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An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables

Rahmani Andebili, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TII.2017.2761336
  3. Abstract:
  4. An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors
  5. Keywords:
  6. And renewable energies ; Charging management (CHM) ; distribution system reconfiguration (DSR) ; Mathematical model ; Meters ; Optimization ; plug-in electric vehicles (PEV) ; power loss ; Stochastic processes ; Charging (batteries) ; Cost reduction ; Electric losses ; Electric power distribution ; Electric vehicles ; Instruments ; Linear programming ; Mathematical models ; Model predictive control ; Predictive control systems ; Random processes ; Renewable energy resources ; Stochastic control systems ; Stochastic models ; Stochastic systems ; Vehicles ; Charging managements ; Distribution system reconfiguration ; Plug in electric vehicle (PEV) ; Power-losses ; Renewable energies ; Uncertainty ; Plug-in electric vehicles
  7. Source: IEEE Transactions on Industrial Informatics ; 2017 ; 15513203 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/8064677