Reinforcement Learning for the scheduling of EV charging systems in a Smart Grid context
Felipe Spoturno
Reinforcement Learning for the scheduling of EV charging systems in a Smart Grid context.
Rel. Michela Meo, Daniela Renga. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2022
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Abstract
In the last decades global warming has aroused increasing attention as new temperature records are being registered worldwide and new violent and unpredictable meteorological events occur. Italy has just registered the second hottest summer in the last 200 years and different countries have registered the hottest temperatures ever. Climatologists agree that these temperatures are five times more probable due to climate change and they are caused by the increasing amount of CO2 and other greenhouse effect gases released on the atmosphere due to human activity. Organizations at national and international level are constantly studying and monitoring the production of greenhouse effect gases and for both Europe and the USA it emerges that in the last years the domestic transportation sector is responsible for about 22-27% of emissions, and that the energy production sector is responsible for about 25% of emissions.
If combined, different enabling technologies that has been developed in the last decades can give an enormous contribution to the reduction of emissions produced by domestic transportation
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