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Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems

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Abstract

The transportation and electric sectors are by far the largest producers of greenhouse emissions in the United States while they consume a significant amount of the national energy. The ever rising demand for these systems, the growing public concern on issues like global warming or national security, along with emerging technologies that promise great synergies between both (plug-in hybrid vehicles or electrified rail), creates the necessity for a new framework for long-term planning. This paper presents a comprehensive methodology to investigate long-term investment portfolios of these two infrastructures and their interdependencies. Its multiobjective nature, based on the NSGA-II evolutionary algorithm, assures the discovery of the Pareto front of solutions in terms of cost, sustainability and resiliency. The optimization is driven by a cost-minimization network flow program which is modified in order to explore the solution space. The modular design enables the use of metrics to evaluate sustainability and resiliency and better characterize the objectives that the systems must meet. An index is presented to robustly meet long-term emission reduction goals. An example of a high level representation of the continental United States through 2050 is presented and analyzed using the present methodology.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50011, USA

    Eduardo Ibanez & James D. McCalley

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  1. Eduardo Ibanez
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  2. James D. McCalley
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Correspondence to Eduardo Ibanez.

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This work was supported in part by the US National Science Foundation under Award 0835989.

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Ibanez, E., McCalley, J.D. Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems. Energy Syst 2, 151–169 (2011). https://doi.org/10.1007/s12667-011-0031-z

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