A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations

Daqaq, Fatima; Hassan, Mohamed H.; Kamel, Salah; Hussien, Abdelazim G.

doi:10.1038/s41598-023-41608-1

Cited by 15 publications

(3 citation statements)

References 66 publications

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“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”

Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

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Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

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Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

Self Cite

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“… 3 , 12 , 17 while minimum cost is utilized in refs. 13 , 15 , 16 . When all resources have the same generation cost function, then the minimum cost and minimum losses provide similar results.…”

Section: Problem Formulationmentioning

confidence: 99%

“…DG size and allocation in distribution networks have been investigated in several studies to optimize one or more system variables, such as maximizing the hosting capacity and minimizing the total energy losses 3 , minimizing the real power losses and enhancing the VSI 9 , diminishing both the power losses and the voltage deviation 10 , maximizing DG hosting capacity 11 , minimizing active power losses, reactive power losses, voltage deviation, and maximizing the VSI 12 , minimizing costs and enhancing the economic efficiency of the power systems 13 , 14 , and minimize fuel cost, real power losses, emission cost, and voltage deviations 15 , 16 . Also, several types of DGs (wind, PV, and fuel cell) have been optimized for loss reduction and voltage profile enhancement in 9 , energy losses and emission reduction in 17 , and minimizing the injected power into the grid in 18 .…”

Section: Introductionmentioning

confidence: 99%

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Hamza,

Yousef,

Ali

et al. 2024

Sci Rep

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Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

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