National Renewable Energy Laboratory 

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A national laboratory of the U.S. Department of Energy

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NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

A Multi-Year Analysis of 

Renewable Energy Impacts in 

California: Results from the 

Renewable Portfolio Standards 

Integration Cost Analysis 

Preprint 

M. Milligan (Consultant) 

National Renewable Energy Laboratory 

H. Shiu 

California Wind Energy Collaborative

B. Kirby 

Oak Ridge National Laboratory

K. Jackson 

Dynamic Design Engineering, Inc.

Presented at WINDPOWER 2006 Conference and Exhibition 

Pittsburgh, Pennsylvania 

June 4-7, 2006 

Conference Paper 

NREL/CP-500-40058 

August 2006 
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A Multi-Year Analysis of Renewable Energy Impacts in California: 

Results from the Renewable Portfolio Standards 

Integration Cost Analysis*

Michael Milligan, Consultant 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

USA 

303-384-6927 

Michael_Milligan@nrel.gov 

Henry Shiu 

California Wind Energy Collaborative 

Department of Mechanical and Aeronautical Engineering 

University of California, Davis 

One Shields Avenue 

Davis, CA 95616 

530-752-2261 

hjshiu@ucdavis.edu 

Brendan Kirby 

Oak Ridge National Laboratory 

P.O. Box 2008 

Oak Ridge, TN 37831 

USA 

865-576-1768 

kirbybj@ornl.gov 

Kevin Jackson 

Dynamic Design Engineering, Inc. 

123 C St. 

Davis, CA 95616 

530-753-7961 

KevinJacksonPhD@aol.com 

*This work has been authored by an employee of the Midwest Research Institute under Contract No. DE-AC36-

99GO10337 with the U.S. Department of Energy. The United States Government retains and the publisher, by 

accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paidup, irrevocable, worldwide license to publish or reproduce the publishd form of this work, or allow others to do so, 

for United States Goverment purposes. 

1 
Abstract 

California’s Renewable Portfolio Standard (RPS, Senate Bill 1078) requires the state’s investorowned utilities to obtain 20% of their energy mix from renewable generation sources. To 

facilitate the imminent increase in the penetration of renewables, the California Energy 

Commission (CEC), in support of the California Public Utility Commission (CPUC), initiated a 

study of integration costs in the context of RPS implementation. This effort estimated the impact 

of renewable generation in the regulation and load-following time scales and calculated the 

capacity value of renewable energy sources using a reliability model. The analysis team, 

consisting of researchers from the National Renewable Energy Laboratory (NREL), Oak Ridge 

National Laboratory (ORNL), and the California Wind Energy Collaborative (CWEC), 

performed the study in cooperation with the California Independent System Operator (CaISO), 

the Pacific Gas and Electric Company (PG&E), and Southern California Edison (SCE). The 

study was conducted over three phases and was followed by an analysis of a multi-year period. 

In Phase I, integration costs were identified and defined and methodologies were developed to 

valuate them. The methodologies were then applied to an analysis of 2002 renewable generation 

in California. In Phase II, the generator attributes, including technology and siting, that affect 

integration costs were studied. Phase III presented recommendations for practical 

implementation of regular integration cost calculation and included some minor revisions to the 

capacity value methodology and the 2002 analysis results. The multi-year analysis applied the 

integration cost methodologies to a three year period from 2002 to 2004; it also included new 

recommendations to address the difficulties with data encountered over the course of the study. 

This paper presents results from the multi-year analysis and the Phase III recommendations. 

Executive Summary 

The California Renewable Portfolio Standard requires a “least-cost, best-fit” strategy for 

selecting new-generation projects to fulfill its renewable energy supply goals. This explicitly 

includes indirect integration costs in the bid-evaluation process. In Phase I of the California RPS 

Integration Costs Study, integration costs were identified, valuation methodologies were defined, 

and a 1-year analysis of 2002 was performed. 

This report documents a multi-year analysis of integration costs, applying the previously defined 

methodologies to a 3-year period from 2002 to 2004. The multi-year analysis provided 

opportunities to verify the consistency of the methodologies, further examine the practical issues 

associated with integration cost analysis, and to study the impact of renewable generation on 

integration costs over several years. The scope of the California RPS Integration Costs Study 

included all renewable generation within the state, including solar, geothermal, biomass, and 

wind. In this paper, we focus only on wind generation. 

The methodologies are straightforward and were applied with little modification from their 

implementation in the previous 1-year analysis; the changes that were made are documented 

herein. The input data required for the analysis, however, was more problematic. Data quality 

and confidentiality issues hindered the progress of the study. The most critical data issues were 

ultimately resolved by using a combination of datasets from the California Independent System 

Operator (CaISO), Southern California Edison (SCE), and Pacific Gas and Electric (PG&E); 

performing extensive manual reviews of the data using custom-developed programs; and training 

2 
personnel who had access to the data to perform the analyses. However, outstanding data issues 

limited the analysis, as described in this paper. 

Overall, the multi-year integration cost analysis results were reasonable, consistent with the 

analysis results of the previous 1-year dataset and, in some cases, verified with alternate 

approaches. The capacity credit metric is effective load-carrying capability (ELCC) and is 

expressed as a percentage of the plant rated capacity. The results of the capacity credit analysis 

are summarized in Figure 1. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2002 2003 2004

ELCC % of Rated Capacity

Wind (Northern Cal)

Wind (San Gorgonio)

Wind (Tehachapi)

Fig. 1. Capacity credit analysis for 2002 through 2004 

The capacity credit analysis uses a conventional medium gas unit as a benchmark. Because of 

inconsistencies in the nameplate capacities provided for the generation aggregates, results are 

presented relative to both reported nameplate capacity and annual peak generation. 

Wind capacity values ranged from 24% to 39% based on reported nameplate capacity, with both 

regional and inter-annual variation. This is reasonable given wind’s variable nature. The results 

were verified using an alternate method. 

The results of the regulation analysis are summarized in Figure 2. The left panel shows the 

breakdown for each wind resource area. The right panel shows how the regulation cost of wind 

compares with the regulation cost of the overall system. 

3 
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

2002 2003 2004 3-Yr Avg

Regulation Cost $/MWh

Wind (Northern

California)

Wind (San Gorgonio)

Wind (Tehachapi)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

2002 2003 2004 3-Yr Avg

Regulation Cost $/MWh

Wind (Total)

System (Total)

Fig. 2. Results of the regulation analysis: (left) breakdown for each wind resource 

area, (right) regulation cost of wind compared with the regulation cost of the overall 

system 

The resources studied have fairly minor impacts on total system regulation requirements. There 

is some inter-annual variation; in most cases, the changes follow the cost trend of actual 

regulation commitment by CaISO between 2002 and 2004. Because of the sheer size of total 

load, its regulation cost is consistently very close to that of the total system requirement. The 

regulation costs of the wind aggregates range between $0.24/MWh and $0.70/MWh, ignoring the 

anomalously low value for wind in San Gorgonio in 2002. While these values are higher than the 

results for biomass and geothermal, they are still quite modest. The regulation costs imposed by 

wind are reasonable, given that there are no apparent mechanisms that tie wind plant 

performance to the power system’s needs ― either favorably or unfavorably ― in the regulation 

time frame. 

The results of the load-following analysis are summarized in Table 1. 

Table 1. Results of Load-Following Analysis 

2002 2003 2004 

AVERAGE MINIMUM AVERAGE MAXIMUM AVERAGE MINIMUM AVERAGE MAXIMUM AVERAGE MINIMUM AVERAGE MAXIMUM

ERROR 

MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) 

Load forecast alone -1945 100% 2112 100% -1600 100% 2151 100% -1439 100% 1529 100% 

Load scheduling alone -4747 244% 1302 62% -4021 251% 2158 100% -3700 257% 1776 116% 

Scheduling bias -5337 274% 1708 81% -3336 208% 1534 71% -3016 210% 1634 107% 

Combined load forecast and renewable resource scheduling error 

Wind (Northern Cal) -1946 100% 2148 102% -1591 99% 2203 102% -1419 99% 1554 102% 

Wind (San Gorgonio) -1930 99% 2142 101% -1581 99% 2163 101% -1443 100% 1545 101% 

Wind (Tehachapi) -1931 99% 2177 103% -1569 98% 2181 101% -1435 100% 1544 101% 

The combined load forecast and renewable resource scheduling error values in Table 1 indicate 

that renewable generators do not have a significant effect on the total energy requirements from 

4 
the short-term load-following market at current penetration levels. The minimum scheduling bias 

was more than 200% greater than the combined forecast and scheduling error, implying that 

ample depth is available in the short-term generator stack to handle incremental energy 

requirements. 

A complementary methodology for analyzing ramping capability and requirements is also 

presented with a preliminary analysis. The ramping capability of thermal generators responding 

in the load-following time frame appears to very large and capable of supporting a large amount 

of renewables. The ramping requirements of variable renewable generation appear to be 

significantly lower than the requirement of the total system load and the capability available in 

the CaISO control area. 

Provided the necessary data with sufficient quality, integration cost analysis becomes a relatively 

quick and straightforward process. An Integration Cost Analyst (ICA) is proposed to perform 

and report on integration cost analysis on a regular basis. It is recommended that the California 

Energy Commission or CPUC dedicate personnel and resources to perform the functions of an 

ICA. However, given the data issues encountered during this study, the tasks of 

handling/preparing data and analyzing integration costs should be made distinct and separate. 

