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Title: A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations

Abstract

The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve a global optimum. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of chargingmore » stations over multiple years. Here, case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [2];  [1];  [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1573972
Alternate Identifier(s):
OSTI ID: 1573108
Report Number(s):
INL/JOU-18-45750-Rev000
Journal ID: ISSN 0142-0615
Grant/Contract Number:  
AC07-05ID14517; AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Electrical Power and Energy Systems
Additional Journal Information:
Journal Volume: 117; Journal Issue: C; Journal ID: ISSN 0142-0615
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Electric vehicle; Receding horizon control; Highly efficient; Smart charging; Centralized charging coordination; Aggregator

Citation Formats

Yi, Zonggen, Scoffield, Don, Smart, John G., Meintz, Andrew, Jun, Myungsoo, Mohanpurkar, Manish U., and Medam, Anudeep. A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations. United States: N. p., 2019. Web. doi:10.1016/j.ijepes.2019.105661.
Yi, Zonggen, Scoffield, Don, Smart, John G., Meintz, Andrew, Jun, Myungsoo, Mohanpurkar, Manish U., & Medam, Anudeep. A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations. United States. doi:10.1016/j.ijepes.2019.105661.
Yi, Zonggen, Scoffield, Don, Smart, John G., Meintz, Andrew, Jun, Myungsoo, Mohanpurkar, Manish U., and Medam, Anudeep. Tue . "A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations". United States. doi:10.1016/j.ijepes.2019.105661.
@article{osti_1573972,
title = {A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations},
author = {Yi, Zonggen and Scoffield, Don and Smart, John G. and Meintz, Andrew and Jun, Myungsoo and Mohanpurkar, Manish U. and Medam, Anudeep},
abstractNote = {The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve a global optimum. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Here, case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.},
doi = {10.1016/j.ijepes.2019.105661},
journal = {International Journal of Electrical Power and Energy Systems},
number = C,
volume = 117,
place = {United States},
year = {2019},
month = {11}
}

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