NREL AnalysisUFTO
May 8, 2002
Walter ShortAnalysis Group ManagerPrincipal Policy Analyst
National Renewable Energy Laboratory
Operated for the U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel
2
What Is Energy Analysis?Understanding current and future characteristics, roles and
interactions among technology, policy, and markets and using that understanding to inform decisions that are relevant to
advancing EE and RE technologies from concept to commercial application
Markets
Technology
Policy
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Types of Analyses Decision Type/User
• R&D Program Direction (Program Managers/Researchers)– Identify technical issues/opportunities– Establish R&D priorities– Determine which technical thrust(s) to continue/end/modify– Select designs/materials/approaches
• Program/Policy Formulation (Portfolio Managers, Policy Makers)– Establish/Justify Program Budgets or Policy Option– Set program portfolio priorities– Determine how effective/productive a program/policy has
been– Decide between alternative program or policy options
• Technology Choice (Energy Market Decision Makers)– Decide between alternatives– Determine whether or not to invest– Diagnose operating problems/application opportunities
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NREL Analysis Portfolio
R&D ProgramDirectionProgram/PolicyFormulationTechnologyChoice
•Analysis efforts represent about 10% of the Laboratory’s total research activity•FY01 total research volume ~$215M
9%
31%60%
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Managing Analysis Capabilities
Planning andTechnology Management
Bobi GarrettDeputy: Jack Darnell
Planning andTechnology Management
Bobi GarrettDeputy: Jack Darnell
ES&HRandall McConnell
DirectorRichard Truly
DirectorRichard Truly
Chief CounselDonald Hagengruber
NREL National Advisory Council
Science andTechnology
Stanley Bull
Science andTechnology
Stanley Bull
LaboratoryOperationsJerry Bellows
LaboratoryOperationsJerry Bellows
Communications andStakeholder Relations
Jessie Harris
Communications andStakeholder Relations
Jessie Harris
NREL Fellows Council
Research & Development Centers
Energy Analysis Office
Energy & Environmental Applications Office
60% R&D Program Direction30% Program/Policy Formulation10% Technology Choice
60% Policy/ProgramFormulation
30% R&D Program Direction10% Technology Choice
60% Technology Choice30% R&D Program Direction10% Policy/Program
Formulation
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Analysis Staff Education
05
1015202530354045
Econo
mics Math
Eng/Phy
Sci
Envir S
ci
Social
Sci
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Level of Education
Years Experience
NREL Analysis Staff Capabilities
< 10 Years
10-20 Years
>20 Years
Bachelors
Masters
PhDs
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DOE Stated Expectations for a ‘World Class’ Analysis Capability
• Establish NREL as the ‘hub’ of analytical efforts for the DOE Office of Energy Efficiency and Renewable Energy (EERE). The ‘hub’ shouldhave the ability to– access the best talent to address the pieces– synthesize the results of individual efforts into clear and succinct briefing
papers that communicate the key messages to EERE executive management
– deal with both quick response and longer term analysis – Be aware of and keep EERE informed of analysis efforts funded
elsewhere• Act as a strategic advisor to EERE
– anticipate and bring new ideas forward– be ahead of them, not just react– keep them out of trouble– provide a view into the programs
• Establish a presence among analysis stakeholders– visibility in both traditional and non-traditional forums, especially in D.C.
