Upgrade of the PV Power Prediction System and Implementation of the Key Technologies

doi:10.11930/j.issn.1004-9649.2014.10.142.5

Electric Power ›› 2014, Vol. 47 ›› Issue (10): 142-147.DOI: 10.11930/j.issn.1004-9649.2014.10.142.5

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Upgrade of the PV Power Prediction System and Implementation of the Key Technologies

CUI Yang^{1, 2}, CHEN Zhen-hong^{1, 2}, CHEN Chi^{1, 2}, TANG Jun^{1, 2}, GU Chun³

1. Hubei Meteorological Service Center, Wuhan 430070, China;
2. Meteorological Energy Development Center of Hubei Province,Wuhan 430074, China;
3. Wuhan Science and Technology Co., Ltd. Ding Sheng, Wuhan 430077, China

Received:2014-06-19 Online:2014-10-18 Published:2015-12-10
Supported by:
This work is supported by China Special Fund for Meteorological Research in the Public Interest(GYHY201006036, GYHY201306048); Special Found of Small Business in CMA(Letter No.3[2014] of CMA); Special Found for Science and Technology Development in Hubei Meteorological Bureau (2013Q06)

Abstract

Abstract: The “Solar Power Generation Forecasting System V2.0” was developed in early 2012. As the national energy industry standard- the “Functional Specification for Photovoltaic Power Prediction System” (2014) is to be issued soon, it's necessary to upgrade this system to further perfect its function and improve its suitability. The upgrade mainly consists the following respects. Firstly, the B/S technical architecture is added to achieve the network publishing of forecasting products through Silverlight 4.0. Secondly, the combined forecast method is included in the upgrade to integrate various forecasting methods as to improve the forecast accuracy. Thirdly, the Google Earth will be incorporated to show the geographic information of plant which can enhance the display performance of the system. Finally, the upgrade strengthens the standardized management of data and divides the system by power generation units. The upgraded system has been applied to several PV power plants and it will help to effectively schedule the photovoltaic power generation and improve the power generation efficiency.

Key words: photovoltaic power generation, power prediction system, Silverlight 4.0, combined forecast method, Google Earth

CLC Number:

TM615

CUI Yang, CHEN Zhen-hong, CHEN Chi, TANG Jun, GU Chun. Upgrade of the PV Power Prediction System and Implementation of the Key Technologies[J]. Electric Power, 2014, 47(10): 142-147.

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References

[1] 中国电力网. 2013年光伏十大新闻[EB/OL].（2014-01-08）[2014-06-01]. http://www.chinapower.com.cn/newsarticle/1202/new1202414.asp
[2] 张伯泉,杨宜民. 风力和太阳能光伏发电现状及发展趋势[J].中国电力,2006,39（6）：65-69.
ZHANG Bo-quan, YANG Yi-min. Status and trend of wind/photovoltaic power development [J]. Electric Power, 2006, 39(6):65-69.
[3] 张岚,张艳霞,郭嫦敏,等.基于神经网络的光伏系统发电功率预测[J]. 中国电力,2010,43（9）：75-78.
ZHANG Lan, ZHANG Yan-xia, GUO Chang-min, et al .Photovoltaic system power forecasting based on neutral networks[J]. Electric Power, 2010, 43(9): 75-78.
[4] LORENZ E, HURKA J, HEINEMANN D, et al . Irradiance forecasting for the power prediction of grid-connected photovoltaic systems[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2009, 2(1): 2-10.
[5] 丁宇宇,丁杰,周海,等. 基于全天空成像仪的光伏电站水平面总辐射预报[J]. 中国电机工程学报,2014,34（1）：50-56.
DING Yu-yu, DING Jie, ZHOU Hai, et al . Forecasting of global horizontal irradiance in photovoltaic power stations based on the total sky imager [J]. Proceedings of the CSEE, 2014, 34(1): 50-56.
[6] 代倩,段善旭,蔡涛,等. 基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J]. 中国电机工程学报,2011,31（34）：28-35.
DAI Qian, DUAN Shan-xu, CAI Tao, et al . Short-term PV generation system forecasting model without irradiation based on weather type clustering [J]. Proceedings of the CSEE, 2011, 31(34): 28-35.
[7] 陈正洪,李芬,成驰,等. 太阳能光伏发电预报技术原理及其业务系统[M]. 北京：气象出版社,2011.
[8] BATES J M, GRANGER C W J. The combination of forecasts. Operational Research Quarterly, 1969, 20(4): 451-468.
[9] MARINO L, ADOLFO L T, MIGUEL A. Perez-Toledano, et al . Providing RIA user interfaces with accessibility properties[J]. Journal of Symbolic Computation, 2011, 46(2): 207-217.
[10] 陈强,姜立新,帅向华,等. Google Earth在地震应急中的应用[J]. 地震,2008,28（1）：121-128.
CHEN Qiang, JIANG Li-xin, SHUAI Xiang-hua, et al . The application of google earth in earthquake emergency[J].Earthquake, 2008, 28(1): 121-128.
[11] 何明琼,成驰,陈正洪,等. 太阳能光伏发电预报效果评价[J].水电能源科学,2011,29（12）：196-199.
HE Ming-qiong, CHENG Chi, CHEN Zheng-hong, et al .Prediction effect assessment of solar PV power generation[J]. Water Resources and Power, 2011, 29(12): 196-199.

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Upgrade of the PV Power Prediction System and Implementation of the Key Technologies

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