Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynami-cally analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy con-sumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational effi-ciency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply se-curity in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A systematic framework for dynamically analyzing the real-time reliability of IESs is proposed in this paper, integrating different machine learning methods and statistics. The method uses a bootstrap-based Extreme Learning Machine to forecast probability distributions and a stacked auto-encoder model to simulate the dynamic behavior of IESs. Additionally, variables representing the transient properties of natural gas pipeline networks are used to quantify energy supply security, improving the effectiveness of reliability assessment. The study demonstrates the method's ability to effectively evaluate the reliability of IESs, providing valuable information for system operation and management.