文章摘要
黎敏,林湘宁,张哲原,等.超短期光伏出力区间预测算法及其应用[J].电力系统自动化,2019,43(3):10-16. DOI: 10.7500/AEPS20180322001.
LI Min,LIN Xiangning,ZHANG Zheyuan, et al.Interval Prediction Algorithm for Ultra-short-term Photovoltaic Output and Its Application[J].Automation of Electric Power Systems,2019,43(3):10-16. DOI: 10.7500/AEPS20180322001.
超短期光伏出力区间预测算法及其应用
Interval Prediction Algorithm for Ultra-short-term Photovoltaic Output and Its Application
DOI:10.7500/AEPS20180322001
关键词: 粒子群优化  边界估值理论  区间预测  光伏出力预测
KeyWords: particle swarm optimization (PSO)  lower upper bound estimation (LUBE) method  interval prediction  photovoltaic output prediction
上网日期:2018-12-11
基金项目:国家自然科学基金重点项目(51537003)
作者单位E-mail
黎敏 三峡大学电气与新能源学院, 湖北省宜昌市 443002  
林湘宁 强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074  
张哲原 强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074 zheyuan_zhang@foxmail.com 
翁汉琍 三峡大学电气与新能源学院, 湖北省宜昌市 443002  
摘要:
      光伏出力预测能为电力系统经济安全运行提供重要依据,传统预测方法多为确定性点预测,其结果一般有不同程度的误差,概率性区间预测方法能有效描述光伏出力的不确定性因而逐步受到重视。针对超短期光伏出力区间预测问题,提出一种基于粒子群优化与边界估值理论的预测模型,用于光伏出力区间预测。通过利用粒子群算法对边界估值理论的输出权值进行优化,能够直接、快速地寻找最优的预测区间上下限,从而克服传统区间预测方案中计算量大与需要数据分布假设的限制,实现对超短期光伏出力的区间预测。最后,基于澳大利亚昆士兰大学光伏电站实例仿真验证模型,评估不同置信水平下模型的区间预测性能,并与传统的点预测方案进行对比,结果表明,所提出模型能生成高质量的超短期光伏出力区间预测,能够为光电并网安全稳定运行提供更好的决策支持。
Abstract:
      Photovoltaic (PV) output prediction can provide an important basis for the economical and safe operation of power system. The traditional prediction methods mostly belong to deterministic point predictions, the results of which generally have different degrees of error. The probability interval prediction method is gradually adopted because of its ability to effectively describe the uncertainty of PV output. For the interval prediction problem of ultra-short-term PV output, a prediction model based on particle swarm optimization (PSO) and lower upper bound estimation (LUBE) is proposed for PV output interval prediction. The upper and lower limits of interval prediction could be optimized quickly and directly by using PSO-LUBE, thus the problems of the large computational complexity and the assumption of data distribution in the traditional interval prediction scheme are solved. Case studies based on real PV power station data from the University of Queensland are conducted, the interval prediction performance of the model under different confidence levels is evaluated and compared with the traditional point prediction scheme. The results show that the proposed model can generate high-quality ultra-short-term PV output interval prediction, which can provide better decision support for safe and stable operation of grid-connected PV.
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