Multi-objective differential evolution for optimal power flow

Author

Abido, M.A. ; Al-Ali, N.A.

Author_Institution

Electr. Eng. Dept., King Fahd Univ. of Pet. & Miner., Dhahran

fYear

2009

fDate

18-20 March 2009

Firstpage

101

Lastpage

106

Abstract

This paper presents a multiobjective differential evolution (MODE) based approach to solve the optimal power flow (OPF) problem. OPF problem has been treated as a true multiobjective constrained optimization problem. Different objective functions and different operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. Also, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE-30 bus test system show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well distributed Pareto solutions.

Keywords

Pareto optimisation; evolutionary computation; fuzzy set theory; load flow; power system planning; statistical analysis; IEEE-30 bus test system; OPF problem; clustering algorithm; distributed Pareto solutions; fuzzy set theory; multiobjective constrained optimization; multiobjective differential evolution; operational constraints; optimal power flow; power system operation; power system planning; Clustering algorithms; Constraint optimization; Costs; Fuels; Load flow; Particle swarm optimization; Power generation; Power system management; Power system planning; Power system security; Optimal power flow; differential evolution; evolutionary algorithms; multiobjective optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering, Energy and Electrical Drives, 2009. POWERENG '09. International Conference on

Conference_Location

Lisbon

Print_ISBN

978-1-4244-4611-7

Electronic_ISBN

978-1-4244-2291-3

Type

conf

DOI

10.1109/POWERENG.2009.4915212

Filename

4915212