Abstract
In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.
Highlights
Electric services companies are repeatedly working for generation scheduling and reasonable operational state to optimize the generating cost based on effective security limits and power transfer confinements
Statistical analysis showed that Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is a robust and reliable optimization method to solve optimal power flow (OPF) problems
The IEEE30 − bus test scheme is applied for the suggested HFPSO and the original Particle Swarm Optimization (PSO) algorithms to investigate the effectiveness of the suggested HFPSO method
Summary
Electric services companies are repeatedly working for generation scheduling and reasonable operational state to optimize the generating cost based on effective security limits and power transfer confinements. The optimal power flow (OPF) is an essential and complex optimization technique in electrical power system operations to adjust and optimize the control settings with various constraints sit [1] [2] [3] [4]. Main objective of the OPF problem to obtain the optimize scheduling of particular control variables based on limitation of system constraints sit [5] [6] [7]. These constraints consists of equality and inequality constraints. Equality constrains includes power flow or balance equations, whereas the inequality constrains sphere the dependent and decision variables within its limits
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