Safe-AutoSAC: AutoML-enhanced safe deep reinforcement learning for integrated energy system scheduling with multi-channel informer forecasting and electric vehicle demand response

This publication has 45 references indexed in Scilit:

• To charge or not to charge? Using Prospect Theory to model the tradeoffs of electric vehicle users
  Sustainability Science, 2024
• Costs, carbon footprint, and environmental impacts of lithium-ion batteries – From cathode active material synthesis to cell manufacturing and recycling
  Applied Energy, 2023
• A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning
  Applied Energy, 2023
• Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach
  Applied Energy, 2023
• Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach
  Applied Energy, 2022
• Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach
  Applied Energy, 2022
• Novel data-driven energy management of a hybrid photovoltaic-reverse osmosis desalination system using deep reinforcement learning
  Applied Energy, 2022
• Integrated demand response for congestion alleviation in coupled power and transportation networks
  Applied Energy, 2021
• Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets
  Applied Energy, 2020
• Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing
  Applied Energy, 2018