为评估孤岛型微电网的可靠性以及分析储能装置对系统可靠性的影响,提出基于序贯蒙特卡洛模拟法的孤岛微电网可靠性评估方案。首先,以孤岛型风柴储混合微电网系统作为研究对象,搭建分布式电源等关键设备的出力模型;其次,制定可靠性评估...为评估孤岛型微电网的可靠性以及分析储能装置对系统可靠性的影响,提出基于序贯蒙特卡洛模拟法的孤岛微电网可靠性评估方案。首先,以孤岛型风柴储混合微电网系统作为研究对象,搭建分布式电源等关键设备的出力模型;其次,制定可靠性评估指标体系、不同的储能运行策略及负荷削减策略,在此基础上提出基于序贯蒙特卡洛模拟法的孤岛风柴储微电网可靠性评估算法;最后,通过对改进的RBTS Bus 6 F4孤岛系统进行仿真,量化分析了储能装置的运行策略、储能容量、容量配置对系统可靠性水平的影响。结果表明:合理的储能策略、适当增大储能容量以及适宜的储能系统配置方案等均可提高系统可靠性。展开更多
针对风、光资源的分布特点,结合发电系统设备的运行状态,建立含风力、光伏、储能的发电系统可靠性数学模型。定义风光储冗余容量比和出力偏移度两个指标来衡量含风光储发电系统的可靠性。采用序贯蒙特卡洛模拟法,构建含风光储发电系统...针对风、光资源的分布特点,结合发电系统设备的运行状态,建立含风力、光伏、储能的发电系统可靠性数学模型。定义风光储冗余容量比和出力偏移度两个指标来衡量含风光储发电系统的可靠性。采用序贯蒙特卡洛模拟法,构建含风光储发电系统的可靠性评估。利用可靠性测试系统(roy billinton test system,RBTS),分析不同协调运行方式、风光容量配置比和储能容量下含风光储的发电系统的可靠性指标。研究表明,合理的风光储配置能有效提高电能利用率,增强跟踪负荷能力,改善发电系统的可靠性。展开更多
Accuracy of simulated permeability can be improved using soft data during the process of simulation. Integrating soft data with hard data, a method based on COSISIM （sequential indicator cosimulation） was proposed t...Accuracy of simulated permeability can be improved using soft data during the process of simulation. Integrating soft data with hard data, a method based on COSISIM （sequential indicator cosimulation） was proposed to reconstruct permeability. The algorithm COSISIM extends the SISIM （sequential indicator simulation） algorithm to handle secondary data. At the difference of SISIM, data must already be an indicator-coded prior to using COSISIM. The soft data were integrated with hard data using the Markov-Bayes algorithm and must be coded into indicators before they are used. This method was tested on a regional simulation of permeability. The simulated results and the original distribution of permeability were compared. The experimental results demonstrate that this method is practical.展开更多
文摘为评估孤岛型微电网的可靠性以及分析储能装置对系统可靠性的影响,提出基于序贯蒙特卡洛模拟法的孤岛微电网可靠性评估方案。首先,以孤岛型风柴储混合微电网系统作为研究对象,搭建分布式电源等关键设备的出力模型;其次,制定可靠性评估指标体系、不同的储能运行策略及负荷削减策略,在此基础上提出基于序贯蒙特卡洛模拟法的孤岛风柴储微电网可靠性评估算法;最后,通过对改进的RBTS Bus 6 F4孤岛系统进行仿真,量化分析了储能装置的运行策略、储能容量、容量配置对系统可靠性水平的影响。结果表明:合理的储能策略、适当增大储能容量以及适宜的储能系统配置方案等均可提高系统可靠性。
文摘针对风、光资源的分布特点,结合发电系统设备的运行状态,建立含风力、光伏、储能的发电系统可靠性数学模型。定义风光储冗余容量比和出力偏移度两个指标来衡量含风光储发电系统的可靠性。采用序贯蒙特卡洛模拟法,构建含风光储发电系统的可靠性评估。利用可靠性测试系统(roy billinton test system,RBTS),分析不同协调运行方式、风光容量配置比和储能容量下含风光储的发电系统的可靠性指标。研究表明,合理的风光储配置能有效提高电能利用率,增强跟踪负荷能力,改善发电系统的可靠性。
基金Supported by the National Natural Science Foundation of China（50874005）
文摘Accuracy of simulated permeability can be improved using soft data during the process of simulation. Integrating soft data with hard data, a method based on COSISIM （sequential indicator cosimulation） was proposed to reconstruct permeability. The algorithm COSISIM extends the SISIM （sequential indicator simulation） algorithm to handle secondary data. At the difference of SISIM, data must already be an indicator-coded prior to using COSISIM. The soft data were integrated with hard data using the Markov-Bayes algorithm and must be coded into indicators before they are used. This method was tested on a regional simulation of permeability. The simulated results and the original distribution of permeability were compared. The experimental results demonstrate that this method is practical.
