Abstract
This study presents a new turbine layout optimisation approach using a grid-based problem formulation for improved design performance and computational efficiency for industrial-scale applications. A particle swarm optimisation algorithm is employed in the wind turbine layout optimisation, in which a micro-siting function is proposed to allow solutions 50 m of deviation while maximising energy capture without compromising maritime navigation or search and rescue operations. Solutions are assessed by a wind farm model, comprising the Larsen wake model, a multiple wake effect summation method, and a rotor-effective wind speed calculation. A novel look-up function is populated by on-the-fly algorithm and is used to reduce the number of model evaluations by approximately 95%. A gigawatt scale hypothetical site is presented to test the model on a realistically complex scenario. A set of design solutions generated by the algorithm are compared to empirical designs, with the algorithm outperforming the empirical solutions by 7.55% on average, in terms of net-present-value of energy capture minus the capital cost of turbines. The numerical efficiency and design effectiveness are examined and further improvements discussed.
Highlights
As the demand for renewable energy generation increases and onshore usable space is limited, developers have increasingly looked to offshore wind projects for grid-connected renewable energy
The overwhelming majority of layout studies use the Jensen wake model [1], in which a linearly expanding wake region is assumed behind a turbine, with a velocity deficit decreasing with downstream distance but radially uniform across the wake area [2]
This was expanded by Katic et al to allow for the aggregation of multiple wake effects and to calculate cluster efficiency, and the Katic et al.’s approach was believed to be sufficient for mean energy production, it is not sufficient for considering turbine loading or for use in detailed cost modelling of projects [3]
Summary
As the demand for renewable energy generation increases and onshore usable space is limited, developers have increasingly looked to offshore wind projects for grid-connected renewable energy. This 10 × 10 grid formulation has since been used in many wind farm layout problems [10,11,12] In these studies, turbines are placed within the defined 100 discrete grid positions to maximise energy capture by assessing wake interactions across the site. Turbines are placed within the defined 100 discrete grid positions to maximise energy capture by assessing wake interactions across the site While this is valuable to examine the efficacy of the algorithms, the test cases are far from representative of a real offshore wind farm site. Key hypothetical wind farm data are provided in the Appendix
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
