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This study describes an efficient technique for reliability assessment of composite power generation–transmission system. The state-space partitioning (SSP) method combines the advantages of the state enumeration method (SEM) and the Monte Carlo simulation (MCS). The SEM is quite effective at handling low-order contingencies that constitute a significant portion of the total state space. However, it is computationally inefficient for higher order contingencies. On the other hand, the MCS is very effective at sampling larger state spaces. With the SSP, the system state space is partitioned into two regions. The system states are first enumerated in descending probability order using the fast sorting technique (FST). Then the remainder of the state space is sampled using the MCS to catch low-probability states. Since the likelihood of encountering failed states in the non-enumerated part of the state space is higher than in the full state space, the simulation converges much faster than a direct MCS (DMCS). A comparison of SSP with the SEM and DMCS using IEEE reliability test system shows that the SSP considerably reduces the computational effort. The effectiveness of the proposed method is also demonstrated on a part of the China southern power grid. [ABSTRACT FROM AUTHOR]

Increasing exploitation of wind energy requires the development of adequate models and techniques for reliability assessment of wind farms (WFs) and of electric power systems including wind energy conversion systems. A novel approach to WF modelling is investigated for reliability assessment, which is based on the universal generating functions. Such an approach combines the use of the z-transform and composition operators, allowing to account for all the factors affecting the WF performance. The results, both theoretical and numerical, of the case study, give evidence of the effectiveness of the proposed approach in terms of accuracy as well as of flexibility and efficiency of the solving algorithm. [ABSTRACT FROM AUTHOR]