Smart Grid Using Artificial Intelligence
Along with the development of smart grids, the wide adoption of electric vehicles (EVs) is seen as a catalyst to the reduction of emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and, therefore, help optimize the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimize costs and, at the same time, avoid users being stranded.
On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilize artificialintelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state of the art in this space
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