Sitting and sizing of aggregator controlled park for plug-in hybrid electric vehicle based on particle swarm optimization (original) (raw)

Abstract

Environmental constraints, high and unstable fuel prices, limitation on fuel resources have led to emergence of Plug-in Hybrid Electric Vehicles (PHEVs). In order to launch the regulation service for grid-use of electric-drive vehicles, a smart control interface called an aggregator between the grid and the vehicles has been developed. In this paper, a particle swarm optimization (PSO), as well as its modified version (MPSO) based approach is presented for optimal sitting and sizing of aggregator controlled public car park for vehicle fleets in modern power system, which is convenient to the optimal charger control of PHEVs. The optimal location and sizing is calculated by minimizing the power loss and voltage deviations. The proposed approach is tested on IEEE 14 bus system.

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Acknowledgments

This work was supported in part by the National Science Foundation of China (grant no. 61005090, 61034004, 91024023, 61075064), the Ph.D. Programs Foundation of Ministry of Education of China (grant no. 20100072110038), and the Program for New Century Excellent Talents in University of Ministry of Education of China.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Key Lab of Embedded System and Computer-Service, MOE, Tongji University, 201804, Shanghai, China
    Tian Lan, Qi Kang, Jing An & Lei Wang
  2. School of Electrical and Electronic Engineering, Shanghai Institute of Technology, 201418, Shanghai, China
    Jing An
  3. Shanghai Research Institute of MicroElectronics, Peking University, 210203, Shanghai, China
    Wei Yan
  4. School of Software and Microelectronics, Peking University, 100871, Beijing, China
    Wei Yan

Authors

  1. Tian Lan
  2. Qi Kang
  3. Jing An
  4. Wei Yan
  5. Lei Wang

Corresponding author

Correspondence toQi Kang.

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Lan, T., Kang, Q., An, J. et al. Sitting and sizing of aggregator controlled park for plug-in hybrid electric vehicle based on particle swarm optimization.Neural Comput & Applic 22, 249–257 (2013). https://doi.org/10.1007/s00521-011-0687-2

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