A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems

• Published in: Energy (Oxford) Vol. 193; p. 116817
• Main Authors: El Sehiemy, Ragab A., Selim, F., Bentouati, Bachir, Abido, M.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2020; Elsevier BV
• Subjects: Algorithms; case studies; Computer simulation; Economics; Emission minimization; Emissions control; Enhancing voltage profile; Multiple objective analysis; Optimal power flow; Optimization algorithms; Parameter robustness; Pareto front multi-objective optimization; Particle swarm optimization; particles; Power flow; Power loss; Sensitivity analysis; System effectiveness; Techno-economic benefits in power systems; Voltage; Voltage stability; Enhancing voltage profile; Pareto front multi-objective optimization; Emission minimization; Optimal power flow; Techno-economic benefits in power systems
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.116817

Optimal Power Flow (OPF) problem is one of the most widely nonlinear optimization problems in power systems. This paper proposes a novel hybrid optimization algorithm that combines the merits of salp swarm optimization (SSO) algorithm with particle swarm optimization (PSO) algorithm for solving the OPF problem. The proposed hybrid method is considered to accomplish economic, environmental and technical benefits. The proposed method is applied to single and multi-objective optimization problems with different objective functions such as generation cost minimization, emission reduction, transmission power loss minimization, voltage profile improvement, and voltage stability enhancement. To prove the capability of the proposed hybrid optimization algorithm, 18 case studies are employed and tested on three standard test systems. The proposed PSO–SSO algorithm achieves significantly the effectiveness and robustness of the OPF results for the cases considered. The simulation results demonstrate that the proposed method leads to superior levels of techno-economic-environmental benefits compared with those reported in the literature. In addition, the sensitivity analysis study confirms that the proposed hybrid method produces robust results against parameter variations. •A novel hybrid optimization algorithm is proposed for solving OPF problem.•A merge between Salp- and particle-swarm optimization algorithms is implemented.•Significant benefits for single and multi-objective case studies are achieved.•The capability of the proposed hybrid optimization algorithm is proven for three IEEE standard systems.•Robustness of the proposed hybrid algorithm is assessed compared with previous methods.