Ice cover on transmission lines is a significant issue that affects the safe operation of the power system.Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disa...Ice cover on transmission lines is a significant issue that affects the safe operation of the power system.Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents.However,under extreme weather conditions,strong instantaneous wind can cause tension sensors to fail,resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models.In this paper,we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments.The model expands scarce raw data by the Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP)technique,records historical environmental information by a recurrent neural network,and evaluates the ice warning levels by a classifier.At each time point,the model diagnoses whether the current sensor failure is due to icing or strong winds.If it is determined that the wire is covered with ice,the icing thickness will be calculated after the wind-induced tension is removed from the ice-wind coupling tension.Our new model was evaluated using data from the power grid in an area with extreme weather.The results show that the proposed model has significant improvements in accuracy compared with traditional models.展开更多
The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids.This study was based on energy blockchai...The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids.This study was based on energy blockchain,and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid,which is expected to enable reliable,affordable,and clean energy supply in smart communities.In the proposed system,economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity.The reduction of energy surplus realized by distributed energy production and P2P energy trading,within the smart grid results in less transmission loss and lower requirements for costly upgrading of existing grids.By adopting energy blockchain and smart contract technologies,energy secure trading with a low risk of privacy leakage could be accommodated.The prototype is examined through a case study,and the feasibility and efficiency of the proposed mechanism are further validated by scenario analysis.展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China(SGXJDK00GYJS2400035).
文摘Ice cover on transmission lines is a significant issue that affects the safe operation of the power system.Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents.However,under extreme weather conditions,strong instantaneous wind can cause tension sensors to fail,resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models.In this paper,we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments.The model expands scarce raw data by the Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP)technique,records historical environmental information by a recurrent neural network,and evaluates the ice warning levels by a classifier.At each time point,the model diagnoses whether the current sensor failure is due to icing or strong winds.If it is determined that the wire is covered with ice,the icing thickness will be calculated after the wind-induced tension is removed from the ice-wind coupling tension.Our new model was evaluated using data from the power grid in an area with extreme weather.The results show that the proposed model has significant improvements in accuracy compared with traditional models.
基金Fundings that permitted this research were granted by Australia CRC for Low Carbon Living through the Project“Integrated Carbon Metrics(ICM)”(RP2007)the National Natural Science Foundation of China(51908064)the Natural Science Foundation of Hunan Province(2021JJ30717).
文摘The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids.This study was based on energy blockchain,and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid,which is expected to enable reliable,affordable,and clean energy supply in smart communities.In the proposed system,economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity.The reduction of energy surplus realized by distributed energy production and P2P energy trading,within the smart grid results in less transmission loss and lower requirements for costly upgrading of existing grids.By adopting energy blockchain and smart contract technologies,energy secure trading with a low risk of privacy leakage could be accommodated.The prototype is examined through a case study,and the feasibility and efficiency of the proposed mechanism are further validated by scenario analysis.
