基于无爬坡事件定义标准晴空集的短期光伏功率预测

doi:10.11930/j.issn.1004-9649.202212013

中国电力 ›› 2023, Vol. 56 ›› Issue (9): 187-195.DOI: 10.11930/j.issn.1004-9649.202212013

基于无爬坡事件定义标准晴空集的短期光伏功率预测

郭洪武¹, 车建峰², 闫钇汛³, 王丽婕⁴

1. 国网内蒙古东部电力有限公司赤峰供电公司，内蒙古赤峰 024000;
2. 中国电力科学研究院有限公司，北京 100192;
3. 中国铁道出版社有限公司，北京 100054;
4. 北京信息科技大学自动化学院，北京 100192

收稿日期:2022-12-02 修回日期:2023-05-24 发布日期:2023-09-20
作者简介:郭洪武(1985-),男,硕士,高级工程师,从事电力系统控制技术研究,E-mail:254785478@qq.com;王丽婕(1983-),女,通信作者,博士,副教授,从事新能源功率预测技术研究,E-mail:wanglijie_0203@126.com
基金资助:
国网内蒙古东部电力有限公司科技项目（SGMDCF00YWJS2000769）。

Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event

GUO Hongwu¹, CHE Jianfeng², YAN Yixun³, WANG Lijie⁴

1. Chifeng Power Supply Company, State Grid Inner Mongolia East Electric Power Co.. Ltd., Chifeng 024000, China;
2. China Electric Power Research Institute, Beijing 100192, China;
3. China Railway Publishing House Company Limited, Beijing 100054, China;
4. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China

Received:2022-12-02 Revised:2023-05-24 Published:2023-09-20
Supported by:
This work is supported by Science and Technology Project of State Grid Inner Mongolia East Electric Power Co., Ltd. (No.SGMDCF00YWJS2000769).

摘要/Abstract

摘要： 光伏功率的输出受季节、气象条件及其他因素的影响具有随机性和不确定性，恶劣天气下功率输出具有较强的波动性也加大了预测的难度。提出了一种基于无爬坡事件定义标准晴空集的短期光伏功率预测模型。通过爬坡定义提取一天内均为无爬坡事件的样本点，将其定义为一个标准晴空集，并与历史实际功率做差，得到的差值作为输出目标变量，以数值天气预报作为输入变量，采用长短期记忆模型对差值进行建模预测，最后将标准晴空集与该预测差值做差，间接得到预测的光伏输出功率值。通过对某光伏电站进行仿真，并进行算例对比，所提模型的短期光伏功率预测精度提高了2%~4%，在恶劣天气下，该方法可以将平均绝对误差和均方根误差降低3%左右，验证了所提模型的性能和有效性。

关键词: 光伏功率短期预测, 非爬坡样本提取, 标准晴空集, 长短期记忆模型

Abstract: Photovoltaic power output is influenced by season, weather conditions, and other factors with randomness and uncertainty. It is difficult to predict the power output under bad weather with strong volatility. Therefore, this paper proposed a short-term photovoltaic power prediction model based on the standard clear sky set with no climbing event definition. The sample points with no climbing events in one day were extracted by the climbing definition and defined as a standard clear sky set, and the difference between them and the historical actual power was made. The obtained difference was used as the output target variable, and the numerical weather forecast was used as the input variable. The long-short term memory (LSTM) model was used to model and forecast the difference. Finally, the predicted photovoltaic output power was obtained indirectly by making the difference between the standard clear sky set and the predicted difference. Through the simulation of a photovoltaic power station and comparison of calculation examples, the short-term photovoltaic power prediction accuracy of the proposed model was improved by 2%~4%. In severe weather, this method can reduce mean absolute error (MAE) and root-mean-square error (RMSE) by about 3%, which verifies the prediction performance and effectiveness of the proposed model.

Key words: short-term photovoltaic power prediction, non-climbing sample extraction, standard clear sky set, long-short term memory model

郭洪武, 车建峰, 闫钇汛, 王丽婕. 基于无爬坡事件定义标准晴空集的短期光伏功率预测[J]. 中国电力, 2023, 56(9): 187-195.

GUO Hongwu, CHE Jianfeng, YAN Yixun, WANG Lijie. Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event[J]. Electric Power, 2023, 56(9): 187-195.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202212013

