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Wind Integration: What Have We

Learned?

Michael Milligan

Consultant

National Renewable Energy Laboratory

Planning for Wind Workshop

NW Power & Conservation Council

Portland, OR

Dec 5, 2003

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Utility Wind Interest Group

• Interest in UWIG has surged as more utilities have evaluated/adopted wind

• “Clearing house” for operational issues, solutions, etc.

• www.uwig.org

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2000

3600

0 6 12 180 6 12 18

Time (Hour of the Day)

Sys

tem

Loa

d

Seconds to minutesRegulation

Minutes to hoursLoad Following

Days

0

25

50

75

100

Sy

ste

m L

oa

d

0 6 12 18 24Time (Hour of the day)

Daily scheduling/unit commitment

Time-frames: Power System Operations

CyclesTransient stability & short-circuit

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Brief Outline

• Planning horizon– Geographic

benefit/reliability

• Operational horizons– Load following– Reserve allocation based on

reliability modeling

• Overview of CA Renewable Portfolio Standard Integration Study– Capacity value– Regulation– Load following

• Summary of Operations Studies

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Geographically Disperse Wind Development

• Two projects:– Joint project

with Minnesota Department of Public Service (Commerce)

– Joint project with Iowa Wind Energy Institute

-30

-20

-10

0

10

20

30

1-H

our

MW

Diffe

rence

AlgonaAlta

ArlingtCedar

EstherForest

InwoodRadclif

RedoakSibley

SutherlTurin

Combine

Site

Iowa First-Order Difference RangesSimulated Power Production 7/97

Maximum, Minimum, Mean+stdev, Mean-stdev

Iowa One-Hour Delta Hourly Output

-20

-10

0

10

20

30

MW

Diffe

rence (

1-h

our)

AlbertaBrewster

CrookstonCurrie

LuverneCombined

Site

1st Difference in Hourly MW OutputMinnesota Wind Sites, July '96

Maximum hourly difference Minimum hourly difference

25 -MW Clusters

One-Hour Delta Hourly Output

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Modeling Methods

• Minnesota: Dynamic fuzzy search to maximize system reliability

• Iowa: Dynamic fuzzy search to maximize two separate objective functions– Economic benefit– System reliability– Corroboration of the

economic benefit results with a genetic algorithm

Genetic Algorithm and Fuzzy Search Results: Economic Sites

0

5

10

15

20

25

30

35

# o

f 50-M

W C

luste

rs

12

34

56

78

910

1112

1314

1516

1718

1920

Solution Rank

Alg Alt Est For Rad Sib

G G G G G G G G

Tradeoff: Reliability vs. Economics

93

94

95

96

97

98

99

100

Pe

rce

nt

of

Op

tim

al C

ap

acity C

red

it

97.5 98.0 98.5 99.0 99.5 100.0 Percent of Optimal Economic Value

Best-fit Simulated

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Iowa Load Following Study

• 8 wind scenarios• Wind capacity

– 800 MW– 1,600 MW (22.7% of

peak load)

• Scenario 1– 1,300 MW at one site

• All other scenarios– Geographic spread

based on optimal locations

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Wind/No Wind Load Following

-600

-400

-200

0

200

400

600

800

1 25 49 73 97 121 145

Hour of Week

Lo

ad

Fo

llo

win

g

Re

qu

ire

me

nt

(MW

)

No Wind 1600 MW/wind 800 MW/wind

How Does Wind Affect 1-Hour Load Following?

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Standard Deviation of Load-Following Requirements

0

50

100

150

200

250

1 2 3 4 5 6 7 8

Case Number

Std

Dev

(M

W)

Load 800 MW/wind 1600 MW/wind

Variability of Load Following With/Without Wind

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800 MW Wind: Max/Min Change in 1-hr Ramp Requirements

-80 -60 -40 -20 0 20 40

1

2

3

4

5

6

7

8

Case

MW Change from No-wind

Down-ramp

Up-ramp

Largest Single-hour Difference at 800 MW Penetration

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Largest Single-hour Difference at 1600 MW Penetration

1600 MW Wind: Max/Min Change in 1-hr Ramp Requirements

-500 -400 -300 -200 -100 0 100 200 300

1

2

3

4

5

6

7

8

Case

MW Change from No-wind

Down-ramp

Up-ramp

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Load-Following Allocation: No Forecast Errors

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 200 400 600 800 1000 1200 1400 1600 1800

Wind Capacity

Win

d I

mp

act

on

Lo

ad F

oll

ow

ing

(P

erce

nt

of

Rat

ed)

Case 1 Max(Cases2:8)

Load Following Allocated to Wind

Difference due to geographic dispersion

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Allocation of Variation to Wind: Improvements to Wind Persistence Forecast: Good Load Forecast

0

10

20

30

40

50

60

70

400 600 800 1000 1200 1400 1600

Wind Plant Capacity

Std

Dev

(M

W)

Wind/Persistence 20% Improvement 40% Improvement

Imbalance Impact of Wind Increases with Penetration

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Iowa Load Following Conclusions

• Geographically disperse wind causes an increase in the standard deviation of load following requirements of about 2.5% of rated capacity at 22.7% penetration rate with a backward-looking analysis

• Geographically disperse wind causes an increase in the standard deviation of imbalances of about 4% of rated capacity with a simple wind forecast at 22.7% penetration rate

• Results will depend on wind regime, loads, and would be expected to differ in other situations

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Reliability-based Reserve Allocation

• Examine how much of the fraction of operating reserve that should fall on a wind power plant

• Method should be based on reliability theory and practice, and take probability of various system failures into account

• Should provide market signals that encourage reliability and accurate wind forecasts

