基于空间相关性的分布式光伏实用化功率预测及误差分析

doi:10.11930/j.issn.1004-9649.202004223

中国电力 ›› 2021, Vol. 54 ›› Issue (7): 185-191,207.DOI: 10.11930/j.issn.1004-9649.202004223

基于空间相关性的分布式光伏实用化功率预测及误差分析

邵尹池¹, 袁绍军², 孙荣富³, 吴林林¹, 陈璨¹

1. 国网冀北电力科学研究院（华北电力科学研究院有限责任公司），北京 100045;
2. 国网冀北电力有限公司承德供电公司，河北承德 067000;
3. 国网冀北电力有限公司，北京 100054

收稿日期:2020-04-28 修回日期:2020-10-25 发布日期:2021-07-12
作者简介:白万荣(1985-),男,硕士,高级工程师,从事电力网络与信息安全研究,E-mail:baiwanrong@yeah.net;邵洁(1981-),女,通信作者,副教授,从事电力系统智能化处理、机器视觉分析研究,E-mail:shaojie@shiep.edu.cn
基金资助:
国家电网公司科技项目(52010619010D)

Practical Method and Error Analysis for Distributed Photovoltaic Power Prediction Based on Spatial Correlation

SHAO Yinchi¹, YUAN Shaojun², SUN Rongfu³, WU Linlin¹, CHEN Can¹

1. State Grid Jibei Electric Power Research Institute (North China Electric Power Research Institute Co., Ltd.), Beijing 100045, China;
2. State Grid Chengde Power Supply Company, Chengde 067000, China;
3. State Grid Jibei Electric Power Company, Beijing 100054, China

Received:2020-04-28 Revised:2020-10-25 Published:2021-07-12
Supported by:
This work is supported by Science and Technology Project of SGCC (No.52010619010D)

摘要/Abstract

摘要： 低压接入的分布式光伏容量激增，其出力随机性对调度部门的负荷预测以及电网安全运行带来严峻挑战。提出一种适用于低压接入的分布式光伏功率预测方法，首先基于空间相关性原理为分布式光伏匹配集中式光伏电站作为功率预测参照，通过日发电量数据建立二者的出力关系，推算分布式光伏在运容量；随后利用参照电站的功率预测值对分布式光伏的功率预测进行初步推算；定义分布式光伏容量折算系数，详细推导出该方法的3类误差来源，提出一种基于分时电量比的误差修正策略。以张家口地区分布式光伏为例，验证了修正后的功率预测方法具有较好的预测精度。

关键词: 分布式光伏, 功率预测, 误差分析, 出力特性, 低压配电网

Abstract: With the rapid rise of distributed photovoltaic (PV) generation capacity in the low-voltage distribution network, severe challenges have been brought up to the load forecasting of the dispatching department and the security of the grid operation because of its randomness of output. A practical power prediction method for low-voltage distributed PV is proposed. Firstly, a single concentrated PV station is chosen as reference for the distributed PV based on the spatial correlation principle of adjacent regions. Then the output relationship between distributed PV and reference station is established through existing daily generation data in order to calculate the operating capacity of distributed PV. Next, the predicted power of distributed PV is calculated by the output relationship with the power prediction data of the reference station. Thirdly, based upon the definition of the conversion factor of distributed PV, three kinds of error sources related to the proposed method are deduced. Hence, an error correction scheme for capacity constraint and performance difference between distributed PV and reference station is proposed. Case studies using a group of typical distributed PVs in Zhangjiakou area has verified the effectiveness of the method.

Key words: distributed PV, power prediction, error correction, output characteristics, distribution network

邵尹池, 袁绍军, 孙荣富, 吴林林, 陈璨. 基于空间相关性的分布式光伏实用化功率预测及误差分析[J]. 中国电力, 2021, 54(7): 185-191,207.

SHAO Yinchi, YUAN Shaojun, SUN Rongfu, WU Linlin, CHEN Can. Practical Method and Error Analysis for Distributed Photovoltaic Power Prediction Based on Spatial Correlation[J]. Electric Power, 2021, 54(7): 185-191,207.

