TY  - EJOU
AU  - Wang, Lixiao 
AU  - Li, Jiaqi 
AU  - Li, Haifeng 
AU  - Lu, Yongshi 
AU  - Deng, Xiaoshi 
AU  - Lin, Tongping 
AU  - Li, Dingxiao 
AU  - Li, Rui 

TI  - Correlation-Aware Dynamic-Price-Feedback Ordered Charging for Vehicle–Grid Coordination
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - Random vehicle arrivals, heterogeneous charging demands, and the station-level limit on aggregate electric vehicle (EV) charging power pose simultaneous challenges to event-driven ordered charging in terms of causal information constraints, charging economics, and aggregate-load coordination. Existing full-information multiobjective methods generally rely on the complete daily EV set and iterative solution procedures, whereas fixed time-of-use pricing or single-objective load-smoothing strategies cannot continuously adapt to vehicle arrival events. Moreover, preserving only the marginal distributions of charging-behavior variables may neglect their joint dependence, thereby affecting consistency between scenario generation and scheduling evaluation. To address these issues, this paper proposes Correlation-Aware Dynamic-Price-Feedback Ordered Charging for Vehicle–Grid Coordination (CDPFOC). CDPFOC uses a Gaussian Copula to generate representative scenarios that preserve the joint dependence structure of charging behaviors. It forms event-level economic feedback through a Dynamic Feedback Module (DFM), implements load balancing through an Adaptive Load-Envelope Module (ALM), and updates only the unexecuted future charging plans of vehicles that have arrived and remain online. Monte Carlo experiments in four representative scenarios show that, under a unified real-time dynamic settlement price and a 9 MW station-level EV aggregate charging-power hard limit, CDPFOC records either the lowest dynamic settlement cost or a tie for the lowest in all scenarios. Relative to uncontrolled charging, it reduces the dynamic settlement cost by 0.36%–18.68% and the peak-to-valley difference (PV) by 1.73%–17.55%, while attaining a 100.00% schedulable energy delivery rate and zero station-level capacity violations. Ablation results further indicate that the DFM primarily provides economic guidance, whereas the ALM primarily suppresses charging-power concentration; together, they coordinate charging economics and load smoothing without using individual information from vehicles that have not yet arrived.
KW  - Electric vehicles; ordered charging; charging-behavior correlation; Gaussian Copula; dynamic price feedback; event-driven scheduling

DO  - 10.32604/ee.2026.087390