Risk and resilience based residential electric vehicle integration framework for restoration of modern power distribution networks

The restoration of power distribution networks following extreme events is a critical challenge, particularly when traditional utility-owned mobile power sources (MPSs) are limited in number, capacity, and deployment flexibility. Residential electric vehicles (EVs) present a scalable and decentralized alternative due to their widespread availability and bidirectional charging capabilities. However, the large-scale integration of non-predetermined residential EVs into power restoration remains under-explored. This paper proposes the Individually-Owned EV Integration Innovative Framework (IIIF) to optimize residential EV participation in grid restoration. The IIIF introduces a novel EV user behaviour model, suggesting a 24% likely improvement in participation prediction accuracy used in the restoration process and leading to more effective EV mobilization. Additionally, a dynamic clustering framework is integrated to enhance EV allocation to public EV charging points (EVCPs), increasing EVCP utilization and optimizing EV distribution to reduce congestion and waiting times. The IIIF also incorporates an advanced EV dispatch model, which optimizes routing decisions to minimize power consumption and enhance discharging efficiency, leading to improved power delivery compared to conventional strategies. To ensure resilience, the IIIF integrates a risk assessment and backup strategy, activating MPSs when EVs alone are insufficient to meet restoration demand. Performance evaluation on the IEEE 123-bus system demonstrates that the IIIF reduces power restoration time by 27% and decreases total operational costs by 19% compared to conventional EV-based and MPS-only restoration approaches. These findings establish the IIIF as a benchmark framework for resilient, cost-effective, and large-scale EV integration in power distribution network restoration. • Proposed a novel EV framework for resilient power restoration after rare events. • Developed a robust EV behaviour model for predicting participation in restoration. • Introduced dynamic clustering to optimize EV allocation to charging points. • Integrated risk assessment to complement EVs with utility mobile power sources. • Achieved superior performance on IEEE 123-bus test systems and real-world cases.