MOPSO using probabilistic and deterministic criteria based on OHL's thermal ratings

Abstract

Abstract-A Population Intelligent (PI) methodology calledParticle Swarm Optimization has recently been applied to power system networks with the view to minimize the computational burden of Monte Carlo Simulation in the reliability domain. This paper presents a novel Multi Objective Particle Swarm optimization (MOPSO) methodology which adapts traditional binary PSO to multi objective PSO and intelligently prunes the state space by using the thermal capacity of transmission lines derived from the more detailed modelling of OHLs. For the implementation of the algorithm, deterministic metrics are used to evaluate the loading of the lines with the view to further enhance the efficiency of the proposed method. The IEEE 24-bus RTS is used under different case studies to validate that the filtering based methodology achieves computational effectiveness as well as improves network performance indices.

Electricity Network Reliability Planning and Evaluation Considering Flexible Solutions

“…Nonetheless, this assumption is flawed because in reality the system is always exposed to risk of failure, and subsequently to customer outages in spite of the operator's ability to minimize this risk by implementing post fault corrective actions. Therefore, probabilistic methods have been proposed to tackle these problems [2]- [6].…”