TY - JOUR
T1 - Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm
AU - Lu, Maxim
AU - Abedinia, Oveis
AU - Bagheri, Mehdi
AU - Ghadimi, Noradin
AU - Shafie-Khah, Miadreza
AU - Catalão, João P.S.
N1 - Funding Information:
The work of M. Lu, O. Abedinia and M. Bagheri was supported in part by the Program-Targeted Funding of the Ministry of Education and Science of the Republic of Kazakhstan through the Innovative Materials and Systems for Energy Conversion and Storage for 2018–2020 under Grant BR05236524 and in part by the Faculty Development Competitive Research Grant of Nazarbayev University under Grant SOE2018018. The work of M. Shafie-khah was supported in part by FLEXIMAR-project (Novel marketplace for energy flexibility), which has received funding from Business Finland Smart Energy Program, 2017–2021. The work of J. P. S. Catalão was supported by FEDER funds through COMPETE 2020 and by the Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017).
Publisher Copyright:
© This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.
AB - One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.
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U2 - 10.1049/iet-stg.2019.0334
DO - 10.1049/iet-stg.2019.0334
M3 - Article
AN - SCOPUS:85098917397
SN - 2515-2947
VL - 3
SP - 914
EP - 923
JO - IET Smart Grid
JF - IET Smart Grid
IS - 6
ER -