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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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A robust MPPT framework based on GWO-ANFIS controller for grid-tied EV charging stations.

Debabrata Mazumdar1, Pabitra Kumar Biswas1, Chiranjit Sain2

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This study introduces an enhanced grey wolf optimized ANFIS controller for multi-energy integrated electric vehicle charging stations. This approach optimizes charging schedules for dependability, efficiency, and sustainability.

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Transportation Systems

Background:

  • Growing popularity of electric vehicles (EVs) necessitates advancements in charging infrastructure.
  • Current charging solutions reliant on fossil fuels are inadequate for climate goals.
  • Integrating renewable energy sources is crucial for sustainable EV charging.

Purpose of the Study:

  • To propose an energy-efficient charging terminal for electric vehicles.
  • To enhance the dependability, environmental advantages, and charging efficiency of EVs.
  • To address the integration challenges between transportation and power networks for EV charging.

Main Methods:

  • Development of an enhanced grey wolf optimized (GWO) adaptive neuro-fuzzy inference system (ANFIS) controller.
  • Integration of Maximum Power Point Tracking (MPPT), standby battery systems, and solar power.
  • Utilization of neural network-integrated grids and Proportional-Integral-Derivative (PID) controllers.
  • Simulation and assessment using MATLAB/Simulink 2018a software with four case studies.

Main Results:

  • Demonstration of a viable route for efficient and sustainable EV charging infrastructure.
  • Validation of the proposed controller's effectiveness in managing multi-energy sources.
  • Assessment of the system's performance under various conditional scenarios.
  • Confirmation of improved charging efficiency and dependability for electric vehicles.

Conclusions:

  • The proposed multi-energy integrated EV charging station offers a sustainable solution.
  • The enhanced GWO-ANFIS controller effectively manages renewable energy integration and EV charging.
  • The developed system provides a promising framework for future EV charging infrastructure.