A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles

TLDR

The STM is designed to aid power system evaluation and planning by providing average and probabilistic nodal voltage and branch loading metrics. The STM combines power system and transportation modeling, employing OD analysis, EU MERGE data, and Monte Carlo simulation to estimate time‑varying EV charging loads on each busbar in a high‑density UK distribution network. The STM identifies critical network components requiring upgrades and shows that smart charging improves network performance compared to dumb charging at 0 %, 25 %, and 50 % penetration levels.

Abstract

A Spatial–Temporal model (STM) was developed to evaluate the impact of large scale deployment of plug-in electric vehicles (EVs) on urban distribution networks. The STM runs based on the integration of power system analysis and transportation analysis. Origin–Destination (OD) analysis from intelligent transportation research was used to model the EV mobility. Based on the EV technical and market information provided by the EU MERGE project and the output of OD analysis, a Monte Carlo simulation method was developed within the STM to obtain the EV charging load of each load busbar over time. The STM aims to facilitate power system evaluation and planning, and is able to provide both average values and probabilities of nodal bus voltages and branch loadings. The STM is able to identify the critical network components that will require to be upgraded. A high customer density urban network from the United Kingdom Generic Distribution System combined with geographic information was used as a test system. Two EV charging strategies, “dumb” charging and “smart” charging, were simulated and compared under different EV penetration levels (0%, 25% and 50%) to verify the effectiveness of STM.