Year: 2025
Type: article
Abstract: The rapid growth of electric vehicles (EVs) and photovoltaic (PV) generation creates substantial power peaks that strain local electrical infrastructure. Coordinated bidirectional charging can mitigate these challenges while delivering benefits such as lower costs, improved PV utilization, and reduced emissions. This paper develops a framework for fleet charging that combines station assignment with a two-stage scheduling approach. A heuristic assignment method allocates EVs to uni- and bidirectional charging stations, ensuring efficient use of limited infrastructure. Building on these assignments, charging power is optimized in two stages: a Mixed-Integer Linear Program (MILP) generates day-ahead schedules from forecasts, while an intraday heuristic-based MILP adapts them to unplanned arrivals and forecast errors through lightweight re-optimization. A Python -based simulator is developed to evaluate the framework under stochastic PV, load, price, and EV conditions. Results show that the approach reduces costs and emissions compared to alternative methods, improves the utilization of bidirectional infrastructure, scales efficiently to large fleets, and remains robust under significant uncertainty, highlighting its potential for practical deployment.
Source: Automation
Authors: Shiwei Shen, Syed Irtaza Haider, Razan Habeeb, Frank H. P. Fitzek
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
Awards: EXC 2050: Centre for Tactile Internet with HumanintheLoop (CeTI), Verbundvorhaben: DymoBat - KI unterstützte Kommunikationstechnologien zur dynamischen Optimierung von Mobilität und Energiespeichern zur Frequenzstabilisierung und Energieversorgung, Teilvorhaben Universität Dresden: Kommunikationstechnologien
OpenAlex ID: W4415820743