MV-LV Rule-Based Control of EV Charging Using Limited Network and Monitoring Data

Abstract

The widespread adoption of electric vehicles (EVs) will increase the peak demand of existing medium voltage (MV) and low voltage (LV) distribution networks, affecting assets and customer voltages. An alternative to costly reinforcements is the adequate real-time control of EV charging. However, most studies that investigate such control often require detailed electrical network models and real-time smart meter data. While distribution companies have MV network models, LV ones are rare. And despite the rising adoption of smart meters, their real-time usage is uncommon. This paper proposes a hierarchical rule-based control of EV charging in unbalanced, large-scale MV-LV networks. Only using asset associations and limited monitoring (from transformers and charging points), it calculates adequate EV charging setpoints across the MV-LV networks, successfully managing high EV penetrations in large areas. Its effectiveness is compared to a linearized AC Optimal Power Flow (OPF)-based approach using a real Australian MV-LV network with 3,300+ residential customers, considering high EV and photovoltaic (PV) penetrations. Results demonstrate that the proposed approach can mitigate voltage and thermal issues as effectively as the benchmark OPF-based approach, and with limited impacts on EV charging delays. It provides a practical, implementable solution for distribution companies to control EVs in large-scale networks.