Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck
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.
Year of publication: |
2012
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Authors: | Su, Wencong ; Chow, Mo-Yuen |
Published in: |
Applied Energy. - Elsevier, ISSN 0306-2619. - Vol. 96.2012, C, p. 171-182
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Publisher: |
Elsevier |
Subject: | Plug-in Hybrid Electric Vehicle (PHEV) | Plug-in Electric Vehicle (PEV) | Electric Vehicle (EV) | Smart Grid | Estimation of Distribution Algorithm (EDA) | Particle Swarm Optimization (PSO) |
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