Integrating Traditional, Variable, Renewable,
Distributed, and Demand-Side Response Resources
GRIDSCHOOL 2010
MARCH 8-12, 2010  RICHMOND, VIRGINIA
INSTITUTE OF PUBLIC UTILITIES
ARGONNE NATIONAL LABORATORY
Thomas D. Veselka
Center for Energy, Economic, and Environmental Systems Analysis
Decision and Information Sciences Division
ARGONNE NATIONAL LABORATORY
tdveselka@anl.gov  630.252.6711
Do not cite or distribute without permission
MICHIGAN STATE UNIVERSITY
GridSchool 2010
The Grand Challenge of Integrating Renewable Resources with Variable and Intermittent
Production into the Grid Is the Ability to Respond to Rapid and Unpredictable Fluctuations
45
40
Total of 3 Sites
The thermal system
or loads need to
adjust quickly
30
25
Rapid Ramping
20
Wind is not Always
Available when
Needed Most
15
10
5
Day of the Month in August
21
20
19
18
17
16
0
15
Wind Output (MW)
35
Veselka - 02
GridSchool 2010
Wind Probability Profiles Vary
Seasonally and by Time of Day
(MW)
Wind Production
Generation
(MWh)
90
On Average, Wind Output Decreases in the
Morning When Load Is Rapidly Increasing.
The Opposite Occurs in the Evening.
80
70
February - 4 AM
February - 6 PM
August - 4 AM
August - 6 PM
All Hours of Year
60
Winter Wind Is Greater
Than Summer Wind
50
40
30
20
10
0
Summer Nighttime Wind
Is Less than Daytime Wind
0
25
50
75
100
Exceedance Probability
(%) (%)
Exceedance
Probability
Veselka - 03
GridSchool 2010
Wind Resources Vary Widely Across the United States
Often the Best Wind Resources Are Far from Major Load Centers
MISO
Transmission
PJM
765 kV
Veselka - 04
GridSchool 2010
The U.S. has Installed the Most Wind Capacity in the World, but the
Percent Penetration Rate (% Production) Is Relatively Small
U.S. recently became the world leader in wind power with over 8 GW installed in
2008 and 25 GW total installed capacity (AWEA, Feb 09)
Veselka - 05
GridSchool 2010
Wind Capacity by State
Veselka - 06
GridSchool 2010
U.S. Wind Capacity Growth
Source: AWEA 2009
Veselka - 07
GridSchool 2010
Does Wind Power Influence Market Operations?
Midwest ISO Wind Power and MN Hub Prices, May 11-17, 2009:
http://www.midwestiso.org/
180.0
Wind power
ramping events
4000.0
3500.0
130.0
80.0
2500.0
30.0
-20.0 1
2000.0
1500.0
25
49
73
97
121
145
Wind Power [MW]
Price [$/MWh]
3000.0
1000.0
-70.0
500.0
-120.0
0.0
Negative prices (LMPs)
Day Ahead price
Time [hour]
Real Time price
Wind power
Veselka - 08
GridSchool 2010
United States Photovoltaic Solar Resource Map
Veselka - 09
GridSchool 2010
There Are Several Different Types of Photovoltaic
Technologies, Each of Which Has its Own Set of Attributes
One Size Does Not Fit All
Luminescent
Solar
Concentrators
with
Multijunction
Cells (~40%)
Veselka - 010
GridSchool 2010
Photovoltaic Efficiencies Have Increased Dramatically Since
the Mid-1970’s and Are Expected to Continue Improve
Source: http://en.wikipedia.org/wiki/Solar_cell
Veselka - 011
Public Service Company of Colorado Solar Study
GridSchool 2010
200 MW of PV & 200 MW CSP with 800 MWh Storage
Source: http://www.xcelenergy.com/SiteCollectionDocuments/docs/PSCo_SolarIntegration_020909.pdf
Veselka - 012
GridSchool 2010
Generation (kW)
Spanish PV Study: Annual Hourly Photovoltaic Output
6
7
8
9
10
11
12
13
14
Source: http://www.icrepq.com/ICREPQ'09/abstracts/520-ramon-abstract.pdf
15
16
17
18
19
20
21
22
Veselka - 013
GridSchool 2010
Most States Have a Renewable Portfolio Standard (RPS)
WA: 15% by 2020*
MN: 25% by 2025
MT: 15% by 2015
☼ OR: 25% by 2025
