Sharif Digital Repository

This chapter presents the optimal switch deployment in distribution systems. First, an explanation regarding different types of switches and their functionality is introduced. Then, a fundamental description of fault management procedure in distribution networks is presented. Thereafter, the mathematical formulation of optimal fault management process is described. Optimal switch deployment problem is formulated in the format of mixed integer programming (MIP). The impact of remote controlled switch (RCS) and manual switch (MS) is scrutinized on the interruption cost once they are installed either individually or simultaneously. The concept of switch malfunctions is explained and the... 

Numerical calculation of Value-at-Risk (VaR) for large-scale portfolios poses great challenges to financial institutions. The problem is even more daunting for large fixed-income portfolios as their underlying instruments have exposure to higher dimensions of risk factors. This article provides an efficient algorithm for calculating VaR using a historical grid-based approach with volatility updating and shows its efficiency in computational cost and accuracy. Our VaR computation algorithm is flexible and simple, while one can easily extend it to cover other nonlinear portfolios such as derivative portfolios on equities and FX securities. © 2022  

The great share of interruptions in distribution networks motivates distribution decision makers to establish various reliability enhancement strategies. Amongst, deploying remote controlled switch (RCS) can make a crucial contribution to the reduction of interruption costs. Nevertheless, the stochastic nature of contingencies affects the RCS worth and imposes substantial financial risk to RCS deployment projects. This paper proposes a mathematical model to consider the risk in the optimal RCS deployment problem. The model determines the number and location of RCSs such that the expected profit is maximized while financial risk is minimized. The risk is modeled through conditional... 

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... 

The great share of interruptions in distribution networks motivates distribution decision makers to establish various reliability enhancement strategies. Amongst these strategies, deploying remote controlled switch (RCS) can make a crucial contribution to the reduction of interruption costs. Nevertheless, the stochastic nature of contingencies affects RCS worth and imposes substantial financial risk to RCS deployment projects. This paper proposes a mathematical model to consider the risk in the optimal RCS deployment problem. The model determines the number and location of RCSs such that the expected profit is maximized while financial risk is minimized. The risk is modeled through... 

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... 

Existing real-time pricing (RTP) schemes charge the whole customer's consumption at one highly volatile time varying price, which, in turn, imposes financial risk to participating customers. This paper presents a two-part RTP scheme aiming at efficient distribution system operation while mitigating customers' risk level imposed by volatile RTP prices. Our proposed RTP scheme offers two different RTP price signals. The first RTP price signal is used to charge customer's consumption up to its customer baseline load (CBL) which presents customer's consumption in the absence of RTP signals. The second RTP price signal is announced a day ahead of the actual operation, and can be updated in real... 

Distributed Generation (DG) has received increasing attention during the last decade. Advantage and constraints of DG application are well known to both DG owner and electric utility. Various technologies are available for DG units among them gas GenSet is, in particular, more attractive to the investors as the technology provides the control on DG generation. However, there are various financial risks associated with dispatchable DG units that prohibit wide private investment in such technologies. This paper examines the use of financial tools to manage dispatchable DG economic risks. A comprehensive framework has been proposed to consider various economic risks to DG owner. Suitable models... 

In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the... 

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...