孙怡文,邢海军,梅丘梅,全文斌,郭啟振,庄世杰
SUN Yiwen,XING Haijun,MEI Qiumei,QUAN Wenbin,GUO Qizhen,ZHUANG Shijie

• 1:上海电力大学电气工程学院
• 2:浙江华云清洁能源有限公司

摘要(Abstract)：

在新型电力系统加速发展的背景下，新能源大规模接入电网给电力系统的可靠性带来全新挑战。为提高新型电力系统可靠性评估的精准度，考虑风电-光伏出力的相关性，提出了一种基于混合Copula风光联合出力模型的新型电力系统可靠性评估方法。首先，依据赤池信息准则、贝叶斯信息准则及平方欧式距离评价准则，筛选合适的Copula函数构建混合Copula模型，生成风光联合出力场景。然后，构建新型电力系统可靠性评估指标，结合拉丁超立方抽样与重要抽样法开展可靠性评估。最后，利用某地区风光历史数据，以改进的IEEE RTS79系统为算例，验证了所提方法的准确性，并分析了风光装机比例和新能源渗透率对新型电力系统可靠性的影响。
The rapid development of modern power systems, driven by large-scale integration of renewable energy, introduces new challenges to grid reliability. To enhance the accuracy of reliability assessment, this paper proposes a novel methodology based on a hybrid Copula model that captures the correlation between wind and photovoltaic(PV) power generation. First, the optimal Copula function is selected using the Akaike information criterion(AIC), Bayesian information criterion(BIC), and squared Euclidean distance, enabling the generation of joint wind-PV output scenarios. Next, a set of reliability assessment indices for modern power systems is established, and evaluations are performed via Latin hypercube sampling(LHS) coupled with importance sampling(IS). Case studies—using historical wind-PV data from a regional grid and a modified IEEE RTS-79 test system—validate the method's accuracy. The analysis further quantifies the impacts of wind-PV capacity ratios and renewable penetration levels on system reliability.

关键词(KeyWords)： 新型电力系统;可靠性评估;Copula函数;风电;光伏
modern power systems;reliability assessment;Copula function;wind power;PV

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(52007112)

作者(Author): 孙怡文,邢海军,梅丘梅,全文斌,郭啟振,庄世杰
SUN Yiwen,XING Haijun,MEI Qiumei,QUAN Wenbin,GUO Qizhen,ZHUANG Shijie