(Xcel: 30% by 2020)
MI: 10% + 1,100 MW
ND: 10% by 2015
(large
by 2015*
utilities)*
SD: 10% by 2015
5% - 10% by 2025 (smaller utilities)
WI: Varies by utility;
10% by 2015 goal
☼ NV: 25% by 2025*
☼ CO: 20% by 2020
IA: 105
(IOUs)
MW
10% by 2020 (co-ops & large munis)*
CA: 20% by 2010
VT: (1) RE meets any increase
in retail sales by 2012;
(2) 20% RE & CHP by 2017
KS: 20% by 2020
UT: 20% by 2025*
☼ NY: 24% by 2013
☼ OH: 25% by 2025†
☼ IL: 25% by 2025
VA: 15% by 2025*
☼ NC: 12.5% by 2021
(IOUs)
10% by 2018 (co-ops & munis)
☼ NM: 20% by 2020
New RE: 10% by 2017
☼ NH: 23.8% by 2025
☼ MA: 15% by
2020
+ 1% annual increase
I Renewables)
RI:(Class
16% by 2020
CT: 23% by 2020
☼ MO: 15% by 2021
☼ AZ: 15% by 2025
ME: 30% by 2000
☼ PA: 18% by 2020†
☼ NJ: 22.5% by 2021
☼ MD: 20% by 2022
☼ DE: 20% by 2019*
☼ DC: 20% by 2020
(IOUs)
10% by 2020 (co-ops)
TX: 5,880 MW by
HI: 40% by 2030
State renewable portfolio standard
State renewable portfolio goal
Solar water heating eligible
Source: www.dsireusa.org
/ September 2009
2015
☼ Minimum solar or customer-sited requirement
*†
Extra credit for solar or customer-sited renewables
29 states &
DC
have an RPS
5 states have goals
Includes separate tier of non-renewable alternative resources
Standards Should be Consistent
with Renewable Resources & Needs
Veselka - 014
GridSchool 2010
Currently, Volatility in Production from Variable Resources Are Accommodated
by Changing Thermal Unit and Hydroelectric Power Plant Production Levels
The Greater the Operational Flexibility of Dispatchable Units,
the more Variability the Grid Will Accommodate
Min
Output
Load Following
Range
Output (MW)
Operating
Capacity
Cold
Start
Time
Minimum
Down
Time
Minimum
Up
Time
Time
Veselka - 015
GridSchool 2010
Some Technologies Are Able to Come On-line Quickly to Respond to
Rapid Load Changes while Others Respond More Slowly
Weeks for Shutdown and Startup
Nuclear Steam
Diesel Generator
Gas Turbine
Combined Cycle
Hydroelectric
Fossil Steam
0
5
10
15
20
25
Cold Start Time (Hours)
Nuclear Steam
Diesel Generator
Gas Turbine
Combined Cycle
Some Hydropower Plants Change Very Quickly
Hydroelectric
Fossil Steam
0
2
4
6
8
10
Ramp Rates (%/min)
12
14
16
18
20
Veselka - 016
GridSchool 2010
The Load Following Range Is Restricted by the
Output Minimum and Generation Capacity
Nuclear Steam
Diesel Generator
Gas Turbine
Combined Cycle
Hydroelectric
Fossil Steam
0
10
20
30
40
50
60
70
80
90
100
Minimum Output (% of Capacity)
Minimum
Output
Cold
Start
Time
Minimum
Down
Time
Time
Load Following
Range
Production (MW)
Operating Capacity
Minimum
Up
Time
Veselka - 017
GridSchool 2010
Ideally, Units Are Dispatched Based on Production Cost
Max Load
NGCC
41 $/MWh
Cycling
Coal
32 $/MWh
Coal
25 $/MWh
Nuclear
12 $/MWh
Resource
Stack
GT
Load (MW)
Supply (MW)
Gas
Turbines
80 $/MWh
Highest
Production
Costs
NGCC
Min Load
Cycling Coal
Base Load Coal
Nuclear
Lowest
Production
Costs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 018
Unfortunately, a Steam Plant (e.g., Cycling Coal) Does not
Have the Flexibility to Operate at a Very Low Output Level
GridSchool 2010
Nuclear Steam
Diesel Generator
Max Load
Gas Turbine
Combined Cycle
Hydroelectric
GT
Fossil Steam
0
10
20
30
40
50
60
70
80
90
Highest Production
Costs
100
Load (MW)
Minimum Output (% of Capacity)
GT
Operations
NGCC
Cycling Coal
Base Load Coal
Nuclear
Min
Load
Lowest
Production
Costs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 019
GridSchool 2010
Wind Production Will Serve Some of the Load.
