基于实测数据的阿克苏光伏电站相关性分析
Relevance Analysis of Aksu Photovoltaic Power Station Based on Measured Data
DOI: 10.12677/SG.2019.91001, PDF,   
作者: 钱 勇, 薛安成, 李业成, 杨可文：新能源电力系统国家重点实验室(华北电力大学)，北京
关键词: 相关性；光伏电站；实测数据；云量因子；地理位置；Relevance； Photovoltaic Power Station； Measured Data； Cloud Cover Factor； Geographical Position
摘要: 光伏发电的不确定性、间歇性和波动性特征，会造成光伏发电弃光问题，掌握其特性是提高消纳的重要基础。本文基于实测数据分析光伏电站相关性。首先，给出了同一光伏电站不同时段出力的相关性和同一时段不同光伏电站出力的相关性计算方法；其次，从理论上分析云量因子和地理位置对于光伏电站出力相关性的影响；最后，以新疆阿克苏光伏电站为例，利用实测数据说明了云量因子和地理位置对同一光伏电站出力相关性的影响，以及不同光伏电站出力相关性的影响。
Abstract: Uncertainty, intermittence and fluctuation of photovoltaic power generation will cause photovoltaic power generation to abandon light, and mastering its characteristics is an important basis for improving absorption. In this paper, the correlation of photovoltaic power plants is analyzed based on measured data. Firstly, the calculation method of the correlation between the output of the same photovoltaic power station at different time periods and the output of the same photovoltaic power station at different time periods is given. Secondly, the influence of cloud factor and geographic location on the output correlation of photovoltaic power station is analyzed theoretically. Finally, taking Aksu photovoltaic power station in Xinjiang as an example, the influence of cloud factor and geographic location on the output correlation of the same photovoltaic power station and the output correlation of different photovoltaic power stations are illustrated by the measured data.
文章引用：钱勇, 薛安成, 李业成, 杨可文. 基于实测数据的阿克苏光伏电站相关性分析[J]. 智能电网, 2019, 9(1): 1-10. https://doi.org/10.12677/SG.2019.91001

参考文献

[1] Woyte, A., Van, V., Belmans, R., et al. (2006) Voltage Fluctuations on Distribution Level Introduced by Photovoltaic Systems. IEEE Transactions on Energy Conversion, 21, 202-209.
[Google Scholar] [CrossRef] 
[2] 张程飞, 刘纯, 王跃峰, 等. 基于模糊多目标优化的电网日前风电接纳能力评估模型[J]. 电网技术, 2015, 39(2): 426-431.
[3] 田建芳, 毛亚珊, 翟桥柱, 等. 基于风电消纳能力评估的安全约束经济调度方法[J]. 电网技术, 2015, 39(9): 2398-2403.
[4] Perez, R., Kivalov, S., Schlemmer, J., et al. (2012) Short-Term Irradiance Variability: Preliminary Estimation of Station Pair Correlation as a Function of Distance. Solar Energy, 86, 2170-2176.
[Google Scholar] [CrossRef] 
[5] Hollands, K.G.T. and Huget, R.G. (1990) A Probability Densitity Function for the Clearness Index, with Applications. Solar Energy, 30, 195-209.
[6] Graham, V.A. and Hollands, K.G.T. (1990) A Method to Generate Synthetic Hourly Solar Radion Globally. Solar Energy, 44, 333-341.
[7] 姚荃. 考虑不确定因素的光伏发电出力预测[D]: [硕士学位论文]. 北京: 华北电力大学, 2012.
[8] 张曦, 康重庆, 张宁, 黄越辉, 刘纯, 徐健飞. 太阳能光伏发电的中长期随机特性分析[J]. 电力系统自动化, 2014, 38(6): 6-13.
[9] 张慧玲, 喻洁, 韩红卫, 冯其芝. 间歇性新能源发电相关性分析[J]. 电气自动化, 2015, 37(5): 30-34.
[10] 汪海瑛, 白晓民. 并网光伏的短期运行备用评估[J]. 电力系统自动化, 2013, 37(5): 55-60.
[11] 李芝娟. 基于实测数据的风电场群功率特性研究[D]: [硕士学位论文]. 北京: 华北电力大学, 2015.
[12] 崔杨, 李焕奇, 张节潭, 严干贵, 穆钢, 王茂春. 计及功率相关性的光伏电站群置信容量评估方法[J]. 太阳能学报, 2017, 38(4): 1039-1044.

为你推荐