Increased single-phase electric vehicle (EV) charging often causes phase unbalance in low-voltage networks (LVNs). This paper presents a dynamic assessment and mitigation framework that uses a linear model predictive control (MPC) approach with successive linearisation and constraint tightening to implement a fully device-agnostic control method. The approach is implemented on public EV data from Leeds and two representative UK low-voltage feeders from the D-Suite network set, comprising an urban feeder and a rural feeder. The proposed device-agnostic control was implemented through idealised balancing actuators placed at selected feeder buses. Since this is a dynamic case, the EV charging scenarios were constructed at 15-minute resolution under different EV penetration scenarios and phase-skew conditions so that the time-varying effect of EV charging on feeder unbalance could be captured more realistically.

Two conclusions were drawn for the two feeders. In the urban feeder, most scenarios remained within the voltage unbalance factor (VUF) limit of less than 2% and the voltage constraint of ±10%, showing that the feeder was relatively robust under the tested charging conditions. In contrast, the rural feeder showed phase unbalance violation under the strongly skewed scenario. Before control was applied to the rural feeder, the maximum VUF reached 4.09%, and the feeder exceeded the 2% VUF limit for a prolonged period, showing that sustained non-compliance can arise when EV charging is concentrated unevenly across phases. After the control framework was applied, the maximum VUF was reduced to 1.95%, and all phase unbalance violations were resolved, while the voltage remained within acceptable limits. This shows that the proposed control framework can effectively mitigate severe time-varying phase unbalance in the rural feeder under the tested condition.

Additional testing on urban feeders indicated that they could also become vulnerable to VUF violations under more concentrated and increased charging. In this stressed urban case, the device-agnostic control reduced the duration of the violations, although some minimal violations remained. This indicates that the proposed control framework is effective.

These results show that EV-induced phase unbalance should be addressed on a case-by-case basis, depending on the feeder in question, and that the effectiveness of mitigation depends strongly on feeder characteristics, EV charging usage and available control capability. The study is relevant to EV grid integration in low-voltage distribution systems and provides a practical framework for assessing and mitigating time-varying phase unbalance in representative low-voltage networks.