CTDE-MADDPG: V2G scheduling and capacity-constrained energy sharing in bidirectional renewable-assisted charging clusters along intercity highways

This publication has 30 references indexed in Scilit:

• An intelligent control strategy considering power balance, operating-costs and user-demands in multi-microgrids with V2G
  Electric Power Systems Research, 2025
• A comparative study of multi-objective and neuroevolutionary-based reinforcement learning algorithms for optimizing electric vehicle charging and load management
  Applied Energy, 2025
• Reinforcement learning-based multi-objective smart energy management for electric vehicle charging stations with priority scheduling
  Energy, 2025
• Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service
  Energy, 2024
• A deep reinforcement learning-based charging scheduling approach with augmented Lagrangian for electric vehicles
  Applied Energy, 2024
• A multi-agent deep reinforcement learning paradigm to improve the robustness and resilience of grid connected electric vehicle charging stations against the destructive effects of cyber-attacks
  Energy, 2024
• Bi-level planning of electric vehicle charging station in coupled distribution-transportation networks
  Electric Power Systems Research, 2024
• Unlocking the potential of smart EV charging: A user-oriented control system based on Deep Reinforcement Learning
  Electric Power Systems Research, 2024
• Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model
  Electric Power Systems Research, 2023
• Distributed real-time economic dispatch for islanded microgrids with dynamic power demand
  Applied Energy, 2023