Multi-Agent Based Simulation for Investigating Centralized Charging Strategies and their Impact on Electric Vehicle Home Charging Ecosystem

TL;DR

This study uses multi-agent simulation to evaluate centralized EV charging strategies, demonstrating their effectiveness in managing grid loads and improving user satisfaction in a realistic home charging ecosystem.

Contribution

It introduces a multi-agent simulation model to analyze centralized EV charging strategies and their impact on grid stability and user satisfaction over long-term scenarios.

Findings

Earliest-deadline-first and Round Robin strategies perform best at 100% EV adoption.

Centralized strategies can effectively manage load profiles and prevent grid overloads.

Simulation captures detailed ecosystem dynamics with realistic adoption and usage patterns.

Abstract

This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, which is essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing grid infrastructure, particularly in managing the increasing electricity demand and mitigating the risk of grid overloads. Centralized EV charging strategies are investigated due to their potential to optimize grid stability and efficiency, compared to decentralized approaches that may exacerbate grid stress. Utilizing a multi-agent based simulation model, the study provides a realistic representation of the electric vehicle home charging ecosystem in a case study of Strib, Denmark. The findings show that the Earliest-deadline-first and Round Robin perform best with 100% EV adoption in terms of EV user satisfaction. The simulation considers a realistic adoption curve, EV charging strategies, EV models, and driving patterns to capture the full ecosystem dynamics over a long-term period with high resolution (hourly). Additionally, the study offers detailed load profiles for future distribution grids, demonstrating how centralized charging strategies can efficiently manage grid loads and prevent overloads.