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-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.

Publication DOI: https://doi.org/10.3390/su132111924
Additional Information: 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.
Uncontrolled Keywords: offshore wind power,optimization,energy efficiency,energy resources,clean energies
Publication ISSN: 2071-1050
Last Modified: 04 Jul 2024 07:22
Date Deposited: 24 May 2023 15:29
Full Text Link:
Related URLs: https://www.mdp ... 050/13/21/11924 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-11
Published Online Date: 2021-10-28
Accepted Date: 2021-10-26
Authors: Marcelino, Carolina G.
Avancini, João V. C.
Delgado, Carla A. D. M.
Wanner, Elizabeth F. (ORCID Profile 0000-0001-6450-3043)
Jiménez-Fernández, Silvia
Salcedo-Sanz, Sancho



Version: Published Version

License: Creative Commons Attribution

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