考虑季节因素的光伏出力主-噪成分概率特性分析及场景构建方法

马昊天; 刘科研; 盛万兴; 何开元

doi:10.19912/j.0254-0096.tynxb.2023-1333

PDF(8771 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 154-164. DOI: 10.19912/j.0254-0096.tynxb.2023-1333

考虑季节因素的光伏出力主-噪成分概率特性分析及场景构建方法

马昊天, 刘科研, 盛万兴, 何开元

作者信息 +

PROBABILITY CHARACTERISTIC ANALYSIS AND SCENARIO MODELING METHOD FOR MAIN-NOISE COMPONENTS OF PHOTOVOLTAIC POWER OUTPUT CONSIDERING SEASONAL FACTORS

Ma Haotian, Liu Keyan, Sheng Wanxing, He Kaiyuan

Author information +

文章历史 +

摘要

提出一种考虑季节因素的光伏电站出力多场景模拟生成方法,通过对不同季节下的采集数据在基础分量（主成分）和随机分量（噪声成分）两方面分别进行分析与构建,从而实现光伏电站输出功率在不同季节下的有效建模。首先采取基于Beta分布的概率化模型实现光伏出力基础分量的最优模拟,并给出不同季节下的Beta参数区间估计方法;其次,利用有色噪声模型来模拟光伏出力随机分量的不确定特征,针对不同季节下光伏出力具备不同的波动强度,给出随机分量噪声参数构建方法;之后,利用所给出的基础分量和随机分量联合模拟不同季节下的光伏电站出力分布,完成光伏季节场景的构建;最后,基于中国东南某地区光伏电站实测数据进行案例分析,验证所提方法的适用性。

Abstract

The proportion of photovoltaic power generation systems connected to the transmission and distribution grid has increased year by year. Because the output of photovoltaic power stations in different seasons usually has a large difference in output characteristics, the traditional universal photovoltaic output simulation method is difficult to cope with the requirements of simulation scenarios under various conditions. Addressing this challenge, the present study introduces a multi-scenario simulation approach for accurately modeling the seasonal power outputs of photovoltaic plants. This approach involves a detailed analysis of data gathered across different seasons, deconstructing it into fundamental（principal） components and stochastic (noise） elements. Thus, the output power of photovoltaic power stations can be effectively modeled in different seasons. In our proposed framework, firstly, a probabilistic model based on Beta distribution is adopted to simulate the optimal base component of PV output, and the Beta parameter interval estimation method under different seasons is given. Secondly, the colored noise model is used to simulate the uncertainty characteristics of the random component of photovoltaic power output. According to the different fluctuation intensity of photovoltaic power output in different seasons, the construction method of random component noise parameters is given. Then, the base component and random component are used to simulate the output distribution of photovoltaic power stations in different seasons, and the construction of photovoltaic seasonal scenarios is completed. Finally, a case study based on the measured data of a photovoltaic power station in southeast China is carried out to verify the applicability of the proposed method.

导出引用

马昊天, 刘科研, 盛万兴, 何开元. 考虑季节因素的光伏出力主-噪成分概率特性分析及场景构建方法[J]. 太阳能学报. 2024, 45(12): 154-164 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1333

Ma Haotian, Liu Keyan, Sheng Wanxing, He Kaiyuan. PROBABILITY CHARACTERISTIC ANALYSIS AND SCENARIO MODELING METHOD FOR MAIN-NOISE COMPONENTS OF PHOTOVOLTAIC POWER OUTPUT CONSIDERING SEASONAL FACTORS[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 154-164 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1333

