Integrating Traditional, Variable, Renewable, Distributed, and Demand-Side Response Resources G RID...

Integrating Traditional, Variable, Renewable, Distributed, and Demand-Side Response Resources

GRIDSCHOOL 2010MARCH 8-12, 2010 RICHMOND, VIRGINIA

INSTITUTE OF PUBLIC UTILITIESARGONNE NATIONAL LABORATORY

Thomas D. VeselkaCenter for Energy, Economic, and Environmental Systems Analysis

Decision and Information Sciences DivisionARGONNE NATIONAL LABORATORY

[email protected] 630.252.6711

Do not cite or distribute without permission

MICHIGAN STATE UNIVERSITY

Veselka - 02

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

0

5

10

15

20

25

30

35

40

4515

16

17

18

19

20

21

Day of the Month in August

Win

d O

utp

ut

(MW

)

Total of 3 Sites

Wind is not Always Available when Needed Most

Rapid Ramping

The thermal system or loads need to adjust quickly

Veselka - 03

GridSchool 2010

Wind Probability Profiles Vary Seasonally and by Time of Day

0

10

20

30

40

50

60

70

80

90

0 25 50 75 100Exceedance Probability (%)

Gene

ratio

n (M

Wh)

February - 4 AMFebruary - 6 PMAugust - 4 AMAugust - 6 PMAll Hours of Year

Exceedance Probability (%)

Win

d P

rod

uct

ion

(M

W)

Summer Nighttime Wind Is Less than Daytime Wind

Winter Wind Is Greater Than Summer Wind

On Average, Wind Output Decreases in the Morning When Load Is Rapidly Increasing.The Opposite Occurs in the Evening.

Veselka - 04

GridSchool 2010

Wind Resources Vary Widely Across the United StatesOften the Best Wind Resources Are Far from Major Load Centers

TransmissionTransmission

MISO

PJM765 kV

Veselka - 05

GridSchool 2010

The U.S. has Installed the Most Wind Capacity in the World, but the Percent Penetration Rate (% Production) Is Relatively SmallU.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 - 06

GridSchool 2010

Wind Capacity by State

Veselka - 07

GridSchool 2010

U.S. Wind Capacity Growth

Source: AWEA 2009

Veselka - 08

GridSchool 2010

Does Wind Power Influence Market Operations?

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

3500.0

4000.0

-120.0

-70.0

-20.0

30.0

80.0

130.0

180.0

1 25 49 73 97 121 145

Win

d Po

wer

[MW

]

Pric

e [$

/MW

h]

Time [hour]Day Ahead price Real Time price Wind power

Negative prices (LMPs)

Wind power ramping events

Midwest ISO Wind Power and MN Hub Prices, May 11-17, 2009:http://www.midwestiso.org/

Veselka - 09

GridSchool 2010

United States Photovoltaic Solar Resource Map

Veselka - 010

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 withMultijunctionCells (~40%)

Veselka - 011

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 - 012

GridSchool 2010

Public Service Company of Colorado Solar Study200 MW of PV & 200 MW CSP with 800 MWh Storage

Source: http://www.xcelenergy.com/SiteCollectionDocuments/docs/PSCo_SolarIntegration_020909.pdf

Veselka - 013

GridSchool 2010

Spanish PV Study: Annual Hourly Photovoltaic OutputG

ener

atio

n (k

W)

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Source: http://www.icrepq.com/ICREPQ'09/abstracts/520-ramon-abstract.pdf

Veselka - 014

GridSchool 2010

Most States Have a Renewable Portfolio Standard (RPS)

Source: www.dsireusa.org / September 2009

State renewable portfolio standard

State renewable portfolio goal

Solar water heating eligible *† Extra credit for solar or customer-sited renewables

Includes separate tier of non-renewable alternative resources

WA: 15% by 2020*

CA: 20% by 2010

☼ NV: 25% by 2025*

☼ AZ: 15% by 2025

☼ NM: 20% by 2020 (IOUs)

10% by 2020 (co-ops)

HI: 40% by 2030

☼ Minimum solar or customer-sited requirement

TX: 5,880 MW by 2015

UT: 20% by 2025*

☼ CO: 20% by 2020 (IOUs)

10% by 2020 (co-ops & large munis)*

MT: 15% by 2015

ND: 10% by 2015

SD: 10% by 2015

IA: 105 MW

MN: 25% by 2025

(Xcel: 30% by 2020)

