Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with othe...Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs.Therefore,there is the need for a smart structure to handle the new situation.This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs.The upper-level problem is decomposed into a set of low-level market clearing problems.Both electricity and heat markets are simultaneously modeled in this paper.DERs,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity markets.In addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat market.Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.展开更多
Rational design of a robust carbon matrix has a profound impact on the performance of flexible/wearable lithium/sulfur batteries.Herein,we demonstrate a freestanding three-dimensional super-aligned carbon nanotube (SA...Rational design of a robust carbon matrix has a profound impact on the performance of flexible/wearable lithium/sulfur batteries.Herein,we demonstrate a freestanding three-dimensional super-aligned carbon nanotube (SACNT) matrix reinforced with a multi-functionalized carbon coating for flexible,high-areal sulfur loading cathode.By employing the sulfur/nitrogen co-doped carbon (SNC)"glue",the joints in the SACNT scaffold are tightly welded together so that the overall mechanical strength of the electrode is significantly enhanced to withstand the repeated bending as well as the volume change during operation.The SNC also shows intriguing catalytic effect that lowers the energy barrier of Li ion transport,propelling a superior redox conversion efficiency.The resulting binder-free and current collector-free sulfur cathode exhibits a high reversible capacity of 1,079 mAh·g^-1 at 1 C,a high-rate capacity of ~ 800 mAh·g^-1 at 5 C,and an average capacity decay rate of 0.037% per cycle at 2 C for 1,500 cycles.Impressively,a large-areal flexible Li/S pouch cell based on such mechanically robust cathode exhibits excellent capacity retention under arbitrary bending conditions.With a high areal sulfur loading of 7 mg·cm^-2,the large-areal flexible cathode delivers an outstanding areal capacity of 6.3 mAh·cm^-2 at 0.5 C (5.86 mA·cm^-2),showing its promise for realizing practical high energy density flexible Li/S batteries.展开更多
Machine-learning(ML)techniques hold the potential of enabling efficient quantitative micrograph analysis,but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully ...Machine-learning(ML)techniques hold the potential of enabling efficient quantitative micrograph analysis,but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated.We collected thousands of scanning electron microscopy(SEM)micrographs for molecular solid materials,in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions.We then built ML models to predict the ultimate compressive strength(UCS)of consolidated molecular solids,by encoding micrographs with different image feature descriptors and training a random forest regressor,and by training an end-to-end deep-learning(DL)model.Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way.As a remedy,we explored intensity normalization techniques.It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness,but microscope-induced intensity variations can be difficult to eliminate.展开更多
基金funded partially by the National Science Foundation(NSF)(No.1917308)the British Council(No.IND/CONT/GA/18-19/22)
文摘Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs.Therefore,there is the need for a smart structure to handle the new situation.This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs.The upper-level problem is decomposed into a set of low-level market clearing problems.Both electricity and heat markets are simultaneously modeled in this paper.DERs,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity markets.In addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat market.Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.
基金the National Key R&D Program of China (No.2016YFB0100100)the National Natural Science Foundation of China (Nos.21433013 and U1832218)+1 种基金CAS-Queensland Collaborative Science Fund (No.121E32KYSB20160032)the CAS-DOE Joint Research Program (No. 121E32KYSB20150004).
文摘Rational design of a robust carbon matrix has a profound impact on the performance of flexible/wearable lithium/sulfur batteries.Herein,we demonstrate a freestanding three-dimensional super-aligned carbon nanotube (SACNT) matrix reinforced with a multi-functionalized carbon coating for flexible,high-areal sulfur loading cathode.By employing the sulfur/nitrogen co-doped carbon (SNC)"glue",the joints in the SACNT scaffold are tightly welded together so that the overall mechanical strength of the electrode is significantly enhanced to withstand the repeated bending as well as the volume change during operation.The SNC also shows intriguing catalytic effect that lowers the energy barrier of Li ion transport,propelling a superior redox conversion efficiency.The resulting binder-free and current collector-free sulfur cathode exhibits a high reversible capacity of 1,079 mAh·g^-1 at 1 C,a high-rate capacity of ~ 800 mAh·g^-1 at 5 C,and an average capacity decay rate of 0.037% per cycle at 2 C for 1,500 cycles.Impressively,a large-areal flexible Li/S pouch cell based on such mechanically robust cathode exhibits excellent capacity retention under arbitrary bending conditions.With a high areal sulfur loading of 7 mg·cm^-2,the large-areal flexible cathode delivers an outstanding areal capacity of 6.3 mAh·cm^-2 at 0.5 C (5.86 mA·cm^-2),showing its promise for realizing practical high energy density flexible Li/S batteries.
基金The authors would like to thank Donald Loveland,Jize Zhang,and Piyush Karande for prototype codes and helpful discussions.This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344was supported by the LLNL-LDRD Program under Project No.19-SI-001。
文摘Machine-learning(ML)techniques hold the potential of enabling efficient quantitative micrograph analysis,but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated.We collected thousands of scanning electron microscopy(SEM)micrographs for molecular solid materials,in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions.We then built ML models to predict the ultimate compressive strength(UCS)of consolidated molecular solids,by encoding micrographs with different image feature descriptors and training a random forest regressor,and by training an end-to-end deep-learning(DL)model.Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way.As a remedy,we explored intensity normalization techniques.It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness,but microscope-induced intensity variations can be difficult to eliminate.
