基于卫星遥感的超短期分布式光伏功率预测

doi:10.19595/j.cnki.1000-6753.tces.210311

Abstract
Figure/Table
References
Related Citation (3)

Download: PDF (1467 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Photovoltaic (PV) output prediction is of great significance for power grid dispatching. In this paper, an ultra short-term distributed PV power prediction method based on satellite remote sensing is proposed for the distributed PV power station without irradiance measurement device. Firstly, the SWR grid is spatio-temporal predicted based on Res-UNet model, and then the predicted SWR grid is spatially interpolated to obtain the future irradiance of the ground distributed stations. Finally, the LSTM model with codec is constructed to predict the PV output. Res-UNet can fully learn the spatio-temporal correlation of the SWR grid, and LSTM can better learn the annual and daily periodicity of irradiance by introducing daily coding and time coding. The power experiments on real PV power stations show that, compared with the PV power prediction method that takes the irradiance of numerical weather forecast as the input, the PV power prediction method that takes the irradiance predicted by the Res-UNet+ interpolation as the input realizes ultra short term power prediction with higher accuracy.

Key words： Ultra short-term photovoltaic power prediction satellite remote sensing Res-UNet irradiance spatio temporal correlation short wave radiation(SWR)

Received: 11 March 2021

PACS:

TM615

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Xiaoyan
	Wang Jue
	Yao Tiechui
	Zhang Pei
	Chi Xuebin

Cite this article:

Liu Xiaoyan,Wang Jue,Yao Tiechui等. Ultra Short-Term Distributed Photovoltaic Power Prediction Based on Satellite Remote Sensing[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1800-1809.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210311 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I7/1800