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NREL Core Analysis Capabilities
Renewables•Solar•Wind•Bioenergy•Hydrogen
Energy Efficiency•Hybrid Vehicles•Alternative Fuels•Buildings
Distributed EnergySystems
• Hybrids• Village Power• Interconnection
Technology
Tools/Methods
•GIS ResourceData
•System Models- Hybrid Power- Hybrid Vehicles- Building Systems
• Modeling of Renewables
•Life-Cycle Analysis•Internet Delivery
Issues
Renewable Energy•Technology •Policy•Markets•Scenarios
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Product Examples
Formal Products• NREL Reports• Issues Briefs• Journal Articles• Web Based Data and Models• PresentationsInformal• Advice• Data/Information
NREL Analysis
NREL Analysis Examples•R&D Program Direction•Program/Policy Formulation•Technology Choice
National Renewable Energy Laboratory
Operated for the U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel
R&D Program Direction
RE Technology Characterizations Bioenergy Life Cycle AssessmentWind Energy Techno-economic Tradeoffs
National Renewable Energy Laboratory
Operated for the U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel
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Renewable Energy Technology Characterizations
NREL Lead: Larry Goldstein
Guideline developmentIn-depth RE technology awarenessFinancial analysis
NREL Capabilities
Policy makers, analysts, modelers, mediaUsers/Use
Capital, O&M, and efficiency estimates to 2030 for all major RE power technologies and DG technologiesLevelized cost of energy, based on standard assumptions
Results
Develop standard guidelines for the characterization Build on the best available information and experience Collaborate with EPRI; EPRI/DOE reportConduct review workshops
Methodology
Develop a standard reference for the future cost and performance of renewable power technologiesObjectives
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Sample Characterization ResultsResidential PV
0
10
20
30
40
1990 2000 2010 2020 2030
Systemefficiency %System Cost$/Wp
LCOEcents/kWh
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Bioenergy Life Cycle AssessmentNREL Lead: Maggie Mann
Process modelingMass and energy balancesSystem definition and study conceptualization
NREL Capabilities
Industry, policy makers, analysts, modelers, mediaUsers/Use
Total environmental profileNet energy balanceResearch recommendations in area of highest impact
Results
Inventory: mass and energy balances -> air, water, and solid waste emissions, energy and other resourcesImprovement: process design changes, materials, etc.Impact assessment and sensitivity analysis
Methodology
Assess environmental impacts from cradle to graveIdentify environmental impacts we can work to reduceCompare new processes to the status quo
Objectives
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LCAs for Bioenergy Conducted by NREL
Biomass IGCCCurrent and advanced coal systemsDirect-fired biomass combustionNatural gas combined cycleCoal / biomass cofiringSteam methane reformingWind/electrolysisH2 from biomassEthanol from corn stoverBiodiesel from soybeans
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Life Cycle GWP and Energy Balance for Advanced IGCC Technology using Energy Crop Biomass
Future, wide-spread potential
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Strong Computer Modeling Skills, Unique to NREL. Staff and Facilities for Advanced Development and Testing Not Available in Industry.
NREL Capabilities
Program managers and research staff / R&D prioritiesIndustry Partners / Select Advanced Components to Implement
Users/Use
Exercise and Validate Design ToolsIdentify Most Promising Areas for ResearchSelect Components for Detailed Design and Testing
Results
Advanced System Aerodynamic and Structural DesignsIdentify Performance ImprovementsModel Overall Cost of Energy ImpactsPerform Tradeoffs Between Competing Technologies
Methodology
Turbine Design Impacts on Cost of EnergyIdentify Cost Effective System and Component InnovationsIdentify Possible Research Paths
Objectives
Wind Energy Technoeconomic TradeoffsNREL Lead: Alan Laxson
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Wind Energy Technoeconomic TradeoffsNREL Capabilities
• Primary Research Agency for Aerodynamic and Structural Design Codes - Computer Modeling
• Extensive Aerodynamic and Structural Design Skills
• Cost Analysis and Cost Comparisons• Staff and Facilities for Advanced
Development and Testing Not Available in Industry
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Wind Energy Technoeconomic TradeoffsWind Turbine Design Process
AtmosphericConditions
InflowStructure
Loads on Structure
TurbineResponse
