Every year, transmission congestion costs billions ofdollars for electricity customers. This clearly identifies the criticalneed for more transmission capacity and also poses big challengesfor power grid reliability i...Every year, transmission congestion costs billions ofdollars for electricity customers. This clearly identifies the criticalneed for more transmission capacity and also poses big challengesfor power grid reliability in stressed conditions due to heavyloading and in uncertain situations due to variable renewableresources and responsive smart loads. However, it becomesincreasingly difficult to build new transmission lines, whichtypically involve both economic and environmental constraints.In this paper, advanced computing techniques are developedto enable a non-wire solution that realizes unused transfercapabilities of existing transmission facilities. An integratedsoftware prototype powered by high-performance computing(HPC) is developed to calculate ratings of key transmission pathsin real time for relieving transmission congestion and facilitatingrenewable integration, while complying with the North AmericanElectric Reliability Corporation (NERC) standards on assessingtotal transfer capabilities. The innovative algorithms include: (1)massive contingency analysis enabled by dynamic load balancing,(2) parallel transient simulation to speed up single dynamicsimulation, (3) a non-iterative method for calculating voltagesecurity boundary and (4) an integrated package consideringall NERC required limits. This tool has been tested on realisticpower system models in the Western Interconnection of NorthAmerica and demonstrates satisfactory computational speedusing parallel computers. Various benefits of real-time path ratingare investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in pathlimits. These technologies would change the traditional goals ofpath rating studies, fundamentally transforming how the grid isoperated, and maximizing the utilization of national transmissionassets, as well as facilitating integration of renewable energy andsmart loads.展开更多
The COVID-19 was firstly reported in Wuhan,Hubei province,and it was brought to all over China by people travelling for Chinese New Year.The pandemic coronavirus with its catastrophic effects is now a global concern.F...The COVID-19 was firstly reported in Wuhan,Hubei province,and it was brought to all over China by people travelling for Chinese New Year.The pandemic coronavirus with its catastrophic effects is now a global concern.Forecasting of COVID-19 spread has attracted a great attention for public health emergency.However,few re-searchers look into the relationship between dynamic transmission rate and preventable measures by authorities.In this paper,the SEIR(Susceptible Exposed Infectious Recovered)model is employed to investigate the spread of COVID-19.The epidemic spread is divided into two stages:before and after intervention.Before intervention,the transmission rate is assumed to be a constant since individual,community and government response has not taken into place.After intervention,the transmission rate is reduced dramatically due to the societal actions or measures to reduce and prevent the spread of disease.The transmission rate is assumed to follow an exponential function,and the removal rate is assumed to follow a power exponent function.The removal rate is increased with the evolution of the time.Using the real data,the model and parameters are optimized.The transmission rate without measure is calculated to be 0.033 and 0.030 for Hubei and outside Hubei province,respectively.After the model is established,the spread of COVID-19 in Hubei province,France and USA is predicted.From results,USA performs the worst according to the dynamic ratio.The model has provided a mathematical method to evaluate the effectiveness of the government response and can be used to forecast the spread of COVID-19 with better performance.展开更多
基金supported by the U.S.Department of Energy,Advanced Research Projects Agency-Energy(ARPAE)and Office of Electricity Delivery and Energy Reliability through its Advanced Grid Modeling Program.Pacific Northwest National Laboratory(PNNL)is operated by Battelle for the DOE under Contract DE-AC05-76RL01830.
文摘Every year, transmission congestion costs billions ofdollars for electricity customers. This clearly identifies the criticalneed for more transmission capacity and also poses big challengesfor power grid reliability in stressed conditions due to heavyloading and in uncertain situations due to variable renewableresources and responsive smart loads. However, it becomesincreasingly difficult to build new transmission lines, whichtypically involve both economic and environmental constraints.In this paper, advanced computing techniques are developedto enable a non-wire solution that realizes unused transfercapabilities of existing transmission facilities. An integratedsoftware prototype powered by high-performance computing(HPC) is developed to calculate ratings of key transmission pathsin real time for relieving transmission congestion and facilitatingrenewable integration, while complying with the North AmericanElectric Reliability Corporation (NERC) standards on assessingtotal transfer capabilities. The innovative algorithms include: (1)massive contingency analysis enabled by dynamic load balancing,(2) parallel transient simulation to speed up single dynamicsimulation, (3) a non-iterative method for calculating voltagesecurity boundary and (4) an integrated package consideringall NERC required limits. This tool has been tested on realisticpower system models in the Western Interconnection of NorthAmerica and demonstrates satisfactory computational speedusing parallel computers. Various benefits of real-time path ratingare investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in pathlimits. These technologies would change the traditional goals ofpath rating studies, fundamentally transforming how the grid isoperated, and maximizing the utilization of national transmissionassets, as well as facilitating integration of renewable energy andsmart loads.
基金This work is supported by National Key R and D Program of China(No.2017YFC0803300)National Science Foundation of China(Grant Nos.7204100828,91646201,U1633203)High-tech Discipline Con-struction Funding for Universities in Beijing(Safety Science and Engi-neering)and Beijing Key Laboratory of City Integrated Emergency Re-sponse Science.
文摘The COVID-19 was firstly reported in Wuhan,Hubei province,and it was brought to all over China by people travelling for Chinese New Year.The pandemic coronavirus with its catastrophic effects is now a global concern.Forecasting of COVID-19 spread has attracted a great attention for public health emergency.However,few re-searchers look into the relationship between dynamic transmission rate and preventable measures by authorities.In this paper,the SEIR(Susceptible Exposed Infectious Recovered)model is employed to investigate the spread of COVID-19.The epidemic spread is divided into two stages:before and after intervention.Before intervention,the transmission rate is assumed to be a constant since individual,community and government response has not taken into place.After intervention,the transmission rate is reduced dramatically due to the societal actions or measures to reduce and prevent the spread of disease.The transmission rate is assumed to follow an exponential function,and the removal rate is assumed to follow a power exponent function.The removal rate is increased with the evolution of the time.Using the real data,the model and parameters are optimized.The transmission rate without measure is calculated to be 0.033 and 0.030 for Hubei and outside Hubei province,respectively.After the model is established,the spread of COVID-19 in Hubei province,France and USA is predicted.From results,USA performs the worst according to the dynamic ratio.The model has provided a mathematical method to evaluate the effectiveness of the government response and can be used to forecast the spread of COVID-19 with better performance.
