The present paper proposes the impact of the air temperature on electricity demand as expected. It is clear that the annual maximum load is recorded versus the years starting by the year 2009 up to 2012. At present, t...The present paper proposes the impact of the air temperature on electricity demand as expected. It is clear that the annual maximum load is recorded versus the years starting by the year 2009 up to 2012. At present, the graph fitting technique is applied with some mathematical and computational tools based on the actual values of the years 2009 up to 2012 considering the lower values, the higher values and the average values of the annual maximum loads for Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.展开更多
The present paper proposes the impact of the air temperature on electricity demand as expected. The annual maximum load is recorded versus the years starting by the year 2009. At present, the graph fitting was applied...The present paper proposes the impact of the air temperature on electricity demand as expected. The annual maximum load is recorded versus the years starting by the year 2009. At present, the graph fitting was applied with some mathematical and computational tools considering the lower values, the higher values and the average values of the annual maximum loads of Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.展开更多
Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience ...Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades.However,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data.Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance.There is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems.To address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States.Through the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve archetype.The results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal curves.Triangular curves characterize resilience behavior based on three fundamental properties:1.critical functionality threshold,2.critical functionality recovery rate,and 3.recovery pivot point.Trapezoidal archetypes explain resilience curves based on 1.duration of sustained function loss and 2.constant recovery rate.The longer the duration of sustained function loss,the slower the constant rate of recovery.The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.展开更多
文摘The present paper proposes the impact of the air temperature on electricity demand as expected. It is clear that the annual maximum load is recorded versus the years starting by the year 2009 up to 2012. At present, the graph fitting technique is applied with some mathematical and computational tools based on the actual values of the years 2009 up to 2012 considering the lower values, the higher values and the average values of the annual maximum loads for Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.
文摘The present paper proposes the impact of the air temperature on electricity demand as expected. The annual maximum load is recorded versus the years starting by the year 2009. At present, the graph fitting was applied with some mathematical and computational tools considering the lower values, the higher values and the average values of the annual maximum loads of Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained.
基金supported by the National Science Foundation under Grant CMMI-1846069(CAREER).
文摘Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades.However,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data.Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance.There is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems.To address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States.Through the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve archetype.The results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal curves.Triangular curves characterize resilience behavior based on three fundamental properties:1.critical functionality threshold,2.critical functionality recovery rate,and 3.recovery pivot point.Trapezoidal archetypes explain resilience curves based on 1.duration of sustained function loss and 2.constant recovery rate.The longer the duration of sustained function loss,the slower the constant rate of recovery.The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.
