Comprehensive elasticity evaluation method for power distribution network

By constructing a multi-source space-time cube model and a multi-energy dynamic game model, combined with an edge-cloud collaborative digital twin system, the dynamic response and collaborative optimization problems of the distribution network under extreme natural disasters are solved, and efficient resilience assessment and decision support are achieved.

CN120724801APending Publication Date: 2025-09-30KAIFENG POWER SUPPLY COMPANY STATE GRID HENAN ELECTRIC POWER
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Patent Information

Application Number
CN202510492243.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-18
Publication Date
2025-09-30

AI Technical Summary

Technical Problem

Existing distribution network resilience assessment methods have shortcomings in dynamic response, spatial modeling and multi-energy coordination, and are difficult to adapt to the real-time changes of extreme natural disasters, resulting in an increased risk of power outages for critical loads, large errors in load loss assessment, low resource utilization and delayed decision-making.

Method used

A multi-source space-time cube model, a multi-energy dynamic game model, and an edge-cloud collaborative digital twin system are adopted, combined with Geohash grid division, Shapley value weight distribution, and virtual-real data synchronization protocol to achieve dynamic weight adjustment and millisecond-level strategy preview, and optimize multi-energy coordination and spatial heterogeneity assessment.

Benefits of technology

It improves the critical load protection rate, reduces load loss assessment errors, improves resource utilization and decision response efficiency, and meets millisecond-level decision-making requirements.

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Abstract

A comprehensive elasticity evaluation method for a power distribution network relates to the technical field of power systems, and comprises the following steps: S1, constructing a multi-source space-time cube model of the power distribution network, and dividing a geographic area into hexagonal grid units; s2, establishing a multi-energy dynamic game model, distributing energy weights based on an improved Shapley value, and realizing multi-agent collaborative optimization through a weight attenuation mechanism driven by output deviation; s3, deploying an edge-cloud collaborative digital twin system, and compressing the equipment model by adopting a model pruning technology; s4, calculating a spatial heterogeneity Gini coefficient, a time persistence toughness index and an energy alternative green elastic equivalent; s5, outputting a dynamic decision support scheme based on a B / S architecture visualization platform; according to the method, the space-time evolution characteristics of the disaster events are combined with the dynamic matching of the contribution degree of the multi-energy main body, so that the problems of static weight distribution defect of elastic evaluation and response lag of multi-energy cooperative scheduling in the prior art are solved.
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