A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem
| Main Author: | |
|---|---|
| Publication Date: | 2022 |
| Other Authors: | , , , |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10400.22/22474 |
Summary: | Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies. |
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A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market ProblemLocal electricity marketsParticle Swarm OptimizationPeer-to-Peer transactionsSensitivity analysisSwarm intelligenceEnergy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.IEEEREPOSITÓRIO P.PORTOVieira, MiguelFaia, RicardoLezama, FernandoCanizes, BrunoVale, Zita2023-03-14T10:56:38Z20222022-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.22/22474eng10.1109/CEC55065.2022.9870290info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-02T03:32:42Zoai:recipp.ipp.pt:10400.22/22474Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:59:50.326232Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| title |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| spellingShingle |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem Vieira, Miguel Local electricity markets Particle Swarm Optimization Peer-to-Peer transactions Sensitivity analysis Swarm intelligence |
| title_short |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| title_full |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| title_fullStr |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| title_full_unstemmed |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| title_sort |
A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem |
| author |
Vieira, Miguel |
| author_facet |
Vieira, Miguel Faia, Ricardo Lezama, Fernando Canizes, Bruno Vale, Zita |
| author_role |
author |
| author2 |
Faia, Ricardo Lezama, Fernando Canizes, Bruno Vale, Zita |
| author2_role |
author author author author |
| dc.contributor.none.fl_str_mv |
REPOSITÓRIO P.PORTO |
| dc.contributor.author.fl_str_mv |
Vieira, Miguel Faia, Ricardo Lezama, Fernando Canizes, Bruno Vale, Zita |
| dc.subject.por.fl_str_mv |
Local electricity markets Particle Swarm Optimization Peer-to-Peer transactions Sensitivity analysis Swarm intelligence |
| topic |
Local electricity markets Particle Swarm Optimization Peer-to-Peer transactions Sensitivity analysis Swarm intelligence |
| description |
Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-03-14T10:56:38Z |
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conference object |
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info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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http://hdl.handle.net/10400.22/22474 |
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http://hdl.handle.net/10400.22/22474 |
| dc.language.iso.fl_str_mv |
eng |
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eng |
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10.1109/CEC55065.2022.9870290 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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IEEE |
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IEEE |
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