Empirical comparisons show that PUI-MAPPO (multi-agent proximal policy optimization) achieves the best performance among all PUI-enhanced variants, and ablation studies further validate the individual effectiveness of the PUI urgency mechanism, the dynamic threshold framework, and the adaptive reward function.
This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop by integrating Large Language Model (LLM).
Electric vehicle (EV) parking lots can create concentrated charging demand that couples operating cost, grid capacity use, and carbon emissions. This paper proposes a reinforcement-learning-guided two-stage multi-energy optimization framework for carbon-aware EV parking lot charging. The reinforcement learning (RL) lay...
Ride-hailing services using autonomous electric vehicles (AEVs) at transportation hubs face strong demand fluctuations and limited charging resources. During peak periods, pricing decisions directly affect passenger demand, while charging decisions determine vehicle availability. Treating these two decisions independ...
A hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem is proposed and demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charg...
The scheduling problem of battery electric buses (BEBs) has been widely studied. However, most existing work focuses only on either operation or charging. In reality, these two aspects are interdependent and should be considered from a lifecycle perspective. This study proposes a cooperative scheduling strategy for BEB...
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