LoadSEER Satellite Analytics

Transcription

Spatial Forecasting
[email protected]
513 479 1235
or 513 762 7621
312 Walnut St Suite 1600, Cincinnati Ohio USA 45202
Copyright 2006-2012 Integral
Analytics
Integral to Consultants & Utilities
Putting advanced analytics into software to manage costs & risks
WindStore
GridStore
SolarSEEK
Optimal
Demand
Dispatching
Transformer
Aging Analytics/
Load Leveling
DRPricer
Optimal Demand Response
Bill Insure
Fixed Bill
Analytics
Outline
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What is spatial forecasting?
How is spatial forecasting used?
Spatial forecasting issues
Methodology
How does Integral Analytics perform
spatial forecasting?
Analytics
What Is Spatial Forecasting?
• Typical load forecast
– Utility service area
– Project volume and timing
• What - When
• Spatial load forecast
– Small area: a few acres to square mile
– Project volume, timing, and location
• What - When - Where
Analytics
What Is Spatial Forecasting?
• Not all forecasts of load by location are truly
spatial forecasts
– Small area forecasts are not necessarily spatial
• Trending of loads for a substation or an equipment
area is not a spatial forecast
• Spatial forecast for an area incorporates
impacts of economic activity in surrounding
areas
– Land use type which drives intensity of use
– Forecast of land use impacted by growth in
surrounding areas
Analytics
How Is Spatial Forecasting Used?
• Traditional use: T&D planning
– Plan system expansion
– Reliably meet new customer loads
• Additional Drivers of Spatial Forecasts
– Budget constraints
– Need for resource optimization
– Regulatory requirements
– Forecast accuracy
– Integration of alternate resources, e.g., DG /
renewable
Analytics
Spatial Forecasting Issues
• What – When - Where
– Level of locational detail required?
– Length of forecast horizon?
– Methodology?
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Small area trends on loads
Incorporate economic drivers
Utilize GIS: Geographical Information System data
Blending of locational economic data with T&D facility
areas
– Weather impacts?
– Uncertainty?
Analytics
Methodology
• Level of locational detail
– Equipment area
– Grid of small areas
• Length of forecast horizon
– Short-term vs. long-term
• Load Class
– Customer class level loads by location
– End-use detail
Analytics
Methodology
Polygon / Equipment Based Area
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Grid / Small Area
Methodology
• Customer Classes
– Residential
– Commercial : e.g., office, retail, small , large
– Industrial
• Energy use: high, low
• Industry group
• End Uses
– HVAC, water heating,
– Motors, cooking, refrigeration, lighting
– Process heat, computers, data centers
Analytics
Methodology
• Forecast Method Options
– Small area forecasts
• Areas forecast independently
– Trend extrapolation
– S – Curves (Gompertz)
• Issues:
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Little use of economic and demographic data
Difficulty with Green Field development
Difficulty with redevelopment of existing space
Inability to integrate with activity of surrounding areas
Simpler to develop, but less accurate
Analytics
Methodology
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Methodology
S - Curve
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Methodology
– Spatial forecasts
• Area forecasts coordinated
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Simulation model
Uses detailed data: demographic and economic
Gravity model for density of economic activity
Class locational preferences drive the forecast
• Issues:
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Acquire and organize detailed land use data
Greater detail requires resources
Higher complexity, but higher accuracy
Facilitates targeting of energy efficiency and load
management resources as well as electric vehicles
Analytics
Methodology
Customer Classes
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Methodology
Urban Pole
A major employment area has a large and wide influence on the areas around it: every one wants
to get as close to it as they can. This circular, decreasing value function of distance, centered at
the downtown area, represents this influence.
Analytics
Methodology
Proximity Score
0 - 100
•
¼ mi
Euclidean Distance to an object
Methodology
– Geographic forecasts
• Forecast of a larger area
– Econometric model
– Uses demographic and economic data
• Issues:
– Lacks detailed data for smaller areas
– Areas too big for T&D planning purposes
Analytics
™
Spatial Electric Expansion & Risk
Space-time calculator. Geographic calculator. We created this tool to give
distribution planners the ability to quickly extrapolate complex geographical
knowledge across space and time in order to plan for economic growth and
system expansion.
