Dynamic Reliability Assessment Framework for Integrated Energy Systems Based on the Improved Universal Generating Function

With the development of science and technology in the field of energy, the concept of Integrated Energy System (IES) has attracted more and more researchers' attention. However, it is difficult to evaluate the reliability of the coupled energy system due to the complex systems structure and dynamics in subsystems. This leads to heavy computational burdens for evaluation processing. To overcome this gap, in this paper, a novel systematic reliability assessment framework is proposed to analyze the dynamic reliability of IESs. The data-driven model and improved universal generating function are combined in this method. The components' random behavior is represented by improved universal generating function (IUGF) models. All these IUGF models are aggregated by an operator to build the system IUGF model. The operator is defined as an IES model which is described by a data-driven model. The efficiency and accuracy are validated by comparing the results from the proposed method with that from Monte Carlo simulation. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results indicate that the role of P2G is becoming more and more important in IESs with the increasing penetration level of renewable sources. The synergism of P2G and energy storage devices has the best effect on improving the system's reliability.