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考虑爬坡特性与预测区间优化的电热水器集群功率区间预测
作者:
作者单位:

1.河北省分布式储能与微网重点实验室(华北电力大学),河北省保定市 071003;2.新能源电力系统国家重点实验室(华北电力大学),河北省保定市 071003;3.国网河北省电力有限公司,河北省石家庄市 050000

摘要:

根据电热水器(EWH)集群功率的波形特征,提出了一种考虑爬坡特性和区间优化的EWH集群功率短期区间预测方法。首先,针对EWH负荷功率的不确定性,提出了一种考虑样本分布多源异构特性的结合集合经验模态分解(EEMD)、主成分分析(PCA)和多核相关向量机(MKRVM)的高精度组合点预测模型。然后,为获得期望预测覆盖率下宽度更窄的预测区间,综合区间预测覆盖率、区间平均宽度和累积宽度偏差等区间评价指标,设计了一种核密度估计(KDE)与粒子群优化相结合的改进预测区间优化方法,改善了MKRVM-KDE区间结构性能,避免了参数选择的随意性。最后,采用EWH聚合功率数据对算法有效性进行了验证。结果表明,该预测方法具有较高的预测精度和较好的清晰度,能够提供高质量的预测区间。

关键词:

基金项目:

国家重点研发计划“政府间国际科技创新合作/港澳台科技创新合作”重点专项项目(2018YFE0122200);国家电网公司科技项目(KJGW2018-014)。

通信作者:

作者简介:

余洋(1982—),男,通信作者,博士,副教授,硕士生导师,主要研究方向:电力储能技术、柔性负荷建模与调度。E-mail:yym0401@163.com
权丽(1995—),女,硕士研究生,主要研究方向:柔性负荷预测与调度等。E-mail:1057863783@qq.com
贾雨龙(1989—),男,博士研究生,主要研究方向:需求响应、负荷聚合建模。E-mail:jiayulong1110@126.com


Interval Prediction of Aggregated Power for Electric Water Heaters Considering Ramp Characteristic and Prediction Interval Optimization
Author:
Affiliation:

1.Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province (North China Electric Power University), Baoding 071003, China;2.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Baoding 071003, China;3.State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China

Abstract:

According to the waveform feature of the aggregated power for electric water heaters (EWHs), a short-term interval prediction method is proposed, which considers the ramp characteristic and interval optimization. Firstly, in view of the uncertainty of load power for EWH, a combinatorial point prediction model with high-precision considering multi-source heterogeneous characteristics of the sample distribution is presented, which combines with ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and multi-kernel relevant vector machine (MKRVM). Secondly, to obtain a narrower prediction interval with the expected prediction coverage, the evaluation indices of the interval prediction coverage, interval average width, and cumulative width deviation are combined to design an improved prediction interval optimization method integrating the kernel density estimation (KDE) and particle swarm optimization, which enhances the performance of MKRVM-KDE in interval structure and avoids the randomness in parameter selection. Finally, the aggregated power data of EWH is used to verify the effectiveness of the approach. The results show that the prediction method has high prediction accuracy and better clarity, and it can also provide prediction intervals with high quality.

Keywords:

Foundation:
This work is supported by the Key Special Project of National Key R&D Program of China “International Scientific and Technological Innovation Cooperation Between Governments/Scientific and Technological Innovation Cooperation Between Hong Kong, Macao and Taiwan” (No. 2018YFE0122200) and State Grid Corporation of China (No. KJGW2018-014).
引用本文
[1]余洋,权丽,贾雨龙,等.考虑爬坡特性与预测区间优化的电热水器集群功率区间预测[J].电力系统自动化,2021,45(1):88-96. DOI:10.7500/AEPS20191022006.
YU Yang, QUAN Li, JIA Yulong, et al. Interval Prediction of Aggregated Power for Electric Water Heaters Considering Ramp Characteristic and Prediction Interval Optimization[J]. Automation of Electric Power Systems, 2021, 45(1):88-96. DOI:10.7500/AEPS20191022006.
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  • 收稿日期:2019-10-22
  • 最后修改日期:2020-02-11
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  • 在线发布日期: 2021-01-05
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