E3S Web Conf.
Volume 216, 2020Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2020)
|Number of page(s)||6|
|Published online||14 December 2020|
Siting and sizing of wind farms taking into account stochastic nature of generation
1 Melentiev Energy Systems Institute, Department of Complex and Regional Problems in Energy, 130 Lermontov str., Irkutsk, Russia
2 Melentiev Energy Systems Institute, Department of Electric Power Systems, 130 Lermontov str., Irkutsk, Russia
3 Azerbaijan Scientific-Research and Designed-Prospecting Power Engineering Institute, Hasanbey Zardabi av. 94, Baku, Azerbaijan
* Corresponding author: email@example.com
The article deals with the problem of the negative impact of wind farms stochastic generation on power grid. One of the ways to reduce the stochasticity of the wind farms generation is their geographically distributed siting. A technique for sizing and distributed siting of wind farms from the standpoint of the influence on the variability of the total generated power is proposed. Modeling of wind power generation with hourly detailing is carried out using the developed Wind-MCA software based on data from archives of long-term observations of ground-based weather stations. The optimal distribution of wind turbines in potential locations is based on a genetic algorithm. The objective function is the coefficient of variation of the power generated by all wind farms in the sites under consideration, depending on the number of wind turbines in their composition. The genetic algorithm is implemented using the built-in MATLAB function. The proposed technique is applied to assess the capacity options and sites for wind farms in the Zabaykalsky Krai. The solution providing the minimum value of the coefficient of variation of the wind farms generated power and high value of the wind farms capacity utilization factor has been obtained.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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