This Production Reduces the Loads that Are Served by other Generating Resources
Load/Wind Output (MW)
Max Load
Min Load
Wind
Generation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 020
GridSchool 2010
Dispatchable Units Serve a Load Profile that Typically, but not Always, Has
Greater Fluctuations Relative to the Case where there Is no Wind
Load (MW)
New Max
New Min
Larger Range of Operations
Wind Typically Increases
Resultant Load Changes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 021
Unit Dispatch with Wind Results in Less Thermal
Generation & Associated Air Emissions
Load (MW)
Highest
O&M
Costs
Coal May Operate
Less Efficiently
@ Min Gen
GT
GridSchool 2010
Without Wind
NGCC
Cycling Coal
With Wind
Base Load Coal
Nuclear
Lowest
O&M
Costs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 022
GridSchool 2010
Wind and other Renewable Technologies Will
Reduce Greenhouse Gas Emissions
Nuclear Steam
Diesel Generator
Gas Turbine
Combined Cycle
Hydroelectric
Fossil Steam
0
100
200
300
400
500
600
CO2 Emissions (kg CO2/BOE)
20 Percent Wind by 2030 Report: CO2 Emissions Are
Estimated at 25 Percent Lower Than a No-Wind Scenario
Veselka - 023
GridSchool 2010
As a Result of Variable Resource Generation Some
Units Will Operate at a Different Efficiency Point
100
Net Electrical Efficiency (%)
90
Hydro
80
70
60
Combined Cycle
50
Nuclear
40
Diesel
Fossil Steam
30
20
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fraction of Full Load
Veselka - 024
Unit Dispatch with Greater Nighttime Wind
GridSchool 2010
Base Load Coal Unit May Need to Be Taken Off-line for Several Hours
Load (MW)
Highest
O&M
Costs
GT
Without Wind
NGCC
Cycling Coal
Sell
With Wind
Base Load Coal
Nuclear
Lowest
O&M
Costs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 025
GridSchool 2010
Pumped Storage Plants Can Be Used to Help
Smooth Out Loads Served by Other Dispatchable Resources
Upper
Reservoir
Pump
Lower
Reservoir
Substation
Energy is Consumed
When Pumping
React to Sudden
Changes in Variable
Resource Production
Load (MW)
Fill Load Valley (Consume)
to Utilize Low Cost
Production and Avoid
Expensive Shutdown Costs
Hour of the Day
Veselka - 026
2,500
2,250
Least-Cost Resource
Stack Before Outage
Unprepared
Slow Transition
2,000
1,750
1,500
Coal Steam
Partially
Loaded
1,200W
1,250
Cycling Coal
1,000
750
40 $/MWh
Base
Coal
25 $/MWh
500
250
0
Nuclear
8 $/MWh
Supply
40$/MWh
Operational Problems
After the outage it will take
hours for the system to
reach the least-cost state
of operations
All demand will not be
served
Marginal
Cost
Supply - Nuclear Unit Out of Service (MW)
Supply Stack without Maintenance (MW)
When a Unit Is Forced Out of Service, the System
Responds by Altering the Dispatch
2,500
2,250
GridSchool 2010
Least-Cost Resource
Stack After Outage
2,000
1,750
1,500
1,200W
1,250
1,000
750
Gas Turbines
80 $/MWh
80$/MWh
NGCC
60 $/MWh
Cycling Coal
40 $/MWh
500
250
0
Base
Coal
25 $/MWh
Supply Marginal
Cost
Veselka - 027
GridSchool 2010
2,500
2,250
2,000
1,750
1,500
Fast Transition
1,200 MW Load
1,250
Gas Turbines
1,000
750
500
250
0
NGCC
60 $/MWh
