nav emailalert searchbtn searchbox tablepage yinyongbenwen piczone journalimg journalInfo journalinfonormal searchdiv qikanlogo popupnotification paper paperNew
2025, 03, v.56 98-109
考虑多重不确定性的概率暂态稳定约束最优潮流
基金项目(Foundation): 国家自然科学基金项目(52307109); 湖北省自然科学基金项目(2022CFB825)
邮箱(Email): a5425656684@163.com;
DOI: 10.13928/j.cnki.wrahe.2025.03.008
摘要:

【目的】针对高比例可再生能源电力系统并网的多种不确定性,为维持系统运行稳定性和经济性,提出了一种考虑多重不确定性的概率暂态稳定约束最优潮流(Probabilistic Transient Stability Constrained Optimal Power Flow, PTSCOPF)方法。【方法】首先,在综合考虑多重不确定性的影响下,建立了风电出力、负荷、故障类型、故障位置及故障清除时间不确定性变量的概率模型;其次,基于机会约束优化理论构建了电力系统PTSCOPF模型;之后,结合基于Gauss-Hermite积分的多点估计法和Gram-Charlier级数对随机变量进行确定性处理,再联合粒子群优化(Particle Swarm Optimization, PSO)算法和蚁群优化(Ant Colony Optimization, ACO)算法以实现PTSCOPF模型的有效求解;最后,在改进后的IEEE 39节点算例系统上进行了仿真。【结果】采用基于Gauss-Hermite积分的多点估计法计算得到的输出随机变量标准差的相对误差和均值均小于2.0%,该方法以27.9 s的计算时间和62 610·h-1的期望成本实现了对系统运行方式的优化,优化后系统暂态稳定系数值为62.4。【结论】结果表明:在故障发生后,该方法在综合考虑多重不确定性因素的情况下,可以在较好地兼顾系统经济性的同时,以较低计算时间成本实现系统暂态稳定性的可靠提升,使系统过渡到暂态稳定状态,保障了电力系统的安全稳定运行。

Abstract:

[Objective]In view of the various uncertainties of grid-connected power systems with a high proportion of renewable energy, in order to maintain the stability and economy of the system, a probabilistic transient stability constrained optimal power flow(PTSCOPF) method considering multiple uncertainties is proposed.[Methods]Firstly, taking into account the uncertain factors, probabilistic models of uncertainty variables of wind power output, load, fault type, fault location and fault clearance time are established. Secondly, the PTSCOPF model based on chance constraint optimization theory is constructed. Then, combined with the multipoint estimation method based on Gauss-Hermite integral and the Gram-Charlier series, the random variables are treated deterministically. Then particle swarm optimization(PSO) algorithm and ant colony optimization(ACO) algorithm are combined to realize the effective solution of PTSCOPF model. Finally, simulations are conducted on the modified IEEE 39-bus test system.[Results]The relative error and mean of the standard deviation of the output random variables computed using the multi-point estimation method based on Gauss-Hermite integration are less than 2.0%, and the method in this paper achieves the optimization of the way the system operates with a computation time of 27.9 s and an expected cost of 62 610 $·h-1, and the value of the transient stability index of the system is 62.4 after the optimization.[Conclusion]The results show that after the occurrence of the fault, under the condition of considering multiple uncertain factors, the proposed method in this paper can realize the reliable improvement of the transient stability of the system with low computational time cost while taking into account the economy of the system, so that the system can transition to the transient stable state, which guarantees the safe and stable operation of the power system.

参考文献

[1] 肖先勇,郑子萱.“双碳”目标下新能源为主体的新型电力系统:贡献、关键技术与挑战[J].工程科学与技术,2022,54(1):47-59.XIAO Xianyong,ZHENG Zixuan.New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality:contribution,key techniques,and challenges[J].Advanced Engineering Sciences,2022,54(1):47-59.

[2] WANG K,WEI W,XIAO T,et al.Power system preventive control aided by a graph neural network-based transient security assessment surrogate[J].Energy Reports,2022,8:943-951.

[3] 王耀函,张扬帆,赵庆旭,等.低电压穿越过程中风电机组载荷特性联合仿真研究[J].发电技术,2024,45(4):705-715.WANG Yaohan,ZHANG Yangfan,ZHAO Qingxu,et al.Joint simulation study on load characteristics of wind turbines in low voltage ride through process[J].Power Generation Technology,2024,45(4):705-715.

[4] 潘晓杰,徐友平,解治军,等.堆栈式集成学习驱动的电力系统暂态稳定预防控制优化方法[J].发电技术,2023,44(6):865-874.PAN Xiaojie,XU Youping,XIE Zhijun,et al.Power system transient stability preventive control optimization method driven by stacking ensemble learning[J].Power Generation Technology,2023,44(6):865-874.

