energy center state utility forecasting group (sufg) alternative resources and energy capacity...

ENERGY CENTERState Utility Forecasting Group (SUFG)


Alternative Resources and Energy Capacity

Presented by:Douglas J. GothamPurdue University

Presented to:Institute of Public Utilities

56th Annual Regulatory Studies Program

August 12, 2014



Main Presentation Topics

• Current status of renewable resource development

• Estimating capacity credits for variable resources using probabilistic methods

• Thermal system operating considerations• Optimal and least-cost capacity expansion

paths

2



3Source: EERE/WPA 2013

U.S. led global growth in wind power with over 13 GW added in 2012. The 60 GW total installed capacity ranks 2nd to China’s 75 GW.



4

Wind Penetration in US States (Capacity)



Wind Penetration in US States

Source: AWEA 2012 5



6

US Wind Capacity Growth Slowed Down substantially in 2013

Source: EERE/WINDexchange

Headwinds against future growth include

• Lack of clarity about federal tax incentives

• Low natural gas prices

• Modest electricity demand growth

• Limited near term demand from state RPSs

• Growing competition from solar in some regions



7

United States Wind Resource Map (80 meter)



US PV Solar Resource Map

8



US PV Installations

9

Source: SEIA



Global PV Market

10



US CSP Installations

11

Source: SEIA, NREL



12Source: DSIRE

Renewable Portfolio StandardsMarch 2013



Intermittency

• All generators have some amount of uncertainty when it comes to availability– Mechanical failure– Environmental factors

• Some renewable resources experience this problem on a far greater scale

13



Intermittency Problems

• Operational– Low output + high demand– High output + low demand– Rapid change in output

• Scheduling– Unit commitment– Gas purchase

• Planning14



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

WindProduction

Source: Veselka (Argonne National Laboratory)



Intermittency in Planning

• Amount– How much power will the intermittent

resource produce when it is needed most?• Type

– How will the intermittent resource impact the appropriate mix of resources?

16



• Load and wind generation exhibit a strong negative correlation, with the negative correlation being stronger during the summer months

0

50

100

150

200

250

300

350

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1 3 5 7 9 11 13 15 17 19 21 23

MW

MW

Hour

2004-2006 Hourly Average Load and Wind

Average LoadAverage Wind

Load and Wind Patterns



Load and Solar Patterns• Solar power is also intermittent, but is

more predictable than wind– “Forecast for tonight – dark” – George

Carlin– Cloud cover can introduce short-term

variations in output• Solar power is positively correlated with

load– Solar intensity tends to be greater in the

summer 18



Capacity Value of Intermittent Generation

• Rule of thumb method– Simple and easy to understand– There is no standard approach and little to

no scientific basis for value– May not account for geographic variability

of load and resource

19




• Historical availability– Use the percentage of full output that is

available when the system peak demand occurs

– Accounts for local factors– Large variations from year to year– Small sample size of historical

observations

20



MISO Wind Availability on Peak (% of Nameplate)

Summer Availability Capacity Credit

2005 11.5%

2006 56.0% 20%

2007 2.1% 20%

2008 12.4% 20%

2009 1.5% 20%

2010 21.6% 8%

2011 44.2% 12.9%

2012 9.8% 14.7%

2013 52.6% 13.3%

2014 14.1%21



Indiana Wind PPA Simulated Availability on Peak

22

Date of Annual Peak Demand

Peak Load (MW)

Wind Generation (MW) *

Wind Output as % of Wind Capacity

8/3/2004 19,201 57 7.4%

8/3/2005 20,065 174 22.6%

7/31/2006 20,791 231 30.0%

* Wind generation is based on simulated output from NREL data for appropriate locations of Indiana utility PPAs at the time of the study (770MW)



23

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 1010111112121313141415151616171718181919202021212222232324240

5,000

10,000

15,000

20,000

25,000

0

50

100

150

200

250

300

350

400

450

500

Load and Load Net of WindAnnual Peak 8/3/04

Load

Load Net of Wind

Day Hour

Lo

ad (

MW

)

Win

d O

utp

ut

(MW

)




• Effective Load Carrying Capability– The amount of new load that can be added

with a given amount of new generation while maintaining a constant loss of load probability

– MISO uses this method to determine the capacity credit shown earlier

24



Loss of Load Probability• aka - loss of load expectation

– given an expected demand for electricity and a given set of supply resources with assumed outage rates, what is the likelihood that the supply will not be able to meet the demand?

• Generally used to meet a minimum standard, such as 1 day in 10 years– or about 0.000274

25



All Power Generators Experience Outages Some Outages Such as Maintenance Schedules Are Planned

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

On-Line Capacity (MW)

Planned Outages Forced Outages On-Line Capacity Load

Total System Capacity

Resulting Reserve Margin

PlannedOutages

Source: Veselka



All Power Generators Experience Outages

Due to Mechanical Problems Some Outages Are not Known in Advance

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Load andOn-Line Capacity

(MW)

Planned & Maintenance Outages Forced Outages On-Line Capacity Load

Total System Capacity

Source: Veselka



Intermittent Resources in a LOLP• It is not as simple as adding another

generator• A wind or PV generator will often operate

at a level lower than full capacity– Rather than two states (on/off), there is a

distribution of possible states• fossil-fueled generators may have multiple states

as well (partial outages, de-rates)

– That distribution differs at various times of the day or year 28



Wind Probability Profiles Should Be Used When Constructing “with Wind” Resultant Load Probability Curves

