| | Distributed Energy Resources in Practice |
| | 1,68 | | MB |
| | 209 | | stron |
| | 5085 | | ID | Lawrence Berkeley National Laboratory |
| | 2004 | | rok |
| | Table of Contents |
| | Abstract. i |
| | Acknowledgments. iii |
| | Table of Contents. vii |
| | List of Tables xi |
| | Appendix Tables xiii |
| | List of Figures . xv |
| | Appendix Figures xvii |
| | Acronyms and Definitions . xix |
| | Executive Summary . xxi |
| | 1. Introduction 1 |
| | 1.1 Background. 1 |
| | 1.2 The Distributed Energy Resource-Customer Adoption Model 2 |
| | 1.3 Purpose of Research 3 |
| | 1.3.1 Analyze, Describe, and Disseminate DER Site Project Experience. 3 |
| | 1.3.2 Describe Real-World Issues Involved with DER Adoption Decision-Making and System Design |
| | 4 |
| | 1.3.3 Validate DER-CAM Financial Estimates and Technology Adoption Decisions . 4 |
| | 1.3.4 Improve DER-CAM Accuracy and Expand its Capabilities Based on Real-World Experience 5 |
| | 1.3.5 Establish Contacts with Relevant DER Sites for Future Research . 5 |
| | 1.3.6 Methodology & Application Summary 5 |
| | 2. Methodology 9 |
| | 2.1 Site Selection Procedures 9 |
| | 2.1.1 Candidate Site List Compilation. 9 |
| | 2.1.2 Required and Desired Site Characteristics 9 |
| | 2.1.3 Final Site Selection . 10 |
| | 2.2 Data Requirements for Each Site 12 |
| | 2.2.1 Utility Provider and Applicable Tariff Schedules: . 12 |
| | 2.2.2 Performance and Cost Characteristics for each of the DG Technologies Considered 12 |
| | 2.2.3 Load Data 13 |
| | 2.2.4 Financial Analysis.13 |
| | 2.2.5 Special Constraints Faced By the Site 16 |
| | 2.3 Tariff Information . 16 |
| | 2.4 DOE-2 Load Development . 17 |
| | 2.5 Automation Manager 18 |
| | 2.6 Scenarios Considered for Each Site 19 |
| | 2.6.1 Description of the Six Scenarios. 19 |
| | 2.6.2 Graphical Representation of Scenario Results 21 |
| | 2.7 Sensitivity Analysis 22 |
| | 2.7.1 Spark Spread Sensitivity. 22 |
| | 2.7.2 Standby Charge Sensitivity. 23 |
| | 2.7.3 Flat Rate Electricity Sensitivity 25 |
| | 2.8 Assumptions of Modeling Process 25 |
| | 2.9 Including Rebates and Grants for DER Technologies in Model 28 |
| | 2.9.1 CPUC Self-generation Incentive Program, : . 28 |
| | 2.9.2 New York State Funding for Energy Efficiency and DER. 30 |
| | 2.9.3 DOD and CERL Climate Change Fuel Cell program. 30 |
| | 3. The Test Cases 31 |
| | 3.1 Summary of the Test Cases. 31 |
| | 3.2 Case A: A&P Waldbaum’s Supermarket, Hauppauge, NY 33 |
| | 3.2.1 The Decision-Making Process 35 |
| | 3.2.2 Description of the Data Collection Process 42 |
| | 3.2.3 Assumptions of Modeling Process 45 |
| | 3.2.4 Model Results .46 |
| | 3.2.5 Discussion of Results50 |
| | 3.2.6 Limitations of this Analysis 55 |
| | 3.2.7 Observed Outcomes of Installed Technology. 56 |
| | 3.2.8 Conclusions from A&P Test Site Analysis. 56 |
| | 3.3 Case B: Guarantee Savings Building, Fresno California 59 |
| | 3.3.1 The Decision-Making Process 61 |
