(313b) Design and Operation Planning for Delivery and Storage of Hydrogen Gas Via Pipelines

Nations around the world have set ambitious goals for decarbonization, with the U.S. targeting zero emissions by 2050 [1]. Meeting this goal requires, among others, increasing the contribution of renewable generation of electricity and heat, reducing renewable curtailments, and replacing carbon feedstock across the power, industrial, and transportation sectors. In this context, using fungible carbon-free energy carriers such as hydrogen and ammonia, gains prominence. These carriers present multiple benefits; to name a few, they allow for storing energy in a more cost-effective manner than, e.g. batteries, absorbing fluctuations in renewable power generation, [2] and can connect energy supply and demand (traditional and new hydrogen users) across vast geographic areas.

The use of such energy carriers does, however, encounter several practical barriers, including developing the means to produce them economically in a distributed fashion (e.g., at locations where renewable energy is abundant), establishing the technologies to utilize them in place of current energy sources (for transportation, industrial and residential heat, and power generation, etc.), and establishing the infrastructure for conveying them from the source to the points of use.

Developing this infrastructure involves considerable effort and expense. This calls for an optimization approach where the decisions involve tradeoffs between capital and operating cost while meeting a (stochastic, but likely increasing) material demand. Focusing specifically on hydrogen infrastructure, the available design tools (e.g., HDSAM, H2A) are in relatively incipient stages [3][4]. The models capture the physical phenomena (compressible flow of gas) and related costs (compressor stations, pipelines) in procedural formats, that are not amenable to optimization calculations. Optimization models exist for e.g., natural gas pipelines, but are focused on system operations and control, rather than design [5][6].

Motivated by the above, in this work we present a novel modeling and optimization framework for designing hydrogen pipelines. We adopt a bottom-up approach, whereby the fundamental physics are captured along with a representation of the unique physical properties of hydrogen and hydrogen-natural gas blends. A modular structure allows for customizable construction of representations of large-scale pipeline networks. We capture the costs of relevant units (compressors, pipeline segments). An equation-oriented representation is used, which allows us to use a standard process simulator (gPROMS in this case) to simulate the model and solve the relevant optimization problems. Several case studies on developing the hydrogen infrastructure in Texas are presented, focusing on minimizing the levelized cost of hydrogen (LCOH).

References:

[1] Cresko, Joe, et al. U.S. Department of Energy’s Industrial Decarbonization Roadmap, 1 Sept. 2022, https://doi.org/10.2172/1961393.

[2] Denholm, Paul, and Trieu Mai. “Timescales of energy storage needed for reducing renewable energy curtailment.” Renewable Energy, vol. 130, Jan. 2019, pp. 388–399, https://doi.org/10.1016/j.renene.2018.06.079.

[3] Elgowainy, Amgad, and Krishna Reddi. Hydrogen Delivery Scenario Analysis Model (HDSAM) V4.5, Argonne National Laboratory, 9 Feb. 2024, hdsam.es.anl.gov/downloadCounter/click.php?id=6.

[4] H2A: Hydrogen Analysis Production Models, NREL, Nov. 2022, www.nrel.gov/hydrogen/h2a-production-models.html.

[5] Jalving, Jordan, and Victor M. Zavala. “An optimization-based state estimation framework for large-scale natural gas networks.” Industrial & Engineering Chemistry Research, vol. 57, no. 17, 13 Mar. 2018, pp. 5966–5979, https://doi.org/10.1021/acs.iecr.7b04124.

[6] Liu, Kai, et al. “Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty.” Chemical Engineering Science, vol. 215, Apr. 2020, p. 115449, https://doi.org/10.1016/j.ces.2019.115449.