☼ MO: 15% by 2021

WI: Varies by utility;

10% by 2015 goal

MI: 10% + 1,100 MW by 2015*

☼ OH: 25% by 2025†

ME: 30% by 2000New RE: 10% by 2017

☼ NH: 23.8% by 2025☼ MA: 15% by

2020+ 1% annual

increase(Class I Renewables)RI: 16% by 2020

CT: 23% by 2020

☼ NY: 24% by 2013

☼ NJ: 22.5% by 2021

☼ PA: 18% by 2020†

☼ MD: 20% by 2022☼ DE: 20% by 2019*☼ DC: 20% by 2020

VA: 15% by 2025*

☼ NC: 12.5% by 2021 (IOUs)

10% by 2018 (co-ops & munis)

VT: (1) RE meets any increase in retail sales by

2012; (2) 20% RE & CHP by 2017

29 states & DC

have an RPS5 states have goals

KS: 20% by 2020

☼ OR: 25% by 2025 (large utilities)*

5% - 10% by 2025 (smaller utilities)

☼ IL: 25% by 2025

Standards Should be Consistent with Renewable Resources & Needs

http://www.dsireusa.org/

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GridSchool 2010

Currently, Volatility in Production from Variable Resources Are Accommodated by Changing Thermal Unit and Hydroelectric Power Plant Production Levels

Time

Ou

tpu

t (M

W)

OperatingCapacity

Ram

p U

p R

ate

Ram

p D

ow

n R

ateMinOutput

Cold Start Time

MinimumDownTime

MinimumUp

Time

Lo

ad F

ollo

win

g

Ran

ge

The Greater the Operational Flexibility of Dispatchable Units, the more Variability the Grid Will Accommodate

Veselka - 016

GridSchool 2010

Some Technologies Are Able to Come On-line Quickly to Respond to Rapid Load Changes while Others Respond More Slowly

0 2 4 6 8 10 12 14 16 18 20

Fossil Steam

Hydroelectric

Combined Cycle

Gas Turbine

Diesel Generator

Nuclear Steam

Ramp Rates (%/min)

0 5 10 15 20 25

Fossil Steam

Hydroelectric

Combined Cycle

Gas Turbine

Diesel Generator

Nuclear Steam

Cold Start Time (Hours)

Weeks for Shutdown and Startup

Some Hydropower Plants Change Very Quickly

Veselka - 017

GridSchool 2010

The Load Following Range Is Restricted by the Output Minimum and Generation Capacity

0 10 20 30 40 50 60 70 80 90 100

Fossil Steam

Hydroelectric

Combined Cycle

Gas Turbine

Diesel Generator

Nuclear Steam

Minimum Output (% of Capacity)

Time

Pro

du

ctio

n (

MW

)

Operating Capacity

Ram

p U

p

Rat

e

Ram

p D

ow

n

Rate

Cold Start Time

MinimumDownTime

MinimumOutput

Lo

ad F

ollo

win

g

Ran

ge

MinimumUp

Time

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GridSchool 2010

GTHighest

ProductionCosts

NGCC

Cycling Coal

Base Load Coal

Ideally, Units Are Dispatched Based on Production Cost

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

Max Load

Min Load

NuclearLowestProductionCosts

Nuclear12 $/MWh

Coal25 $/MWh

NGCC41 $/MWh

Cycling Coal

32 $/MWh

Gas Turbines80 $/MWh

ResourceStack

Su

pp

ly (

MW

)

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GridSchool 2010

0 10 20 30 40 50 60 70 80 90 100

Fossil Steam

Hydroelectric

Combined Cycle

Gas Turbine

Diesel Generator

Nuclear Steam

Minimum Output (% of Capacity)

Unfortunately, a Steam Plant (e.g., Cycling Coal) Does not Have the Flexibility to Operate at a Very Low Output Level

GTHighest Production

Costs

NGCC

Cycling Coal

Base Load Coal

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

Max Load

Min Load

NuclearLowestProduction Costs

Ram

p U

p

Ram

p Dow

n

GT Operations

Veselka - 020

GridSchool 2010

Wind Production Will Serve Some of the Load.This Production Reduces the Loads that Are Served by other Generating Resources

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad/W

ind

Ou

tpu

t (M

W)