This would also benefit other recent and current studies that require similar data. A data-handling 

person is proposed who would coordinate with data sources (CaISO, Investor Owned Utilities, or 

IOUs, etc.) and the ICA to ensure the availability of good data quality as needed. 

Introduction 

The California Renewable Portfolio Standard requires a “least-cost, best-fit” strategy for 

selecting new-generation projects to fulfill its renewable energy supply goals. This explicitly 

includes indirect integration costs in the bid evaluation process. In Phase I of the California RPS 

Integration Costs Study, integration costs were identified, valuation methodologies were defined, 

and a 1-year analysis of 2002 was performed. 

The multi-year analysis documented in this report applies the integration cost valuation 

methodologies detailed in Phase III to a new multi-year dataset. The new analysis spans 2002 to 

2004 and provided opportunities to verify the consistency of the methodologies and to further 

examine the practical issues associated with integration cost analysis. The methodologies were 

originally developed to be straightforward and were applied with little or no modification to 

capacity credit, regulation, and load following. They are described herein, along with the 

analysis results. The methodologies, however, require good quality data, and the difficulties 

encountered in assembling an adequate dataset hampered the analysis. Because these data issues 

will remain relevant to any future study, they are also detailed below. Finally, based on the 

experiences garnered from performing the multi-year analysis and resolving the data quality 

issues, recommendations are provided for future analyses. 

Capacity Credit 

Based on discussion with utilities and other stakeholders throughout the RPS Integration Cost 

project, several refinements were made to the ELCC calculation of renewable technologies. The 

Phase I report modeled the renewable variable generation using a probabilistic approach. This 

method is similar to what is often done with conventional units that have multiple output 

settings, each with an associated partial forced outage rate. As a result of extensive feedback 

5 
during public workshops, the probabilistic method was replaced with a more direct approach that 

uses actual hourly output of the renewable generators. 

The probabilistic approach is more appropriate as an indicator of future performance, where 

there are considerable uncertainties surrounding the timing of the power delivery from certain 

resources. Directly using hourly output is more appropriate for measuring past contributions to 

capacity from a variable resource. It does not consider alternative timing of the power delivery 

from variable resources, as does the probabilistic method. However, when multiple years of data 

are analyzed, this is not a significant limitation. Therefore, single-year estimates should be 

considered as such and would be expected to vary somewhat from year to year. This was 

discussed in detail and applied in the Phase III update to the 1-year capacity credit study. In the 

multi-year study, we continued to use the direct hourly method. 

Other improvements were made in the input data. For the multi-year analysis, we utilized 

renewable generation data directly from the IOUs. This allowed us to bypass some data from 

CaISO’s Plant Information (PI) system that suffered from data errors. Those errors were 

sometimes difficult to detect because the renewable generation data was aggregated, which 

tended to obscure the errors. The data errors caused artificial offsets to actual generation and 

injected unrealistic ramping behavior over long time periods into the data set. The CaISO data 

also had related problems with the reported nameplate capacity of the generator aggregates. The 

IOU data aggregates used for the multi-year analysis were the ones that most closely matched the 

CaISO data used in the regulation and load-following analyses. 

We were able to obtain 1-minute hydro data from CaISO and used hourly averages of these data 

directly in the multi-year analysis. This is an improvement over the hydro modeling previously 

used. In Phase III, an optimal dispatch of hydro was used based on CEC information on monthly 

minimum and maximum flows and rough estimates of pond-storage and pumped hydro data. 

However, a significant portion of hydro energy is run-of-river, which is uncontrollable and 

subject to nature. This is similar to wind and solar, although hydro is less variable than wind and 

has different characteristics than solar. But ultimately, these forms of generation are not 

dispatchable. As discussed further in some of the workshops and the Phase III report, the impact 

of the hydro system on the hourly risk profile is significant. The results below support this view 

and also show the significant effect of interchange to neighboring balancing authorities. 

The outcome of the public workshops during the Phase I work suggested that scheduled 

maintenance from conventional units should be eliminated from the modeling and was excluded 

in the 1-year and multi-year analyses. As was stated in the Phase III report, whether this should 

continue is a policy question. Workshop participants in the earlier phases of this project 

suggested that in principle, the capacity value of renewable generators should be independent 

from conventional maintenance scheduling. 

Multi-Year Reliability Modeling and Discussion 

Power systems experience a wide variety of conditions from year to year. Because load is 

generally sensitive to weather, unusually warm or cool temperatures can cause the load profile in 

a given year to diverge from “normal.” Generation does not always respond in the same way to 

nearly identical load conditions. Because loads can change significantly from year to year, both 

in magnitude and timing, one would expect that reliability indicators such as loss of load 

probability (LOLP) would also change, perhaps significantly. Because LOLP is a key ingredient 

6 
in calculating capacity credit, we began the analysis by collecting the results of the base-case 

reliability model runs for each of the 3-year periods (note that 2004 is represented by data from 

September 2003 to September 2004). Figure 3 is a LOLP-duration curve for each year, plotted 

on the same graph. We can see from the graph that 2004 exhibits a relatively sharp decline in 

LOLP as loads drop off from the annual peak. Much of the annual risk occurs in a smaller 

number of hours, whereas the curves for 2002 and 2003 indicate a more gradual decline. In 2002 

the risk is spread over more hours. The significance of this graph is that the risk profile of the 

CaISO system, as measured by LOLP, changes from year to year. It is not possible a priori to 

determine which hours will have the highest risk, or even to predict the risk profile with 

certainty. 

For a closer view, we generated a series of graphs for the 3-year period that show not only the 

relationship between load and LOLP, but the overall impact that the hydro system and 

interchange have on risk. In general (ignoring hydro and interchange), the highest annual LOLP 

would be expected to occur during the peak hour. However, there are many factors that can cause 

LOLP in near-peak hours to exceed the LOLP on the system peak. Generator schedules, 

exchange schedules, and hydro generation are capable of responding to the high prices that 

accompany peak or near-peak loads, subject to operating constraints. It is, therefore, possible that 

real-time reserves are higher during system peak than at near-peak. These and other factors can 

contribute to a LOLP profile that is similar to, but does not match, the peak load profile. 

Fig. 3. Hourly LOLP ranked from, 2002 through 2004 

In Figure 4, the graph shows a typical load duration curve, in this case for 2002 (the blue line 

with the smooth characteristic). Superimposed on this graph are two additional rankings. The 

first shows the ranking of load by hourly LOLP (red). What the graph shows is that high load 

7 
hours may generally be correlated with high LOLP, but the correlation is weak when we view 

the top 271 hours (the somewhat arbitrary cutoff point was LOLP ≥ 0.000001 days/year). 

The final ranking on the graph (green) is based on the load that remains to be served after hydro 

and interchange have been taken into account. We refer to this as the load, net of hydro and 

interchange. Because forced outage rates of hydro and imports are very low or cannot be 

objectively assessed, standard practice is to ignore forced outage rates for these resources. The 

implication is that the primary impact that hydro and imports have on system risk is to shift the 

timing of risk. For variable resources, such as wind, this further implies that for the generator to 

reduce annual loss of load expectation (LOLE, a measure that is based on LOLP), it must 

provide power during periods of high LOLP after taking account of hydro and imports/exports. 

This can have a significant impact on the LOLP profile, which is apparent from the figure. 

31000

33000

35000

37000

39000

41000

43000

0 50 100 150 200 250

Rank

Load (MW)

Ranked by load

Ranked by LOLP

Ranked by load net hydro and interchange

Fig. 4. Load in 2002 during top risk hours, ranked by load, LOLP, and load net hydro 

and interchange 

Figure 5 takes a closer look at the load net hydro and interchange. The LOLP duration curve is 

not monotonically decreasing as a function of net load. If a variable resource delivers its energy 

during the high LOLP events, it will achieve a relatively high capacity credit. The timing of these 

high LOLP events will not necessarily correspond to highest load events. Similar characteristics 

were found in the 2003 and 2004 data sets and are described in the original report. 

8 
0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

28850 27850 26850 25850 24850 23850

Load net hydro and interchange (MW) ordered from highest to lowest

LOLP (hours/year)

Fig. 5. LOLP in 2002 at top hours of load net hydro and interchange 

ELCC Results 

All ELCC results were calculated based on the method described in the Phase III report and with 

data received from CaISO and the IOUs. The analysis requires a complete year of data for each 

calculation. The input datasets used had complete years for 2002 and 2003, but not 2004. The 

year 2004 is represented by data from mid-September 2003 to mid-September 2004. It is simply 

referred to as “2004” for convenience. 

As in the prior work, ELCC is measured relative to a benchmark unit, a gas combined-cycle 

generator with a 4% forced outage rate and a 7.6% annual maintenance rate. All wind resources 

were modeled as time series, using the actual hourly generation provided by the IOUs for the full 

year. Transactions (interchange) and hydro were also represented by actual hourly data, obtained 

from CaISO. We note that in February 2002 there were some errors in the hydro data, which we 

patched through a combination of interpolation and pattern matching. Because LOLP during the 

month of February is so close to zero, this will not impact the results. 

During the processing of the data for the analysis, some discrepancies were uncovered in the 

reported nameplate capacities of some of the generation aggregates. In prior work we reported 

capacity value as a percent of the annual maximum hourly generation for the resource in 

question. In the results below we have represented capacity value in three ways: (1) MW, (2) 

percent of maximum hourly output for the year, and (3) as percent of rated capacity as indicated 

by the IOU providing the generation data. In the case of wind, the relatively large discrepancy 

9 
between actual generation and nameplate generation is probably an artifact of the older 

technology that still exists in some areas in California. 

We believe that modern and future wind turbine technology will be more reliable than some past 

technology has been, minimizing this capacity discrepancy. If wind generation were to receive 

capacity payments, the wind operator would have an incentive to keep the turbines running and 

in good repair, especially during high load or LOLP events. Although we generally believe that 

capacity value should be represented as a percentage of nameplate capacity, this depends on 

having accurate nameplate values. The PG&E nameplate estimates do not match the maximum 

wind generation, nor do they match the estimates from SCE. Although this is not conclusive, it 

suggests that caution should be used in interpreting these capacity values. All of the data issues 

introduced above are discussed in further detail in a later section of this paper. 

Table 2 shows the capacity value results expressed in terms of annual peak generation. To 

clarify, to calculate the relative ELCC for this table, the ELCC (in MW) is divided by the 

maximum hourly generation, for the resource in question, over the year. 

Table 2. Capacity Credit Analysis Results Based on Annual Peak Generation 

2002 2003 2004 

Resource ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

Wind (Northern Cal) 160 489 33% 170 463 37% 205 462 44% 

Wind (San 

Gorgonio) 138 325 42% 89 317 28% 89 332 27% 

Wind (Tehachapi) 168 584 29% 191 568 34% 167 571 29% 

The capacity results for wind are different than those of the Phase III report. There are a couple 

of reasons for these differences. The hydro dataset used in this analysis is actual hydro, hourly, 

for the full year. In the Phase III work we were constrained to work with modeled hydro. Second, 

the generation aggregates used for the Phase I and Phase III 1-year analyses differ somewhat 

from those in the current work. 

10 
Table 3 shows the results in terms of the reported nameplate capacity. As can be seen in the 

table, the percentage capacity values are generally lower than in Table 2. For the wind resources, 

we would expect the capacity value to decline when we use reported capacity as the basis of the 

capacity value, and we believe that using an accurate measure of nameplate capacity is the most 

appropriate metric. 

To get an idea of the impact that hydro and interchange have on the LOLP profile, we removed 

them and re-ran the analysis. We show the results in terms of annual peak generation (Table 4) 

and in terms of reported rated capacity. 

Table 3. Capacity Credit Analysis Results based on 

Rated Capacity Reported by the IOUs 

2002 2003 2004 

Resource ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

Wind (Northern Cal) 160 679 24% 170 679 25% 205 680 30% 

Wind (San 

Gorgonio) 138 357 39% 89 362 24% 89 362 25% 

Wind (Tehachapi) 168 652 26% 191 659 29% 167 659 25% 

11 
Table 4. Capacity Credit Results with Hydro and Interchange Removed; 

Results Based on Annual Peak generation 

2002 2003 2004 

Resource ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Annual 

peak 

generation 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

Wind (Northern Cal) 129 489 26% 129 463 28% 179 462 39% 

Wind (San 

Gorgonio) 124 325 38% 69 317 22% 93 332 28% 

Wind (Tehachapi) 175 584 30% 167 568 29% 178 571 31% 

A comparison of the wind capacity values from Table 4 with the Phase III results shows a much 

closer correspondence. For example, in the Phase III report Altamont (Northern California) had a 

capacity value of 26% (based on maximum generation), San Gorgonio 31%, and Tehachapi 29%. 

The obvious outlier is San Gorgonio. The relatively good correspondence between some of these 

values may however be spurious because there are substantial differences in the data sets used in 

the two analyses. Assuming accurate data, Table 5 provides the most accurate assessment of the 

capacity values that would have occurred in the absence of interchange and hydro. 

12 
Table 5. Capacity Credit Results with Hydro and Interchange Removed; 

Results Based on Rated Capacity reported by the IOUs 

2002 2003 2004 

Resource ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

ELCC 

(MW) 

Reported 

rated 

capacity 

(MW) 

Capacity 

credit 

(relative 

ELCC) 

Wind (Northern Cal) 129 679 19% 129 679 19% 179 680 26% 

Wind (San Gorgonio) 124 357 35% 69 362 19% 93 362 26% 

Wind (Tehachapi) 175 652 27% 167 659 25% 178 659 27% 

Based in part on comments received by Solargenix during the Phase I discussions, we calculated 

the capacity factor for each renewable based on SCE’s definition of the peak period: weekdays 

during the months of June through September (except holidays) between 12:00 p.m. and 6:00 

p.m. As an alternative, we also included the month of May (Piwko et al., 2005). Table 6 shows 

the results of these calculations based on annual peak generation, and Table 7 shows the same 

information based on rated capacity. 

13 
Table 6. Capacity Factor over Peak Hours Based on Annual Peak Generation 

2002 2003 2004 

Resource 

May 

through 

September

June 

through 

September

May 

through 

September

June 

through 

September

May 

through 

September

June 

through 

September 

Solar 85% 90% 70% 76% 85% 89% 

Wind (Northern 

Cal) 

27% 27% 29% 30% 37% 35% 

Wind (San 

Gorgonio) 

41% 39% 28% 26% 34% 30% 

Wind (Tehachapi) 36% 33% 28% 28% 33% 29% 

Table 7. Capacity Factor over Peak Hours Based on Rated Capacity Reported by the IOUs 

2002 2003 2004 

Resource 

May 

through 

September

June 

through 

September

May 

through 

September

June 

through 

September

May 

through 

September

June 

through 

September 

Wind (Northern 

Cal) 

19% 19% 20% 20% 25% 24% 

Wind (San 

Gorgonio) 

37% 36% 25% 23% 31% 28% 

Wind (Tehachapi) 32% 30% 24% 24% 29% 25% 

Table 8 collects results from Table 3 and Table 4. All ELCC values in the table are expressed as 

a percentage of the rated capacity and the peak capacity factors are all calculated based on the 

period from June through September and use rated capacity in the denominator. In each case, we 

also calculated the 3-year average. For some of the wind resource areas we have an excellent 

match between the 3-year average ELCC and the 3-year average peak capacity factors. 

14 
Unfortunately, this close match does not extend to the Northern California wind resource, which 

differs by about 5%. 

Table 8. ELCC Compared to Peak Capacity Factors (June through September, 

Weekdays Excluding Holidays, 12:00 p.m. to 6:00 p.m.) for 3 years, 

Based on Rated Capacity Reported by the IOUs 

2002 2003 2004 3-Year Average 

Resource 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

Wind (Northern Cal) 24 19 25 20 30 24 26 21 

Wind (San 

Gorgonio) 

39 36 24 23 25 28 29 29 

Wind (Tehachapi) 26 30 29 24 25 25 27 26 

Table 9 summarizes some of the key results as above, but instead uses ELCC values from the 

runs that exclude hydro and interchange. All ELCC values in the table are expressed as a 

percentage of the rated capacity and the peak capacity factors are all calculated based on the 

period from June through September and use rated capacity in the denominator. In this case, we 

have a match between the results for the Northern California wind area and have a 2% difference 

in San Gorgonio. Because the hydro and interchange data were removed from these ELCC 

calculations, the ELCC results are not quite as accurate because of the missing resources. 

However, because of the lack of interchange and hydro, the relationship between load and LOLP 

is more straightforward. 

15 
Table 9. ELCC with Hydro and Interchange Excluded Compared to Peak 

Capacity Factors (June through September, Weekdays Excluding Holidays, 

12:00 p.m. to 6:00 p.m.) for 3 years, Based on Rated Capacity Reported by the IOUs 

2002 2003 2004 3-Year Average 

Resource 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

ELCC 

(% of rated 

capacity) 

Peak 

capacity 

factor 

Wind (Northern Cal) 19 19 19 20 26 24 21 21 

Wind (San 

Gorgonio) 

35 36 19 23 26 28 27 29 

Wind (Tehachapi) 24 30 25 24 27 25 26 26 

Discussion of Results 

During the prior phases of this project, there have been numerous discussions regarding whether 

nameplate capacity assignments for existing wind resource areas were correct. Although it is 

useful to measure capacity value in MW, it is difficult to properly interpret the effectiveness of 

the resource if its rated capacity is unknown. Although this has been an issue in this project, we 

believe that it will be less of a problem with new wind facilities. If capacity payments are to be 

made to renewable (or other) generators, the incentive provided by the payment should be an 

inducement to ensure generator availability. Perhaps even more important is the evolution in 

wind turbine technology. Modern turbines are quite unlike many older turbines currently 

installed in California. Combined with taller towers and larger rotors, energy can be generated at 

lower wind speeds than with older technology. We expect that the capacity credit, however 

calculated, will be significantly different for modern/future wind turbines. Going forward, we do 

not believe that large numbers of turbines will be unaccounted for if good engineering and 

business practices are followed. 

With the uncertainties surrounding data quality during this project, it is hard to know the extent 

to which data inaccuracies influence the results. We have much better confidence in the revised 

data sets used for this analysis than in the past. Data confidentiality issues have made it difficult 

to fully assess the results, particularly given the confidential aggregations of renewable 

generators. 

The ELCC for the wind generators that were calculated for this project indicate the reliability 

contribution to the generator fleet in California. There are many moving parts that are captured 

by the model as a snapshot. For example, there may be significant synergies between hydro 

operations and wind (and other renewable technologies). Based on discussions during this 

project it appears that the hydro system is dispatched independently of the variable generation. 

16 
With improvements in forecasting, especially for wind, it is possible that some incremental 

reliability can be gained by exploiting these potential synergies. 

We ran several alternative scenarios to calculated ELCC. It is clear that hydro and interchange 

make a difference in the LOLP profile and, therefore, on the ELCC of variable generators. It is 

also evident that ELCC results are not necessarily transparent. We found a reasonably good 

correspondence between ELCC and capacity factors that were calculated over the peak period. 

Whether to use ELCC or a capacity factor approximation is a policy decision. The overriding 

factor that would seem to favor ELCC is that it is a rigorous method that explicitly considers risk 

via the LOLP equation. Any approximation method will fall short. Conversely, a simpler method 

such as that considered above can come close and our examples showed that over 3 years, 

differences in methods may become less important. Simple methods also have the advantage of 

transparency and ease of reproduction. 

Regulation 

The method for calculating regulation costs was developed by Kirby and Hirst (2000). The 

methodology and its results are described below with background material presented in the 

original Multi-year Report. The regulation analysis methodology has been applied to a variety of 

other control areas to quantify the ancillary service impacts of loads and variable resources. It 

determines the regulation and load-following impacts to the control area. These impacts are the 

result of fluctuations in aggregate load and/or uncontrolled generation that must be compensated. 

Once the requirements are quantified, the method then determines the costs incurred in terms of 

greater amounts of purchased regulating capacity and greater use of the short-term energy 

markets. 

Ancillary Services Terminology and Overview 

Terminology associated with ancillary services has not been standardized across the utility 

industry and this sometimes has led to confusion. It is important to distinguish between the 

impacts imposed upon the power system and the resources or services the CaISO utilizes to 

compensate for these impacts. The impacts in the regulation time frame are imposed upon the 

power network by loads, uncontrolled generators, and transactions. The resources or services that 

compensate for these impacts are supplied by generators responding to AGC and the ADS. 

Regulation and load following are intimately related; both continuously balance aggregate load 

and generation within the control area. The two services differ in the time frame over which they 

operate with regulation operating minute-to-minute while load following operates over a ten 

minute or longer time frame. In 1996 the Federal Energy Regulatory Commission (FERC) 

defined six ancillary services in its Order 888. This order did not discuss load following. Perhaps 

because of this omission, most utilities and ISOs do not include load following in their tariffs. 

The absence of this service required some ISOs to acquire much more regulation than they 

otherwise would need. Perhaps because of these problems, FERC, in its notice on RTOs, 

proposed to require that RTOs operate real-time balancing markets (FERC 1999). The 

responsive resources for these supplemental energy markets are generators that can change 

output every ten minutes as needed to follow load. 

The CaISO obtains responsive resources to achieve the required real-time balancing of 

generation and load from the hourly regulation markets and the short-term energy markets. The 

17 
alignment between the impacts that the CaISO must meet and the services it procures to meet 

those impacts is not perfect. Resources procured through the regulation markets, for example, 

could be used to provide load following, accommodate energy imbalance, or even supply base 

energy if there were no other alternatives. Load following itself is not a service which the CaISO 

procures directly. The CaISO meets its load-following needs through short-term energy 

transactions, including both AGC generators and the supplemental energy market. Loadfollowing results are discussed in a later section of this paper. 

Definition of Regulation and Load Following 

Loads within a control area can be decomposed into three components: base energy, load 

following, and regulation, as shown for a hypothetical weekday morning in Figure 6. Starting at 

a base energy of 3566 MW, the smooth load-following ramp (blue) is shown rising to 4035 MW. 

Regulation (red) consists of the rapid fluctuations in load around the underlying trend, shown 

here on an expanded scale to the right with a ±55 MW range. Combined, the three elements 

serve a total load (green) that ranges from 3539 MW to 4079 MW during the 3 hours depicted. 

3000

3200

3400

3600

3800

4000

4200

7:00 8:00 9:00 10:00

Load and Load Following (MW)

-60

-30

0

30

60

90

120

150

Regulation (MW)

Regulation

Total Load and

Load Following

Fig. 6. Decomposition of hypothetical weekday morning load 

18 
The system responses to the second and third components are called load following and 

regulation. These two services ensure that, under normal operating conditions, a control area is 

able to balance generation to load. The two services are briefly defined (FERC 1996, Hirst and 

Kirby 1998, NERC2002) as follows: 

• Regulation is the use of online generating units that are equipped with AGC and that can 

change output quickly (MW/minute) to track the moment-to-moment fluctuations in 

customer loads and to correct for the unintended fluctuations in generation. In so doing, 

regulation helps to maintain interconnection frequency, manage differences between actual 

and scheduled power flows between control areas, and match generation to load within the 

control area. This service can be provided by any appropriately equipped generator that is 

connected to the grid and electrically close enough to the local control area that physical and 

economic transmission limitations do not prevent the importation of this power. 

• Load following is the use of online generation equipment to track the intra- and inter-hour 

changes in customer loads. Load following differs from regulation in three important 

respects. First, it occurs over longer time intervals than does regulation, 10 minutes or more 

rather than minute to minute. Second, the load-following patterns of individual customers can 

be highly correlated with each other, whereas the regulation patterns are largely uncorrelated. 

Third, load-following changes are often predictable (e.g., because of the weather dependence 

of many loads) and have similar day-to-day patterns. 

There is no hard-and-fast rule to define the temporal boundary between regulation and load 

following. If the time chosen for the split is too short (e.g., 5 minutes), too much of the 

fluctuations will appear as load following and not enough as regulation. If the boundary is too 

long (e.g., 60 minutes), too much of the fluctuations will show up as regulation and not enough 

as load following. But, in each case, the total is unchanged and is captured by one or the other of 

these two services. A 15-minute rolling average is recommended here to separate regulation from 

load following. The rolling average for each 1-minute interval should be calculated as the mean 

value of the seven earlier values of the variable, the current value, and the subsequent seven 

values. For load, we use the following equations: 

Load Followingt = Loadestimated-t = mean (Lt-7 , Lt-6 , ... , Lt , Lt+1 , ... , Lt+7) Equation 1 

Regulationt = Loadt - Loadestimated-t Equation 2 

This method is somewhat arbitrary and imperfect. It is arbitrary in that the time-averaging period 

(15 minutes as recommended here) and the temporal aggregation of raw data (1 minute) cannot 

be predetermined. In principle, the control-area characteristics (dynamics of generation and load 

and the short-term energy market interval) should determine these two factors (Hirst and Kirby 

2002). The 15-minute rolling average is recommended because it provides good temporal 

segregation and captures the characteristics of California’s supplemental energy market. 

In practice, system operators cannot know future values of load. They generally produce shortterm forecasts of these values to aid in generation-dispatch decisions. While aggregate load 

forecasts are typically well developed and a short-term energy market now operates in 

California, short-term forecast methodologies for non-dispatchable conventional and renewable 

generators are not. The rolling average has proven to be a reasonable analytical substitute in 

studying other control areas. The rolling average, like the system operator through the use of the 

19 
short-term energy market, is constantly moving the regulating units back to the center of their 

operating range. If consistent, robust short-term forecasts are available and verified for all of the 

renewable generation technologies, this analysis can be performed without the use of a rolling 

average. 

The use of the rolling average rather than the short-term forecasts can impact the allocation of 

variability between the regulation and load-following services slightly. Significantly, the method 

assures that total variability is captured in one or the other service and that there is no double 

counting. The distinctions between regulation and load following are discussed in another 

section of this paper. 

Regulation Analysis Methodology 

The regulation analysis methodology quantifies the regulation impacts of loads and generating 

resources within a control area. These impacts are the result of fluctuations in aggregate load 

and/or uncontrolled generation that must be compensated. Once the requirements are quantified, 

the method then determines the costs incurred in terms of greater amounts of purchased 

regulating capacity. 

The regulation requirement of the entire system is first determined by taking the standard 

deviation of the 1-minute regulation values (applying Equation 2) for the total system. This is 

done hourly because the regulation market clears hourly. It is then possible to calculate 

individual contributions to that total requirement. Regulation aggregation is nonlinear; there are 

strong aggregation benefits. It takes much less regulation effort to compensate for the total 

aggregation than it would take if each load or generator compensated for its regulation impact 

individually. An allocation method should do the following: 

• Recognize positive and negative correlations 

• Be independent of sub-aggregations 

• Be independent of the order in which loads or resources are added to the system 

• Allow dis-aggregation of as many or few components as desired. 

The method used here meets these criteria. It was developed to analyze the impacts of 

nonconforming loads on power system regulation and works equally well when applied to nondispatchable or uncontrolled generators. The allocation method does not require knowledge of 

each individual’s contribution to the overall requirement. Specific individual contributions can 

be calculated based upon the total requirement and the individual’s performance. Because 

regulation is composed of short, minute-to-minute fluctuations, the regulation component of each 

individual is often largely uncorrelated with those of other individuals. If each individual’s 

fluctuations (represented by the standard deviation, σi) is completely independent of the 

remainder of the system, the total regulation requirement (σT) would equal the following: 

= ∑ 2

T σσ i Equation 3

where i refers to an individual and T is the system total. 

20 
For the case of uncorrelated contributions, the share of regulation assigned to each individual is 

Sharei = 

2

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

T

i

σ

σ

 Equation 4 

The more general allocation method, presented in Equation 5, accommodates any degree of 

correlation and any number of individuals. This allocation method is more complex but no more 

data-intensive than the previous method. This method yields results that are independent of any 

sub-aggregations. In other words, the assignment of regulation to generator (or load) gi is not 

dependent on whether gi is billed for regulation independently of other non-AGC generators (or 

loads) or as part of a group. In addition, the allocation method rewards (pays) generators (or 

loads) that reduce the total regulation impact. 

The general allocation method (Equation 5) is recommended for analysis of the impacts of 

various individual renewable generators on the overall system’s regulation requirements: 

T

i

T i iT Share

σ

σσσ

2

222

− −+ = Equation 5 

Calculated hourly regulation requirements are compared with actual hourly regulation purchases 

by the CaISO and hourly regulation self-provided by scheduling coordinators. Typically, three to 

five standard deviations of regulating reserves are carried to assure adequate Control 

Performance Standards (CPS) performance. Total regulation requirements are then allocated 

back to individuals. Hourly regulation costs are used to allocate the cost of regulation back to 

individuals. All of the CaISO’s regulation requirements are allocated based on the short-term 

variability impacts of the loads and renewable generators. 

Data Requirements 

Studying regulation requires 1-minute, synchronized, integrated-energy, time-series data for total 

control area load and the individual renewable resources of interest. 

At a minimum, the data list must include time series data for the following: 

• Total load 

• Each renewable generator of interest. 

Experience has shown that it is also wise to perform an energy balance around the control area to 

assure data integrity. This requires 1-minute data for total generation, net actual imports/exports, 

net scheduled imports/exports, system frequency (and the frequency bias), and area control error 

(ACE). The data list should include 1-minute, synchronized, integrated-energy, and time-series 

data for the following: 

• Total generation 

• Net actual imports/exports 

21 
• Net scheduled imports/exports 

• Area control error 

• Frequency (and frequency bias) – often provided as a deviation from scheduled frequency. 

Regulation analysis requires only one system data element plus one for each renewable generator 

of interest, each minute. Verifying data integrity requires an additional five system data elements 

each minute. 

The CaISO runs hourly markets for regulation up and regulation down. Price and quantity data 

from these markets are used to determine practical quantities and costs of procured regulating 

resources. Scheduling coordinators are also allowed to self-provide regulation. The amount of 

self-provided regulation must be added to the amount of purchased regulation to obtain the total 

regulation amount. There is no price associated with self-provided regulation so the market price 

of the purchased regulation for the same hour is used to calculate the total dollar value of 

regulation for each hour: 

• Hourly regulation-up price 

• Hourly regulation-down price 

• Hourly MW of regulation-up procured (hour ahead and real-time) 

• Hourly MW of regulation-down procured (hour ahead and real-time) 

• Hourly MW of regulation-up self-provided 

• Hourly MW of regulation-down self-provided. 

Analysis Changes from Phase I and Phase III 

In the spring of 2005, an independent review* of the Phase I report revealed that the calculation 

of the total system compensation requirement did not include the renewable generators’ 

variability along with the total load variability. Only the total load variability was included. The 

methodology implementation description above now explicitly includes the individual 

generators, as well as the load. 

Later, a 1-minute data misalignment was discovered in the wind data for San Gorgonio used in 

the Phase I analysis. The misalignment only affected the regulation results because its effect is 

suppressed by the hourly and 10-minute averaging used by the capacity credit and loadfollowing calculations. A revised set of results for the Phase I regulation analysis is presented in 

Table 10. This includes the complete calculation of the total system compensation requirement 

and synchronized data for San Gorgonio. 

 

*

 The independent review was performed by Matthew Barmack of Analysis Group, Inc. When he could not duplicate 

the Phase I regulation results, we investigated further and found the omission in the total system compensation 

calculation. We are grateful to Matthew. 

22 
Table 10. Original and Corrected Results of the Phase I (1 year, 2002) 

Regulation Analysis; Negative Values are Costs to the System 

Regulation Cost 

($/MWh or mills/kWh) 

Resource 

Original Corrected 

Total System -0.42 -0.44 

Total Load -0.42 -0.41 

Wind (Altamont) 0.00 -0.22 

Wind (San Gorgonio) -0.46 -0.08 

Wind (Tehachapi) -0.17 -0.53 

Wind (Total) -0.17 -0.33 

The results for the total system and for load remain approximately the same because the load 

represents the majority of variability in the entire system. However, because the variability of the 

individual generators was not originally included in the total system regulation requirement, the 

amount of variability allocated to each generator was understated. The decrease in San Gorgonio 

is not a result of including its variability in the total regulation requirement, but because of the 

correction of the 1-minute misalignment in its generation data (a calculation with the original 

misaligned data indeed results in a cost increase). The cost for San Gorgonio is several times 

lower than the other wind regions. This may be an anomaly, as shown in the multi-year results 

for San Gorgonio, below. The results are discussed further along with the multi-year analysis 

results in the following section. 

The datasets used in the multi-year analysis vary somewhat from the datasets used in the Phase I 

1-year analysis. The CaISO multi-year dataset has expanded aggregates in an attempt to better 

represent the generators being studied. However, the multi-year dataset exhibited new types of 

errors. To address these errors, the multi-year dataset was reviewed and checked for errors using 

data from PG&E and SCE as bases of comparison. 

The multi-year analysis replaced the Altamont aggregate with an aggregate including plants from 

Altamont, Solano, and Pacheco; this was necessary to more closely match the corresponding 

PG&E data aggregate that it was compared against. Because of gaps in the 2002 biomass and 

solar data, the 2002 biomass and solar regulation analyses were run normally, but the runs for the 

other generation aggregates excluded biomass and solar from their calculation of the total system 

compensation requirement. This was considered a reasonable approximation because results 

from the 2002 1-year analysis are available for comparison. All of the data issues are detailed in 

the original report. 

23 
Multi-Year Regulation Analysis Results and Discussion 

The methodology described above was applied to the CaISO multi-year dataset. The results of 

the multi-year analysis appear in Table 11. 

Table 11. Results of Regulation Analysis of Multi-year Dataset; 

Negative Values are a Cost 

Regulation Cost 

($/MWh or mills/kWh)a

Resource 

2002 2003 2004 

Total System -0.42 -0.47 -0.39 

Total Load -0.41 -0.46 -0.36 

Wind (Northern California) -0.24 -0.40 -0.33 

Wind (San Gorgonio) -0.09 -0.43 -0.58 

Wind (Tehachapi) -0.57 -0.70 -0.56 

Wind (Total) -0.36 -0.53 -0.47 

a

 Using $/MWh as a metric for regulation is both useful and dangerous. It is useful because what we 

really want to know is how much this ancillary service (something we are forced to buy but don’t 

really want) adds to the cost of electricity (something that does useful work for us and we do want to 

purchase). In that sense a metric that is in the same units ($/MWh) as the commodity we are 

purchasing is very useful. It is dangerous because the amount of regulation required and the price have 

almost nothing to do with the amount of energy consumed or produced. The amount of regulation 

depends upon the short-term volatility of the generation or load, not the energy consumption or 

production. Use $/MWh in reference to regulation with great caution.

The 2002 results from the multi-year analysis and the 1-year analysis (Table 10) match well. 

There is some minor variation, but this is expected as the composition of the generation 

aggregates are not exactly identical. 

In general, regulation costs increased slightly from 2002 to 2003 and then fell again in 2004, 

although not to previous levels. The calculated regulation purchase amount and costs are scaled 

from actual regulation commitment and purchase data from the CaISO Open Access Same Time 

Information Systems (OASIS) database, which is shown in Table 12. 

24 
Table 12. Actual Regulation Amounts Committed 

in the CaISO Control Area, 2002-2004 

2002 2003 2004 

Regulation up, self provided (MW-hr) 1,855,270 1,769,493 1,972,175 

Regulation down, self provided (MW-hr) 2,078,057 1,797,975 2,073,533 

Regulation up, procured (MW-hr) 1,659,438 1,116,009 1,109,265 

Regulation down, procured (MW-hr) 1,627,342 1,488,440 1,255,973 

Total regulation (MW-hr) 7,220,107 6,171,916 6,410,947 

Total value ($) 98,270,561 109,357,025 88,141,708 

Average regulation price ($/MW-hr) 13.61 17.72 13.75 

In Table 12 above, note that MW-hr is the commitment of one MW of capacity for 1 hour and is 

not the same as MWh, a unit of energy. Also, as stated above, there is no price associated with 

self-provided regulation so the market price of the purchased regulation for the same hour is used 

to calculate the total dollar value of regulation for each hour. 

Between 2002 and 2003, the actual amount of regulation committed over the entire CaISO 

control area decreased by 15%. However, the average price increased by 30%, resulting in a net 

increase in cost of 11%. From 2003 to 2004, the amount of regulation committed stayed 

approximately the same with a 4% increase. The price returned to 2002 levels resulting in a net 

cost decrease of 19% between 2003 and 2004. 

The calculated regulation costs for the total system requirement and total load follow this pattern 

closely. In all 3 years, the regulation costs of the total load are very close to that of the total 

system requirement, a result of the sheer size of the load. The results could have been different 

only if one or more of the other studied resources had a dramatic regulation impact. A single 

large arc furnace, for example, would have sufficient impact to alter the cost of regulation for the 

rest of the load. None of the resources studied have that sort of regulation impact. In fact, the 

generating resources studied have quite minor impacts on total system regulation requirements. 

Ignoring the outlying low value of San Gorgonio in 2002 for now, the regulation costs of the 

wind aggregates range from $0.24/MWh to $0.70/MWh. Not unexpectedly the wind plants 

25 
impose a small regulation burden on the power system within the same order of magnitude as 

load when evaluated on a per MWh basis. This was expected because there is no apparent 

mechanism that would tie the wind-plant performance to the power system’s needs in the 

regulation time frame. The regulation burden is low because there is no mechanism that ties 

wind-plant fluctuations to aggregate load fluctuations in a compounding way either. Wind and 

load minute-to-minute fluctuations appear to be uncorrelated. Hence, they greatly benefit from 

aggregation. 

The variation in regulation costs across the three wind regions may be a result of geography, 

technology, and turbine numbers. The Northern California wind aggregate, for example, has 

lower costs all 3 years than the other two regions (again, ignoring San Gorgonio in 2002), 

possibly because it is composed of the largest numbers of turbines (eWPRS 2005). 

The inter-annual changes in regulation costs for Tehachapi follow the overall trend of actual 

regulation commitment in the CaISO control area. The Northern California wind aggregate does 

too, but to a lesser extent between 2003 and 2004 when the cost increased 67%. San Gorgonio is 

unique among all the resources studied, showing a 378% jump between 2002 and 2003 and then 

further increase instead of a decline between 2003 and 2004. The $0.09/MWh value for 2002 is 

significantly lower than any of the other annual wind regulation results. San Gorgonio’s 

individual variability is not significantly lower in 2002 than 2003. There are also no known 

mechanisms that would correlate (or not correlate) its fluctuations in the regulation time frame to 

the rest of the system any differently in 2002 than in any other year. The 2002 value therefore 

remains anomalous. It was confirmed with the results from the analysis of the 2002 1-year 

dataset, but it may be possible that there are underlying, undetected issues with the 2002 San 

Gorgonio data in both the 1-year and multi-year datasets. The 2003 and 2004 results are more 

consistent with the results of the other regional wind aggregates. 

Overall, the regulation analysis results are reasonable. Because (1) inter-annual variations 

exhibited by some resources were disproportionate to changes in actual purchases amounts, (2) 

large amounts of new capacity will be installed in the future, and (3) technology and operation 

changes may have a significant effect, the continued understanding of regulation impacts and 

costs would benefit from more analysis over future years. Analysis with the methodology as 

described remains straightforward, given the availability of sufficient quality data. 

Load Following 

In this section we focus on the renewable resource impacts in the load-following time frame, 

which generally encompasses periods ranging from 10 minutes up to a few hours. 

Overview 

Load and generation must be continuously balanced on a nearly instantaneous basis in an electric 

power system. This is one of the characteristics that makes supplying electricity different from 

providing any other public good such as natural gas, water, telephone service, or air-traffic 

control. It is a physical requirement that does not depend on the market structure. How load and 

generation are balanced does depend, in part, on the structure of the electricity markets. One 

benefit of interconnecting multiple control areas is that balancing load and generation within a 

single control area does not have to be perfect. The North American Electric Reliability Council 

(NERC) has established rules governing how well each control area must balance load and 

26 
generation. Control Performance Standards 1 and 2 (CPS1 and CPS2) establish statistical limits 

on how well each control area must balance minute-to-minute fluctuations. Inadvertent 

interchange accounts track longer-term differences. In all cases, the total system remains in 

balance (otherwise blackouts occur). When one control area fails to balance its load with its 

generation, generation in another control area provides the balance. 

The balancing of aggregate load with aggregate generation is accomplished through several 

services that are distinguished by the time frame over which they operate. As discussed above, 

regulation and load following (which, in competitive spot markets such as in California, is 

provided by the intra-hour workings of the real-time energy market) are the two services required 

to continuously balance generation and load under normal conditions 

There is no hard-and-fast rule to define the temporal boundary between regulation and load 

following. In the PJM region, New York, New England, and Ontario, load following is defined 

as the 5-minute ramping capability of a generator. In Texas it is a 15-minute service, and in 

Alberta and California it is a 10-minute service. 

Interestingly, control area operators do not need to specifically procure load following; it is 

obtained from the short-term energy market with generators responding to real-time energy 

prices. In the CaISO control area, this is known as the supplemental energy market. Regulation, 

however, requires faster response than can be obtained from units responding to market signals 

alone. Instead, generators (and potentially storage and/or responsive load) offer capacity that can 

be controlled by the system operator’s AGC system to balance the power system. 

Control areas are not able and not required to perfectly match generation and load. CPS1 

measures the relationship between the control area’s area ACE and the interconnection frequency 

on a 1-minute average basis. CPS1 values can be either “good” or “bad.” When frequency is 

above its reference value, undergeneration benefits the interconnection by lowering frequency 

and leads to a good CPS1 value. Overgeneration at such times, however, would further increase 

frequency and lead to a bad CPS1 value. CPS1, although recorded every minute, is evaluated and 

reported on an annual basis. NERC sets minimum CPS1 requirements that each control area 

must exceed each year. 

CPS2, a monthly performance standard, sets control-area-specific limits on the maximum 

average ACE for every 10-minute period. Control areas are permitted to exceed the CPS2 limit 

no more than 10% of the time. This 90% requirement means that a control area can have no more 

than 14.4 CPS2 violations per day, on average, during any month. 

Methodology Description 

Integration of large amounts of renewable generators could potentially increase errors between 

scheduled and actual generation. Increases in scheduling error combined with the existing error 

in load forecasting could change the composition or size of the “generator stack” which responds 

to load-following needs. If such a distortion of the generator stack occurred it could shift the 

market to marginal generators, whose costs are higher. That could increase the price of energy 

across the market and thus create implicit costs which were imposed on the entire system by the 

renewable generators. 

The analysis focused on the potential impacts to the generator stack caused by scheduling error. 

The methodology looks at the impact of renewable generators on the total system scheduling 

27 
error. If renewable generators create systematic errors that significantly increase the need for 

generation resources, then they could have a material effect on the composition of the generator 

stack or the ex-post price for energy. 

The analysis methodology first determines system forecasting and scheduling errors for a 

benchmark case without renewable generators. CaISO prepares hour-ahead forecasts of its 

generation requirements, which represent its best estimate of actual system load. The scheduling 

coordinators provide schedules for generation that are designed to economically meet the 

forecasted needs. The scheduling coordinators typically schedule significantly less generation 

than is needed during peak-demand periods and rely upon the hour-ahead market to provide the 

balance. The difference between the forecasted load and the scheduled load is defined as the 

scheduling bias. Forecast and scheduling errors in the benchmark case provide an indication of 

the variability inherent in operating the utility grid and are important because they define the 

normal range of errors without renewable generation impacts. 

The scheduling errors for each renewable generator under study are then calculated. The 

difference between the actual and forecasted load is the load-forecasting error. Worst case 

scheduling was used to estimate the impacts of the renewable generators. Bids for the hour-ahead 

market are due 150 minutes prior to each market cycle. The scheduled output for the hour-ahead 

market was defined by a simple persistence model, assuming that output 150 minutes in the 

future would be equal to output at the present time. For solar generators it was assumed that 

scheduled output was equal to what it had been on the previous day at the same time period. 

The total system error including the renewable resources was calculated by combining the 

system forecast error (without renewables) with the additional scheduling error produced by the 

renewable resources. The forecasting error including renewable generators was then compared 

against the benchmark case and reviewed to identify significant differences. The goal of this 

analysis was to determine if the renewable resources significantly changed the total system error, 

thereby potentially modifying the generator bid stack. 

Multi-Year Analysis Results and Discussion 

The load forecasts prepared by CaISO provide the best estimate of the upcoming system load 

conditions. Figure 7 presents a graphical comparison of the hour-ahead forecast load and the 

actual load for an example period of several days. Because it is not possible to perfectly predict 

the load in the hour ahead time frame, there will always be some forecast error. 

28 
Fig. 7. Forecast and actual load over a 3-day sample period 

The load schedule is created by the scheduling coordinators based on forecast information from 

CaISO and conditions in the energy markets. The hour-ahead schedule as compared to the actual 

load is presented in Figure 8 for several example days in September. During peak hours the 

scheduled load is typically well below the actual load with the difference made up by the hourahead market. This indicates that the hour-ahead market can be relied upon for large amounts of 

power to meet short-term needs. 

Fig. 8. Scheduled and actual load over a 3-day sample period 

29 
The difference between the scheduled load and the forecast load is the scheduling bias. It is 

typically negative (scheduled generation is less than forecast load) and, interestingly, reaches the 

largest negative values during peak summer hours when the power system is typically under the 

most stress. The scheduled load provided by the scheduling coordinators is often thousands of 

megawatts less than the forecast load provided by CaISO. Over the 3-year analysis period, the 

scheduled generation was as much as 5832 MW less than forecast load during peak hours. The 

average minima and maxima of the scheduling bias during peak hours are shown in Figure 9 

over the 3-year analysis period. The large negative bias of the hour-ahead schedules provides an 

indication of the amount of generation assets available in the short-term energy market. The data 

implies that the scheduling coordinators are comfortable with the depth of the generator stack; 

they can call up several thousand megawatts of generation whenever it might be needed. The 

scheduling bias was used as a proxy for estimating the depth of the generator stack. It was used 

for comparison purposes in determining the significance of renewable impacts on the system 

error. 

The hour-ahead schedules for each renewable generation resource were developed using a simple 

persistence model. This model provides a schedule of renewable output for the hour-ahead 

market and is a conservative (worst-case) approach. Use of true forecasting models will reduce 

scheduling error and reduce the significance of renewable impacts from those calculated here. 

Figure 9 presents an example of actual output and scheduled output for a wind generator using 

the simple persistence model to calculate the schedule. The resource scheduling error was 

calculated as the difference between the resource’s scheduled generation and its load-following 

component of generation; with the hourly data used in this analysis, the hourly generation values 

were used directly as the value of the resource’s load-following component. The forecasting 

error including the scheduling error was then calculated by adding the resource scheduling error 

to the load forecasting error. 

Fig. 9. Actual and scheduled wind generation over a 3-day sample period; a 

simple persistence model was used to produce the schedule 

30 
We compared the average minimum and maximum forecasting error during peak hours (noon to 

6 p.m.) as a means of evaluating the significance of the renewable generator impacts. The results 

for the three analysis years are presented in Table 13. Negative values indicate that incremental 

energy purchases were required to compensate for under-generation or unexpected load. Positive 

values indicate over-generation or lower demand than expected, requiring generators in the 

short-term energy market to decrement their output. The minimum forecasting error was changed 

by no more than two percentage points by any of the renewable resources with slight 

improvements in some cases. The impact on the maximum forecasting error was similarly small. 

This indicates that at current penetration levels, the scheduling error of the renewables do not 

have a significant effect on the total energy requirements from the short-term market. The 

minimum scheduling bias reduced over the years but remained more than 200% greater than the 

load forecast error. This implies ample depth in the generator stack to handle incremental energy 

requirements. The analysis concluded that the impact on unit commitment was too small to 

measure. 

Table 13. Results of multi-year analysis of forecast and scheduling errors during peak 

hours 

2002 2003 2004 

AVERAGE MINIMUM AVERAGE MAXIMUM AVERAGE MINIMUM AVERAGE MAXIMUM AVERAGE MINIMUM AVERAGE MAXIMUM 

ERROR 

MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) MW 

Compared 

to load 

forecast 

error alone 

(%) 

Load forecast alone -1945 100% 2112 100% -1600 100% 2151 100% -1439 100% 1529 100% 

Load scheduling alone -4747 244% 1302 62% -4021 251% 2158 100% -3700 257% 1776 116% 

Scheduling bias -5337 274% 1708 81% -3336 208% 1534 71% -3016 210% 1634 107% 

Combined load forecast and renewable resource scheduling error 

Wind (Northern Cal) -1946 100% 2148 102% -1591 99% 2203 102% -1419 99% 1554 102% 

Wind (San Gorgonio) -1930 99% 2142 101% -1581 99% 2163 101% -1443 100% 1545 101% 

Wind (Tehachapi) -1931 99% 2177 103% -1569 98% 2181 101% -1435 100% 1544 101% 

Discussion of the Ramping Capability Analysis Results 

It is possible to calculate a lower bound to the ramping capability within a given control area 

using public databases. In our experience some significant capabilities could not be estimated 

and more ramping capability exists than we were able to measure (Kirby and Milligan 2006). It 

appears that there is a very large amount of ramping capability in the CaISO control area during 

31 
most hours of the 2002 analysis year we studied. This ramping capability is a natural result of the 

resource mix that has developed. Because each increase or decrease of renewable generation 

does not need to be matched one-for-one by another generator, the ability to absorb moderate or 

even large quantities of wind, solar, and other renewables appears significant for most of the 

year. 

The CaISO control area appears to have significant ramping resources available from thermal 

generation that is partially loaded and physically able to respond. CaISO, like most ISOs, 

operates energy markets that clear several times an hour, providing access to the ramping 

capabilities of the generators active in the energy markets. Control areas that do not have access 

to fluid intra-hour markets still have the physical capabilities of the generators but may not have 

access to that capability simply based on the hourly market structure. This lack of access denies 

the generators the ability to position themselves (ramp) to sell as much energy as customers 

want, forces the control-area operator to use additional regulating resources instead, and forces 

consumers to pay for the inefficiency. 

There may be significant opportunities for neighboring control areas to assist each other in the 

load-following time frame as well. This is partly a natural consequence of the ability of larger 

control areas to better manage variability, whether caused by load, wind, or a combination with 

other resources. It is also a consequence of additional capability being inherently available from 

a larger pool of generators. 

Assessing the ramping capability of a control area with public data presents some challenges. 

Because some data are unreported, and because of the shortcomings of our method, it is not 

possible to obtain an accurate measure. However, having said that, we think that this type of 

analysis can be useful in several ways. The estimates provided by this approach provide a lower 

bound on the load-following capability in a control area. The approach is transparent, which 

makes it possible to more easily understand how the more complex methods embodied in 

production simulation models work. The data set we used was based on data reported as part of 

the Continuous Emissions Monitoring System (CEMS), which captures data from all power 

plants with emissions. The approach could easily be extended to include data from non-CEMSreporting resources. For entities that have access to such data, a more detailed analysis would be 

possible, and would provide a better estimate of the load-following capability of the control area. 

Data Issues 

A variety of data issues were encountered in the various datasets used in the analysis. They are 

discussed below along with the methods used to address them. 

Confidentiality 

Although the need to preserve the confidentiality of much of the study data is recognized, data 

confidentiality significantly impeded the study at several occasions. Establishing the initial data 

nondisclosure agreement (NDA) with CaISO was a very lengthy process. The experience 

garnered from the completion of this first NDA was valuable later in the study, as new study 

participants were able to receive draft NDAs from CaISO quickly. 

Some other NDA processes were not as successful. In particular, SCE and NREL were unable to 

reach a confidentiality agreement even after numerous exchanges between their lawyers. 

32 
Consequently, another analyst had to be trained to perform the capacity credit analysis, delaying 

the progress of the study. 

Even with NDAs in place, the data released was aggregated because of concerns about the 

proprietary nature of power generation data from individual plants. Data aggregation aggravated 

data issues in the CaISO 1-year and multi-year datasets, as discussed later in this report. Later in 

the study, CaISO made a notable effort to allow the study analysts to view non-aggregated data 

while on-site at the CaISO offices; again, this is discussed later. 

Manageability 

The sheer size of the data is a problem, particularly with 1-minute data as in the CaISO 1-year 

and multi-year datasets. To assemble the renewable aggregates, CaISO had to extract more than 

eighty pieces of raw data, each with 525,600 values per year. Even with automated retrieval 

scripts, extensive computer time was required to query such a large volume, especially in the 

case of the 3-year dataset. Because the disk space requirement for storing all of the individual 

data items was considered to be too great, CaISO calculated aggregated values as the individual 

data items were being retrieved; only the aggregated value was stored and individual data values 

were immediately discarded. The lack of ready availability of non-aggregated data later hindered 

the data review process. 

Performing the data review and error checks for so much data was also a time-intensive process. 

Because of the difficulties introduced by aggregation, the effectiveness of automated data checks 

was limited, and all of the CaISO 1-minute data required manually review. The errors discovered 

in the 1-year and multi-year datasets revealed an underlying problem. Because much of CaISO’s 

data is stored automatically and is never used for operations or in any other way, it does not 

undergo any inspection except for generic automated tests by the PI system. Much of the data is 

therefore recorded without any verification of the quality of the data or the actual recording 

process. 

Lossy Compression 

CaISO’s PI system records more than 180,000 pieces of data, some sampled many times a 

minute. To store so much data, a lossy compression scheme is used. Lossless compression uses 

algorithms that reduce the size of data while maintaining complete fidelity; when the data is 

uncompressed, it is exactly identical to what it was before compression was applied. Lossy 

compression sacrifices some accuracy for large improvements in size reduction; when the data is 

uncompressed, it is not exactly identical to what it was originally, but the changes should be 

negligible. The PI system uses the “Swinging Door” algorithm, a lossy scheme with configurable 

settings that trade-off data fidelity and size. Ideally, information removed by compression is 

insignificant. However, the regulation analysis tracks even small fluctuations over short time 

periods. Data compressed without consideration for this type of calculation may affect the 

analysis when regulation impacts are small. Inspection of the data and regulation results suggests 

that the effects of compression might be significant only at impact levels when the regulation 

cost is negligible anyway. 

33 
Recommendations 

The Phase III report made several recommendations about the implementation of integration cost 

analysis. Based on experiences from the multi-year analysis, the following additional 

recommendations pertaining to data reporting/collection and an ICA are proposed. 

Data Reporting and Collection 

Most of time and effort required for the multi-year analysis was dedicated to data collection and 

processing. The actual calculations and review of the results were relatively straightforward. 

Specific recommendations are, therefore, made for the handling of data for future integration 

cost analysis. 

In Phase III of the study, it was proposed that data collection should be performed by an 

Integration Cost Analyst, a CEC or CPUC staff tasked with performing and reporting on regular 

integration cost analysis. Given the complex data quality issues described in this report and the 

need for similar data in other recent and current studies such as the CEC’s Strategic Value 

Analysis and Intermittency Analysis Project, it is now recommended that data handling and 

integration cost analysis be separated into two distinct tasks. A data handling entity would be 

responsible for collecting, reviewing, storing, and providing data for integration cost analysis 

and, possibly, associated data for other studies. In Phase III, it was assumed that data collection 

and processing was essentially an accounting function that would be highly automated. While 

this eventually may become true, given the data issues described in this report, data handling is 

more appropriately an engineering task. The data-handling person would have to meet the 

following requirements and perform the following duties: 

• Satisfy confidentiality requirements of CaISO, IOUs, and other sources to access data 

• Provide a database that securely stores data and that can be easily queried for both manual 

and automated data input and retrieval 

• Coordinate with CaISO, IOUs, and other sources to receive data on a frequent, regular basis 

(a 1-month basis is recommended) 

• Jointly develop a reporting standard with the data sources for incoming data and, as 

necessary, develop tools to process various data types and formats 

• Jointly develop an automated reporting system so that data are transferred from the sources to 

the data-handling person automatically 

• Update data requests as necessary as new generators come online and other changes occur 

• Review and verify the quality of incoming data and flag or correct bad data 

• Coordinate with CaISO, IOUs, and other sources as necessary to ensure that the quality of 

data they are collecting and recording is sufficient for the intended analyses (as ongoing 

integration cost calculation is presumed for the future, this process should begin 

immediately) 

• Coordinate with the Integration Cost Analyst to ensure that the required data is collected with 

sufficient quality and provided to the ICA on a frequent, regular basis; again, a 1-month basis 

is recommended 

34 
• Jointly develop a reporting standard with the Integration Cost Analyst and an automated 

system for transfer of data from the data-handling person to the ICA. 

One of the key aspects of the proposed data-handling process is that the assurance of data quality 

is a shared responsibility between the data sources (CaISO, IOUs, etc.), the data-handling person, 

and the Integration Cost Analyst. The task otherwise becomes disproportionately difficult to 

manage and complete. 

It is also important to collect and review data on a frequent and regular basis. Many of the 

difficulties encountered with the processing of the datasets for the multi-year analysis were the 

result of working with such a large, lumped number of data at once. As originally proposed in 

Phase III, it is recommended that data be documented monthly in arrears for the previous month. 

Processing data on a frequent basis not only keeps the task more manageable, but allows errors 

and issues to be identified and corrected before they propagate into a larger amount of data over 

an extended period. Automated data reporting would simplify the collection process, but the data 

review will always include some manual inspection. 

Integration Cost Analyst 

An ICA was introduced in Phase III and is recommended again with some revisions to the 

original description of qualifications and responsibilities. The function of the ICA is to perform 

regular analysis and reporting of integration costs. It is proposed that the CEC or CPUC 

designate one or more staff to assume this role. Specifically, the ICA would have to meet the 

following requirements and perform the following duties: 

• Satisfy confidentiality requirements of CaISO, IOUs, and other sources to access data 

• Coordinate with the data-handling person and, as necessary, the various data sources to 

ensure that all required data is of sufficient quality and is received on a frequent, regular 

basis in a consistent format (again, it is recommended that data be received on a monthly 

basis) 

• Review incoming data as it is received to verify data quality 

• Annually perform integration cost analysis 

• Prepare annual reports documenting the results of the integration cost analysis. 

Assuming the availability of good data, the calculations involved in integration cost analysis are 

relatively straightforward and can be highly automated. Once procedures are established and 

refined, it is estimated that the ICA will require approximately 1 to 2 days per month to perform 

data-handling tasks and approximately 2 additional weeks each year to conduct the integration 

cost calculations, perform an analysis of the results, and generate a report. 

35 
Conclusions and Summary 

The multi-year analysis of the impact of wind energy in California quantified the impacts on the 

regulation and load following time frames, and applies a reliability model to calculate the 

capacity value of wind. Throughout the project, data quality and consistency were problematic, 

and for the multiyear study, most of these issues were resolved by using combined datasets from 

the CaISO, SCE, and PG&E. The final data sets used for the analysis were judged to be 

reasonably good, but it is possible that some data errors remain. Because the analysts had limited 

access to the data, it is not possible to guarantee that the study results are accurate, although we 

believe that most, if not all, significant errors in the data set have been removed. 

For the three-year period covered by this study, the results were generally consistent, although 

there were some minor expected variations. Capacity value of wind is heavily influenced by the 

timing of the wind energy delivery relative to load, and this relationship would be expected to 

vary somewhat from year to year and from location to location. To quantify the impacts of power 

interchange and hydro system on system risk, as measured by LOLP and related metrics, we ran 

scenarios to calculate ELCC after removing hydro and net imports. The finding indicated that 

there are important and significant interactions between these variable and reliability, and these 

interactions have a discernable impact on capacity value. A simple capacity factor metric was 

applied to wind generation during the summer peak period and this matched the ELCC values 

relatively well when the hydro and imports were removed. Over the three-year period the 

approximation matched quite well at all wind locations. 

The regulation results from earlier phases of the study were found to be in error as a result of 

calculation error and the shift between standard time and daylight savings time. The multiyear 

results were relatively consistent, and because the impact of wind in the regulation time frame is 

relatively small and because wind is uncorrelated with load in this time frame, variations would 

be expected. However, the cost impact of wind on the power system is similar to the impact of 

load, measured in terms of cost per kWh or MWh. 

The load following results also show a relatively small impact of wind at the current penetration 

level. The minimum scheduling bias is more than double the combined forecast and scheduling 

error, which implies ample depth of the dispatch stack to absorb the additional variability that 

wind contributes during this time frame. A complementary analysis of the ramping capability of 

the fleet of emitting power plants in the CaISO system also shows that far more ramping 

capability exists in the system than is currently required by the existing load and wind 

generation. 

Because of the sheer volume and complexity of the data set required for this type of analysis, the 

prior recommendation of an integration cost analyst was expanded to also include a data 

handling person who could check the data continuously to ensure data quality and consistency. 

These results can be briefly summarized as follows: 

• wind capacity value of the existing fleet is in the mid-20s range as a percent of rated 

capacity. The 3-year modified ELCC values (excluding hydro and interchange) match peak 

period capacity factors quite well. 

• regulation impacts of wind are the same relative order of magnitude as the regulation impact 

of load. Costs are moderate and vary somewhat from year to year and by resource location 

36 
• load-following impacts of wind in California appear to be very small and are dwarfed by the 

magnitude of unscheduled generation. 

• data issues are significant in a project of this size. Ongoing quality assessment and regular 

data sampling would significantly improve the data quality and would result in a higher 

quality assessment of the impact of renewable generation on indirect costs. 

References 

eWPRS (electronic Wind Performance Reporting System). Web site at wprs.ucdavis.edu/, 

accessed in October 2005. 

FERC (U.S. Federal Energy Regulatory Commission). (1996). Promoting Wholesale 

Competition Through Open Access Non-Discriminatory Transmission Services by Public 

Utilities; Recovery of Stranded Costs by Public Utilities and Transmitting Utilities, Final 

Rule, Docket Nos. RM95-8-000 and RM94-7-001, Order No. 888. Washington, D.C.: 

Federal Energy Regulatory Commission. April 24, 1996. 

FERC (U.S. Federal Energy Regulatory Commission). (1999). Regional Transmission 

Organizations, Notice of Proposed Rulemaking, Docket No. RM99-2-000. Washington, 

D.C.: Federal Energy Regulatory Commission. May 13, 1999. 

Hirst, E. and B. Kirby. (1998). “Unbundling Generation and Transmission Services for 

Competitive Electricity Markets: Examining Ancillary Services.” NRRI 98-05. 

Columbus, OH: National Regulatory Research Institute. 

Hirst, E. and B. Kirby. (2000). “Measuring Generator Performance in Providing the Regulation 

and Load-Following Ancillary Services.” ORNL TM/2000-383. Oak Ridge, TN: Oak 

Ridge National Laboratory. 

Kirby, B. and E. Hirst. (2000). “Customer Specific Metrics for the Regulation and LoadFollowing Ancillary Services”, ORNL/CON-474, Oak Ridge, TN: Oak Ridge National 

Laboratory. 

Kirby, B. and M. Milligan. (2005). “A Method and Case Study for Estimating The Ramping 

Capability of a Control Area or Balancing Authority and Implications for Moderate or 

High Wind Penetration.” NREL/CP-500-38153. Golden, CO: National Renewable 

Energy Laboratory. (Available at www.nrel.gov/docs/fy05osti/38153.pdf). 

NERC (North American Electric Reliability Council). (2002). NERC Operating Manual.

Princeton, NJ: North American Electric Reliability Council. 

Piwko, R., D. Osborn, R. Gramlich, G. Jordan, D. Hawkins, and K. Porter. (2005). “Wind 

Energy Delivery Issues.” IEEE Power and Energy, Vol. 3, No. 6. 

37 
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A Multi-Year Analysis of Renewable Energy Impacts in California: 

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14. ABSTRACT (Maximum 200 Words)

California’s Renewable Portfolio Standard (RPS, Senate Bill 1078) requires the state’s investor-owned utilities to obtain 

20% of their energy mix from renewable generation sources. To facilitate the imminent increase in the penetration of 

renewables, the California Energy Commission (CEC), in support of the California Public Utility Commission (CPUC), 

initiated a study of integration costs in the context of RPS implementation. This effort estimated the impact of renewable 

generation in the regulation and load-following time scales and calculated the capacity value of renewable energy 

sources using a reliability model. The analysis team, consisting of researchers from the National Renewable Energy 

Laboratory (NREL), Oak Ridge National Laboratory (ORNL) and the California Wind Energy Collaborative (CWEC), 

performed the study in cooperation with the California Independent System Operator (CaISO), the Pacific Gas and 

Electric Company (PG&E), and Southern California Edison (SCE). The study was conducted over three phases and was 

followed by an analysis of a multi-year period. This paper presents results from the multi-year analysis and the Phase III 

recommendations. 

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wind energy; renewable portfolio standard; RPS; California 

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