LoadSEER System
- LoadSEER-GIS (Geographic Information System) is a spatial forecasting desktop application that is
used by Area Senior Engineers to model long term load horizons for the System as a whole and each division, planning
area, bank, and circuit respectively. LoadSEER-GIS replicates urban development processes based on historical land
use change, zoning information from government, customer rate class from utilities, and the energy model of
consumption patterns.
- LoadSEER-FIT (Forecast Integration Tool) is a load trending web-based application that is used by
Distribution Engineers to adjust planning area, bank, and circuit forecasts with their local knowledge and judgment.
ESRI
Personal
Geodatabase
Area & Division
Forecast
Approval
Load Adjustments
Planning Area
Forecast
Approval
LoadSEER FIT –
Web client
SQL Server Central
Repository:
Load Forecasting Database
LoadSEER GIS –
Desktop client
Area Forecast
Aggregation
Bank/Circuit Peak Load
Normalization & Trending
System Load Allocation
and Optimization
Customer class based
spatial load growth
simulation
Confidential and Proprietary
GIS Conceptual Program Workflow
A. What does my Service Territory look like?
I. Land Use
II. Transportation
III. Large Area Employment
B. How does my Service Territory grow?
I. Growth Rates
a. Corporate Forecast
b. Local / State Planning
II. Growth Drivers
a. Proximity, Surround, Urban Pole factors
b. Preference coefficients
C. What does Seasonal Peak Load look like?
I. 24 hour peak load – one acre density
- Data Types & Schema
- Points
- Lines
- Polygons
Land Use
Polygons
Transportation
Urban Pole
Lines
Points
Land Use & Customer Load
Electric Grid &
Customer Load Data
Land Use Classes
Land Use
1
A
C
R
E
C
O
N
V
E
R
S
I
O
N
Commercial
Residential –
Suburban
Industrial
Vacant Land
Travel Time
Urban Pole (Center City)
Euclidean Distance
Activity Centers
A major employment area has a large and wide influence on the areas around it: every one wants to get as close to it as they can.
This circular, decreasing value function of distance, centered at the downtown area, represents this influence.
Customer Preference Survey
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Customers are recruited to participate in an online market research study which
is specifically designed to quantify the types of trade-offs customers make when
they decide to live or build within specific areas.
In all cases, particular attention is paid to nonlinear preferences to different
distances from attributes, such as a Central Business District, Hospitals, and
Highways, which informs LoadSEER’s specific Regional Factors.
Historical Land Change Modeling
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Historical land-use classification maps are established for prior years to
consistently represent the Study Area’s geography across time. Using these
historical land-use maps, we quantify the spatial land-use changes that
historically have occurred within the study area, across time and space.
Once modeling is complete, a full set of Proximity and Surround factor
Preference Weight Coefficients will be generated for LoadSEER’s long range
forecasts.
Model calibration
Income Segments vs. Distance from Downtown
*Avg. Utilities is the Average Preference Score for each Segment. Notice: Preferences Scores
change nonlinearly with distance. For example, high income customer show a significant
increase in preference between 2 – 5 miles away.
Model calibration
Income Segments vs. Distance from Hospital
*Avg. Utilities is the Average Preference Score for each Segment. Notice: Customer show
significant preference for locating within 10 miles of a hospital.
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Model calibration
Income Segments vs. Distance from Highway
*Avg. Utilities is the Average Preference Score for each Segment. Notice: The preference to
located near highways peaks at 2 – 5 miles; customers want to live close, but not too close to
a highway.
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Tract Size
• Minimum number of contiguous acres required
to allocate growth for each class.
Residential = 1 acre
Commercial = 3 acre
Heavy Industrial = 10 acre
Analytics
Satellite Image Analysis… 1991
The image cannot be display ed. Your computer may not hav e enough memory to open the image, or the image may hav e been corrupted. Restart y our computer, and then open the file again. If the red x still appears, y ou may hav e to delete the image and then insert it again.
The image cannot be display ed. Your computer may not hav e enough memory to open the image, or the image may hav e been corrupted. Restart y our computer, and then open the file again. If the red x still appears, y ou may hav e to delete the image and then insert it again.
