Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm. (1st August 2020)

• Record Type: Journal Article
• Authors: Liu, Feng; Ju, Xinglong; Wang, Ning; Wang, Li; Lee, Wei-Jen
• Is Part Of: Energy conversion and management. Volume 217(2020)
• Publication Date: 2020-08-01
• Subjects: Wind farm layout optimization -- Wind turbine -- Adaptive genetic algorithm -- Neural networks surrogate model -- Monte-Carlo simulation; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.112964
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A bi-criteria identification and relocation mechanism is given in genetic algorithm. A response surface built on neural networks to guide the relocation of wind turbines. The Influence Matrix is introduced to for wind turbine bi-criteria selection. Abstract: The existence of wake effect can affect the total power generation of a wind farm. To alleviate the impact of wake effect, numerous algorithms under the paradigm of evolutionary computation have been proposed to find the optimal layout of wind turbines. Previously, inspired by the self-adjustment capability among the individuals of a species in the natural world, we empowered the genetic algorithm (GA) with self-adaptivity and found that, by relocating the least efficient wind turbine to a new location with the help of a surrogate response surface of the power generation distribution, the performance of GA can be significantly improved. Following previous research, we discovered another major bottleneck that can cause the algorithm to be trapped into a suboptimal solution. A new bi-criteria identification and relocation (BCIR) mechanism is introduced to different versions of GA, including the conventional GA and our previous improved versions of GA. The introduction of BCIR does not require additional computation complexity. The effectiveness of this new mechanism is verified by conducting extensive experiments in two case studies, and both results show significant improvement over GA after adopting the new mechanism of BCIR.