pso algorithm

Implementing the energy storage improves power load response, and network reliability, as well as reduces the need to build new power capacity in the electricity Using the energy storage improves responsiveness power into load, increases network reliability, and reduces the need to build new power capacity in the electricity industry. Regarding the economic- environmental benefits of using energy storage in the electricity industry, the main objective of this research is to investigate the application of electrical network’s energy storage with the aim of minimizing losses, environmental pollution, and system fuel costs. In this regard, three scenarios have been designed under the multi-objective particle swarm optimization (PSO) algorithm, which in scenario number 1, network consumption load is provided only by diesel generators. In scenario number 2, the renewable energy sources of wind and solar are added to the network, and in scenario number 3 further diesel generator and wind turbine and solar panels, energy storages are added to the network, and the PSO algorithm for optimal placement of the storage devices is performed. The results show that the most efficient result for the designed purposes can be achieved by solving the model under scenario number 3. Accordingly, the amount of network losses, fuel costs, and pollution in motion from the first scenario (base scenario) to the third scenario shows a decrease of 432 kW, 13.7 thousand dollars, and 75 kg, respectively. These results can help to optimum usage of energy storage devices in order to improve sustainability and network security, losses decreasing, and pollution decreasing in the electricity industry.

The vision of the Islamic Republic of Iran in the 1404 horizon illustrates a bright future for the country, a program that is more qualitative than a little. To achieve the objectives of the vision document, there is a need for a quantitative analysis of the indicators discussed in the document. To achieve this, economic exports as one of the major macroeconomic indicators have been simulated and predicted by Pso algorithms for Iran and Turkey. Using the bands pattern, using the four criteria of mean deviation (MSE), root mean square deviation (RMSE), absolute error percentage (MAPE) and absolute error (MAE) simulation have been performed. The results indicate that the gap between exports of  two countries has continued since 2000, with the current trend of Turkey's exports going to double the export of Iran's economy.

Exchange Rate uncertainty means an unpredictable change in the exchange rate that can cause a great impact on other economic variables and institutions such as banks and credit institutions especially in developing countries. In this study, using monthly data from the 2009/3- 2013/9, the effect of recent exchange rate crisis on Iranian banks and financial institutions is estimated and predicted. To do that, models such as VAR, VECM, Genetic and PSO Algorithm are employed. The results of estimating the VAR model indicates that there is a negative relationship between exchange rate volatility and stock market returns. Further, Co-integration test results imply the existence of an indirect long-run relationship between these two variables. By simulation of linearly and exponentially models using Genetic and PSO algorithms to compare the accuracy of these models with estimated VAR model, it was determined that VAR model can predict more accurately. Prediction results showed that persistence of exchange rate fluctuation can lead to first a diminishing increase followed by reduction in the index. However, reduction of exchange rate can lead to an increase in financial and credit institution's stock index.