DOI: 10.19585/j.zjdl.202509002

参考文献(References)：

• [1]徐青山，蔡霁霖，李淋，等.含多状态机组风电场的发输电系统可靠性评估加速方法[J].中国电机工程学报，2018,38(24):7248-7257.XU Qingshan,CAI Jilin,LI Lin,et al. Acceleration method for evaluating the reliability of composite power system considering wind farms with multi-state wind turbines[J].Proceedings of the CSEE,2018,38(24):7248-7257.
• [2] AHMAD W,HASAN O,AWWAD F,et al.Formal reliability analysis of an integrated power generation system using theorem proving[J].IEEE Systems Journal,2020,14(4):4820-4831.
• [3] CHEN F,LI F X,FENG W,et al.Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms[J].Electric Power Systems Research,2019,166:73-82.
• [4] GBADAMOSI S L,NWULU N I.Reliability assessment of composite generation and transmission expansion planning incorporating renewable energy sources[J].Journal of Renewable and Sustainable Energy,2020,12(2):026301.
• [5]李雪，李佳奇，张儒峰，等.计及风电出力相关性和条件价值风险的电力系统概率可用输电能力评估[J].电工技术学报，2023,38(15):4162-4177.LI Xue,LI Jiaqi,ZHANG Rufeng,et al.Probabilistic available transfer capability assessment in power system considering conditional value-at-risk and correlated wind power[J].Transactions of China Electrotechnical Society,2023,38(15):4162-4177.
• [6]李晏君，何雨微，盛方，等.兼顾风-光-荷不确定性与相关性的配电网多目标重构方法[J].浙江电力，2024,43(8):20-27.LI Yanjun,HE Yuwei,SHENG Fang,et al. A multiobjective reconfiguration method for distribution networks considering the wind-photovoltaic-load uncertainty and correlation[J].Zhejiang Electric Power,2024,43(8):20-27.
• [7]刘智彬，苏本庆.考虑出力相关性的海上风电接入系统固定成本分摊方法[J].浙江电力，2024,43(3):38-46.LIU Zhibin,SU Benqing. A fixed cost allocation method for offshore wind power integration systems considering output power correlation[J]. Zhejiang Electric Power,2024,43(3):38-46.
• [8]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.
• [9]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 Yu,ZHANG Bingzhe,PANG Huizhen,et al.Research on wind speed forecasting method based on hybrid copula optimization algorithm[J].Acta Energiae Solaris Sinica,2022,43(10):192-201.
• [10] WEI D X,WANG J Z,LI Z W,et al.Wind power curve modeling with hybrid copula and grey wolf optimization[J]. IEEE Transactions on Sustainable Energy,2022,13(1):265-276.
• [11] TU Q Y,MIAO S H,YAO F X,et al.Forecasting scenario generation for multiple wind farms considering timeseries characteristics and spatial-temporal correlation[J].Journal of Modern Power Systems and Clean Energy,2021,9(4):837-848.
• [12] ZHANG W B,XING H J,SUN Y W,et al. Multi-time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition[J]. IET Generation,Transmission&Distribution,2023,17(17):3921-3935.
• [13] LIN K S,CHEN Y X.Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals[J]. Reliability Engineering&System Safety,2021,207:107327.
• [14] JIA B J,ZHOU J Z,TANG Z Y,et al.Effective stochastic streamflow simulation method based on Gaussian mixture model[J].Journal of Hydrology,2022,605:127366.
• [15] HU B,PAN C C,SHAO C Z,et al.Decision-dependent uncertainty modeling in power system operational reliability evaluations[J].IEEE Transactions on Power Systems,2021,36(6):5708-5721.
• [16]罗魁，郭剑波，马士聪，等.海上风电并网可靠性分析及提升关键技术综述[J].电网技术，2022,46(10):3691-3703.LUO Kui,GUO Jianbo,MA Shicong,et al.Review of key technologies of reliability analysis and improvement for offshore wind power grid integration[J].Power System Technology,2022,46(10):3691-3703.
• [17]陶思钰，江福庆.集群化发展模式下风电场预测、规划及控制关键技术综述[J].电力工程技术，2024,43(1):86-99.TAO Siyu,JIANG Fuqing. Review of the key technologies of wind farm cluster prediction, planning and control[J].Electric Power Engineering Technology,2024,43(1):86-99.
• [18]王勃，孙勇，李振元，等.基于Logistic函数与分位数回归的风电机组功率曲线建模方法[J].电测与仪表，2025,62(3):112-120.WANG Bo,SUN Yong,LI Zhenyuan,et al.Wind turbine power curve modeling method with Logistic functions based on quantile regression[J]. Electrical Measurement&Instrumentation,2025,62(3):112-120.
• [19]李士哲，王霄慧，刘帅.考虑多变量相关性改进的风电场Transformer中长期预测模型[J].智慧电力，2024,52(4):62-68.LI Shizhe,WANG Xiaohui,LIU Shuai. Improved transformer medium and long term prediction model of wind farm considering multivariate correlation[J].Smart Power,2024,52(4):62-68.
• [20]林旭，李军，杨德健，等.基于功率轨迹预设的双馈风电机组虚拟惯量控制策略[J].智慧电力，2023,51(10):47-53.LIN Xu,LI Jun,YANG Dejian,et al.Virtual inertia control strategy for doubly-fed induction generator based on preset power trajectory[J].Smart Power,2023,51(10):47-53.
• [21] OH U,LEE Y,CHOI J,et al. Reliability evaluation of power system considering wind generators coordinated with multi-energy storage systems[J]. IET Generation,Transmission&Distribution,2020,14(5):786-796.
• [22] MEHRABANI A,ALLAH AALAMI H,PIRAYESH NEGHAB A.Integrated planning framework for microgrid incorporating the flexibility provision capabilities of demand response programs[J].International Journal of Electrical Power&Energy Systems,2024,155:109521.
• [23]罗佳怡，邢海军，全文斌，等.极端高温下新型电力系统韧性评估及差异化规划[J].电力系统自动化，2025,49(3):135-144.LUO Jiayi,XING Haijun,QUAN Wenbin,et al. Resilience assessment and differentiated planning of new power systems under extreme high temperatures[J].Automation of Electric Power Systems,2025,49(3):135-144.
• [24] KHANI M,SAFDARIAN A. Effect of sectionalizing switches malfunction probability on optimal switches placement in distribution networks[J].International Journal of Electrical Power and Energy Systems,2020,119:105973.
• [25] PANDIT D,NGUYEN N,ELSAIAH S,et al.Analytical assessment of time-varying reliability and penetration limit of PV-integrated systems[J].IEEE Transactions on Industry Applications,2022,58(6):6886-6898.