TurbulenceSimulation
Aerodynamic Codes
Fatigue Evaluation
Final Design &Viability
Structural Dynamics & Simulation Codes
COEEstimates
COE Models
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COE Model Summaryrotor study task 3 configurations, costs/kWh
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
cost
, c/k
Wh
O & MRepl't costsBalance of stnTowerControlsDrive trainRotor
3 blade upwind 3 blade downwind
2 blade upwind
2 blade downwind
Portfolio/Policy Formulation
PV Value of Benefits Distributed Power Consumer Analysis Scenarios for a Clean Energy Future Renewable Energy Modeling in NEMS
National Renewable Energy Laboratory
Operated for the U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel
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•Detailed understanding of technology and market•Good rapport with stakeholders to assess benefits
NREL Capabilities
•Consumers, energy service providers and policy decision makersUsers/Use
•A matrix of stakeholders, with value attributes •Quantified benefits to each stakeholder group•Basis for policy development to capture and allocate value
Results
•Define the characteristics of the impacts due to PV usage•Identify the beneficiaries of the impacts, both plus and minus•Quantify the range of benefits to the stakeholders•Identify situations where the benefits can be captured and sold
Methodology
•Develop quantified attribute values for PV technology •Better understanding of the integrated economic impact of new and different technology
Objectives
PV – Value of BenefitsNREL Lead: Christy Herig
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PV Value Connection Matrix
Stakeholder
DOE Legisltr NGOEnergyDemand/Capacity
DistributionTransmission
GenerationEnvironmentBIPV
Matrl ReplaceLoad Manage
ReliabilityEconomicDevelopmentUncertainty/Risk
DemandSupply
Fuel PriceElec Price
EnvironmentRegs
FederalIndustry
PVSupportMatrls
Bldrs/Dvlprs
Government
Local StateResidntl Comrcl
ConsumersValueAttribute IOU MUNI REC
Electric Service Providers
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Value of PV for Government
Analysis for AZ Environmental Portfolio Standard
38,000 tonsSMOG NOx emissions avoided by 2020
32,000 tonsAcid rain SOx emissions avoided by 2020
12 million tonsGlobal warming CO2emissions avoided by 2020
$200 millionWage, salary, and state income tax revenue (1998-2020)
600 jobsJobs created by 2000
ResultsParameter
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Distributed Power Consumer AnalysisNREL Lead: Bill Babiuch
Collaborators: Antares Group Inc. and Harris Interactive
Objective: To better understand consumer preferences for grid-connected distributed power systems
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Methodology• National random sample of 1,200
respondents
• Data collected through Harris Poll Online Survey
• Two-part survey
– Traditional Q&A survey
– Conjoint survey
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Traditional Preference Question
If you were going to purchase an electricity generation system for your home, indicatehow important each of the following product attributes would be:
(Circle one per item)
Not VeryImportant Important
Price 1 2 3 4 5 6 7 8 9Maintenance 1 2 3 4 5 6 7 8 9Backup Power 1 2 3 4 5 6 7 8 9Environmental Impact 1 2 3 4 5 6 7 8 9Noise 1 2 3 4 5 6 7 8 9Standard Warranty 1 2 3 4 5 6 7 8 9
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Sample of On-Line Conjoint Survey
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Attribute Preference FindingsComparison of Conjoint Results
vs. Average Attribute Rating
0
1
2
3
4
5
6
7
8
9
10
Cost GridIndependence,Back-up Power
MaintenanceRequired
Noise Air Emissions Warranty VisualAppearance
Attribute
Conjoint Results Average Attribute Rating
30Conjoint ResultsAverage Attribute Rating
0
3
6
9
12
15
18
21
24
27
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Strong technology backgroundImproved modeling capabilityIncreased collaboration with other laboratories
NREL Capabilities
Wide range of users: policy makers, analysts, NGOs, legislators, media, internationalUsers/Use
Smart policies can significantly reduce CO2, SO2, and oil demandEconomic benefits of such policies exceed their costsUncertainties are large, but unlikely to change the conclusions
Results
Scenario-based approach - BAU, moderate, advanced policiesNEMS modeling baselineMacroeconomic impacts based on Stanford EMF analysesExternal Review Committee and Sector Expert Groups
Methodology
Assess the potential of public policies and programs to foster efficient and clean energy technology solutions to the range of energy/environmental problems facing the U.S.
