Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems

Energy ◽  
2021 ◽  
Vol 216 ◽  
pp. 119245
Author(s):  
Linfei Yin ◽  
Tao Wang ◽  
Baomin Zheng
Keyword(s):  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flow Problems ◽  
Optimal Power

scholarly journals A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2270 ◽  
Author(s):  
Sirote Khunkitti ◽  
Apirat Siritaratiwat ◽  
Suttichai Premrudeepreechacharn ◽  
Rongrit Chatthaworn ◽  
Neville Watson
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transmission Losses ◽  
Dragonfly Algorithm ◽  
Flow Problems ◽  
Hybrid Optimization Algorithm ◽  
Optimal Power

In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.

Sign in / Sign up

Export Citation Format

Share Document