Differential evolution-based efficient multi-objective optimal power flow
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This paper proposes a novel-efficient evolutionary-based multi-objective optimization (MOO) approaches for solving the optimal power flow (OPF) problem using the concept of incremental load flow model based on sensitivities and some heuristics. This paper is useful in robust decision-making for the system operator. The main disadvantage of meta-heuristic-based MOO approach is computationally burdensome. The motivation of this paper is to overcome this drawback. By using the proposed efficient MOO approach, the number of load flows to be performed is reduced substantially, resulting to the solution speed up. Here, three objective functions, i.e., generator fuel cost minimization, loss minimization, and L index minimization are considered. The proposed approach can effectively handle the complex non-linearities, discontinuities, discrete variables, and multiple objectives. The potential and suitability of the proposed efficient MOO approach is tested on the IEEE 30 bus system. The results obtained with the proposed efficient MOO approach are also compared with the meta-heuristic-based non-dominated sorting genetic algorithm-2 (NSGA-II) technique. In this paper, the proposed efficient MOO approach is implemented using the differential evolutionary (DE) algorithm. However, it is a generic one and can be implemented with any type of evolutionary algorithm.
KeywordsMulti-objective optimization Optimal power flow Pareto optimal solutions Sensitivity Fuel cost Transmission loss Voltage stability
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Conflict of interest
The authors declare that they have no conflict of interest.
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