Dynamic line rating considering short term reliability and convergence time

Dynamic line ratings (DLR) can improve the ampacity of overhead transmission lines by short term overload. This paper conducts ACOPF incorporating DLR considering short term reliability. The convergence time of different reliability evaluation methods is predicted by using the correlation with topological vulnerability metrics. Moreover, a Markov chain based analysis of two population based algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) is studied, and a state aggregation based technique is proposed to find a computational tractable way to study the relationship of the second largest eigen value of underlying Markov chain with convergence rate. A decision tree and kernelized linear regression are used for regression analysis, and the statistical significance of regression models are tested through chi-square analysis. Temporal decomposition is used to decouple the reliability analysis and heat balance equations (HBE) with the ACOPF model to incorporate DLR and reliability metrics in multi-interval dispatch. The sub problems are solved through analytical targeted cascading (ATC). The applications of proposed method was tested and demonstrated on 3 bus system and 32 bus Nordic System. Moreover, the proposed scheme aids in clearing congestion, improving the plant load factor of wind turbines while maintaining the tolerable levels for loss of load probability.