Satellite Image Analysis… Change detection
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Enhance with Building
Descriptions
Enhance with Circuit lines &
Nodes
Weather and Economic Modeling
Within LoadSEER FIT Model
LoadSEER FIT Forecasts Are Normalized For Both Weather AND
Economy At The Same Time
For each feeder class combination, it is typically the case that there are
summer MWH histories, possibly by customer class. In the case of the
MWH model, we use CDD or cooling degree days, in conjunction with
specific econometric variables.
Following is one possible set of variables used, to control for both summer
weather and the economy, modeled across sets of feeders.
Real Gross Regional Product
Non-Manufacturing Employment
Manufacturing Employment
Real Total Retail Sales
Real Income: Total Personal
Real Income: Earnings - Farm Proprietors Profits
Agricultural Employment
CDD = cooling degree days
FIT Model
LoadSEER methods employ not 1, but 3 key sets of models
For increased accuracy in load forecasting
For increased defensibility with regulators and management
To enhance convergent validity (triangulation)
1 Traditional area regression forecast used by distribution planners is based
upon historical MW peaks (for past X years).
2 Spatial GIS forecast using LoadSEER algorithms (GIS) and satellite history
3 A MWH model to forecast economic growth, controlled for weather
This 3rd model is used to increase accuracy and defensibility, and serves to
enhance the GIS based spatial forecasts. This includes normalization for
BOTH weather and economy
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MWH Model uses CDD/HDD and Regression FIT uses hourly temps
LoadSEER employs a forecast blending process
• Blend GIS with MWH Model
• Adjusted forecast – align with Corporate aggregate forecast
• T&D planner can blend traditional trend model with the adjusted
GIS/MWH model
Analytics
LoadSEER View
1 in 10 Year
Weather Risk
Weather Normal
1 in 2 Year
Capacity
Economic
Predictor
Variable
Weather
Predictor
Variable
Your Final Forecast appears in
red and blends Global
Corporate vs. Local Regression
Economy Now Trumps Weather Risk
LoadSEER uses multiple methods to forecast loads.
Spatial analytics from satellite data for growth trending.
MWH model with load factor conversions, consistent with Corporate Forecast.
Local Regression models with:
Weather Impact
150 Economic Variables
Recently, economic risk is trumping weather risk. Need for “economic normalization” vs.
weather normal.
Added complexities of renewable, solar, and EV loads added as layers.
Analytics
Increased Accuracy in New LoadSEER model
Several sources of increased accuracy in LoadSEER
Forecasts BOTH temperature and economic data (Moody’s)
Economic downtown caused poor model fits on temp alone
Spatial forecasts more accurate at a local level vs. corporate
Accommodates impact of special load additions at specific locations
Added the MWH model to enhance accuracy, and improve economic forecast
Uses load research per load area to estimate weather risk
Enables accurate consistency check to overall Corporate Forecast
Incorporates a pooled model for forecasting circuits for MWH model
Forecasts feeder growth more accurately than if done individually
Enables automatic key driver identification, so LoadSEER finds best fit
Enables layering in of new classes (e.g., solar, DG, EV, etc.)
More localized IRP strategy
Possibility of adding gas end uses (e.g., gas air conditioning).
Solar forecast, EV forecasts, other DG/ Smart Grid programs.
Land Use Forecast (2011)
Land Growth Classes
2 = Residential - Rural
3 = Residential - Suburban
4 = Residential - Multi/Dense
5 = Residential - High Rise
6 = Retail Commercial (Including Parking Lots)
8 = Business Parks
12 = Light and Medium Industrial
15 = Institutional (Schools, Churches)
Analytics
Land Growth Classes
8 = Business Parks
Analytics
Land Growth Classes
8 = Business Parks
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Land Growth Classes
8 = Business Parks
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Land Growth Classes
8 = Business Parks
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Land Growth Classes
8 = Business Parks
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Uncertainty
• While spatial forecasts improve T&D
planning, it makes sense to test the plan
under multiple scenarios to improve
robustness of the plan
• Example scenarios
– New plant location
– Major plant expansion
– Faster or slower load growth
Analytics
Incorporating EV adoption forecasts
into Grid Planning Risk
Market Research and GIS Modeling
Analytics
Web Based Choice Example
Analytics
Web Based Choice Example
Analytics
Market Simulation
Example Share Forecast For Simple Market of 4 Cars
Shares Vary With Competition, Gas Prices, Fueling, Etc.
