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Multi-Objective Smart Charge Control...

Das, Ridoy.

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Multi-Objective Smart Charge Control of Electric Vehicles.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Objective Smart Charge Control of Electric Vehicles./
Author:	Das, Ridoy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	284 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Contained By:	Dissertations Abstracts International82-03B.
Subject:	Alternative energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28119667
ISBN:	9798662568075

Multi-Objective Smart Charge Control of Electric Vehicles.
Das, Ridoy.

Multi-Objective Smart Charge Control of Electric Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 284 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (Ph.D.)--University of Northumbria at Newcastle (United Kingdom), 2020.

This item must not be sold to any third party vendors.

With the increasing integration of electric vehicles and renewable energy sources in electricity networks, key opportunities in terms of a cleaner environment and a sustainable energy portfolio are unlocked. However, the widespread deployment of these two technologies, can entail significant challenges for the electricity grid and in a larger context for the society, when they are not optimally integrated. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are being proposed as crucial solutions to achieve economic, technical and environmental benefits in future smart grids. The implementation of these technologies involves a number of key stakeholders, namely, the end-electricity user, the electric vehicle owner, the system operators and policy makers. For a wider and efficient implementation of the smart grid vision, these stakeholders must be engaged and their aims must be fulfilled. However, the financial, technical and environmental objectives of these stakeholders are often conflicting, which leads to an intricate paradigm requiring efficient and fair policies. With this focus in mind, the present research work develops multi-objective optimisation algorithms to control the charging and discharging process of electric vehicles. Decentralised, hybrid and real-time optimisation algorithms are proposed, modelled, simulated and validated. End user energy cost, battery degradation, grid interaction and CO2 emissions are optimised in this work and their trade-offs are highlighted. Multi-criteria-decision-making approaches and game theoretical frameworks are developed to conciliate the interests of the involved stakeholders. The results, in the form of optimal electric vehicle charging/discharging schedules, show improvements along all the objectives while complying with the user requirements. The outcome of the present research work serves as a benchmark for informing system operators and policy makers on the necessary measures to ensure an efficient and sustainable implementation of electro-mobility as a fundamental part of current and future smart grids.

ISBN: 9798662568075Subjects--Topical Terms:

3436775
Alternative energy.
Subjects--Index Terms:

Multi-objective

Multi-Objective Smart Charge Control of Electric Vehicles.
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500 $a Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
500 $a Advisor: Putrus, Ghanim.
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520 $a With the increasing integration of electric vehicles and renewable energy sources in electricity networks, key opportunities in terms of a cleaner environment and a sustainable energy portfolio are unlocked. However, the widespread deployment of these two technologies, can entail significant challenges for the electricity grid and in a larger context for the society, when they are not optimally integrated. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are being proposed as crucial solutions to achieve economic, technical and environmental benefits in future smart grids. The implementation of these technologies involves a number of key stakeholders, namely, the end-electricity user, the electric vehicle owner, the system operators and policy makers. For a wider and efficient implementation of the smart grid vision, these stakeholders must be engaged and their aims must be fulfilled. However, the financial, technical and environmental objectives of these stakeholders are often conflicting, which leads to an intricate paradigm requiring efficient and fair policies. With this focus in mind, the present research work develops multi-objective optimisation algorithms to control the charging and discharging process of electric vehicles. Decentralised, hybrid and real-time optimisation algorithms are proposed, modelled, simulated and validated. End user energy cost, battery degradation, grid interaction and CO2 emissions are optimised in this work and their trade-offs are highlighted. Multi-criteria-decision-making approaches and game theoretical frameworks are developed to conciliate the interests of the involved stakeholders. The results, in the form of optimal electric vehicle charging/discharging schedules, show improvements along all the objectives while complying with the user requirements. The outcome of the present research work serves as a benchmark for informing system operators and policy makers on the necessary measures to ensure an efficient and sustainable implementation of electro-mobility as a fundamental part of current and future smart grids.
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