Fit Entropy-Based Dynamic Communication Resource Slicing-Optimization Method in Smart Distribution Grids on the...

Area of Science:

• Electrical Engineering
• Computer Science
• Network Optimization

Background:

• The proliferation of new energy and smart technologies drives increased demand for inter-device communication in smart distribution grids.
• Existing communication infrastructure faces challenges in supporting the growing variety and volume of communication services.

Purpose of the Study:

• To propose an optimization method for limited communication network resources in medium-low-voltage smart distribution grids.
• To enhance the deployment of service function chains for flexible virtual resource combination.

Main Methods:

• Modeling the distribution grid as a three-domain (service, logical, physical) structure.
• Transforming the problem into a hierarchical bipartite hypergraph-matching problem.
• Developing two matching optimization algorithms: 'business domain-logic domain-physical domain integration' and a two-stage approach.

Main Results:

• The proposed algorithms effectively address the complex combinatorial optimization problem.
• Simulation results show significant improvements in service success rates.
• Enhanced resource utilization and overall network utility were demonstrated.

Conclusions:

• The developed optimization methods are effective for managing communication resources in smart distribution grids.
• The algorithms provide a scalable solution for diverse and massive communication service demands.
• Fit entropy and network utility are key metrics for optimizing smart grid communication networks.