Rebecca Widiss and Kevin Porter Exeter Associates, Inc. SPSC

Variable Generation
Forecasting in the
West
Rebecca Widiss and Kevin Porter
Exeter Associates, Inc.
SPSC Webinar
December 18, 2013
Today’s Webinar
Project Overview
VG Forecasting Basics
Costs & Metrics
Forecasting Uses
Forecasting Practices
Solar Forecasting
Lessons Learned & Next Steps
Photo Credit: Peter Haas via Wikimedia Commons
http://commons.wikimedia.org/wiki/File%3APower_Lines_DSC1993w.jpg
Project Overview
Project History
▪ Project an update of a report that was done for the SPSC, NREL and DOE in
2011 and published by NREL in 2012: www.nrel.gov/docs/fy12osti/54457.pdf
▪ Intent was to interview several operating entities (OEs) in the West about their
approach to variable generation (VG) forecasting
▪ Funding provided by the U.S. Department of Energy’s Office of Electricity Delivery
and Energy Reliability, technical support provided by NREL
4
Current Project
▪ Interviews with thirteen operating entities
▪ Twenty-eight questions in the survey covering:
•
•
•
•
•
Needs assessment
Forecast timeframes,
scopes, types, and sources
Probabilistic forecasting
Incorporating curtailments
and outages
Data collection requirements
and challenges
•
•
•
•
•
•
The impact of FERC Order
•
764 or sub-hourly scheduling •
System operator training
Control-room integration
•
Forecast costs
Cost-benefit analysis
Reductions to operating
reserve requirements
Accuracy measurements
Utility-scale and “Behind-theMeter” solar forecasting
Coordination between
operating entities
▪ Three deliverables: report, summary table, and webinar
▪ We gratefully acknowledge the continued support of DOE and technical assistance of NREL
5
Interviewees
▪ Alberta Electric System Operator
(AESO)
▪ Arizona Public Service (APS)
▪ Bonneville Power Administration (BPA)
▪ California Independent System
Operator (CAISO)
▪ Glacier Wind
▪ Idaho Power
▪ Portland General Electric (PGE)
▪ Puget Sound Electric (PSE)
▪ Sacramento Municipal Utility District
(SMUD)
▪ Southern California Edison (SCE)
▪ Turlock Irrigation District (Turlock)
▪ Xcel Energy
▪ Pacific Gas and Electric (PG&E)
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VG Levels and Forecasting Practices
Glacier
Wind
Idaho
Power
PG&E*
PGE
PSE
n/a**
1,759
n/a***
2,140
4,328
5,660
399
669
n/a
550
823
0
2,700
137****
2,215
6
3,263
0
2-3
n/a
2
0.5
150
1,400
3
390+
2008
2009
2004
2009
2011
2000s
2007
2007
2011
1980s
2009
2008
X
X
X
X
X
X
X
X
X
X
X
To come
X
Operating Entity
AESO
APS
BPA
Average Load
8,604
4,500
6,000
Wind Capacity
1,088
290
4,516
Solar Capacity
0
481
2010
X
Year Forecasting Began
Wind Forecast
Solar Forecast
X
CAISO
21,57935,781
X
*PG&E, SMUD, and SCE also receive PIRP forecasts from CAISO
**Glacier Wind has no load
SMUD*
SCE*
Xcel
Turlock Energy
1,200 13,000*** 245-336
X
X
4,000
To come
***Load, wind, and solar capacity for SCE and PG&E included in CAISO totals
****Turlock’s Tuolumne Wind Project is in BPA’s service area
▪ Variable generation is rising swiftly
▪ SCE had 383 MW of solar online in 2011; that number has more than tripled
▪ In December 2013, the CO PUC approved Xcel’s acquisition of 450 MW of wind and 170 MW of
solar at prices equal to or below the company’s cost of conventional generation
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VG Forecasting Basics
Common Forecasts and Their Applications
Type
Content
Weather Situational
Provides severe weather alerts
Awareness
Primary Use
Important because storms can
lead to rapid changes in VG
Intra-day
Typically provides 10-minute power Helps grid operators anticipate
values for next few hours
near-term changes in VG and
take appropriate action
Day-ahead
Provides hourly power values for
the next few days
Often used in the unit
commitment process
Nodal
Aggregates forecasts for each
node or transmission delivery point
Helpful in transmission
congestion planning
Persistence
Simply assumes that current output Useful for very short-term
levels will continue
decisions (<1 hour)
Ensemble
Aggregates output values from two
or more forecasts
Helps compensate for the error
inherent in any forecast
