SCENE GENERATION METHOD CONSIDERING DYNAMIC CORRELATION OF WIND AND PHOTOVOLTAIC OUTPUTS

Gao Fan; Bao Daorina; Di Yanqiang; Zhang Shaohua

doi:10.19912/j.0254-0096.tynxb.2023-1873

PDF(5921 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (8) : 256-264. DOI: 10.19912/j.0254-0096.tynxb.2023-1873

SCENE GENERATION METHOD CONSIDERING DYNAMIC CORRELATION OF WIND AND PHOTOVOLTAIC OUTPUTS

Gao Fan¹, Bao Daorina¹, Di Yanqiang², Zhang Shaohua³

Author information +

History +

Abstract

A hybrid Copula function combined with multivariate normal distribution is proposed to generate scenarios of wind and photovoltaic powers in response to the uncertainty. Based on the historical data of wind power and photovoltaic power, the joint distribution model of wind and PV is established, and the joint distribution sequence of wind and PV outputs with dynamic correlation is further generated by the multivariate normal distribution and covariance matrix. The random distribution number of wind and photovoltaic power scenarios are generated by the Markov Chain Monte Carlo Sampling method, and the corresponding wind and PV outputs scenarios are obtained by the inverse transformation method; the output scenarios generated by the different methods are compared, and the fitting effect and coupling effect of the wind power and photovoltaic power scenarios under dynamic correlation are analyzed. The results verify the validity of the proposed method, and the output scenarios can better portray the uncertainty of single output and coupled output of wind and PV power system.

Key words

wind and PV outputs / hybrid Copula function / multivariate normal distribution / dynamic correlation / scene generation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Gao Fan, Bao Daorina, Di Yanqiang, Zhang Shaohua. SCENE GENERATION METHOD CONSIDERING DYNAMIC CORRELATION OF WIND AND PHOTOVOLTAIC OUTPUTS[J]. Acta Energiae Solaris Sinica. 2024, 45(8): 256-264 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1873