Objectives
Scenarios For a Clean Energy FutureA Multi-Laboratory Study Led by ORNL, NREL, LBNL
NREL Lead: Walter Short
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CEF Key PoliciesAdvanced Scenario
Buildings Industry- Efficiency standards for equipment - Voluntary programs- Voluntary labeling and deployment programs - Voluntary agreements
Transportation Electric
- Voluntary fuel economy agreements with auto manufacturers - Restructuring- "Pay at the pump" auto insurance - RPS and PTC
- Double Federal R&D - Domestic Carbon TradingCross Sector Policies
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Reliance on R&D delays carbon reductions from the transportation sector, but by 2020 emission reductions are large.
Electric sector
policies
Electric sector policies account for a third of the carbon reductions in the Advanced scenario.
CEF Sectoral Contributions to Carbon Reductions
2100
(9 - 10% Reduction in 2020)
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Renewable Energy in theNational Energy Modeling System
NREL lead: Walter Short
• Background: NEMS is the official energy market model of the U.S. government. As such it is used in many policy forums, e.g. EIA Annual Energy Outlook, Kyoto analysis, multi-pollutant analysis, CEF study.
• Objective: Ensure renewable energy technologies are appropriately represented in NEMS
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Selected NEMS Constraintson Wind Energy
• 90% of U.S. wind resource is penalized for site access by a factor of 3 times capital cost
• No projects developed in one region to serve another region
• Intermittency: max 10% of regional generation
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Wind Energy Constraints in NEMS -2020 Sensitivities
0
50
100
150
200
250
Referen
ce
+ $10
0/ton
ne
- long-te
rm ac
cess
mult
- growth
multipli
ers
- 1 GW
limit
+ inte
rregio
nal b
uilds
- interm
ittenc
y
GW
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Non-Blended Ethanol Demand in NEMS
0
0.5
1
1.5
2
2000 2005 2010 2015 2020
Qua
ds
AEO2000
$0/gallonhalf gas price
Technology Choice
HOMER Hybrid Optimization Green Power Market Assessment ADVISOR Hybrid Electric Vehicle Systems Analysis
National Renewable Energy Laboratory
Operated for the U.S. Department of Energy by Midwest Research Institute • Battelle • Bechtel
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•Expertise in RE and hybrid technologies•Expertise in optimization methodologies•International experience in village power system implementation
NREL Capabilities
•~ 1000 users in >90 countries•Industry, utilities, government, academia, off-grid consumers
Users/Use
•User friendly software for rural electrification planning, system design, market, and technology development analysis•Improves understanding of hybrid systems by decision-makers•Technology comparisons on a user-defined, level playing field
Results
•Site-specific economic optimization with user inputs•Hourly simulation of load and generation/storage operation•Intelligent grid-search with sensitivity analysis •Being expanded to grid-connected system modeling
Methodology
•Improve decision-making about remote hybrid power systems •Evaluate cost/performance goals for technology development•Perform market analysis for competing small power technologies
Objectives
HOMER Hybrid OptimizationNREL Lead: Peter Lilienthal
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HOMER Hybrid Optimization
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•Nationally and internationally recognized group of market analysts -- NREL is considered the place to go for green power market data, information, and advice.
NREL Capabilities
•DOE/NREL programs•Green power suppliers; other industry stakeholders; popular press
Users/Use
•Analysis products and other information for public consumption•Green Power Network website•Presentations at Green power conferences and workshops
Results
•Develop relationships with industry players•Collect market data; develop and perform analysis activities•Disseminate analysis results and other market information
Methodology
•Assess the impact of customer choice on RE market demand.•Identify and analyze factors impacting the success of green power markets.
