考虑出力相关性的海上风电接入系统固定成本分摊方法A fixed cost allocation method for offshore wind power integration systems considering output power correlation
刘智彬,苏本庆
LIU Zhibin,SU Benqing
摘要(Abstract):
在海上风电规模化开发、集群化并网的背景下,如何回收海上风电接入系统的输电成本,公平合理地向各风电场分摊已成为目前海上风电行业亟待解决的问题。针对该问题,提出了一种考虑风电出力相关性的海上风电接入系统固定成本分摊方法。首先,利用藤Copula函数构建多个海上风电场的出力相关性模型,同时假定系统负荷需求模型服从正态分布,发电机服从0-1概率分布;然后,结合蒙特卡洛抽样和顺流概率潮流追踪法计算各海上风电场接入系统的固定成本分摊费用;最后,通过算例仿真分析,验证了所提方法的合理性和有效性。
In the context of large-scale development and clustering integration of offshore wind power, fairly and equitably recovering the transmission costs of offshore wind power integration systems and allocating them to wind farms has become a pressing issue in the offshore wind power industry. To address this challenge, a method for allocating fixed costs in offshore wind power integration systems, taking into account the correlation of wind power output, is proposed. Firstly, the Vine copula function is employed to construct a correlation model for the output of multiple offshore wind farms. Additionally, it is assumed that the system's load demand model follows a normal distribution, and the generators follow a [0,1] probability distribution. Then, using a combination of Monte Carlo sampling and the probability flow power flow tracking method, the fixed cost allocation for each integrated offshore wind farm is calculated. Finally, through case simulation analysis, the reasonableness and effectiveness of the proposed method are validated.
关键词(KeyWords):
海上风电;风电出力相关性;固定成本分摊;藤Copula;潮流追踪
offshore wind farm;correlation of wind power output;fixed cost allocation;Vine copula;power flow tracking
基金项目(Foundation): 国家自然科学基金资助项目(51777126)
作者(Author):
刘智彬,苏本庆
LIU Zhibin,SU Benqing
DOI: 10.19585/j.zjdl.202403005
参考文献(References):
- [1]高晨,赵勇,汪德良,等.海上风电机组电气设备状态检修技术研究现状与展望[J].电工技术学报,2022,37(增刊1):30-42.GAO Chen,ZHAO Yong,WANG Deliang,et al. Research status and prospect of condition based maintenance technology for offshore wind turbine electrical equipment[J].Transactions of China Electrotechnical Society,2022,37(S1):30-42.
- [2]周保中,刘敦楠,张继广,等.“风光火一体化”多能互补项目优化配置研究[J].发电技术,2022,43(1):10-18.ZHOU Baozhong,LIU Dunnan,ZHANG Jiguang,et al.Research on optimal allocation of multi-energy complementary project of wind-solar-thermal integration[J].Power Generation Technology,2022,43(1):10-18.
- [3]吴倩,王洋,王琳媛,等.计及波动平抑与经济性的风光储系统中混合储能容量优化配置[J].电测与仪表,2022,59(4):112-119.WU Qian,WANG Yang,WANG Linyuan,et al.Optimal capacity allocation of hybrid energy storage system in wind-solar-battery system considering fluctuation smoothing and economy[J].Electrical Measurement&Instrumentation,2022,59(4):112-119.
- [4]曹文斌,潘武略,戚宣威,等.海上风电高抗匝间保护误动分析及对策建议[J].浙江电力,2023,42(1):54-62.CAO Wenbin,PAN Wulue,QI Xuanwei,et al.Maloperation analysis and suggested countermeasures for inter-turn protection of high-voltage shunt reactors in offshore wind power transmission[J].Zhejiang Electric Power,2023,42(1):54-62.
- [5]符杨,刘阳,黄玲玲,等.海上风电场集群接入系统组网优化[J].中国电机工程学报,2018,38(12):3441-3450.FU Yang,LIU Yang,HUANG Lingling,et al.Optimization of grid integration network for offshore wind farm cluster[J].Proceedings of the CSEE,2018,38(12):3441-3450.
- [6]梅书凡,檀勤良,代美.考虑风光出力季节性波动的储能容量配置[J].电力工程技术,2022,41(4):51-57.MEI Shufan,TAN Qinliang,DAI Mei.Energy storage capacity configuration considering seasonal fluctuation of wind and photovoltaic output[J].Electric Power Engineering Technology,2022,41(4):51-57.
- [7]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 Shuqiang,JIN Tianran,LI Zhiwei,et al. Wind power scenario generation for multiple wind farms considering temporal and spatial correlations[J].Power System Technology,2019,43(11):3997-4004.
- [8]仉梦林,胡志坚,王小飞,等.基于动态场景集和需求响应的二阶段随机规划调度模型[J].电力系统自动化,2017,41(11):68-76.ZHANG Menglin,HU Zhijian,WANG Xiaofei,et al.Two-stage stochastic programming scheduling model based on dynamic scenario sets and demand response[J].Automation of Electric Power Systems,2017,41(11):68-76.
