Posters

Abstract

The micro-siting of Wind Turbines (WTs) in wind farm planning is one of the most critical steps of wind site planning. The layout helps determine the optimum annual energy production of the farm as well as the cost of the project. Several factors affect the placement of WTs such as the wind speed, direction of the wind, height, and terrain as well as the quality of the data collected for it. An optimization algorithm of this multi-criteria is utilized at a test site in North Macedonia at a height of 1000 metres above sea level with wind data from the year 2014. The raw data is analysed using Symphonie Data Retriever, and the wind potential is evaluated on which a Weibull Distribution curve is fitted. The scaled data is represented on a 3D MATLAB model. This is followed by WTs selection from several WTs with different parameters such as hub height and rotor diameter. The location of each wind turbine is also shown using Georeferenced points on Google Earth Pro software which details the site of the wind farm. The total number of WTs will give the annual energy production of the site. This is estimated using the Power Coefficient (Cp) of the WTs and the capacity factor (CF) and the wind speed data available. The techno-economic analysis of this gives the Levelized Cost of Energy (LCOE) produced from the wind farm as well as the initial investment and costs required for the maintenance and operation of the wind farm for its lifecycle. The payback period of the project will justify the initial investment in the project which has a lifecycle of 20 years. The optimization algorithm can be then used for other sites in North Macedonia to boost its Renewable energy sector and reduce the dependencies on coal-fired power plants and reduce pollution.