• Strbac/Kirschen (Electricity Journal, October 2000) model fulfills these goals, except doesn’t consider wind – Milligan (AWEA/EWEA 2001) adapts to wind

• I use 1-hour wind forecast errors as “outage” rates for system reliability calculations

0%

2%

4%

6%

Reserv

e a

s %

of R

ate

d C

apacity

0 25 50 75% Forecast Error Reduction (from base)

Maximum Reserve Fractionand Decreasing Forecast Errors

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Effect of Geographic Diversity

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Fra

ctio

n:

Re

se

rve

s t

o R

ate

d C

ap

acity

1 3 5 7 9 11 13 15Day Window Size

Estherville 1600 MW Wind Plant

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Fra

ctio

n:

Re

se

rve

s t

o R

ate

d C

ap

acity1 3 5 7 9 11 13 15

Day Window Size

Combined 1600 MW Wind Plant

Geographic diversity is roughly equivalent to the 50% forecast reduction case.

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Implications

• Worse-case scenario analyzed shows the reserve allocation at about 5.5% of rated capacity of the wind plant

• Average is less than 1% of wind capacity• Improvements in forecast will reduce wind’s risk• Wind does contribute to EUE (risk)• …but at a very low rate relative to rated capacity• Geographic dispersion reduces composite forecast

error and reserve allocation

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California RPS Integration Study Project Team

• Primary investigators in Methods Group:– David Hawkins, California ISO – Brendan Kirby, ORNL– Yuri Makarov, California ISO– Michael Milligan, NREL

• California Wind Energy Collaborative– Kevin Jackson– Henry Shiu

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Transmission investments

Indirect costs

Remarketing costs

Integration costs

Total cost

Bid price

Direct cost

These are the costs incurred to incorporate the electricity from a generation source into a real-time electricity supply.

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• Identify significant characteristics of California’s load and installed renewable and conventional generators.

• Define and implement methodologies for evaluating the capacity credit for renewables.

• Provide a comparison of the capacity credit between various renewable and conventional generators.

• Define and implement methodologies for evaluating integration costs.

• Provide a comparison of the magnitude of load following and regulation services for various renewable and conventional technologies.

• The final report documenting the one year analysis results of existing generation resources has been released for public comment.

Analysis of Existing Generation

Phase I

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Data Processing

• OASIS: Open Access Same-Time Information System

• CAISO Power Information (PI) system

• Error removal

• Data storage error– Results from PI system data compression– The standard deviation of data storage error is

160 MW or ±0.6% of the average annual load.

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ELCC Results

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Wind-A Wind-G Wind-T Biomass Solar Geo (1) Geo (2)

Renewable Technology

Per

cen

t o

f R

ated

C

apac

ity/

Ref

eren

ce U

nit

Capacity Credit Relative to Gas Reference Unit

Regulation Cost Results

• Used ORNL method, CA regulation prices

• A negative price means there is a cost imposed on the system.

• A positive price means there is a benefit provided to the system.

• The baseline for comparison is a generator with constant output and a regulation price of zero.

• Caution: regulation is a capacity service; cost in $/MWh as a convenience

Regulation Cost ($/MWh)

Total Load -0.42

Medium Gas 0.08

Biomass 0.00

Geothermal -0.10

Solar 0.04

Wind (Altamont) 0.00

Wind (San Gorgonio)

-0.46

Wind (Tehachapi) -0.17

Wind (Total) -0.17

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Load Following Analysis

• Deviations between the scheduled generation and the actual load requirements are compensated through purchases from the CAISO supplemental energy market.

• The system operator must compensate for aggregate scheduling error, individual errors must be viewed in the context of the full system.

• Market participants provide CAISO with bids for the hour ahead energy market and create the “stack” of available generators.

• The purpose of the load following analysis was to determine if the renewable generators affected the size or composition of the “stack” and therefore changed the cost for the load following service.

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Scheduled Hour Ahead Load

COMBINED FORECAST ERROR AND RENEWABLE SCHEDULING ERROR

Average Minimum Average Maximum

RESOURCE MW

Compared to forecast error w/out renewables

(%) MW

Compared to forecast error w/out renewables

(%)

Forecast error without renewables

-1909 100% 2220 100%

Biomass -1897 99% 2218 100%

Geothermal -1878 98% 2221 100%

Solar -1870 98% 2220 100%

Wind (Altamont) -1909 100% 2272 102%

Wind (San Gorgonio) -1898 99% 2226 100%

Wind (Tehachapi) -1884 99% 2281 103%

Wind (total) -1870 98% 2377 107%

Scheduling bias -5076 266% 1747 79%

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California Preliminary Conclusions

• Capacity credit for wind is low but non-zero– Phase II will examine higher penetration, newer

technology, and different locations

• Regulation impact of wind is small– Because of data storage error these results are not

precise, but the regulation cost adders should be used until more accurate results can be obtained in Phase II

– Will examine this issue in Phase II

• Load following impact has negligible impact on supplemental energy stack at this penetration– No cost adders for wind can be justified at this time

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Other Results

Study (Penetration) Time-Scale Cost ($/MWh)

Hirst PJM (0.06-0.12%) Reg, Imb 0.05-0.30

Electrotek Xcel (3.5%) Reg, LF, Res, UC

1.85

PacifiCorp (20%) Imb, Res 5.50

Hirst BPA (6%) Reg, LF, Imb, DA

1.47-2.27

WE Energies (4%-29%) Reg, LF, UC 1.90-2.92

CA RPS Integration (4%) (Preliminary Phase I)

Reg, LF 0.17

Reg=regulation, IMB=imbalance, LF=load following,Res=reserves, UC=unit commitment, DA=day ahead market