Max Load

Min Load

WindGeneration

Ram

p U

p

Ram

p Dow

n

Veselka - 021

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

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

New Max

New Min

Wind Typically Increases Resultant Load Changes

Larger Range of O

perations

Ram

p U

p

Ram

p Dow

n

Veselka - 022

GridSchool 2010

NGCC

GT

Cycling Coal

Nuclear

Base Load Coal

LowestO&M Costs

HighestO&M Costs

Unit Dispatch with Wind Results in Less Thermal Generation & Associated Air Emissions

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Hour of the Day

Lo

ad (

MW

)

Without Wind

With Wind

Coal May OperateLess Efficiently @ Min Gen

Veselka - 023

GridSchool 2010

0 100 200 300 400 500 600

Fossil Steam

Hydroelectric

Combined Cycle

Gas Turbine

Diesel Generator

Nuclear Steam

CO2 Emissions (kg CO2/BOE)

Wind and other Renewable Technologies Will Reduce Greenhouse Gas Emissions

20 Percent Wind by 2030 Report: CO2 Emissions Are Estimated at 25 Percent Lower Than a No-Wind Scenario

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GridSchool 2010

As a Result of Variable Resource Generation Some Units Will Operate at a Different Efficiency Point

20

30

40

50

60

70

80

90

100

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Net

Elec

tric

al Effi

cien

cy (%

)

Fraction of Full Load

Combined Cycle

Hydro

Diesel

Nuclear

Fossil Steam

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GridSchool 2010

NGCC

GT

Cycling Coal

Nuclear

Base Load Coal

LowestO&M Costs

HighestO&M Costs

Unit Dispatch with Greater Nighttime Wind Base Load Coal Unit May Need to Be Taken Off-line for Several Hours

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

Without Wind

With Wind

Sell

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GridSchool 2010

Pump

Energy is Produced

When Generating

Energy is ConsumedWhen Pumping

Substation

Upper Reservoir

LowerReservoir

Pumped Storage Plants Can Be Used to Help Smooth Out Loads Served by Other Dispatchable Resources

Fill Load Valley (Consume) to Utilize Low Cost Production and Avoid Expensive Shutdown Costs

React to Sudden Changes in Variable Resource Production

Lo

ad (

MW

)

Hour of the Day

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GridSchool 2010

When a Unit Is Forced Out of Service, the System Responds by Altering the Dispatch

Nuclear8 $/MWh

Base Coal

25 $/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply MarginalCost

1,200W

40$/MWh

Su

pp

ly S

tac

k w

ith

ou

t M

ain

ten

an

ce

(M

W)

Cycling Coal40 $/MWh


NGCC60 $/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply MarginalCost

1,200W

80$/MWh

Su

pp

ly -

Nu

cle

ar

Un

it O

ut

of

Se

rvic

e (

MW

)


Coal Steam Partially Loaded

Base Coal

25 $/MWh

UnpreparedSlow Transition

Operational ProblemsAfter the outage it will take hours for the system to reach the least-cost state of operations

All demand will not be served

Least-Cost ResourceStack Before Outage

Least-Cost ResourceStack After Outage

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GridSchool 2010

Spinning Reserves Help Alleviate Operational Problems Associated with Random Outages

Nuclear8 $/MWh

Base Coal25 $/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply MarginalCost

Su

pp

ly S

tac

k w

ith

ou

t O

uta

ge

s (

MW

)



NGCC60 $/MWh

1,200 MW80$/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply

Su

pp

ly -

Nu

cle

ar

Un

it O

ut

of

Ser

vic

e (M

W)


Base Coal

25 $/MWh

NGCC60 $/MWh

Gas Turbines

1,200 MW Load

80 $/MWh

Fast TransitionSimultaneously Ramp Operations

Spinning ReservesNGCC 250 MWNG Steam 110 MWOil Steam 110 MWGas Turbines 30 MWTotal 500 MW

Veselka - 029

GridSchool 2010

System Operators Need to Make Certain that Ramping Resources Are Available

Nuclear8 $/MWh

Base Coal25 $/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply

Su

pp

ly S

tac

k w

ith

ou

t M

ain

ten

an

ce

(M

W)


