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Welcome to the California Energy Commission
Public Interest Energy Research Program: Final Project Report
cover of report Renewable Distributed Generation Assessment: City of Palo Alto Utilities Case Study

Publication Number: CEC-500-2005-029
Publication Date: January 2005

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Executive Summary

Introduction

In an effort to contribute to the baseline knowledge of distributed generation value, this case study reports the methodology and results of the combined economic and engineering analysis performed by Energy and Environmental Economics, Inc. (E3) and Electrotek Concepts (ETK) under a California Energy Commission (CEC) PIER program-funded contract. The aim of this research project is to develop a methodology for evaluating the potential renewable distributed generation (RDG) applications within the municipal utility planning process. The resulting methodology from this research will be integrated with nine other related research projects occurring in parallel to this RDG Assessment project to further the greater goals of the CEC PIER program. Figure 1 maps how this RDG Assessment Project relates to the other research areas under this program.

The following discussion comprises one of four case studies for the application of the RDG Assessment methodology. This case study describes the analytical process and associated results for the City of Palo Alto Utilities (CPAU) distribution system. The analysis results for the remaining three municipal utilities are provided as separate cases study reports for the San Francisco Public Utilities Commission/ Hetch Hetchy (SF PUC), Alameda Power and Telecom (Alameda P&T), and Sacramento Municipal Utilities District (SMUD).

Purpose

Numerous detailed screening studies for large transmission and distribution systems have identified several elements of value that distributed generation can provide. These include capital deferral, reduced losses, reduced O&M costs, and risk reduction. These elements focus on cost reduction to the wires company or an integrated utility. Although it has been postulated that distributed renewable generation can provide enhanced reliability, very little in the way of quantitative analysis has been completed to include the reliability impact in DG evaluation. This research builds upon this body of work and is focused on utility’s internal planning processes.

As such, the purpose of this Renewable Distributed Generation Assessment project is to develop a sound and replicable methodology for evaluating RDG within a utility planning process. The methodology developed jointly by E3 and ETK was applied in four municipal utility case studies throughout Northern California with the goal of facilitating the installation of cost-effective RDG systems in California.

The core contributions of this research include the following:

  • Analysis of the local system impacts and benefits that accrue directly to a municipal UDC in a localized network


  • Expansion of the evaluation methodology to evaluate the impacts on local system reliability, including value to both the customers and the UDC


  • Incorporation of uncertainty for elements of RDG project value such as local load growth, wholesale energy prices, and capital costs for equipment

Project Objective

The overall objective is to accelerate the deployment of renewable distributed generation by fully accounting for all benefits. The specific objectives of the project are to (1) identify the best locations for distributed renewable generation (DG) in a local Utility Distribution Company (UDC) system, (2) include reliability impacts in the analysis, and (3) assess the impact of load growth and generator performance uncertainty on the results.

The key measure of success of this project is establishing an understanding of the merits of distributed renewable generation in distribution systems in general, embodied in the comprehensive application to four example distribution systems. Successful completion of this research will result in reduced overall system costs, enhanced local reliability, and increased resource diversity. The key anticipated outcome is an established and verified methodology and readily accessible tools for rapid assessment of distributed renewable technologies that can be applied to any distribution network.

Results

The results of the CPAU case study RDG Assessment project are two-fold. First, this project represents a successful application of the RDG Assessment methodology developed by E3 and ETK. Second, the results provide CPAU with valuable information for future decision making that includes the specific benefits RDG could provide on their distribution system.

Highlights of the assessment results provided in this report include:

  1. CPAU has a compact distribution system with low losses.


  2. Voltage changes from RDG switching on/off are within system tolerance boundaries.


  3. No major system upgrades would be required with an RDG installation.


  4. The most economically favorable RDG technologies on CPAU system are:


  5. 800 kW biogas-fueled internal combustion engine with CHP


  6. 3 MW biogas-fueled internal combustion engine with CHP


  7. Solar PV has a relatively high 60% coincidence factor in Palo Alto.


  8. Some locations within the City can achieve 5% higher effective capacity boost by reducing losses during peak hours. However, if the generator is too large, a negative loss savings could result.


