Thesis
Improving computational efficiency of wind energy resource assessment through novel statistical wake and access models
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2025
- Thesis identifier
- T17287
- Person Identifier (Local)
- 202268958
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- With the rapid growth of the wind energy sector and, consequently, site development, accurate and efficient resource assessment is becoming increasingly relevant. In-depth assessment requires a multitude of complex considerations, including the study of highly stochastic environmental conditions such as wind field and wave patterns. Understanding these conditions is essential to many Key Performance Indicators such as Energy Yield, O&M cost, accessibility and Levelized Cost of Energy (LCOE). To achieve this, computationally complex techniques such as Monte Carlo simulations are typically employed, however these methods are rarely feasible for comparing long timespans or large areas. This thesis focuses on improving the computational efficiency of resource assessment for wind energy by producing novel methods in two key areas of environmental forecasting: access prediction and wake modelling. The improved efficiency enables quicker resource assessment and comparison of large areas to optimise site selection. The first section proposes a highly accurate and efficient Markov Chain model, based on a weather and accessibility state space, to estimate several key accessibility traits, including the probability of instant access and estimated delay time of repairs due to weather. This model has competitive accuracy with existing probabilistic methods but with improved computational cost, allowing for assessing a three-year North Sea dataset in an order of minutes. This model may be used instead of more intensive Monte-Carlo simulations whilst still producing high accuracy and associated confidence. The second section derives and solves a Fourier approximation of a Gaussian wake based on the Bastankhah Porte-Agel model [1], to be implemented in Strathfarm, a holistic wind farm simulation software aiming to produce medium-fidelity results in real-time. The current implementation requires computationally intensive look-up tables based on polynomial approximations. The proposed method derives an efficiently calculable polynomial in terms of farm conditions only, removing the need for extremely large look-up table storage and access. This new method significantly improves efficiency whilst maintaining the accuracy of the current methodology within 10%. [References in thesis text]
- Advisor / supervisor
- Hart, Edward J.
- Feuchtwang, Julian
- Kazemi Amiri, Abbas Mehrad
- Resource Type
- DOI
- Date Created
- 2024
- Embargo Note
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File | 2025-04-08 | University of Strathclyde |