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Due to the unstable situation of the atmosphere, the wind power forecasting of the wind farms (WFs) will be so complex. This unsteady provides an opportunity in order to investigate the wind power probabilistic prediction. This paper proposed a novel method based on Gaussian Processes (GPs) to improve the probabilistic prediction of a group or regional of WFs. Covariance Functions (CFs), known as Kernel, are the key ingredient in using GPs. One of the most usually of these functions is Squared Exponential (SE), which is applied with other functions to the model of the proposed method. Thus, different groupings of CFs are investigated comprehensively. Additionally, evaluating the accuracy of the prediction is carried out. This study went through two types of comparisons of dynamic and static GP as well as direct and indirect prediction plan. The result of the comparison between dynamic and static GP revealed that the dynamic GP generates keen Prediction Intervals (PIs). Besides, comparing the accuracy of direct and indirect prediction plan, it shows that indirect prediction strategy brings about wider PIs with higher coverage probability on the part of net demand prediction. Moreover, the proposed model provides precise results of forecasted energy in every time step. • Assessment of PWPP and DWPP to search about their advantages and disadvantages. • Using GP models as static, dynamic, direct, and indirect for evaluating results. • Using of different CFs in the GP models to reach comprehensive evaluation. • In comparison to other methods, DY–IN–GP defined as the best method. [ABSTRACT FROM AUTHOR]

This article aimed to present a new approach which consists of three-points, and concerns each diode pattern separately. Distinct from traditional curve fitting processes, this approach is based on the simplistic view which flourishes the potential abilities in order to provide the efficient solutions with the least repetitions. The basic reason of this study is to substitute the three – point approach with the previous used approach and set it as core point. By using mentioned points, it reinforces the usage of improved shark smell optimization algorithm and regulates the left parts efficiently. Consequently, it decreases the elaborateness of algorithms. According to the conducted parts comparisons on the three case studies, proved that the presented method has more exactness than the traditional methods. As well as, the investigated the proposed method has high efficiency and presents the optimized solution in various cells type. Also the achieved solutions are stable and is compared with over 17 various algorithms which in whole of the standard deviation of root mean square error for three type of cells is lower than the determined scales in other studies. By having these values, the suggested approach had been predicted to work efficiently in all circumstances like offline analysis and online controlling and it requires an exact, steady approach exploitation tool. • Application of new parameter in the diode model based three basic points. • Application of ISSO algorithm to improves the left parts off the curves fitting. • Application of proposed method to evade the extra bounds parts in the optimization. • Considering the ISSO due to better global research, convergence, & controlling. • Application of proposed pattern for numerical database of the real PV cell. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes two controlling methods for SVC in smart grid with connected EV charger. • SMC, ASRFC with a HVC are separately applied in the grid. • This paper presents usage of proposed methods for decreasing size of capacitor and value of apparent power loss. • The proposed scheme is validated through computer simulations of a 22.9-kV grid. Abstract Electric Vehicles (EVs) play a pivotal role in reducing the emissions of greenhouse gases in the world. Currently demand for EVs has abruptly increased for transportation and other applications. Using more EVs causes an increasing load on the power system. Random EV charging yields voltage distortion, which results in an unbalanced grid. In a distributed system, the connection of EVs to the grid may diminish the power factor, which increases the apparent power loss. The Static VAR Compensator (SVC), which is a FACTS device, is applied to the power system to compensate the voltage distortions and apparent power loss. In this paper, two controlling methods are contemplated and tested in a SVC to reduce the apparent power loss and minimize the capacitor size of the SVC in comparison with the conventional control method in a SVC. Proposed methods are also compared with the conventional control method used for SVC. The Slide Mode Controller (SMC) and Asymmetric Synchronous Reference Frame Controller (ASRFC) with a Harmonic Voltage Compensator (HVC) are separately applied in the grid. This paper presents usage of SMC and ASRFC methods for decreasing size of capacitor and value of apparent power loss through computer simulations of a 22.9-kV grid. With application of new controllers, apparent power loss of grid will be reduced as compared with conventional method of SVC controller. Moreover, the size of SVC capacitor can also be reduced. [ABSTRACT FROM AUTHOR]