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Public Interest Energy Research Program: Final Project Report
cover of report Renewable Distributed Generation Assessment: Sacramento Municipal Utility District Case Study

Publication Number: Energy Commission-500-2005-028
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 (Energy Commission) 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 Energy Commission PIER program. Figure 1 maps how this RDG Assessment Project relates to the other research areas under this program.

The following discussion comprises the results of the third of four case studies for the application of the RDG Assessment methodology. This case study describes the analytical process and associated results for the Sacramento Municipal Utility District (SMUD) 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 the City of Palo Alto Utilities (CPAU).

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 with readily accessible tools for rapid assessment of distributed renewable technologies, applicable to any distribution network.

Results

The results of the SMUD 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 SMUD 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 these findings:

  • It is difficult to find cost-effective RDG. Certain opportunities do exist, but they require an array of favorable circumstances to generate net benefits.


  • Two of the three cost-effective technologies identified were combined heat & power (CHP). The ability to capture and use waste heat creates an additional income stream for RDG projects that can tip the balance towards cost-effectiveness. CHP is only an option with combustion-based RDG technologies.


  • The modeling of a 1.5 MW wind turbine resulted in the third cost-effective technology. Even though a wind turbine of this size is technically not distributed, if an adequate wind resource exists in SMUD territory, this could result in a cost-effective renewable project.


  • RDG's ability to defer distribution projects can provide economic benefits. However, the overall effect of the distribution deferral benefits is expected to be modest.
Engineering Screening
  • No likely operational problems (e.g. voltage/overcurrent) were indicated for the three RDG cases that we evaluated. Among these, we note the following:


  • 13.5 MW of RDG sited for loss reduction resulted in a net peak incremental loss reduction of 5%.


  • 20 MW of dispersed solar PV resulted in only 2% peak loss reduction.


  • 13.5 MW DG sited for released capacity is likely best for reliability improvement from feeder capacity.
Reliability Screening
  • Solar PV can achieve a high (45%) incremental capacity value due to high coincidence with SMUD's summer peak.


  • Well-sited RDG yields a 3-4% reliability 'bonus' when operating at peak load due to reducing losses as well as supplying load.


  • Well-sited dispatchable RDG can yield incremental capacity increases of two to three times the generation output.
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 SMUD 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 Sacramento Municipal Utility District (SMUD). SMUD is one of four distribution system evaluated under the RDG Assessment project conducted under the auspices of the Energy Commission PIER Renewables program. In addition to SMUD, the three other distribution systems evaluated include Alameda Power & Telecom (Alameda P&T), the San Francisco PUC / Hetch Hetchy (SF PUC), and the City of Palo Alto Utilities (CPAU). 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, Sacramento Municipal Utility District, avoided costs, reliability analysis, uncertainty analysis