NGCC60 $/MWh

Gas Turbines

6 AMLoad

1200 MW

7 AMLoad

1500 MW

0

250

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply

Nuclear8 $/MWh

Base Coal25 $/MWh


NGCC60 $/MWh

Gas Turbines

Old GT120 $/MWh

500

0 Supply

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Nuclear8 $/MWh

Base Coal25 $/MWh


8 AMLoad

1800 MW

Spinning Reserves500 MW



NGCC60 $/MWh

Gas Turbines

Old GT120 $/MWh

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GridSchool 2010

Usually, the Grid Can Accommodate Relatively Small Amounts of Wind Generation

Nuclear8 $/MWh

Base Coal25 $/MWh

0

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply

Su

pp

ly S

tac

k w

ith

ou

t M

ain

ten

an

ce

(M

W)


NGCC60 $/MWh

6 AMLoad

1200 MW

7 AMLoad

1500 MW

0

250

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Supply

Nuclear8 $/MWh

Base Coal25 $/MWh


NGCC60 $/MWh

Gas Turbines

Old GT120 $/MWh

500

0 Supply

250

500

750

1,000

1,250

1,500

1,750

2,500

2,250

2,000

Nuclear8 $/MWh

Base Coal25 $/MWh


8 AMLoad

1800 MW




NGCC60 $/MWhRamp up

due towind decreaseWIND

WIND

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GridSchool 2010

The Unpredictability of Wind Compounds Grid Integration Problems Forecast of Wind Power Production Levels Can Be Made for the Next Few Days

Source: Iberdrola Renewables, 2009

Eyeballing: Looks pretty good

Mean absolute error is 9.3%

But devil is in the details (ramps)

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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

Errors in SCADA information and

wind farm operation

Error in meteorological

forecasts

Errors in wind-to-power conversion

process

Phase Error

Magnitude Error

Veselka - 033

GridSchool 2010

Technology Improvements Are Alleviating Some Problems Example: The Danish Horns Rev Wind Farm Is Providing Regulation (Frequency Response) and Balancing Response

Source: Smith et al., IEEE Power and Energy Magazine, Vol. 7. No.2, 2009.

Control Wind Output with Blade Pitch

(Spill Energy)

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GridSchool 2010

Historical Winter Load Shapes and Wind Generation in the Midwest

Source: http://www.dis.anl.gov/pubs/65610.pdf

Wind ~ 14% of Load

Unit Commitment Study

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GridSchool 2010

Problem: Given that Wind Forecasts Have Errors, Make an Economic Unit-Commitment Schedule that Is Reliable


Costs: Generation, Unserved Energy, & StartupConstraint: Ramping, Up & Down Time, Min Output, & Maintain Reserves

Wind can be curtailed (spilled energy)

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GridSchool 2010

Unit Commitment Results Using Various Modeling Methodologies and Assumptions


Stochastic UC gives higher commitment & more available operating reserves

Similar result for deterministic UC w/additional reserve requirement

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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


Veselka - 038

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

Desirability Rating1 Very Low2 Moderately Low3 Average4 Moderately High5 Very High

* 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.

Variability Issues

Zero Fuel Costs

Veselka - 039

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

Thank you for your attention

Source: BOR

EXTRA SLIDESMaximum

Variable ResourceCapacity Credit

Veselka - 042

GridSchool 2010

Reserve Capacity Is Needed to Serve Load when One or More Generators Are Out of Service

Peak Load Forecast

Years

MW

Upper RM

Lower RM

Total System Capacity

Existing System Capacity

New Capacity Additions

Engineering GuidelineBuild 15% to 20%

more capacity thanthe peak load

Veselka - 043

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 - 044

GridSchool 2010

Loads Can Also Be Viewed as Probabilistic Events Step 1: Chronological Loads

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

Max Load

Min Load

Veselka - 045

GridSchool 2010

Sorted Summer Load ProfileStep 2: Load Exceedance Curve

hr4

hr 17

Lo

ad (

MW

)

Max Load

Min Load

Time

Lo

ad

Some Information Is Lost Such as Load Changes Over Time

hr17

hr4

0 100Exceedance Probability (%)

Veselka - 046

GridSchool 2010

NGCC

GT

Cycling Coal

Base Load Coal

Nuclear

Unit Production Levels Can Be Estimated Using a Load Duration Curve


Lo

ad (

MW

)

Max LoadIs NeverExceeded

Time

Lo

ad

Min LoadIs Always Exceeded

Information Such as Unit Ramping and Frequency of Unit Starts/Stops Are Lost

Veselka - 047

GridSchool 2010

NGCC

GT

Cycling Coal

Base Load Coal

When a Supply Resource Is Forced Out of Service Other Resources Are Dispatched to Serve the Load

