4.7 Article

Optimal decentralized valley-filling charging strategy for electric vehicles

Journal

ENERGY CONVERSION AND MANAGEMENT
Volume 78, Issue -, Pages 537-550

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2013.11.011

Keywords

Electric vehicle; Valley-filling; Autonomous charging strategy; Smart grid; Charge pattern

Funding

  1. National Natural Science of China (NSFC) [51275264]
  2. Ministry of Science and Technology (MOST) of China [2010DFA72760]
  3. Tsinghua University Initiative Scientific Research Program [2010THZ08116]

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Uncoordinated charging load of electric vehicles (EVs) increases the peak load of the power grid, thereby increasing the cost of electricity generation. The valley-filling charging scenario offers a cheaper alternative. This study proposes a novel decentralized valley-filling charging strategy, in which a day-ahead pricing scheme is designed by solving a minimum-cost optimization problem. The pricing scheme can be broadcasted to EV owners, and the individual charging behaviors can be indirectly coordinated. EV owners respond to the pricing scheme by autonomously optimizing their individual charge patterns. This device-level response induces a valley-filling effect in the grid at the system level. The proposed strategy offers three advantages: coordination (by the valley-filling effect), practicality (no requirement for a bidirectional communication/control network between the grid and EV owners), and autonomy (user control of EV charge patterns). The proposed strategy is validated in simulations of typical scenarios in Beijing, China. According to the results, the strategy (I) effectively achieves the valley-filling charging effect at 28% less generation cost than the uncoordinated charging strategy, (2) is robust to several potential affecters of the valley-filling effect, such as (system-level) inaccurate parameter estimation and (device-level) response capability and willingness (which cause less than 2% deviation in the minimal generation cost), and (3) is compatible with device-level multi-objective charging optimization algorithms. (C) 2013 Elsevier Ltd. All rights reserved.

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