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This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm’s layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing the layout with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.
|Number of pages||11|
|Publication status||Published - 19 Jun 2016|
|Event||35th International Conference on Ocean, Offshore and Arctic Engineering - Busan, Korea, Republic of|
Duration: 19 Jun 2016 → 24 Jun 2016
|Conference||35th International Conference on Ocean, Offshore and Arctic Engineering|
|Country||Korea, Republic of|
|Period||19/06/16 → 24/06/16|
- offshore wind farms
- renewable energy generation
- farm layout optimization
- cost assessment
- electrical infrastructure
Pillai, A. C., Chick, J., Johanning, L., Khorasanchi, M., & Barbouchi, S. (2016). Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer. Paper presented at 35th International Conference on Ocean, Offshore and Arctic Engineering, Busan, Korea, Republic of.