References

[1] 申建建, 王月, 程春田, 等. 水风光多能互补发电调度问题研究现状及展望[J]. 中国电机工程学报, 2022, 42(11): 3871-3885.
SHEN J J, WANG Y, CHENG C T, et al.Research status and prospect of generation scheduling for hydropower-wind-solar energy complementary system[J]. Proceedings of the CSEE, 2022, 42(11): 3871-3885.
[2] 万家豪, 苏浩, 冯冬涵, 等. 计及源荷匹配的风光互补特性分析与评价[J]. 电网技术, 2020, 44(9): 3219-3226.
WAN J H, SU H, FENG D H, et al.Analysis and evaluation of the complementarity characteristics of wind and photovoltaic considering source-load matching[J]. Power system technology, 2020, 44(9): 3219-3226.
[3] ZHAO Y, LIU Q Y, KUANG J W, et al.Modeling multivariate dependence by nonparametric pair-copula construction in composite system reliability evaluation[J]. International journal of electrical power and energy systems, 2021, 124: 106373.
[4] HAN Q, WANG T Y, CHU F L.Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China[J]. Renewable and sustainable energy reviews, 2022, 161: 112319.
[5] 马喜平, 何世恩, 姚寅, 等. 计及风速不确定性及相关性的风电场分区虚拟惯量估计[J]. 电力系统保护与控制, 2022, 50(10): 123-131.
MA X P, HE S E, YAO Y, et al.Virtual inertia estimation of wind farm zones with wind speed uncertainty and correlation[J]. Power system protection and control, 2022, 50(10): 123-131.
[6] RAMÍREZ A F, VALENCIA C F, CABRALES S, et al. Simulation of photo-voltaic power generation using copula autoregressive models for solar irradiance and air temperature time series[J]. Renewable energy, 2021, 175: 44-67.
[7] LI Q S, ZHANG Y, CHENG F, et al.Generation expansion planning for Guizhou Province based on the complementary characteristics of wind and solar[J]. Energy reports, 2022, 8: 574-584.
[8] 黄宇, 张冰哲, 庞慧珍, 等. 基于混合Copula优化算法的风速预测方法研究[J]. 太阳能学报, 2022, 43(10): 192-201.
HUANG Y, ZHANG B Z, PANG H Z, et al.Research on wind speed forecasting method based on hybrid Copula optimization algorithm[J]. Acta energiae solaris sinica, 2022, 43(10): 192-201.
[9] MORF H.A validation frame for deterministic solar irradiance forecasts[J]. Renewable energy, 2021, 180: 1210-1221.
[10] ARRIETA-PRIETO M, SCHELL K R.Spatio-temporal probabilistic forecasting of wind power for multiple farms: a Copula-based hybrid model[J]. International journal of forecasting, 2022, 38(1): 300-320.
[11] 许彪, 徐青山, 黄煜, 等. 基于藤Copula分位数回归的光伏功率日前概率预测[J]. 电网技术, 2021, 45(11): 4426-4435.
XU B, XU Q S, HUANG Y, et al.Day-ahead probabilistic forecasting of photovoltaic power based on vine Copula quantile regression[J]. Power system technology, 2021, 45(11): 4426-4435.
[12] HAN S, QIAO Y H, YAN J, et al.Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network[J]. Applied energy, 2019, 239: 181-191.
[13] LI J H, ZHOU J S, CHEN B.Review of wind power scenario generation methods for optimal operation of renewable energy systems[J]. Applied energy, 2020, 280: 115992.
[14] 赵书强, 金天然, 李志伟, 等. 考虑时空相关性的多风电场出力场景生成方法[J]. 电网技术, 2019, 43(11): 3997-4004.
ZHAO S Q, JIN T R, LI Z W, et al.Wind power scenario generation for multiple wind farms considering temporal and spatial correlations[J]. Power system technology, 2019, 43(11): 3997-4004.
[15] 丁明, 宋晓皖, 孙磊, 等. 考虑时空相关性的多风电场出力场景生成与评价方法[J]. 电力自动化设备, 2019, 39(10): 39-47.
DING M, SONG X W, SUN L, et al.Scenario generation and evaluation method of multiple wind farms output considering spatial-temporal correlation[J]. Electric power automation equipment, 2019, 39(10): 39-47.
[16] 曲凯, 李湃, 黄越辉, 等. 面向新能源消纳能力评估的年负荷序列建模及场景生成方法[J]. 电力系统自动化, 2021, 45(1): 123-131.
QU K, LI P, HUANG Y H, et al.Modeling and scenario generation method of annual load series for evaluation of renewable energy accommodation capacity[J]. Automation of electric power systems, 2021, 45(1): 123-131.
[17] 初壮, 孙健浩, 赵蕾, 等. 考虑风光与负荷时序特性的主动配电网分布式电源优化配置[J]. 电力建设, 2022, 43(11): 53-62.
CHU Z, SUN J H, ZHAO L, et al.Optimal configuration of distributed power generation in active distribution network considering the characteristics of wind power and load time series[J]. Electric power construction, 2022, 43(11): 53-62.
[18] 樊晓伟, 王瑞妙, 朱小军, 等. 考虑风光荷时序波动特性的配电网投资策略多目标优化[J]. 可再生能源, 2023, 41(2): 268-276.
FAN X W, WANG R M, ZHU X J, et al.Multi-objective optimization of distribution network investment strategy considering the time series fluctuation of wind power generation, photovoltaic power generation and power load[J]. Renewable energy resources, 2023, 41(2): 268-276.
[19] 谢中华. MATLAB统计分析与应用: 40个案例分析[M]. 北京: 北京航空航天大学出版社, 2010: 191-192.
XIE Z H.Statistical analysis and application of MATLAB: analysis of 40 cases[M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2010: 191-192.
[20] 卢锦玲, 於慧敏. 基于混合Copula的风光功率相关结构分析[J]. 太阳能学报, 2017, 38(11): 3188-3194.
LU J L, YU H M.Dependence structure analysis of wind and PV power based on hybrid Copula[J]. Acta energiae solaris sinica, 2017, 38(11): 3188-3194.
[21] 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.
[22] WAHBAH M, MOHANDES B, EL-FOULY T H M, et al. Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling[J]. Energy conversion and management, 2022, 266: 115811.
[23] XU L J, WANG Z W, LIU Y F.The spatial and temporal variation features of wind-Sun complementarity in China[J]. Energy conversion and management, 2017, 154: 138-148.
[24] 要金铭, 赵书强, 韦子瑜, 等. 基于场景分析的电力系统日前调度及其快速求解方法[J]. 电力自动化设备, 2022, 42(9): 102-110.
YAO J M, ZHAO S Q, WEI Z Y, et al.Day-ahead dispatch and its fast solution method of power system based on scenario analysis[J]. Electric power automation equipment, 2022, 42(9): 102-110.
[25] 张辰毓, 许刚. 分布式多元随机动态场景生成及快速精准场景降维算法[J]. 电网技术, 2022, 46(2): 671-681.
ZHANG C Y, XU G.Distributed multivariate random dynamic scenario generation and fast & accurate scenario simplified algorithm[J]. Power system technology, 2022, 46(2): 671-681.
[26] PINSON P, GIRARD R.Evaluating the quality of scenarios of short-term wind power generation[J]. Applied energy, 2012, 96: 12-20.