A Cloud-Edge Communication Resource Slicing Allocation Method for Data Monitoring in Integrated Energy Systems

With the continuous growth in both volume and variety of monitoring services in integrated energy systems, the disparities between time scales and tasks of heterogeneous energy flow data monitoring pose significant challenges to rational resource allocation and efficient data transmission. To address these challenges, a monitoring resource slicing allocation method with low-cost optimization is presented for energy flow monitoring services in integrated energy systems. Firstly, a dynamic network slicing method for heterogeneous energy flow is proposed, which realizes rational resource allocation for diverse monitoring tasks and time scales through an integrate-then-slice approach. Secondly, a data transmission strategy with a cost representation method for network slicing is proposed. By establishing precise modeling of the complete data monitoring process within each slice, it solves the quantitative problem of data monitoring costs. Thirdly, an adaptive monitoring resource slice allocation algorithm is proposed, which addresses the cost optimization problem in data monitoring under slicing modes through optimized allocation of both intra-slice and inter-slice resources. Finally, tests are conducted on an integrated energy system in China. The results demonstrate that the proposed method successfully achieves data monitoring of heterogeneous energy flows across multiple time scales, while significantly reducing the data monitoring costs.