Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore

Abstract This paper proposes a multi-population genetic algorithm (MPGA) program for wind turbine layout optimization in a wind farm which aims at extracting the maximum power in a minimum investment cost. The MPGA program is applied to a 2 km×2 km wind farm considering three different wind scenarios, i.e. (a): constant wind speed of 12 m/s with fixed wind direction; (b): constant wind speed of 12 m/s with variable wind direction and (c): variable wind speed of 8 m/s, 12 m/s, 17 m/s with variable wind directions. Compared with previous studies, the results of power generation cost of energy and wind farm efficiency are improved in the paper using MPGA which validate that MPGA works effectively in wind turbine layout optimization with wind farm. Additionally, a case study of wind turbine layout configuration op1timization using the MPGA program in a hypothetical offshore wind farm located in Hong Kong southeastern water is attempted using the 20 years׳ wind data from 1992 to 2011. The optimization result is beneficial for the assessment of the offshore wind power potential in Hong Kong. It is indicated that, under the optimization of wind turbines layout in the wind farm, 39.14×108 kWh/year electricity can be converted from the offshore wind farm, which occupies 9.09% of the 2012 electricity consumption in Hong Kong. Results show that the multi-population genetic algorithm program can be applied in any real-world wind farms for wind turbine layout optimization when the wind farm boundary is determined as well as the local wind condition is obtained.

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