Reliability Evaluation of Composite Systems with Renewable Energy Sources

Abstract

This thesis presents simple yet effective methodologies for accelerating Monte Carlo simulation (MC) in power system reliability assessment and also studies the impact of correlation between generated renewable power and loads towards the estimated reliability indices. Monte Carlo simulation can be broadly classified into two approaches---sequential and non-sequential simulations. Sequential MC is a very flexible method for reliability assessment since it can sequentially imitate the random nature of system components. We improve the computational efficiency of sequential MC using Latin Hypercube Sampling (LHS) as a variance reduction technique. The performances of sequential LHS and MC are compared. Results indicate the better performance of LHS over MC as the variances of resulting reliability indices are reduced.

Non-sequential MC usually converges faster than sequential MC; however, calculating frequency and duration (F\&D) indices using this technique requires additional computation. Such additional computation can be very heavy and complicated due to correlation between components in the system integrated with renewable energy sources. In order to reduce such complication, the correlation is captured by a non-aggregate Markov model. In addition, a hybrid enumeration and conditional probability approach is proposed to calculate F\&D indices. The proposed non-sequential MC converges much faster than sequential MC while precisely taking into account the correlation. Non-sequential MC with independent sampling is conducted in order to observe the impact of correlation. In comparison with sequential MC and the proposed method, independent non-sequential MC provides largely biased indices, indicating the enormous impact of correlation towards resulting indices.

Computational performance of both sequential and non-sequential MCs can be further improved by integrating a pattern classifier into the simulation. We propose techniques for improving precision and construction efficiency of a classifier in power system reliability assessment. Construction efficiency of a classifier can be enhanced by means of worsening components reliability. In addition, relaxed decision boundary is proposed to improve precision of a classifier. Results show that the proposed techniques outperform conventional methodologies in terms of both precision and construction efficiency of a classifier. Computational time taken is also dramatically reduced.