Sharif Digital Repository

Distribution automation (DA) application for fault management in the electricity distribution networks is one of the main potential remedial actions to reduce customers' outage times and hence improve service reliability. As a result, various automation schemes have been developed and introduced in different countries and by different venders. The impacts of implementing a specific automation scheme on reliability indices of a given distribution system can be different from other available automation schemes. For this reason, when comparing the available automation schemes with each other or with the other reliability improvement measures, it is necessary to perform a course of reliability... 

Distribution automation (DA) application for fault management in the electricity distribution networks is one of the main potential remedial actions to reduce customers' outage times and hence improve service reliability. As a result, various automation schemes have been developed and introduced in different countries and by different venders. The impacts of implementing a specific automation scheme on reliability indices of a given distribution system can be different from other available automation schemes. For this reason, when comparing the available automation schemes with each other or with the other reliability improvement measures, it is necessary to perform a course of reliability... 

Statistical analysis of stochastic dynamical systems is of considerable importance for engineers as well as scientists. Engineering applications require approximate statistical methods with a trade-off between accuracy and simplicity. Most exact and approximate methods available in the literature to study stochastic differential equations (SDEs) are best suited for linear or lightly nonlinear systems. When a system is highly nonlinear, e.g., a system with multiple equilibria, the accuracy of conventional methods degrades. This problem is addressed in this article, and a novel method is introduced for statistical analysis of special types of essentially nonlinear SDEs. In particular,... 

Remote controlled switch deployment in distribution networks is usually justified via comparing the required investment with the expected decrease in interruption costs. Although the investment is known prior to the implementation, the stochastic nature of contingencies imposes significant uncertainty on interruption costs. The uncertainty may impose substantial financial risks on the distribution company. This paper aims at evaluating the financial risk and key affecting parameters. For doing so, a step-by-step method is presented to evaluate the risk. The method captures the stochastic nature of contingencies through a sequential Monte Carlo simulation approach. A mathematical formulation... 

Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that...