@Article{ee.2026.087390,
AUTHOR = {Lixiao Wang, Jiaqi Li, Haifeng Li, Yongshi Lu, Xiaoshi Deng, Tongping Lin, Dingxiao Li, Rui Li},
TITLE = {Correlation-Aware Dynamic-Price-Feedback Ordered Charging for Vehicle–Grid Coordination},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/28158},
ISSN = {1546-0118},
ABSTRACT = {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.},
DOI = {10.32604/ee.2026.087390}
}