提出了一种考虑储能管理并计及源-荷不确定性的主动配电网运行风险评估方法。针对分布式电源出力的概率随机性,根据点估计法将分布式电源的出力离散化,定量描述分布式电源的概率输出。同时,设定电池储能的运行策略,计算储能的输出,结合...提出了一种考虑储能管理并计及源-荷不确定性的主动配电网运行风险评估方法。针对分布式电源出力的概率随机性,根据点估计法将分布式电源的出力离散化,定量描述分布式电源的概率输出。同时,设定电池储能的运行策略,计算储能的输出,结合主动配电网线路、变压器等设备的随机故障考虑故障下高渗透分布式电源所带来的运行风险,构建主动配电网的电量不足期望值(expected energy not supplied,EENS)以及严重度(severity index,SI)2个运行风险评估指标,并利用2m+1点估计法对主动配电网的运行风险进行精确评估。算例表明,所提出的考虑储能管理并计及源-荷不确定性的主动配电网运行风险评估方法能够用于主动配电网运行风险的快速计算。展开更多
Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing the...Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler （PMS）. Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%-61%.展开更多
文摘提出了一种考虑储能管理并计及源-荷不确定性的主动配电网运行风险评估方法。针对分布式电源出力的概率随机性,根据点估计法将分布式电源的出力离散化,定量描述分布式电源的概率输出。同时,设定电池储能的运行策略,计算储能的输出,结合主动配电网线路、变压器等设备的随机故障考虑故障下高渗透分布式电源所带来的运行风险,构建主动配电网的电量不足期望值(expected energy not supplied,EENS)以及严重度(severity index,SI)2个运行风险评估指标,并利用2m+1点估计法对主动配电网的运行风险进行精确评估。算例表明,所提出的考虑储能管理并计及源-荷不确定性的主动配电网运行风险评估方法能够用于主动配电网运行风险的快速计算。
文摘Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler （PMS）. Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%-61%.
