Abstract
A hybrid BCMPO technique for optimal scheduling of electric vehicle aggregators under market price uncertainty is proposed in this manuscript. The proposed method is a combined performance of balancing composite motion optimization (BCMO) and political optimizer (PO); thus, it is known as a BCMPO system. The hybrid optimization system is used in this work to explore the programming of electric vehicle aggregators under market price uncertainty. The proposed electric vehicle aggregator contributes to the market price for increasing profits. To model the uncertainty of market price with the BCMPO system, the higher and lower levels of the upstream grid prices are utilized. The results of the proposed algorithm are utilized to build numerous charging and discharging approaches that may be used via the operator for a robust EV aggregator scheduling under the uncertainty of the upstream grid price. At that time, the proposed model accomplished on MATLAB/Simulink platform performance is related to the existing systems, such as genetic algorithm (GA), particle swarm algorithm (PSO) and elephant herding optimization (EHO). The profit of EVs aggregator vs. robust profit using the proposed technique is 10110.
Published Version
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