Arnold Schwarzenegger Governor OPTIMIZATION OF NOVEL

91 Pages·2011·5.9 MB·English

Arnold Schwarzenegger
Governor
OPTIMIZATION OF NOVEL DISTRIBUTED
ENERGY NETWORKS TO REDUCE

GREENHOUSE GAS EMISSIONS IN T
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CALIFORNIA
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Prepared For:
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California Energy Commission

Public Interest Energy Research Program R
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Prepared By:
Stanford University

Sept. 21st, 2007
CEC-500--02-004

Prepared By:
1Department of Biological Sciences, Stanford University
2Renewable and Appropriate Energy Laboratory, University of California
3Energy and Resources Group, University of California
4Goldman School of Public Policy, University of California
Whitney G. Colella1
Stephen H. Schneider (PI)1
Daniel M. Kammen2,3,4
Stanford, California, 94305-5020
Commission Contract No. 500-02-004, Subcontract MEX-06-03
Commission Work Authorization No: Insert: #

Prepared For:
Public Interest Energy Research (PIER) Program
California Energy Commission

Gina Barkalow Project Manager
Beth Chambers Contract Manager

Kelly Birkinshaw
Program Area Lead
PIER Environmental Area

Laurie ten Hope
Office Manager
Energy Systems Research

Martha Krebs
Deputy Director
ENERGY RESEARCH & DEVELOPMENT DIVISION

B.B. Blevins
Executive Director

DISCLAIMER
This report was prepared as the result of work sponsored by the California Energy Commission. It does not necessarily represent the views of the
Energy Commission, its employees or the State of California. The Energy Commission, the State of California, its employees, contractors and
subcontractors make no warrant, express or implied, and assume no legal liability for the information in this report; nor does any party represent
that the uses of this information will not infringe upon privately owned rights. This report has not been approved or disapproved by the California
Energy Commission nor has the California Energy Commission passed upon the accuracy or adequacy of the information in this report.
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Acknowledgments
This study was funded by the California Energy Commission Public Interest Energy Research
Program. The research team is very appreciative of the Energy Commission’s support for this
research project and the research team especially wishes to thank Ed Vine for his guidance
during the project.
The research team also wishes to thank the following individuals (in alphabetical order, by
affiliation):
California Air Resources Board (CARB):
• Richard Bode, Larry Hunsaker, and Webster Tasat
California Energy Commission:
• Gerry Bemis, Guido Franco, Adam Pan, and Angela Tanghetti
Calpine Inc. (The Geysers):
• Barbara McBride and Mitch Stark
Competitive Power Ventures Inc.:
• Michael Hatfield
Department of Energy’s (DOE) Energy Information Administration (EIA):
• Tom Leckey, Perry Lindstrom, Kevin Lillis, Channele Wirman, and Orhan Yildiz
Lawrence Berkeley National Laboratory (LBNL):
• Stephane de la Rue du Can, Jonathan Koomey, and Jayant Sathaye
Stanford University:
• Sarah Jo Chadwick, Marilyn Cornelius, Scott Gould, Aditya Jhunjhunwala, Susan
Kulakowski, Patricia Mastrandrea, Dean Murray, Tee Sing Tang, Nigel Teo, and Wallace
Wong
Sun Microsystems:
• Kenneth Russell
The research team also would like to thank the anonymous reviewers for their comments on the
draft report.
Please cite this report as follows:
Colella, W.G., S.H. Schneider, and D.M. Kammen (Stanford University and University of
California at Berkeley). 2007. Optimization of Novel Distributed Energy Networks to Reduce
Greenhouse Gas Emissions in California. California Energy Commission, PIER Energy-Related
Environmental Research Program. CEC-500-2007-XXX.
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Preface
The Public Interest Energy Research (PIER) Program supports public interest energy research
and development that will help improve the quality of life in California by bringing
environmentally safe, affordable, and reliable energy services and products to the marketplace.

The PIER Program, managed by the California Energy Commission (Energy Commission),
conducts public interest research, development, and demonstration (RD&D) projects to benefit
California.

The PIER Program strives to conduct the most promising public interest energy research by
partnering with RD&D entities, including individuals, businesses, utilities, and public or
private research institutions.

PIER funding efforts are focused on the following RD&D program areas:

Buildings End(cid:0)Use Energy Efficiency

Energy Innovations Small Grants

Energy(cid:0)Related Environmental Research

Energy Systems Integration

Environmentally Preferred Advanced Generation

Industrial/Agricultural/Water End(cid:0)Use Energy Efficiency

Renewable Energy Technologies

Transportation

Optimization of Novel Distributed Energy Networks to Reduce Greenhouse Gas Emissions in California
is the final draft report for the Optimization of Novel Distributed Energy Networks to Reduce
Greenhouse Gas Emissions in California project (contract number XXX(cid:0)XX(cid:0)XXX, work
authorization number [insert #] or grant number [insert #] CIEE Contract MR-043?) conducted
by Stanford University and the University of California at Berkeley. The information from this
project contributes to PIER’s Energy(cid:0)Related Environmental Research Program.

