Two methods are proposed, for the simulation of wind farms with asynchronous generators in the load flow analysis. Both methods are based on the steady-state model of the induction machine. The first involves improving the conventional PQ bus, and the second involves modeling the generators in steady-state in the bus where the wind farm is located. The two sets of results are then compared.

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.

According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with present challenges and future scope on the mentioned technologies. A conclusion is drawn that smart technologies can lead to better water resource management, which can lead to the reduction of water scarcity worldwide. High implementation cost may act as a barrier to the implementation of SWT in developing countries, whereas data security and its reliability along with system ability to give accurate results are some of the key challenges in its field implementation.

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.