风力、光伏等新能源发电输出功率具有波动性,为减小功率波动对电网的影响,提出平抑功率波动的储能优化配置方法。将频谱分析和低通滤波相结合,根据新能源输出功率频谱分析结果,结合并网功率波动率约束、储能充放电效率及荷电状态(state ...风力、光伏等新能源发电输出功率具有波动性,为减小功率波动对电网的影响,提出平抑功率波动的储能优化配置方法。将频谱分析和低通滤波相结合,根据新能源输出功率频谱分析结果,结合并网功率波动率约束、储能充放电效率及荷电状态(state of charge,SOC),在频率波动范围内确定最佳的一阶低通滤波器的截止频率,得到经滤波和修正的并网联络线功率及储能充放电补偿功率,从而确定满足平滑出力运行控制需求的最优储能额定功率、容量和初始SOC。采用南京地区某屋顶光伏实测数据及波动要求,对该方法进行验证,结果表明采用此方法能以较小储能容量将光伏输出功率波动从27.3%降低到1.62%,且在整个周期内储能不会过充过放。展开更多
电压暂降的经济管理是当前电能质量投资和需求侧管理工作中亟待解决的问题。提出了基于交互式逼近理想解的排序法(technique for order preference by similarity to idealsolution,TOPSIS)的电压暂降投资最优分配策略。该策略在定义负...电压暂降的经济管理是当前电能质量投资和需求侧管理工作中亟待解决的问题。提出了基于交互式逼近理想解的排序法(technique for order preference by similarity to idealsolution,TOPSIS)的电压暂降投资最优分配策略。该策略在定义负荷点的电压暂降敏感度指标的基础上,综合考虑了不同类型的电压暂降治理技术水平的差异。交互式TOPSIS方法的运用能够有效地提取负荷点的优势信息,避免了决策过程中的主观性和盲目性,使电压暂降投资的分配结果更加合理。展开更多
Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies...Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies.Under such circumstances,it is desirable to consider a generalized case-cohort design,where only a fraction of cases are sampled.In this article,we propose the inference procedure for the additive hazards regression under the generalized case-cohort sampling.Asymptotic properties of the proposed estimators for the regression coefcients are established.To demonstrate the efectiveness of the generalized case-cohort sampling,we compare it with simple random sampling in terms of asymptotic relative efciency.Furthermore,we derive the optimal allocation of the subsamples for the proposed design.The fnite sample performance of the proposed method is evaluated through simulation studies.展开更多
分析了宏基站(Macrocell)和家庭基站(Femtocell)共同构成的异构网络场景下的多个业务的资源分配问题,并提出了一种基于用户体验(Quality of experience,QoE)的高效的分配算法.主要考虑了语音、视频和数据三种业务.为了使所有业务的QoE...分析了宏基站(Macrocell)和家庭基站(Femtocell)共同构成的异构网络场景下的多个业务的资源分配问题,并提出了一种基于用户体验(Quality of experience,QoE)的高效的分配算法.主要考虑了语音、视频和数据三种业务.为了使所有业务的QoE之和达到最大化,先根据三类业务的不同属性设置相应的速率限制,在此基础上,利用模拟退火智能算法分别对频谱资源块和功率资源进行优化分配.仿真结果表明算法有效的提高了资源的利用效率,避免了多余资源分配到无法提升QoE的业务上,实现了资源的最优化分配.展开更多
文摘风力、光伏等新能源发电输出功率具有波动性,为减小功率波动对电网的影响,提出平抑功率波动的储能优化配置方法。将频谱分析和低通滤波相结合,根据新能源输出功率频谱分析结果,结合并网功率波动率约束、储能充放电效率及荷电状态(state of charge,SOC),在频率波动范围内确定最佳的一阶低通滤波器的截止频率,得到经滤波和修正的并网联络线功率及储能充放电补偿功率,从而确定满足平滑出力运行控制需求的最优储能额定功率、容量和初始SOC。采用南京地区某屋顶光伏实测数据及波动要求,对该方法进行验证,结果表明采用此方法能以较小储能容量将光伏输出功率波动从27.3%降低到1.62%,且在整个周期内储能不会过充过放。
文摘电压暂降的经济管理是当前电能质量投资和需求侧管理工作中亟待解决的问题。提出了基于交互式逼近理想解的排序法(technique for order preference by similarity to idealsolution,TOPSIS)的电压暂降投资最优分配策略。该策略在定义负荷点的电压暂降敏感度指标的基础上,综合考虑了不同类型的电压暂降治理技术水平的差异。交互式TOPSIS方法的运用能够有效地提取负荷点的优势信息,避免了决策过程中的主观性和盲目性,使电压暂降投资的分配结果更加合理。
基金supported by the Fundamental Research Fund for the Central Universitiessupported by National Natural Science Foundation of China(Grant No.11301545)supported by National Natural Science Foundation of China(Grant No.11171263)
文摘Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies.Under such circumstances,it is desirable to consider a generalized case-cohort design,where only a fraction of cases are sampled.In this article,we propose the inference procedure for the additive hazards regression under the generalized case-cohort sampling.Asymptotic properties of the proposed estimators for the regression coefcients are established.To demonstrate the efectiveness of the generalized case-cohort sampling,we compare it with simple random sampling in terms of asymptotic relative efciency.Furthermore,we derive the optimal allocation of the subsamples for the proposed design.The fnite sample performance of the proposed method is evaluated through simulation studies.
文摘分析了宏基站(Macrocell)和家庭基站(Femtocell)共同构成的异构网络场景下的多个业务的资源分配问题,并提出了一种基于用户体验(Quality of experience,QoE)的高效的分配算法.主要考虑了语音、视频和数据三种业务.为了使所有业务的QoE之和达到最大化,先根据三类业务的不同属性设置相应的速率限制,在此基础上,利用模拟退火智能算法分别对频谱资源块和功率资源进行优化分配.仿真结果表明算法有效的提高了资源的利用效率,避免了多余资源分配到无法提升QoE的业务上,实现了资源的最优化分配.
