  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.
