Closed-loop corrective EV demand response with composite customer directrix load for renewable energy accommodation in distribution networks

With the increasing penetration of renewable energy sources (RES) in distribution network, the coordination of demand-side electric vehicle (EV) becomes crucial for RES accommodation. By introducing the concept of customer directrix load, this paper proposes a novel closed-loop corrective strategy to enhance the effectiveness of EV demand response. Firstly, a robust composite customer directrix load (CCDL) model is constructed to enhance resilience against uncertainties in EV responses, which integrates control characteristics and response fluctuations to provide tailored full-time guidance for heterogeneous EVs. On the basis, considering that the implementation of demand response is a sequential coupled process, a closed-loop correction mechanism is further proposed to facilitate the multi-stage coordination across RES prediction, CCDL optimization, and deviation feedback. By developing a direction-aware combined prediction model to cope with asymmetric prediction effects, and designing a policy-based gradient descent method for closed-loop refinement, the proposed mechanism enables self-correction against uncertain deviations for demand response implementation. Numerical comparison using real-world case demonstrates that the constructed CCDL model could effectively promote the EV-coordinated RES accommodation, while the proposed deviation correction mechanism can improve the demand response accuracy by 17.88% over traditional sequential mode.