Assessing EV Charging Impacts on Power Distribution Systems: A Unified Co-Simulation Framework

TL;DR

The paper presents a unified, high-fidelity co-simulation framework to evaluate the impacts of large-scale EV charging on electric distribution networks. It combines OpenDSS-based detailed distribution modeling with high-resolution synthetic data from SMART-DS and EV-load projections from the DOE EVI-X toolbox, augmented by GIS-enabled visualization. The framework identifies critical network components likely to require upgrades and supports scenario-based planning for diverse charging patterns and adoption levels. Key findings show that unmanaged charging can substantially increase line loading and losses, while coordinated or managed charging can mitigate grid stress, underscoring the framework's practical utility for utilities and policymakers. The modular design ensures easy adaptation to different regions, feeder configurations, and future grid conditions, making it a valuable tool for proactive grid reinforcement and investment planning.

Abstract

The growing adoption of electric vehicles (EVs) is expected to significantly increase demand on electric power distribution systems, many of which are already nearing capacity. To address this, the paper presents a comprehensive framework for analyzing the impact of large-scale EV integration on distribution networks. Using the open-source simulator OpenDSS, the framework builds detailed, scalable models of electric distribution systems, incorporating high-fidelity synthetic data from the SMART-DS project. The study models three feeders from an urban substation in San Francisco down to the household level. A key contribution is the framework's ability to identify critical system components likely to require upgrades due to increased EV loads. It also incorporates advanced geospatial visualization through QGIS, which aids in understanding how charging demands affect specific grid areas, helping stakeholders target infrastructure reinforcements. To ensure realistic load modeling, the framework uses EV load profiles based on U.S. Department of Energy projections, factoring in vehicle types, charging behaviors, usage patterns, and adoption rates. By leveraging large-scale synthetic data, the model remains relevant for real-world utility planning. It supports diverse simulation scenarios, from light to heavy EV charging loads and distributed vs. centralized charging patterns, offering a practical planning tool for utilities and policymakers. Additionally, its modular design enables easy adaptation to different geographic regions, feeder setups, and adoption scenarios, making it suitable for future studies on evolving grid conditions.

Figures (12)

• Figure 1: Schematic of the proposed framework for analyzing the impact of EV chargers on power distribution systems. The framework is organized into three integrated layers, i.e., data, computation, and Visualization. Geographic and electrical data are first collected and processed using GIS and DOE datasets. EV load profiles are then estimated using the EVI-X modeling suite and assigned to load buses. Finally, time-series power flow simulations are performed using OpenDSSDirect to evaluate system-level impacts, including voltage profiles, line loading, and energy losses.
• Figure 2: Modification of a JSON dictionary entry representing a power distribution line. The original entry (left) includes basic electrical properties, while the updated entry (right) incorporates additional visualization attributes such as "line_width", "line_opacity", and "line_color" to reflect changes in power flow for geospatial rendering.
• Figure 3: Geographic distribution of EV charging stations with power outputs ranging from 50 kW to 150 kW, located within and around San Jose, Sunnyvale, Cupertino, and Santa Clara. The blue circle indicates the area of interest used for spatial analysis.
• Figure 4: One line diagram of PU15 sub-region in San Jose, Sunnyvale, and Cupertino. EV charging stations are determined as: Red Points: below 50 kW (Level 1), Yellow points:between 50 kW and 150 kW (Level 2),Blue points: between 150 kW and 350 kW (Level 3), Green Points: more than 350 kw (Level 4).
• Figure 5: 24-hour EV charging demand profile for Scenario 1, based on a fleet of 350,000 vehicles in the San Jose–Sunnyvale–Cupertino area. The fleet includes 50% BEVs and 50% PHEVs, with most vehicles (80%) charging at home using a mix of Level 1 and Level 2 chargers. Workplace charging also uses a 50/50 mix of Levels 1 and 2. Home charging starts immediately at low speed, while workplace charging starts immediately at full speed. The peak total demand reaches about 130 MW around hour 19.38.