@inproceedings {pub6188,
	title = {Combination of Charging Policies for Fair and Bias-aware Electric Vehicle Charging},
	author = {Steffen Limmer AND Felix Lanfermann AND Andrea Castellani},
	year = {2025},
	month = {December},
	abstract = {Controlled charging can be employed to coordinate the charging of multiple electric vehicles (EVs) under power limitations, such as those imposed by transformer capacity constraints. However, controlled charging may result in reduced satisfaction of the EV users{\textquoteright} charging requirements compared to uncontrolled charging. It is desirable to distribute this (dis-)satisfaction fairly among users. Additionally, one might be interested in avoiding systematic bias, which disadvantages groups of users with certain characteristics. The present paper proposes an approach for the automated design of fair and bias-aware charging policies by combining multiple manually designed policies through multi-objective optimization. For the detection of bias in EV charging control, a hierarchical clustering approach is introduced. In simulation experiments, the proposed method reduces bias by an average of 5 \% while improving efficiency and fairness by an average of 4 \% and 9 \%, respectively, compared to manually designed charging policies.},
	publisher = {IEEE},
	booktitle = {IEEE Innovative Smart Grid Technologies (ISGT) Europe 2025}
}