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参考文献

[1] 国家能源局. 国家能源局关于进一步落实分布式光伏发电有关政策的通知[A]. 北京, 2014.
[2] 裴哲义, 丁杰, 李晨, 等. 分布式光伏并网问题分析与建议[J]. 中国电力, 2018, 51(10):80-87
PEI Zheyi, DING Jie, LI Chen, et al. Analysis and suggestion for distributed photovoltaic generation[J]. Electric Power, 2018, 51(10):80-87
[3] 贡晓旭, 金强, 王基, 等. 典型功能区分布式光伏接入对配电网建设改造的影响[J]. 中国电力, 2016, 49(3):166-171
GONG Xiaoxu, JIN Qiang, WANG Ji, et al. Effects of distributed PV connected to grid on distribution network constructions in typical functional zones[J]. Electric Power, 2016, 49(3):166-171
[4] 王洪坤, 葛磊蛟, 李宏伟, 等. 分布式光伏发电的特性分析与预测方法综述[J]. 电力建设, 2017, 38(7):1-9
WANG Hongkun, GE Leijiao, LI Hongwei, et al. A review on characteristic analysis and prediction method of distributed PV[J]. Electric Power Construction, 2017, 38(7):1-9
[5] BELHAOUAS N, CHEIKH M, AGATHOKLIS P, et al. PV array power output maximization under partial shading using new shifted PV array arrangements[J]. Applied Energy, 2017, 187:326-337.
[6] 王晶, 黄越辉, 李驰, 等. 考虑空间相关性和天气类型划分的多光伏电站时间序列建模方法[J]. 电网技术, 2020, 44(4):1376-1384
WANG Jing, HUANG Yuehui, LI Chi, et al. Time series modeling method for multi-photovoltaic power stations considering spatial correlation and weather type classification[J]. Power System Technology, 2020, 44(4):1376-1384
[7] 张程熠, 唐雅洁, 李永杰, 等. 适用于小样本的神经网络光伏预测方法[J]. 电力自动化设备, 2017, 37(1):101-106
ZHANG Chengyi, TANG Yajie, LI Yongjie, et al. PV power forecast based on neural network with a small number of samples[J]. Electric Power Automation Equipment, 2017, 37(1):101-106
[8] ZHANG Haixiang, CHENG Lilin, DING Tao, et al. Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network[J]. Generation, Transmission & Distribution, 2018, 12(20):4557-4567.
[9] 甄皓. 有限信息下基于深度学习模型的小型分布式光伏电站功率预测[J]. 上海节能, 2020(4):302-308
ZHEN Hao. Power prediction of small distributed photovoltaic power plant based on deep learning model with limited information[J]. Shanghai Energy Conversation, 2020(4):302-308
[10] 侯伟, 肖健, 牛利勇, 等. 基于灰色理论的光伏发电系统出力预测方法[J]. 电气技术, 2016(4):53-58
HOU Wei, XIAO Jian, NIU Liyong. Analysis of power generation capacity of PV power generation system in electric vehicle charging station[J]. Electrical Engineering, 2016(4):53-58
[11] 龚莺飞, 鲁宗相, 乔颖, 等. 光伏功率预测技术[J]. 电力系统自动化, 2016, 40(4):140-151
GONG Yingfei, LU Zongxiang, QIAO Ying, et al. An overview of photovoltaic energy system output forecasting technology[J]. Automation of Electric Power Systems, 2016, 40(4):140-151
[12] 李伟, 王冰, 陈献慧, 等. 基于气象因子权重相似日的短期光伏功率预测[J]. 广东电力, 2018, 31(4):59-64
LI Wei, WANG Bing, CHENG Xianhui, et al. Prediction on short-term photovoltaic power based on similar day with meteorological factor weight[J]. Guangdong Electric Power, 2018, 31(4):59-64
[13] 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.
AI Yiyang. Very short-term solar irradiance forecast based on sky images[D]. Hangzhou:Zhejiang University, 2018.
[14] 朱想, 居蓉蓉, 程序, 等. 组合数值天气预报与地基云图的光伏超短期功率预测模型[J]. 电力系统自动化, 2015, 39(6):4-10
ZHU Xiang, JU Rongrong, CHENG Xu, et al. Controller architecture design for MMC-HVDC physical simulation system[J]. Automation of Electric Power Systems, 2015, 39(6):4-10