  9. Specific optimal locations for RDG may change with the Alma substation closure.
Conclusion and Recommendations

Upon initiation of this research project, the specific goals in the five-year, ten-year, and fifteen-year timeframe were identified. These included development of a robust methodology to evaluate local area resources and moving this type of analysis towards standard industry practice. The completion of the CPAU RDG Assessment represents the first step in achieving these goals.

Recommendations including the implications of the use of this methodology in California and proposed next steps are described in the Final Report for the Renewable Distributed Generation Assessment project which captures the results from all four applications of this newly developed evaluation methodology.



Abstract

This case study presents the results of the second application of a renewable distributed generation assessment methodology conducted for City of Palo Alto Utilities (CPAU). CPAU is one of four distribution systems evaluated under the RDG Assessment project conducted under the auspices of the CEC PIER Renewables program. In addition to CPAU, the three other distribution systems evaluated include Alameda Power & Telecom (Alameda P&T), the San Francisco PUC / Hetch Hetchy, and Sacramento Municipal Utility District. The overall objective of this project is to accelerate the deployment of renewable energy systems in a distributed generation mode by fully accounting for all benefits.

Keywords: renewable distributed generation, assessment methodology, municipal utility planning, City of Palo Alto Utilities, avoided costs, reliability analysis, uncertainty analysis



