Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid's security, reliability and resiliency.
This paper proposes an algorithm for the calculation of short circuit forces on the high current busbars of electric arc furnace (EAF) transformers based on the method of images using an analytical solution for the electromagnetic force between two adjacent current carrying rectangular conductor. The theory of images is used to account for the impact of the tank walls on the short circuit forces on the busbars. The proposed method uses an iterative algorithm and increases the number of image layers to achieve the desired accuracy. A 30 MVA EAF transformer is investigated as a case study and the results are compared to 2D finite element analysis (FEA). ANSYS software is used for the FEA. The proposed algorithm converges very fast, so that only in its first iteration, the convergence error is less than 0.2%. The comparisons show that the short circuit force calculations using the proposed method, conform to the 2D FEA results. However, the short-circuit calculation time using the proposed method is about 20 times faster than the 2D FEA with the same relative error. Therefore it can be used as a faster alternative for the FEA. The proposed method is characterized by fast convergence, simple calculations and high precision.
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