(658e) Robust Multi-Period and Multi-Objective Strategic Planning of Hydrogen Networks | AIChE

(658e) Robust Multi-Period and Multi-Objective Strategic Planning of Hydrogen Networks


Ogumerem, G. S. - Presenter, Texas A&M University
Tso, W. W., Texas A&M University
Demirhan, C. D., Texas A&M University
Kim, C., Texas A&M University
Pistikopoulos, E., Texas A&M Energy Institute, Texas A&M University
As renewable energies gain more traction due to improving efficiencies and decreasing costs, there will be a gradual shift away from dependence on fossil fuels toward the deployment of carbon-neutral energy technologies. One such energy vector is hydrogen, which has enormous potential as a renewable fuel for vehicle mobility and electricity generation. However, its integration into the energy landscape is currently strained by the dearth of enabling infrastructure. Several options exist for the production, storage, distribution and retailing of hydrogen to end users, but it is unresolved how to develop this infrastructure given different short- and long-term technological and economic actualizations.

What is the most energy efficient, environmentally benign, and cost effective pathways to deliver hydrogen to the consumer considering these uncertainties? To answer this question, several groups have investigated multi-objective supply chain optimization for hydrogen infrastructural development [1-3]. A few have considered a multi-period approach [4-6], and even fewer have considered uncertainty in the supply chain [7, 8].

Here, we propose a multi-period and multi-objective mixed-integer programming approach to the optimal planning of hydrogen infrastructures. By incorporating recent advances in robust optimization [9] in the strategic planning model, we address uncertainty by designing robust hydrogen networks that are protected against different technological and economic realizations. In the United States, California is taking the lead with more than 20 hydrogen retailing stations spread across the state [9]. To demonstrate our method, we present a scenario analysis using California as a case study.

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2. Hugo, A., P. Rutter, S. Pistikopoulos, A. Amorelli, and G. Zoia, Hydrogen infrastructure strategic planning using multi-objective optimization. International Journal of Hydrogen Energy, 2005. 30(15): p. 1523-1534.

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4. Liu, P., A. Whitaker, E.N. Pistikopoulos, and Z. Li, A mixed-integer programming approach to strategic planning of chemical centres: A case study in the UK. Computers & Chemical Engineering, 2011. 35(8): p. 1359-1373.

5. Almansoori, A. and N. Shah, Design and operation of a future hydrogen supply chain: Multi-period model. International Journal of Hydrogen Energy, 2009. 34(19): p. 7883-7897.

6. Li, Z., D. Gao, L. Chang, P. Liu, and E.N. Pistikopoulos, Hydrogen infrastructure design and optimization: A case study of China. International Journal of Hydrogen Energy, 2008. 33(20): p. 5275-5286.

7. Guillén, G., F.D. Mele, M.J. Bagajewicz, A. Espuña, and L. Puigjaner, Multiobjective supply chain design under uncertainty. Chemical Engineering Science, 2005. 60(6): p. 1535-1553.

8. Almansoori, A. and N. Shah, Design and operation of a stochastic hydrogen supply chain network under demand uncertainty. International Journal of Hydrogen Energy, 2012. 37(5): p. 3965-3977.

9. Guzman, Y.A., L.R. Matthews, and C.A. Floudas. New a priori and a posteriori probabilistic bounds for robust counterpart optimization: I. Unknown probability distributions. Computers & Chemical Engineering, 2016. 84: p. 568-598.

10. EPA, C., Hydrogen Fuel Cell Electric Vehicle Deployment and Hydrogen Fuel Station Network Development, in AB 8, A.E. Report, Editor. 2016: California.