Abstract
Big data analytics and plug-in electric vehicle (PEV) are the important elements of smart grids in the future. This paper introduces a data-driven charging strategy for PEV-based taxis, where driving behaviors of taxis and load profiles of buildings are characterized by data analysis to make the risk-averse decision on PEV charging. First, the framework of data driven risk-averse PEV charging is introduced, where a stochastic game model is proposed. Specifically, the pricing mechanism-based charging cost and the conditional value-at-risk (CVaR) measurement are used to determine the objective function for each PEV. Then, the existence of a generalized Nash equilibrium and its seeking algorithm is studied. The convergence analysis of the algorithm is also given. Second, the big data analysis of the statistical information about PEV-based taxis and the load profile of buildings are presented by applying various data process techniques. Finally, the performance of the method is numerically illustrated by the case study via real global positioning system information of 490 PEV-based taxis and the smart meter data from local commercial buildings.
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