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AI and social anthropology for large-scale Vehicle-to-Grid schemes

Rigas Emmanouil S., Akasiadis Charilaos, Vardaki Evangelia, Chalkiadakis Georgios

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Περίληψη

The use of Electric Vehicles (EVs) is a main pathway to decrease CO2 emissions. Apart from mobility services, EVs can also utilize their batteries when they are not en route to increase the storage capacity of the electricity network. This in turn can improve the utilization of energy from renewable sources that are characterized by intermittent production. In the so-called Vehicle-to-Grid (V2G) energy provision, drivers offer their EVs as temporary energy storage units, but this comes with substantial losses in comfort or equipment degradation costs. In this paper we discuss how, departing from the strict algorithmic and mechanism design techniques, social anthropology can help AI in designing V2G schemes that will be explainable and thus easily comprehensible and acceptable by laypeople. We argue that intertwining the scheduling algorithms and mechanisms with socio-anthropological aspects can: enhance the effectiveness of V2G incentivization mechanisms; help engage and associate people with the use and control of green energy technologies; boost the creation of novel and highly realistic large-scale V2G simulation platforms; and create a new and challenging interdisciplinary research area.

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