刘梦依;邱晓燕;张志荣;赵长枢;赵有林;张楷;

• 1:智能电网四川省重点实验室(四川大学)

摘要(Abstract)：

风电-光伏机组的大量接入对传统的无功优化模型提出了新的挑战。提出了计及风光出力相关性的配电网多目标无功优化模型,采用粒子群优化神经网络(particle swarm algorithm-BP neural network,PSO-BP)依据过去天气预报和风电出力的历史数据训练得到风电预测出力曲线,利用综合场景概率法生成光伏出力曲线。用斯皮尔曼相关系数将风光之间的出力相关性量化,再考虑风机和光伏机组共同参与无功优化。采用多目标粒子群算法(multi-obj ective particle swarm optimization,MOPSO)求解模型,以改进的IEEE 33节点配电网系统作为仿真样本,求得兼顾网损和电压偏差的Pareto最优解集,从中选择最优方案。算例结果验证了风光出力相关性对无功优化的影响,以及分布式电源接入配电网能有效降低网损和提高节点电压。在实际运行中,各地区的风光出力均满足一定的自然规律,可以以斯皮尔曼相关系数大小为参考依据,实现分布式电源(distributed generation,DG)和静止无功补偿装置(static var compensator,SVC)的协同优化运行,为配电网的安全经济运行保驾护航。

关键词(KeyWords)： 分布式电源;PSO-BP神经网络;相关性;多目标无功优化

Abstract:

Keywords:

基金项目(Foundation): 四川省科技厅重点研发项目(2017FZ0103)~~

作者(Author): 刘梦依;邱晓燕;张志荣;赵长枢;赵有林;张楷;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.1206

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