Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C...

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Autores principales:	Carolina G. Marcelino, João V. C. Avancini, Carla A. D. M. Delgado, Elizabeth F. Wanner, Silvia Jiménez-Fernández, Sancho Salcedo-Sanz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	offshore wind power optimization energy efficiency energy resources clean energies Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ab018756461540c681effa82d9046dc6
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spelling	oai:doaj.org-article:ab018756461540c681effa82d9046dc62021-11-11T19:37:09ZDynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms10.3390/su1321119242071-1050https://doaj.org/article/ab018756461540c681effa82d9046dc62021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11924https://doaj.org/toc/2071-1050In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.Carolina G. MarcelinoJoão V. C. AvanciniCarla A. D. M. DelgadoElizabeth F. WannerSilvia Jiménez-FernándezSancho Salcedo-SanzMDPI AGarticleoffshore wind poweroptimizationenergy efficiencyenergy resourcesclean energiesEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11924, p 11924 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	offshore wind power optimization energy efficiency energy resources clean energies Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	offshore wind power optimization energy efficiency energy resources clean energies Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Carolina G. Marcelino João V. C. Avancini Carla A. D. M. Delgado Elizabeth F. Wanner Silvia Jiménez-Fernández Sancho Salcedo-Sanz Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
description	In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.
format	article
author	Carolina G. Marcelino João V. C. Avancini Carla A. D. M. Delgado Elizabeth F. Wanner Silvia Jiménez-Fernández Sancho Salcedo-Sanz
author_facet	Carolina G. Marcelino João V. C. Avancini Carla A. D. M. Delgado Elizabeth F. Wanner Silvia Jiménez-Fernández Sancho Salcedo-Sanz
author_sort	Carolina G. Marcelino
title	Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
title_short	Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
title_full	Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
title_fullStr	Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
title_full_unstemmed	Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
title_sort	dynamic electric dispatch for wind power plants: a new automatic controller system using evolutionary algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ab018756461540c681effa82d9046dc6
work_keys_str_mv	AT carolinagmarcelino dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms AT joaovcavancini dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms AT carlaadmdelgado dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms AT elizabethfwanner dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms AT silviajimenezfernandez dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms AT sanchosalcedosanz dynamicelectricdispatchforwindpowerplantsanewautomaticcontrollersystemusingevolutionaryalgorithms
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Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

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