Power management optimization of electric vehicles for grid frequency regulation: comparative study

Mohamed Y. Metwly; Mohamed Ahmed; Mostafa S. Hamad; Ayman S. Abdel-Khalik; Eman Hamdan; Noha A. Elmalhy

doi:10.1016/j.aej.2022.10.030

Power management optimization of electric vehicles for grid frequency regulation: comparative study

Mohamed Y. Metwly^*, Mohamed Ahmed, Mostafa S. Hamad, Ayman S. Abdel-Khalik, Eman Hamdan, Noha A. Elmalhy

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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Abstract

Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.

Original language	English
Pages (from-to)	749-760
Number of pages	12
Journal	Alexandria Engineering Journal
Volume	65
Early online date	29 Dec 2022
DOIs	https://doi.org/10.1016/j.aej.2022.10.030
Publication status	Published - 15 Feb 2023

Funding

This work was submitted on 4- August- 2022 and achieved by the financial support of ITIDAs ITAC collaborative funded project under the category type of advanced research projects (ARP) and Grant No ARP2020.R29.7. This work was achieved by the financial support of ITIDAs ITAC collaborative funded project under the category type of advanced research projects (ARP) and grant number ARP2020.R29.7.

Keywords

contingency cases
electric vehicles (EVs)
multi-objective optimization
secondary frequency regulation (SFR)
vehicle-to-grid (V2G)

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10.1016/j.aej.2022.10.030Licence: CC BY-NC-ND 4.0

Metwly-etal-AEJ-2023-Power-management-optimization-of-electric-vehicles-for-grid-frequency-regulationFinal published version, 2.46 MBLicence: CC BY-NC-ND 4.0

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@article{7308ce4da2cc492fae8d13c9be9d3357,

title = "Power management optimization of electric vehicles for grid frequency regulation: comparative study",

abstract = "Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.",

keywords = "contingency cases, electric vehicles (EVs), multi-objective optimization, secondary frequency regulation (SFR), vehicle-to-grid (V2G)",

author = "Metwly, {Mohamed Y.} and Mohamed Ahmed and Hamad, {Mostafa S.} and Abdel-Khalik, {Ayman S.} and Eman Hamdan and Elmalhy, {Noha A.}",

year = "2023",

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doi = "10.1016/j.aej.2022.10.030",

language = "English",

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TY - JOUR

T1 - Power management optimization of electric vehicles for grid frequency regulation

T2 - comparative study

AU - Metwly, Mohamed Y.

AU - Ahmed, Mohamed

AU - Hamad, Mostafa S.

AU - Abdel-Khalik, Ayman S.

AU - Hamdan, Eman

AU - Elmalhy, Noha A.

PY - 2023/2/15

Y1 - 2023/2/15

N2 - Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.

AB - Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.

KW - contingency cases

KW - electric vehicles (EVs)

KW - multi-objective optimization

KW - secondary frequency regulation (SFR)

KW - vehicle-to-grid (V2G)

U2 - 10.1016/j.aej.2022.10.030

DO - 10.1016/j.aej.2022.10.030

M3 - Article

AN - SCOPUS:85140641825

SN - 1110-0168

VL - 65

SP - 749

EP - 760

JO - Alexandria Engineering Journal

JF - Alexandria Engineering Journal

ER -

Power management optimization of electric vehicles for grid frequency regulation: comparative study

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Keywords

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