Optimal reactive power dispatch problem: A comprehensive study on meta-heuristic algorithms

Document Type : Original Article


1 Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran

2 Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran.

3 Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.

4 School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.

5 West Regional Electric Company, Kermanshah, Iran.

6 Department of Electrical Engineering, Faculty of Technical and Vocational, Islamabad-e Gharb Branch, Technical and Vocational University (TVU), Kermanshah, Iran.


The main mission of modern power systems is to supply the load in the most economical and reliable methods. One of the most challenging issues in this regard is the Optimal Reactive Power Dispatch (ORPD), since the crucial focus of planning and operation studies is mainly on only supplying the active power. The primary purpose of the ORPD issue, as a complex and nonlinear problem, is to identify the relevant control variables to minimize some objective functions, i.e. active power losses considering the system constraints. As the literature review shows, the application of meta-heuristic techniques to find the optimal solution to the ORPD problem is of great importance in this field. This paper, as a comparative case study, attempts to investigate the capability of some powerful meta-heuristic optimization algorithms to tackle the ORPD problem. The control variables are the generated power by the power plants, the voltage magnitude of PV buses, the installed capacity of parallel capacitors, and on-load transformer tap changers. All the simulations were implemented on the two case study systems, including the IEEE 30-, and 57-buses. The applied meta-heuristic algorithms to the problem are Orthogonal Crossover based Differential Evolution (OXDE), Hybrid Grey Wolf Optimization, and Particle Swarm Optimization Algorithm (HGWO-PSO), Sine Cosine Algorithm (SCA), and Hybrid PSO and Genetic Algorithm (HPSO-GA).


Main Subjects

Volume 5, Issue 3
September 2021
Pages 67-77
  • Receive Date: 27 July 2020
  • Revise Date: 07 November 2020
  • Accept Date: 23 November 2020
  • First Publish Date: 23 November 2020