A Two-Stage Optimal Electric Vehicles Charging Methodology Based on Aggregators Considering Grid Reliability and Operational Efficiency

Ali Ihsan Aygun; Md Shamim Hasan; Aniket Joshi; Sukumar Kamalasadan

doi:10.1109/TIA.2024.3462909

A Two-Stage Optimal Electric Vehicles Charging Methodology Based on Aggregators Considering Grid Reliability and Operational Efficiency

Ali Ihsan Aygun, Md Shamim Hasan, Aniket Joshi, Sukumar Kamalasadan

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

2 Scopus citations

Abstract

This paper presents a two-stage optimal charging methodology for electric vehicles that considers grid impact, load management, and optimal charging based on pricing. The charging method is distributed based on aggregators (complying with orders such as FERC 2222). The approach considers peak loading time and optimally schedules charging so that cost savings can be integrated into customer pricing as incentives. The advantages of this proposed architecture include a) demand response and maintaining load balance, b) supporting grid stability, and c) allowing prioritized charging. It is observed that the approach has significant improvement in cost saving, valley filling, and fully providing the EV charging requirements for the customers.

Original language	English
Pages (from-to)	940-954
Number of pages	15
Journal	IEEE Transactions on Industry Applications
Volume	61
Issue number	1
DOIs	https://doi.org/10.1109/TIA.2024.3462909
State	Published - 2025
Externally published	Yes

Keywords

ADMM
Electric vehicles (EVs)
charging cost minimization
peak shaving
smart charging algorithm
valley filling

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10.1109/TIA.2024.3462909

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abstract = "This paper presents a two-stage optimal charging methodology for electric vehicles that considers grid impact, load management, and optimal charging based on pricing. The charging method is distributed based on aggregators (complying with orders such as FERC 2222). The approach considers peak loading time and optimally schedules charging so that cost savings can be integrated into customer pricing as incentives. The advantages of this proposed architecture include a) demand response and maintaining load balance, b) supporting grid stability, and c) allowing prioritized charging. It is observed that the approach has significant improvement in cost saving, valley filling, and fully providing the EV charging requirements for the customers.",

keywords = "ADMM, Electric vehicles (EVs), charging cost minimization, peak shaving, smart charging algorithm, valley filling",

author = "Aygun, \{Ali Ihsan\} and Hasan, \{Md Shamim\} and Aniket Joshi and Sukumar Kamalasadan",

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AU - Joshi, Aniket

AU - Kamalasadan, Sukumar

PY - 2025

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AB - This paper presents a two-stage optimal charging methodology for electric vehicles that considers grid impact, load management, and optimal charging based on pricing. The charging method is distributed based on aggregators (complying with orders such as FERC 2222). The approach considers peak loading time and optimally schedules charging so that cost savings can be integrated into customer pricing as incentives. The advantages of this proposed architecture include a) demand response and maintaining load balance, b) supporting grid stability, and c) allowing prioritized charging. It is observed that the approach has significant improvement in cost saving, valley filling, and fully providing the EV charging requirements for the customers.

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KW - charging cost minimization

KW - peak shaving

KW - smart charging algorithm

KW - valley filling

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A Two-Stage Optimal Electric Vehicles Charging Methodology Based on Aggregators Considering Grid Reliability and Operational Efficiency

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Keywords

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