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Multimodal Transportation Flows in Energy Networks with an Application to Crude Oil Markets

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Abstract

Network models of energy markets have been beneficial for analyses and decision-making to tackle challenges related to the production, distribution and consumption of energy in its various forms. Despite the growing awareness of environmental and safety impacts of fuel transfer, such as emissions, spills and other harmful effects, existing energy models for various types of networks are yet to fully capture modal distinctions which are relevant to providing pathways to limiting these impacts. To address this deficit in detailed multimodal analyses, we have built on recent work to develop a partial-equilibrium model that incorporates the representation of multimodal fuel transfer within energy networks. In a novel application to the North American crude oil market, we also demonstrate that our model is a useful tool for exploring avenues for reducing the risks of light and heavy crude oil transportation across this region. The results we obtain indicate that a combined strategy of rail loading restrictions, pipeline deployments and a discontinuation of the oil export ban is most effective in reducing the transportation of crude oil by rail and thereby mitigating the associated risks.

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Notes

  1. General Algebraic Modeling Systems (GAMS), release 23.9.5; GAMS Development Corporation, https://www.gams.com/.

  2. We calculate end-use costs as in Huppmann and Egging (2014).

  3. Kilian (2014) provides a comprehensive background on the effects of this “shale revolution” on prices and infrastructure in the US.

  4. National Energy Board, “Canadian crude oil exports by rail—quarterly data”: https://www.neb-one.gc.ca/nrg/sttstc/crdlndptrlmprdct/stt/cndncrdlxprtsrl-eng.html.

  5. US Energy Information Administration, “Petroleum & other liquids”: http://www.eia.gov/petroleum/data.cfm.

  6. Canada’s National Energy Board: https://www.neb-one.gc.ca/nrg/sttstc/crdlndptrlmprdct/index-eng.html.

  7. Canadian Association of Petroleum Producers, http://www.capp.ca/publications-and-statistics/crude-oil-forecast

  8. US Energy Information Administration, International Energy Statistics: http://tinyurl.com/gp3tb6b.

  9. A history and map of the PADD system are available at https://www.eia.gov/todayinenergy/detail.cfm?id=4890.

  10. Since 2013, Pemex has been in transition to involve private participation for better performance in the industry (Zamora 2014).

  11. Information on refining capacities from the Canadian Fuels Association: http://www.canadianfuels.ca/The-Fuels-Industry/Fuel-Production/.

  12. These include: RBN Energy, Hart Energy, Genscape, BNSF, Canadian Pacific, Canadian National, Meritage Midstream, Howard Energy Partners, and Rangeland Energy

  13. North American crude-by-rail data available from Oil Change International at http://priceofoil.org/rail-map/.

  14. Pipeline delivery statistics are available from the 2014 and 2015 “US Liquids Pipeline Usage & Mileage Report” published by AOPL/API (Association of Oil Pipelines/American Petroleum Institute) at http://www.aopl.org/news-public-policy/reports-2/.

  15. These data were sourced from the Canadian Association of Petroleum Producers (CAPP, http://www.capp.ca/publications-and-statistics/crude-oil-forecast) and a compilation by P. Coutsoukis (http://www.theodora.com/pipelines/north_america_oil_gas_and_products_pipelines.html).

  16. Some of the major systems and pipeline operators include: Colonial, Enbridge/Lakehead, Keystone, Marathon, Mid-Valley, Pony Express, Seaway, Spearhead, and TransCanada.

  17. See G. Collins, “California crude trains: How much oil is actually coming in and where is it coming from?” North America Shale Blog, 2015 (http://bit.ly/1HpU4El).

  18. This “Final Rule”—“Hazardous Materials: Enhanced Tank Car Standards and Operational Controls for High-Hazard Flammable Trains”—was developed in collaboration with the Pipeline and Hazardous Safety Administration in 2014. Available at http://federalregister.gov/r/2137-AE91(Federal Register).

  19. See 2015 reports on the “oil train rules” by J. Mouawad at http://nyti.ms/1bmdr6G and http://nyti.ms/1AVFv7V.

  20. See New York Times report by C. Davenport, 2015, at http://nyti.ms/1MN5hpL.

  21. A description of the Energy East pipeline project is available from the NEB at http://bit.ly/1kBcNr1.

  22. See CBC News article, “Hydro-Québec raises concerns about Energy East pipeline,” 2015 (http://bit.ly/1T5qgEa).

  23. See 2015 Bismark Tribune article by N. Smith at http://bit.ly/1jrcbEq

  24. See Bakken Magazine article for more information regarding the Dakota Access pipeline approval at http://bit.ly/1S8z8Gt.