中图分类号： TM615

参考文献

[1] THERISTIS M, LIVERA A, JONES C B, et al.Nonlinear photovoltaic degradation rates: modeling and comparison against conventional methods[J]. IEEE journal of photovoltaics, 2020, 10(4): 1112-1118.
[2] 刘科研, 盛万兴, 马晓晨, 等. 基于多种群遗传算法的分布式光伏接入配电网规划研究[J]. 太阳能学报, 2021, 42(6): 146-155.
LIU K Y, SHENG W X, MA X C, et al.Planning research of distributed photovoltaic source access distribution network based on multi-population genetic algorithm[J]. Acta energiae solaris sinica, 2021, 42(6): 146-155.
[3] 黄雨薇, 彭道刚, 姚峻, 等. 基于SSA和K均值的TD-BP神经网络超短期光伏功率预测[J]. 太阳能学报, 2021, 42(4): 229-238.
HUANG Y W, PENG D G, YAO J, et al.Ultra-short-term photovoltaic power forecast of TD-BP neural network based on SSA and K-means[J]. Acta energiae solaris sinica, 2021, 42(4): 229-238.
[4] MIKOFSKI M A, HOLMGREN W F, NEWMILLER J, et al.Effects of solar resource sampling rate and averaging interval on hourly modeling errors[J]. IEEE journal of photovoltaics, 2023, 13(2): 202-207.
[5] 刘洁, 林舜江, 梁炜焜, 等. 基于高阶马尔可夫链和高斯混合模型的光伏出力短期概率预测[J]. 电网技术, 2023, 47(1): 266-275.
LIU J, LIN S J, LIANG W K, et al.Short-term probabilistic forecast for power output of photovoltaic station based on high order Markov chain and Gaussian mixture model[J]. Power system technology, 2023, 47(1): 266-275.
[6] 刘科研, 盛万兴, 秦梦雅, 等. 计及多种扰动源的有源配电网电能质量分析方法[J]. 电网技术, 2020, 44(6): 2303-2309.
LIU K Y, SHENG W X, QIN M Y, et al.Power quality analysis for active distribution network considering multiple disturbance sources[J]. Power system technology, 2020, 44(6): 2303-2309.
[7] 郭一飞, 高厚磊, 田佳. 引入聚类分析的光伏出力建模及其在可靠性评估中的应用[J]. 电力系统自动化, 2016, 40(23): 93-100.
GUO Y F, GAO H L, TIAN J.Photovoltaic output modeling by introducing clustering analysis and its application in reliability evaluation[J]. Automation of electric power systems, 2016, 40(23): 93-100.
[8] 姬广龙, 袁越, 范绚然, 等. 基于Cornish-Fisher展开的配电网电压越限风险评估[J]. 太阳能学报, 2020, 41(1): 358-366.
JI G L, YUAN Y, FAN X R, et al.Risk assessment of voltage limit violation on distribution network based on Cornish-Fisher expansion[J]. Acta energiae solaris sinica, 2020, 41(1): 358-366.
[9] LI S W, LI Q, QI W H, et al.Two variable-weather-parameter models and linear equivalent models expressed by them for photovoltaic cell[J]. IEEE access, 2020, 8: 184885-184900.
[10] 汪惟源, 窦飞, 程锦闽, 等. 一种风光联合出力概率模型建模方法[J]. 电力系统保护与控制, 2020, 48(10): 22-29.
WANG W Y, DOU F, CHENG J M, et al.A modeling method for a wind and photovoltaic joint power probability model[J]. Power system protection and control, 2020, 48(10): 22-29.
[11] RAMAKRISHNA R, SCAGLIONE A, VITTAL V, et al.A model for joint probabilistic forecast of solar photovoltaic power and outdoor temperature[J]. IEEE transactions on signal processing, 2019, 67(24): 6368-6383.
[12] 高文森, 樊艳芳, 王一波, 等. 串联直流升压型光伏电站经VSC-HVDC并网控制策略研究[J]. 太阳能学报, 2019, 40(1): 87-95.
GAO W S, FAN Y F, WANG Y B, et al.Integration control strategy of series dc step-up type photovoltaic power plant via VSC-HVDC[J]. Acta energiae solaris sinica, 2019, 40(1): 87-95.
[13] JIN J X, YE L, LI J C, et al.Wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain[J]. CSEE journal of power and energy systems, 2022, 8(3): 757-768.
[14] AGOUA X G, GIRARD R, KARINIOTAKIS G.Probabilistic models for spatio-temporal photovoltaic power forecasting[J]. IEEE transactions on sustainable energy, 2019, 10(2): 780-789.
[15] MAHMOUD Y, EL-SAADANY E F. A photovoltaic model with reduced computational time[J]. IEEE transactions on industrial electronics, 2015, 62(6): 3534-3544.
[16] WU W, WANG K Y, HAN B, et al.A versatile probability model of photovoltaic generation using pair copula construction[J]. IEEE transactions on sustainable energy, 2015, 6(4): 1337-1345.
[17] REN Z Y, YAN W, ZHAO X, et al.Chronological probability model of photovoltaic generation[J]. IEEE transactions on power systems, 2014, 29(3): 1077-1088.
[18] TABONE M D, CALLAWAY D S.Modeling variability and uncertainty of photovoltaic generation: a hidden state spatial statistical approach[J]. IEEE transactions on power systems, 2015, 30(6): 2965-2973.
[19] 廖文龙, 杨德昌, 葛磊蛟, 等. 基于像素卷积生成网络的可再生能源场景生成[J]. 高电压技术, 2022, 48(4): 1320-1331.
LIAO W L, YANG D C, GE L J, et al.Renewable energy scenario generation based on pixel convolutional generative networks[J]. High voltage engineering, 2022, 48(4): 1320-1331.
[20] 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 J, HUANG Y H, LI C, 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.
[21] 朱瑞金, 廖文龙, 王玥珑, 等. 基于生成矩匹配网络的光伏和风电随机场景生成[J]. 高电压技术, 2022, 48(1): 374-385.
ZHU R J, LIAO W L, WANG Y L, et al.Stochastic scenarios generation for wind power and photovoltaic system based on generative moment matching network[J]. High voltage engineering, 2022, 48(1): 374-385.
[22] 张文强, 陈静, 杨伟, 等. 基于VAE-CGAN的光伏不确定性建模方法[J]. 电网技术, 2021, 45(4): 1273-1280.
ZHANG W Q, CHEN J, YANG W, et al.Photovoltaic uncertainty modeling based on VAE-CGAN[J]. Power system technology, 2021, 45(4): 1273-1280.
[23] 杨宏, 苑津莎, 张铁峰. 一种基于Beta分布的风电功率预测误差最小概率区间的模型和算法[J]. 中国电机工程学报, 2015, 35(9): 2135-2142.
YANG H, YUAN J S, ZHANG T F.A model and algorithm for minimum probability interval of wind power forecast errors based on Beta distribution[J]. Proceedings of the CSEE, 2015, 35(9): 2135-2142.