1. Load Forecast and Scenarios
David Bailey
Customer Energy & Forecasting Manager
Soyean Kim
Rate Design Manager

2. LTERP Forecast 3 step process: Base Forecast Monte Carlo Scenarios
As used for the 2016 PBR Update
Provides a common starting point
Monte Carlo
Business as usual but incorporates recent volatility for several measures
Scenarios
All the new factors not part of “business as usual”

3. Step 1: Base forecast
All information presented is before incremental DSM and other savings

4. 2016 Load Forecast by Rate Group (GWh)

5. 2016 Customers by Rate Group
Total 2016F wholesale customers 36, %
Total 2016F Direct customers 133, %
Total 2016F Direct and Indirect customers 169,611
Total 2014 wholesale customers 35,797 – 21.5%
Total 2014 direct customers 130,572 – 78.5%
Total 2014 customers 166,369

6. Wholesale Customers Load %

7. Annual Load Forecast

8. 2016 Peak Demand Forecast Summer peak is 604/767 = 79%
Before DSM & other savings

9. Step 2: Monte carlo
All information presented is before incremental DSM and other savings

10. Long-Term Load Forecast
Applies to the “business as usual” scenario
Large degree of uncertainty inherent in the long term forecast
Rapidly changing market conditions and technology options introduce additional uncertainty
Monte Carlo simulation allows a quantitative assessment of the long term uncertainty
Upper range (P90) tied to 90% probability
Lower range (P10) tied to 10% probability

11. Monte Carlo Process Identify major influencing factors
Assign probability distribution
Apply random sampling

12. Major Influencing Factors
In the model as random variables:
Population
GDP
Weather

13. Residential Forecast Probability Distribution
Uncertainty increases with time

14. Annual Gross Load Forecast
Maximum range from base is +/-5%
Biggest uncertainty from Industrial, then Wholesale
Commercial forecast to be most stable
Residential variation +/-6%
Commercial +/- 4%
Wholesale +/- 13%
Industrial +/- 24%
5% deviation

15. Peak Forecast

16. Step 3: scenarios

17. Scenarios We will add scenarios to the Monte Carlo (MC) results
Some future scenarios will increase load and some will reduce load
Additions will be added to the high MC case while deductions will be removed from the low MC case
Hybrid scenarios (eg. some EV and some DG) will land somewhere in the middle

18. High Load Forecast Scenario
Continued low DG growth
High EV growth
FBC promotes charging stations and EV range improves
Higher gasoline prices
High gas-to-electricity switching (e.g. gas to ASHP)
Government policy focused on environment, electrification and GHG emission reductions with higher carbon tax and subsidies for green technologies like EV
Natural gas rates rise more than electricity rates (partially due to increasing carbon tax) driving fuel switching
High climate change scenario

19. Low Load Forecast Scenario
High DG growth (includes rooftop solar, wind, home batteries, CHP)
Low EV growth due to other technology like fuel cell vehicles and low gasoline prices
Low gas-to-electricity switching
Government policy less focused on environment so no increases to carbon tax and no subsidies for green technology
Government policies favour positive role for natural gas in BC for domestic use
Natural gas rates remain low relative to electricity rates
Low climate change scenario

20. Questions? Feedback on scenarios?

21. Backup Slides

22. Definitions Load – the annual load measured in GWh
Demand – the peak measured in MW
MWh
A typical single family home uses 12 MWh per year.
A typical restaurant uses 65 MWh per year
A typical 24 hr convenience store uses MWh per year
A typical grocery store uses 1,200 MWh per year
GWh
1,000 MWh
Larger industrial/commercial customers typically use over 10 GWh
A large shopping mall can use 10 GWh
A large hospital can use 20 GWh
PV – Photovoltaic or solar panel
DG – Distributed generation
EV – Electric Vehicle
Monte Carlo - A modeling technique that uses experienced volatility in different measures to forecast future volatility.
ASHP – Air source heat pumps
CHP – Combined heat and power