[1] 徐正阳, 路志英, 刘洪. 基于泄漏积分型回声状态网络的在线学习光伏功率预测[J]. 电力系统及其自动化学报, 2018, 30(2): 1-7.
Xu Zhengyang, Lu Zhiying, Liu Hong.Online-learning PV power forecasting based on leaky-integrator echo state network[J]. Proceedings of the CSU-EPSA, 2018, 30(2): 1-7.
[2] 朱玥, 顾洁, 孟璐. 基于EMD-LSTM的光伏发电预测模型[J]. 电力工程技术, 2020, 39(2): 51-58.
Zhu Yue, Gu Jie, Meng Lu.Photovoltaic power generation prediction model based on EMD-LSTM[J]. Electric Power Engineering Technology, 2020, 39(2): 51-58.
[3] 许建峰, 曹庆仁. 中国分布式光伏并网发电现状及应用研究[J]. 能源与节能, 2020, 177(6): 51-54.
Xu Jianfeng, Cao Qingren.Research on the status and application of distributed photovoltaic grid-connected powergeneration in China[J]. Energy and Energy Conservation, 2020, 177(6): 51-54.
[4] 李文甲. 光伏—光热—热化学互补的太阳能利用理论、方法与系统 [D]. 北京: 中国科学院大学(中国科学院工程热物理研究所), 2018.
[5] 马群. 含分布式光伏的配电网无功优化研究[J]. 信息技术, 2020, 44(3): 85-89.
Ma Qun.Reactive power optimization of distribution network with access of distributed photovoltaic[J]. Information Technology, 2020, 44(3): 85-89.
[6] 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.
[7] 时珉, 许可, 王珏, 等. 基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测[J]. 电工技术学报, 2021, 36(11): 2298-2305.
Shi Min, Xu Ke, Wang Jue, et al.Short-term photovoltaic power forecast based on grey relational analysis GeoMAN model[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2298-2305.
[8] 周楠, 徐潇源, 严正, 等. 基于宽度学习系统的光伏发电功率超短期预测[J]. 电力系统自动化, 2021, 45(1): 55-64.
Zhou Nan, Xu Xiaoxiao, Yan Zheng, et al.Ultra-short-term forecasting of photovoltaic power generation based on broad learning system[J]. Automation of Electric Power Systems, 2021, 45(1): 55-64.
[9] Giacomuzzi S, Langwasser M, Carne G D, et al.Smart transformer-based medium voltage grid support by means of active power control[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(4): 285-294.
[10] 田剑刚, 张沛, 彭春华, 等. 基于分时长短期记忆神经网络的光伏发电超短期功率预测[J]. 现代电力, 2020, 37(6): 629-638.
Tian Jiangang, Zhang Pei, Peng Chunhua, et al.Ultra short-term forecast of photovoltaic generation based on time-division long short-term memory neural networks[J]. Modern Electric Power, 2020, 37(6): 629-638.
[11] 许寅, 李佳旭, 王颖, 等. 考虑光伏出力不确定性的园区配电网日前运行计划[J]. 电力自动化设备, 2020, 40(5): 85-94, 106.
Xu Yin, Li Jiaxu, Wang Ying, et al.Day-ahead operation plan for campus distribution network considering uncertainty of photovoltaic output[J]. Electric Power Automation Equipment, 2020, 40(5): 85-94, 106.
[12] 路志英, 周庆霞, 李鑫, 等. 基于地基云图图像特征的光伏功率预测[J]. 电力系统及其自动化学报, 2020, 32(8): 70-76.
Lu Zhiying, Zhou Qingxia, Li Xin, et al.Photovoltaic power prediction based on image features of ground cloud image[J]. Proceedings of the CSU-EPSA, 2020, 32(8): 70-76.
[13] 张华彬, 杨明玉. 基于最小二乘支持向量机的光伏出力超短期预测[J]. 现代电力, 2015, 32(1): 70-75.
Zhang Huabin, Yang Mingyu.Ultra-short-term forecasting for photovoltaic power output based on least square support vector machine[J]. Modern Electric Power, 2015, 32(1): 70-75.
[14] 马磊, 黄伟, 李克成, 等. 基于Attention-LSTM的光伏超短期功率预测模型[J]. 电测与仪表, 2021, 58(2): 146-152.
Ma Lei, Huang Wei, Li Kecheng, et al.Photovoltaic ultra-short-term power prediction model based on Attention-LSTM[J]. Electrical Measurement & Instrumentation, 2021, 58(2): 146-152.
[15] 张小萌, 白恺, 柳玉, 等. 大规模分布式光伏短期集群功率预测综述[J]. 华北电力技术, 2017 (6): 1-7.
Zhang Xiaomeng, Bai Kai, Liu Yu, et al.Overview for large-scale distributed photovoltaic power prediction with clustered method[J]. 华北电力技术, 2017(6): 1-7.
[16] 张柏林, 拜润卿, 智勇, 等. 基于空间相关性的分布式光伏超短期预测技术研究[J]. 陕西电力, 2017, 45(5): 22-26.
Zhang Bolin, Bai Runqing, Zhi Yong, et al.Distributed PV ultra-short term power forecast technology based on spatial correlation[J]. Shanxi Electric Power, 2017, 45(5): 22-26.
[17] 艾逸阳. 基于地基云图的超短期光伏电站辐照度预测研究[D]. 杭州:浙江大学, 2018.
[18] 杨春熙, 韩威, 高志球. 基于GRU神经网络的太阳辐照度短期预测[J]. 中国科技论文, 2020, 15(1): 8-14.
Yang Chunxi, Han Wei, Gao Zhiqiu.Short-term forecasting for solar irradiance using GRU neural network[J]. China Sciencepaper, 2020, 15(1): 8-14.
[19] Lorenz E, Kühnert J, Heinemann D.Short term forecasting of solar irradiance by combining satellite data and numerical weather predictions[C]// Proceedings of the 27th European PV Solar Energy Conference (EUPVSEC), Frankfurt, Germany, 2012: 4401-4405.
[20] 李冬辉, 尹海燕, 郑博文, 等. 改进的LSTM方法在冷水机组传感器故障检测中的应用[J]. 电工技术学报, 2019, 34(11): 2324-2332.
Li Donghui, Yin Haiyan, Zheng Bowen, et al.Application of improved LSTM method in sensor fault detection of the chiller[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2324-2332.
[21] 孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674.
Sun Bingxiang, Ren Pengbo, Chen Yuzhe, et al.Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 666-674.
[22] Kirkland E J.Advanced computing in electron microscopy[M]. Boston: Springer, 2010.
[23] Ronneberger O, Fischer P, Brox T.U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2015: 234-241.
[24] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al.Deep residual learning for image recognition[C]// Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2016: 770-778.
[25] 马原, 张雪敏, 甄钊, 等. 基于修正晴空模型的超短期光伏功率预测方法[J]. 电力系统自动化, 2021, 45(11): 44-51.
Ma Yuan, Zhang Xuemin, Zhen Zhao, et al.Ultra-short-term photovoltaic power prediction method based on modified clear-sky model[J]. Automation of Electric Power Systems, 2021, 45(11): 44-51.
[26] 王晨, 寇鹏. 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测[J]. 电工技术学报, 2020, 35(13): 2723-2735.
Wang Chen, Dou Peng.Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735.