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Smart charge-optimizer: Intelligent electric vehicle charging and discharging.

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Electric Vehicles (EVs) can strain the electrical grid. This study introduces two intelligent charging strategies, LSTM-ILP and Q-learning, to manage EV charging and discharging effectively, reducing grid load and user costs.

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Area of Science:

  • Electrical Engineering
  • Sustainable Energy Systems
  • Artificial Intelligence

Background:

  • The widespread adoption of Electric Vehicles (EVs) is crucial for a low-carbon economy but poses risks to electrical grid reliability due to high electricity consumption.
  • Overloading of power grid transformers is a significant concern associated with the rapid development of EVs.
  • Effective charging and discharging scheduling strategies are essential to mitigate the negative impacts of EVs on the power grid.

Purpose of the Study:

  • To explore and evaluate two intelligent strategies for scheduling EV charging and discharging to reduce the overload of power grid transformers.
  • To minimize the peak-to-average ratio of the grid load through peak shaving and valley filling.
  • To reduce EV charging costs for users while ensuring their mobility needs are met.

Main Methods:

  • Coupling Long Short-Term Memory (LSTM) with Integer Linear Programming (ILP) to optimize charging and discharging schedules, focusing on minimizing delays.
  • Applying Q-learning, a reinforcement learning technique, to determine optimal EV charging/discharging actions based on state-of-charge and grid demand.
  • Utilizing intelligent scheduling to manage the integration of EVs into the electrical system.

Main Results:

  • Both the LSTM-ILP and Q-learning strategies demonstrated success in reducing the peak-to-average ratio of the grid load.
  • The implemented strategies effectively lessened the influence of EV charging demands on the electrical grid.
  • The research validated the capability of intelligent scheduling to balance grid stability and user charging needs.

Conclusions:

  • Intelligent charging and discharging scheduling strategies, such as LSTM-ILP and Q-learning, are effective in managing the grid impact of Electric Vehicles.
  • These methods contribute to a more reliable and sustainable energy future by optimizing EV integration.
  • The research provides a viable approach to minimize grid strain and reduce EV user charging costs simultaneously.