持续功率曲线能反映长时间波动特性规律,通过研究已建设光伏集群持续功率曲线,建立预测模型揭示不同规模集群的汇聚演化规律,最终得到待建光伏集群的持续功率曲线。首先,利用层次聚类算法确定光伏集群汇聚规模的分层顺序,得到装机容量逐层递增的光伏集群,并提出汇聚效应指标验证顺序的有效性;其次,为了更好地判断和预测光伏持续功率曲线的变化趋势,对持续功率曲线进行出力场景划分;最后,为避免单一模型预测偏差,在各出力场景下,通过改进的信息熵组合预测模型掌握汇聚过程中规模演变规律,完成规划待建设集群持续功率曲线的预测。利用河北某地区实测数据仿真结果表明：验证聚类方法得到的集群分层顺序更能体现汇聚效应,并有效提高预测精度;出力场景划分准确刻画集群持续功率曲线汇聚趋势;通过模型对比表明分场景下改进信息熵组合预测模型更能精确完成待建光伏集群持续功率特性的量化分析。
Abstract
The continuous power curve can reflect the law of long-term fluctuation characteristics. By studying the known continuous power curve of photovoltaic clusters, a prediction model is established to reveal the convergence evolution law of clusters of different scales, and finally, the continuous power curve of the photovoltaic cluster to be built is obtained. Firstly, the hierarchical clustering algorithm is used to determine the hierarchical order of the aggregation scale of photovoltaic clusters, and the photovoltaic clusters with the installed capacity increasing layer by layer are obtained, and propose aggregation effect indicators to verify the effectiveness of the sequence. Secondly, in order to better predict the change trend of the photovoltaic continuous power curve, and divide the output scene of the continuous power curve. Finally, in order to avoid the prediction deviation of a single model, in each output scene, the improved information entropy combination prediction model is used to grasp the scale evolution law in the aggregation process and complete the prediction of the continuous power curve of the cluster to be built. The simulation results using the measured data in a certain area in Hebei show that the cluster hierarchical order obtained by verifying the clustering method can better reflect the convergence effect and effectively improve the prediction accuracy; the output scene division accurately describes the convergence trend of the continuous power curve of the cluster; and the improved information entropy combination prediction model can more accurately complete the quantitative analysis of the continuous power characteristics of the photovoltaic cluster to be built.
关键词
光伏发电 /
集群层级划分 /
汇聚效应 /
持续功率曲线 /
组合预测
Key words
PV power /
hierarchical clustering /
smoothing effect /
duration curve /
combined prediction
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] YU S W, ZHENG Y L, LI L X.A comprehensive evaluation of the development and utilization of China’s regional renewable energy[J]. Energy policy, 2019, 127: 73-86.
[2] BROŻYNA J, STRIELKOWSKI W, FOMINA A, et al. Renewable energy and EU 2020 target for energy efficiency in the Czech republic and Slovakia[J]. Energies, 2020, 13(4): 965.
[3] ZHANG W D, LIU Z M.Simulation and analysis of the power output fluctuation of photovoltaic modules based on NREL one-minute irradiance data[C]//2013 International Conference on Materials for Renewable Energy and Environment. Chengdu, China, 2013: 21-25.
[4] MILLS A,AHLSTROM M,BROWER M.Understanding variability and uncertainty of photovoltaics for integration with the electric power system[R]. DE20121048297, 2011.
[5] 吴振威, 蒋小平, 马会萌, 等. 多时间尺度的光伏出力波动特性研究[J]. 现代电力, 2014, 31(1): 58-61.
WU Z W, JIANG X P, MA H M, et al.Study on fluctuations characteristics of photovoltaic power output in different time scales[J]. Modern electric power, 2014, 31(1): 58-61.
[6] 陈逍潇, 张粒子, 杨萌, 等. 考虑光伏发电功率波动性的AGC备用容量分析方法[J]. 电力系统自动化, 2015, 39(22): 16-21, 52.
CHEN X X, ZHANG L Z, YANG M, et al.A method for AGC reserve capacity analysis considering photovoltaic power fluctuation characteristics[J]. Automation of electric power systems, 2015, 39(22): 16-21, 52.
[7] 崔杨, 穆钢, 刘玉, 等. 风电功率波动的时空分布特性[J]. 电网技术, 2011, 35(2): 110-114.
CUI Y, MU G, LIU Y, et al.Spatiotemporal distribution characteristic of wind power fluctuation[J]. Power system technology, 2011, 35(2): 110-114.
[8] 尹佳楠, 葛延峰, 高凯. 风电场群出力的汇聚效应分析[J]. 电测与仪表, 2015, 52(5): 104-108.
YIN J N, GE Y F, GAO K.Analysis on clustering effect of wind generations[J]. Electrical measurement & instrumentation, 2015, 52(5): 104-108.
[9] 刘燕华, 田茹, 张东英, 等. 风电出力平滑效应的分析与应用[J]. 电网技术, 2013, 37(4): 987-991.
LIU Y H, TIAN R, ZHANG D Y, et al.Analysis and application of wind farm output smoothing effect[J]. Power system technology, 2013, 37(4): 987-991.
[10] 穆钢, 杨修宇, 严干贵, 等. 基于风电场群汇聚演变趋势的场群持续功率特性预测方法[J]. 中国电机工程学报, 2018, 38(增刊1): 32-38.
MU G, YANG X Y, YAN G G, et al.Prediction method of the durative characteristic for wind farm cluster based on cumulative evolution tendency[J]. Proceedings of the CSEE, 2018, 38(S1): 32-38.
[11] 崔杨, 李焕奇, 严干贵, 等. 计及汇聚特性的光伏电站群集中外送输电容量优化配置方法[J]. 电网技术, 2015, 39(12): 3491-3496.
CUI Y, LI H Q, YAN G G, et al.An optimization method to determine integrated power transmission capacity of clustering photovoltaic plants based on clustering effect[J]. Power system technology, 2015, 39(12): 3491-3496.
[12] 崔杨, 曲钰, 仲悟之, 等. 基于改进Shapley值的风电汇聚趋势性分状态量化方法[J]. 电网技术, 2019, 43(6): 2094-2102.
CUI Y, QU Y, ZHONG W Z, et al.Research on sub-state quantization method of wind convergence trend based on improved Shapley value[J]. Power system technology, 2019, 43(6): 2094-2102.
[13] 穆钢, 崔杨, 严干贵. 确定风电场群功率汇聚外送输电容量的静态综合优化方法[J]. 中国电机工程学报, 2011, 31(1): 15-19.
MU G, CUI Y, YAN G G.A static optimization method to determine integrated power transmission capacity of clustering wind farms[J]. Proceedings of the CSEE, 2011, 31(1): 15-19.
[14] 姚宏民, 杜欣慧, 秦文萍. 基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法[J]. 太阳能学报, 2020, 41(9): 184-190.
YAO H M, DU X H, QIN W P.PV power forecasting approach based on density peaks clustering and general regression neural network[J]. Acta energiae solaris sinica, 2020, 41(9): 184-190.
基金
国网浙江省电力有限公司科技项目（5211DS220009）
PDF(2517 KB)