Objectives
Green Power Market AssessmentNREL Lead: Blair Swezey
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U.S. Green Pricing Programs
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Statistics for UtilityGreen Pricing Programs
0.01.02.03.04.05.06.07.08.09.0
10.011.0
Utility Program
(¢/kWh)
median value = 2.5¢/kWh
17.6
Participation Rates for Utility
Green Pricing Programs
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
%
Utility Programs
Green Power Program Assessment
Utility Programs
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ADVISOR Demonstration
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ADVISOR: Preliminary hybrid electric vehicle (HEV) design evaluation and optimization
• ADVISOR = ADvanced VehIcle SimulatOR– simulates conventional, electric, or hybrid vehicles (series,
parallel, or fuel cell)• Created in 1994 to support DOE Hybrid Program• Provides capability to quickly evaluate component impacts on
vehicle system attributes • Open source code provided
for flexibility• Downloaded by over 3800
people around the world since first released in 1998
• Users provide componentdata, validation, andfeedback for future development
0
500
1000
1500
2000
2500
3000
3500
4000
Apr-97 Jan-98 Nov-98 Aug-99 May-00 Feb-01 Nov-01
Num
ber
of D
ownl
oads v3.1
(2/12/01)
v1.1(5/9/97)
v2.1.1(4/13/99)
v2.2.1(11/23/99)
v2.0(9/15/98)
v3.0(8/23/00)
v1.2.1(4/23/98)
v3.2(8/21/01)
http://www.nrel.gov/transportation/analysis
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What is it?What is it?A collection of integrated software modeling tools tools and processesand processes that enable the evaluation, design and optimization of new energy saving automotive
technologies such as HEVs and Fuel Cells.
Digital Functional Vehicle
What is new?What is new?Simulation, modeling, safety and costing tools are readily available to the automotive industry. What is missing is a workable synergy between these toolssynergy between these toolsto make them effective enablers of new effective enablers of new technologiestechnologies.
Why?Why?NREL and their automotive industry partners work together to identify and remove technical barriers of remove technical barriers of these technologiesthese technologies.
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Improving the loads prediction capability using an accurate tire model would assist in minimizing vehicle
weight while creating durable vehicle structure
Tire Modeling at NREL
NREL ParametricTire Data
(Geometric, Material,Loading, Modeling) Parametric Solid Model
FEA Model
Execution at ORNLSupercomputerFEA Results
DOE
OptimizationEnrich -
Data Base3600 CPU hours
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Structural Foam Applications for Side-impact Crashworthiness in Aluminum Structures
Structural Foam
Reinforcement B-pillar outer
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Applying Optimization TechniquesFuel Cell Hybrid SUV
• Maximize fuel economy of Fuel Cell Hybrid SUV
• Coupling of sizing with control strategy leads to improved solution (56.5 mpgge, up from starting point of 41 mpgge)
• Multiple local optimums in HEV design space
Traditonal: local Non-traditional: global
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Battery AnalysisUsed realistic cycles to obtain heat generation and heat capacity data using battery cyclers and calorimeter.
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Air-Conditioning Optimization
• ADVISOR-SINDA/FLUINT Link Now Operational
• These connections make A/C optimization possible in vehicle context
Vehicle Solar LoadEstimator(VSOLE)
Transient A/CSystem Model
(SINDA/FLUINT)
ADVISOR
VEHICLEFUEL
ECONOMY
VEHICLEEMISSIONS
Solar loadSolar load
CompressorCompressorPower & cabin tempPower & cabin temp
• VSOLE now included with ADVISOR 3.2 distribution
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“These are very powerful tools and essential in the development of our hybrid vehicles at DaimlerChrysler.”Min Sway-Tin, Supervisor HEV Electrical EngineeringHEV Platform EngineeringDaimlerChrysler Corp.
“ADVISOR has been invaluable in Delphi's development of codes to predict the performance of stop/start and integrated starter generator vehicles.”
John MacBainStaff Research Engineer
Delphi Automotive Systems
What Does Industry Have to Say?
“… We have found this collaboration to be very helpful since the NREL team brings new, fresh, out-of-the-box ideas and high level technical expertise.”
Tsung-Yu Pan, Ph.D.Senior Technical Specialist, Manufacturing Systems
Ford Research Laboratory
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Analysis VisionNREL is recognized in the U.S. and around the world as a credible and leading source of knowledge on the economic,
environmental, and social implications of renewable energy and energy efficiency technologies, systems, markets, and policies.