For more information about the PIER Program, please visit the Energy Commission’s website at
www.energy.ca.gov/pier or contact the Energy Commission at 916(cid:0)654(cid:0)5164.
Table of Contents
Preface..........................................................................................................................................................v
Abstract.......................................................................................................................................................ix
Executive Summary...................................................................................................................................1
1.0 Introduction.............................................................................................................................10
1.1. Motivation: Reduced Energy Consumption.......................................................................11
1.1.1. Distributed CHP FCSs Can Be More Efficient than Conventional Power.............11
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1.1.2. Distributed CHP FCSs Can Recover Heat at Higher Efficiency.............................12
1.1.3. California Can Save 1/6th of Energy Demand with Distributed CHP FCSs........13
1.2. Motivation: Reduced GHG Emissions.................................................................................15
1.2.1. Distributed CHP FCSs Can Reduce GHG Emissions by 65% or More..................15
1.2.2. California Can Significantly Reduce GHG with FCSs only with CHP..................16
1.3. Motivation: The Necessity of Detailed, Real-Time Simulations......................................20
1.4. Motivation: Addressing Technical Barriers and Knowledge Gaps.................................22
1.5. Background: Game-Changing Designs for Installing FCSs..............................................25
1.5.1. Stand Alone (SA) or Networked (NW).......................................................................25
1.5.2. Heat, Electricity, and No Load Following (HLF, ELF and NLF)............................26
1.5.3. Variable Heat-to-Power Ratio (VHP) or Fixed Heat-to-Power Ratio (FHP).........26
1.5.4. Methods to Achieve a Rapidly VHP...........................................................................28
1.5.5. Impediments to Thermal Networks............................................................................29
1.5.6. MERESS Model Focus...................................................................................................29
1.6. Project Objectives....................................................................................................................29
1.7. Report Organization...............................................................................................................30
2.0 Methods: The Optimization Model MERESS.....................................................................32
2.1. Installation and Operating Strategies Evaluated...............................................................32
2.2. Model Capabilities..................................................................................................................33
2.3. Optimization Function...........................................................................................................33
2.4. Input Data................................................................................................................................34
2.4.1. Electricity and Heating Demand Load Curves for Buildings.................................34
2.4.2. Cooling Demand Load Curves....................................................................................37
2.4.3. FCS Operating Data.......................................................................................................37
2.4.4. FCS Financial Data.........................................................................................................39
2.4.5. Government Incentives.................................................................................................41
2.4.6. Carbon Tax......................................................................................................................41
2.4.7. Competing Generator Data..........................................................................................42
2.4.8. Networking.....................................................................................................................44
3.0 Results......................................................................................................................................45
3.1. Scenario A – No State or Federal Incentives, No Carbon Tax..........................................47
3.2. Scenario B-1 – Full State and Federal Incentives, No Carbon Tax...................................48
3.3. Scenario B-2 – Maximizing Savings by Building Type and Load....................................49
3.4. Scenario C – Full Government Incentives, $20/tonne CO Tax.......................................52
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3.5. Scenario D – Full Government Incentives, $100/tonne CO Tax....................................52
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3.6. Summary Trends Based on Scenarios B, C, and D.............................................................53
3.7. Scenario E-1: Strategies for Maximizing Reductions in CO ............................................55
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3.8. Scenario E-2 – Building Types for Maximizing CO Reductions.....................................56
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3.9. Identification of Policy Options............................................................................................59
4.0 Conclusions and Recommendations....................................................................................62
4.1. Building Types........................................................................................................................62
4.2. Network Configurations........................................................................................................63
4.3. Policy Recommendations......................................................................................................64
4.4. Recommendations for Future Research..............................................................................65
4.5. Benefits to California..............................................................................................................66
4.6. Final Conclusions....................................................................................................................67
References..................................................................................................................................................68
Glossary of Abbreviations and Acronyms...........................................................................................72
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List of Figures
Figure 1: Summary of Scenario A results...............................................................................................3
Figure 2: Summary of Scenario B results................................................................................................4
Figure 3: Summary of Scenario C results................................................................................................5
Figure 4: Summary of Scenario D results...............................................................................................6
Figure 5: Summary of Scenario E results................................................................................................6
Figure 6: Fuel cell systems situated as distributed generators..........................................................12
Figure 7. California energy flows in 1999, with a net primary resource consumption of 8375
trillion BTU (8.375 Quads) (Kaiper 2003)..............................................................................................14
Figure 8: Change in CO in California by county with different fuel cell types installed.............18
2
Figure 9: U.S. Urban Driving Cycle, a test of desired vehicle speed over time (EPA 2007)..........20
Figure 10: Real-time electricity demand from a detached house the weekend of May 6, 1996.
(Colella 2002(a))........................................................................................................................................21
Figure 11. Comparison of SA and NW operating strategies..............................................................25
Figure 12. Comparison of load following operating strategies.........................................................26
Figure 13. Comparison of fixed vs. variable heat-to-power operating strategies...........................28
Figure 14. Sample measured input data for building load curves showing electricity demand
from five different building types over one representative week during winter...........................36
Figure 15. Sample measured input data for building load curves showing heating demand from
five different building types over one representative week during winter....................................36
Figure 16. Best strategies for cost savings for building owners........................................................53
Figure 17. Best strategies for sales revenue for fuel cell manufacturers..........................................54

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List of Tables
Table 1. Comparison of CO emissions from conventional generators and fuel cells...................15
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Table 2. Operating strategies investigated...........................................................................................33
Table 3. Model input data for FCS operation.......................................................................................38
Table 4. Model input data for FCS costs...............................................................................................41
Table 5. Model input data for competing generators.........................................................................43
Table 6. Key inputs and results for scenario runs...............................................................................47
Table 7. Simulation results for a scenario with no incentives............................................................47
Table 8. Simulation results for a scenario with government incentives...........................................49
Table 9. Most economical building load curves with SA installation..............................................51
Table 10. Simulation results with government incentives and $20/tonne CO carbon tax..........52
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Table 11. Simulation results with government incentives and $100/tonne CO carbon tax.........52
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Table 12. Best strategies for maximum CO reductions.....................................................................55
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Table 13. Best building type load curves for maximum CO reductions under Strategy IV........57
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Table 14. Best building type load curves for maximum CO reductions under Strategy V..........58
2

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