Large-Scale Network Layout Optimization for Radial Distribution Networks by Parallel Computing

Abstract Background Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. Results We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. Conclusions We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.

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Reconfiguration of radial distribution networks: application of heuristic methods on large-scale networks

IEE Proceedings - Generation Transmission and Distribution ◽

10.1049/ip-gtd:19952155 ◽

1995 ◽

Vol 142 (6) ◽

pp. 631 ◽

Cited By ~ 34

Author(s):

G. Peponis

Keyword(s):

Radial Distribution ◽

Large Scale ◽

Distribution Networks ◽

Heuristic Methods ◽

Radial Distribution Networks ◽

Large Scale Networks

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Multi-tier service restoration through network reconfiguration and capacitor control for large-scale radial distribution networks

Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351) ◽

10.1109/pica.1999.779398 ◽

2003 ◽

Cited By ~ 13

Author(s):

K.N. Miu ◽

Hsiao-Dong Chiang ◽

R.J. McNulty

Keyword(s):

Radial Distribution ◽

Large Scale ◽

Distribution Networks ◽

Network Reconfiguration ◽

Service Restoration ◽

Radial Distribution Networks

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Multi-tier service restoration through network reconfiguration and capacitor control for large-scale radial distribution networks

IEEE Transactions on Power Systems ◽

10.1109/59.871725 ◽

2000 ◽

Vol 15 (3) ◽

pp. 1001-1007 ◽

Cited By ~ 40

Author(s):

Karen Nan Miu ◽

Hsiao-Dong Chiang ◽

R.J. McNulty

Keyword(s):

Radial Distribution ◽

Large Scale ◽

Distribution Networks ◽

Network Reconfiguration ◽

Service Restoration ◽

Radial Distribution Networks

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A Cost-Benefit Analysis of Capacitor Allocation Problem in Radial Distribution Networks Using an Improved Stochastic Fractal Search Algorithm

Complexity ◽

10.1155/2020/8811674 ◽

2020 ◽

Vol 2020 ◽

pp. 1-32

Author(s):

Phuoc Tri Nguyen ◽

Thi Nguyen Anh ◽

Dieu Vo Ngoc ◽

Tung Le Thanh

Keyword(s):

Radial Distribution ◽

Large Scale ◽

Cost Benefit Analysis ◽

Search Algorithm ◽

Cost Benefit ◽

Distribution Networks ◽

Optimal Number ◽

Solution Quality ◽

Radial Distribution Networks ◽

Stochastic Fractal Search

This research proposes a modified metaheuristic optimization algorithm, named as improved stochastic fractal search (ISFS), which is formed based on the integration of the quasiopposition-based learning (QOBL) and chaotic local search (CLS) schemes into the original SFS algorithm for solving the optimal capacitor placement (OCP) in radial distribution networks (RDNs). The test problem involves the determination of the optimal number, location, and size of fixed and switched capacitors at different loading conditions so that the network total yearly cost is minimized with simultaneous fulfillment of operating constraints. Also, the hourly on/off scheduling plans of switched shunt capacitors (SCs) considering a modified cost objective function are obtained. The proposed ISFS algorithm has been tested on two IEEE 69-bus and 119-bus RDNs and a practical 152-bus RDN. For clarifying the effectiveness and validation of the ISFS, the simulated results have been compared with those of other previously utilized solution approaches in the literature as well as the original SFS. From result comparison, the proposed ISFS outperforms other previous approaches regarding solution quality and statistical performance for the compared cases, especially in the complex and large-scale networks. Notably, compared with the original SFS, the proposed ISFS shows a significantly better performance in all the tested cases.

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