[5] YANG Y,SONG A,LIU H,et al.Parallel computing of multi-contingency optimal power flow with transient stability constraints[J].Protection and Control of Modern Power Systems,2018,3(2):1-10.

[6] YUAN H,XU Y.Preventive-corrective coordinated transient stability dispatch of power systems with uncertain wind power[J].IEEE Transactions on Power Systems,2020,35(5):3616-3626.

[7] 孙景强,房大中,周保荣.基于轨迹灵敏度的电力系统动态安全预防控制算法研究[J].电网技术,2004(21):26-30.SUN Jingqiang,FANG Dazhong,ZHOU Baorong.Study on preventive control algorithm for dynamic security of power systems based on trajectory sensitivity method[J].Power System Technology,2004(21):26-30.

[8] 杨健,韦化,覃秀君.基于二阶正交配置法的暂态稳定约束最优潮流[J].中国电机工程学报,2017,37(1):64-73.YANG Jian,WEI Hua,QIN Xiujun.Transient stability constrained optimal power flow based on second-order orthogonal collocation method[J].Proceedings of the CSEE,2017,37(1):64-73.

[9] 杨跃,刘友波,刘俊勇,等.基于神经网络预测校核的暂态稳定预防控制[J].电网技术,2018,42(12):4076-4084.YANG Yue,LIU Youbo,LIU Junyong,et al.Preventive transient stability control based on neural network security predictor[J].Power System Technology,2018,42(12):4076-4084.

[10] SU T,LIU Y,ZHAO J,et al.Deep belief network enabled surrogate modeling for fast preventive control of power system transient stability[J].IEEE Transactions on Industrial Informatics,2021,18(1):315-326.

[11] MORSHED M J,HMIDA J B,FEKIH A.A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems[J].Applied Energy,2018,211:1136-1149.

[12] 茆美琴,周松林,苏建徽.基于风光联合概率分布的微电网概率潮流预测[J].电工技术学报,2014,29(2):55-63.MAO Meiqin,ZHOU Songlin,SU Jianhui.Probabilistic power flow forecasting of microgrid based on joint probability distribution about wind and irradiance[J].Transactions of China Electrotechnical Society,2014,29(2):55-63.

[13] 石东源,蔡德福,陈金富,等.计及输入变量相关性的半不变量法概率潮流计算[J].中国电机工程学报,2012,32(28):104-113.SHI Dongyuan,CAI Defu,CHEN Jinfu,et al.Probabilistic load flow calculation based on cumulant method considering correlation between input variables[J].Proceedings of the CSEE,2012,32(28):104-113.

[14] 董晓阳,苏宏升,罗世昌.考虑风电出力相关性的概率最优潮流计算[J].三峡大学学报(自然科学版),2019,41(5):90-95.DONG Xiaoyang,SU Hongsheng,LUO Shichang.Probabilistic optimal power flow calculation considering correlation of wind power outputs[J].Journal of China Three Gorges University(Natural Sciences),2019,41(5):90-95.

[15] 李钰洋,王增平.基于高斯求积的智能配电网三相概率潮流点估计法[J].电网技术,2022,46(2):709-717.LI Yuyang,WANG Zengping.Three-phase probabilistic load flow for smart distribution network based on Gauss-quadrature-based point estimate method[J].Power System Technology,2022,46(2):709-717.

[16] ZHU J H,WANG J S,ZHENG Y,et al.Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices[J].Renewable Energy,2024,232:121087.

[17] AYVAZ A,ISTEMIHAN GENC V M.Information-gap decision theory based transient stability constrained optimal power flow considering the uncertainties of wind energy resources[J].IET Renewable Power Generation,2020,14(11):1946-1955.

[18] XIA B,WU H,YANG W,et al.Parametric transient stability constrained optimal power flow solved by polynomial approximation based on the stochastic collocation method[J].Energies,2022,15(11):4127.

[19] 钱仲豪,胡骏,沈思辰,等.考虑条件风险价值的多源协调优化运行策略[J].发电技术,2023,44(6):781-789.QIAN Zhonghao,HU Jun,SHEN Sichen,et al.Multi-power coordinated optimization operation strategy considering conditional value at risk[J].Power Generation Technology,2023,44(6):781-789.

[20] PENG S,TANG J,LI W.Probabilistic power flow for AC/VSC-MTDC hybrid grids considering rank correlation among diverse uncertainty sources[J].IEEE Transactions on Power Systems,2016,32(5):4035-4044.

[21] WONG W C,CHUNG C Y,CHAN K W,et al.Quasi-Monte Carlo based probabilistic small signal stability analysis for power systems with plug-in electric vehicle and wind power integration[J].IEEE Transactions on Power Systems,2013,28(3):3335-3343.

