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This paper aims to evaluate the effect of optimal use of rooftop photovoltaic (PV) systems on improving the loss of life (LOL) of distribution transformers, reducing power losses as well as the unbalance rate of the 69-bus distribution network.

The problem is studied in three scenarios, considering different objective functions as multi-objective optimization in balanced and unbalanced operations. Meta-heuristic golden ratio optimization method (GROM) is used to determine the optimal size of the rooftop PV in the network.

The simulation results show that in all scenarios, the GROM by optimally installing the rooftop PV is significantly capable to reduce the transformer distribution loss of loss, unbalance rate and power loss as well as reduce the temperature of the oil and transformer winding. Also, the lowest %LOL, power loss and unbalance rate occurred in the second scenario for the balanced network and first scenario, respectively. In addition, the results showed that the unbalance of the network results in increased power losses and LOL of the distribution transformer.

The better capability of GROM is proved compared with the grey wolf optimization algorithm with better objective function and by achieving better values of LOL, unbalance rate and power loss. The results also showed that the %LOL, unbalance and power losses are weakened compared to without considering the PV cost but the achieved results are realistic and cost-effective.

Operational or event risk is not a new phenomenon for financial services companies. However, its measurement, as part of integrated risk management programs, has been the subject of recent focus. Property and casualty insurers have measured components of this risk class as part of the pricing and underwriting process. Although all financial services firms are exposed to direct and indirect (e.g., reputational) costs of operational risk events, few financial services firms actually measure “operational risk.” This article explores ways in which this may be done in practice.

In recent years, several methodologies for measuring portfolio credit risk have been introduced that demonstrate the benefits of using internal models to measure credit risk in the loan book. These models measure economic credit capital and are specifically designed to capture portfolio effects and account for obligor default correlations. An example of an integrated market and credit risk model that overcomes this limitation is given in Iscoe et al. [1999], which is equally applicable to commercial and retail credit portfolios. However, the measurement of portfolio credit risk in retail loan portfolios has received much less attention than the commercial credit markets. This article proposes a methodology for measuring the credit risk of a retail portfolio, based on the general portfolio credit risk framework of Iscoe et al. The authors discuss the practical estimation and implementation of the model. They demonstrate its applicability with a case study based on the credit card portfolio of a North American financial institution. They also analyze the sensitivity of the results to various assumptions.

The Iranian banking industry has been greatly affected by dramatic changes in macroeconomic conditions over the past several decades owing to volatile oil revenues, changing fiscal and monetary policies, and the imposition of US sanctions. The main objective of this paper is to estimate potential credit losses in the Iranian banking sector due to macroeconomic shocks and assess the minimum economic capital requirements under the baseline and distressed scenarios. The paper also contrasts the applications of linear and nonlinear models in estimating the impacts of macroeconomic shocks on financial institutions.

The paper uses a multistage approach to derive the portfolio loss distribution for banks. In the first step, the dynamic relationship between the selected macroeconomic variables are estimated using a VAR model to generate the stress scenarios. In the second step, the default probabilities are estimated using a quantile regression model and the results are compared with those of the conventional linear models. Finally, the default probabilities are simulated for a one-year time horizon using Monte-Carlo method and the portfolio loss distribution is calculated for hypothetical portfolios. The expected loss includes the loss given default for loans drawn randomly and uniformly distributed and exposed at default values when loans are assigned a fixed value.

The results indicate that the loss distributions under all scenarios are skewed to the right, with the linear model results being very similar to those of quantile at the 50% quantile, but very unlike those at the 10% and 90% quantiles. Specifically, the quantile model for the 90% (10%) quantile generates estimates of minimum economic capital requirement that are considerably higher (lower) than those using the linear model.

The study has focused on credit risk because of lack of data on other types of risk at individual bank level. The future studies can estimate the aggregate economic capital using a risk aggregation approach and a panel data (not presently available), which could further improve the accuracy of the estimates.

The fiscal and monetary authorities in developing countries, specially oil-exporting countries, can follow the risk assessment approach to assess the health of their banking system and adapt policies to mitigate the impacts of large macroeconomic shocks on their financial markets.