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

Max Load

Min Load

NuclearForcedOut ofService

Time

Lo

ad

Veselka - 048

GridSchool 2010

NGCC

GT

Cycling Coal

Base Load Coal

Alternatively, the Load Curve Can Be Adjusted While Including the Out-of-service Unit

121 2 543 6 7 98 1110 18 2019 2322 242113 14 16 1715

Hour of the Day

Lo

ad (

MW

)

New Max

New Min

Nuclear

Original Curve

Load not Served by the Nuclear Unit Is Satisfied by Other Units in the Resource Stack

NuclearCapacity

Time

Lo

ad

Veselka - 049

GridSchool 2010

NGCC

GT

Cycling Coal

Base Load Coal

The Same Methodology Can Be Applied to a Load Duration Curve


Lo

ad (

MW

)

Nuclear

New Max

New Min

NuclearCapacity

Original Curve

NGCC

GT

Cycling Coal

Base Load Coal

There Is Some Probability that a Unit Does Not Operate


Lo

ad (

MW

)

We don’t know with certainty if the nuclear unit will be either on or off at some point in the future

NuclearCapacity

EquivalentLoad CurveAccountsfor NuclearOutages

Nuclear

Weighted Average Curve of Nuclear Unit On & Off

Nuclear OffNuclearOn

Area = Outage Rate/100 X Nuclear Capacity

Likewise All Units Are “Convolved”Into the Load Duration Curve

Exceedance Probability (%) Nuclear

NGCC

GT

Cycling Coal

Base Load Coal

0 100

Lo

ad (

MW

)

Operating System Capacity

Nuclear

Cycling Coal

Base Coal

GT

NGCCEnergyNot Served

Original Curve

Total Capacity + Peak Load

Final EquivalentLoad Curve AccountingFor All Unit Outages

Loss of Load Probability

Exceedance Probability (%)0 100

Lo

ad (

MW

)

Without Wind

With Wind

Using Historical Hourly Wind Data and Corresponding Hourly Loads by Location a Net Load Exceedance Curve Can Be Constructed

There Is a chance that all wind turbines produce zero power at the time of peak load

There Is a chance that all wind turbines produce maximum power at the time of minimum load

The Firm Capacity Credit for Wind Can Be Based on a System Reliability Measure

Exceedance Probability (%)0 100

Lo

ad (

MW

)

Operating System Capacity

Total Capacity + Peak Load

Nuclear

Cycling Coal

Base Coal

GT

NGCC

Firm Capacity Credit (% of Capacity)

Wind: 5-20Coal: 80-95

Nuclear: 90-95NGCC: 85-90

Without Wind

With Wind

Loss of Load Probability

Reliability Increase with Wind

Wind FirmCapacityCredit

Engineering GuidelineTypically 5% to 15% of wind turbine capacity is applied toward the reserve margin

Years

MW

Capacity Credit

De

sig

n C

ap

ac

ity

Derated

Veselka - 054

GridSchool 2010

A Lot of Wind Capacity Is Needed to Meet Renewable Portfolio Standards (e.g., 20% Energy)

0

10

20

30

40

50

2010 2015 2020 2025 2030 2035

Cap

acit

y (G

W)

EXISTINGCAPACITY

THERMAL CAPACITYTO BE ADDED

A lot of wind capacity is needed to get a relatively

small capacity credit

WINDCAPACITYCREDIT (20%)

In this example, wind installed capacity is greater than the thermal capacity additions

Veselka - 055

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

Lev

eliz

ed C

ost

($)

Capacity Factor (%)

0 100

GT NGCC Coal Nuclear

1000

No

rmal

ized

Lo

ad (

%)

Nuclear

Coal

NG

CC

GT

Exceedance Probability (%)

Without Wind

With Wind

100

Veselka - 056

GridSchool 2010

The “Optimal” Expansion Solution in Terms of Economics Can Be Approximated with More Sophisticated Mathematical Models

Pre-planning– existing plus committed units

Planning period (20+ years)– first year an uncommitted unit could

operate Post-planning period

– operate plants past last year– compute salvage value

A Dynamic Programming (DP) AlgorithmIs One Method for Solving Problems

Time Years

State

(Expansion O

ption)

“Best” Plan Over Time

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 …..

Integrating Traditional, Variable, Renewable, Distributed, and Demand-Side Response Resources G RID...

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