Virtual power plants can participate in power market transactions by combining wind and solar power generation and energy storage technologies, They participate in energy market transactions for aggregating distributed energy and user-side resources, Meanwhile, the carbon trading market is an effective tool to control the greenhouse effect, As a market subject with unlimited potential, VPP of industrial park plays its environmental benefits by participating in the carbon trading market, It is able to promote the growth of VPP benefits and reduce carbon emissions, Firstly, this paper introduces the composition of virtual power plants and the carbon trading market, Secondly, this paper puts forward the influence of the uncertainty of wind turbines on virtual power plants, Thirdly, a virtual power plant model is build under the uncertainty of wind turbines, Finally, the operation of virtual power plants is optimized considering carbon trading, so as to maximize the benefits and explore the carbon reduction capacity of virtual power plants
Recently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology. However, many existing methods in this area fail to fully leverage all available information from dynamic networks. Additionally, most of these methods are supervised or semi-supervised algorithms that require labeled data, which may not always be feasible in real-world scenarios. In this paper, we propose AddAG-AE, a general dynamic graph anomaly-detection framework that can fuse node attributes and spatiotemporal information to detect anomalies in an unsupervised manner. The framework consists of two main components. The first component is a feature extractor composed of a dual autoencoder, which captures a joint representation of both the network structure and node attributes in a latent space. The second component is an anomaly detector that combines a Long Short-Term Memory AutoEncoder (LSTM-AE) and a predictor, effectively identifying abnormal snapshots among most normal graph snapshots. Compared with baselines, experimental results show that the method proposed has broad applicability and higher robustness on three datasets with different sparsity.
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