Simultaneous network reconfiguration and capacitor allocations using a novel dingo optimization algorithm

Ayanlade, Samson Oladayo; Jimoh, Abdulrasaq; Ogunwole, Emmanuel Idowu; Aremu, Abdullahi; Jimoh, Abdulsamad Bolakale; Owolabi, Dolapo Eniola

doi:10.11591/ijece.v13i3.pp2384-2395

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…These equations capture the essence of how dingoes surround their prey and work together as a cohesive unit. By applying these mathematical models, the effectiveness of this encircling behavior in the context of optimization algorithms can be simulated and analyzed [24].…”

Section: Encirclingmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Ayanlade,

Ariyo,

Jimoh

et al. 2023

Sustainability

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Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions.

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Section: Encirclingmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Ayanlade,

Ariyo,

Jimoh

et al. 2023

Sustainability

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Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Neda

2024

Electr Eng

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Reviews, Challenges, and Insights on Computational Methods for Network Reconfigurations in Smart Electricity Distribution Networks

El-Fergany

2023

Arch Computat Methods Eng

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Power losses and voltage profiles in electricity distribution networks are a problem, particularly in developing nations. Many techniques have reportedly been used in the previous ten years to address this issue. Among other solutions, network reconfigurations (NRs) are regarded as one of the most practical. It is important to conduct a periodic update survey on this subject because the electricity radial distribution networks (RDNs) are continually evolving. Therefore, a thorough evaluation of the various techniques to address the issues with NRs along distribution networks is provided in this manuscript. There is discussion of several mathematical, traditional, heuristic-based, and machine-learning strategies. It is important to understand how the radiality is achieved as well as methods for resolving distribution load flow, particularly with greater R/X ratios. The most typical test cases used in the literature are listed. In order to enrich this review and make it useful to others, more than 200 articles (the majority of which were published in the last five years) are referenced inside the body of this text. The final conclusions and related future insights are presented. At last, this work is an invaluable resource for anyone involved in this field of study because it offers a comprehensive literary framework that can serve as the foundation for any future research on NRs and its prospective difficulties. Therefore, academics can use this framework to enhance previous formulations and approaches as well as suggest more effective models.

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Simultaneous network reconfiguration and capacitor allocations using a novel dingo optimization algorithm

Cited by 4 publications

References 25 publications

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Reviews, Challenges, and Insights on Computational Methods for Network Reconfigurations in Smart Electricity Distribution Networks

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