Cycling Coal
40 $/MWh
Base Coal
25 $/MWh
80 $/MWh
Spinning Reserves
NGCC
NG Steam
Oil Steam
Gas Turbines
Total
Nuclear
8 $/MWh
Supply
Marginal
Cost
250 MW
110 MW
110 MW
30 MW
500 MW
Supply - Nuclear Unit Out of Service (MW)
Supply Stack without Outages (MW)
Spinning Reserves Help Alleviate Operational
Problems Associated with Random Outages
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
1,200 MW
80$/MWh
Simultaneously
Ramp Operations
Gas Turbines
80 $/MWh
NGCC
60 $/MWh
Cycling Coal
40 $/MWh
500
250
0
Base
Coal
25 $/MWh
Supply
Veselka - 028
GridSchool 2010
Supply Stack without Maintenance (MW)
System Operators Need to Make Certain that
Ramping Resources Are Available
2,500
6 AM
2,500
7 AM
2,500
8 AM
2,250
Load
1200 MW
2,250
Load
1500 MW
2,250
Load
1800 MW
2,000
Spinning Reserves
2,000
Spinning Reserves
2,000
Spinning Reserves
1,750
1,750
Old GT
120 $/MWh
1,500
1,500
Old GT
120 $/MWh
1,500
1,250
1,250
Gas Turbines
500 MW
1,750
Gas Turbines
1,000
750
500
250
0
NGCC
60 $/MWh
Cycling Coal
40 $/MWh
Base Coal
25 $/MWh
Nuclear
8 $/MWh
Supply
500 MW
NGCC
60 $/MWh
1,250
Cycling Coal
1,000
750
Base Coal
25 $/MWh
750
0
NGCC
60 $/MWh
Cycling Coal
Base Coal
25 $/MWh
500
500
250
Gas Turbines
40 $/MWh
1,000
40 $/MWh
500 MW
Nuclear
8 $/MWh
Supply
250
0
Nuclear
8 $/MWh
Supply
Veselka - 029
GridSchool 2010
Supply Stack without Maintenance (MW)
Usually, the Grid Can Accommodate Relatively
Small Amounts of Wind Generation
2,500
6 AM
2,500
2,500
7 AM
8 AM
2,250
Load
1200 MW
2,250
Load
1500 MW
2,250
Load
1800 MW
2,000
Spinning Reserves
2,000
Spinning Reserves
2,000
Spinning Reserves
500 MW
500 MW
1,750
1,750
1,750
1,500
1,500
Old GT
120 $/MWh
1,250
1,250
Gas Turbines
WIND
1,000
750
500
250
0
NGCC
60 $/MWh
Cycling Coal
40 $/MWh
Base Coal
25 $/MWh
Nuclear
8 $/MWh
Supply
NGCC
60 $/MWh
Ramp up
due to
wind
decrease
1,500
1,250
Cycling Coal
1,000
750
Base Coal
25 $/MWh
750
0
NGCC
60 $/MWh
Cycling Coal
Base Coal
25 $/MWh
500
500
250
WIND
40 $/MWh
1,000
40 $/MWh
500 MW
Nuclear
8 $/MWh
Supply
250
0
Nuclear
8 $/MWh
Supply
Veselka - 030
GridSchool 2010
The Unpredictability of Wind Compounds Grid Integration Problems
Forecast of Wind Power Production Levels Can Be Made for the Next Few Days
Eyeballing: Looks pretty good
Mean absolute error is 9.3%
But devil is in the details (ramps)
Source: Iberdrola Renewables, 2009
Veselka - 031
GridSchool 2010
Wind Forecasts Are Far from Perfect in the Short-Term
and Much Worse in the Long-Term
• Error depends on
several factors
–Prediction horizon
–Time of the year
–Terrain complexity
–Model inputs and model
types
–Spatial smoothing effect
–Level of predicted power
Error in
meteorological
forecasts
Errors in wind-topower conversion
process
Errors in SCADA
information and
wind farm operation
Magnitude Error
Phase Error
Veselka - 032
Technology Improvements Are Alleviating Some Problems
GridSchool 2010
Example: The Danish Horns Rev Wind Farm Is Providing
Regulation (Frequency Response) and Balancing Response
Control Wind
Output with
Blade Pitch
(Spill Energy)
Source: Smith et al., IEEE Power and Energy Magazine, Vol. 7. No.2, 2009.
