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.

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
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
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
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
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
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
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
 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
Other Partners Direct Work with NREL

10. 10
Other Sources of Data

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
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
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
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
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
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
• 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
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
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19. 19
Thank you!
• We need …
annabelle.pratt@nrel.gov , julieta.giraldez@nrel.gov