Algorithm for Transportation Pathways and Patterns Through Pipeline Network: A Case Study in California for the Power Generation Sector

Zemin Eitan Liu; Diego Moya; Zhenlin Chen; Wennan Long; Liang Jing; Bo Ren; Haoyu Tang; Muhammad Y. Jabbar; Farah Ramadan; James Littlefield; Mohammad S. Masnadi

doi:10.1016/j.eng.2025.11.005

Engineering ›› :202511005 DOI: 10.1016/j.eng.2025.11.005

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Algorithm for Transportation Pathways and Patterns Through Pipeline Network: A Case Study in California for the Power Generation Sector

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Abstract

Decarbonization and energy transition in modern energy systems require integrated tools that can analyze complex supply pathways, optimize infrastructure choices, and evaluate policy impacts. This study presents the algorithm for transportation pathways and patterns through pipeline network (ATP3), an open-source framework combining high-resolution supply-chain traceability, cost- and emissions-aware network optimization, and scenario simulation. ATP3 reconstructs real-world flows by matching supply and demand nodes across an entire pipeline network, integrating the entropy weight method (EWM)-based allocation and a minimum-cost flow formulation with scenario-driven computational simplification. In a California power generation case study, ATP3 accurately identified 154 processing-to-plant supply routes and 134 upstream field linkages. In addition, when using the EWM-based 40% baseline allocation, the model determined that 90.53% of natural gas used for electricity was imported—primarily from Arizona (48.66%), Oregon (27.43%), and Nevada (14.44%)—with Arizona supplying the largest single external volume (1034.51 MMscf d⁻¹, here MMscf is million standard cubic feet, and 1 MMscf ≈ 28 317 m³). In-state production accounted for only 201.37 MMscf d⁻¹ (9.47%). Meanwhile, EWM allocation reduced transportation costs by 2.76% compared to uniform allocation by favoring geographically proximate sources. These results demonstrate the ability of ATP3 to bridge granular infrastructure mapping with system-level planning, offering a robust and versatile platform for life-cycle assessment, infrastructure planning, and policy evaluation across power, transportation, and industrial sectors. The continuously updated resource is available via the GitHub repository.

Keywords

Transmission pipeline network / Natural gas / Energy transmission / Pathway planning and optimization / Geospatial supply-chain traceability / Power generation

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Zemin Eitan Liu, Diego Moya, Zhenlin Chen, Wennan Long, Liang Jing, Bo Ren, Haoyu Tang, Muhammad Y. Jabbar, Farah Ramadan, James Littlefield, Mohammad S. Masnadi. Algorithm for Transportation Pathways and Patterns Through Pipeline Network: A Case Study in California for the Power Generation Sector. Engineering 202511005 DOI:10.1016/j.eng.2025.11.005

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References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Littlefield J, Rai S, Skone TJ. Life cycle GHG perspective on US natural gas delivery pathways. Environ Sci Technol 2022; 56(22):16033-42.

[2]	Liu ZE, Long W, Chen Z, Littlefield J, Jing L, Ren B, et al. A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning. Energy AI 2024; 18:100434.

[3]	Saad DM, Sodwatana M, Sherwin ED, Brandt AR. Energy storage in combined gas-electric energy transitions models: the case of California. Appl Energy 2025; 385:125480.

[4]	Lyu M, Zhang C, Bao X, Song J, Liu Z. Effects of the substitution rate of natural gas on the combustion and emission characteristics in a dual-fuel engine under full load. Adv Mech Eng 2017; 9(12):1-8.

[5]	Burns D, Grubert E. Attribution of production-stage methane emissions to assess spatial variability in the climate intensity of US natural gas consumption. Environ Res Lett 2021; 16(4):044059.

[6]	Lochran S. GNOME: a dynamic dispatch and investment optimisation model of the European natural gas network and its suppliers. Oper Res Forum 2021; 2:67.

[7]	Guo Z, Liu Z, Shuai S. Evolution and future development of vehicle fuel specification in China. Report. Warrendale: SAE Technical Paper; 2021.

[8]	Welder L, Ryberg DS, Kotzur L, Grube T, Robinius M, Stolten D. Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany. Energy 2018; 158:1130-49.

[9]	Welder L, Stenzel P, Ebersbach N, Markewitz P, Robinius M, Emonts B, et al. Design and evaluation of hydrogen electricity reconversion pathways in national energy systems using spatially and temporally resolved energy system optimization. Int J Hydrogen Energy 2019; 44(19):9594-607.

[10]	Samsatli S, Staffell I, Samsatli NJ. Optimal design and operation of integrated wind-hydrogen-electricity networks for decarbonising the domestic transport sector in great Britain. Int J Hydrogen Energy 2016; 41(1):447-75.

[11]	Moreno-Benito M, Agnolucci P, Papageorgiou LG. Towards a sustainable hydrogen economy: optimisation-based framework for hydrogen infrastructure development. Comput Chem Eng 2017; 102:110-27.

[12]	Roman-White SA, Mallikarjuna Prasanna D, McCullagh A, Ravikumar AP, Allen DT, Chivukula K, et al. Gas pathing: improved greenhouse gas emission estimates of liquefied natural gas exports through enhanced supply chain resolution. ACS Sustain Chem Eng 2024; 12(46):16956-66.

[13]	Gabriel SA, Kydes AS, Whitman P. The national energy modeling system: a large-scale energy-economic equilibrium model. Oper Res 2001; 49(1):14-25.

