A hierarchical charging control of plug-in electric vehicles with simple flexibility model. (1st November 2019)

• Record Type: Journal Article
• Authors: Wu, Di; Radhakrishnan, Nikitha; Huang, Sen
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Electric vehicle supply equipment -- Hierarchical control -- Model predictive control -- Plug-in electric vehicles -- Smart charging; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113490
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could add a considerable amount of load onto the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. On the other hand, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes a hierarchical charging scheduling and control framework to enable PEVs for grid services while meeting vehicle owners' travel needs. The proposed control framework consists of coordination and vehicle layers. In the vehicle layer, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, charging equipment power rating, battery energy state, and upcoming trip information. The controller also controls vehicle charging power in real-time. In the coordination layer, with the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window based on grid services to be provided. The proposed charging coordination approach can help reduce computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. The proposed method is studied using a prototypical feeder developed at Pacific Northwest National Laboratory and detailed trip information extracted from National Household Travel Survey.