Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering p...Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering properties in these systems were compared with those in non-polar systems. The results show that the ACSs in bi-polar systems are larger than those in the non-polar systems. In uni-polar systems the behavior of clustering property differs: at the lower ionic concentration (10%), repulsive force is not strong enough to break clusters, but may greatly weaken them. The clusters are eventually cracked into smaller ones only when concentration of uni-polar charged particles is large enough. In this work, the ionic concentration is 20%. The relationship between ACS and shear rates follows power law in a exponent range of 0.176-0.276. This range is in a good agreement with the range of experimental data, but it is biased towards the lower limit slightly.展开更多
Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent orga...Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time.展开更多
分布式光伏与数字孪生理念相结合,是应对大规模并网光伏群控挑战的有效方法,也是推进新型电力系统建设的重要内容。该文提出一种面向分布式光伏群调群控的数字孪生方法,拟从光伏一致性表征、孪生模型重建、功率推演预测3个方面支撑孪生...分布式光伏与数字孪生理念相结合,是应对大规模并网光伏群控挑战的有效方法,也是推进新型电力系统建设的重要内容。该文提出一种面向分布式光伏群调群控的数字孪生方法,拟从光伏一致性表征、孪生模型重建、功率推演预测3个方面支撑孪生系统构建。基于K-means算法提出光伏一致性表征方法,利用皮尔逊系数加权构造气象因子与光伏并网节点的电压灵敏度作为指标,对配电网电压影响相似的光伏进行聚类;以运动恢复结构(structure from motion,SFM)为原理提出基于加速稳健特征(speeded up robust features,SURF)算法的高质量相机位姿估计方法与低误差稀疏点云重建方法,再结合多视角立体视觉(multiple view stereo,MVS)原理提出点云稠密化方案,形成基于SFM-MVS的高保真光伏模型重建算法;基于长短期记忆网络(long-short term memory,LSTM)网络提出光伏短期功率预测方法,利用皮尔逊系数加权处理输入的光照、温度数据,并提出残差补偿机制和物理意义约束两部分提高预测结果准确性。文章最后通过实验,验证集群划分的合理性、光伏模型重建优越性以及功率预测的准确性。展开更多
随着高比例、大规模分布式光伏并网以及电动汽车的普及,如何发挥电动汽车灵活性、实现配电网分布式光伏与本地电动汽车负荷灵活性资源的友好协调是当前需要解决的重要问题。为此,提出了考虑电动汽车与分布式光伏协同的配电网集群划分与...随着高比例、大规模分布式光伏并网以及电动汽车的普及,如何发挥电动汽车灵活性、实现配电网分布式光伏与本地电动汽车负荷灵活性资源的友好协调是当前需要解决的重要问题。为此,提出了考虑电动汽车与分布式光伏协同的配电网集群划分与运行策略。首先,建立电动汽车可调充电功率灵活性聚合模型,提出基于Louvain算法的改进模块度指标配电网分布式集群划分方法;其次,基于历史数据信息生成电动汽车多时间尺度充电场景,提出考虑电动汽车充电灵活性的分布式集群协同优化模型;最后,采用同步交替方向乘子法(synchronous alternating direction multiplier method,SADMM)实现各集群优化模型的分布式求解。仿真结果表明,利用电动汽车充电灵活性参与配电网协同运行可有效提高分布式光伏利用率,并且在满足电动汽车用户充电需求的同时保证了配电网电压运行安全。展开更多
基金Projects(50474037, 50874087) supported by the National Natural Science Foundation of ChinaProject (BK2006078) supported by the Natural Scientific Funds of Jiangsu Province,China
文摘Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering properties in these systems were compared with those in non-polar systems. The results show that the ACSs in bi-polar systems are larger than those in the non-polar systems. In uni-polar systems the behavior of clustering property differs: at the lower ionic concentration (10%), repulsive force is not strong enough to break clusters, but may greatly weaken them. The clusters are eventually cracked into smaller ones only when concentration of uni-polar charged particles is large enough. In this work, the ionic concentration is 20%. The relationship between ACS and shear rates follows power law in a exponent range of 0.176-0.276. This range is in a good agreement with the range of experimental data, but it is biased towards the lower limit slightly.
文摘Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time.
文摘分布式光伏与数字孪生理念相结合,是应对大规模并网光伏群控挑战的有效方法,也是推进新型电力系统建设的重要内容。该文提出一种面向分布式光伏群调群控的数字孪生方法,拟从光伏一致性表征、孪生模型重建、功率推演预测3个方面支撑孪生系统构建。基于K-means算法提出光伏一致性表征方法,利用皮尔逊系数加权构造气象因子与光伏并网节点的电压灵敏度作为指标,对配电网电压影响相似的光伏进行聚类;以运动恢复结构(structure from motion,SFM)为原理提出基于加速稳健特征(speeded up robust features,SURF)算法的高质量相机位姿估计方法与低误差稀疏点云重建方法,再结合多视角立体视觉(multiple view stereo,MVS)原理提出点云稠密化方案,形成基于SFM-MVS的高保真光伏模型重建算法;基于长短期记忆网络(long-short term memory,LSTM)网络提出光伏短期功率预测方法,利用皮尔逊系数加权处理输入的光照、温度数据,并提出残差补偿机制和物理意义约束两部分提高预测结果准确性。文章最后通过实验,验证集群划分的合理性、光伏模型重建优越性以及功率预测的准确性。
文摘随着高比例、大规模分布式光伏并网以及电动汽车的普及,如何发挥电动汽车灵活性、实现配电网分布式光伏与本地电动汽车负荷灵活性资源的友好协调是当前需要解决的重要问题。为此,提出了考虑电动汽车与分布式光伏协同的配电网集群划分与运行策略。首先,建立电动汽车可调充电功率灵活性聚合模型,提出基于Louvain算法的改进模块度指标配电网分布式集群划分方法;其次,基于历史数据信息生成电动汽车多时间尺度充电场景,提出考虑电动汽车充电灵活性的分布式集群协同优化模型;最后,采用同步交替方向乘子法(synchronous alternating direction multiplier method,SADMM)实现各集群优化模型的分布式求解。仿真结果表明,利用电动汽车充电灵活性参与配电网协同运行可有效提高分布式光伏利用率,并且在满足电动汽车用户充电需求的同时保证了配电网电压运行安全。
