||Pillai, A.C.,Chick, J., Johanning, L., Khorasanchi, M. and Pelissier, S. Optimisation of Offshore Wind Farms Using a Genetic Algorithm, Proceedings of the Twenty-fifth (2015) International Offshore and Polar Engineering Conference, Kona, Big Island, Hawaii, June 21-26 2015, 2015. https://doi.org/10.5286/UKERC.EDC.000866. Cite this using DataCite
||Pillai, A.C.,Chick, J., Johanning, L., Khorasanchi, M. and Pelissier, S.
||A modular framework for the optimisation of an offshore wind farm using a discrete genetic algorithm is presented. This approach uses a bespoke grid generation algorithm to define the discrete positions that turbines may occupy, thereby implicitly satisfying navigational and search and rescue constraints through the wind farm. The presented methodology takes a holistic approach, optimising both the turbine placement and intra-array cable network while minimising the levelised cost of energy and satisfying real-world constraints. This tool therefore integrates models for the assessment of the energy production including wake losses, the optimisation of the intra-array cables, and the estimation of the costs of the project over the lifetime. This framework will allow alternate approaches to wake and cost modelling as well as optimisation to be benchmarked in the futuref the costs of the project over the lifetime. This framework will allow alternate approaches to wake and cost modelling as well as optimisation to be benchmarked in the future.
This work was partly funded via IDCORE, the Industrial Doctorate Centre for Offshore Renewable Energy, which trains research engineers whose work in conjunction with sponsoring companies aims to accelerate the deployment of offshore wind, wave and tidal-current technologies