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Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck

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  • Su, Wencong
  • Chow, Mo-Yuen

Abstract

There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques.

Suggested Citation

  • Su, Wencong & Chow, Mo-Yuen, 2012. "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, Elsevier, vol. 96(C), pages 171-182.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:171-182
    DOI: 10.1016/j.apenergy.2011.11.088
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    References listed on IDEAS

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    1. BŠrbel M. R. Stadler & Peter F. Stadler & Max Shpak & GŸnter P. Wagner, 2001. "Recombination Spaces, Metrics, and Pretopologies," Working Papers 01-02-011, Santa Fe Institute.
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