Table of Contents

Abstract

Executive Summary

City Of Palo Alto Utilities Case Study

1.0 Introduction

1.1. Background

1.1.1. Overview of Analysis

1.2. Summary of Results for CPAU

1.2.1. Economic Screening Analysis

1.2.2. Engineering Screening Analysis

1.3. Report Organization

2.0 Economic Screening Analysis

2.1. Avoided Costs

2.1.1. General Avoided Cost Methodology

2.1.2. Generation Avoided Costs

2.1.3. Transmission Avoided Costs

2.1.4. Distribution Avoided Costs

2.2. DG Economic Screening

2.2.1. Calculation of Costs and Benefits

2.2.2. Results of Economic Screening Analysis

2.3. Applying the RDG Screening Results

3.0 Engineering Screening Analysis

3.1. Overview

3.2. Description of Analysis

3.3. Power Flow Characteristics

3.3.1. Peak Load Snapshots

3.3.2. Annual Load Characteristics

3.4. RDG Siting Analysis

3.4.1. Small (100 kW) Test Generator

3.4.2. Large (2,000 kW) Test Generator

3.5. Comparison of Cases

3.5.1. Base Case (No RDG)

3.5.2. Reference Case 1: 10 MW of Distributed 500-kW Generators

3.5.3. Reference Case 2: 4 MW of Distributed PV

3.6. Proposed RDG Cases

3.6.1. 570 kW Photovoltaic (PV) Solar At Selected Sites

3.6.2. 730 kW Water Pump Regeneration

3.6.3. 2 MW Combined Heat & Power (CHP) Near VA Hospital

3.6.4. 2 MW Peaking Generation Near VA Hospital

3.6.5. 10 MW CHP Generation Near VA Hospital

3.6.6. 10 MW CHP Generation on QR Substation

3.7. Voltage Change Impact Screen

3.8. Overcurrent Protection Impact Screen

3.9. Annual Energy Simulation Comparison

3.10. Observations

4.0 Load and Resource Analysis

4.1. Local Area Load Shapes

4.2. RDG Output Characteristics

4.3. Summaries of Demands and Savings

4.3.1. Reference Case 1: 10 MW CHP Generation Characteristics

4.3.2. Photovoltaic Characteristic

4.3.3. 570 kW Solar Photovoltaic Case

4.3.4. 730 kW Water Pump Regeneration

4.3.5. 2 MW Combined Heat & Power (CHP) Near VA Hospital

4.3.6. 2 MW Peaker Near VA Hospital

4.3.7. 10 MW CHP Near VA Hospital

4.3.8. 10 MW CHP Near QR Substation

4.4. Conclusions

5.0 Reliability Analysis

5.1. Engineering Reliability Analysis Overview

5.2. Reliability Evaluation

5.2.1. Basic Concept

5.3. Distribution Reliability

5.4. Case Evaluation

5.4.1. Base Case

5.4.2. Reference Case 1 (10 MW of 500-kW CHP Generators)

5.4.3. Reference Case 2 (4 MW PV Generation)

5.5. Proposed DG Cases

5.5.1. 570 kW Photovoltaic (PV) Solar At Selected Sites

5.5.2. 730 kW Water Pump Regeneration

5.5.3. 2 MW Combined Heat & Power (CHP) Near VA Hospital

5.5.4. 2 MW Peaking Generation Near VA Hospital

5.5.5. 10 MW CHP Generation Near VA Hospital

5.5.6. 10 MW CHP Generation on QR Substation

5.6. Valuing EEN and Losses

5.7. Economic Evaluation of Reliability Impacts

5.8. Customer Value of Reliability Improvement

5.8.2. Deferral Benefit of DG

5.8.3. VRI and Deferral Benefit Interaction

5.8.4. Additional Uses of Reliability Valuations

5.8.5. Relative Customer VRI

5.8.6. VRI for Project Justification

6.0 Uncertainty Analysis

6.1. Scenario Analysis for Key Inputs

6.2. Generation Market Prices

6.3. Transmission Prices.139

6.4. Distribution Avoided Costs

6.5. RDG Capital Costs, Fuel Costs, and Capacity Factors

6.6. Results of Uncertainty Analysis

6.6.1. 5MW Biogas

6.6.2. 50 kW Solar PV

6.6.3. 1.5 MW Wind Generator

6.7. Renewable Generation Premium

6.7.1. Effect on the cost tests

6.7.2. Results with inclusion of renewable premium

7.0 Conclusions

References

Glossary


Appendices

APPENDIX A: COST AND PERFORMANCE OF RENEWABLE DG TECHNOLOGIES


List of Figures

Figure 1: CEC PIER Program Research Project Structure

Figure 2: RDG Analysis Process Diagram

Figure 3: Net Benefit Range For Key Uncertainties From The TRC Test Perspective

Figure 4: Power Flow In The CPAU System

Figure 5: Optimal Locations For Small Generation (100 Kw) On The CPAU System With Respect To Reducing Peak Load Losses

Figure 6: Optimal Locations For Large RDG (2 MW) With Respect To Loss Reduction At Peak Load

Figure 7: Avoided Generation Costs

Figure 8: Comparison Of Base, High, And Low Scenarios For Avoided Generation Costs

Figure 9: Potential Indirect Benefits Of RDG Installation

Figure 10: Cost Test Results For 800 Kw Biogas Caterpillar W/CHP

Figure 11: Cost Test Results For 800 Kw Biogas Caterpillar Without CHP

Figure 12: Cost Test Results For 50 Kw Solar PV

Figure 13: Cost Test Results For 10 Kw Wind Generator

Figure 14: Test Results For 1.5 MW Wind Generator

Figure 15: Power Flow in CPAU Distribution System

Figure 16: Circuit Plot With Line Section Thickness Proportional To Losses

Figure 17: Annual Energy Load Shape.47 Figure 18: Annual Energy Loss Shape

Figure 19: Shape Of Energy Exceeding Normal (EEN) Line And Transformer Ratings At 150 MW Load Level

Figure 20: Shape Of Energy Exceeding Normal (EEN) Line And Transformer Ratings At 195 MW Load Level

Figure 21: Darker colors indicate more optimal locations for small generation (100 kW) on CPAU system with respect to reducing peak load losses

Figure 22: Darker colors indicate more optimal locations for small DG (100 kW) with respect to releasing distribution capacity

Figure 23: Darker colors indicate less optimal locations for small DG (100 kW) with respect to releasing distribution capacity

Figure 24: Darker colors indicate less optimal locations for small DG (100 kW) with respect to reducing losses

Figure 25: Darker colors indicate more optimal locations for a large DG (2 MW) with respect to loss reduction at peak load

Figure 26: Darker colors indicate the least optimal locations for large DG (2MW) with respect to losses

Figure 27: Darker colors indicate more optimal locations for a large DG (2 MW) with respect to released capacity

Figure 28: Darker colors indicate less optimal locations for a large DG (2 MW) with respect to released capacity

Figure 29: "Optimal" Locations (Yellow Circles) For 10 MW Of DG Sited For Maximum Loss Reduction At Peak Load

Figure 30: Assumed Locations For Solar PV Units Totaling 4 MW

Figure 31: Yellow circles indicate approximate generator locations for proposed CPAU PV solar case