Example
Type
Price
Fueling
time
Fueling
duration
Impact on
Reducing
Global
Warming
Share of
Preference at
Gasoline
Price Level
$4 / Gallon
Share of
Preference at
Gasoline
Price Level
$5 / Gallon
Share of
Preference at
Gasoline Price
Level $6 /
Gallon
Share of
Preference at
Gasoline
Price Level
$8 / Gallon
Share of
Preference
at Gasoline
Price Level
$10 / Gallon
Standard
$25,000
Any time
2 Minutes
No impact
87.37%
79.98%
71.86%
54.16%
38.52%
PHEV
$40,000
Any time
1 hour
High Impact
8.36%
14.44%
20.82%
38.15%
54.66%
EV
$50,000
No fueling
between 3 –
6 pm
5 hours
High Impact
2.05%
3.36%
5.61%
6.30%
5.93%
Scooter
< $7,000
Any Time
5 hour
Medium Impact
2.22%
2.21%
1.71%
1.39%
0.89%
Scenario applied here uses $4 gasoline, 10 cent electricity, 4 cars (over simplified here) and an expensive EV option at
$50,000 versus the hybrid option at $40,000. Share forecast varies significantly by demographics and market segments.
This is simply an average. Fueling convenience benefits and image factors are just as important as costs and economics.
Analytics
Prius Vehicle
Prius Type Vehicle
The popularity of the Toyota Prius presents an interesting dichotomy in
customer segment preferences. Where we see increased likelihood in
the purchase of this type of vehicle among customer segments which
are _____________________, we see a marked decrease in likely
popularity among __________________(note the negative sign on the
Vehicle parameter estimate.
Scoring Function for Prius Type Vehicle = - 0.2862+1.544 * VAR1
(combination of 3 demographics) + 2.1171 * Economic VAR - 2.2901 *
Specific Vehicle type.
Analytics
Adding EV to Load Growth
Segments Tend To Cluster
(“Birds of a Feather Flock Together”)
Forecasted Adoption of Car Type C
If segment clusters are large enough,
or charge rates are high enough,
local circuits and transformers may be at risk
Page 6
Adding EV to Distribution Loads
All Segments (T&D focus)
Page 10
Adding EV to Distribution Loads
Scoring Process
30
50
All customers scored using clustering and regression,
to get Prob Value,35eventually replaced with actual
purchase (Prob=100%) 65
40
45
70
85
35
65
80
30
30
60
70
20
Page 11
Fine Tuning Within Hot Spots
LoadSEER Spatial Forecasting
Identifies “Hot Spots”
Smart Grid Analytics
Identifies Which Service Transformers
Are At Risk From Load Growth + EV
Analytics
Service Transformer Analytics
Averaged, Load Research
profiled load shapes are
nice and smooth….
…. AND MISLEADING.
In reality, service
transformers experience
much more load volatility.
Load At Risk analytics
accurately models loads.
Analytics
Load At Risk is defined more precisely than “at 95 degrees” since more than
temperature drives the loads.
Load Normalization is more accurate than Weather Normalization.
September 4pm Load At Risk
Each hour has its own distribution
Every other year, 1 in 2 years
1 in 5 years
Graphic representation of
full Load At Risk Simulations
1 in 10 years
1 in 100 years
10%
Percentiles
1st 5th 10th
50th
5%
1% Load at Risk
90th 95th 99th
(avg)
Analytics
Percentiles
Perhaps
Under-loaded
or
“Over”-Sized
For
Cold Load
Pickup
At Risk Adding
Electric Vehicle
Analytics
At Risk Now
What Does It All Mean?
• Simple trend based forecast methods may
be simpler to implement, but have lower
accuracy.
• Spatial forecast methods incorporate
economic and demographic growth impacts
from adjacent locations.
– Increases accuracy
• Spatial forecasting methods can add
capability to include the impacts of solar, EV,
storage, or other localized effects.
Analytics
QUESTIONS ?
[email protected]
513 479 1235
or 513 762 7621
312 Walnut St Suite 1600, Cincinnati Ohio USA 45202
Analytics

LoadSEER Satellite Analytics

Transcription

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