Effective Load Carrying Capability of Wind Generation: Initial Results with Public Data

1. Effective Load Carrying Capability of Wind Generation: Initial Resultswith Public Data Presented by Ed Kahn at UCEI POWER Conference March 18,2005

2. 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

3. German grid rules imposes large integration costs (from Sacharowitz)

4. Overview of ELCC study • Background • 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 aregenerally reasonable, but their implementation is unclear • The use of confidential ISO data defeats the purpose of transparency • Data issues • Public dataon 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%

5. 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)

6. 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

7. 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

8. The coincidence of wind generation and demand is low

9. 2002 Base Case: Top LOLP Hours vs. Wind Generation

10. Sensitivity cases • Disaggregated wind data • Representation of hydro dispatch • Sensitivity to forced outage rates • 2003 data • 1999 data including adjustments for high LOLE levels

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

12. Sensitivity Tests: Wind Regions and 2002 and 1999 Data

13. 1999 Data with 2691 MW of Additional Perfect Capacity • 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%)

14. Conclusions • ELCC methods are a reasonable approach to determining the capacity value of wind generation, but • Implementing 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