Abstract
In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.
Highlights
In 1962, the optimal power flow (OPF) problem was presented by cf. Carpenter [1,2]
A non-dominated sorting and crowding distance based multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) algorithm was designed for multi-objective optimal power flow (MOOPF) problems
Non-dominated sorting and Euclidean approaches are formulated and used in the algorithm to calculate Pareto optimal front and optimal solution in a single run as a main contribution; Considering various conflicting objective functions, and the approach is implemented on two standard test systems; The effectiveness of the proposed approach is compared with multi-objective particle swarm optimization (MOPSO) and literature
Summary
In 1962, the optimal power flow (OPF) problem was presented by cf. Carpenter [1,2]. It is an effective, non-linear optimization method in an electrical power system. Various MOOPF based evolutionary methods have been developed in the present research These multi-objective algorithms are efficient as compared to classical methods because these algorithms can find a final Pareto optimal value in one execution. A non-dominated sorting and crowding distance based multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) algorithm was designed for MOOPF problems. A novel hybrid multi-objective optimization algorithm is designed and applied to OPF problems; Non-dominated sorting and Euclidean approaches are formulated and used in the algorithm to calculate Pareto optimal front and optimal solution in a single run as a main contribution; Considering various conflicting objective functions, and the approach is implemented on two standard test systems; The effectiveness of the proposed approach is compared with MOPSO and literature.
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