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.

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

“…In addition, the uncertainty of RESs (Wang et al, 2008;Zhang et al, 2021) affects the electric-thermal coupling conversion and demand response implementation process. Thus, in this paper situational awareness techniques are employed to effectively mitigate the uncertainty risk posed by RESs (Lin et al, 2022) and to provide safe and economic operation of the ECS. In summary, the main contributions of this paper are summarized as follows:…”

“…Due to the specific heat capacity and thermal characteristics of the heat transfer medium, the temperature of the heated medium consistently lags behind the temperature changes in the heat transfer medium. Therefore, changing the heat supply amount within a certain time will not affect the ambient temperature within the heated space (Lin et al, 2022). Based on the above analysis, this section models the thermal inertia characteristic of the heating area of the thermal system.…”

Research on the Implementation Method of Situational Awareness Strategy by Fusing Computational Migration Models

“…Currently, many scholars have conducted in-depth research on situational awareness strategies and achieved certain research results. Literature [15] proposes a decision tree-based situational prediction model for Vehicular Network Security, by categorizing specific attributes and using the information gain rate to construct a decision tree, thus realizing the situational prediction of Vehicular Network Security, which improves the accuracy of prediction, but ignores the long-distance nature of the vehicle traveling data; Literature [16] proposes a Long Short-Term Memory (LSTM)-based and Multiple Attention Approach (MADA) hybrid model for predicting a given time series, which outperforms most of the tested methods in terms of symmetric mean absolute percentage error, but fails to extract features from the time series data, and has low warning accuracy; Literature [17] proposes a spatio-temporal neural network (GCN-DHSTNet) model, which is modeled by a graphical convolution network, and dynamically learns, based on global spatial relations of traffic among nodes, the spatio-temporal characteristics of traffic data, and then simultaneously deal with the complex and dynamic spatio-temporal dependence of traffic flow, the model effectively captures the dynamic temporal correlation, but it can only be used when the node distances are small; Literature [18] based on the Gram's angular disparity field theory to understand the temporal correlation of deviation changes, and establish a convolutional neural network model to predict the future trend of the system, the method of predicting the subsequent postures with high accuracy but without considering the computational load; literature [19] proposes a situation prediction model named IPSO-ABiLSTM, which is based on Improved Particle Swarm Optimization (IPSO) and Attention Fused Bidirectional Long and Short-Term Memory (ABiLSTM), which is able to converge the parameters of the neural network quickly, but without processing the data itself.…”

Image Tampering Recognition Algorithm Based on Improved YOLOv5s

“…Wherein, m ^(n, n ;`) represents the European distance between the central store of the prediction box and the real box, q represents the diagonal distance of the minimum closure area that can contain both the prediction box and the real box, α represents the hyperparameter, and v represents the difference in aspect ratio. Compared with the previous loss function, the convergence speed and detection accuracy of CIOU loss have been significantly improved, but the v in the formula reflects the difference in the aspect ratio, rather than the real difference between the width and height and their confidence [13], which sometimes hinders the optimization similarity of the model. To solve this problem, EIOU lose is used as the loss function of the bounding box, as shown in formula (1)(2)(3)(4).…”