Veselka - 033
Historical Winter Load Shapes and
Wind Generation in the Midwest
GridSchool 2010
Unit Commitment Study
Wind ~ 14% of Load
Source: http://www.dis.anl.gov/pubs/65610.pdf
Veselka - 034
GridSchool 2010
Problem: Given that Wind Forecasts Have Errors, Make
an Economic Unit-Commitment Schedule that Is Reliable
Costs: Generation, Unserved Energy, & Startup
Constraint: Ramping, Up & Down Time, Min Output, & Maintain Reserves
Source: http://www.dis.anl.gov/pubs/65610.pdf
Wind can be curtailed (spilled energy)
Veselka - 035
Unit Commitment Results Using Various Modeling
Methodologies and Assumptions
Source: http://www.dis.anl.gov/pubs/65610.pdf
GridSchool 2010
Stochastic UC gives
higher commitment
& more available
operating reserves
Similar result for
deterministic UC
w/additional reserve
requirement
Veselka - 036
GridSchool 2010
Comparison of Costs (30 day simulation)
Results Based on Fixed Unit Commitments and Real-Time Economic Dispatch
 The potential value of forecasting illustrated by perfect forecast (D1)
 Deterministic UC with point forecast (D2) appears too risky
 Deterministic UC w/add reserves (D3) and stochastic UC (S1) give similar total cost
Source: http://www.dis.anl.gov/pubs/65610.pdf
Veselka - 037
GridSchool 2010
Finding the “Best” Mix of Generating Capacity to Backup Variable
Resources While Keeping Costs Reasonable Is Challenging
Technology
Construction Cost
Operating Cost*
Operating
Flexibility**
Fossil Steam
2
4
2
Hydroelectric
2
3
4
Combined Cycle
3
3
3
Gas Turbine
5
2
5
Diesel Generator
4
1
5
Nuclear Steam
1
5
1
* Operating costs includes fuel costs and fixed and variable operating and maintenance costs
** Operating flexibility is the unit’s ability to respond to load changes and includes ramp rates, cold start time, etc.
Desirability Rating
1 Very Low
2 Moderately Low
3 Average
4 Moderately High
5 Very High
Zero Fuel Costs
Variability Issues
Veselka - 038
GridSchool 2010
Suggested Reading: DOE’s 20% Wind by 2030 Report
 Explores “a modeled energy scenario in which
wind provides 20% of U.S. electricity by 2030”
 Describes opportunities and challenges in
several areas
 Turbine technology
 Manufacturing, materials, and jobs
 Transmission and integration
 Siting and environmental effects
 Markets
 Enhanced wind forecasting and better
integration into system operation is one of the
challenges
 DOE is funding several research projects in
this area
Veselka - 039
Thank you for your attention
Source: BOR
EXTRA SLIDES
Maximum
Variable Resource
Capacity Credit
GridSchool 2010
Reserve Capacity Is Needed to Serve Load when One or
More Generators Are Out of Service
MW
Total System Capacity
Upper RM
Peak Load Forecast
Lower RM
New Capacity Additions
Existing System Capacity
Engineering Guideline
Build 15% to 20%
more capacity than
the peak load
Years
Veselka - 042
GridSchool 2010
Variable Resources Have a Capacity Value Which Can Be
Approximated Using Probabilistic Methodologies
Probability of 3 sixes = 1/6 x 1/6 x 1/6
= 1/216
= less than %0.5
Veselka - 043
GridSchool 2010
Loads Can Also Be Viewed as Probabilistic Events
Step 1: Chronological Loads
Load (MW)
Max Load
Min Load
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 044
GridSchool 2010
Sorted Summer Load Profile
Step 2: Load Exceedance Curve
Max Load
Load
Load (MW)
Some Information Is Lost
Such as Load Changes
Over Time
hr
17
hr
4
Time
hr
17
Min Load
hr
4
0
Exceedance Probability (%)
100
Veselka - 045
GridSchool 2010
Unit Production Levels Can Be
Estimated Using a Load Duration Curve
Information Such as Unit
Ramping and Frequency of
Unit Starts/Stops Are Lost
Load
GT
Load (MW)
Max Load
Is Never
Exceeded
NGCC
Time
Cycling Coal
Min Load
Is Always
Exceeded
Base Load Coal
Nuclear
0
100
Exceedance Probability (%)
Veselka - 046
GridSchool 2010
When a Supply Resource Is Forced Out of Service Other
Resources Are Dispatched to Serve the Load
Load (MW)
GT
Load
Max Load
NGCC
Time
Min Load
Cycling Coal
Base Load Coal