This is the first paper estimating the portfolio loss distribution for the Iranian banks under turbulent macroeconomic conditions using linear and nonlinear models. The case study can be applied to other developing and emerging countries, particularly those highly dependent on natural resources, prone to extreme macroeconomic shocks.

This research studies the impact of introducing distributed generators (DGs) into a distribution network. The aim of this paper is to optimally site DGs based on economic, environmental and reliability indices are presented.

The considered network was modelled by using the network’s line parameters and capacity of the load bus with the help of Power System Analysis Toolbox. The location of the DG is based on voltage stability index and power loss reduction index. The DG energy sources considered are the diesel generator, solar photo-voltaic (PV) and wind generator, and the objectives were to minimize cumulative cost while maximizing reliability of the network. The Advanced Interactive Multidimensional Modelling System was used for the mathematical modelling.

The obtained results in the cases of introducing renewable energy into a network improves network performance. The benefits of renewable energy on the distribution network measured in terms of electricity production cost, gas emission cost, fuel cost and value of energy not supplied were positive. The research also showed that the total benefit of renewable energy reduces as the price of the renewable generators increases.

This paper introduces a new approach to determining the optimal location of DG for reducing line losses and improved voltage profile. A new cost modelling function based on external grid power transfer cost, technical losses and cost because of the various energies source is also introduced.

The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion.

The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model.

This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades.

The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

In the USA, private insurance companies serve as an integral part of the delivery and risk sharing of the federal crop insurance program. Governed by the Standard Reinsurance Agreement (SRA), private crop insurance companies must designate an eligible crop insurance contract to the assigned risk, developmental, or commercial funds. While the SRA restricts the private sector delivery system in a number of ways, the assignment of contracts to crop insurance funds, however, is left solely to the discretion of individual crop insurance companies. Thus, as to the companies' profitability viewpoint, the optimal selection of the crop insurance funds is the most important task. Therefore, the purpose of this paper is to provide a decision framework for crop insurance companies to make optimal decisions regarding the purchases of crop reinsurance. This information and framework may also be useful for crop insurance firms in China when considering crop reinsurance decisions.

The paper studied three commonly used parametric loss distributions and presented a general guideline to choose the most profitable fund within the company's risk bearing level.

The paper finds many important features in the commonly used loss distributions, which are useful to maximize the company's underwriting returns.

The paper provides a general decision framework for optimally ceding risks to reinsurance. While this paper focused on agricultural insurance decisions by firms, the concept could be applied to general reinsurance decisions.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

An economic analysis is presented of the Standard Reinsurance Agreement (SRA), the contract governing the relationship between the Federal Crop Insurance Corporation and the private insurance companies that deliver crop insurance products to farmers. The paper outlines provisions of the SRA and describes the modeling methodology behind the SRA simulator, a computer program developed to assist crop insurers and policy makers in assessing the economic impact of the Agreement. The simulator is then used to analyze how the SRA affects returns from underwriting crop insurance. The results are presented in aggregate and also at the regional and individual company levels.

This paper aims to achieve an optimal design for distribution transformers considering cost and power losses. Particle swarm optimization (PSO) algorithm is used as an optimization tool for minimizing the objective functions of design procedure which are cost and electrical and iron losses.

In this paper, distribution transformer losses are considered as operating costs. Also, transformer construction cost which depends on the amount of iron and copper in the structure is assumed as its initial cost. In addition, some other important constraints such as appropriate ranges of transformer efficiency, voltage regulation, temperature rise, no-load current, and winding fill factor are investigated in the design procedure. The PSO algorithm is applied to find optimum amount of needed copper and iron for a typical distribution transformer. Moreover, transformer impedance considered as a constraint to achieve an acceptable voltage regulation in the design process.

It is shown that the proposed design procedure provides a simple and effective approach to estimate the flux and current densities for minimizing the active part cost and active power losses which means reduction in amount of transformer total owning cost (TOC).

The methodology advances a proposal for reducing distribution transformers costs using PSO algorithm. The approach considers the aforementioned constraints and TOC to minimize the active part cost and maximize the efficiency. It is demonstrated that a designed transformer will not be optimum when the transformer losses over years are not considered in design procedure. Finally, the results prove the effectiveness of the proposed procedure in designing cost-effective distribution transformers from its initial cost until its whole life.