Optimization of Reactive Power Dispatch Considering DG Units Uncertainty By Dandelion Optimizer Algorithm
Abstract
The optimal reactive power dispatch (ORPD) is a vital problematic widely discussed in power system engineering, where ORPD be located one of optimal power flow (OPF) sub-difficulties which is complex and nonlinear problem and can be expressed as solitary or multi objective designed for optimizing the reactive power, the power losses, and capacity of the network transfer power. Distributed generation (DGs) rise network reservations due to haphazard behaviour, accordingly that optimal power flow is no elongated approachable, and the probabilistic optimal power flow (POPF) necessity remain studied. This paper explains solving for a POPF problem using Taguchi orthogonal array technique (TOAT) or Taguchi method (TM) based on orthogonal arrays (OAs) for modelling and correlation between uncertainties of the DGs and using new optimization algorithm to increases processing speed and accuracy. Lately proposed prevailing and dependable meta-heuristic algorithm identified as Dandelion optimizer (DO) has remained running on the standard IEEE 30 bus for solving the ORPD problematic and defined the optimal-mix combination of dispatchable DG, to make the most of several techno-economic and societal paybacks at the same time. The simulation outcomes by means of the new proposed algorithm once tested through TM method and compared it performance with the results from other algorithms such as Genetic algorithm (GA), Killer Whale Algorithm (KWA), Prairie dog optimization algorithm (PDO),and Whale optimization algorithm (WOA) prove that the (DO) optimizer is the most superior among all and lead to minimizing voltage deviation and system power losses minimization through high speed and minimum calculation time. Finally, with this method, optimize reactive power dispatch is obtained. The results on IEEE 30-bus test network show the OA based on TM have excellent accuracy, high speed, and simplicity in the POPF issue solving.
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DOI (PDF): https://doi.org/10.20508/ijrer.v12i4.13573.g8606
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