[15] 张家安, 王琨玥, 陈建, 等. 基于空间相关性的分布式光伏出力预测[J]. 电力建设, 2016, 4(2):203-208
ZHANG Jia'an, WANG Kunyue, CHEN Jian, et al. Research on prediction of distributed photovoltaic output considering spatial relevance[J]. Electric Power Construction, 2016, 4(2):203-208
[16] XWEGNON G, GIRARD R, KARINIOTSKIS G. Short-term spatial-temporal forecasting of photovoltaic production[J]. IEEE Transactions on Sustainable Energy, 2018, 9(2):538-546.
[17] 刘博, 科德平, 李鹏, 等. 低压配电台区分布式光伏发电功率辨识方法[J]. 电力系统自动化, 2019, 43(19):111-117
LIU Bo, KE Deping, LI Peng, et al. Identification method of distributed photovoltaic power in low-voltage distribution networks[J]. Automation of Electric Power Systems, 2019, 43(19):111-117
[18] 焦田利, 章坚民, 李雄, 等. 基于空间相关性的大规模分布式用户光伏空间分群方法[J]. 电力系统自动化, 2019, 43(21):97-102
JIAO Tianli, ZHANG Jianmin, LI Xiong, et al. Spatial clustering method for large-scale distribution user photovoltaic based on spatial correlation[J]. Automation of Electric Power Systems, 2019, 43(21):97-102
[19] 严华江, 章坚民, 胡瑛俊, 等. 考虑空间相关性的分布式光伏发电出力预测及误差评价指标研究[J]. 浙江电力, 2020, 39(3):54-60
YAN Huajiang, ZHANG Jianmin, HU Yingjun, et al. Distributed photovoltaic power generation output prediction based on spatial correlation and error evaluation indexes[J]. Zhejiang Electric Power, 2020, 39(3):54-60
[20] AGOUA X G, GIRARD R, KARINIOTAKIS G. Short-term spatio-temporal forecasting of photovoltaic power production[J]. IEEE Transaction on Sustainable Energy, 2017(99):1-11.
[21] 乔颖, 孙荣富, 丁然, 等. 基于数据增强的分布式光伏电站群短期功率预测(一):方法框架与数据增强[J]. 电网技术, 2021, 45(5):1799-1808.
QIAO Ying, SUN Rongfu, DING Ran et al. Distributed photovoltaic station cluster gridding short-term power forecasting part 1:methodology and data augmentation[J]. Power System and Technology,2021,45(5):1799-1808.
[22] 李佳儒. 光伏发电输出功率的预测方法研究[D]. 沈阳:沈阳农业大学, 2018.
LI Jiaru. Methods and research on forecasting output power of photovoltaic power generation[D]. Shenyang:Shenyang Agricultural University, 2018.
[23] 吕斌斌, 方勤斌, 王国帮, 等. 分布式家庭屋顶光伏异常数据识别与修复方法[J]. 电气传动自动化, 2018, 40(2):1-5, 20
LYU Binbin, FANG Qinbin, WANG Guobang, et al. Method for identification and restoration of abnormal power data in distributed household rooftop photovoltaic device[J]. Electric Drive Automation, 2018, 40(2):1-5, 20
[24] 黄通. 含随机功率修正的时间序列回归光伏功率预测方法研究[D]. 南京:南京航空航天大学, 2017.
HUANG Tong. Study on PV power prediction method based on multiple linear regression with time-series and stochastic error correction[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2017.
[25] 郭辉, 杨国清, 姚李孝, 等. 基于综合相似日和功率相关性的光伏电站预测功率修正[J]. 电网与清洁能源, 2018, 34(9):52-58
GUO Hui, YANG Guoqing, YAO Lixiao, et al. Correction of predictive power of PV plants based on integrated similar days and power correlations[J]. Power System and Clean Energy, 2018, 34(9):52-58

基于空间相关性的分布式光伏实用化功率预测及误差分析

Practical Method and Error Analysis for Distributed Photovoltaic Power Prediction Based on Spatial Correlation

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基于空间相关性的分布式光伏实用化功率预测及误差分析

Practical Method and Error Analysis for Distributed Photovoltaic Power Prediction Based on Spatial Correlation

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