Integrated Python-PowerFactory Framework for Mitigating Voltage Violations Through Electric Vehicle Demand Response

The rapid rise in electric vehicle (EV) adoption presents significant challenges to low-voltage (LV) distribution networks, mainly due to the added demand from residential EV charging. This paper represents a simulation-based analysis of the voltage of a residential LV network with 100% EV penetration. An integrated Python-PowerFactory framework was developed to perform time-series analysis of the voltage of a LV network comprising 36 households. Using the integrated framework, the objective of this paper is to investigate the impact of high EV penetration on LV network voltage stability and to develop a mitigation strategy using EV demand response. Without EVs, the network's voltage profile stayed within the limit. However, widespread voltage breaches were noted during EV integration, with voltage levels falling below the 0.94 pu threshold. To address this issue, the largest EV loads were dynamically lowered by 20% when the voltage dropped below 0.94 pu as part of a voltage-responsive demand response (DR) strategy. With the help of the control algorithm, voltage violations were successfully mitigated, increasing network stability without requiring more equipment. The automation significantly enhances scalability and repeatability, making it a practical solution for real-world smart grid applications.