Article: An edge computing-based fast restoration for urban medium- and low-voltage distribution networks Journal: International Journal of Critical Infrastructures (IJCIS) 2026 Vol.22 No.9 pp.1 - 21 Abstract: The rapid and reliable restoration of urban medium- and low-voltage distribution networks is paramount for sustaining economic activities and social well-being. However, conventional centralised restoration methods are increasingly inadequate due to their inherent limitations in handling communication delays, heterogeneous real-time data integration, and the high computational complexity of stochastic optimisation, leading to prolonged outages and reduced service reliability. To address these challenges, this research proposes a fast power recovery method based on distributed edge computing for urban medium- and low-voltage distribution networks. The method enhances restoration efficiency through localised data processing, improved temporal performance, and the integration of heterogeneous data sources. Employing Box-Cox-combined Z-scale conversion for non-Gaussian temporal datasets and principal component-enhanced Dempster-Shafer deduction for information amalgamation, the approach transmutes multi-criteria recovery into singular-goal optimisation via decision matrices. Probabilistic voltage restrictions are reformulated as definitive quadratic mixed-integer constraints through sample average approximation, while second-degree conical relaxation manages non-linear current equations to establish a tractable mixed-integer quadratically constrained programming framework. Experimental outcomes demonstrate 2.89-minute recovery intervals, success probabilities exceeding 95%, and 0.82-0.91 load distribution equilibrium, exhibiting superior performance relative to comparative methodologies. Inderscience Publishers - linking academia, business and industry through research

Title: An edge computing-based fast restoration for urban medium- and low-voltage distribution networks

Authors: Zhaoyu Wu; Weirui Cai; Genyuan Zhang; Weichen Long; Lizhong Peng

Addresses: Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guang'zhou, 510630, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guang'zhou, 510630, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guang'zhou, 510630, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guang'zhou, 510630, China ' Dongfang Electronics Corporation, Yantai, 264000, China

Abstract: The rapid and reliable restoration of urban medium- and low-voltage distribution networks is paramount for sustaining economic activities and social well-being. However, conventional centralised restoration methods are increasingly inadequate due to their inherent limitations in handling communication delays, heterogeneous real-time data integration, and the high computational complexity of stochastic optimisation, leading to prolonged outages and reduced service reliability. To address these challenges, this research proposes a fast power recovery method based on distributed edge computing for urban medium- and low-voltage distribution networks. The method enhances restoration efficiency through localised data processing, improved temporal performance, and the integration of heterogeneous data sources. Employing Box-Cox-combined Z-scale conversion for non-Gaussian temporal datasets and principal component-enhanced Dempster-Shafer deduction for information amalgamation, the approach transmutes multi-criteria recovery into singular-goal optimisation via decision matrices. Probabilistic voltage restrictions are reformulated as definitive quadratic mixed-integer constraints through sample average approximation, while second-degree conical relaxation manages non-linear current equations to establish a tractable mixed-integer quadratically constrained programming framework. Experimental outcomes demonstrate 2.89-minute recovery intervals, success probabilities exceeding 95%, and 0.82-0.91 load distribution equilibrium, exhibiting superior performance relative to comparative methodologies.

Keywords: edge computing; medium and low voltage distribution network; rapid restoration of power supply; DS reasoning mechanism.

DOI: 10.1504/IJCIS.2026.152365

International Journal of Critical Infrastructures, 2026 Vol.22 No.9, pp.1 - 21

Received: 13 Nov 2025
Accepted: 10 Jan 2026
Published online: 16 Mar 2026 *

Title: An edge computing-based fast restoration for urban medium- and low-voltage distribution networks

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