考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶...考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶段分布鲁棒低碳经济优化模型,构建了基于Kullback-Leibler(KL)散度的概率分布模糊集,同时利用条件风险价值量化了极端场景下的尾部风险,使得模型能够同时考虑概率分布不确定性以及处于最坏概率分布中极端场景下的尾部损失;此外,将阶梯型碳交易机制并入所提分布鲁棒模型中,通过合理利用柔性资源和储能装置,增强系统运行的灵活性,在兼顾运行风险的前提下,降低碳排放量的目标。再者,为了提高计算效率,在列和约束生成算法(column-and-constraint generation method,C&CG)和Multi-cut Benders分解算法的基础上提出了双循环分解算法。最后,在基于改进的IEEE RTS 79测试系统中验证了所提模型及算法的有效性。展开更多
Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex m...Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement.展开更多
为了提高系统在不确定运行环境下应对故障的调控能力,以含柔性直流输电技术(voltage source converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出一种综合风险管控与多阶段校正控制的储能分布鲁棒优化配置方...为了提高系统在不确定运行环境下应对故障的调控能力,以含柔性直流输电技术(voltage source converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出一种综合风险管控与多阶段校正控制的储能分布鲁棒优化配置方法。通过在考虑最恶劣概率分布的情况下进行储能的配置决策,改善传统鲁棒规划方法过于保守的问题,提高潜在尾部风险度量结果的鲁棒性;同时,将日内校正控制划分成无故障、故障短期与长期3个校正阶段,并通过最大化所配置储能、柔性负荷以及VSC的快速校正能力,弥补常规调控装置难以快速响应指令的缺陷。针对所提配置模型,在列和约束生成算法(column and constraint generation,C&CG)和Multi-cut Benders分解算法的基础上,提出一种双循环分解快速求解算法,通过返回多割约束的方式,降低模型规模、提高求解效率。最终,在改进的IEEE RTS 79测试系统中验证所提模型与方法的有效性。展开更多
Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertain...Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.展开更多
文摘考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶段分布鲁棒低碳经济优化模型,构建了基于Kullback-Leibler(KL)散度的概率分布模糊集,同时利用条件风险价值量化了极端场景下的尾部风险,使得模型能够同时考虑概率分布不确定性以及处于最坏概率分布中极端场景下的尾部损失;此外,将阶梯型碳交易机制并入所提分布鲁棒模型中,通过合理利用柔性资源和储能装置,增强系统运行的灵活性,在兼顾运行风险的前提下,降低碳排放量的目标。再者,为了提高计算效率,在列和约束生成算法(column-and-constraint generation method,C&CG)和Multi-cut Benders分解算法的基础上提出了双循环分解算法。最后,在基于改进的IEEE RTS 79测试系统中验证了所提模型及算法的有效性。
文摘Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement.
文摘为了提高系统在不确定运行环境下应对故障的调控能力,以含柔性直流输电技术(voltage source converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出一种综合风险管控与多阶段校正控制的储能分布鲁棒优化配置方法。通过在考虑最恶劣概率分布的情况下进行储能的配置决策,改善传统鲁棒规划方法过于保守的问题,提高潜在尾部风险度量结果的鲁棒性;同时,将日内校正控制划分成无故障、故障短期与长期3个校正阶段,并通过最大化所配置储能、柔性负荷以及VSC的快速校正能力,弥补常规调控装置难以快速响应指令的缺陷。针对所提配置模型,在列和约束生成算法(column and constraint generation,C&CG)和Multi-cut Benders分解算法的基础上,提出一种双循环分解快速求解算法,通过返回多割约束的方式,降低模型规模、提高求解效率。最终,在改进的IEEE RTS 79测试系统中验证所提模型与方法的有效性。
文摘Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.
