Modeling the arrangement of turbines for onshore wind power plants under varying wind conditions

Abstract

Wind field layout optimization concerning various parameters is a major point in planning and will influence the revenue for the whole life of the installation. Besides the obvious impact of wind distribution also other parameters like connection costs and levelized costs of energy influence the optimum layout and have to be included in a realistic optimization algorithm. In this paper the sophisticated optimization of wind field layout with of two fundamentally different heuristic algorithms is investigated. To do so, detailed real-world data from an existing wind field in Bogdanci, Macedonia is utilized by employing real wind field data we are able to calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind fields. Different layouts were designed using sophisticated algorithms for handling the resulting high-dimensional, highly nonlinear optimization problem. In particular, a nondominated sorting genetic algorithm (NSGA) and a mixed discrete particle swarm optimization algorithm (MD-PSO) were applied. Both optimization algorithms established bi-objective fitness functions, in particular- minimizing the levelized cost of energy and maximizing the capacity factor. By comparing the results obtained with the existing layout, it is established that both optimization algorithms are adequate in determination of wind power plant layouts. It is proven that the implementation of sophisticated optimization methods can results in essential savings during the whole lifetime of the wind field.