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Effective Load Carrying Capability of Wind Generation: Initial Resultswith Public Data

Presented by Ed Kahn

at

UCEI POWER Conference

March 18,2005

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Background: What are wind integration costs?

Its all about reserves

Wind power has an intermittent output profile

Some kind of back-up requirement is necessary to accommodate fluctuations

Extreme cases

German grid rules require that wind power look exactly like thermal (next slide)

Wind Collaborative study of regulation requirements: there are no back-up costs

What is effective load carrying capability ?

A measure of the incremental impact of new resources on planning reserves; based on traditional reliability methods, i.e. loss of load probability (LOLP)

Related to resource adequacy issues Stoft LICAP testimony for the NE ISO proposes a transformation of LOLP for making

capacity payments

Distinct from operating reserve issues

Transmission costs are yet another integration issue

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German grid rules imposes large integration costs (from Sacharowitz)

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Overview of ELCC studyBackground

California Wind Collaborative RPS Integration Study finds the capacity credit for wind generation is roughly equal to its capacity factor (about 25%)

SCE asked Analysis Group to review study methods and make an independent assessment

Methodology

LOLP methods used in the RPS Study are generally reasonable, but their implementation is unclear

The use of confidential ISO data defeats the purpose of transparency

Data issues

Public data on loads, imports and hydro dispatch must be used in the absence of the ISO data available to the RPS study

Initial results

Base case results do not replicate RPS study results

ELCC for 2002 is 13% using SCE wind data, not the 22-23.9% found in the RPS study

ELCC for 2003 is about 20% and for 1999 about 10%

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Methodology

Formally, we define the probability that in a given hour available capacity is less than load. We call this the LOLP for hour i, or LOLPi

LOLPi = Pr (∑ Cj < Li), (1)

where Cj is the random variable representing the capacity of generator j in hour i and Li is the load in hour i.

The annual LOLE index is defined over all hours of the year i as

LOLE = ∑ LOLPi. (2)

Effective load carrying capacity (ELCC) is the amount of new load, call it ∆L, that can be added to a system at the initial LOLE, which we call LOLEI, after a new unit with capacity ∆Cmax is added. If we denote the random variable representing the available capacity of ∆Cmax by ∆C, then solving (3) for ∆L gives an implicit definition of ELCC.

LOLEI = ∑ Pr (∑ Cj + ∆C < Li + ∆L) (3)

In normalized form

ELCC = ∆L/∆Cmax (4)

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Data issues

RPS study relied on ISO data for 2002 that is not publicly available

Hourly hydro generation data

Proprietary database for outages

This analysis relies upon public data from ISO released by FERC in connection with the Western Energy Markets Investigation

Hourly hydro from 2000 used as a proxy for 2002

Adjustments to load required to account for SMUD withdrawal from ISO control area

Forced outage rates from Henwood database

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Initial results

ELCC depends upon coincidence of wind output with high LOLP hours

LOLP is highly correlated with load The coincidence of wind generation and summer peak demand is low (see next slide)

There is year to year variation in this correlation

Correlation was comparatively low in 2002; higher in 2003

ELCC is also sensitive to the concentration of LOLE in time; i.e. is 95% of LOLE in the top 20 hours or the top 50 hours

We use “perfect load shaving” hydro dispatch in base case; this tends to concentrate the LOLE to the top load hours

The LOLE can be spread out more by tightening the supply/demand balance and raising the total LOLE

RPS results appear to have a bigger LOLE spread than Analysis Group base case (see Figures 3.1 and 3.2 and compare with following chart)

It is unclear why the RPS study finds 50 high LOLP hours instead of 20

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The coincidence of wind generation and demand is low

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2002 Base Case: Top LOLP Hours vs. Wind Generation

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Sensitivity cases

Disaggregated wind data

Representation of hydro dispatch

Sensitivity to forced outage rates

2003 data

1999 data including adjustments for high LOLE levels

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Sensitivity Cases: Hydro Dispatch, Low Forced Outage Rates and 2003 Data

Year Outage Rate Hydro Dispatch LOLE ELCC

2002 base Perfect 0.15924 13.0%

2002 base Imperfect 0.27262 14.0%

2002 5% Perfect 0.00055 11.5%

2002 5% Imperfect 0.00139 15.0%

2003 base Perfect 4.68E-05 21.5%

2003 base Imperfect 0.00023 20.5%

2003 5% Perfect 8.91E-09 21.5%

2003 5% Imperfect 9.58E-08 21.0%

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Sensitivity Tests: Wind Regions and 2002 and 1999 Data

Year Aggregation Hydro Dispatch LOLE ELCC

2002 Total QF Perfect 0.15923 13.0%

2002 Antelope Perfect 0.15911 10.19%

2002 Devers Perfect 0.15926 19.17%

2002 Vincent Perfect 0.15907 8.57%

1999 Total QF Perfect 22.9611 16.50%

1999 Antelope Perfect 22.9601 16.67%

1999 Devers Perfect 22.9698 14.17%

1999 Vincent Perfect 22.9689 20.00%

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1999 Data with 2691 MW of Additional Perfect Capacity

Year Aggregation Hydro Dispatch LOLE ELCC

1999 Total QF Perfect 2.399 10.00%

1999 Antelope Perfect 2.401 11.11%

1999 Devers Perfect 2.400 6.67%

1999 Vincent Perfect 2.400 13.33%

ELCC does not depend on LOLE, when LOLE is low, but the 1999 Results show LOLE is high (about 23 hours/year)

We add enough capacity to reduce LOLE to the “1 day in ten years” level (about 2.4 hours/year)

This reduces ELCC by 40% (from 16.5% to 10%)

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Conclusions

ELCC methods are a reasonable approach to determining the capacity value of wind generation, butImplementing these methods raises issues

The RPS Integration relies on confidential data used in a non-transparent way

Year to year variation is substantial

This raises policy questions about how to compensate wind generators for capacity

Other estimates of wind integration costs in the RPS study need further examination