Abstract
With the rapid development of grid-connected wind power, analysing and describing the probability density distribution characteristics of wind power fluctuation has always been a hot and difficult problem in the wind power field. In traditional methods, a single distribution function model is used to fit the probability density distribution of wind power output fluctuation; however, the results are unsatisfying. Therefore, a new distribution function model is proposed in this work for fitting the probability density distribution to replace a single distribution function model. In form, the new model includes only four parameters which make it easier to implement. Four statistical index models are used to evaluate the distribution function fits with the measured probability data. Simulations are designed to compare the new model with the Gaussian mixture model, and results illustrate the effectiveness and advantages of the newly developed model in fitting the wind power fluctuation probability density distribution. Besides, the fireworks algorithm is adopted for determining the optimal parameters in the distribution function model. The comparison experiments of the fireworks algorithm with the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA) are carried out, which shows that the fireworks algorithm has faster convergence speed and higher accuracy than the two common intelligent algorithms, so it is useful for optimizing parameters in power systems.
Highlights
Owning to the increased efficiency of clean energy, and the need to reduce pollution and fuel consumption, development in renewable energy sources, including wind power plants, is of great significance [1]
The fitting effect of the distribution function can be quantified by means of residual sum of squares (RSS), root mean square error (RMSE), determination coefficient and adjusted determination coefficient, etc
To verify the proposed distribution model (NDM), we compare it with the second-order Gaussian mixture model (SGDM) and the fifth-order Gaussian mixture model (FGDM) by fitting the wind power fluctuation probability density function for each to the wind farm fluctuation data distribution (PFDD)
Summary
Owning to the increased efficiency of clean energy, and the need to reduce pollution and fuel consumption, development in renewable energy sources, including wind power plants, is of great significance [1]. Describing the probability density distribution characteristics of wind power fluctuation has always been a challenge in grid-connected wind power operation analysis. Zhang et al [15] and Yang et al [16] described the change law of wind power prediction error using the normal distribution and optimized beta distribution model, and the wind power fluctuation range was estimated from the distribution function characteristics. Owning to the “trailing” distribution characteristics, some researchers have chosen single distribution functions such as normal distribution, t location-scale distribution, generalized extreme value distribution and Weibull distribution for the purpose of fitting wind power probability density distribution [12,13,14,15,16,17,18,19,24,25]. A new distribution function model is proposed to fit the measured power probability distribution data of wind farm groups, and the model includes only four parameters, which will greatly reduce the complexity and computation
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