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HDSAM 2.0: Expanded Capabilities, Enhanced Results in Hydrogen Delivery Modeling Marianne Mintz, Amgad Elgowainy, Jerry Gillette, Mark Paster, Matt Ringer (NREL), Daryl Brown (PNNL), Matt Hooks (TIAX), and Bruce Kelly (Nexant) NHA Annual Meeting April 1, 2008
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Presentation Outline Enhancements in Version 2.0 vs. Version 1.0 –Pathway representation –Data –Modeling Demand profile and component sizing Results Conclusions and next steps 2
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3 HDSAM 2.0 Incorporates common building blocks from DOE’s H2A models: −Delivery Components and Forecourt (Refueling Station) models −Discounted cash flow analysis −Common financial assumptions and fuel properties Flexible, easy to use tool: –Programmed as series of Microsoft EXCEL spreadsheets –Runs with either H2A defaults or user inputs –User-friendly interface to quickly and easily define scenarios of interest –Posted on USDOE website (www.hydrogen.energy.gov) with Users’ Guidewww.hydrogen.energy.gov –Technical support by EERE help desk Automatically links and sizes components into optimized pathways Provides structure for efficiently examining new technologies, delivery pathways, operating targets and packaging options Provides “snap shot” of delivery cost resulting from input assumptions. Not a transition model.
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4 Enhancements in HDSAM 2.0: Better Pathway Representation Components sized to meet refueling demand profile (replaces capacity factor set for entire pathway). Pathway storage optimized for peak demand (plant outage, summer peak, Friday peak, hourly peak, HOF peak). Variable capacity refueling stations (50 – 6000 kg/day). Additional pathways (mixed-mode deliveries, handling of plant outage/summer peak).
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5 Enhancements in HDSAM 2.0: Better Data Improved cost data (liquid handling, pipelines, compressors, storage, labor, indirect capital, O&M) and installation factors. Revised cost equations (compressors, pipelines, liquefiers, storage tubes). Revised fuel demand profiles (hourly, daily, seasonally).
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6 V 2.0 Equations Increase Some Costs, Decrease Others Vaporizers Liquid Storage Large Compressors Liquid Pumps Distribution Pipelines Vaporizer costs now cover a range of capacities (vs. two refueling-station sizes). Liquid storage now reflects large vessels (vs. refueling station- sizes). Liquid pump costs now are double HDSAM 1.0. Central pipeline compressors now reflect greater scale economies. Distribution pipeline costs now vary by location as well as diameter.
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7 V 2.0 Refueling Station Demand Profile Anchors Optimization of Component Capacity & Storage Throughout Pathway SunMTWThFSat 2 peaks @ 8 am & 3 pm almost no cars over night Also seasonal variation! Winter need is 70% and 90% of summer in the US North and South, respectively. increase 2 peaks @ 8 am & 3 pm almost no cars over night 1 peak @ noon almost no cars over night 12.0% 14.5%
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Enhancements in HDSAM 2.0: Better Modeling Refueling station (e.g., cascade vs. low-pressure storage, compressor/electrical, evaporator/pump, boil-off recovery). Refueling station optimization (both GH2 and LH2, based on total refueling station cost). Pipeline geometry (4-ring capability, separate downtown calculations.) Practical limitation on size of components (e.g., liquefier, compressors). Land area calculations (refueling station, terminals). Additional user options (energy use, CO 2 emissions.) 8
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9 Overview of HDSAM Scenario Definition Components & Other Sub-Models Results Trans/Distribution Mode Refueling Station kg/d 50 1500 3000 4500 6000 Refueling Module Energy and GHG Emissions Module
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10 Demand/Supply Profiles Drive Component Sizing winter summer (13 weeks) 10% above average Production average demand Average seasonal demand [kg/day] year [kg/day] S M T W R F S Friday Hourly Profile Peak hour profile (HOF=100% in the 1 st 3min) 3min 5min 55min GH Terminal H2 Production
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11 Results Show Sensitivity to Demand Cost drops rapidly with increasing demand, up to about 100 tpd. Scale matters for pipeline and liquid delivery, less so for compressed gas truck. High pressure gaseous truck may be attractive at low demand, despite uncertain characterization. HDSAM 1.0 V 2.0 reflects same pattern, although all estimates are higher. Cost of LH2 delivery is higher in V 2.0 due to 200 tpd size limit. HDSAM 2.0
