Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm
<p dir="ltr">To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This ha...
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2023
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| _version_ | 1864513534750097408 |
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| author | Ali S. Alghamdi (17541711) |
| author2 | Mohana Alanazi (17541558) Abdulaziz Alanazi (9509776) Yazeed Qasaymeh (17541561) Muhammad Zubair (728141) Ahmed Bilal Awan (17541714) M. G. B. Ashiq (17541717) |
| author2_role | author author author author author author |
| author_facet | Ali S. Alghamdi (17541711) Mohana Alanazi (17541558) Abdulaziz Alanazi (9509776) Yazeed Qasaymeh (17541561) Muhammad Zubair (728141) Ahmed Bilal Awan (17541714) M. G. B. Ashiq (17541717) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali S. Alghamdi (17541711) Mohana Alanazi (17541558) Abdulaziz Alanazi (9509776) Yazeed Qasaymeh (17541561) Muhammad Zubair (728141) Ahmed Bilal Awan (17541714) M. G. B. Ashiq (17541717) |
| dc.date.none.fl_str_mv | 2023-08-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.32604/cmes.2023.029453 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Stochastic_Programming_for_Hub_Energy_Management_Considering_Uncertainty_Using_Two-Point_Estimate_Method_and_Optimization_Algorithm/30338893 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Environmental engineering Information and computing sciences Artificial intelligence Stochastic energy hub scheduling Energy profit Uncertainty Hong’s two-point estimate method improved artificial rabbits optimization |
| dc.title.none.fl_str_mv | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat and power (CHP) systems, steam boilers, energy storage, and electric cars in the day-ahead market. The standard ARO algorithm is developed to mimic the foraging behavior of rabbits, and in this work, the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique. The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO, particle swarm optimization (PSO), and salp swarm algorithm (SSA). The findings show that, in comparison to previous approaches, the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity, gas, and heating markets by satisfying the operational and energy hub limitations. Additionally, the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995% as compared to deterministic planning.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Modeling in Engineering & Sciences<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.32604/cmes.2023.029453" target="_blank">https://dx.doi.org/10.32604/cmes.2023.029453</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5a46203761c55bac13dc765bc7e4ef5e |
| identifier_str_mv | 10.32604/cmes.2023.029453 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30338893 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization AlgorithmAli S. Alghamdi (17541711)Mohana Alanazi (17541558)Abdulaziz Alanazi (9509776)Yazeed Qasaymeh (17541561)Muhammad Zubair (728141)Ahmed Bilal Awan (17541714)M. G. B. Ashiq (17541717)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringEngineering practice and educationEnvironmental engineeringInformation and computing sciencesArtificial intelligenceStochastic energy hub schedulingEnergy profitUncertaintyHong’s two-point estimate methodimproved artificial rabbits optimization<p dir="ltr">To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat and power (CHP) systems, steam boilers, energy storage, and electric cars in the day-ahead market. The standard ARO algorithm is developed to mimic the foraging behavior of rabbits, and in this work, the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique. The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO, particle swarm optimization (PSO), and salp swarm algorithm (SSA). The findings show that, in comparison to previous approaches, the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity, gas, and heating markets by satisfying the operational and energy hub limitations. Additionally, the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995% as compared to deterministic planning.</p><h2>Other Information</h2><p dir="ltr">Published in: Computer Modeling in Engineering & Sciences<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.32604/cmes.2023.029453" target="_blank">https://dx.doi.org/10.32604/cmes.2023.029453</a></p>2023-08-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.32604/cmes.2023.029453https://figshare.com/articles/journal_contribution/Stochastic_Programming_for_Hub_Energy_Management_Considering_Uncertainty_Using_Two-Point_Estimate_Method_and_Optimization_Algorithm/30338893CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303388932023-08-03T03:00:00Z |
| spellingShingle | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm Ali S. Alghamdi (17541711) Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Environmental engineering Information and computing sciences Artificial intelligence Stochastic energy hub scheduling Energy profit Uncertainty Hong’s two-point estimate method improved artificial rabbits optimization |
| status_str | publishedVersion |
| title | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| title_full | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| title_fullStr | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| title_full_unstemmed | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| title_short | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| title_sort | Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Engineering practice and education Environmental engineering Information and computing sciences Artificial intelligence Stochastic energy hub scheduling Energy profit Uncertainty Hong’s two-point estimate method improved artificial rabbits optimization |