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Title: Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

Abstract

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Authors:
; ORCiD logo; ; ; ORCiD logo; ; ORCiD logo
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1411516
Report Number(s):
NREL/JA-5D00-70597
Journal ID: ISSN 1949-3053
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Smart Grid; Journal Volume: 8; Journal Issue: 6
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; automatic demand response; charging stations; electric vehicles; real-time price; PV system

Citation Formats

Chen, Qifang, Wang, Fei, Hodge, Bri-Mathias, Zhang, Jianhua, Li, Zhigang, Shafie-Khah, Miadreza, and Catalao, Joao P. S. Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations. United States: N. p., 2017. Web. doi:10.1109/TSG.2017.2693121.
Chen, Qifang, Wang, Fei, Hodge, Bri-Mathias, Zhang, Jianhua, Li, Zhigang, Shafie-Khah, Miadreza, & Catalao, Joao P. S. Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations. United States. doi:10.1109/TSG.2017.2693121.
Chen, Qifang, Wang, Fei, Hodge, Bri-Mathias, Zhang, Jianhua, Li, Zhigang, Shafie-Khah, Miadreza, and Catalao, Joao P. S. Wed . "Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations". United States. doi:10.1109/TSG.2017.2693121.
@article{osti_1411516,
title = {Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations},
author = {Chen, Qifang and Wang, Fei and Hodge, Bri-Mathias and Zhang, Jianhua and Li, Zhigang and Shafie-Khah, Miadreza and Catalao, Joao P. S.},
abstractNote = {A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.},
doi = {10.1109/TSG.2017.2693121},
journal = {IEEE Transactions on Smart Grid},
number = 6,
volume = 8,
place = {United States},
year = {Wed Nov 01 00:00:00 EDT 2017},
month = {Wed Nov 01 00:00:00 EDT 2017}
}