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The surging adoption of electric vehicles (EV) calls for accurate andefficient approaches to coordinate with the power grid operation. By beingresponsive to distribution grid limits and time-varying electricity prices, EVcharging stations can minimize their charging costs while aiding grid operationsimultaneously. In this study, we investigate the economic benefit ofvehicle-to-grid (V2G) using real-time price data from New York State and areal-world charging network dataset. We incorporate nonlinear battery modelsand price uncertainty into the V2G management design to provide a realisticestimation of cost savings from different V2G options. The proposed controlmethod is computationally tractable when scaling up to real-world applications.We show that our proposed algorithm leads to an average of 35% charging costsavings compared to uncontrolled charging when considering unidirectionalcharging, and bi-directional V2G enables additional 18% cost savings comparedto unidirectional smart charging. Our result also shows the importance of usingmore accurate nonlinear battery models in V2G controllers and evaluating thecost of price uncertainties over V2G.

Vehicle-to-Grid Fleet Service Provision considering Nonlinear Battery Behaviors