Showing 2 results for Load Restoration.
Mahdi Arabsadegh, Aref Doroudi,
Volume 21, Issue 4 (11-2025)
Abstract
This paper presents an advanced methodology for post-storm power system restoration. A real-time Condition Index (CI)-based classification scheme is introduced to categorize circuit breakers into high-reliability (Type A) and moderate-reliability (Type B) groups. Leveraging this classification, a genetic algorithm (GA) optimizes microgrid configurations to maximize power restoration probabilities by explicitly modeling the stochastic failure risks associated with circuit breakers under severe weather conditions. The approach was validated on the IEEE 118-bus system with five critical breakers deactivated due to storm conditions. The GA achieved a 92.5% load restoration after 200 iterations, surpassing a baseline Monte Carlo simulation that attained 85.2%. Computational efficiency was significantly improved, reducing execution time to approximately 15 minutes compared to 60 minutes for traditional methods, with enhanced accuracy indicated by a 1.8% error margin versus 7.5%. Key contributions include utilizing live CI data for dynamic breaker classification, which resulted in a 20% reduction in computational time, and demonstrating scalability and effectiveness on large-scale test systems such as the 118-bus network. The methodology's performance decreases to 78.3% load restoration when more than 14 breakers are compromised. Future research will focus on integrating detailed storm modeling—including wind speed profiles—and incorporating renewable energy resources to enhance grid resilience.
Mahdi Arabsadegh,
Volume 22, Issue 3 (9-2026)
Abstract
This article proposes an innovative framework for enhancing resilience in power grid restoration that integrates dynamic breaker failure modeling with structural topology analysis. Unlike conventional approaches focusing solely on breaker health metrics, our method introduces a novel Structural Importance Coefficient (SIC) quantifying each breaker’s criticality through graph-theoretic measures (betweenness/closeness centrality) and cascading failure impact. The hybrid probabilistic-physical failure model combines Weibull-Bayesian degradation analysis with environmental stressors (humidity, temperature) to estimate real-time malfunction probabilities. A hierarchical optimization algorithm then prioritizes repairs by jointly optimizing SIC and health status, achieving: (1) 28% faster critical load recovery, (2) 40% reduction in repair resource waste via strategic SIC-based allocation, and (3) adaptive microgrid formation under uncertainty. Validated on IEEE 39/118-bus systems, the framework demonstrates superior performance compared to Monte Carlo-based methods (e.g., 35% higher load restoration during storms) while requiring no historical data archives. Key innovations include the SIC metric for topology-aware decision-making and a two-stage optimization protocol balancing local breaker conditions with global network resilience. Practical implementation is highlighted through SCADA-compatible modules.
