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

  • Olufolajimi OkeEmail author
  • Daniel Huppmann
  • Max Marshall
  • Ricky Poulton
  • Sauleh Siddiqui
Article

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.

Keywords

Crude-by-rail Energy networks Transportation Market equilibrium Mixed complementarity problem Infrastructure 

Glossary

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

Notes

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.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.International Institute of Applied Systems Analysis (IIASA)LaxenburgAustria
  3. 3.German Institute for Economic Research (DIW Berlin)BerlinGermany
  4. 4.Department of Applied Mathematics and StatisticsThe Johns Hopkins UniversityBaltimoreUSA
  5. 5.Center for Systems Science and EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  6. 6.Department of Civil and Environmental EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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