Reliability importance of renewable energy sources to overall generating systems

• Main Authors: Niu, Ming, Xu, Ning Zhou, Kong, Xin, Ngin, Hoon Tong, Ge, Yang Yang, Liu, Jing Song, Liu, Yi Tao
• Other Authors: Experimental Power Grid Centre
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Environmental engineering; Component Importance; Converters
• Online Access: https://hdl.handle.net/10356/146653
• Institution: Nanyang Technological University

Over the years, probabilistic nature of renewable energy sources (RES) and its influence on power system adequacy have been well studied. However, rather less attention has been paid to the impact of RES unit itself’s and its power conversion system’s (PCS’) reliability, as well as their various connection topologies. This paper devises a comprehensive sensitivity study on how each of these elements can affect overall generating system reliability. given the plethora of RES configurations and components, it is of import to identify the most vulnerable element in RES. In this work, component importance is extended, for the first time, to generating capacity adequacy assessment (HLI). Measurement index is the centerpiece in reliability importance. New indices have to be introduced to facilitate the study. While the physical meaning of previously developed indices is lost, in this study indices are proposed based on traditional importance measures, of which the physical meaning are strictly retained and consistent with the definitions. With the proposed assessment technique, components in various RES configuration can be ranked according to their reliability importance. It is found in the numerical study that different importance measures (such as risk-achievement based measures and risk-reduction based measures) can result in different rankings. Studies on contributing factors of the reliability importance are also performed. As more and more RES gaining foothold in generating systems, the proposed technique assist to achieve targeted reliability level of the system, by easily identifying and prioritizing reliability improvement tasks among various units/components in the increasing complex system.