23. Electrical End Use Shares of Annual KWh Consumption FBC (Direct) Residential Customers
from the 2012 REUS
Other includes, chargers, electronics, small electric devices, heating elements, outdoor grills, exterior lights, pool heaters, spa heaters and motors not listed

24. Base Methodology Overview
Load Class
Customers
UPC
Load
% of Total
Residential
BC STATS regression
3 year average of normalized actuals
Calculated UPC X Customers
39.4%
Commercial
CBOC GDP regression
Calculated Load/Customers
Regression using CBOC GDP forecast
22.8%
Wholesale
Survey
28.1%
Industrial
Survey + Sector GDP
9.1%
Lighting
Trend Analysis
0.4%
Irrigation
5 Year Average
1.2%
Load Forecast Technical Cmte
Majority of the methods were established following the series of Load Forecast Tech Cmte meetings in 2012.
Residential
Changes
Using population instead of housing starts in 2014 PBR.
CoK
Customers
BC Stats regression of customer count on FBC service area population.
Historical (how many years) + 20 year forecast from BC Stats.
Regress historical BC Stats population vs our historical YE customer counts to develop a model.
Regression stat is 94%
Then use the model and their forecast to develop our forecast.
F statistic: Is the result statistically significant? Are the means between two datasets significantly different? The null hypothesis is that they are. If the F value is less than F Critical, then we can reject the null hypothesis.
P value:
UPC
Three year average of the most recent three years normalized actual use rates.
No statistically significant trend.
to get an avg then held that constant for
UPC around 12 MWh.
12.24 for 2015
12.33 for 2014
Commercial
May 2014 CBOC report. Short term (5 yr).
GDP forecast %.
2013 CBOC report GDP forecast error rate 0.5% forecast : actual.
CBOC performance is on par with other agencies.
Load
Also regressed against GDP.
Wholesale
Survey
Penticton 60%
Summerland 15%
Grand Forks 8%
Nelson 15%
BCH Kingsgate 2%
BCH Ladreau 2%
Penticton growth approx. 2%, all others less than 1%.
Industrial
49 customers
70% response by customer (incl CoK)
80% response by volume (incl CoK)
10 customers. 6 responses.
Mall and hospital and BC Fruits Sun Rype
Lighting
Trend
Is this a time series?
Starting in 2006
1,600 customers
Irrigation
5 yr avg
No significant trend so we use an average.
1,100 customers?

25. Residential UPC
Residential Load with CoK
Before savings
Before-savings forecast
Forecast Methodology: 3-year average of normalized loads

26. Residential Customer Count
Residential Customer Count with Cok
Forecast
Forecast Methodology: BC stats regression

27. Residential Load Forecast
Residential Load with CoK
Before savings
Before-savings forecast
Forecast Methodology: Calculated UPC x Customers

28. Commercial Load Forecast
Commercial Forecast with CoK
Before-savings forecast
Forecast Methodology: Regression using CBOC GDP forecast

29. Commercial Customer Count
Forecast
Forecast Methodology: CBOC GDP regression

30. Industrial Load Forecast
Industrial Load w/CoK
Before-savings forecast
Forecast Methodology: Survey and CBOC Sector GDP

31. Wholesale Load Forecast
Before-savings forecast
Forecast Methodology: Survey

32. Irrigation Load Forecast
No CoK
Before-savings forecast
Forecast Methodology: 5-year average

33. Lighting Load Forecast
Before-savings forecast
Forecast Methodology: Trend Analysis

34. Peak Forecast

35. Residential
6% deviation from high to base at 2035

36. Commercial
4% deviation from high to low at 2035

37. Wholesale
13% deviation at 2035

38. Industrial
24% deviation at 2035

39. Peak Monthly Variation

40. Comparison of 2012 and 2016 LTERP Gross Load
Before DSM and other savings

41. 2016 Total Direct and Indirect (Wholesale) Customers