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

Carolina G. Marcelino, João V. C. Avancini, Carla A. D. M. Delgado, Elizabeth F. Wanner, Silvia Jiménez-Fernández and Sancho Salcedo-Sanz
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Carolina G. Marcelino: Department of Signal Processing and Communications, Universidad de Alcalá (UAH), Alcalá de Henares, 28805 Madrid, Spain
João V. C. Avancini: Institute of Computing, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, Brazil
Carla A. D. M. Delgado: Institute of Computing, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, Brazil
Elizabeth F. Wanner: Department of Computation, Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Belo Horizonte 30421-169, Brazil
Silvia Jiménez-Fernández: Department of Signal Processing and Communications, Universidad de Alcalá (UAH), Alcalá de Henares, 28805 Madrid, Spain
Sancho Salcedo-Sanz: Department of Signal Processing and Communications, Universidad de Alcalá (UAH), Alcalá de Henares, 28805 Madrid, Spain

Sustainability, 2021, vol. 13, issue 21, 1-20

Abstract: 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.

Keywords: offshore wind power; optimization; energy efficiency; energy resources; clean energies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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