Adaptive Neuro-Fuzzy Optimization of Wind Farm Project Investment Under Wake Effect

Abstract

The engineering planning of a wind farm generally includes critical decision-making, regarding the layout of the turbines in the wind farm, the number of wind turbines to be installed and the types of wind turbines to be installed. Two primary objectives of optimal wind farm planning are to minimize the cost of energy and to maximize the net energy production or to maximize wind farm efficiency. The optimal wind turbine placement on a wind farm could be modified by taking economic aspects into account. The net present value (NPV) and internal rate of return (IRR) are two of the most important criteria for project investment estimating. To assess the investment risk of wind power project, this paper constructed a process which initially simulated maximal NPV with adaptive neuro-fuzzy (ANFIS) method and then evaluated the IRR based on it. Afterwards, ANFIS simulated maximal IRR and then evaluated the NPV based on it. ANFIS shows very good learning and prediction capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system. The aim of this paper is to develop a model to determine economically optimal layouts for wind farms which include the aerodynamic interactions between the turbines, the various cost factors and wind regime.