Transcript Document

ENERGY CENTER
State Utility Forecasting Group (SUFG)
Methods for Forecasting
Supply and Demand
Presented by:
Douglas J. Gotham
Purdue University
Presented to:
Institute of Public Utilities
56th Annual Regulatory Studies Program
August 12, 2014
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Using the Past to Predict the Future
• What is the next number in the following
sequences?
0, 2, 4, 6, 8, 10, ….
0, 1, 4, 9, 16, 25, 36, ....
0, 1, 2, 3, 5, 7, 11, 13, ....
1, 3, 7, 15, 31, ....
0, 1, 1, 2, 3, 5, 8, 13, ....
8, 6, 7, 5, 3, 0, ….
8, 5, 4, 9, 1, 7, ….
2
ENERGY CENTER
State Utility Forecasting Group (SUFG)
A Simple Example
1000
1100
1010
1080
1020
1060
1030
1020
1040
1000
1050
960
?
940
1040
980
920
?
?
900
1
2
3
4
5
6
3
ENERGY CENTER
State Utility Forecasting Group (SUFG)
A Little More Difficult
1000
1700
1100
1600
1210
1500
1331
1400
1464
1300
1200
1610
1100
?
1000
?
900
?
1
2
3
4
5
6
4
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Much More Difficult
18831
22,000
18794
18193
21,000
19944
20855
20,000
20858
19275
19,000
19054
18,000
20315
21002
?
?
17,000
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Much More Difficult
• The numbers on the previous slide were
the summer peak demands for Indiana
from 2002 to 2011
• They are affected by a number of
factors
– Weather
– Economic activity
– Price
– Interruptible customers called upon
– Price of competing fuels
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Question
• How do we find
a pattern in
these peak
demand
numbers to
predict the
future?
25000
20000
15000
10000
5000
0
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Methods of Forecasting
•
•
•
•
•
•
Palm reading
Tea leaves
Tarot cards
Ouija board
Crystal ball
Polling
•
•
•
•
•
Astrology
Dart board
Sheep entrails
Hire a consultant
Wishful thinking
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Alternative Methods of
Forecasting
• Top-down
– trend analysis (aka time series)
– econometric
• Bottom-up
– survey-based
– end-use
• Hybrid
– statistically-adjusted end-use
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Time Series Forecasting
• Linear Trend
– Fit the best straight line to the historical data and
assume that the future will follow that line
• works perfectly in the 1st example
– Many methods exist for finding the best fitting line; the
most common is the least squares method
Y    X
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Time Series Forecasting
• Polynomial Trend
– Fit the polynomial curve to the historical data and
assume that the future will follow that line
– Can be done to any order of polynomial (square, cube,
etc.) but higher orders are usually needlessly complex
Y    1 X  2 X  ...
2
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Time Series Forecasting
• Logarithmic Trend
– Fit an exponential curve to the historical data and
assume that the future will follow that line
• works perfectly for the 2nd example
Y  
X
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Example
• Use linear time series analyses to
project Indiana peak demand from 2010
to 2029 using historical observations
over 3 time periods
– 1980-2009
– 1990-2009
– 2000-2009
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Trend 1 - Starting in 1980
30000
25000
20000
15000
Actual
Trend
10000
5000
0
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1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Trend 2 - Starting in 1990
30000
25000
20000
15000
Actual
Trend
10000
5000
0
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Trend 3 - Starting in 2000
30000
25000
20000
15000
Actual
Trend
10000
5000
0
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Comparison
30000
25000
Actual
20000
Trend 1
Trend 2
Trend 3
15000
10000
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Results
Year
Trend 1
Trend 2
Trend 3
2010
20981
20963
20793
2015
22766
22716
22376
2020
24551
24470
23959
2025
26337
26224
25542
2030
28122
27978
27124
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Advantages
• Relatively easy
• The statistical functions in most
commercial spreadsheet software
packages will calculate many of these
for you
• Requires little data
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Disadvantages
• Does not account for changing
circumstances
• Choice of historical observations can
impact results
• May not work well when there is a lot of
variability in the historical data
– If the time series curve does not perfectly
fit the historical data, there is model error
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Acceptability
• Trend analysis was a popular
forecasting methodology until the 1970s
• The inability to handle changing
conditions led to considerably
inaccurate forecasts
• They have been largely discredited
– MISO’s forecasting whitepaper lists it as an
“unacceptable” method
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Econometric Forecasting
• Econometric models attempt to quantify the
relationship between the parameter of
interest (output variable) and a number of
factors that affect the output variable.