Table of Contents

Abstract

Executive Summary

Sacramento Muncipal Utilities District Case Study

1.0 Introduction

1.1. Background

1.2. Overview of Energy Commission PIER Deliverables

1.3. Analysis Process and Interrelationship of Deliverables

1.4. Deliverable Conveyance

1.5. Summary of Results for SMUD

1.5.1. Engineering Screening Analysis

1.5.2. Economic Screening Analysis

2.0 Economic Screening Analysis

2.1. General Avoided Cost Methodology

2.2. Generation Avoided Costs

2.2.1. Generation Avoided Costs Methodology

2.2.2. Generation Avoided Cost Results

2.3. Transmission Avoided Costs

2.3.1. Transmission Avoided Costs Methodology

2.3.2. Transmission Avoided Cost Results

2.4. Distribution Avoided Costs

2.4.1. Distribution Avoided Costs Methodology

2.4.2. Distribution Avoided Cost Results

2.5. RDG Economic Screening

2.5.1. Calculation of Benefits and Costs

3.0 Engineering Screening Analysis

3.1. Overview

3.2. Description of Analysis

3.3. Power Flow Characteristics

3.3.1. Losses

3.4. DG Siting Analysis

3.4.1. Optimal Locations for Loss Reduction

3.4.2. Optimal Locations for Released Capacity

3.4.3. Reference Case 1: Optimally Sited for Loss Reduction

3.4.4. Reference Case 2: Optimally Sited for Released Capacity

3.4.5. Reference Case 3: 20 MW Distributed PV Generation

3.5. DG Operational Feasibility

3.5.1. Voltage Regulation Screen

3.5.2. Overcurrent Protection Screen

4.0 Load and Resource Analysis

4.1. Local Area Load Shapes

4.2. Renewable DG Output Characteristics

4.3. Summaries of Demands and Savings

4.3.2. Conclusions

5.0 Reliability Analysis

5.1. Reliability Evaluation

5.2. Basic Concept

5.3. Distribution Reliability

5.4. Case Evaluation

5.4.1. Reference Case 1: 13.5 MW of 500-kW Generators Optimally Sited for Losses

5.4.2. Reference Case 2: 13.5 MW of 500-kW Generators Optimally Sited for Released Capacity

5.4.3. Reference Case 3: 20 MW PV Generation

5.5. Economic Reliability Analysis

5.5.1. Customer Value of Reliability Improvement

5.5.2. Deferral Benefit of DG

5.5.3. VRI and Deferral Benefit Interaction

5.5.4. Additional Uses of Reliability Valuations

6.0 Uncertainty Analysis

6.1. Scenario Analysis for Key Inputs

6.1.1. Generation Market Prices

6.1.2. Transmission Prices

6.1.3. Distribution Avoided Costs

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

6.2. Results of Uncertainty Analysis

6.2.1. 800 kW Biogas

6.2.2. 50 kW Solar PV

6.2.3. 500 kW Biodiesel

7.0 Conclusions

References

Glossary


Appendices

Appendix A: Cost and Performance of Renewable DG technologies


List of Figures

Figure 1: Energy Commission PIER Program Research Project Structure

Figure 2: RDG Analysis Process Diagram

Figure 3: Power flow in the SMUD System

Figure 4: Optimal Locations For Small Generation (100 Kw) On The SMUD System With Respect To Reducing Peak Load Losses

Figure 5. Optimal Locations For 1,000 Kw Generation On SMUD Area B System With Respect To Reducing Peak Load Losses

Figure 6. Optimal Locations For 5,000 Kw Generation On SMUD Area B System With Respect To Reducing Peak Load Losses

Figure 7: Net Benefit Range For Key Uncertainties From The TRC Test Perspective

Figure 8: Avoided Generation Costs

Figure 9: Comparison Of Base, High, And Low Scenarios For Avoided Generation Costs

Figure 10: SMUD Area B Distribution Avoided Costs Resulting From Deferral Of Sylvan-Auburn Substation Second Bank

Figure 11: Power Flow Diagram For Area B

Figure 12: Partial Map Of Area B

Figure 13: Locations In Red Are Most Optimal For 100 Kw Generation On SMUD Area B System With Respect To Reducing Peak Load Losses

Figure 14: Red Areas Are Least Optimal For Siting 100 Kw Generation With Respect To Loss Reduction

Figure 15: Optimal Locations For 1,000 Kw Generation On SMUD Area B System With Respect To Reducing Peak Load Losses

Figure 16: Optimal Locations For 5,000 Kw Generation On SMUD Area B System With Respect To Reducing Peak Load Losses

Figure 17: Optimal Locations (Red) For 100 Kw Generation On SMUD Area B System With Respect To Releasing Capacity

Figure 18: Areas In Red Are Least Optimal For Releasing Capacity With 100 Kw Generators

Figure 19: Optimal Locations For 1,000 Kw Generation On SMUD Area B System With Respect To Releasing Capacity

Figure 20: Optimal Locations For 5,000 Kw Generation On SMUD Area B System With Respect To Releasing Capacity

Figure 21: Incremental Loss Reduction In Percent Of Last DG Unit Added

Figure 22: Optimal Siting For 13.5 MW Of 500 Kw Generators For Loss Reduction

Figure 23: Optimal Siting For 13.5 MW Of 500 Kw Generators For Released Capacity

Figure 24: Distribution Of 20 MW Of PV Generation For Reference Case 3

Figure 25: Darker (blue) colors represent areas with larger change in fault currents for 13.5 MW generator case, generators sited for loss reduction

Figure 26: Darker (blue) colors represent areas with larger change in fault currents for 13.5 MW generator case, generators sited for released capacity

Figure 27: SMUD Area B Annual Energy Load Shape, Normalized

Figure 28: SMUD Area B Peak Demand Shape

Figure 29: Topographical Representation Of SMUD Area B Peak Load Shape

Figure 30: 'Optimal' Locations (yellow circles) Assumed for 13.5 MW of DG for Reference Case 1

Figure 31: Impact Of 13.5 MW Generation Optimally Sited For Losses, Operated As Baseload Generation For A Typical 7-Day Load Profile During Summer Peak Loading