Figure 32: Generator Locations For Water Pump Regenerative Case

Figure 33: Generator Location For Generator Near VA Hospital

Figure 34: Dispatch Characteristic Assumed For The 2 MW Peaking Generation

Figure 35: Optimal Locations For A 10 MW Generator Based On Loss Reduction

Figure 36: Optimal Locations For A 10 MW Generator Based On Released Capacity At 195 MW Loading Level

Figure 37: Generator Location For 10 MW CHP On QR Substation Case

Figure 38: Area Impacted By Increased Short Circuit Currents From 10 MW CHP Generator On QR Substation

Figure 39: Area Impacted By Increased Short Circuit Currents From 10 MW CHP Generator On VA Hospital Circuit

Figure 40: CPAU System-Wide Annual Load Shape

Figure 41: Topographical Representation Of CPAU System-Wide Load Shape

Figure 42: "Optimal" Locations (Yellow Circles) Assumed For 10 MW Of DG For Reference Case 1

Figure 43: Impact Of 10 MW Optimally-Sited CHP Generation

Figure 44: Assumed Locations For Solar PV Units Totaling 4 MW For Reference Case 2

Figure 45: PV Output Shape

Figure 46: Side-by-Side Comparison of CPAU Load Contour and assumed Solar PV Output Contour

Figure 47: Yellow Circles Indicate Approximate Generator Locations For Proposed CPAU PV Solar Case

Figure 48: Impact Of 570 Kw Solar PV Case On Total Load For A Peak Summer Week

Figure 49: Generator Locations For Water Pump Regenerative Case

Figure 50: Impact Of 730 Kw Water Pump Regeneration Case On Total Load For A Peak Summer Week

Figure 51: Generator Location For Generator Near VA Hospital

Figure 52: Impact Of 2 MW Of Continuously-Operating CHP Generation Located Near The VA Hospital

Figure 53: Assumed Annual Dispatch Characteristic For The Peaking Generator

Figure 54: Impact Of 2 MW Of Peaking Generation Located Near The VA Hospital

Figure 55: Impact Of 10 MW Of Continuously-Operating CHP Generation Located Near The VA Hospital

Figure 56: Generator Location For 10 MW CHP On QR Substation Case

Figure 57: Impact Of 10 MW Of Continuously-Operating CHP Generation Located On A QR Substation Feeder

Figure 58: Basic Concept Of Unserved Energy (UE) And Energy Exceeding Normal (EEN)

Figure 59: Evaluating The Impact Of DG On The Power Delivery System Capacity

Figure 60: How DG Might Affect Distribution Reliability

Figure 61: Shape Of Energy Exceeding Normal (EEN) Line And Transformer Ratings At 150 MW Load Level

Figure 62: Shape Of Energy Exceeding Normal (EEN) Line And Transformer Ratings At 195 MW Load Level

Figure 63: EEN Computed For 10 MW Of Baseload

Figure 64: Losses Computed For 10 MW Of CHP Generation Optimally Sited In 500 Kw Units For Losses

Figure 65: Depiction Of The Impact Of The Generation In Reference Case 1 On The Peak Hourly EEN As Compared To The Base Case

Figure 66: Depiction Of The Impact Of The Generation In Reference Case 1 On The Total EEN As Compared To The Base Case

Figure 67: Capacity Increase With Respect To EEN For 4 MW Of Solar Photovoltaic Generation Uniformly Distributed Throughout The System (Reference Case 2)

Figure 68: Capacity Increase With Respect To Losses For 4 MW Of Solar Photovoltaic Generation Uniformly Distributed Throughout The System (Reference Case 2)

Figure 69: Depiction Of The Impact Of The Generation In Reference Case 2 (4 MW Solar PV) On The Peak Hourly EEN As Compared To The Base Case

Figure 70: Depiction Of The Impact Of The Generation In Reference Case 2 (4 MW Solar PV) On The Total EEN As Compared To The Base Case

Figure 71: Comparison Of CPAU Load Shape And Assumed Output Of Solar PV Generation For A Typical Summer Week

Figure 73: Capacity Comparison Based On EEN Of Proposed CPAU Solar PV Case With The Base Case

Figure 74: Capacity Comparison (Based On Primary Losses) Of Proposed CPAU Solar PV Case With The Base Case

