By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compa...
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By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compared to the bus-branch model, the node-breaker model provides higher granularity in describing grid components and can dynamically reflect changes in equipment status, thus improving the efficiency of grid dispatching and operation. This paper proposes a spatiotemporal datamodeling method based on a graphdatabase. It elaborates on constructing graph nodes, graph ontology models, and graph entity models from grid dispatch data, describing the construction of the spatiotemporal node-breaker graph model and the transformation to the bus-branch model. Subsequently, by integrating spatiotemporal data attributes into the pre-built static grid graph model, a spatiotemporal evolving graph of the power grid is constructed. Furthermore, the concept of the “Power Grid One graph” and its requirements in modern power systems are elucidated. Leveraging the constructed spatiotemporal node-breaker graph model and graph computing technology, the paper explores the feasibility of grid situational awareness. Finally, typical applications in an operational provincial grid are showcased, and potential scenarios of the proposed spatiotemporal graph model are discussed.
Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countrie...
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Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countries start to resume economic activities aiming at a "living with COVID " new normal, a short-term load forecasting technique incorporating the epidemic's effects is of great significance to both power system operation and a smooth transition. In this context, this paper proposes a novel short-term load forecasting method under COVID-19 based on graph representation learning with heterogeneous features. Unlike existing methods that fit power load data to time series, this study encodes heterogeneous features relevant to electricity consumption and epidemic status into a load graph so that not only the features at each time moment but also the inherent correlations between the features can be exploited;Then, a residual graph convolutional network (ResGCN) is constructed to fit the non-linear mappings from load graph to future loads. Besides, a graph concatenation method for parallel training is introduced to improve the learning efficiency. Using practical data in Houston, the annual, monthly, and daily effects of the crisis on power load are analyzed, which uncovers the strong correlation between the pandemic and the changes in regional electricity utilization. Moreover, the forecasting performance of the load graph-based ResGCN is validated by comparing with other representative methods. Its performance on MAPE and RMSE increased by 1.3264 and 15.03%, respectively. Codes related to all the simulations are available on & nbsp;https://***/YoungY6/ResGCN-for-Short-term-power-load-forecasting-under-COVID-19.
To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graphdatabase & computing technology is proposed in this paper. A node and edge level paral...
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To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graphdatabase & computing technology is proposed in this paper. A node and edge level parallel iterative algorithm is implemented by using a graph computing algorithm combined with a message passing mechanism. IEEE testing cases and actual operation data of a provincial power system in China are used to verify the method, then expand the MP 10790 to over 100k and 1 million nodes systems for the calculation stress test on a conventional server. (C) 2017 The Authors. Published by Elsevier Ltd.
To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graphdatabase & computing technology is proposed in this paper. A node and edge level paral...
详细信息
To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graphdatabase & computing technology is proposed in this paper. A node and edge level parallel iterative algorithm is implemented by using a graph computing algorithm combined with a message passing mechanism. IEEE testing cases and actual operation data of a provincial power system in China are used to verify the method, then expand the MP 10790 to over 100k and 1 million nodes systems for the calculation stress test on a conventional server.
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