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

TL;DR

This paper demonstrates that incorporating nonlinear battery models and real-time price data into vehicle-to-grid management significantly reduces charging costs, with bi-directional V2G providing additional savings.

Contribution

It introduces a realistic, scalable control algorithm that accounts for nonlinear battery behaviors and price uncertainties in V2G systems, improving cost efficiency.

Findings

35% cost savings with unidirectional V2G

18% additional savings with bi-directional V2G

Nonlinear battery models improve V2G control accuracy

Abstract

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