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Nuclear
Forced
Out of
Service
Veselka - 047
Alternatively, the Load Curve Can Be Adjusted While
Including the Out-of-service Unit
GridSchool 2010
Load
New Max
GT
Load not Served by the
Nuclear Unit Is Satisfied
by Other Units in the
Resource Stack
Load (MW)
Time
NGCC
New Min
Nuclear
Capacity
Cycling Coal
Original
Curve
Base Load Coal
Nuclear
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the Day
Veselka - 048
GridSchool 2010
The Same Methodology Can Be Applied to
a Load Duration Curve
New Max
Nuclear
Capacity
Load (MW)
GT
NGCC
Cycling Coal
New Min
Base Load Coal
Original
Curve
Nuclear
0
Exceedance Probability (%)
100
Veselka - 049
There Is Some Probability that a Unit Does Not Operate
Nuclear
Capacity
Area = Outage Rate/100 X Nuclear Capacity
Nuclear Off
Load (MW)
Nuclear
On
GT
We don’t know with
certainty if the nuclear unit
will be either on or off at
some point in the future
Weighted Average Curve
of Nuclear Unit On & Off
NGCC
Cycling Coal
Equivalent
Load
Curve
Accounts
for Nuclear
Outages
Base Load Coal
Nuclear
0
Exceedance Probability (%)
100
Total Capacity + Peak Load
Likewise All Units Are “Convolved”
Into the Load Duration Curve
GT
NGCC
Energy
Not
Served
Cycling
Coal
Operating System Capacity
Base
Coal
GT
Nuclear
Final Equivalent
Load Curve Accounting
For All Unit Outages
Load (MW)
NGCC
Cycling Coal
Base Load Coal
Nuclear
0
Loss of
Load Probability
Exceedance Probability (%)
Original
Curve
100
Using Historical Hourly Wind Data and
Corresponding Hourly Loads by Location a Net
Load Exceedance Curve Can Be Constructed
Load (MW)
There Is a chance that all wind
turbines produce zero power at
the time of peak load
Without Wind
With Wind
There Is a chance that all wind
turbines produce maximum power
at the time of minimum load
0
Exceedance Probability (%)
100
The Firm Capacity Credit for
Wind Can Be Based on a
System Reliability Measure
Total Capacity
+ Peak Load
Firm Capacity Credit
(% of Capacity)
Load (MW)
MW
Operating System Capacity
GT
Years
Without Wind
NGCC
Reliability Increase
with Wind
Cycling
Coal
With Wind
Base
Coal
Nuclear
0
Loss of
Load Probability
Exceedance Probability (%)
100
Derated
Typically 5% to 15% of wind
turbine capacity is applied
toward the reserve margin
Design Capacity
Engineering Guideline
Wind: 5-20
Coal: 80-95
Nuclear: 90-95
NGCC: 85-90
Wind Firm
Capacity
Credit
Capacity Credit
GridSchool 2010
A Lot of Wind Capacity Is Needed to Meet
Renewable Portfolio Standards (e.g., 20% Energy)
50
In this example, wind installed
capacity is greater than the
thermal capacity additions
Capacity (GW)
40
A lot of wind
capacity is needed
to get a relatively
small capacity credit
30
THERMAL
CAPACITY
TO BE ADDED
20
EXISTING
CAPACITY
10
WIND
CAPACITY
CREDIT (20%)
0
2010
2015
2020
2025
2030
2035
Veselka - 054
GridSchool 2010
In Addition to Hourly Operations, Variable Resource Technologies Will Affect
both the Amount and Type of New Thermal Capacity Built in the Future
NGCC
Coal
Nuclear
Levelized Cost ($)
GT
100
0
Capacity Factor (%)
GT
Without
Wind
NGCC
Normalized Load (%)
100
Coal
With Wind
Nuclear
0
Exceedance Probability (%)
100
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GridSchool 2010
The “Optimal” Expansion Solution in Terms of Economics Can
Be Approximated with More Sophisticated Mathematical Models
A Dynamic Programming (DP) Algorithm
Is One Method for Solving Problems
“Best” Plan
Over Time
 Pre-planning
– existing plus committed units
 Planning period (20+ years)
Time
Years
State
(Expansion Option)
Important Considerations
 Existing grid resources
 Unit operating flexibility
 Ancillary services
 Wind variability & uncertainty
 Technical minimum output levels
 Transmission constraints
 Load profiles and uncertainty
 Fuel costs
 …..
– first year an uncommitted unit could
operate
 Post-planning period
– operate plants past last year
– compute salvage value
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