In recent years some researchers have focused on dividing of large distribution grids to autonomous Microgrids (MGs). The benefits of MGs consist of their ability to increase the reliability of distribution networks and reduce the power losses. The distribution resources within the MGs can balance the gap between limited generation capacity and actively growing demands. In this paper, we have proposed new dynamic boundaries for MGs to gain the flexibility in the grid. The proposed method is based on finding the Optimal state of switches, sizing and siting of distributed energy resources (DES) in an MG-based distribution network. A bi-Level optimization approach is used to solving the proposed problem. In the upper Level of the optimization problem, the sizing and siting of DER is implemented and the system is updated to Optimal switching in lower Level. The stochastic model of wind, solar and load demand is represented. The 94 buses distribution network is modified to the MG-based distribution network for testing and validating the proposed model. The Particle swarm optimization (PSO) is applied to minimize the objective function of upper Level and Genetic algorithm (GA) is used for minimization of lower Level. According to the results, Optimal planning of the autonomous MGs can improve the performance of distribution network operation.

In this paper, a novel risk-based, two-objective (technical and economical) Optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

The purpose of the current study is to determine the optimum Level of the governmentchr('39')s debt for the period of the sixth plan period of the economic, social a eviation of the target variables economic growth rate, inflation rate, real GDP and nd cultural development of the government. Therefore, a function of inter periodic loss welfare has been devised for the policy maker in which the squared d consumer price index from the targeted amount in the sixth plan. To do so, the dynamic optimization was used in which the control amounts including tax revenues, governmentchr('39')s current payments, governmentchr('39')s acquisition of non-financial assets and liquidity. The result of this study not only determines the Optimal Level of the public debt, but it also shows tax revenues and Optimal liquidity is less than the sixth plan amount and the current payments as well as the Optimal acquisition of non-financial assets is more than the approved amount of the economic sixth plan. Also, the Optimal economic growth rate is less than 8% in compare with the rate of the Sixth Economic Development Plan that shows the Optimal inflation rate for this duration about 13%. Based on the results, it is suggested that policymakers adjust the economic growth rate and inflation rate in their planning

Government budget is an important policy-making tool to achieve allocation, distribution and stabilization goals that affect the economic performance of society through variables of government expenditures and revenues,accordingly, determining the Optimal Level of budget components is always important for the policy-making system. Therefore, the purpose of the current study is to assess the Optimal Level of government budget components for the period of the Sixth Economic, Social and Cultural Development Plan. To do so, the random Optimal control approach was used in which the control amounts include tax revenues, government's consumption expenditures, government's acquisition of non-financial assets and liquidity. The result of this study shows Optimal tax revenues, non-tax revenues, government's current payments, and government's acquisition of non-financial assets is less than the sixth plan amount and secondly, the largest gap between the Optimal amounts and the amount of the Sixth Plan is related to government's acquisition of non-financial assets.

The paper explains the importance and the incomes of inflationary finance, the maximum income, and the corresponding inflation rate, and the Optimal Level of application of this instrument. The empirical researches suggest that except for the hyper-inflationary cases, inflationary finance does not play a significant role in financing government expenditures.The calculation of inflationary finance income and its maximum amount and the corresponding inflation rate has been a concern for economists. The results of investigations suggest that usually the seignior age increases with an increase in monetary base and inflation rate. However, in many studies, maximum income is achieved by a certain rate of inflation. When the inflation exceeds the certain rate inflationary tax will decrease. Incorporating the welfare costs of inflation in inflationary finance income calculation will yield the Optimal inflationary finance. The study shows that, first, the social cost of seignior age income, and the tax should be equal. Secondly, a one percent increase in government income as a fraction of gross domestic product GDP will increase the inflation by 1.8 percent.

Banking capital and risk decisions are affected by laws, private incentives and capital market pressure. Focusing on examining these cases will explicitly provide an updated insight into empirical evidence on the impact of auditors 'regulations on capital and risk, and provide an assessment of the evolution of banks' decision making mechanisms. Therefore, this research has determined the factors affecting the Optimal Level of risk and Optimal capital structure in Iranian banks using Delphi-Fuzzy approach based on in-depth interview with experts and content analysis. After identifying the effective factors using content analysis and content analysis, 15 experts were interviewed. The Delphi-Fuzzy approach was used to determine the effect of the final factors. The results show that the most important factors affecting efficiency are physical capital cost, manpower cost, cost of funds, investment in securities and net facility. In addition, among the various factors, the variables of facility coverage rate, risk capacity and legal requirement will be the most important factors affecting the Optimal Level of risk in banks. Finally, minimum capital, minimum capital risk and minimum leverage ratio were considered as the most effective factors in determining Optimal capital structure.

Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the Optimal number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs.   Methods This study employs the two-stage D-Optimal method to optimize the combination of FMS elements and equipment. The D-Optimal response Level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response Level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two stages of the D-Optimal design are defined as follows: In the first stage, all combinations within these categories are investigated. In the second stage, the optimized combination from the first stage is analyzed to determine the best configuration. Experiments in the top category from the first stage are simulated and further evaluated in the second stage of the D-Optimal method.   Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-Optimal design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-Optimal design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-Optimal method and the second phase indicate that the Optimal combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current Level and a higher degree of automation for this production line.   Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining Optimal combinations requires accurate calculations.

In order to study the spatial structure of oriental beech (Fagus orientalis Lipsky) stand at Optimal stage, this research was carried out in the Caspian forests of Iran. The studied site consisted of a natural stand located at Sangdeh district (Mers-e-se) with three hectare area. It is located between 1900 and1950 m.a.s.l. Six sample plots each with 0.36 ha area, were systematically selected and all trees (1244 stem) within the plots were measured and recorded. Using increment borer, the mean age of the stand was estimated as 137 years; the age difference of trees was 40 years. Results showed that the stand has a closed canopy cover and distribution of stem number per diameter class was more or less homogenous (Bell shape) with a semi even-aged structure. Using Ripley's K function, the distribution of trees within the stand was random.