Data-Driven Situation Awareness of Electricity-Gas Integrated Energy System considering Time Series Features

Abstract

Clean and low-carbon electricity-gas integrated energy system (EGIES) is being developed rapidly in order to meet the dual-carbon target. Situation awareness can provide an early warning of operational risks to the EGIES, which is helpful for its promotion and application. In this paper, a data-driven situation awareness method of EGIES considering time series features is proposed. The state and deviation vectors of EGIES are solved at the situation perception level based on the state estimation. The recurrence plot (RP) is used at the situation comprehension level to extract the time series features of historical deviations, and the operating state of future deviations is encoded in the form of labels. A convolutional neural network (CNN) is established at the situation projection level to project the future operating state of the EGIES based on the RP of the historical deviations. A case study of EGIES coupling a 14-node power system with a 7-node gas system shows that the projection accuracy of the proposed method increases by 2.07 and 3.04% compared with the long-short term memory (LSTM) neural network and the support vector machine (SVM), respectively.