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6.3_DOE-OE Microgrid Cost Study_Pratt_EPRI/SNL Microgrid Symposium

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Presentation from the EPRI-Sandia Symposium on Secure and Resilient Microgrids: DOE-OE Microgrid Cost Study, presented by Annabelle Pratt, National Renewable Energy Laboratory, Baltimore, MD, August 29-31, 2016.

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6.3_DOE-OE Microgrid Cost Study_Pratt_EPRI/SNL Microgrid Symposium

  1. 1. NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. DOE - OE Microgrid Cost Study Annabelle Pratt for Julieta Giraldez EPRI-Sandia National Laboratories Secure, Resilient Microgrid Symposium Baltimore, August 30th 2016
  2. 2. 2 Objective • Identify the costs of components, integration and installation of U.S. microgrids and project cost improvements and technical accelerators over the next 5 years and beyond  Information could then be used to develop R&D agendas for the development of the next generation microgrids
  3. 3. 3 Commercial/ Industrial 6% Community 11% Utility Distribution 15% Institutional/ Campus 10%Military 5% Remote 53% Direct Current 0% Objective • Scope of microgrids  Key Market Segments – Commercial/Industrial – Community New Microgrid Power Capacity Market Share by Segment, World Markets: 2Q 2016 (Source: Navigant Research) Commercial/Industri al… Community 12% Utility Distribution 12% Institutional/Camp us 27% Military 13% Remote 23% Direct Current 0% New Microgrid Power Capacity Market Share by Segment US Market: 2Q 2016 – Campus/Institutional – Utility Distribution – Remote
  4. 4. 4 Expected Outcome • Contribute to providing better transparency and standardization in the reporting of microgrid costs  Better able to determine individual components’ contributions to total system price  Develop granular factors and eliminate subjective pricing parameters that may influence customer system value  Identify differences – across system configurations – across market segment and component – between installation costs, component prices, and system prices Source: Charge Bliss
  5. 5. 5 Challenge • Particularly challenging to generalize costs  Every installation has unique design and architecture characteristics that affect the overall cost of the individual microgrid components  E.g., unit costs per size such as $/MW installed DG capacity may vary from one design to another because of application requirements Cost projections made under defined assumptions and scenarios
  6. 6. 6 Current Practices • Companies do internal market research • Market Analysis Companies (Navigant Research & GTM)  Mainly track projects and report costs in ranges of $/MW of Capacity Installed  Do not include any breakdown of costs  No standardization in reporting costs o Microgrid per DOE definition? o Brown field/Green field projects o Existing assets 6
  7. 7. 7 Approach • Collect and classify microgrid cost database:  Along with key industry partners, examine existing microgrid cost databases  Classify microgrid costs and identify the range of possible microgrid applications and functionalities to divide the market into segments  Identify costs, technical drivers and barriers • Develop bottom-up model for projecting future microgrid costs • Build automated microgrid cost database for future use
  8. 8. 8  Sent survey to Microgrid Tracker contacts, inviting them to provide cost information – ~ 45 projects with partial or full breakdown of costs  Still waiting on several responses  Expected to provide detailed breakdown on costs on ~ 70 projects Data Collection 8  Querying database to down-select projects  Sent survey to collect info – Stage of the project, final component sizes, etc. – ~ 50 users responded and 10 are willing to provide cost information  Access to GTM’s U.S. Microgrid Market Quarterly Update – 237 project entries; over 2.5 GW of U.S capacity – Total or partial cost information on 95 projects  Subcontract being signed
  9. 9. 9 Other Partners Direct Work with NREL
  10. 10. 10 Other Sources of Data
  11. 11. 11 Existing Microgrid Cost Study Data • Characteristics to validate NREL’s database and determine the focus for the data collection effort o Regional o Capacity per Market Segment in MW o # Projects per Market Segment o Capacity by DER o # Projects with breakdown of controls and soft costs
  12. 12. 12 MG Cost Study Data – by Location State [MW] Projects New York 312.7 19 California 94.6 11 Connecticut 20.4 7 Marlyland 67.6 5 Alaska 37.1 5 New Jersey 37.2 4 Texas 140 3 Oregon 23.3 3 New Mexico 4.3 2 Colorado 31.1 1 Pennsylvania 16 1 Utah 11.2 1 Illinois 9.4 1 Florida 7.0 1 Vermont 6.5 1 Washington 5 1 Delaware 4.9 1 Maine 1.6 1 Hawaii 0.2 1
  13. 13. 13 MG Cost Study Data - by Capacity Campus/Institutiona l 53.7% Commercial 3.5% Community 36.5% Remote 6.4% MG Cost Study Project Data by Capacity Campus/Institut ional 47.0% Commercial 26.0% Community 20.2% Remote 6.8% GTM Data by Capacity Campus/Instituti onal 47.7% Commercial 8.1% Community 15.1% Remote 29.1% Navigant Data by Capacity 51% 38%
  14. 14. 14 MG Cost Study Data - by # Projects Campus/Institution al 31.1% Commercial 14.9% Community 40.5% Remote 13.5% MG Cost Study Project Data by # Projects Campus/Institut ional 40.1% Commercial 16.7% Community 26.6% Remote 16.7% GTM Data by # Projects Campus/Instituti onal 24.7% Commercial 21.3% Community 21.3% Remote 32.6% Navigant Data by # Projects 39% 12%
  15. 15. 15 MG Cost Study Data - by DER Capacity Diesel 17.1% Natural Gas 7.1% CHP 58.1% Solar 9.9% Wind 1.5% Storage 5.7% Fuel Cell 0.7% MG Cost Study Data by DER Capacity
  16. 16. 16 MG Cost Study Data – by Non-DER Costs • Of the 74 projects in current database  31 have soft cost breakdown  29 have microgrid controls costs • Special emphasis  Controls/Software costs  System Integration costs  “Soft costs”  What ranges in % of total project costs?  How do project costs without system control and/or “soft costs” compare with projects with such data?
  17. 17. 17 • No linear relationship found in the normalized cost in $/MW with regards to characteristic and design variables: • The team is currently working on multi-regression and quantile regression models  Size of the dataset is small for statistical analysis models  In any attempt to subdivide the dataset, the size of the subgroups are too small to provide any meaningful results Preliminary Results  size, % energy storage, % renewable energy penetration, etc. 17
  18. 18. 18 Lessons Learned • Data Collection effort takes time!  Most of the companies that have the data are not in the business of providing data… o Data not readily available o It is not part of their daily job! • Existing microgrid databases only track projects but do not contain detailed cost information • A lot of microgrid sites contain legacy equipment and are built in phases  Considerable effort goes in homogenizing the dataset 18
  19. 19. 19 Thank you! • We need … annabelle.pratt@nrel.gov , julieta.giraldez@nrel.gov

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