[22] VAAHEDI E,LI W,CHIA T,et al.Large scale probabilistic transient stability assessment using BC Hydro′s on-line tool[J].IEEE Transactions on Power Systems,2000,15(2):661-667.

[23] 崔凯,房大中,钟德成.电力系统暂态稳定性概率评估方法研究[J].电网技术,2005(1):44-49.CUI Kai,FANG Dazhong,CHUNG Takshing.Study on probabilistic assessment method for power system transient stability[J].Power System Technology,2005(1):44-49.

[24] FANG D Z,JING L,CHUNG T S.Corrected transient energy function-based strategy for stability probability assessment of power systems[J].IET Generation,Transmission & Distribution,2008,2(3):424-432.

[25] 苏童,刘友波,沈晓东,等.深度学习驱动的电力系统暂态稳定预防控制进化算法[J].中国电机工程学报,2020,40(12):3813-3824.SU Tong,LIU Youbo,SHEN Xiaodong,et al.Deep learning-driven evolutionary algorithm for preventive control of power system transient stability[J].Proceedings of the CSEE,2020,40(12):3813-3824.

[26] 李庆扬,王能超,易大义.数值分析(第5版)[M].北京:清华大学出版社,2008:116-124.LI Qingyang,WANG Nengchao,YI Dayi.Numerical Analysis (5th edition)[M].Beijing:Tsinghua University Press,2008:116-124.

[27] 张立波,程浩忠,曾平良,等.基于Nataf逆变换的概率潮流三点估计法[J].电工技术学报,2016,31(6):187-194.ZHANG Libo,CHENG Haozhong,ZENG Pingliang,et al.A three-point estimate method for solving probabilistic load flow based on inverse Nataf transformation[J].Transactions of China Electrotechnical Society,2016,31(6):187-194.

[28] 王涛,王淳,李成豪.基于Copula函数及Rosenblatt变换的含相关性概率潮流计算[J].电力系统保护与控制,2018,46(21):18-24.WANG Tao,WANG Chun,LI Chenghao.Probabilistic load flow calculation based on Copula function and Rosenblatt transformation considering correlation among input variables[J].Power System Protection and Control,2018,46(21):18-24.

[29] 韩海腾,高山,吴晨,等.基于Nataf变换的电网不确定性多点估计法[J].电力系统自动化,2015,39(7):28-34.HAN Haiteng,GAO Shan,WU Chen,et al.Uncertain power flow solved by multi-point estimate method based on Nataf transformation[J].Automation of Electric Power Systems,2015,39(7):28-34.

[30] ZHAO Y G,ONO T.New point estimates for probability moments[J].Journal of Engineering Mechanics,2000,126(4):433-436.

[31] ZHANG P,LEE S T.Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion[J].IEEE Transactions on Power Systems,2004,19(1):676-682.

[32] 董光德,李道明,陈咏涛,等.基于粒子群优化与卷积神经网络的电能质量扰动分类方法[J].发电技术,2023,44(1):136-142.DONG Guangde,LI Daoming,CHEN Yongtao,et al.Power quality disturbance classification method based on particle swarm optimization and convolutional neural network[J].Power Generation Technology,2023,44(1):136-142.

[33] HAJLAOUI R,GUYENNET H,MOULAHI T.A survey on heuristic-based routing methods in vehicular ad-hoc network:Technical challenges and future trends[J].IEEE Sensors Journal,2016,16(17):6782-6792.

[34] LI W,ZHANG T,WANG R,et al.Weighted indicator-based evolutionary algorithm for multimodal multiobjective optimization[J].IEEE Transactions on Evolutionary Computation,2021,25(6):1064-1078.

[35] 苏靖程,王志强,屈江江,等.基于BP神经网络和支持向量回归的燃煤电厂空气预热器压差预测[J].发电技术,2023,44(4):550-556.SU Jingcheng,WANG Zhiqiang,QU Jiangjiang,et al.Pressure difference prediction of air preheater in coal-fired power plant based on BP neural network and support vector regression[J].Power Generation Technology,2023,44(4):550-556.

基本信息:

DOI:10.13928/j.cnki.wrahe.2025.03.008

中图分类号:TM712

引用信息:

[1]李沉融,吴梓宁.考虑多重不确定性的概率暂态稳定约束最优潮流[J].水利水电技术(中英文),2025,56(03):98-109.DOI:10.13928/j.cnki.wrahe.2025.03.008.

基金信息:

国家自然科学基金项目(52307109); 湖北省自然科学基金项目(2022CFB825)

引用

GB/T 7714-2015 格式引文
MLA格式引文
APA格式引文
检 索 高级检索