Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

Jalel Ben Hmida, Mohammad Javad Morshed, Jim Lee and Terrence Chambers
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Jalel Ben Hmida: Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Mohammad Javad Morshed: Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Jim Lee: Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Terrence Chambers: Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

Energies, 2018, vol. 11, issue 11, 1-23

Abstract: The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

Keywords: multiobjective optimization; optimal power flow; metaheuristic; wind energy; photovoltaic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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