Figure 75: Assumed Dispatch Characteristic For Regenerative Pump Case

Figure 76: Capacity Comparison, Based On EEN, Of Proposed CPAU Regenerative Pumping Case With The Base Case

Figure 77: Capacity Comparison, Based On Losses, Of Proposed CPAU Regenerative Pumping Case With The Base Case

Figure 78: Capacity Comparison, Based On EEN, Of 2 MW Of CHP Generation Near The VA Hospital With The Base Case

Figure 79: Capacity Comparison, Based On Losses, Of 2 MW Of CHP Generation Near The VA Hospital With The Base Case

Figure 80: Dispatch Characteristic Assumed For The 2 MW Peaking Generation

Figure 81: Capacity Comparison, Based On EEN, Of 2 MW Of Peaking Generation Located Near The VA Hospital With The Base Case

Figure 82: Capacity Comparison, Based On Losses, Of 2 MW Of Peaking Generation Located Near The VA Hospital With The Base Case

Figure 83: Capacity Comparison, Based On EEN, Of 10 MW Of Peaking Generation Located Near The VA Hospital With The Base Case

Figure 84: Capacity Comparison, Based On EEN, Of 10 MW Of Peaking Generation Located Near The VA Hospital With The Base Case

Figure 85: Capacity Comparison, Based On EEN, Of 10 MW Of CHP Generation Located Near The QR Substation With The Base Case

Figure 86: Capacity Comparison, Based On Losses, Of 10 MW Of CHP Generation Located Near The QR Substation With The Base Case

Figure 87: Comparison Of EEN Versus MW Load Computed For All Cases

Figure 88: Comparison Of Annual Losses Versus MW Load For All Cases

Figure 89: Typical Range Of Reported Values For Customer Value Of Service (VOS)

Figure 90: RDG Assessment Analysis Process Flow Diagram

Figure 91: EEN-Based T&D Deferral

Figure 92: VRI and T&D Deferral

Figure 93: Comparison Of Base, High, And Low Avoided Generation Costs

Figure 94: Net benefit range for key uncertainties from the TRC test perspective

Figure 95: Sensitivity Analysis For 5 MW Biogas Generator From The TRC Test Perspective

Figure 96: Range Of Net Benefits For 50 Kw Solar PV From The TRC Test Perspective

Figure 97: Sensitivity Analysis For 50 Kw Solar PV From The TRC Test Perspective

Figure 98: Range Of Net Benefits For A 1.5 MW Wind Generator From The TRC Test Perspective

Figure 99: Sensitivity Analysis For 1.5 MW Wind Generator From The TRC Test Perspective


List of Tables

Table 1: Benefit/Cost Ratio Results for CPAU RDG Screening Analysis (using base-case economic assumptions)

Table 2: Avoided generation cost forecast method by period

Table 3: Screening Model Generation Avoided Cost Inputs

Table 4: Time-Of-Use Period Definitions

Table 5: Distribution Avoided Cost Calculation Inputs

Table 6: Questions addressed by the various cost tests

Table 7: Benefits and costs of various test perspectives included in our modeling

Table 8: Performance Characteristics For RDG Technologies And DG Operating Using Renewable Fuels

Table 9: 2004 RDG fuel prices

Table 10: Generation benefits by test perspective

Table 11: Rates used in analysis.28

Table 12: Results of CPAU RDG screening, under base-case assumptions

Table 13: Voltage drop for different amounts of DG distributed as shown in Figure 16

Table 14: Maximum voltage change for generator on/off

Table 15: Comparative Impacts of The Various DG Options on Overcurrent Protection of CPAU System

Table 16: Comparison of Annual Energy Savings for DG Options

Table 17: Comparison Of Annual and Peak Loss Savings for DG Options

Table 18: Purchased Power and Demand Savings

Table 19: Annual Loss Savings

Table 20: Loss Savings at Peak Load

Table 21: Mid-range customer value of service (VOS) estimates

Table 22: Value of Reliability Improvement (Year 2004)

Table 23: Results Of RDG Screening Under Base Case Assumptions, Without Inclusion Of Renewable Premium

Table 24: Results Of RDG Screening Under Base Case Assumptions, With Inclusion Of Renewable Premium

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