基于大数据技术风电机组容量可信度计算

在大规模风电并网的前提下,风电容量可信度计算对电力系统可靠运行具有重要意义。论文基于电量不足期望(LOEE)可靠性指标,考虑风电场间时空相关性的影响因素,采用非序贯蒙特卡洛法对风电容量可信度进行计算。风电容量可信度的计算需要的数据有风速、风电出力、风电机组地理位置信息等数据,由于计算所需的数据量大、类型多、来源广等特点,提出基于Hadoop架构的大数据技术计算风电容量可信度,针对Hadoop架构存在的机架感知不平衡及存储数据间缺乏相关性问题,引入机架感知配置法和哈希桶存储算法对其进行改进,提高了数据存储及数据处理的效率,减少计算时间,通过实例验证文中所提方法的有效性。

Based on Big Data Technology

Under the premise of large-scale wind power integration, the reliability calculation of wind power capacity is of great significance for the reliable operation of power systems. Based on the LOEE reliability index, considering the influencing factors of space-time correlation between wind farms, the non-sequential Monte Carlo method is used to calculate the reliability of wind capacity. The data required for the calculation of wind power capacity credibility include wind speed, wind power output, and wind turbine geographic location information. Due to the large amount of data required for calculation, many types, and wide sources, the big data technology calculation based on Hadoop architecture is proposed. Wind power capacity credibility, for the rack-aware imbalance in the Hadoop architecture and the lack of correlation between storage data, the rack-aware configuration method and the hash bucket storage algorithm are introduced to improve the data storage and data processing. Efficiency, reduce calculation time, and verify the effectiveness of the method through simulation.