Solution of multi-objective optimal power flow using efficient meta-heuristic algorithm
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An efficient meta-heuristic algorithm-based multi-objective optimization (MOO) technique for solving the multi-objective optimal power flow (MO-OPF) problem using incremental power flow model based on sensitivities and some heuristics is proposed in this paper. This paper is aimed to overcome the drawback of traditional MOO approach, i.e., the computational burden. By using the proposed efficient approach, the number of power flows to be performed is reduced substantially, resulting the solution speed up. In this paper, the generation cost minimization and transmission loss minimization are considered as the objective functions. The effectiveness of the proposed approach is examined on IEEE 30 and 300 bus test systems. All the simulation studies indicate that the proposed efficient MOO approach is approximately 10 times faster than the evolutionary-based MOO algorithms. In this paper, some of the case studies are also performed considering the practical voltage-dependent load modeling. The simulation results obtained using the proposed efficient approach are also compared with the evolutionary-based Non-dominated Sorting Genetic Algorithm-2 (NSGA-II) and the classical weighted summation approach.
KeywordsEvolutionary algorithms Generation cost Multi-objective optimal power flow Pareto optimal solutions Sensitivity Transmission loss
This work was supported by Institute for Information & Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIP) (No. B0186-16-1001. Form factor-free Multi-input and output Power Module Technology for Wearable Devices).
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