A Data-Driven Flexibility Envelope Modeling Framework for Pilot-Scale Residential EV Fleet Aggregations Using Smart Meter Data

The gradual substitution of internal combustion engines with electric vehicles (EVs) gives a lot of flexibility potential to the power systems; however, this potential is not fully utilized because there are no utility-deployable, data-driven approaches to the measurement of EV charging flexibility based on already accessible infrastructure of utilities. This paper presents a data-driven framework for constructing flexibility envelopes for pilot-scale residential EV fleet aggregations using smart-meter data without the need for EV-level submetering, direct user input, or vehicle-specific data. The proposed framework integrates non-invasive load monitoring (NILM) and multi-feature clustering to identify EV charging sessions using aggregate whole-home smart-meter readings and to identify prevailing charging behavior patterns. Based on these behaviorally defined clusters, time-dependent power and energy flexibility sheaths are built, therefore defining practicable charging paths, adjustable energy capacity, and peak-reduction potential both at the cluster level and at the fleet level. One year of residential smart-meter data from ten EV-equipped households of the Pecan Street Dataport is used to validate. The findings indicate that EV charging episodes could be determined from 15-minute smart-meter records with high recall (0.83-0.91) and moderate-to-high precision (0.77-0.85), and that the charging behavior will naturally fall into a few explainable patterns with specific flexibility properties. At the fleet scale, the derived envelopes show that about 60% of total EV charging energy could be shifted over time, temporarily allowing up to a 50% of coincident peak charging demand, which allows meeting historical user charging demands. Unlike forecasting-based techniques, the current technique provides operationally significant flexibility limits that can be effectively fitted in demand-response, aggregation, and grid-optimization applications. The results highlight the feasibility of using the available smart-meter infrastructure to describe the flexibility of the EV fleet in residential aggregation settings on a pilot basis.