An Automated Framework for Assessing Electric Vehicle Charging Impacts on a Campus Distribution Grid

• URL: http://arxiv.org/abs/2509.16218v1
• Date: Tue, 09 Sep 2025 01:17:59 GMT
• Title: An Automated Framework for Assessing Electric Vehicle Charging Impacts on a Campus Distribution Grid
• Authors: Mohammadreza Iranpour, Sammy Hamed, Mohammad Rasoul Narimani, Silvia Carpitella, Kourosh Sedghisigarchi, Xudong Jia,
• Abstract summary: This paper introduces a unified and automated framework designed to assess the impact of electric vehicle (EV) charging on distribution feeders and transformers at California State University, Northridge (CSUN)<n>Our main contribution is the development of a flexible testbed that integrates Julia, a high-performance programming language for technical computing, with PowerWorld Simulator via the EasySimauto.jl package.<n>The proposed system offers a valuable tool for evaluating transformer loading, feeder utilization, and overall system stress.
• Score: 0.6978367196609415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a unified and automated framework designed to dynamically assess the impact of electric vehicle (EV) charging on distribution feeders and transformers at California State University, Northridge (CSUN). As EV adoption accelerates, the resulting increase in charging demand imposes additional stress on local power distribution systems. Moreover, the evolving nature of EV load profiles throughout the day necessitates detailed temporal analysis to identify peak loading conditions, anticipate worst-case scenarios, and plan timely infrastructure upgrades. Our main contribution is the development of a flexible testbed that integrates Julia, a high-performance programming language for technical computing, with PowerWorld Simulator via the EasySimauto.jl package. This integration enables seamless modeling, simulation, and analysis of EV charging load profiles and their implications for campus grid infrastructure. The framework leverages a real-world dataset collected from CSUN's EV charging stations, consisting of 15-minute interval measurements over the course of one year. By coupling high-resolution data with dynamic simulations, the proposed system offers a valuable tool for evaluating transformer loading, feeder utilization, and overall system stress. The results support data-driven decision-making for EV infrastructure deployment, load forecasting, and energy management strategies. In addition, the framework allows for scenario-based studies to explore the impact of future increases in EV penetration or changes in charging behavior. Its modular architecture also makes it adaptable to other campus or urban distribution systems facing similar electrification challenges.

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