Document Type : Research article


1 Bakhtar Regional Company, Arak, Iran.

2 Department of Education and Training of Isfahan Province, District 4 Management, Isfahan, 81458-13331, Iran

3 Department of Electrical Engineering, Afarinesh University, Borujerd, Lorestan, Iran.

4 Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran

5 School of Electrical and Electronic Engineering University College Dublin, Dublin, Ireland


Restructuring the distribution system in the presence of distributed generation (DG) sources is an effective solution to reduce the cost of power generation and improve the technical and economic parameters of the network. In this paper, the optimal design of the location and capacity of DGs in the distribution system is modeled as a multi-objective optimization problem. The technical parameters of the problem include network losses, voltage stability index, and voltage profile improvement. Also, the economical parameters of the problem are the capital and operation costs of the system. The optimization problem presented in this paper is mixed-integer nonlinear programming (MINLP), so the enhanced imperialist competition algorithm (EICA) is adopted to minimize the objective function. In this algorithm, adding a new implementation phase to the ICA increases searchability and thus enhances the algorithm's efficiency over ICA. The proposed method is first implemented on the standard IEEE 33-bus system. Then, a real network has been incorporated to optimize the technical and economic parameters. The analysis and comparison of the results demonstrate the efficacy of the suggested algorithm in the optimal design of the system compared to the original ICA. In this article, we were finally able to bring the voltage profile and voltage stability index of the "Rahdarkhaneh" distribution feeder close to 1 pu and also significantly reduce the network losses.

Graphical Abstract

Improving the Technical and Economic Indexes of Distribution Network by Three-Stage Enhanced Imperialist Competitive Algorithm