1. Optimizing Hydrogen Pipeline Deployment in Real Geographic Regions Nils Johnson Joan Ogden Yueyue Fan National Hydrogen Association Conference May 4, 2010

2. Part I: Introduction

3. Motivation Major barrier to introduction of H 2 vehicles is the development of supporting fuel infrastructure What magnitude of infrastructure is required? How much will it cost? Need for spatially-explicit modeling in real geographic regions Potential benefits from economies-of-scale by aggregating demand of multiple cities What might a regional hydrogen supply network look like?

4. The Study Area What is the least-cost pipeline network for connecting production and demand?

5. Old Method: Shortest Distance Shortest Distance Pathways Between All Coal Plants and Demand Centers Optimal Hydrogen Pipeline Network (5% Market Penetration) Existing Pipeline Network

6. Old Method: Issues 1.Redundant pipeline segments 2.Ignores intermediate junction points 3.Difficult to isolate individual pipeline segments and assign flows 4.Optimization based on length only 5.Number of production facilities must be specified

7. Objectives Develop a hydrogen pipeline optimization tool Find least cost pipeline network for connecting production facilities to demand centers Consider pipeline length AND diameter Determine H 2 flows along each pipeline segment and optimal location and size of facilities Apply the tool to a real geographic region Provide insight into the deployment of H 2 infrastructure Identify costs of deployment

8. Part II: The Model

9. The H 2 Pipeline Optimization Model Minimize Decision Variables:Input Parameters: x ij units of hydrogen transported from node i to node j (tonnes/day) C f, C p fixed capital cost for building a production facility, constructing a pipeline (thousand$/yr) aiai units of hydrogen produced at node i (tonnes/day) VfVf variable cost for producing hydrogen (thousand$/tonne) f is 1, if facility is built at node i with size s; 0, otherwise Q f, Q p capacity of a facility, pipeline (tonnes/day) y ijd 1, if pipeline is constructed from node i to node j with diameter d; 0, otherwise RiRi demand at node i (tonnes/day) L ij length of pipeline segment (km) B is production at previously built facility (tonnes/day) Sets: N network nodes R demand nodes F facility nodes D pipeline diameters (4, 10, 20, 30, 42-inch) S facility sizes (300, 600, 900, 1200 t/day) Mixed integer linear programming model

10. The H 2 Pipeline Optimization Model Capacity ConstraintsNon-negativity Constraints Binary Constraints Mass Balance ConstraintOne Pipeline Constraint Existing Plant ConstraintOne Plant Constraint

11. Part III: The Case Study

12. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): ?? 1% Refueling Stations: ~150 ~250,000 0 0 0 ??

13. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): Refueling Stations: $9.1 Billion 5% 679 1.2 Million 1 2,620 1,735 1,195

14. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): Refueling Stations: $13.9 Billion 10% 1,408 2.4 Million 2 3,837 2,710 1,741

15. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): Refueling Stations: $25.1 Billion 25% 3,561 6.1 Million 5 5,935 3,752 3,182

16. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): Refueling Stations: $42.2 Billion 50% 7,233 12.5 Million 9 8,604 4,544 5,660

17. Infrastructure Design with Pipeline Distribution Market Penetration: H 2 Demand (tonnes/day): H 2 Vehicles: Production Facilities: Distribution Pipelines (km): Transmission Pipelines (km): Cumulative Capital Cost (2005$): Refueling Stations: $58.2 Billion 75% 10,825 18.7 Million 13 10,778 5,288 8,087

18. Comparison of Old and New Methods

19. Cost Model GIS Model: Infrastructure modeled at 5 MP levels Uses existing GIS data Engineering Economic Model: Calculates capital and annual O&M costs for each component Costs quantified for each build phase modeled in the GIS Based on several cost studies, but primarily H2A, NAS, and Princeton Revenue Requirements Model: Identifies annualized cash flows based on when infrastructure is built Includes replacement costs, corporate income tax, and depreciation Calculates breakeven cost of hydrogen over specified planning periods

20. Levelized Costs (very preliminary – do not cite) Feedstock Cost: COAL: $2/mmbtu 2005$/kg Optimistic Market Penetration Rate 1% in 2020 and reaches 69% MP in 2049 $8.48 $4.91 $3.87

21. Part IV: Conclusions

22. Conclusions Successfully developed a tool for optimizing H 2 supply networks Identifies the number, location, and size of production facilities Specifies the optimal transmission pipeline network, including the flow and diameter along each pipeline segment Significantly streamlines H 2 infrastructure analysis by reducing analyst time and providing improved results

23. Future Work Examine impact of different diameter classes and plant sizes Develop an alternative model that examines top-down infrastructure deployment Develop a similar model for intra-city distribution pipelines

24. Acknowledgements National Energy Technology Laboratory (NETL) STEPS Program Sponsors For Further Information: Nils Johnson njohnson@ucdavis.eduhttp://steps.ucdavis.edu

25. Part IV: Extra Slides

26. Modeling Infrastructure Deployment 5 % 10 % 25 % 50 % 75 % Market Penetration Period 1Period 2Period 3