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The Basic Steps to Creating a Wind Forecast
▪ Large-scale Numeric Weather
Prediction (NWP) models predict
weather conditions
▪ Run by agencies such as NOAA; used
for a wide variety of applications
▪ Tend to have limited spatial resolution
▪ Local weather data gathered to
supplement NWP
▪ Statistical models used to account
for subtle influences of local terrain
Image credit: National Center for Atmospheric Research
(http://www.mmm.ucar.edu/prod/rt/wrf/wrf20/2013121112/wsp.hr18.png)
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A Few Notes on Solar Forecasting
▪ At a very early stage, where wind
forecasting was 10 years ago
▪ Clouds, water vapor, and aerosols
affect how much solar radiation
reaches the earth (insolation)
▪ Hour-ahead forecasts tend to rely on
statistical models, satellite images of
water vapor channels, and sky imagers
▪ Day-ahead forecasts tend to use
physical models that simulate
atmospheric processes
Photo credit: Hong Kong Observatory
(http://www.hko.gov.hk/wxinfo/aws/imager/sky.htm)
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Improving Accuracy
▪ Gathering data from more sites,
especially at hub heights
▪ Improving model formulas, training
models with historic data
▪ Shortening scheduling intervals and
updating forecasts more frequently
▪ Improving staff understanding of and
access to forecasts
Plot of System-Wide Wind Forecast Error Versus Forecast Time
Horizon, with Error Expressed as Mean Absolute Error as a
Percentage of Installed Wind MW
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Costs & Metrics
Costs
▪ Wind forecast costs have gone down
dramatically since 2011
▪ In 2011, one of the few OEs willing to
share hard numbers was paying
$1,000 per plant per month
▪ In 2013, several respondents
confirmed a ballpark cost of $300$400 per month per wind plant
▪ Declining costs foster a “shopping”
mentality
Action
Operating Entity
Using multiple
vendors
•
•
•
•
•
Running trial
with multiple
vendors
• BPA
• Glacier Wind
• SMUD
BPA
Glacier Wind
PGE
PSE
SCE
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Cost-Benefit Analysis
▪ There has been a striking shift away
from C-B analysis since 2011
▪ Half of interviewees say it simply is
not worth the effort—VG forecasting
is so clearly beneficial:
▪ It’s a no-brainer – AESO
▪ Anyone with large amounts of
renewables should forecast – PG&E
▪ One day of less hedging probably
takes care of the yearly cost of
forecasting – PGE
▪ Xcel tracks the value of a 1%
reduction in forecast Mean Average
Error by market
Region
Annual Value
Public Service
Company of CO
$1,300,000
Southwestern
Public Service Co.
$250,000
▪ SCE says its ROI significant,
PG&E plans formal C-B analysis,
SMUD interested
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Accuracy
▪ Almost every respondent says accuracy is on the rise—a few shared their track
records:
Operating Entity
2012 Report
Current Report
AESO
Day-ahead MAPE: 13.0%
Day-ahead MAPE: 12.8%
CAISO
Day-ahead MAE: <15%
Day-ahead MAE: <10%
Glacier Wind
Hour-ahead MAE: 10% better than
persistence, at best
Hour-ahead MAE: 20-25% better
than persistence
Idaho Power
Day-ahead MAE: 12%
Day-ahead MAE: 13%
SCE
Day-ahead RMSE: 13-20%
Day-ahead RSME: 8-13%
▪ Interviewed OEs credit gains to improved forecasting techniques, seasoned
vendors, experienced users, trained models, and growing portfolio size
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Reserve Requirements
▪ Many interviewees believe that VG
forecasting has led to lower
operating reserve requirements
(than if such forecasting were not put
into place)
▪ Some anecdotal evidence for this:
▪ AESO buying the same reserves as
five years ago, while its wind portfolio
has doubled
▪ But no interviewee provided formal
quantification
The Damsel of the Holy Grail
Image credit: Dante Gabriel Rossetti via Wikimedia
Commons (http://tinyurl.com/lv26jen)
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Forecasting Uses
Core Applications of Forecasting
▪ Almost every OE interviewed has expanded forecasting uses since 2011
▪ Biggest change: the use of forecasting in forward unit commitment has more than
doubled!
