An Edge Computing-Based Fast Restoration for Urban Medium- and Low-Voltage Distribution Networks

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 0.82-0.91load distribution equilibrium, exhibiting superior performance relative to comparative methodologies.