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Title: A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model

Abstract

The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could have significant effects on the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. Even though PEVs increase demand on the electric grid, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes an innovative scheduling and control framework to enable smart PEV charging for grid services while meeting PEV owners’ travel needs. In the proposed framework, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, EVSE power rating, battery energy state, and upcoming trip information. With the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window for the given grid services. The proposed charging coordination strategy can help reduce the 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 prototypicalmore » feeder developed at Pacific Northwest National Laboratory and detailed trip information extracted from the National Household Travel Survey.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1547373
Report Number(s):
PNNL-SA-136731
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 253
Country of Publication:
United States
Language:
English

Citation Formats

Wu, Di, Radhakrishnan, Nikitha, and Huang, Sen. A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.113490.
Wu, Di, Radhakrishnan, Nikitha, & Huang, Sen. A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model. United States. doi:10.1016/j.apenergy.2019.113490.
Wu, Di, Radhakrishnan, Nikitha, and Huang, Sen. Fri . "A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model". United States. doi:10.1016/j.apenergy.2019.113490.
@article{osti_1547373,
title = {A Hierarchical Charging Control of Plug-in Electric Vehicles with Simple Flexibility Model},
author = {Wu, Di and Radhakrishnan, Nikitha and Huang, Sen},
abstractNote = {The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could have significant effects on the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. Even though PEVs increase demand on the electric grid, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes an innovative scheduling and control framework to enable smart PEV charging for grid services while meeting PEV owners’ travel needs. In the proposed framework, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, EVSE power rating, battery energy state, and upcoming trip information. With the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window for the given grid services. The proposed charging coordination strategy can help reduce the 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 the National Household Travel Survey.},
doi = {10.1016/j.apenergy.2019.113490},
journal = {Applied Energy},
number = ,
volume = 253,
place = {United States},
year = {2019},
month = {11}
}