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Special Issue "Coordinated Control of Wind Power in Power Systems with a Large Share of Renewables"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: 20 April 2023 | Viewed by 1625
Please submit your paper and select the Journal "Energies" and the Special Issue "Coordinated Control of Wind Power in Power Systems with a Large Share of Renewables" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the journal editor Adele Min ([email protected]) before submitting.

Special Issue Editors

Dr. Anca D. Hansen
E-Mail Website
Guest Editor
Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
Interests: wind power plants modelling, integration and control; ancillary services provision and coordination from and between wind power plants; operation and optimization of distribution networks by employing WPPs technical capabilities
Special Issues, Collections and Topics in MDPI journals
Dr. Kaushik Das
E-Mail Website
Guest Editor
Department of Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark
Interests: hybrid power plants; integration of renewables in power system studies; grid integration and control; weather dependency of power systems; active distribution networks

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the coordination of wind power plants with either other wind power plants or any other source such as PV, storage, STATCOM to provide grid services, in order to maintain the stability of future entire renewable energy integrated power systems.

This Special Issue aims to gather contributions which provide agile insights and answers to a variety of future research challenges for improved integration of a large share of renewable generation plants, providing industry and society with new solutions. The contributions to this Special Issue can involve but need not be limited to the following topics:

  • Co-ordination among wind and renewable generations to provide enhanced grid services (grid following, grid supporting and grid forming) both for interconnected and island networks;
  • Co-ordination among wind turbines in a single wind power plant to provide grid services;
  • Co-ordination of wind turbines together with other components inside wind power plants such as supercapacitors, STATCOM, and on-load tap changers to provide enhanced grid services;
  • Co-ordinated control of wind power plants, especially as a cluster of large offshore wind power plants connected to a power system at one point of connection either through HVAC or HVDC connection to provide enhanced grid services;
  • Co-ordination of wind power together with other sources—solar power and storage controlled through a hybrid power plant controller to provide enhanced grid services;
  • Co-ordination of other network assets including TSO/DSO coordination for networks with a large share of renewable generations;
  • Co-ordinated control of wind power plants together with power to x (hydrogen, methane, etc.)

Prof. Dr. Anca D. Hansen
Dr. Kaushik Das
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • enhanced grid services (grid following, grid supporting, and grid forming)
  • wind power plant coordinated control
  • wind power coordination with other existing assets, i.e., on-tap-changers, statcom
  • renewable generations
  • tso/dso coordination
  • offshore wind power plants
  • hybrid wind power plants

Published Papers (1 paper)

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Research

Article
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study
Energies 2022, 15(7), 2327; https://doi.org/10.3390/en15072327 - 23 Mar 2022
Cited by 7 | Viewed by 1147
Abstract
Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent [...] Read more.
Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models. Full article
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