Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.

Electric vehicle (EV) technology has revolutionized the transportation sector in the last few decades. The adoption of EVs, along with the advancement of smart grid technologies and Renewable Energy Sources (RES), has introduced new concepts in the automobile and power industries. Vehicle-Grid Integration (VGI) or Vehicle-to-Grid (V2G) is a technology revolutionizing both the transport and electric power sectors. From a V2G perspective, these sectors are complementary and mutually beneficial. For the power sector, mitigation of voltage and frequency excursions and the prospect of grid stabilization on the brink of uncertainties owing to the dynamics in the grid scenario are very important. This article focuses on various aspects of EV-power grid integration. The tremendous benefits of this technology, as presented in the literature, are reviewed. Furthermore, the concerns and the implementation challenges are reviewed in detail in this work.