dec Model Statistics Iterations 1 Adjusted Observations 89 Deg. of Freedom for Error 80 R Squared Adjusted R Squared AIC 9.84 BIC Model Sum of Squares 127,196, Sum of Squared Errors 1,364, Mean Squared Error 17, Std. Error of Regression Mean Abs. Dev. (MAD) Mean Abs. % Err. (MAPE) 2.70% Durbin Watson Statistic 2.137

24 Contribution by End-Use Predicted Cooling Other Heating

25 Comparison with Class Buildup Peak SAE annual peaks approximately 200 MW higher 25

26 Can Combine SAE Peak Forecast with Build-up Hourly Load Forecast Buildup Hourly Load Forecast SAE Peak Demand Forecast 26

27 Comparison with Buildup Shape Higher August and March Loads Lower November Loads 27

28 SAE Peak Model Like the hourly load build-up model, the SAE Peak model captures the impact of differences in customer class and end-use sales growth on peak demand (though does not capture changes in timing of peak demand) Greater visibility into how peak demand forecast is derived Can easily be verified as its estimated using a standard regression model Can quickly update the forecast for changes in forecast assumptions price, efficiency trends, DSM impacts, economic and demographic changes Can assess peak for more extreme weather conditions Can integrate monthly peaks with hourly load profiles and energy forecasts to derive consistent 8,760 hourly load forecasts 28

29 2015 Load Forecast Our plan is to adopt the SAE peak forecasting approach for the 2015 long-term energy and demand forecast. We will also be using MetrixLT for generating hourly load forecast (Currently using MetrixLT combined with SAS). Currently working with state regulators through a series of forecast workshops to gain approval of this approach The combination of MetrixND and MetrixLT provides a set of easy- to-use tools for evaluating different load and peak forecasting approaches and incorporating the load impact of other technologies such as net metering, electric vehicles, and demand response programs 29

30 30 Questions?