2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results
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Publication Date: | 2019 |
Other Authors: | , , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | http://hdl.handle.net/10400.22/17307 |
Summary: | This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems. |
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2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and resultsEvolutionary computationPower systemsMetaheuristicsOptimizationSmart gridsSwarm intelligenceThis paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and Project SIMOCE (ANI|P2020 17690). It also received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. We would like to express our gratitude to the 7 participants of this competition: UNESP-LaPSEE, INESC TEC-CEFET MG, CHARUSAT, UNAL, UFMG, NTU-EEE, UB-UTD.ElsevierRepositório Científico do Instituto Politécnico do PortoLezama, FernandoSoares, JoãoVale, ZitaRueda, JoseRivera, SergioElrich, István2021-05-17T00:30:21Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/17307eng10.1016/j.swevo.2018.05.005info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:06:43Zoai:recipp.ipp.pt:10400.22/17307Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:49.483567Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
title |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
spellingShingle |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results Lezama, Fernando Evolutionary computation Power systems Metaheuristics Optimization Smart grids Swarm intelligence |
title_short |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
title_full |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
title_fullStr |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
title_full_unstemmed |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
title_sort |
2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results |
author |
Lezama, Fernando |
author_facet |
Lezama, Fernando Soares, João Vale, Zita Rueda, Jose Rivera, Sergio Elrich, István |
author_role |
author |
author2 |
Soares, João Vale, Zita Rueda, Jose Rivera, Sergio Elrich, István |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Lezama, Fernando Soares, João Vale, Zita Rueda, Jose Rivera, Sergio Elrich, István |
dc.subject.por.fl_str_mv |
Evolutionary computation Power systems Metaheuristics Optimization Smart grids Swarm intelligence |
topic |
Evolutionary computation Power systems Metaheuristics Optimization Smart grids Swarm intelligence |
description |
This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2021-05-17T00:30:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/17307 |
url |
http://hdl.handle.net/10400.22/17307 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.swevo.2018.05.005 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131458733867008 |