  25. See CBC News report: B. Nicholson, J. MacPherson, “TransCanada to seek US approval for $600M Upland pipeline,” 2015, at http://bit.ly/1VqYuGb.

  26. J. Bordoff presented data suggesting that strong opinions against oil exports still persisted among the general public in 2014 (https://www.eia.gov/conference/2014/pdf/presentations/bordoff.pdf).

  27. For a brief context on Alaska oil shipments, refer to J. A. Dlouhy’s post at http://bit.ly/1WsVhou

  28. See Los Angeles Times report by M. Muskal, 2014, at http://fw.to/GFJiL7J

  29. Details on this plan were reported by B. House et al., “Pelosi, White House support plan allowing US crude oil exports,” Bloomberg Politics, 2015. http://bloom.bg/1P69q61.

  30. See Wall Street Journal report: K. Peterson, “Congress passes $1.15 trillion spending bill,” 2015 (http://on.wsj.com/1OcL9hq)

  31. Report, “Refining US Petroleum” (2015) available at https://www.afpm.org/uploadedFiles/Refining-US-Capacity.pdf.

  32. D. Murtaugh reports on current oil movement trends at LOOP in the Bloomberg article at http://bloom.bg/1zUyB7q.

Abbreviations

API:

American Petroleum Institute

CAPP:

Canadian Association of Petroleum Producers

EIA:

Energy Information Administration (United States)

kbpd:

thousand barrels per day

kbbl:

thousand barrels

MCP:

Mixed Complementarity Problem

mbpd:

million barrels per day

NACOM:

North American Crude Oil Model

NEB:

National Energy Board (Canada)

OPEC:

Organization of the Petroleum Exporting Countries

PADD:

Petroleum Administration Defense District

Pemex:

Petróleos Mexicanos

RW:

Rest of the World (excluding North America)

US:

United States

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Acknowledgments

This work was partially funded by the Gordon Croft Fellowship awarded by the Energy, Environment, Sustainability and Health Institute (E2SHI) at The Johns Hopkins University. Further support was also due to NSF Grant #1745375 [EAGER: SSDIM: Generating Synthetic Data on Interdependent Food, Energy, and Transportation Networks via Stochastic, Bi-level Optimization]. For their valuable comments and suggestions, we thank: Lissy Langer (TU Berlin), Gary Lin and Dr. Felipe Feijoo (Johns Hopkins Center for Systems Science and Engineering). We are also grateful to David Livingston and Eugene Tan (Carnegie Endowment for International Peace) for their time and insightful conversations.

The model in this article is based in part on the multi-fuel energy equilibrium model, MultiMod (Huppmann et al. 2015), developed by Dr. Daniel Huppmann at DIW Berlin as part of the RESOURCES project, in collaboration with Dr. Ruud Egging (NTNU, Trondheim), Dr. Franziska Holz (DIW Berlin) and others (see http://diw.de/multimod). We are grateful to the original developers of MultiMod for sharing their model, which we further extended as part of this work.

Finally, we would like to thank our anonymous reviewers whose critique and recommendations improved the impact and quality of our manuscript.

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Author notes

  1. Olufolajimi Oke

    Present address: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

Authors and Affiliations

  1. Department of Civil Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Olufolajimi Oke, Max Marshall, Ricky Poulton & Sauleh Siddiqui

  2. International Institute of Applied Systems Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria

    Daniel Huppmann

  3. German Institute for Economic Research (DIW Berlin), Mohrenstraße 58, 10117, Berlin, Germany

    Daniel Huppmann

  4. Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Sauleh Siddiqui

  5. Center for Systems Science and Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Olufolajimi Oke, Daniel Huppmann & Sauleh Siddiqui

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Appendix A: Supplementary material

Appendix A: Supplementary material

A complete enumeration of the nodes and arcs, along with the flow calibration details of NACOM are provided in the Supplementary Material document available at https://github.com/MODLJHU/nacom. Data and processing code are also available for download at this location.

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Oke, O., Huppmann, D., Marshall, M. et al. Multimodal Transportation Flows in Energy Networks with an Application to Crude Oil Markets. Netw Spat Econ 19, 521–555 (2019). https://doi.org/10.1007/s11067-018-9387-0

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