Jun 26, 2023 · This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of ...

This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of ...

This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of ...

The proposed bilevel model aims to achieve carbon-oriented day-ahead optimal scheduling for EV aggregators, which is located at the upper level and is expected ...

This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of ...

This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented ...

This paper presents a novel data-driven approach that leverages reinforcement learning to enhance the efficiency and safety of existing energy flexibility ...

Finally, the CMDP is efficiently solved by the proposed augmented Lagrangian-based DRL algorithm featuring the soft actor-critic (SAC) ...

Jun 29, 2024 · This paper provides a comprehensive review of safe RL techniques and their applications in different power system operations and control,.

Chen, et al. An augmented lagrangian-based safe reinforcement learning algorithm for carbon-oriented optimal scheduling of ev aggregators. IEEE Trans Smart ...