Preprints
https://doi.org/10.5194/wes-2024-117
https://doi.org/10.5194/wes-2024-117
25 Sep 2024
 | 25 Sep 2024
Status: this preprint is currently under review for the journal WES.

IMAP-WFO: A holistic optimization tool for bottom fixed offshore wind farm design and control

Niels Roeders, Matteo Capaldo, Sander van Nederveen, and Oriol Colomés

Abstract. Offshore wind farms, critical for sustainable energy production, face the challenge of optimization among many parameters influencing key performance indicators in competitive ways. This research introduces the novel Integrative Maximized Aggregated Preference Wind Farm Optimization (IMAP-WFO) framework – a comprehensive tool designed to enhance flexibility, accuracy, and uncertainty quantification in offshore wind farm design and operation. Existing methods often fall short due to limitations in adaptability and precision, especially when modeling complex multi-physical behaviors under uncertain conditions. IMAP-WFO overcomes these limitations by combining advanced statistical techniques and simulation methods. At its core are parametric design performance functions, capturing critical aspects of wind farm behavior, including energy production, material usage, and structural fatigue. These functions rely on Kriging meta-models. To address inherent uncertainty, Monte Carlo simulations provide a probabilistic assessment of outcomes. IMAP-WFO's true innovation lies in translating technical functions into socio-economic objectives, including sustainability metrics, annual energy production, capital expenditure, operational expenditure, model uncertainty, and lifetime fatigue. Stakeholders can dynamically weigh these objectives based on their preferences. A validation process ensures the accuracy of design performance functions, comparing simulated results with real-world data. IMAP-WFO's application is demonstrated through case studies: optimizing the levelized cost of energy and exploring wind farm control strategies.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Niels Roeders, Matteo Capaldo, Sander van Nederveen, and Oriol Colomés

Status: open (until 04 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-117', Anonymous Referee #1, 14 Nov 2024 reply
  • RC2: 'Comment on wes-2024-117', Anonymous Referee #2, 18 Nov 2024 reply
Niels Roeders, Matteo Capaldo, Sander van Nederveen, and Oriol Colomés
Niels Roeders, Matteo Capaldo, Sander van Nederveen, and Oriol Colomés

Viewed

Total article views: 251 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
147 34 70 251 8 7
  • HTML: 147
  • PDF: 34
  • XML: 70
  • Total: 251
  • BibTeX: 8
  • EndNote: 7
Views and downloads (calculated since 25 Sep 2024)
Cumulative views and downloads (calculated since 25 Sep 2024)

Viewed (geographical distribution)

Total article views: 257 (including HTML, PDF, and XML) Thereof 257 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2024
Download
Short summary
Offshore wind farms face the challenge of optimization among many parameters influencing key performance indicators. This research introduces a novel farm optimization framework that enhances flexibility, accuracy, and uncertainty quantification in offshore wind farm design and operation. The proposed work has the key innovation in the ability to translate technical functions into socio-economic objectives, where stakeholders can dynamically weigh these objectives based on their preferences.
Altmetrics