Availability augmentation of smart grid systems using Markovian approach and nature inspired algorithms

Area of Science:

• Engineering
• Computer Science
• Operations Research

Background:

• Smart grid systems require high availability for reliable operation.
• Stochastic modeling and nature-inspired algorithms are emerging tools for system optimization.
• Existing methods may not fully capture the complexities of smart grid degradation and uncertainty.

Purpose of the Study:

• To develop a novel stochastic model for smart grid availability.
• To optimize smart grid availability using hybrid nature-inspired algorithms.
• To evaluate the performance and accuracy of the proposed model and algorithms.

Main Methods:

• Developed a novel stochastic model for smart grid systems using Markov birth-death process.
• Integrated degradation, uncertain sensor behavior, and exponential failure/repair laws.
• Employed hybrid nature-inspired algorithms for parameter estimation and availability optimization.
• Utilized Friedman and Wilcoxon tests for statistical validation.

Main Results:

• Sequential SOHBA significantly outperformed traditional SO, HBA algorithms, and Monte Carlo simulations.
• The proposed stochastic model accurately predicted smart grid system availability.
• Numerical results for steady-state availability were derived, and the impact of failure/repair rates was analyzed.

Conclusions:

• The developed stochastic model and sequential SOHBA offer a robust approach for smart grid availability optimization.
• The findings highlight the effectiveness of integrating advanced modeling and optimization techniques for critical infrastructure.
• Further research can explore variations in failure distributions and more complex system architectures.