Model Predictive Control-Based Bidirectional EV Charging Strategy for Grid Load Shaping and Cost Optimization in Smart Grids

Authors

  • K P Uvarajan Department of Electronics and Communication Engineering, KSR College of Engineering, Tiruchengode
  • Rajan.C K S Rangasamy College of Technology

Keywords:

Bidirectional Charging, Vehicle-to-Grid (V2G), Model Predictive Control (MPC), Smart Grids, Peak Load Reduction, Battery Degradation, Charging Optimization

Abstract

Adding electric vehicles (EVs) to smart grids brings issues with handling peak energy demand and setting flexible rates. This study introduces a Model Predictive Control (MPC) framework that helps you locate the best time slots for charging and discharging your EV using V2G technology. To optimize energy flow, the controller looks ahead to forecast the grid’s load and predicts electricity prices and then adjusts charging and discharging times based on user requirements and the state of the battery.

Computer simulations done with MATLAB/Simulink on a fleet of 20 EVs under Time-of-Use pricing conclude that the strategy reduces the peak demand on the grid by 28% and total energy costs by 27.4%, with no EVs falling below a 100% charge level. How stable the grid was improved significantly. This suggests that MPC can ensure EVs are plugged in intelligently, lead to savings and keep the grid stableso MPC is appropriate for use in highly electric vehicle-oriented grids.

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Published

2024-12-25

How to Cite

[1]
K P Uvarajan and Rajan.C, “Model Predictive Control-Based Bidirectional EV Charging Strategy for Grid Load Shaping and Cost Optimization in Smart Grids”, Electronics Communications, and Computing Summit, vol. 2, no. 4, pp. 86–91, Dec. 2024.

Issue

Vol. 2 No. 4 (2024): Electronics, Communications, and Computing Summit -Winter 2024 (Oct-Dec)

Section

Submission

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.