Dual-center control scheme and FF-DHRL-based collaborative optimization for charging stations under intra-day peak-shaving demand

The increasing adoption of electric vehicles (EVs) enhances charging stations’ role in balancing power grid demands through dynamically pricing and adjusting charging power strategies. However, unpredictable nature of EV flow and the necessity for rapid response to real-time peak shaving present complex challenges. To tackle this, a novel dual-center control scheme and learning optimization methods are proposed in this paper. It adopts a station operation model based on queuing theory, considering charging pile capacity constraints. The proposed control scheme, integrating a service price maker (SPM) and charging power controller (CPC), adapts service prices and charging power in response to electricity prices and peak-shaving directives. Employing a model-free Deep Hierarchical Reinforcement Learning (DHRL) approach, the SPM targets economic maximization, while the CPC aims to balance peak-shaving efficacy and user satisfaction. Furthermore, to improve the offline learning efficiency of the algorithm, feature fusion (FF) technology is used to condense high-dimensional data into essential low-dimensional features, improving the algorithm’s learning efficiency. The effectiveness of the proposed FF-DHRL was verified in a simulated charging station system.