# Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehicles

## TL;DR

This paper presents a decentralized, privacy-preserving learning approach for electric vehicle charging that improves Smart Grid reliability, reduces costs, and enhances fairness and driver comfort in a socio-technical context.

## Contribution

It introduces a novel autonomous agent-based method for coordinated EV charging that considers social factors like fairness and discomfort, unlike previous approaches.

## Key findings

- Reduces power peaks and energy costs system-wide.
- Improves driver comfort and fairness in charging.
- Enhances reliability of Smart Grid with high EV penetration.

## Abstract

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.