• Example
– Output variable
– Explanatory variable
•
•
•
•
•
Economic activity
Weather (HDD/CDD)
Electricity price
Natural gas price
Fuel oil price
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Estimating Relationships
• Each explanatory variable affects the output
variable in a different way. The relationships
(or sensitivities) can be calculated via any of
the methods used in time series forecasting
– Can be linear, polynomial, logarithmic, moving
averages, …
Y    1 X1  2 X 2  3 X 3  ...
• Relationships are determined simultaneously
to find overall best fit
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
A Simple Example
• Suppose we have 4
sets of observations
with 2 possible
explanatory
variables
130
Output Y
Variable X1 Variable X2
130
110
100
100
120
113
120
110
114
130
90
121
150
120
120
110
100
80
100
120
140
160
110
100
80
130
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
A Simple Example
• Including both variables provides a
perfect fit
– Perfect fits are not usually achievable in
complex systems
Y = 0.2X1 – 0.1X2 + 100
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Advantages
• Improved accuracy over trend analysis
• Ability to analyze different scenarios
• Greater understanding of the factors
affecting forecast uncertainty
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Disadvantages
• More time and resource intensive than
trend analysis
• Difficult to account for factors that will
change the future relationship between
the drivers and the output variable
– utility DSM programs
– government codes and standards
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Acceptability
• Econometric methods became popular
as trend analysis died out in the 70s
and 80s
• They continue to be used today
• MISO’s forecasting whitepaper lists it as
an “acceptable” method
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Survey-Based Forecasting
• Also referred to as “informed opinion”
forecasts
• Use information from a select group of
customers regarding their future
production and expansion plans as the
basis for a forecast
• Commonly done with large users
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Advantages
• Simplicity
• The ability to account for expected
fundamental changes in customer
demand for large users, especially in
the near-term
– new major user or customer closing a
facility
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Disadvantages
• Tend to be inaccurate beyond first few
years
– most customers do not know what their
production levels will be five or ten years in
the future
– few customers expect to close shop
– new customers after first couple years are
unknown
• Lack of transparency
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Acceptability
• Survey-based forecasts may be
acceptable for short-term applications or
if used in conjunction with another
method in the longer term
• MISO’s forecasting whitepaper lists it as
an “unacceptable” method
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
End Use Forecasting
• End use forecasting looks at individual
devices, aka end uses (e.g., refrigerators)
• How many refrigerators are out there?
• How much electricity does a refrigerator use?
• How will the number of refrigerators change
in the future?
• How will the amount of use per refrigerator
change in the future?
• Repeat for other end uses
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Source: Van Buskirk, Robert. “History and Scope of USA Mandatory
Appliance Efficiency Standards.” (CLASP/LBNL).
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Advantages
• Account for changes in efficiency levels (new
refrigerators tend to be more efficient than
older ones) both for new uses and for
replacement of old equipment
• Allow for impact of competing fuels (natural
gas vs. electricity for heating) or for
competing technologies (electric resistance
heating vs. heat pump)
• Incorporate and evaluate the impact of
demand-side management/conservation
programs
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Disadvantages
• Tremendously data intensive
• Primarily limited to forecasting energy
usage, unlike other forecasting methods
– Most long-term planning electricity
forecasting models forecast energy and
then derive peak demand from the energy
forecast
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Acceptability
• End-use modeling was first developed
in the 1970s but started to gain
popularity with the increase in DSM in
the 1990s
• MISO’s forecasting whitepaper lists it as
an “acceptable” method
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Hybrid Forecasting
• Hybrid models employ facets of both
top-down and bottom-up models
• Most common is called the statisticallyadjusted end-use (SAE) model
• In reality, most end-use models are
hybrid to some degree in that they rely
on top-down approaches to determine
the growth in new devices
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
SAE Models
• SAE models incorporate features of
both econometric and end-use models
• Adjust the end-use estimated loads
using a statistical regression to match
observed loads
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Disadvantages
• Increased model complexity
• More time and resource intensive