Forward Unit Commitment
Intra-Day Unit Commitment
Planning Resources
Hydro, Coal, or Gas Storage Mgmt.
Gen./Trans. Outage Planning
0
2
4
6
8
10
12
19
Additional Applications of Forecasting
▪ Unique circumstances lead to unique uses:
▪ In 2014, wind generators will be allowed to use
AESO’s short-term forecast to bid into its energy
market
▪ BPA uses wind forecasting to meet non-power
objectives (e.g. irrigation, flood control, fisheries
preservation) for the federal hydro facilities it
manages
▪ Because its wind project is outside its service area,
Turlock uses wind forecasts for trading, marketing,
and optimizing schedules
Image credit: Tom Patterson, NPS
via Wikimedia Commons
(http://tinyurl.com/pwlvvw8)
▪ If high wind is predicted, Xcel Energy will reduce
day-ahead gas purchases
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Forecasting Practices
Types and Timeframes
▪ Most of the surveyed OEs use a
suite of forecasts…
▪ Persistence (12 OEs)
▪ Numerical Weather Prediction (12)
▪ Statistical (12)
▪ Weather Situational (8)
▪ Ramp (2)
Spanning multiple timeframes…
14
12
10
8
6
4
2
0
22
Scopes
▪ … With broader geographic coverage
than in 2011
Individual Plant
▪ “Region” is a self-defined term
▪ CAISO looking across Western
Interconnection
▪ SCE looking to develop forecasts by
wind region (e.g. Tehachapi, San
Gorgonio)
Utility / Balancing Area
▪ PGE views the forecast BPA provides
as regional
Commercial Pricing Node
Region
0
5
10
15
▪ Xcel forecasts for commercial pricing
nodes within MISO
23
Data Collection
▪ Few changes since 2011 in types of wind data being gathered:
Operating Entity
Glacier Idaho
Wind Power
AESO
APS
BPA
CAISO
PG&E
PGE
SMUD
SCE
Turlock
Xcel
Wind Speed/Direction
X
X
X
X
X
X
X
X
X
X
X
X
Temperature
X
X
X
X
X
X
X
X
X
X
X
X
Barometric Pressure
X
X
X
X
X
X
X
X
X
X
X
Turbine Location
X
X
X
X
X
X
X
X
X
X
X
X
Turbine Power Output
X
X
X
X
X
X
X
X
X
X
X
X
Turbine Availability
X
X
*
*
X
X
X
X
*
X
X
X
Turbine Outage
X
*
*
*
X
X
X
X
X
X
X
Turbine Power Curve
X
X
**
X
**
X
X
X
X
**
* Receives info from wind plant in total
** Derives an empirical wind curve
▪ AESO, BPA sped up data transmission from generators
▪ Interviewees experimenting with Doppler radar, Lidar, extreme temperature notifications, etc.
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Probabilistic Forecasting
Operating
Entity
AESO
APS
BPA
CAISO
Glacier
Wind
Idaho
Power
PG&E
PGE
PSE
SMUD
Turlock
Xcel
Use of
Ensemble
Forecasts
X
X
Discontinued
Use of
Confidence
Intervals
X
X
To come
X
X
X
To come
To come
X
To come
In Trial
X
X
X
X
X
X
X
Confidence
Interval Range
10% and 90%
▪ Ensemble forecasts and confidence
intervals (CIs) popular in theory
▪ Challenge lies in interpreting them
X
Considering
90%
▪ AESO uses likeliest value, not CI
▪ SMUD looks for forecast “unanimity”
▪ Xcel working on new way to combine
uncertainties from multiple forecasts
90%
80%
80%
80%
75%
▪ BPA using “Super Forecast”
methodology to select best forecast
for each plant for every hour
25
Curtailments and Outages
What info do you
incorporate into your
forecasts?
