Original Article

Research on Multi-Objective Optimization of Smart Grid Based on Particle Swarm Optimization


State Grid Information & communication Branch of Hubei Electric Power Co., Ltd, Wuhan, China


Department of Electronics & Communication Engineering, Model Institute of Engineering and Technology, Jammu, J&K, India


Department of Corporate finance and securities, Tashkent Institute of Finance, Tashkent, Uzbekistan

ELECTRICA 2023; 23: 222-230
DOI: 10.5152/electrica.2022.22047
Read: 117 Downloads: 47 Published: 01 May 2023

Microgrids can benefit from multi-objective optimization dispatch in several ways, including reduced operation costs and improved service dependability. While using the traditional power network optimization method to solve power network planning, the algorithm mostly falls into the local optimal solution, rendering the global optimal solution intractable. In this research, multi-objective power grid planning is applied to an eight-bus system using a multi-objective optimization technique, such as reducing distribution network building costs and losses, planning distribution network growth and fixed capacity, and distributed generation (DG) addressing planning. Improvements are made to the quantum particle swarm optimization algorithm so that it can be applied to solving discrete problems. This report also employs a binary-coded quantum particle swarm optimization technique to design the distribution network without DG and runs the debugged program in MATLAB to compare the final optimization results. Finally, MATLAB software is used to simulate the example, and the corresponding planning results are obtained. From the model verification results, it can be observed that the quantum particle swarm optimization algorithm applied in this research can complete the task of power grid planning well under the premise of ensuring the calculation speed in the multi-objective design of a smart grid.

Cite this article as: F. Long, et al. “Research on multi-objective optimization of smart grid based on particle swarm optimization,” Electrica, 23(2), 222-230, 2023.

EISSN 2619-9831