Ebadollah Amouzad Mahdiraji - Department of Engineering, Sari Branch, Islamic Azad University Sari, Iran
Seyed Mohammad Shariatmadar - Electrical Engineering Department, Naragh Branch, Islamic Azad University Naragh, Iran

Considering the effects of DGs on the new power system, their importance has increased in the future smart grid. The number of DG units installed on the power system is growing significantly and the technical, economical and environmental effects of them on the power system are being analyzed. Currently, considerable technical effects include voltage profile, power loss, quality, reliability, protection, control and stability. The most important factors which affect the technical and the environmental issues include the type, the size and the location where the DG units are installed on the power system. The location and the size of the DG unit should be optimized in order to maximize the advantages and reduce the effects of them on the power system. In some conditions, inappropriate placement can reduce the advantage and even jeopardize the general performance of the system, so finding an optimal location and size of DG units encouraged most of the researchers to investigate this area. So far most of the researches use the DGs with unity power factor in distribution systems that have stable power loads. Moreover, most of the authors did not take the functional cost of DG into consideration and achieved minimization of the power loss and the voltage stability by installing several large scale DGs. In this paper, an attempt was made to investigate the installation location of the multiple distributed generators influenced by the changes of the load levels. In this part, we selected a suitable location to install the DG and investigated the results considering the load models in order to reduce the power loss of the transmission lines, reduce the voltage deviation index and reduce the cost of DG exploitation through optimize genetic algorithm.

multiple distributed generators, optimal siting, power loss, genetic algorithm