A Binary Integer Programming Method for Optimal Wind Turbines Allocation

Nikolaos M. Manousakis, Constantinos S. Psomopoulos, George Ch. Ioannidis and Stavros D. Kaminaris
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Nikolaos M. Manousakis: Department of Electrical and Electronics Engineering, University of West Attica, 250, Thivon & P. Ralli Str., 12244 Egaleo, Athens, Greece
Constantinos S. Psomopoulos: Department of Electrical and Electronics Engineering, University of West Attica, 250, Thivon & P. Ralli Str., 12244 Egaleo, Athens, Greece
George Ch. Ioannidis: Department of Electrical and Electronics Engineering, University of West Attica, 250, Thivon & P. Ralli Str., 12244 Egaleo, Athens, Greece
Stavros D. Kaminaris: Department of Electrical and Electronics Engineering, University of West Attica, 250, Thivon & P. Ralli Str., 12244 Egaleo, Athens, Greece

Clean Technol., 2021, vol. 3, issue 2, 1-12

Abstract: The present study introduces a Binary Integer Programming (BIP) method to minimize the number of wind turbines needed to be installed in a wind farm. The locations of wind turbines are selected in a virtual grid which is constructed considering a minimum distance between the wind turbines to avoid the wake effect. Additional equality constraints are also included to the proposed formulation to prohibit or enforce the installation of wind turbines placement at specific locations of the wind farmland. Moreover, a microscopic wind turbine placement considering the local air density is studied. To verify the efficiency of this proposal, a square site was subdivided into 25 square cells providing a virtual grid with 36 candidate placement locations. Moreover, a virtual grid with 121 vertices related with a Greek island is also tested. All simulations conducted considering the area of geographical territory, the length of wind turbine blades, as well as the capacity of each turbine.

Keywords: binary integer programming; microscopic placement; optimization; wake effect; wind farm; wind turbines (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
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