针对大规模海上风电并网对陆上电网运行带来的影响,结合海上风电与输电系统互联,逐步形成“海上电网”的新趋势,提出一种基于统一潮流控制器(unified power flow controller,UPFC)和拓扑调整的海上风电功率控制策略,对具有互联效应的海...针对大规模海上风电并网对陆上电网运行带来的影响,结合海上风电与输电系统互联,逐步形成“海上电网”的新趋势,提出一种基于统一潮流控制器(unified power flow controller,UPFC)和拓扑调整的海上风电功率控制策略,对具有互联效应的海上电网中的潮流分布进行控制,构建陆上电网最优潮流和含UPFC和拓扑调整的海上风电功率控制两层联合优化运行模型。算例结果表明,提出的UPFC和拓扑调整的联合控制策略可以较好地控制海上风电功率在各个并网点之间的功率分配,实现陆上电网对海上风电功率在各个并网点功率分配的需求,有利于海上风电友好地接入陆上电网。展开更多
针对我国东南沿海省市电网中外来直流电源与海上风电等不可控电源比例不断提升,对受端电网的电源规划与运行稳定提出重要挑战的问题,提出一种考虑海上风电接入与转动惯量约束的电源规划模型。首先,考虑海上风电历史出力数据的季节特征,...针对我国东南沿海省市电网中外来直流电源与海上风电等不可控电源比例不断提升,对受端电网的电源规划与运行稳定提出重要挑战的问题,提出一种考虑海上风电接入与转动惯量约束的电源规划模型。首先,考虑海上风电历史出力数据的季节特征,运用场景分析法描述海上风电不确定性。其次,以年综合成本最低为目标,构建外层优化规划,内层优化运行的双层电源规划模型,并分析了转动惯量水平与频率响应指标的定量关系,通过在内层模型中施加频率变化率(rate of change of frequency,RoCoF)约束保证规划电源结构的惯性支撑能力。采用遗传算法与Cplex求解器对双层规划模型进行求解。最后,算例仿真结果验证了所提模型的可行性与有效性,并分析归纳了海上风电渗透率与外来直流电源比重大小对电源规划结果的影响。展开更多
Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited...Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.展开更多
文摘针对大规模海上风电并网对陆上电网运行带来的影响,结合海上风电与输电系统互联,逐步形成“海上电网”的新趋势,提出一种基于统一潮流控制器(unified power flow controller,UPFC)和拓扑调整的海上风电功率控制策略,对具有互联效应的海上电网中的潮流分布进行控制,构建陆上电网最优潮流和含UPFC和拓扑调整的海上风电功率控制两层联合优化运行模型。算例结果表明,提出的UPFC和拓扑调整的联合控制策略可以较好地控制海上风电功率在各个并网点之间的功率分配,实现陆上电网对海上风电功率在各个并网点功率分配的需求,有利于海上风电友好地接入陆上电网。
文摘针对我国东南沿海省市电网中外来直流电源与海上风电等不可控电源比例不断提升,对受端电网的电源规划与运行稳定提出重要挑战的问题,提出一种考虑海上风电接入与转动惯量约束的电源规划模型。首先,考虑海上风电历史出力数据的季节特征,运用场景分析法描述海上风电不确定性。其次,以年综合成本最低为目标,构建外层优化规划,内层优化运行的双层电源规划模型,并分析了转动惯量水平与频率响应指标的定量关系,通过在内层模型中施加频率变化率(rate of change of frequency,RoCoF)约束保证规划电源结构的惯性支撑能力。采用遗传算法与Cplex求解器对双层规划模型进行求解。最后,算例仿真结果验证了所提模型的可行性与有效性,并分析归纳了海上风电渗透率与外来直流电源比重大小对电源规划结果的影响。
基金sponsored in part by the National Key R&D Program of China under Grant No. 2020YFB1806605the National Natural Science Foundation of China under Grant Nos. 62022049, 62111530197, and 61871254+1 种基金OPPOsupported by the Fundamental Research Funds for the Central Universities under Grant No. 2022JBXT001
文摘Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.
