Joining resilience and reliability evaluation against both weather and ageing causes

Differentiation of resilience from reliability has been a heated topic ever since the emergence of the former upon the limitation, and as a complement, of the latter, while finding the common ground for both has been scarce. This study first picks out the steady-state performance of both to be the common ground, while differentiating their typical causes, namely, the extreme weather for the resilience threats and the component ageing for the reliability challenges. An original evaluation framework developed earlier for the sole reliability is then extended here towards this common ground to accommodate resilience, becoming the joint reliability and resilience evaluation framework. The joint evaluation framework is built upon the Monte Carlo simulation, which embeds the rolling unit commitment as the system operation module to balance the optimality of operation strategy and the punctuality of condition update, and the forecast module with the machine learning technique for generate varied operation conditions. The proposed evaluation framework that joins resilience with reliability not only has its efficacy validated on the 39-bus system, but also deepens the evaluation by analyzing scenarios created with variating the values of key factors that impact resilience, including the typhoon speed, load factor, restoration time, and typhoon direction. Such a joint reliability and resilience evaluation pioneers as an integrated approach to conduct both evaluations in three aspects, namely, differentiating the causes of reliability and resilience harms and paralleling the challenges of both on system performance, upgrading the existing evaluation methods by applying the rolling mechanism to the operation strategy, and offering an open framework to embed complex functions, such as forecast tools enabled by machine learning and proactive responses for resilience enhancement.

Automated summary

This study discusses the differentiation between resilience and reliability, identifying their common ground in steady-state performance. By comparing the causes of resilience threats and reliability challenges, it proposes a joint reliability and resilience evaluation framework, extended with Monte Carlo simulation and machine learning techniques.