Advanced multifunctional composite phase change materials(PCMs)integrating dual-field excitative thermal storage and microwave absorption have been recently highlighted in cutting-edge applications.Herein,we designed ...Advanced multifunctional composite phase change materials(PCMs)integrating dual-field excitative thermal storage and microwave absorption have been recently highlighted in cutting-edge applications.Herein,we designed a metal–organic framework(MOF)derived magnetic NiCo-modified open carbon microcage(NiCo@C)for the encapsulation of PCMs,which simultaneously achieve fast-response multienergy capture and bimode stealth functions:The NiCo@C/PW composite PCMs exhibited a relatively high phase change enthalpy of 130.39 J/g due to the high cavity volume of the NiCo@C microcage.Strikingly,the composite PCMs demonstrated excellent solar-thermal conversion,attributed to the local surface plasmon resonance effect of NiCo nanoparticles and full-spectrum absorption of high graphitized carbon microcage.Simultaneously,composite PCMs harvested high-efficiency magnetic-thermal conversion due to the Néel and Brownian relaxation effects of diffusely embedded magnetic NiCo nanoparticles.More importantly,this thermal energy storage system achieved high-performance microwave absorption with a minimum reflection loss(RL)of−38.1 dB at 11.8 GHz at only 2.35 mm thickness.Our designed all-in-one strategy created an innovative platform for constructing advanced multifunctional microwave-absorbing composite PCMs with thermal storage,dual-energy conversion,microwave absorption,and infrared stealth.展开更多
Generally,energy trading in smart grid is realized by microgrids.Correspondingly,energy trading in energy internet relies on small-scale energy systems,named as Weenergies(WEs).Previous works on the distributed energy...Generally,energy trading in smart grid is realized by microgrids.Correspondingly,energy trading in energy internet relies on small-scale energy systems,named as Weenergies(WEs).Previous works on the distributed energy trading focused on the trading platform or trading mechanism based on distributed communication.However,most ignored the fact that there is no express delivery of energy trading,and the transmission of energy depends on a fixed physical topology.Energy transactions without considering the transmission distance will increase the difficulty of energy scheduling and the transmission cost of energy.Aiming at this problem,an aggregation game among WEs is proposed for two-way multi-energy trading,and a distributed algorithm is designed to solve the Nash equilibrium.Since each WE only needs to communicate with its neighbors to exchange information,this distributed process reduces communication burden and improves information security.Furthermore,a multi-energy transmission optimization model is established to determine the transmission path of the transmission energy,which can minimize the transmission cost.Subsequently,to reduce the influence of real-time fluctuations of renewable energy and load,a receding horizon control algorithm is designed to improve the reliability of the proposed game.Finally,the effectiveness in dealing with two-way multi-energy trading of the proposed strategy is verified through simulations on the five connected WEs.展开更多
Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity a...Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among loads.In line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called STL-CPLE.First,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual components.Then,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature information.This approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.展开更多
Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form...Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.展开更多
基金financially supported by the National Natural Science Foundation of China(NSFC,grant nos.52373261,52377026,and 51902025)Taishan Scholars and Young Experts Program of Shandong Province,China(grant no.tsqn202103057).
文摘Advanced multifunctional composite phase change materials(PCMs)integrating dual-field excitative thermal storage and microwave absorption have been recently highlighted in cutting-edge applications.Herein,we designed a metal–organic framework(MOF)derived magnetic NiCo-modified open carbon microcage(NiCo@C)for the encapsulation of PCMs,which simultaneously achieve fast-response multienergy capture and bimode stealth functions:The NiCo@C/PW composite PCMs exhibited a relatively high phase change enthalpy of 130.39 J/g due to the high cavity volume of the NiCo@C microcage.Strikingly,the composite PCMs demonstrated excellent solar-thermal conversion,attributed to the local surface plasmon resonance effect of NiCo nanoparticles and full-spectrum absorption of high graphitized carbon microcage.Simultaneously,composite PCMs harvested high-efficiency magnetic-thermal conversion due to the Néel and Brownian relaxation effects of diffusely embedded magnetic NiCo nanoparticles.More importantly,this thermal energy storage system achieved high-performance microwave absorption with a minimum reflection loss(RL)of−38.1 dB at 11.8 GHz at only 2.35 mm thickness.Our designed all-in-one strategy created an innovative platform for constructing advanced multifunctional microwave-absorbing composite PCMs with thermal storage,dual-energy conversion,microwave absorption,and infrared stealth.
基金supported by the National Key Research and Development Program of China(2018YFA0702200)National Natural Science Foundation of China(No.62073065).
文摘Generally,energy trading in smart grid is realized by microgrids.Correspondingly,energy trading in energy internet relies on small-scale energy systems,named as Weenergies(WEs).Previous works on the distributed energy trading focused on the trading platform or trading mechanism based on distributed communication.However,most ignored the fact that there is no express delivery of energy trading,and the transmission of energy depends on a fixed physical topology.Energy transactions without considering the transmission distance will increase the difficulty of energy scheduling and the transmission cost of energy.Aiming at this problem,an aggregation game among WEs is proposed for two-way multi-energy trading,and a distributed algorithm is designed to solve the Nash equilibrium.Since each WE only needs to communicate with its neighbors to exchange information,this distributed process reduces communication burden and improves information security.Furthermore,a multi-energy transmission optimization model is established to determine the transmission path of the transmission energy,which can minimize the transmission cost.Subsequently,to reduce the influence of real-time fluctuations of renewable energy and load,a receding horizon control algorithm is designed to improve the reliability of the proposed game.Finally,the effectiveness in dealing with two-way multi-energy trading of the proposed strategy is verified through simulations on the five connected WEs.
基金supported by the National Natural Sci-ence Foundation of China Joint Fund Program(No.U22A20224).
文摘Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among loads.In line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called STL-CPLE.First,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual components.Then,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature information.This approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.
基金jointly supported by National Basic Research Program of China(973 Program)(No.2013CB228202)National Natural Science Foundation of China(No.51477151,No.51361130152)a project by China Southern Power Grid Company(No.WYKJ00000027)
文摘Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas(PtG) technology has attracted much research attention in recent years.PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.
