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2016 Smart Grid R&D Program Peer Review Meeting Microgrid Cost Study Julieta Giraldez NREL August 16 th 2016
2016 Smart Grid R&D Program
Peer Review Meeting
Microgrid Cost Study
Julieta Giraldez
NREL
August 16th 2016
December 2008
Project Title
Objectives & Outcomes
Life-cycle Funding
Summary ($K)
Prior to
FY 16
FY16,
authorized
FY17,
requested
Out-year(s)
0 $375k $325k $0k
Technical Scope
(Note: The life-cycle funding table above should include all FY funds received and to be requested, from the project beginning year to the project ending year)
Objective: Identify the costs of components,
integration and installation of U.S. commercial
microgrids and project cost improvements
accelerators over the next 5 years
Outcome: Provide better insight and standardization
in the reporting of microgrid costs to: 1) determine
individual components’ contributions to total cost, and
2) identify each market segment differences
Phase I - Collect and classify microgrid cost
database:
- Along with key industry partners, examine
existing microgrid cost databases
- Classify microgrid costs using statistical
analysis methods and identify possible groupings
- Investigate avenues for cost reduction
Phase II - Bottom-up model
- Develop bottom-up price analysis methodology
to provide the necessary resolution
- Build automated microgrid cost database
2
Classification Methodology to Map Data Fields to Microgrid Applications
December 2008
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
December 2008
Expected Outcome
• Contribute to providing better insight, transparency and standardization in the reporting of microgrid costs:
Better able to determine individual components’ contributions to total system price
Develop granular factors in microgrid system prices and eliminate subjective pricing parameters that may influence customer system value (price) vs. cost
– e.g. incentives, local rates, etc.
Identify differences – across system configurations (e.g. location, market)
– across market segment and components for significant opportunity for future cost reductions
– between installation costs, component prices, and system prices 4
December 2008
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
5
Cost projections made under defined assumptions and scenarios
December 2008
Current Practices
• Companies do internal market research
• Market Analysis Companies (Navigant Research & GTM) 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
December 2008
Project significance and impact
• The categorization and standardization effort in the way microgrid costs are reported has not been done before
• Help understand the deployment drivers and barriers of microgrid technology
Detailed results will be used to guide R&D efforts aimed at reducing microgrid system prices and to understand the potential benefits of proposed technological improvements
7
December 2008
Technical Approach – Phase I
• Task 1 - 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
• Task 2 - Analysis of microgrid installation costs:
Analyze the information on microgrid installations over the last 5 years using the methodology defined in Task 1
• Task 3 – Identify costs, technical drivers and barriers
Investigate avenues for technology advancements and to drive down the costs to accelerate microgrids
8
December 2008
Technical Approach – Phase II
• Task 4 – Develop bottom-up model:
Develop a bottom-up price analysis methodology in collaboration with industry and account for all materials, labor, land acquisition and preparation costs, and regulatory costs for a microgrid, to provide the necessary resolution
Detailed results can be used to guide R&D efforts aimed at reducing microgrid system prices and to understand the potential benefits of proposed technological improvements
• Task 5 – Build automated microgrid cost database:
Work with the industry partners to build an automated (possibly web interfaced) framework to maintain and update the costs to update the results on an annual base
9
December 2008
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
Performance– Task 1 Data Collection
10
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
December 2008
Performance – Task 1 Data Collection
December 2008
Performance – Task 1 Data Collection
December 2008
Performance – Task 1 MG Cost Database
• Characteristics to validate NREL’s database and determine the focus for the data collection effort
Regional
Capacity per Market Segment in MW
# Projects per Market Segment
Capacity by DER
# Projects with controls and soft costs
13
December 2008
Performance – Task 1 MG Cost Database
• By Location
14
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
December 2008
Performance – Task 1 MG Cost Database
• By Capacity
15
Campus/Institutional 53.7%
Commercial 3.5%
Community 36.5%
Remote 6.4%
MG Cost Study Project Data by Capacity
Campus/Institutional 47.0%
Commercial 26.0%
Community 20.2%
Remote 6.8%
GTM Data by Capacity
Campus/Institutional 47.7%
Commercial 8.1%
Community 15.1%
Remote 29.1%
Navigant Data by Capacity
51%
38%
December 2008
Performance – Task 1 MG Cost Database
• By # Projects
16
51%
38%
Campus/Institutional 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/Institutional 24.7%
Commercial 21.3%
Community 21.3%
Remote 32.6%
Navigant Data by # Projects
39%
12%
December 2008
Performance – Task 1 MG Cost Database
• By DER
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
December 2008
Performance – Task 1 MG Cost Database
• 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?