0

10

20

30

40

50

60

70

80

90

0 25 50 75 100Exceedance Probability (%)

Ge

ne

rati

on

(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

Source: Veselka



Simulation of Intermittent Output

• Rather than calculate the LOLP analytically, it can be estimated using a Monte Carlo simulation– Simulate the state of each generator

(including wind output level) using multiple random draws

– In many cases, the sufficiency of historical data may affect the accuracy of the probability distribution of intermittent resources 30



Impact of Intermittency on Type of Resources Needed

• One approach is to use a load duration curve and load duration curve net of wind (or solar)

• Apply a break-even cost curve to the load duration curve net of wind

31



hr4

hr 17

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

Sort Order (Highest to Lowest)

Lo

ad (

MW

)

Max Load

Min Load

Time

Lo

ad

Some Information Is Lost Such as Load Changes Over Time

hr17

hr4

Source: Veselka



NGCC

GT

Cycling Coal

Base Load Coal

Nuclear

0 100Exceedance Probability (%)

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

Source: Veselka



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

Source: Veselka



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

Resultant Load

Source: Veselka



NGCC

GT

Cycling Coal

Nuclear

Base Load Coal

LowestO&M Costs

HighestO&M Costs

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

Coal May OperateLess Efficiently @ Min Gen

Faster & Often MoreRamping of Thermal Units

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

Source: Veselka



NGCC

GT

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

)

Min Load

NuclearForcedOut ofService

Some Units May Be Stopped & RestartedRevise Unit Commitments

When Base Load Units Are Forced Out-of-Service, It Can Potentially Cause Problems with Technical Minimums at Some Units

Source: Veselka



An

nu

aliz

ed C

ost

($/

MW

-yr)

0 100

GT NGCC Coal

Capacity Factor (%)

GT 0-7%NGCC7-40%

Coal40-85%

Nuclear

Nuclear85-100%

Levelized Capital + Fixed O&M

A Simple Screening Curve Reveals Technology Niches

Source: Veselka



Lev

eliz

ed C

ost

($)

Capacity Factor (%)

0 100

GT NGCC Coal Nuclear

1000 Exceedance Probability (%)

No

rmal

ized

Lo

ad (

%)

Nuclear

Coal

NGCC

GT

100

Combining Screening Curves with the Load Duration Curve Approximates the “Ideal” Capacity Mix Source: Veselka



Lev

eliz

ed C

ost

($)

Capacity Factor (%)

0 100

GT NGCC Coal Nuclear

1000

No

rmal

ized

Lo

ad (

%)

Nuclear

Coal

NGCC

GT

Exceedance Probability (%)

Without Wind

With Wind

100

Expansion Mix Is Affected by Introducing a Variable Supply Resource Source: Veselka



SUFG Study

• SUFG looked at the impact of various levels of wind penetration on resource needs for Indiana

41



Data Sources• Indiana statewide hourly load for 2004-

2006• NREL wind speed estimates for 2004-

2006, using locations from which Indiana utilities are currently purchasing wind power– No Indiana utilities were purchasing wind

power during that period, but it does maintain the chronological relationship between wind and load 42



Costs for New GenerationType Annualized Fixed Cost

(2010 $/MW/Yr)Variable Cost(2010 $/MWh)

PC 542,277 25.34

CC 170,100 37.66

CT 110,353 62.26

Wind 403,430 0.00

43



Effect of Natural Gas Prices

• Projected natural gas prices were low enough that coal units are not cost competitive at any capacity factor

• Natural gas combined cycle are used to meet both baseload and intermediate needs

44



Load Duration Curve +

Break-even Cost Curve w/ Low NG

Prices

45



Additional Resource Requirements (MW)

46

0 MW Wind 1,000 MW Wind 3,000 MW Wind 6,000 MW Wind

CC 875 500 0 0

CT 5,769 5,873 6,016 5,586

Total 6,644 6,373 6,016 5,586

The first 1,000 MW of wind has a capacity value of 271 MW (27.1%), the next 2,000 MW has a capacity value of 357 MW (17.9%), and the last 3,000 MW has a capacity value of 430 MW (14.3%).



New Capacity Requirements

47

0 1000 2000 3000 4000 5000 6000

-1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

CT CC PC Total

Wind Capacity (MW)

Cap

acit

y (M

W)



As Wind Penetration Increases….

• Baseload/cycling resource requirements decrease

• Peaking resource requirements increase (until existing cycling resources start being used as peakers)

• Total resource requirements decrease at a declining rate

48



Solar Impacts

• Unlike wind, solar output is generally positively correlated with demand– both are higher during the day– bright sunshine can increase demand

• Solar usually decreases the need for peaking resources and has less impact on baseload/cycling needs

49



The Duck Curve

50Source: California ISO DR-EE Roadmap



Energy Storage and Demand Response

• Both storage and DR tend to shift demand from peak periods to off-peak periods

• Decreases need for peaking/cycling resources

• Increases need for baseload resources• Total resource requirements decrease

51



Energy Efficiency

• Energy efficiency impacts tend to vary depending on the particular end use affected– lighting programs impact mornings and

evenings more than daylight and middle of the night hours

– LED traffic lights operate at all hours

52



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Further Information

• Doug Gotham– 765-494-0851– [email protected]

• http://www.purdue.edu/discoverypark/energy/SUFG/

mailto:[email protected]

energy center state utility forecasting group (sufg) alternative resources and energy capacity...

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energy capacity

wind power

wind generation

intermittent resource

intermittent resource

solar patternssolar

variable resources

future growth