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Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary

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This study introduces a new method for electric vehicle (EV) rapid charging station (RCS) planning, considering energy losses. The proposed binary lightning search algorithm (BLSA) reduces daily costs by 10% compared to conventional methods.

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Area of Science:

  • Electrical Engineering
  • Environmental Science
  • Optimization Techniques

Background:

  • Electric vehicles (EVs) are crucial for mitigating global warming and pollution.
  • Efficient recharging infrastructure is essential for widespread EV adoption.
  • Existing rapid charging station (RCS) planning methods often overlook specific energy loss components.

Purpose of the Study:

  • To develop a novel approach for optimal rapid charging station (RCS) planning.
  • To incorporate transportation loss, buildup, substation energy loss, and harmonic power loss into the planning model.
  • To introduce and evaluate a new optimization technique, the binary lightning search algorithm (BLSA).

Main Methods:

  • A novel optimization technique, the binary lightning search algorithm (BLSA), was developed.
  • BLSA was applied to optimal RCS planning, considering various cost factors including harmonic power loss.
  • The proposed method was compared against a conventional RCS planning method using the IEEE 34-bus test system.

Main Results:

  • The binary lightning search algorithm (BLSA) demonstrated superior performance compared to other optimization techniques.
  • The proposed RCS planning method, utilizing BLSA, achieved a 10% reduction in daily total cost.
  • The inclusion of harmonic power loss in the planning model led to more comprehensive and cost-effective solutions.

Conclusions:

  • The proposed BLSA-based RCS planning approach is more effective than conventional methods.
  • Integrating harmonic power loss significantly improves the economic efficiency of EV charging infrastructure.
  • This research provides a valuable framework for optimizing the planning of future EV charging networks.