Just outages/
availability
Neither
▪ Nearly every OE interviewed
uses turbine outage/
availability information to
project full (potential) value of
wind plants
▪ If curtailment info available, it
is used for calculating metrics
and for statistical training of
forecasts
O/A &
Curtailments
26
Control Room Integration
▪ VG forecast displays are nearly
universal
▪ Typically automated feeds, often
alongside current weather and
generation
▪ Roles and protocols changing
▪ CAISO combined load and VG
forecasting
▪ Xcel has forecast-based protocols
for curtailment, cycling, unit
decommitment
Photo credit: Design-Build Institute of America
(http://www.dbia.org/awards/Pages/2012-Award-Winners.aspx)
27
Control Room Integration (cont’d)
▪ 1/3+ of respondents integrating forecast values into
operations tools:
▪ AESO – values fed into dispatch decision and short-term
adequacy tools
▪ APS – values fed into dispatch and scheduling tools
▪ CAISO – developing ramping tool to gauge near-term needs
using load, VG forecast, resource commitments, etc.
▪ BPA – values fed into Real-time Reserves Requirement Tool
Image credit: Jorge Maturana
via Wikimedia Commons
(http://tinyurl.com/mesm6uq)
▪ Idaho Power – day-ahead forecast fed into EMS
▪ PGE – working on tool to facilitate tighter scheduling periods
28
Staff Training and Familiarity
▪ Formal training is rare
▪ APS summer prep session
Are operators
familiar with their
VG forecasts?
▪ CAISO 6-wk orientation, biannual course
▪ More often, staff coach one another
informally
▪ Three respondents have meteorologists on
staff to help interpret forecasts (Idaho
Power, PSE, Xcel Energy)
Yes
Getting
there
▪ CAISO looking to add a meteorologist
29
Solar Forecasting
Utility-Scale Solar Forecasting
▪ At a very early stage
▪ Unlike wind, some opting to develop in-house solar
forecasts (APS, Idaho Power, CAISO, SCE)
▪ No consensus on how to do so, nor how easy it will be
▪ Data requirements are still fairly minimal
▪ Combo of: solar insolation, availability, weather info, back
panel temp, system characteristics, power output
▪ SMUD testing four different commercial providers
Photo credit: Chandra Marsono
via flickr (http://tinyurl.com/k3rscnj)
31
“Behind-the-Meter” Solar Forecasting
▪ If solar forecasting is in its infancy,
DG forecasting is at an even more
rudimentary stage
▪ Significant interest and concern
expressed during interviews
▪ Locating DG systems is Step 1
▪ APS mining in-house records
▪ CAISO working with CEC and Clean
Power Research
Is DG solar forecasting a
priority?
Imminent
need
Eventual
need
Not yet a
concern
▪ PG&E using data from CA Solar
Initiative
32
Lessons & Next Steps
Advice and Preferences
Advice for Those Starting Out:
Preferences for the Region:
▪ Start early
▪ Interviewees split on sharing
forecasts
▪ Set realistic expectations
▪ Buy/create what you need
▪ Put data requirements into contracts
▪ Competitive advantage issue
▪ Question of how useful it would be
▪ Also split on the value of guidelines
▪ Use several performance metrics
▪ Fear of “one-size-fits-all” rules
▪ Evolve with your forecast
▪ Yet interest in universal resource
adequacy standard for reserves
34
Trends, Challenges, Recommendations
Common Trends
Common Challenges
Possible Next Steps
▪ Increasing accuracy
of, and confidence
in, wind forecasts
▪ Forecasting the timing
and size of ramps
▪ Use of forecasts in
short-term dispatch
▪ Handling uncertainty
▪ Integrate
forecasting with
faster scheduling
and balancing area
coordination
▪ Rapidly decreasing
wind forecast costs
▪ Early development
of solar forecasts
▪ Integration of
forecasts into
operational tools and
processes
▪ Forecasting for DG solar
▪ Further improving
forecast accuracy
▪ Further integrating
forecasts into grid
operations
▪ Reduce confidence
intervals?
▪ Improve national
forecast models
35
Questions?
Rebecca Widiss
Kevin Porter
[email protected]
[email protected]
410-992-7500
410-992-7500
Photo credit Dennis Brekke via flickr
(http://tinyurl.com/mbvdzkh)
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