18
December 2008
Performance – Task 2 Preliminary Results
• Statistical analysis – any linear relationship?
19
y = 2E+06x R² = 0.7243
$-
$50,000,000
$100,000,000
$150,000,000
$200,000,000
$250,000,000
$300,000,000
$350,000,000
0 20 40 60 80 100 120 140 160
Total Cost of Microgrid Projects $(MW)
December 2008
Performance – Task 2 Preliminary Results
• No further linearity found in the normalized cost in $/MW with regards to characteristic and design variables:
size, energy storage, % renewable energy penetration, etc.
• The team is currently working on multi-regression and quantile regression models that are not providing any further results
Size of the dataset is small for statistical analysis models
In any attempt to subdivide the dataset, the size of the subgroups are too small to provide any meaningful results
As data comes in models are being updated and results generated
20
December 2008
FY 16 & 17 Plan
• Phase I
Complete data collection by August 2016
Final categorization proposal by September 2016
Identify costs and technical drivers and barriers to accelerate Microgrids by November 2016
• Phase II
Review feasibility of bottom-up cost modeling approach
Build automated microgrid cost database
21
December 2008
Lessons Learned
• Data Collection effort takes time!
Most of the companies that have the data are not in the business of providing data…
o Data not readily available
o It is not part of their daily job!
• Existing microgrid databases only track projects but do not contain detailed cost information
• A lot of microgrid sites contain legacy equipment and are built in phases
Considerable effort goes in homogenizing the dataset
22
December 2008
Contact
Julieta Giraldez, NREL 15013 Denver West Parkway, Golden CO, 80401
303-275-4483
23
December 2008
Back-up Slides
24
December 2008
Classification Methodology
• Initial categorization by size
Use statistical methods to subdivide each market segment into capacity ranges looking at standard deviation of normalized costs ($/kW)
Design statistical test to determine capacity groupings if applicable
The team expects that other dimensions will have to be looked at besides size
December 2008
Classification by Size
y = 2E+06x R² = 0.7243
$-
$50,000,000
$100,000,000
$150,000,000
$200,000,000
$250,000,000
$300,000,000
$350,000,000
0 20 40 60 80 100 120 140 160
Total Cost of Microgrid Projects $(MW)
Size in MW
December 2008
Classification by Size
y = 3E+06x R² = 0.2442
$-
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
$30,000,000
$35,000,000
$40,000,000
$45,000,000
$50,000,000
0 2 4 6 8 10
y = 3E+06x R² = 0.0696
$-
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
0 0.5 1 1.5 2 2.5 3 3.5
$-
$100,000,000
$200,000,000
$300,000,000
$400,000,000
0 20 40 60 80 100 120 140 160
Total Cost of Microgrid Projects $(MW)
< 10 MW
< 3 MW
Size in MW
Size in MW
December 2008
Classification Methodology
• Questions/Hypothesis
Are market segments reflective of microgrid costs drivers?
– Regroup market segments if applicable
Do projects within a same market segment and capacity range have wide ranges of normalized costs?
– Identify drivers and propose new classification methodology
December 2008
Preliminary Results within a Market Segment
y = 2E+06x R² = 0.7004
$-
$50,000,000
$100,000,000
$150,000,000
$200,000,000
$250,000,000
$300,000,000
$350,000,000
0 20 40 60 80 100 120 140 160
Total Cost of Campus/Institutional Microgrid Projects $(MW)
y = 3E+06x R² = -0.097
$-
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
$30,000,000
$35,000,000
$40,000,000
$45,000,000
$50,000,000
0 2 4 6 8 10
y = 4E+06x R² = 0.135
0 1 2 3 4 5
< 10 MW
< 3 MW
December 2008
Other dimensions besides size … ?
Cost reduction
Reliability
RE integration
Energy policy
…
Economic opt./market participation
Seamless islanding
Power-flow management
Reduce emissions
…
Applications Functionalities
• Other dimensions:
Number of DER
Type of DER
December 2008
Mapping Exercise - Assumptions
• Map data fields to applications/functionalities
Assumptions will be made with partners & industry
December 2008
Other dimensions
y = 1,941,410.06x R² = 0.95
$-
$50,000,000.00
$100,000,000.00
$150,000,000.00
$200,000,000.00
$250,000,000.00
$300,000,000.00
$350,000,000.00
0 20 40 60 80 100 120 140 160
Single Conventional Generation Source Microgrid Projects $(MW)
Size in MW
