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Abstract:

In the developing countries, there is an interest to attract investments in private and foreign sectors due to the lack of financial resource required for infrastructure projects. Build-Operate-Transfer (BOT) method is one of the approaches in which the private sector is assigned to construct the project and gains its own interest after which it transfers the project to the government. In this paper, according to the proposed case study, a method was obtained to determine the concession period length and the transfer point of project by taking into account both parties' willingness, effects of risks and uncertainty regarding the project's parameters, considering the lack of complete information at least for one party by using bargaining game theory and Monte-Carlo simulation.

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Abstract:
Due to the increasing use of microgrids, investigation of their stability is of special interest. One of the disadvantages of an inverter-based distribution unit is that at any given time, phase and frequency information at the point of common coupling (PCC) is required that can affect the stability. The synchronization techniques include synchronous reference frame phase-locked loop (SRF-PLL), virtual inertia, and constant K. To minimize cost and improve reliability, the minimum DC link capacitance should be determined. In this article, finding the minimum DC link capacitance is defined as an optimization problem where the particle swarm optimization (PSO) algorithm is used for the solving of the problem, where the stability is studied through eigenvalue analysis. In the stability analysis, harmonics and negative sequence of grid voltage, increasing the grid inductance, changes in the grid frequency, and changes in the solar irradiation intensity are considered. The effect of various synchronization methods on the stability of grid-connected inverters and DC link capacitance value is investigated.
 

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Automatic classification of power quality disturbances is the foundation to deal with the power quality problem. From a traditional viewpoint, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection, and classification. However, there are some inherent defects in signal analysis and the procedure of manual feature selection is tedious and imprecise, leading to a low classification accuracy of multiple disturbances. To deal with these problems, this paper presents an automated system for the classification and identification of power quality disturbances. After receiving input signals, the proposed system requires some preprocessing such as changing the range of values by dividing the signals into their basic domains. In the next stage, the RMS value of the signal can be appraised to know the occurrence of the disturbance. If the RMS value of the input signal is not equal to the normal signal, the disturbance is occurring. To identify and classify disturbances, a novel deep learning-based method is developed. In this method, the activation function is expressed by a fuzzy approach. This makes the system more flexible. The benefits of the proposed strategy are separating the disturbances of basic frequency and using the nature of power quality signals as a tool for feature extraction. However, in the traditional method, for example, in empirical mode decomposition, the separation of signals from their components is not conveniently possible. To evaluate the proposed algorithm, a 33-bus distribution power network has been applied. The results reveal good agreement in comparison with other assessment tests.