An Integrated Framework for the Optimal Control of Vehicle-to-Grid Systems

University Of South Carolina At Columbia, Columbia SC

Investigators

Abstract

The research will establish an integrated framework for centralized vehicle-to-grid (V2G) systems using algorithms that incorporate preferences of diverse actors in the system. In the coming decades, plug-in electric vehicles (PEVs) will revolutionize personal transportation as more consumers recognize that dependence on fossil fuels is unsustainable, and as electric vehicles become more affordable. However, the large increase in demand that will accompany the transition to electric-drive transportation can cause our power infrastructure to become overburdened. V2G systems, in which PEVs interact with electric grids, can be used to address this increase in demand and to avoid exacerbated peak loads. This research project addresses the lack of effective ways of regulating charging and discharging while incorporating the multi-faceted complexities such as driving behavior, travel patterns, technical limitations, and social, economic and environmental impacts. This will be accomplished through the design of algorithms for optimal scheduling and control of charging and discharging of PEVs; this will involve the formulation and solving of optimization problems from the perspectives of major stakeholders including electric utilities, owners of commercial charging stations, and customers. To this end, the research will use both offline and online mechanism design in the centralized management and control of PEVs in a V2G system. By addressing some of the issues that currently limit the penetration of PEVs, this research will facilitate the transition to more sustainable transportation and power systems. This research will also inform decisions by policy makers to design effective infrastructure networks for PEVs and incentives for PEV stakeholders. The goal of this study is to design effective mechanisms to facilitate the integration of PEVs into the electric grid. Specific objectives include: (1) Establishment of a detailed driver and traffic profile for V2G systems; (2) Design of hybrid control techniques for battery charging based on performance criteria; (3) Analysis of the cost of employing PEV batteries for energy storage in power grids; (4) Establishment of an integrated framework for managing PEV charging and discharging while incorporating multi-faceted complexities; and (5) Creation of online optimization models that deal with PEV requests per time step. Accomplishing the objectives of the research project will result in novel charging strategies for PEV batteries modeled from predictive methods and the formulation of robust algorithms for optimal scheduling of charging and discharging of PEVs in commercial settings. By taking a systems approach, this study addresses the current gap in research on PEV integration into the power grid, which focuses narrowly on solving problems without considering multiple perspectives. The strategies and models resulting from this work will provide new knowledge on the dynamics and socio-technical dimensions necessary for the effective integration of electrified transportation systems into the electric grid, particularly in a centralized management setting.