https://www.electricpower.com.cn/CN/Y2023/V56/I9/187

参考文献

[1] 张娜, 任强, 刘广忱, 等. 基于VMD-GWO-ELMAN的光伏功率短期预测方法[J]. 中国电力, 2022, 55(5): 57–65
ZHANG Na, REN Qiang, LIU Guangchen, et al. PV power short-term forecasting method based on VMD-GWO-ELMAN[J]. Electric Power, 2022, 55(5): 57–65
[2] 叶琳浩, 申展, 许峰, 等. 考虑光伏不确定性和时序相关性的分布鲁棒光储协同优化配置方法[J]. 南方电网技术, 2023, 17(4): 132–143
YE Linhao, SHEN Zhan, XU Feng, et al. Distributionally robust collaborative optimal allocation method of photovoltaic and energy storage considering photovoltaic uncertainty and temporal correlation[J]. Southern Power System Technology, 2023, 17(4): 132–143
[3] 王海燕, 刘佳康, 邓亚平. 基于预估-校正综合BP神经网络的短期光伏功率预测[J]. 智慧电力, 2023, 51(3): 46–52
WANG Haiyan, LIU Jiakang, DENG Yaping. Short-term photovoltaic power forecasting based on predict-correct combination BP neural network[J]. Smart Power, 2023, 51(3): 46–52
[4] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201–1217
LAI Changwei, LI Jinghua, CHEN Bo, et al. Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201–1217
[5] 吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7): 50–57
JI Xinge, LI Hui, LIU Sijia, et al. Short-term photovoltaic power forecasting based on MIE-LSTM[J]. Power System Protection and Control, 2020, 48(7): 50–57
[6] GUO X F, GAO Y, ZHENG D, et al. Study on short-term photovoltaic power prediction model based on the Stacking ensemble learning[J]. Energy Reports, 2020, 6: 1424–1431.
[7] 朱旭坤, 姚李孝, 杨国清. 基于PSOEM和神经网络的光伏电站功率预测[J]. 电网与清洁能源, 2021, 37(7): 115–120, 135
ZHU Xukun, YAO Lixiao, YANG Guoqing. Power prediction of photovoltaic power plants based on PSOM algorithm and neural network[J]. Power System and Clean Energy, 2021, 37(7): 115–120, 135
[8] 崔佳豪, 毕利. 基于混合神经网络的光伏电量预测模型的研究[J]. 电力系统保护与控制, 2021, 49(13): 142–149
CUI Jiahao, BI Li. Research on photovoltaic power forecasting model based on hybrid neural network[J]. Power System Protection and Control, 2021, 49(13): 142–149
[9] 贾德香, 吕干云, 林芬, 等. 基于SAPSO-BP和分位数回归的光伏功率区间预测[J]. 电力系统保护与控制, 2021, 49(10): 20–26
JIA Dexiang, LÜ Ganyun, LIN Fen, et al. Photovoltaic power interval prediction based on SAPSO-BP and quantile regression[J]. Power System Protection and Control, 2021, 49(10): 20–26
[10] 李丰君, 王磊, 赵健, 等. 基于天气融合和LSTM网络的分布式光伏短期功率预测方法[J]. 中国电力, 2022, 55(11): 149–154
LI Fengjun, WANG Lei, ZHAO Jian, et al. Short-term power prediction method of distributed photovoltaic based on weather fusion and LSTM network[J]. Electric Power, 2022, 55(11): 149–154
[11] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62–69
ZHANG Yunqin, CHENG Qize, JIANG Wenjie, et al. Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 62–69
[12] WANG S, WEI L F, ZENG L A. Ultra-short-term photovoltaic power prediction based on VMD-LSTM-RVM model[J]. IOP Conference Series:Earth and Environmental Science, 2021, 781(4): 042020.
[13] 黄雨薇, 彭道刚, 姚峻, 等. 基于SSA和K均值的TD-BP神经网络超短期光伏功率预测[J]. 太阳能学报, 2021, 42(4): 229–238
HUANG Yuwei, PENG Daogang, YAO Jun, et al. Ultra-short-term photovoltaic power prediction using TD-BP neural network based on SSA and k-means[J]. Journal of solar energy, 2021, 42(4): 229–238
[14] 叶林, 裴铭, 路朋, 等. 基于天气分型的短期光伏功率组合预测方法[J]. 电力系统自动化, 2021, 45(1): 44–54
YE Lin, PEI Ming, LU Peng, et al. Combination forecasting method of short-term photovoltaic power based on weather classification[J]. Automation of Electric Power Systems, 2021, 45(1): 44–54
[15] 朱文立, 张利, 杨明, 等. 考虑日周期性影响的光伏功率爬坡事件非精确概率预测[J]. 电力系统自动化, 2019, 43(20): 31–38
ZHU Wenli, ZHANG Li, YANG Ming, et al. Imprecise probabilistic prediction of photovoltaic power ramp event considering daily periodic effect[J]. Automation of Electric Power Systems, 2019, 43(20): 31–38
[16] 梁志祥, 刘晓明, 牟颖, 等. 基于深度学习的新能源爬坡事件预测方法[J]. 山东大学学报(工学版), 2019, 49(5): 24–28
LIANG Zhixiang, LIU Xiaoming, MU Ying, et al. Prediction method of wind power and PV ramp event based on deep learning[J]. Journal of Shandong University (Engineering Science), 2019, 49(5): 24–28
[17] 崔明建, 孙元章, 柯德平. 基于原子稀疏分解和BP神经网络的风电功率爬坡事件预测[J]. 电力系统自动化, 2014, 38(12): 6–11, 26
CUI Mingjian, SUN Yuanzhang, KE Deping. Wind power ramp events forecasting based on atomic sparse decomposition and BP neural networks[J]. Automation of Electric Power Systems, 2014, 38(12): 6–11, 26
[18] 杨茂, 刘红柳. 超短期风电功率爬坡事件对风电功率实时预测误差的影响研究[J]. 太阳能学报, 2017, 38(3): 571–577
YANG Mao, LIU Hongliu. Study on the influence of ultra-short-term wind power climbing events on real-time prediction error of wind power[J]. Acta Energiae Solaris Sinica, 2017, 38(3): 571–577
[19] HOSSAIN M S, MAHMOOD H. Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast[J]. IEEE Access, 2020, 8: 172524–172533.

基于无爬坡事件定义标准晴空集的短期光伏功率预测

Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

模态框（Modal）标题

基于无爬坡事件定义标准晴空集的短期光伏功率预测

Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics