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Public Interest Energy Research Program: Final Project Report

cover of report California Renewables Portfolio Standard, Renewable Generation Cost Analysis: Multi-Year Analysis Results and Recommendations

Publication Number: CEC-500-2006-064
Publication Date: June 2006
PIER Program Area: Renewable Energy Technologies Research

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Executive Summary


Introduction

The California Renewables 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 previous work2,3, integration costs were identified, valuation methodologies were defined, and a one year analysis of 2002 was performed.


Purpose

The purpose of this report is to document a multi-year analysis of integration costs, and apply the previously defined methodologies to a three year period from 2002 to 2004. The multi-year analysis provides opportunities to verify the consistency of the methodologies, further examine the practical issues associated with integration cost analysis, and to study the impact of renewables on integration costs over several years.

The methodologies are straightforward and were applied with little modification from their implementation in the previous one 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 CaISO, SCE, and PG&E; performing extensive manual reviews of the data using custom developed programs; and training personnel who had access to the data to perform the analyses. However, outstanding data issues limited the analysis, as detailed within the report.


Results

Overall, the multi-year integration cost analysis results were reasonable, consistent with the analysis results of the previous one year dataset and, in some cases, verified with alternate approaches. The method used in this study for determining the capacity credit is the effective load carrying capability, ELCC. ELCC is a way to measure a power plant's capacity contributions based on its impact to system reliability.

The results of the capacity credit analysis are summarized in the following table:

Chart ES-1


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.

Biomass has outage rates comparable to the gas benchmark unit and, therefore, a high capacity credit. The geothermal outage rates are lower than the benchmark unit, resulting in a capacity credit exceeding 100%. The solar values are relatively high, as expected given its natural tendency to track load and the plants' auxiliary gas generators. Wind values ranged from 27% to 44% (based on annual peak generation; 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 the table below. Negative values indicate a cost.

Chart ES-2


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. Geothermal, with a fairly flat output, has a low regulation cost, but a slightly higher value in 2002 when it was block scheduled for part of the year. The regulation costs of the solar and 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 solar results are consistent with the minute-to-minute variability in its generation data. 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 the table below.

Chart ES-3


The combined load forecast and renewable resource scheduling error values above indicate that renewables do not have a significant effect on the total energy requirements from the short term load following market at current penetration levels. The minimum scheduling bias was well over 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 intermittent renewables appear to be significantly lower than the requirement of the total system load and the capability available in the CaISO control area.

At the conclusion of this analysis, a public workshop was held on April 3, 2006, to present and discuss the findings. The workshop generated additional comments from Pacific Gas and Electric Company and Southern California Edison. These comments, and a discussion of the issues they raise, are included as Appendices C and D. Some of the issues are beyond the scope of this analysis and some warrant further examination.


Recommendations

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 which require similar data. A data handling entity is proposed who would coordinate with data sources (CaISO, and IOUs) and the ICA to ensure the availability of good data quality as needed.


Benefits to California

The California Renewables Portfolio Standard challenges the state and its investor owned utilities to increase the amount of energy that is supplied from renewable sources. Meeting this challenge can reduce greenhouse gas emissions, moderate our dependence on natural gas, and mitigate the risks of electricity price volatility. Careful monitoring, reporting and analysis of the renewable energy data will help to provide Californians with the lowest cost and environmentally safest energy in the years ahead. This multi year integration cost analysis is a step forward in meeting this challenge.



Abstract

None available.



Table of Contents

Acknowledgements ii

Preface iii

Table of Contents iv

List of Figures vi

List of Tables viiiii

Abbreviations ix

Nomenclature x

Executive Summary xiii

1 Introduction 1

1.1 Policy Background 1

1.2 Overview of Study 1

2 Capacity Credit 4

2.1 Overview 4

2.2 Definition of Capacity Credit 5

2.3 Methodology and Analysis Description 7

2.3.1 Step-by-Step ELCC Based Capacity Credit Analysis Methodology 7

2.3.2 Analysis Changes from Phase I and Phase III 8

2.4 Multi-Year Analysis Results and Discussion 10

2.4.1 Relationship between reliability, load, interchange, and hydro 10

2.4.2 ELCC results 16

2.4.3 Discussion of results 23

3 Regulation 25

3.1 Overview 25

3.1.1 Ancillary Services 25

3.1.2 Definition of Regulation and Load Following 26

3.2 Regulation Analysis Methodology 28

3.3 Data Requirements 29

3.4 Step-by-Step Regulation Analysis Methodology 30

3.4.1 Analysis Changes from Phase I and Phase III 37

3.5 Multi-Year Analysis Results and Discussion 39

4 Load Following 42

4.1 Overview 42

4.2 Methodology Description 43

4.2.1 Step-by-Step Load Following Analysis Methodology 44

4.2.2 Analysis Changes from Phase I and Phase III 48

4.3 Multi-Year Analysis Results and Discussion 48

4.4 Analysis of Ramping Capability 51

4.4.1 Introduction 51

4.4.2 System Ramping Capability 52

4.4.3 Load Ramping Requirements 54

4.4.4 Renewable Generator Ramping Requirements 56

4.4.5 Discussion of the Ramping Capability Analysis results 63

5 Data 65

5.1 Requirements 65

5.2 Datasets 65

5.2.1 CaISO OASIS Hourly Data 66

5.2.2 CaISO One-Year 2002 Dataset 66

5.2.3 BaseCase Data 68

5.2.4 CaISO Multi-Year Dataset 68

5.2.5 SCE Dataset 76

5.2.6 PG&E Dataset 76

5.3 Data Issues 77

5.3.1 Confidentiality 77

5.3.2 Manageability 78

5.3.3 Lossy Compression 78

5.3.4 Timestamps and Daylight Saving Time 78

5.3.5 Nameplate Capacity 79

5.3.6 Spikes and Dropouts 80

5.3.7 Elevated Data Floors 82

5.3.8 Aggregation and Dataset Comparison 85

5.3.9 Data Gaps 89

6 Recommendations 90

6.1 Data Reporting and Collection 90

6.2 Integration Cost Analyst 91

References 93


Appendix A: Control Performance Standards 94


Appendix B: Regulation Allocation Methodology 96


Appendix C: Comments from Pacific Gas and Electric Company 100

C.1. Received Comments 100

C.2. Response to Comments 106

C.2.1. Capacity Credit 106

C.2.2. Regulation 106

C.2.3. Load Following 107

C.2.4. Ramping Capability 107

C.2.5. Ongoing Study 107


Appendix D: Comments from Southern California Edison 108

D.1. Received Comments 108

D.2. Response to Comments 114

D.2.1. Other Studies 114

D.2.2. Unit Commitment 114

D.2.3. PIRP 114

D.2.4. Ongoing Study 115


LIST OF FIGURES

Figure 1.1 How integration costs fit in the least-cost, best-fit process. 2

Figure 2.1. Hourly LOLP, ranked, 2002-2004. 11

Figure 2.2. Load in 2002 during top risk hours, ranked by load, LOLP, and load net hydro and interchange. 12

Figure 2.3. LOLP in 2002 at top hours of load net hydro and interchange. 13

Figure 2.4. Load in 2003 during top risk hours, ranked by load, LOLP, and load net hydro and interchange. 14

Figure 2.5. LOLP in 2003 at top hours of load net hydro and interchange. 14

Figure 2.6. Load in 2004 during top risk hours, ranked by load, LOLP, and load net hydro and interchange. 15

Figure 2.7. LOLP in 2004 at top hours of load net hydro and interchange. 15

Figure 3.1 Decomposition of hypothetical weekday morning load. 26

Figure 4.1. Forecast and actual load over a three day sample period. 48

Figure 4.2. Scheduled and actual load over a three day sample period. 49

Figure 4.3. Actual and scheduled wind generation over a three day sample period. A simple persistence model was used to produce the schedule. 50

Figure 4.4. Thermal ramping capability and load ramping requirement. The 2002 and 2003 load ramping requirement traces (purple and red) are nearly overlapped. Thermal ramping capabilities typically exceed load ramping requirements. 55

Figure 4.5. The ratio of simultaneous load ramping requirements and thermal generation ramping capability, 2002. Thermal ramping capability exceeds load ramp requirements more than 97% of the time. 55

Figure 4.6. The total wind ramping requirement in California, 2002, calculated from ten minute averages. 56

Figure 4.7. The total wind ramping requirement in May 2002, showing large ramp-up requirements. 57

Figure 4.8. The total wind ramping requirement in February 2002, showing large ramp-down requirements. 58

Figure 4.9. Typical total wind ramping requirements, shown in July 2002. 59

Figure 4.10. Typical total wind ramping requirements, shown in September 2002. 59

Figure 4.11. Solar ramping requirement in California, 2002, calculated from ten minute averages. 60

Figure 4.12. Solar ramping requirements, June 2002. 60

Figure 4.13. Solar ramping requirements in July 2002, showing large ramp-down requirements. 61

Figure 4.14. Solar ramping requirements in September 2002, showing a large ramp-up requirement. 61

Figure 4.15. Comparison of wind ramping requirements calculated with 10 minute and hourly data. 62

Figure 4.16. Ramping requirements for wind and solar aggregates based on hourly data; the requirements are small compared to the load ramping requirement. 62

Figure 4.17. The ramping requirement of wind in Tehachapi based on hourly data over three years. 63

Figure 4.18. The ramping requirement of solar based on hourly data over three years. 63

Figure 5.1. Generation of the biomass aggregate in the CaISO multi-year dataset. Two years from fall of 2002 to fall of 2004. 71

Figure 5.2. Generation of the biomass aggregate in the CaISO multi-year dataset. One week from winter of 2004. 71

Figure 5.3. Generation of the SCE territory geothermal aggregate in the CaISO multi-year dataset. Two weeks in winter of 2002. 72

Figure 5.4. Generation of the SCE territory geothermal aggregate in the CaISO multi-year dataset. One month in spring of 2003. 72

Figure 5.5. Generation of the solar aggregate in the CaISO multi-year dataset. One year from summer of 2003 to summer of 2004. 73

Figure 5.6. Generation of the solar aggregate in the CaISO multi-year dataset. One month in summer of 2004. 73

Figure 5.7. Generation of the Northern California (Altamont, Pacheco, Solano) wind aggregate in the CaISO multi-year dataset. January 2002 to September 2004. 74

Figure 5.8. Generation of the Northern California (Altamont, Pacheco, Solano) wind aggregate in the CaISO multi-year dataset. One month in summer 2004. 74

Figure 5.9. Generation of the San Gorgonio wind aggregate in the CaISO multi-year dataset. January 2002 to September 2004. 74

Figure 5.10. Generation of the San Gorgonio wind aggregate in the CaISO multi-year dataset. One month in summer of 2003. 75

Figure 5.11. Generation of the Tehachapi wind aggregate in the CaISO multi-year dataset. January 2002 to September 2004. 75

Figure 5.12. Generation of the Tehachapi wind aggregate in the CaISO multi-year dataset. One month in summer of 2003. 75

Figure 5.13. One day from the CaISO multi-year dataset showing a large dropout. 81

Figure 5.14. Three days from the CaISO multi-year dataset showing a data spike. 81

Figure 5.15. A twelve hour period from the CaISO multi-year dataset showing a small dropout. 82

Figure 5.16. One day from the CaISO multi-year dataset showing a sharp 40 MW drop suspected to be a partial dropout in the data aggregate. 82

Figure 5.17. One year of data showing artificially elevated data floors. The red trace is from the CaISO multi-year dataset. The light blue trace behind it is corresponding IOU data. 83

Figure 5.18. Almost three years of data showing artificially elevated data floors. The red trace is from the CaISO multi-year dataset. The light blue trace behind it is corresponding IOU data. 83

Figure 5.19. Three years from the CaISO multi-year dataset showing an occurrence of the dropout/interpolation error. 84

Figure 5.20. One week from the CaISO multi-year dataset showing a dropout/interpolation error immediately followed by a data spike. 84

Figure 5.21. Screenshot of one of the programs developed to process the CaISO multi-year dataset using IOU data as a basis of comparison. 87

Figure 5.22. Screenshot of one of the programs developed to process the CaISO multi-year dataset using IOU data as a basis of comparison. 88

Figure B.1. The relationships among the regulation components (A and B) and the total if A and B are positively correlated (top), negatively correlated (middle), or uncorrelated (bottom). 97

Figure B.2. The relationship among the regulation impacts of loads A and B and the total (T) when A and B are neither perfectly correlated nor perfectly uncorrelated. 98

Figure B.3 Application of the vector-allocation method to the case with more than two loads. 99


LIST OF TABLES

Table 2.1. California peak demand hours for four years from 2001-2004. Times are in Pacific Standard Time. 5

Table 2.2. Step-by-step description of ELCC based capacity credit analysis methodology. 8

Table 2.3. Capacity credit analysis results, based on annual peak generation. 17

Table 2.4. Capacity credit analysis results, based on rated capacity reported by the IOUs. 18

Table 2.5. Capacity credit results with hydro and interchange removed; results based on annual peak generation. 19

Table 2.6. Capacity credit results with hydro and interchange removed; results based on rated capacity reported by the IOUs. 20

Table 2.7. Capacity factor over peak hours based on annual peak generation. 20

Table 2.8. Capacity factor over peak hours based on rated capacity reported by the IOUs. 21

Table 2.9. ELCC compared to peak capacity factors (June through September, weekdays excluding holidays, 12:00 p.m. to 6:00 p.m.) for three years, based on rated capacity reported by the IOUs. 22

Table 2.10. 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 three years, based on rated capacity reported by the IOUs. 23

Table 3.1 Regulation inputs/outputs: Verify data consistency. 31

Table 3.2. Regulation inputs/outputs: Calculate total system compensation requirement. 31

Table 3.3. Regulation inputs/outputs: Estimate short term forecast with 15 minute rolling average. 32

Table 3.4. Regulation inputs/outputs: Calculate raw regulation component by subtracting short term forecast. 33

Table 3.5. Regulation inputs/outputs: Calculate total system regulation less resource of interest. 34

Table 3.6. Regulation inputs/outputs: Calculate statistical metrics. 34

Table 3.7. Regulation inputs/outputs: Allocate regulation share for each generator. 35

Table 3.8. Regulation inputs/outputs: Calculate actual regulation share for each generator type. 36

Table 3.9. Regulation inputs/outputs: Calculate actual regulation cost for each generator type. 36

Table 3.10. Original and corrected results of the Phase I (one year, 2002) regulation analysis. Negative values are costs to the system. 38

Table 3.11. Results of regulation analysis of multi-year dataset. Negative values are a cost. 39

Table 3.12. Actual regulation amounts committed in the CaISO control area, 2002-2004. 40

Table 4.1. Load following inputs/outputs: Calculate load forecasting error. 45

Table 4.2. Load following inputs/outputs: Calculate system scheduling error. 45

Table 4.3. Calculate system scheduling bias. 46

Table 4.4. Load following inputs/outputs: Calculate hour ahead schedule of generation resources. 46

Table 4.5. Load following inputs/outputs: Calculate the resource scheduling error. 47

Table 4.6. Results of multi-year analysis of forecast and scheduling errors during peak hours. 51

Table 4.7. Power requirements and generation mix of CaISO in 2002. Data from Platts BaseCase. 52

Table 4.8. Thermal generator ramping capabilities, CaISO in 2002. 54

Table 5.1. Minimum input data requirements for integration cost analysis. 65

Table 5.2. OASIS data used in the multi-year analysis. 66

Table 5.3. CaISO one year 2002 dataset. 67

Table 5.4. CaISO multi-year dataset. 69

Table 5.5. SCE dataset. 76

Table 6.6. PG&E dataset. 77

Table 5.7. Comparison of reported nameplate capacities and annual peak generation of selected generation aggregates from the PG&E and SCE datasets. 80


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