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Keywords

adaptive feedback; grey wolf optimization; provincial distribution network disaster recovery system; dynamic optimization of resources

Abstract

The large-scale integration of renewable energy sources poses significant challenges to provincial distribution network disaster recovery systems, particularly in data processing and resource optimization. Therefore, an intelligent and dynamically optimized resource management method is urgently required to enhance resource utilization efficiency, reduce operational costs, and improve system stability. This paper proposes a dynamic resource optimization method for provincial distribution network disaster recovery systems by integrating an adaptive feedback mechanism with the grey wolf optimization (GWO) algorithm. At the data acquisition layer, a multi-level adaptive feedback mechanism is introduced to realize real-time feedback control. At the resource allocation and system operation layer, an adaptive adjustment mechanism is adopted to enable the system to dynamically perceive the operating status and adjust the optimization strategy according to the needs of different levels. In addition, a multi-objective optimization model is established, which comprehensively considers resource allocation efficiency, energy consumption, cost, and system reliability, and uses the GWO algorithm for global optimization and dynamic allocation of resources. Through simulation and actual application tests, the proposed method shows significant advantages in improving resource utilization efficiency, reducing operating costs, and enhancing system stability. This method can effectively optimize the resource allocation strategy of the provincial distribution network disaster recovery system, improve the robustness and flexibility of the power grid, and has high engineering application value.

DOI

10.19781/j.issn.1673-9140.2026.01.007

First Page

63

Last Page

72

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