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Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage

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This study introduces a deep reinforcement learning (DRL) method to optimize electric vehicle (EV) charging and distribution network (DN) voltage control simultaneously. The approach effectively manages EV charging schedules and voltage stability despite uncertainties in EV behavior and grid conditions.

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • The proliferation of electric vehicles (EVs) presents significant challenges to the stability and operational efficiency of distribution networks (DNs) due to increased charging demands.
  • Coordinating EV charging schedules with DN voltage control is complex due to inherent uncertainties in EV user behavior, grid loads, energy prices, and renewable energy generation.

Purpose of the Study:

  • To develop a robust deep reinforcement learning (DRL) framework for the collaborative optimization of EV charging scheduling and DN voltage control.
  • To address the complexities arising from uncertainties in EV charging patterns and DN operational parameters.

Main Methods:

  • A two-layer deep reinforcement learning (DRL) strategy is proposed, employing a Markov decision process (MDP) model.
  • The upper layer optimizes operating costs for distributed generators and EV charging, while the lower layer manages Volt/Var devices for voltage stability.
  • A deep deterministic policy gradient (DDPG) framework is utilized to train the dual-layer agent, accommodating dynamic state spaces and mixed discrete/continuous action outputs.

Main Results:

  • The proposed DRL approach effectively coordinates EV charging scheduling and distribution network voltage control.
  • Simulations on the IEEE-33 bus test system confirm the method's efficacy in enhancing both EV charging management and grid voltage stabilization.
  • The strategy successfully mitigates operational costs and maintains voltage stability under various uncertain conditions.

Conclusions:

  • The developed DRL-based method provides an effective solution for the intricate problem of collaborative EV charging and DN voltage control.
  • This approach demonstrates significant potential for improving the reliability and efficiency of power distribution systems with high EV penetration.
  • The findings highlight the capability of DRL in handling complex, uncertain, and dynamic optimization problems in smart grids.