Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and load...Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.展开更多
Boreal forests are important carbon sinks and have tremendous potential to mitigate climate change. Aboveground biomass of Siberian larch(Larix sibirica Ledeb.) stands in the Altay Mountains, Northwest China was studi...Boreal forests are important carbon sinks and have tremendous potential to mitigate climate change. Aboveground biomass of Siberian larch(Larix sibirica Ledeb.) stands in the Altay Mountains, Northwest China was studied and allometric equations that are related to the biomass of aboveground components using diameter at breast height(DBH) or both DBH and height(H) as independent variables for L. sibirica trees were derived in this paper. A linear simultaneous equation system by using either DBH or both DBH and H(DBH&H) indices, was used to ensure additivity of the biomass of individual tree components, and was fitted for L. sibirica. Model performance was validated using the jackknifing test. Results indicate that the goodness-of-fit for the regressions was lowest for the needles(R^2 ranging from 0.696 to 0.756), and highest for the stem wood(R^2 ranging from 0.984 to 0.997) and the aggregated biomass components(R^2 ranging from 0.994 to 0.995). The coefficient of determination for each component was only marginally improved in terms of model fit and performance in the biomass equations that used DBH&H as the independent variables compared to that used DBH as the independent variable, and needles yielded an even worse fit. Stem biomass accounted for the largest proportion(87%) of the aboveground biomass. Based on the additive equations that used DBH as the single predicitor in this study, the mean aboveground carbon stock density and the carbon storage values of L. sibirica forests were 74.07 Mg C/hm^2 and 30.69 Tg C, respectively, in the Altay Mountains. Empirical comparisons of published equations for the same species growing in the Altay Mountains of Mongolia were also presented. The mean aboveground carbon stock density estimated for L. sibirica forests was higher in the Chinese Altay Mountains than in the Mongolian Altay Mountains(66.00 Mg C/hm^2).展开更多
基金supported in part by National Natural Science Foundation of China(No.52077076)in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(No.LAPS202118)。
文摘Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.
基金financially supported by the National High-Tech Research and Development Plan of China (2013AA122003)
文摘Boreal forests are important carbon sinks and have tremendous potential to mitigate climate change. Aboveground biomass of Siberian larch(Larix sibirica Ledeb.) stands in the Altay Mountains, Northwest China was studied and allometric equations that are related to the biomass of aboveground components using diameter at breast height(DBH) or both DBH and height(H) as independent variables for L. sibirica trees were derived in this paper. A linear simultaneous equation system by using either DBH or both DBH and H(DBH&H) indices, was used to ensure additivity of the biomass of individual tree components, and was fitted for L. sibirica. Model performance was validated using the jackknifing test. Results indicate that the goodness-of-fit for the regressions was lowest for the needles(R^2 ranging from 0.696 to 0.756), and highest for the stem wood(R^2 ranging from 0.984 to 0.997) and the aggregated biomass components(R^2 ranging from 0.994 to 0.995). The coefficient of determination for each component was only marginally improved in terms of model fit and performance in the biomass equations that used DBH&H as the independent variables compared to that used DBH as the independent variable, and needles yielded an even worse fit. Stem biomass accounted for the largest proportion(87%) of the aboveground biomass. Based on the additive equations that used DBH as the single predicitor in this study, the mean aboveground carbon stock density and the carbon storage values of L. sibirica forests were 74.07 Mg C/hm^2 and 30.69 Tg C, respectively, in the Altay Mountains. Empirical comparisons of published equations for the same species growing in the Altay Mountains of Mongolia were also presented. The mean aboveground carbon stock density estimated for L. sibirica forests was higher in the Chinese Altay Mountains than in the Mongolian Altay Mountains(66.00 Mg C/hm^2).