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12 Because Delivery Is Capital Intensive High pressure gas truck is somewhat less capital intensive at low demand. Capital cost of all delivery modes are comparable at high demand. V 2.0 shows same pattern. Again, unit capital cost tends to flatten beyond 100 tpd. Each mode has a major capital cost challenge, particularly at low demand. HDSAM 1.0 Liquefier Pipeline Gas Terminal HDSAM 2.0
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13 Energy and O&M Are Smaller But Significant for All Modes and Generally Higher in V2.0 HDSAM 1.0 HDSAM 2.0 HDSAM 1.0 HDSAM 2.0
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14 With a Single Size, Refueling Station Costs Are Flat Refueling station capacity is a user input – 100 or 1500 kg/d in V 1.0; 50-6000 kg/d in V 2.0. (All stations are assumed same size). Optimization in V 2.0 results in 24/7 operation of lower- capacity components to meet average demand. Storage accommodates demand peaks and supply shortfalls. HDSAM 1.0 HDSAM 2.0
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Due to Liquefaction’s Energy Intensity, LHT Delivery Emits More GHGs Than GHT or Pipeline Pathways 15 Indianapolis, 20% market penetration, 2700 psi tube trailer, 400 kg/d refueling, 62 mi from city
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16 Conclusions At low demand, compressed gas delivery appears most economic. High pressure tube trailers may be promising, although characterization and cost inputs are uncertain. At higher demand, pipeline delivery is least costly. Distance from central plant to city gate may change relative costs (all results shown assume 100 km). Pipeline costs may be reduced with system “rationalization”, primarily reductions in service mileage. Refueling station costs may be reduced with larger stations. Liquefier and pipeline capital costs are a hurdle, particularly at low demand.
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Next Steps HDSAM 2.0 is now available on the USDOE website (www.hydrogen.energy.gov) with technical support from the EERE help desk.www.hydrogen.energy.gov V 2.0 Users’ Guide will be available by June ‘08. Full documentation will be available by end FY08. V 2.5 will be completed by end 2008. V 2.5 will incorporate: –Additional data (refueling station setbacks, separation distances and operating procedures, terminal size limits) –Revised models (delivery infrastructure to serve multiple urban areas, hydrogen carriers) 17
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18 Thanks to other members of H2A/Nexant project team, USDOE Delivery Tech Team, Fuel Pathway Integration Tech Team, and OFCHIT. Questions? mmintz@anl.gov
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Extra slides 19
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20 HDSAM 2.0 User Interface
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21 Direct/Indirect Costs (as % installed capital) Large-Scale Refueling station Item New Old New Old Site Prep4%12%5%6.5% Engineering and Design 10%32%10%3% Project Contingency 10%25%5%10% Licensing 1 0%1.5%0% Permitting3%4%3% Owner’s Cost12%--- FACTOR1.391.7451.231.225 1 1% licensing fee assumed for liquefiers only
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22 Delivery Scenario Inputs - Compressed GH2 Tube-Trailer Distribution Tractor cost $75,000 $75,000 Tube Trailer cost $225,000 $350,000
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23 Delivery Scenario Inputs- LH2 Truck Distribution Truck Cab cost $75,000 Truck Trailer cost $625,000
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24 Version 1.0 characterized components for 3 pathways with delivery by a single mode. Loading, conditioning and storage are at or adjacent to the plant Liquid Hydrogen (LH) Truck H2 Production 100 or 1500 kg/d Compressed H2 (CH) Truck H2 Production 3 or 7 kpsi 100 or 1500 kg/d H2 Production Gaseous H2 Pipeline 100 or 1500 kg/d HDSAM 1.0 Estimates Delivery Cost for 3 Pathways
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25 HDSAM 2.0 Simulates Nine Pathways H2 Production 1 2 3 LH Terminal H2 Production LH Terminal Transmission Distribution Transmission and Distribution City gate Liquid H2 Delivery
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26 HDSAM 2.0 Simulates Nine Pathways (cont’d) 4 GH Terminal H2 Production Transmission and Distribution H2 Production 5 GH Terminal Compressed Gaseous H2 Delivery
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27 HDSAM 2.0 Simulates Nine Pathways (cont’d) 6 7 GH Terminal Compressed Gaseous H2 Delivery (cont’d) H2 Production City gate TransmissionDistribution
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28 HDSAM 2.0 Simulates Nine Pathways (cont’d) H2 Production 8 9 Transmission Distribution City gate Pipeline Delivery
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