- [9]彭星皓,李艳婷.基于时空协方差函数的风能场景生成方法与应用[J].上海交通大学学报,2023,57(12):1531-1542.PENG Xinghao,LI Yanting.Wind power scenario generation method and application based on spatiotemporal covariance function[J].Journal of Shanghai Jiao Tong University,2023,57(12):1531-1542.
- [10] WANG Y R,LUO Y N.Research of wind power correlation with three different data types based on mixed copula[J].IEEE Access,2018,6:77986-77995.
- [11] XIE Z Q,JI T Y,LI M S,et al.Quasi-monte Carlo based probabilistic optimal power flow considering the correlation of wind speeds using copula function[J].IEEE Transactions on Power Systems,2018,33(2):2239-2247.
- [12]段偲默,苗世洪,李力行,等.计及预测误差动态相关性的多风电场联合出力不确定性模型[J].电力系统自动化,2019,43(22):31-37.DUAN Simo,MIAO Shihong,LI Lixing,et al. Uncertainty model of combined output for multiple wind farms considering dynamic correlation of prediction errors[J].Automation of Electric Power Systems,2019,43(22):31-37.
- [13]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 Ming,SONG Xiaowan,SUN Lei,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.
- [14]何昭辉,曹锐,周成,等.计及时空相关性的分布式风电接入配电网风险评估[J].电力建设,2021,42(7):127-136.HE Zhaohui,CAO Rui,ZHOU Cheng,et al. Operation risk evaluation on distributed wind power connected to distribution network considering temporal-spatial correlation[J].Electric Power Construction,2021,42(7):127-136.
- [15]李奇,潘俞如,邱宜彬,等.基于MVTV Copula方法的多风电场电力系统经济调度分析[J].西南交通大学学报,2021,56(2):339-346.LI Qi,PAN Yuru,QIU Yibin,et al.Economic dispatch for power systems of multiple wind farms based on MVTV copula method[J].Journal of Southwest Jiaotong University,2021,56(2):339-346.
- [16]廖芷燕,李银红.基于R藤Copula-DBN时空相关性建模的风光荷功率概率预测[J].电力自动化设备,2022,42(3):113-120.LIAO Zhiyan,LI Yinhong. Probabilistic forecasting of wind-photovoltaic-load power based on temporal-spatial correlation modelling of Regular Vine Copula-DBN[J].Electric Power Automation Equipment,2022,42(3):113-120.
- [17]邱宜彬,李诗涵,刘璐,等.基于场景D藤Copula模型的多风电场出力相关性建模[J].太阳能学报,2019,40(10):2960-2966.QIU Yibin,LI Shihan,LIU Lu,et al.Correlation modeling of power output among multiple wind farms based on scenario d vine copula method[J].Acta Energiae Solaris Sinica,2019,40(10):2960-2966.
- [18] POUYAFAR S,TARAFDAR HAGH M,ZARE K.Circuit-theory-based method for transmission fixed cost allocation based on game-theory rationalized sharing of mutual-terms[J]. Journal of Modern Power Systems and Clean Energy,2019,7(6):1507-1522.
- [19]奚莉莉.风电并网输电系统固定成本分摊方法研究[D].南京:南京邮电大学,2016.XI Lili.Research on fixed cost allocation method for wind power grid connected transmission system[D]. Nanjing:Nanjing University of Posts and Telecommunications,2016.
- [20]王帅.基于概率潮流的分时效益风电并网固定成本分摊方法[D].上海:上海电机学院,2019.WANG Shuai. The fixed cost allocation method of timesharing benefits for wind power grid-connection based on probability power flow[D].Shanghai:Shanghai Dianji University,2019.
- [21]曹昉,舒雅丽,李成仁,等.混合Copula概率潮流追踪输电固定成本分摊法[J].电力系统及其自动化学报,2018,30(5):111-119.CAO Fang,SHU Yali,LI Chengren,et al.Hybrid copula probabilistic power flow tracing method for allocation of fixed transmission cost[J]. Proceedings of the CSUEPSA,2018,30(5):111-119.
- [22] SKLAR A. Fonctions de répartitionàn-dimensions et leurs marges[J].Publications de l’Institut Statistique de l’Universitéde Paris,1959,8:229-231.
- [23]张博,申建建,程春田,等.基于C藤Copula理论的水风互补系统调峰方法[J].中国电机工程学报,2022,42(15):5523-5535.ZHANG Bo,SHEN Jianjian,CHENG Chuntian,et al.Peak-shaving method of hydro-wind power complementary system based on C-vine copula theory[J].Proceedings of the CSEE,2022,42(15):5523-5535.
- [24]康文韬,霍永胜.计及风电、光伏不确定性和相关性的潮流特性分析[J].现代电力,2022,39(2):246-252.KANG Wentao,HUO Yongsheng.Analysis on the power flow characteristics considering uncertainty and correlation of wind power and photovoltaic station[J].Modern Electric Power,2022,39(2):246-252.