Figure 32: Peaking Generation Dispatch Shape For Top 400 Hours

Figure 33: Impact Of 13.5 MW Generation Optimally Sited For Losses

Figure 34: Optimal Siting For 13.5 MW Of 500 Kw Generators For Released Capacity

Figure 35: Impact Of 13.5 MW Generation Optimally-Sited For Released Capacity,

Figure 36: Impact Of 13.5 MW Generation Optimally-Sited For Released Capacity

Figure 37: Assumed Locations For 20 MW Of Solar PV Units For Reference Case 3

Figure 38: PV Peak Output Shape, Normalized

Figure 39: Coincidence Between Solar PV Output And Load During Peak Demand Week

Figure 40. Side-by-Side Comparison of SMUD Area B Load Contour (left) and Solar PV Output Contour

Figure 41: Impact Of 20 MW Of PV Generation Distributed Uniformly

Figure 42: Graphical Explanation Of EEN And UE

Figure 43: Evaluating The Impact Of DG On The Power Delivery System Capacity

Figure 44: How DG Might Affect Distribution Reliability

Figure 45: Load Shape (Energy) For SMUD Area B (Normalized)

Figure 46: Load Shape (Peak Power) For SMUD Area B

Figure 47: Energy Exceeding Normal (EEN) for Area B

Figure 48: Optimal Siting For 13.5 MW Of 500 Kw Generators For Loss Reduction

Figure 49: Peaking Generator Dispatch Characteristic

Figure 50: EEN Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Maximum Benefit To Distribution System Losses (Baseload)

Figure 51: EEN Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Maximum Benefit To Distribution System Losses (Peaking)

Figure 52: Losses Computed For13.5 MW Of Generation Optimally Sited In 500 Kw Units For Losses (Baseload)

Figure 53: Losses Computed For13.5 MW Of Generation Optimally Sited In 500 Kw Units For Losses (Peaking)

Figure 54: Optimal Siting For 13.5 MW Of 500 Kw Generators For Released Capacity

Figure 55: EEN Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Optimal Released Capacity (Baseload)

Figure 56: EEN Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Optimal Released Capacity (Peaking)

Figure 57: Losses Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Optimal Released Capacity (Baseload)

Figure 58: Losses Computed For 13.5 MW Of Generation Sited In 500 Kw Units For Optimal Released Capacity (Peaking)

Figure 59: Distribution of 20 MW of PV Generation for Reference case 3

Figure 60: Capacity Gain With Respect To EEN For 20 MW Of Solar Photovoltaic Generation Dispersed Throughout The System

Figure 61: Capacity Increase With Respect To Losses For 20 MW Of Solar Photovoltaic Generation Dispersed Throughout The System

Figure 62: Comparison Of SMUD Load Shape And Assumed Output Of Solar PV Generation For A Typical Summer Week

Figure 63. Comparison Of SMUD Annual Load Contour (Left) And Solar PV Annual Output Contour (Right) For Each Week Of The Year

Figure 64: Typical Range Of Reported Values For Customer Value Of Service (VOS)

Figure 65: RDG Assessment Analysis Process Flow Diagram

Figure 66: EEN-Based T&D Deferral

Figure 67: VRI and T&D Deferral

Figure 68: Comparison Of Base, High, And Low Avoided Generation Costs

Figure 69: Net Benefit Range For Key Uncertainties From The TRC Test Perspective

Figure 70: Sensitivity Analysis For 800 Kw Biogas Generator With CHP From The TRC Test Perspective

Figure 71: Range Of Net Benefits For 50 Kw Solar PV From The TRC Test Perspective

Figure 72: Sensitivity Analysis For 50 Kw Solar PV From The TRC Test Perspective

Figure 73: Sensitivity Analysis For A 500 Kw Biodiesel Generator From The TRC Test Perspective

Figure 74: Economic Screening Results For A 500 Kw Biodiesel Generator With 'High' Market Prices, 'High' Transmission Costs, And 'Low' Fuel Costs


List of Tables

Table 1: Benefit/Cost Ratio Results For SMUD RDG Screening Analysis

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 RDG From Five Cost-Effectiveness Test Perspectives Included In Our Modeling

Table 8: RDG Performance Characteristics.

Table 9: Voltage change for 13.5 MW cases

Table 10: Fault Current Changes With 13.5 MW DG Case

Table 11: Purchased Power and Demand Savings

Table 12: Annual Loss Savings

Table 13: Loss Savings at Peak Load

Table 14: Mid-Range Customer Value Of Service (VOS) Estimates

Table 15: Value of Reliability Improvement (Year 2004)

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