| | 3.3.2 Description of Data Collection Process 65 |
| | 3.3.3 Assumptions of Modeling Process 67 |
| | 3.3.4 Model Results .67 |
| | 3.3.5 Discussion of Results70 |
| | 3.3.6 Limitations of this Analysis 74 |
| | 3.3.7 Observed Outcomes of Installed Technology. 75 |
| | 3.3.8 Conclusions from GSB Test Site Analysis . 75 |
| | 3.4 Case C: The Orchid Resort, Mauna Lani, Hawaii 77 |
| | 3.4.1 The Decision-Making Process 78 |
| | 3.4.2 Description of Data Collection Process 82 |
| | 3.4.3 Assumptions of Modeling Process 83 |
| | 3.4.4 Model Results .84 |
| | 3.4.5 Discussion of Results86 |
| | 3.4.6 Limitations of this Analysis 90 |
| | 3.4.7 Observed Outcomes of Installed Technology: 91 |
| | 3.4.8 Conclusions from The Orchid Resort Test Site Analysis . 91 |
| | 3.5 Case D: BD Biosciences Pharmingen. 93 |
| | 3.5.1 The Decision-Making Process:. 94 |
| | 3.5.2 Description of Data Collection Process 99 |
| | 3.5.3 Assumptions of Modeling Process 99 |
| | 3.5.4 Model Results .100 |
| | 3.5.5 Discussion of Results102 |
| | 3.5.6 Limitations of this Analysis 106 |
| | 3.5.7 Observed Outcomes of Installed Technology. 107 |
| | 3.5.8 Conclusions from BD Biosciences Pharmingen Test Site Analysis. 107 |
| | 3.6 Case E: San Bernardino USPS Handling Facility, Redlands, California . 109 |
| | 3.6.1 The Decision Process:. 110 |
| | 3.6.2 Description of the Data Collection Process 112 |
| | 3.6.3 Assumptions of Modeling Process 113 |
| | 3.6.4 Model Results .113 |
| | 3.6.5 Discussion of Results116 |
| | 3.6.6 Limitations of this Analysis 120 |
| | 3.6.7 Observed Outcomes of Installed Technology. 121 |
| | 3.6.8 Conclusions from San Bernardino Test Site Analysis 121 |
| | 4. Other Test Cases . 123 |
| | 4.1 AA Dairy. 124 |
| | 4.2 Alaska USPS. 125 |
| | 4.3 Byron Bergen Schools 125 |
| | 4.4 Compudye. 126 |
| | 4.5 Conde Nast 127 |
| | 4.6 Cortland Memorial Hospital . 127 |
| | 4.7 East Bay Municipal Utility District 128 |
| | 4.8 First National Bank of Omaha 129 |
| | 4.9 Greater Rochester International Airport . 130 |
| | 4.10 Green Mountain Coffee 131 |
| | 4.11 Harbec Plastics 131 |
| | 4.12 International Paper 132 |
| | 4.13 PC Richards 133 |
| | 4.14 Resource Conservation Management . 133 |
| | 4.15 Sea Crest Health Care Facility 133 |
| | 4.16 Southern Container . 134 |
| | 4.17 State University of New York, Buffalo 135 |
| | 4.18 Synagro . 135 |
| | 4.19 Twin Birch Farm. 135 |
| | 4.20 Victoria Packing Corp. 136 |
| | 4.21 Wyoming County Community Hospital . 136 |
| | 5. Lessons in Decision-Making and DER Adoption. 141 |
| | 6. Discussion of Overall Results. 145 |
| | 7. Limitations of Analysis. 157 |
| | 8. Areas for DER-CAM Improvement and Further Study 161 |
| | 8.1.1 Interface features to add to DER-CAM 161 |
| | 8.1.2 Additional data to obtain for DER-CAM 161 |
| | 8.1.3 Capabilities to add to DER-CAM. 161 |
| | 9. Conclusion 165 |
| | 10. References 167 |