Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

In this paper, a procedure is established for calculating the load flow probability density function in an electrical power network, taking into account the presence of wind power generation. The probability density function of the power injected in the network by a wind turbine is first obtained by utilizing a quadratic approximation of its power curve. With this model, the DC power flow of a network is calculated, considering the probabilistic nature of the power injected or consumed by the generators and the loads.

The Economic Dispatch problem consists of minimizing the cost of producing the power demanded by an electrical power system, by means of the suitable dispatching of the power production between the available generators. The difficulty in predicting wind power generation means that penalty and reserve costs must be considered when it is included in the evaluation. Analyzing the output power of each wind turbine individually is not enough when evaluating these costs and the correlation between wind speed values must be considered as another input because it also has an influence. This paper introduces a new method for generating correlated wind power values and explains how to apply the method when evaluating Economic Dispatch. A case study is provided to analyze whether considering correlation in the problem has any influence or not.