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, Sancho Salcedo-Sanz

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Article number11924
    Number of pages20
    Issue number21
    Early online date28 Oct 2021
    Publication statusPublished - Nov 2021

    Bibliographical note

    Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

    Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 754382. This research has been partially funded by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P), by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366), and Brazilian research agencies: CAPES (Finance Code 001) and CNPq.


    • offshore wind power
    • optimization
    • energy efficiency
    • energy resources
    • clean energies


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