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Advantages
• In general, hybrid approaches attempt
to combine the relative advantages and
disadvantages of both model types
• Can better capture externalities that
affect customer decisions when
compared to end-use models
– green options
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Acceptability
• Hybrid models have been gaining in
popularity in recent years
• MISO’s forecasting whitepaper lists it as
an “acceptable” method
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Forecasting Example
• SUFG has electrical energy models for
each of 8 utilities in Indiana
• Utility energy forecasts are built up from
sectoral forecasting models
– residential (end-use & econometric)
– commercial (end-use & econometric)
– industrial (econometric)
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Another Example
• SUFG is developing independent
forecasting models for MISO
– econometric
– individual state level (15 states)
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Another Example
• The Energy Information Administration’s
National Energy Modeling System (NEMS)
projects energy and fuel prices for 9 census
regions
• Energy demand (end-use)
–
–
–
–
residential
commercial
industrial
transportation
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Sources of Uncertainty
• Exogenous assumptions
– forecast is driven by a number of assumptions
(e.g., economic activity) about the future
• Stochastic model error
– it is usually impossible to perfectly estimate the
relationship between all possible factors and the
output
• Non-stochastic model error
– bad input data (measurement/estimation error)
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Energy → Peak Demand
• Constant load factor / load shape
– Peak demand and energy grow at same rate
• Constant load factor / load shape for each
sector
– Calculate sectoral contribution to peak demand
and sum
– If low load factor (residential) grows fastest, peak
demand grows faster than energy
– If high load factor (industrial) grows fastest, peak
demand grows slower than energy
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Energy → Peak Demand
• Day types
– Break overall load shapes into typical day
types
• low, medium, high
• weekday, weekend, peak day
– Adjust day type for load management and
conservation programs
– Can be done on a total system level or a
sectoral level
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Load Diversity
• Each utility does not see its peak demand at
the same time as the others
• 2011 peak demands occurred at:
–
–
–
–
–
–
–
–
Duke Energy – 7/20, 2 PM
Hoosier Energy – 7/21, 6 PM
Indiana Michigan – 7/21, 2 PM
Indiana Municipal Power Agency – 7/21, 2 PM
Indianapolis Power & Light – 7/20, 3 PM
NIPSCO – 7/21, 4 PM
SIGECO – 7/21, 4 PM
Wabash Valley – 7/20, 8 PM
• Statewide peak – 7/21, 4 PM
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Load Diversity Example
2500
2000
2000
2000
2000
1500
A
B
C
1000
500
0
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Example (continued)
6000
5700
5000
4000
3000
Total
2000
1000
0
51
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Load Diversity
• This analysis is normally performed for
all hours of the year
• Thus, the statewide (or regional) peak
demand is less than the sum of the
individual peaks
• Actual statewide/regional peak demand
can be calculated by summing up the
load levels of all utilities for each hour of
the year
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Diversity Factor
• The diversity factor is an indication of
the level of load diversity
• Historically, Indiana’s diversity factor
has been about 96 – 97 percent
– that is, statewide peak demand is usually
about 96 percent of the sum of the
individual utility peak demands
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Resource Expansion Models
• Determine amount, type, and location of
demand/supply resources to be
developed to reliably meet future
electricity demand
• Detailed information on existing
generation resources but limited detail
on existing transmission resources
• EGEAS, NEEM, NEMS, NESSIE,
ReEDS, Strategist
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Production Costing Models
• Simulates the network operation over a
year to determine costs, emissions,
impacts of congestion
• Detailed information on generation and
transmission infrastructure (both
existing and future)
• GE MAPS, GRIDVIEW, PROMOD,
UPLAN
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ENERGY CENTER
State Utility Forecasting Group (SUFG)
Power Flow Models
• Determine adequacy of transmission system
to meet demand without violating physical
constraints (undervoltage, overcurrent, etc.)
• Examine contingencies (can system operate
reliably if a line/transformer/generator goes
down?)
• Load/generator output fixed for a given hour
• Models laws of physics that drive actual path
of power flow (not an optimization)
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• Power World, PSS/e, PSLF
ENERGY CENTER
State Utility Forecasting Group (SUFG)
Further Information
• State Utility Forecasting Group
– http://www.purdue.edu/dp/energy/SUFG/
• Energy Information Administration
– http://www.eia.doe.gov/index.html
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