[14]	Müller-Kirchenbauer J, Ragwitz M, Kneiske T, Klaassen B, Mielich T, Herrmann U. A network modeling systematics for transition paths toward climate neutral gas networks—NeMoSys. Energy Technol 2025; 13(2):2300977.

[15]	Di Lullo G, Oni AO, Gemechu E, Kumar A. Developing a greenhouse gas life cycle assessment framework for natural gas transmission pipelines. J Nat Gas Sci Eng 2020; 75:103136.

[16]	2022 California gas report. Report. Los Angeles: SoCalGas; 2022.

[17]	Seebregts AJ, Goldstein GA, Smekens K. Energy/environmental modeling with the MARKAL family of models. In: Operations Research Proceedings 2001; 2001 Sep 3-5; Duisburg, Germany. Springer; 2001. p. 75-82.

[18]	Calvin K, Patel P, Clarke L, Asrar G, Bond-Lamberty B, Cui RY, et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci Model Dev 2019; 12(2):677-98.

[19]	Capros P, Kannavou M, Evangelopoulou S, Petropoulos A, Siskos P, Tasios N, et al. Outlook of the EU energy system up to 2050: the case of scenarios prepared for European Commission’s “clean energy for all Europeans” package using the PRIMES model. Energ Strat Rev 2018; 22:255-63.

[20]	Sakellaris K, Canton J, Zafeiratou E, Fournié L. METIS-an energy modelling tool to support transparent policy making. Energ Strat Rev 2018; 22:127-35.

[21]	Mintz M, Elgowainy A, Gillette J, Paster M, Ringer M, Brown D, et al. HDSAM 2.0:expanded capabilities, enhanced results in hydrogen delivery modeling. In: Proceedings of The NHA Annual Hydrogen Conference 2008; 2008 Apr 1; Sacramento, CA, USA. National Hydrogen Association; 2008.

[22]	Ulvestad M, Overland I. Natural gas and CO₂ price variation: impact on the relative cost-efficiency of LNG and pipelines. Int J Environ Stud 2012; 69(3):407-26.

[23]	Greene S, Jia H, Rubio-Domingo G. Well-to-tank carbon emissions from crude oil maritime transportation. Transp Res Part D: Transp Environ 2020; 88:102587.

[24]	Zubair M, Chen S, Ma Y, Hu X. A systematic review on carbon dioxide (CO₂) emission measurement methods under PRISMA guidelines: transportation sustainability and development programs. Sustainability 2023; 15(6):4817.

[25]	Winarno E, Hadikurniawati W, Rosso RN. Location based service for presence system using haversine method. In: Proceedings of 2017 International Conference on Innovative and Creative Information Technology (ICITech); 2017 Nov 2-4; Salatiga, Indonesia. IEEE; 2017. p. 1-4.

[26]	Wang G, Cheng Q, Zhao W, Liao Q, Zhang H. Review on the transport capacity management of oil and gas pipeline network: challenges and opportunities of future pipeline transport. Energ Strat Rev 2022; 43:100933.

[27]	Boyles SD, Lownes NE, Unnikrishnan A. Transportation network analysis. Volume I: static and dynamic traffic assignment 2020. 2025. arXiv:2502.05182.

[28]	Ahuja RK, Magnanti TL, Orlin JB. Network flows. Kent: Prentice Hall; 1988.

[29]	Dijkstra EW. A note on two problems in connexion with graphs. In: Apt KR, Hoare T, editors. Edsger wybe dijkstra: his life, work, and legacy. New York City: ACM; 2022. p. 287-90.

[30]	Chvátal V. Linear programming. London: Macmillan; 1983.

[31]	Background information on the structures and functioning of the natural gas production and delivery systems that serve California and the US 2006. Report. Sacramento: California Energy Commission; San Francisco: California Public Utilities Commission; 2006.

[32]	Gill L, Gutierrez A, Weeks T. 2021 SB 100 joint agency report: Achieving 100 percent clean electricity in California, an initial assessment. Report. Sacramento: California Energy Commission; 2021.

[33]	Power plants. HIFLD; 2022.

[34]	Natural gas processing plant survey 2017. Report. Washington, DC: US Energy Information Administration; 2017.

[35]	El-Houjeiri HM, Brandt AR, Duffy JE. Open-source LCA tool for estimating greenhouse gas emissions from crude oil production using field characteristics. Environ Sci Technol 2013; 47(11):5998-6006.

[36]	Chen Z, Zhong R, Long W, Tang H, Wang A, Liu Z, et al. Advancing oil and gas emissions assessment through large language model data extraction. Energy AI 2025; 20:100481.

[37]	Chen Z, Zhong R, Long W, Tang H, Wang A, Liu Z, et al. AI-driven environmental data extraction for energy sector assessment. In: Proceedings of SPE Annual Technical Conference and Exhibition; 2024 Sep 23-25; New Orleans, LA, USA. OnePetro; 2024.

[38]	Innovative data energy applications 2015. Report. Golden: National Renewable Energy Laboratory; 2015.

[39]	Masnadi MS, Long W, Jing L, Liu ZE, Jabbar MY, Chen Z, et al. Global carbon intensity of LNG value chain amid intensifying energy security and climate trade-offs. Res Square 2025.

[40]	California Public Utilities Commission (CPUC). Natural gas and California [Internet]. San Francisco: California Public Utilities Commission (CPUC); [cited 2025 Aug 12]. Available from: https://www.cpuc.ca.gov/industries-and-topics/natural-gas/natural-gas-and-california.