Abstract
Nowadays, there is a growing trend to incorporate renewables in electrical power systems and, in particular, wind energy, which has become an important primary source in the electricity mix of many countries, where wind farms have been proliferating in recent years. This circumstance makes it particularly interesting to understand wind behavior because generated power depends on it. In this paper, a method is proposed to synthetically generate sequences of wind speed values satisfying two important constraints. The first consists of fitting the given statistical distributions, as the generally accepted fact is assumed that the measured wind speed in a location follows a certain distribution. The second consists of imposing spatial and temporal correlations among the simulated wind speed sequences. The method was successfully checked under different scenarios, depending on variables, such as the number of locations, the duration of the data collection period or the size of the simulated series, and the results were of high accuracy.
Highlights
The electricity needs in daily life are assorted, and there are countless actions that consumers carry out daily, such as turning on lights, charging electronic devices or cooking [1].electricity production across the world has a growing perspective due to several causes, among which the new trend to use electric vehicles can be mentioned [2].The negative impact of fossil fuel gas emissions on human health and on the planet health raises the awareness about the harmful effects of such emissions [3], and due to this, different countries have agreed to limit global warming to a value lower than 2 ◦ C concerning preindustrial era [4]
Wind energy prevails both in the current market and in the future production trends, foreseeing a doubling of installed capacity in the ten years in the most conservative case [7], mainly due to the low cost of wind energy produced in large wind farms
The process starts by considering that the random behavior of the wind speed at every location can be cumulatively described by a Weibull probability distribution function, an assumption generally accepted in wind energy studies
Summary
The electricity needs in daily life are assorted, and there are countless actions that consumers carry out daily, such as turning on lights, charging electronic devices or cooking [1].electricity production across the world has a growing perspective due to several causes, among which the new trend to use electric vehicles can be mentioned [2].The negative impact of fossil fuel gas emissions on human health and on the planet health raises the awareness about the harmful effects of such emissions [3], and due to this, different countries have agreed to limit global warming to a value lower than 2 ◦ C concerning preindustrial era [4]. Wind energy prevails both in the current market and in the future production trends, foreseeing a doubling of installed capacity in the ten years in the most conservative case [7], mainly due to the low cost of wind energy produced in large wind farms. This cost can be lower than 0.05 $/kWh in the locations with the highest mean wind speed values [8]
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