Integrated energy systems (IES) have become popular in the sector of energy supply because of advancement of energy intelligent technologies. Advanced adiabatic compressed air energy storage (AA-CAES) is an expansible physical energy storage technology with great potential in peak regulation and renewables accommodation. While AA-CAES improves energy efficiency, internal component failure and partial failure between different subsystems induce multiple operational risks for IES. In this paper, a multi-state Markov model is proposed for reliability evaluation of AA-CAES considering partial function failure. The reliability-oriented dynamic optimal energy flow (DOEF) model and reliability evaluation framework for IES considering AA-CAES are proposed. By considering dynamic temperature characteristics of thermal energy storage (TES) tank, reliability evaluation of IES can be more accurate. Under appropriate assumptions and simplifications, the highly nonlinear dynamic temperature model (DTM) is turned into a linear programming problem. A testing system is used to validate the proposed model and approach.

Power transformers, as essential equipment for electricity transmission, may fail due to insulation degradation. Predicting the failure rate of power transformers precisely is beneficial to decision-making. Currently, uncertainties of the prediction have not been deeply discussed. Besides, prediction accuracy is not high enough. This paper proposes a decomposition-based Bayesian deep learning (BDL) method to predict the failure rate of power transformers. Both the model uncertainty related to distribution of the model's weights and the inherent uncertainty associated with random noise can be captured by BDL. Uncertainties of prediction results are depicted with confidence intervals. Moreover, prediction accuracy is improved using variational mode decomposition (VMD). Numerical experiments have been carried out based on oil chromatographic data of power transformers from the Chongqing grid to validate effectiveness of the proposed method.