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DC Field | Value | Language |
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dc.contributor.author | Alhasnawi, BN | - |
dc.contributor.author | Almutoki, SMM | - |
dc.contributor.author | Hussain, FFK | - |
dc.contributor.author | Harrison, A | - |
dc.contributor.author | Bazooyar, B | - |
dc.contributor.author | Zanker, M | - |
dc.contributor.author | Bureš, V | - |
dc.date.accessioned | 2024-08-11T09:15:21Z | - |
dc.date.available | 2024-08-11T09:15:21Z | - |
dc.date.issued | 2024-08-03 | - |
dc.identifier | Article No.: 105721 | - |
dc.identifier | ORCiD: Bahamin Bazooyar https://orcid.org/0000-0002-7341-4509 | - |
dc.identifier | ORCiD: Firas Faeq K. Hussain https://orcid.org/0000-0003-4087-5592 | - |
dc.identifier | ORCiD: Ambe Harrison https://orcid.org/0000-0002-4353-1261 | - |
dc.identifier | ORCiD: Marek Zanker https://orcid.org/0000-0002-2745-4868 | - |
dc.identifier | ORCiD: Vladimír Bureš https://orcid.org/0000-0001-7788-7445 | - |
dc.identifier.citation | Alhasnawi, B.N. et al. (2024). 'A New Methodology for Reducing Carbon Emissions Using Multi-Renewable Energy Systems and Artificial Intelligence', Sustainable Cities and Society, Vol.114 (1 November 2024), pp. 1 - 20. doi: https://doi.org/10.1016/j.scs.2024.105721. | en_US |
dc.identifier.issn | 2210-6707 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29531 | - |
dc.description | Data availability - (https://www.sciencedirect.com/science/article/pii/S2210670724005468?via%3Dihub#refdata001) / The data used for this research and preparation of this article can be accessed from Brunel University of London repository at: https://doi.org/10.17633/rd.brunel.26391475.v1 | en_US |
dc.description.abstract | Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396% per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437% per day. From the obtained results the proposed algorithm gives superior performance. | en_US |
dc.description.sponsorship | The research has been partially supported by the Faculty of Informatics and Management UHK excellence project “Methodological perspectives on modelling and simulation of hard and soft systems”. | en_US |
dc.format.extent | 1 - 20 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2024 The Authors. Published by Elsevier Ltd.. Under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | DSM | en_US |
dc.subject | HEMS | en_US |
dc.subject | HBA | en_US |
dc.subject | WOA | en_US |
dc.subject | IAROA | en_US |
dc.subject | PV | en_US |
dc.subject | WT | en_US |
dc.title | A New Methodology for Reducing Carbon Emissions Using Multi-Renewable Energy Systems and Artificial Intelligence | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-07-28 | - |
dc.identifier.doi | https://doi.org/10.1016/j.scs.2024.105721 | - |
dc.relation.isPartOf | Sustainable Cities and Society | - |
pubs.publication-status | Published | - |
pubs.volume | 114 | - |
dc.identifier.eissn | 2210-6715 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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FullText.pdf | © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | 4.24 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License