article · Engineering Optimization
This research presents a modified marine predators optimizer designed to address the simultaneous reconfiguration of distribution networks and the allocation of distributed generators. The algorithm adapts standard predator strategies to account for varying environmental and climatic conditions. It was tested against several established computational techniques, including the original marine predators optimizer, genetic algorithms, harmony search, fireworks, firefly, and improved sine-cosine optimizers. Performance evaluations were conducted across single and multiple objectives using standard 33-bus and 69-bus electrical distribution test networks under light, nominal, and heavy loading conditions. The simulation results show that the modified algorithm achieves significant performance improvements over the baseline method and outperforms the alternative optimizers evaluated.
Managing electricity grids requires balancing the placement of local power generators with the physical setup of the network, particularly as electrical demand fluctuates. Using advanced computational methods to optimize these choices helps ensure that power distribution networks run efficiently under various operational loads and shifting operating conditions, supporting better reliability and planning in modern electrical systems.
The method could be integrated into grid-planning software tools used by electrical distribution network operators and utility engineers. Because it has only been demonstrated through simulations on standard 33-bus and 69-bus test systems, the technology remains early-stage research that requires further validation on real-world utility networks before practical commercial deployment.
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A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.
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DOI: 10.1080/0305215x.2021.1897799
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