Download Load Forecast and Scenarios

Load Forecast and Scenarios
David Bailey
Customer Energy & Forecasting Manager
Soyean Kim
Rate Design Manager
1
LTERP Forecast
• 3 step process:
Base Forecast
Monte Carlo
• Business as usual but
incorporates recent
volatility for several
measures
Scenarios
Scenarios
Demand
• As used for the 2016 PBR
Update
• Provides a common
starting point
Base (PBR)
Monte Carlo (business as usual)
Time
• All the new factors not
part of “business as
usual”
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STEP 1: BASE FORECAST
• All information presented is before incremental DSM and other savings
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2016 Load Forecast by Rate Group (GWh)
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2016 Customers by Rate Group
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Wholesale Customers Load %
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Annual Load Forecast
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2016 Peak Demand Forecast
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STEP 2: MONTE CARLO
• All information presented is before incremental DSM and other savings
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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
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Monte Carlo Process
1. Identify major influencing factors
2. Assign probability distribution
3. Apply random sampling using @Risk
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Major Influencing Factors
In the model as random variables:
• Population
• GDP
• Weather
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Residential Forecast Probability
Distribution
Uncertainty increases with time
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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%
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Peak Forecast
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STEP 3: SCENARIOS
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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
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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
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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
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Questions?
Feedback on scenarios?
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Backup Slides
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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 200-300 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
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Electrical End Use Shares of Annual KWh Consumption
FBC (Direct) Residential Customers
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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%
Residential UPC
Before-savings forecast
Forecast Methodology: 3-year average of normalized loads
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Residential Customer Count
Forecast
Forecast Methodology: BC stats regression
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Residential Load Forecast
Before-savings forecast
Forecast Methodology: Calculated UPC x Customers
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Commercial Load Forecast
Before-savings forecast
Forecast Methodology: Regression using CBOC GDP forecast
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Commercial Customer Count
Forecast
Forecast Methodology: CBOC GDP regression
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Industrial Load Forecast
Before-savings forecast
Forecast Methodology: Survey and CBOC Sector GDP
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Wholesale Load Forecast
Before-savings forecast
Forecast Methodology: Survey
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Irrigation Load Forecast
Before-savings forecast
Forecast Methodology: 5-year average
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Lighting Load Forecast
Before-savings forecast
Forecast Methodology: Trend Analysis
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Peak Forecast
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Residential
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Commercial
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Wholesale
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Industrial
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Peak Monthly Variation
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Comparison of 2012 and 2016 LTERP
Gross Load
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2016 Total Direct and Indirect
(Wholesale) Customers
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