Abstract
Choosing the right wind site and estimating the extracted energy of the wind turbines are essential to successfully establishing a wind farm in a specific wind site. In this paper, a method for estimating the extracted energy of the wind farms using several mathematical models is proposed. The estimating method, which was based on five wind turbines, Q1, Q2, Q3, Q4, and Q5 and three wind distribution models, gamma, Weibull, and Rayleigh, was used to suggest suitable specifications of a wind turbine for a specific wind site and maximize the extracted energy of the proposed wind farm. An optimization problem, developed for this purpose, was solved using the whale optimization algorithm (WOA). The suggested method was tested using several potential wind sites in Jordan. The proposed wind farms at these sites achieved the maximum extracted energy, maximum capacity factor (CF), and minimum levelized cost of energy (LCoE) based on the solution of the developed optimization problem. The developed model with Q3 and the Rayleigh distribution function was validated with real measurement data from several wind farms in Jordan. Error analysis showed that the difference between the measured and estimated energy was less than 20%. The study validated the provided model, which can now be utilized routinely for the assessment of wind energy potential at a specific wind site.
Highlights
Throughout history, humanity has always strived to maximize the utilization of natural resources
This paper aims to fill the gap by providing a comprehensive study on the assessment of wind energy potential in Jordan
The utilized algorithms were implemented in MATLAB R2007 b, with the following specifications: Intel® core (TM) i3-2330 M CPU @ 2.20 GHz, Installed memory (RAM): 4.00 GB, System type: 64-bit Operating system
Summary
Throughout history, humanity has always strived to maximize the utilization of natural resources. A good illustration of how this has been accomplished is through renewable energy resources. Renewable energy has become an urgent and critical requirement to address the concerns caused by using fossil fuels. Wind power is the most cost-competitive technology all over the world. The global wind sector hit a new high in 2017 [1]. This increase in wind capacity was attributed to a number of factors by the Global Wind Energy Council (GWEC), including (a) the introduction and deployment of a new generation of turbines with a larger shelf area and higher production; (b) increasing investor confidence; (c) technology and management improvement; and (d) industry maturity
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