An Integrated Approach for Multiarea Reliability Improvement With Risk-Based Contingency Analysis and Optimal Load Curtailment

Line failures, particularly in the form of N-1 contingencies, are a significant cause of interruptions in power grids, negatively affecting system reliability. Effective risk-based contingency analysis (RBCA) is essential in modern power systems to address increasing uncertainties and complexities. This paper proposes an RBCA framework that calculates the probabilistic risk index (RI) based on transmission line severity functions for single- and multiarea bulk power systems. The approach identifies critical transmission lines and least reliable buses, enabling utilization of load curtailment strategies. Meta-heuristic techniques, particle swarm optimization (PSO), and gray wolf optimization (GWO), are employed for optimal load curtailment, achieving approximately 30% reduction in curtailed load compared to the analytical proportional load curtailment approach. The methods chosen for their robustness and ability to balance exploration and exploitation in various optimization problems provide practical insights for system operators to enhance reliability under diverse operating conditions. Furthermore, the study examines the impact of flexible thermal rating (FTR) on multiarea systems considering variations in weather conditions, and a comparison is drawn with the static thermal rating (STR) system. Key reliability indices, including EENS, EDNS, BPECI, MBPCI, and EIC, are determined and analyzed for the proposed study. The proposed approach benefits system operators in preventing outages and formulating contingency action plans and emphasizes the study’s contribution to ensuring a stable and reliable power supply which is critical for modern societal needs. The proposed approach has been tested and validated on IEEE 24 reliability test system (RTS).