This paper presents risk prognosis methods for decision support in long term-asset management by quantifying how different classes of reinvestment strategies influence the reliability of supply. The reinvestment strategies we consider are age based, condition based and risk based, where risk is quantified in terms of expected energy not supplied (EENS). The risk prognosis model combines power system reliability assessment with simulation of the time development of components' technical condition. The condition of the component population is influenced by three different factors in the model: degradation due to aging, forced replacements due to non-repairable failures, and preventive replacement according to the given reinvestment strategy. In demonstrating the methodology we focus on transformers and utilize an existing transformer end-of-life model. An important secondary objective of the work is to quantify the uncertainty in the end-of-life model, and include this uncertainty in the risk prognosis. We show that although there is substantial uncertainty in the end-of-life model, the relative performance of the reinvestment strategies is easily identified. The risk based strategy is seen to outperform the age-based and condition-based strategies giving considerably lower EENS and uncertainty over time.

In the ongoing transition towards a sustainable energy system, the electric power system increases in complexity and must adapt to rapid changes and new uncertainties. Thus, development and application of appropriate probabilistic methods is all the more important. Reliability analysis based on Monte Carlo simulation (MCS) has become increasingly popular, and its strength lies in the ability to account for a large number of random variables, general stochastic processes and assessing the probability distribution of the output. However, power system reliability is governed by relatively rare interruption events which poses a fundamental challenge to MCS. This paper presents a variance reduction technique for Monte Carlo based reliability analysis which combines resampling and the cross entropy (CE) method. The motivation for the work is to mitigate the computational burden related to rare event sampling, while at the same time preserving the flexibility of MCS by introducing few assumptions on the stochastic model. The method is demonstrated on a synthetic test system and gives a speedup of about 10 times compared to a crude simulation.