By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The stu...By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The study shows that Z type aggregate is more sensitive to the feedback of external force than the prism aggregate in the same loading environment, and it indicates that Z type aggregate is more suitable for the research and application of concrete health monitoring. Although the incorporation of RPP would cause the compressive strength of the aggregates and the elastic modulus of hardened cement mortar to reduce slightly within the dosage of RPP by 2.25% because of the polymer film formed in the internal system, this would improve the deformability of the aggregates. In the early loading stage（in the first 60 seconds）, the intelligent concrete specimens implanted with Z type smart aggregate do not show higher sensitivity as expected, although the resistance change rate changes a little bit more, the overall of it is still in balance. Adding RPP could improve the flexibility of smart aggregates exactly, and it plays an active role in prolonging the life of the smart aggregates. By implanting Z type aggregates the damage and failure of the concrete structure could be predicted accurately in this study. The results of this paper will help to promote further research and application of intelligent concrete.展开更多
Background Despite outstanding antiplatelet properties of aspirin and clopidogrel, some patients taking these drugs continue to suffer complications. Antiplatelet resistance appears to be a new prognostic factor in ac...Background Despite outstanding antiplatelet properties of aspirin and clopidogrel, some patients taking these drugs continue to suffer complications. Antiplatelet resistance appears to be a new prognostic factor in acute coronary syndrome patients for clinical events associated with stent thrombosis （ST）. However, there is no optimal method to identify it and assess its correlation to clinical outcomes. This study sought to evaluate the predictive value of antiplatelet resistance assessed by whole blood impedance aggregometry for the risk of early ST in patients with acute coronary syndrome who underwent coronary stenting. Methods Platelet responses to aspirin and clopidogrel in 86 patients with acute coronary syndrome were measured by whole blood impedance aggregometry. Spontaneous platelet aggregation was defined as antiplatelet resistance identified by the increased electrical impedance. The clinical endpoint was early stent thrombosis during 30-day follow-up after coronary stenting. Results The prevalence of aspirin resistance, clopidogrel resistance and dual resistance of combined clopidogrel and aspirin resistance were 19.8%, 12.8% and 5.8% respectively. Diabetes, female and higher platelet counts were more frequently detected in clopidogrel-resistant and dual-resistant patients. During 30-day follow-up, the patients with clopidogrel resistance and dual resistance had higher incidence of early stent thrombosis （18.2% vs. 1.3%, 40.0% vs. 1.2%, P 〈0.05）. Binary Logistic Regression analysis indicated that dual resistance remained an independent predicator for early stent thrombosis （odds ratio 34.064, 95% CI 1.919-604.656, P=-0.016）. Conclusions Antiplatelet resistance assessed by whole blood impedance aggregometry is paralleled to clinical events, and dual antiplatelet resistance is an independent predicator for early stent thrombosis in patients with acute coronary syndrome. As a physiological assessment of platelet reactivity, whole blood impedance aggregometry is a convenient and accurate option for measurin.q antiplatelet resistance and hence predicting early stent thrombosis.展开更多
The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of...The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.展开更多
With the increasing use of renewable resources and electric vehicles(EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage...With the increasing use of renewable resources and electric vehicles(EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter(VSC) based multi-terminal direct current(MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users' demand difference and traveling needs. Based on this, a vehicle-to-grid(V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations(CDGs), aggregated EVs, and energy storage systems(ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.展开更多
This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphi...This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphical method characterizing stability boundary locus is implemented.For a given time delay,the method computes all the stabilizing proportional-integral(PI)controller gains,which constitutes a stability region in the parameter space of PI controller.Secondly,in order to complement the stability regions,a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains.The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder(QPmR)algorithm.展开更多
To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging...To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.展开更多
基金Funded by the Natural Science Foundation of Fujian Province(No.2016J01241)the National Natural Science Foundation of China(No.51608212)the Science&Technology Pillar Program of Fujian Provincial Education Department(No.JA14024)
文摘By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The study shows that Z type aggregate is more sensitive to the feedback of external force than the prism aggregate in the same loading environment, and it indicates that Z type aggregate is more suitable for the research and application of concrete health monitoring. Although the incorporation of RPP would cause the compressive strength of the aggregates and the elastic modulus of hardened cement mortar to reduce slightly within the dosage of RPP by 2.25% because of the polymer film formed in the internal system, this would improve the deformability of the aggregates. In the early loading stage（in the first 60 seconds）, the intelligent concrete specimens implanted with Z type smart aggregate do not show higher sensitivity as expected, although the resistance change rate changes a little bit more, the overall of it is still in balance. Adding RPP could improve the flexibility of smart aggregates exactly, and it plays an active role in prolonging the life of the smart aggregates. By implanting Z type aggregates the damage and failure of the concrete structure could be predicted accurately in this study. The results of this paper will help to promote further research and application of intelligent concrete.
文摘Background Despite outstanding antiplatelet properties of aspirin and clopidogrel, some patients taking these drugs continue to suffer complications. Antiplatelet resistance appears to be a new prognostic factor in acute coronary syndrome patients for clinical events associated with stent thrombosis （ST）. However, there is no optimal method to identify it and assess its correlation to clinical outcomes. This study sought to evaluate the predictive value of antiplatelet resistance assessed by whole blood impedance aggregometry for the risk of early ST in patients with acute coronary syndrome who underwent coronary stenting. Methods Platelet responses to aspirin and clopidogrel in 86 patients with acute coronary syndrome were measured by whole blood impedance aggregometry. Spontaneous platelet aggregation was defined as antiplatelet resistance identified by the increased electrical impedance. The clinical endpoint was early stent thrombosis during 30-day follow-up after coronary stenting. Results The prevalence of aspirin resistance, clopidogrel resistance and dual resistance of combined clopidogrel and aspirin resistance were 19.8%, 12.8% and 5.8% respectively. Diabetes, female and higher platelet counts were more frequently detected in clopidogrel-resistant and dual-resistant patients. During 30-day follow-up, the patients with clopidogrel resistance and dual resistance had higher incidence of early stent thrombosis （18.2% vs. 1.3%, 40.0% vs. 1.2%, P 〈0.05）. Binary Logistic Regression analysis indicated that dual resistance remained an independent predicator for early stent thrombosis （odds ratio 34.064, 95% CI 1.919-604.656, P=-0.016）. Conclusions Antiplatelet resistance assessed by whole blood impedance aggregometry is paralleled to clinical events, and dual antiplatelet resistance is an independent predicator for early stent thrombosis in patients with acute coronary syndrome. As a physiological assessment of platelet reactivity, whole blood impedance aggregometry is a convenient and accurate option for measurin.q antiplatelet resistance and hence predicting early stent thrombosis.
基金supported in part by the National Natural Science Foundation of China(No.51877078)the Fundamental Research Funds for the Central Universities(No.2018MS012)
文摘The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.
基金supported in part by the National Natural Science Foundation of China (No.U2166202)S&T Program of Hebei (No.20312102D)。
文摘With the increasing use of renewable resources and electric vehicles(EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter(VSC) based multi-terminal direct current(MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users' demand difference and traveling needs. Based on this, a vehicle-to-grid(V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations(CDGs), aggregated EVs, and energy storage systems(ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.
基金This work was supported by the Project of Scientific and Technological Research Council of Turkey(TUBITAK)(No.118E744).
文摘This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphical method characterizing stability boundary locus is implemented.For a given time delay,the method computes all the stabilizing proportional-integral(PI)controller gains,which constitutes a stability region in the parameter space of PI controller.Secondly,in order to complement the stability regions,a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains.The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder(QPmR)algorithm.
文摘To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.
