Distribution of distributed generation resources in distribution systems has several advantages, including reducing losses, improving voltage profiles, reducing pollution, and increasing system reliability. However, one of the most important points regarding the placement of these resources in distribution networks is economic issues and the return on investment and the increase in profits from the placement of these resources. On the other hand, due to the privatization of power systems, other distribution networks will not necessarily own the distributed generation resources. Therefore, despite choosing the location of scattered production resources by the owners of scattered production resources and pricing their production power, the selection of purchasing power from each of the scattered production units or the national electricity system is done by the distribution network operator. It will be for supply. Thus, owners of scattered production resources must choose the location and price of the production capacity of their resources in such a way that their profit is maximized and at the same time the amount of payment paid by the network operator is minimized. Therefore, in this paper, the issue of location and optimal pricing of distributed products is considered to increase the profit of the owner of scattered production resources provided that the distribution company pays the minimum payment cost and the method used to solve this problem is the ant-optimization algorithm. It is inspired by the ant's milk hunting mechanism and is a powerful optimization algorithm. Manuscript profile

The dynamic stability problem is one of the challenges that is constantly being discussed in power systems. Meanwhile, one of the most important factors which will have a direct impact on its determination is the system state estimation. To monitor the stability of the power system, one of the determinative factors is the accuracy and speed of the state estimation equations’ input data. Therefore, in this paper, the Factorized Load Flow Method was used as a method for estimating input data of the system stability analysis. In this study, factorized load flow method was presented in full details in terms of theoretical relations and simulation results, and in order to prove its performance efficiency a comparison was made between its results with the results of the Newton-Raphson method. The conducted comparisons and investigations showed that the proposed method can determine the needed inputs for state estimation with high speed and precision. The proposed method was simulated using coding environment of MATLAB software and it was shown that this idea enjoyed an appropriate quality for reducing the computational complexity and increasing the accuracy and speed of state estimation. Manuscript profile

In this study, a new method for designing the damping controller was proposed to improve the transient power system stability in a single machine network connected to an infinite bus. The STATCOM controller problem in a wide area of the system function was considered as an optimization problem with multi-purpose objective function. Also, the Honey Bee Mating Optimization Algorithm was used to determine its parameters. In this paper, a new method for designing a damping controller was presented to improve the transient power system stability in a single machine network connected to an infinite bus. The STATCOM controller design problem in a wide area of system function was considered as an optimization problem with a multi-purpose objective function and the honey bee mating optimization algorithm was used to determine its parameters. As shown, STATCOM improved system stability. For a more comprehensive analysis, the results were tested in three different scenarios. Simulation of the STATCOM controller design for small signal stability was conducted in the MATLAB / SIMULINK environment. The tuning variables in designing the controller were variables c and φ that were used to adjust the modulation index and firing angle of switches. In order to achieve the best possible state, the artificial honey bee colony algorithm was used. Manuscript profile