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A new methodology of peak energy demand reduction using coordinated real-time scheduling of EVs

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Abstract

Extensive usage of electric vehicles (EVs) is very effective, through coordinated scheduling, to provide the solution for scarcity of energy, greenhouse emissions, environmental pollution, and in addition to achieve economic stability and energy security of any nation. India’s pledge of reduction in greenhouse gases emission by 45% till 2030 would catalyze the huge increment in the number of EVs. However, the increased load from the growing number of EVs will lead to the technical challenges in the existing distribution network such as deterioration of voltage profile and increased peak load due to unplanned EV charging stations. Since, the charging load of EVs is very uncertain in terms of amount of charging power, charging location, and charging time due to its movable nature. To address these potential issues, the smart EVs and smart power grid equipped with EV aggregators would help to assess the accurate charging demand at specific times in a day-cycle. This work examines the fundamental difficulties that arise because of EVs charging load to the network at various penetration levels. The simulation and analysis of the EV models by using random values of arrival and departure times, the SoC are used to calculate the charging load of EVs at random time of day-cycle by using Monte-Carlo approach. The EVs are scheduled to take measures to reduce peak and manage the flat load profiles in a coordinated manner based on the charging margin and charging urgency indices. Daily load data of India and IIT Kanpur campus are used for simulation and validation of proposed methodology. The study in this paper shows the result of coordinated scheduling. The load curve peak is shaved up to 7.0%.

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Abbreviations

EVs:

Electric vehicles

SoC:

State of charge

IIT:

India Institute of Technology

NDC:

Nationally determined contributions

UNFCCC:

United Nations Framework Convention on Climate Change

GDP:

Gross domestic product

IEA:

International Energy Agency

PEV:

Plug-in electric vehicles

V2G:

Vehicle-to-grid

G2V:

Grid to vehicles

HEVs:

Hybrid electric vehicles

EVCS:

Electric vehicle charging station

ICE:

Internal combustion engine

PKM:

Passenger-kilometre

IPT:

Intermediate public transport IPT

CAGR:

Compound annual growth rate

FAME:

Faster adoption and manufacturing of EV

NDC:

Nationally determined contributions

IoT:

Internet of things

BU:

Billion unit

CMI:

Charging margin index

CUI:

Charging urgency index

FF-PC:

Flattening factor-partially coordinated

FF-FC:

Flattening factor-fully coordinated

FF-UC:

Flattening factor-un coordinated

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, IET, Dr. RMLA Univ. Ayodhya, Faizabad, India

    Samarendra Pratap Singh

  2. Department of Electrical Engineering, MMMUT Gorakhpur, Gorakhpur, India

    Prabhakar Tiwari

  3. Department of Electrical Engineering, IIT Kanpur, Kanpur, India

    S. N. Singh

Authors
  1. Samarendra Pratap Singh
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  2. Prabhakar Tiwari
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  3. S. N. Singh
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Contributions

SPS contributed to the conceptualization, acquisition, investigation, analysis and interpretation of data, data curation formal analysis, methodology, software, and writing- original draft. PT contributed to supervision, writing–review and validation. SNS done resources, supervision, writing–review, editing and validation.

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Correspondence to Samarendra Pratap Singh.

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There are no conflicts of interest in submitting this paper. All co-authors have seen and agreed with the contents of the manuscript. We do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. We declare that this research article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal’s editorial board decides not to accept it for publication.

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Singh, S.P., Tiwari, P. & Singh, S.N. A new methodology of peak energy demand reduction using coordinated real-time scheduling of EVs. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02407-7

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