A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time

Maritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly r...

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Main Authors: Ilias Sarantakos, Saman Nikkhah, Meltem Peker, Annabel Bowkett, Timur Sayfutdinov, Arman Alahyari, Charalampos Patsios, John Mangan, Adib Allahham, Eleni Bougioukou, Alan Murphy, Kayvan Pazouki
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:Cleaner Logistics and Supply Chain
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772390924000064
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author Ilias Sarantakos
Saman Nikkhah
Meltem Peker
Annabel Bowkett
Timur Sayfutdinov
Arman Alahyari
Charalampos Patsios
John Mangan
Adib Allahham
Eleni Bougioukou
Alan Murphy
Kayvan Pazouki
author_facet Ilias Sarantakos
Saman Nikkhah
Meltem Peker
Annabel Bowkett
Timur Sayfutdinov
Arman Alahyari
Charalampos Patsios
John Mangan
Adib Allahham
Eleni Bougioukou
Alan Murphy
Kayvan Pazouki
author_sort Ilias Sarantakos
collection DOAJ
description Maritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly relying on the electricity network. This interconnection creates multiple challenges due to the complexity of power flow in the port network, uncertainty of vessel arrival time and fluctuation of power generation of renewable energy sources. These uncertainties can lead to an overload in electricity networks and delays in cargo-handling activities, resulting in increased vessel handling times and environmental emissions. This paper presents a joint logistics-electric framework for optimal operation and power management of electrified ports, considering multiple uncertainties in the arrival time of vessels, network demand, and renewable power generation. An optimal power flow method is developed for a real-life port, with consideration for multiple port logistic assets such as cargo handling equipment, reefers, and renewable energy sources. The proposed model ensures feasible port operation for all uncertainty realisations defined by robust optimisation, while minimising operational costs. Simulation results demonstrate that the probability of a network constraint violation can be as high as 70% for an electrified major UK port if the uncertainty in the port operation is neglected, presenting an unacceptable risk of disruption to port activities. Furthermore, such uncertainty can cause 150% increase in emissions if the ships use their auxiliary engine instead of using shore power. The numerical study shows that such challenges can be handled by a 0.3% increase in the robustness in face of uncertainty, while the cost increase in the worst case does not exceed 4.7%. This shows the effectiveness of the proposed method enhancing robustness against uncertainty at the minimum cost.
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spelling doaj-art-d5314bb9718d4d1d9f72a4ff5823d87d2024-11-24T04:14:27ZengElsevierCleaner Logistics and Supply Chain2772-39092024-03-0110100144A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival timeIlias Sarantakos0Saman Nikkhah1Meltem Peker2Annabel Bowkett3Timur Sayfutdinov4Arman Alahyari5Charalampos Patsios6John Mangan7Adib Allahham8Eleni Bougioukou9Alan Murphy10Kayvan Pazouki11School of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom; Corresponding author.Department of Industrial Engineering, Bilkent University, Ankara, TurkeySchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomFaculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, United KingdomPort of Tyne, Maritime House, Tyne Dock, South Shields Newcastle upon Tyne, United KingdomSchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Engineering, Newcastle University, Newcastle upon Tyne, United KingdomMaritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly relying on the electricity network. This interconnection creates multiple challenges due to the complexity of power flow in the port network, uncertainty of vessel arrival time and fluctuation of power generation of renewable energy sources. These uncertainties can lead to an overload in electricity networks and delays in cargo-handling activities, resulting in increased vessel handling times and environmental emissions. This paper presents a joint logistics-electric framework for optimal operation and power management of electrified ports, considering multiple uncertainties in the arrival time of vessels, network demand, and renewable power generation. An optimal power flow method is developed for a real-life port, with consideration for multiple port logistic assets such as cargo handling equipment, reefers, and renewable energy sources. The proposed model ensures feasible port operation for all uncertainty realisations defined by robust optimisation, while minimising operational costs. Simulation results demonstrate that the probability of a network constraint violation can be as high as 70% for an electrified major UK port if the uncertainty in the port operation is neglected, presenting an unacceptable risk of disruption to port activities. Furthermore, such uncertainty can cause 150% increase in emissions if the ships use their auxiliary engine instead of using shore power. The numerical study shows that such challenges can be handled by a 0.3% increase in the robustness in face of uncertainty, while the cost increase in the worst case does not exceed 4.7%. This shows the effectiveness of the proposed method enhancing robustness against uncertainty at the minimum cost.http://www.sciencedirect.com/science/article/pii/S2772390924000064Port electrificationRobust optimisationLogistic-electric operationUncertainty
spellingShingle Ilias Sarantakos
Saman Nikkhah
Meltem Peker
Annabel Bowkett
Timur Sayfutdinov
Arman Alahyari
Charalampos Patsios
John Mangan
Adib Allahham
Eleni Bougioukou
Alan Murphy
Kayvan Pazouki
A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
Cleaner Logistics and Supply Chain
Port electrification
Robust optimisation
Logistic-electric operation
Uncertainty
title A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
title_full A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
title_fullStr A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
title_full_unstemmed A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
title_short A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time
title_sort robust logistics electric framework for optimal power management of electrified ports under uncertain vessel arrival time
topic Port electrification
Robust optimisation
Logistic-electric operation
Uncertainty
url http://www.sciencedirect.com/science/article/pii/S2772390924000064
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