2025年3月29日 周六
首页 > 过刊浏览>2025年第40卷第1期 >163-172. DOI:10.19781/j.issn.1673-9140.2025.01.017
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考虑极端天气的新型电力系统智能化调度方法
作者:
作者单位:

(1.中国南方电网有限责任公司电力调度控制中心，广东 广州 510530;2.南方电网能源发展研究院有限责任公司，广东 广州510530)

通讯作者:

蒙文川（1976—），男，博士，高级工程师，主要从事电网新能源发展规划研究；E?mail: mengwc@csg.cn.

中图分类号:

TM73

基金项目:

南方电网公司管理创新项目（000000KK52210094）


Intelligent dispatching of new power system considering extreme climates
Author:
Affiliation:

(1.Power Dispatching Control Center of China Southern Power Grid Co., Ltd., Guangzhou 510530, China;2.Energy Research Institute of China Southern Power Grid Co., Ltd., Guangzhou 510530, China)

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    摘要:

    随着以新能源为基础的新型电力系统建设的不断推进，近年来风电、光伏等新能源大规模密集接入系统，这虽然为实现“双碳”目标奠定了坚实的基础，但同时也导致极端天气下新型电力系统调度运行面临的挑战不断增大，其中最易出现的问题是风电爬坡事件概率大幅提升，不仅会造成系统频率的大幅频繁波动，还会影响电力电量平衡，严重威胁系统安全稳定运行。为此，在统计分析风电爬坡事件的基础上，提出基于深度自回归(deep auto?regressive, DeepAR)模型的风电爬坡事件的预测方法。首先，结合风机功率与风速之间的关系，分析极端天气下风电爬坡事件对电网调度运行的影响，再建立风电爬坡事件物理模型，分析发生风电爬坡事件时的风电功率统计特征；然后，结合深度自回归模型对风电爬坡事件进行功率预测，分析极端天气下的风电出力曲线；最后，结合风电场实测数据验证所提方法的有效性。验证表明：采用所提方法可提前精准定位极端天气环境下风电爬坡事件出现概率，预期将极大改善未来新型电力系统调度运行面临的不确定性。

    Abstract:

    With the continuous development of new power systems based on new energy, large-scale and intensive wind power, photovoltaic, and other new energy access to the system has laid a solid foundation for the realization of the “carbon peaking and carbon neutrality” goals, but at the same time, it also leads to the increasing challenges faced by the dispatching operation of new power systems under extreme climates, and the most prominent problem is that the probability of wind power ramp events has increased significantly. Wind power ramp events will not only cause great fluctuations in the frequency of the system but also affect the balance of electric power and energy, threatening the safe and stable operation of the system. Through the statistical analysis of wind power ramp events, a predictive method of wind power ramp events based on a deep auto-regressive (DeepAR) model is proposed. Firstly, combined with the relationship between wind power and wind speed, the impact of wind power ramp events on power grid dispatching operations under extreme climates is analyzed. Secondly, a physical model of wind power ramp events is established to analyze the statistical characteristics of wind power when wind power ramp events occur. Then, the DeepAR model is used to perform the power prediction of wind power ramp events, and the wind power output curve under extreme climates is analyzed. Finally, combined with the measured data of the wind power field, the effectiveness of the proposed method is verified. The verification shows that the proposed method can accurately predict the occurrence probability of wind power ramp events under extreme climates in advance, which is expected to greatly improve the uncertainty faced by the dispatching operation of new power systems in the future.

    参考文献
    [1] 张东英,代悦,张旭,等.风电爬坡事件研究综述及展望[J].电网技术,2018，42(6):1783-1792. ZHANG Dongying,DAI Yue,ZHANG Xu,et al.Review and prospect of research on wind power ramp events[J].Power System Technology,2018，42(6):1783-1792.
    [2] HOLTTINEN H，MEIBOM P，ORTHS A，et al．Impacts of large amounts of wind power on design and operation of power systems,results of IEA collaboration[J].Wind Energy,2011,14(2)：179-192.
    [3] 谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(增刊2):335-338. GU Xingkai,FAN Gaofeng,WANG Xiaorong,et al.Summarization of wind power prediction technology[J].Power System Technology,2007,31(sup 2):335-338.
    [4] 宋家康,赵建勇,孙海霞,等.基于多目标协同训练的风电功率预测提升算法[J].电力工程技术,2023,42(6):232-240.SONG Jiakang, ZHAO Jianyong, SUN Haixia,et al. Wind power prediction and improvement algorithm based on multi-objective col-laborative training[J]. Electric Power Engineering Technolo-gy,2023,42(6):232-240.
    [5] 郭燕玲,赵晶,周林,等.山东半岛风电爬坡事件的识别与天气分析研究[J].气候与环境研究,2017,22(1):97-107. GUO Yanling,ZHAO Jing,ZHOU Lin,et al.A study on the identification and synoptic analysis of wind ramp events in shandong peninsula[J].Climatic and Environmental Research,2017,22(1):97-107.
    [6] 丁立,乔颖,鲁宗相,等.高比例风电对电力系统调频指标影响的定量分析[J].电力系统自动化,2014,38(14):1-8. DING Li,QIAO Yin,LU Zongxiang,et al.Impact on frequency regulation of power system from wind power with high penetration[J].Automation of Electric Power Systems,2014,38(14):1-8.
    [7] 迟永宁,刘燕华,王伟胜,等.风电接入对电力系统的影响[J].电网技术,2007,31(3):77-81. CHI Yongning,LIU Yanhua,WANG Weisheng,et al.Study on impact of wind power integration on power system[J].Power System Technology,2007,31(3):77-81.
    [8] 李媛媛,邱跃丰,马世英,等.风电机组接入对系统小干扰稳定性的影响研究[J].电网技术,2012,36(8):50-55. LI Yuanyuan,QIU Yuefeng,MA Shiying,et al.Impact of grid-connected wind turbine generators on small signal stability of power grid[J].Power System Technology,2012,36(8):50-55.
    [9] 刘洪波，刘永发，任阳，等．高风电渗透率下考虑系统风电备用容量的储能配置[J]．发电技术，2024，45(2)：260-272. LIU Hongbo, LIU Yongfa, REN Yang, et al．Energy storage config-uration considering the system wind power reserve capacity under high wind power permeability[J]．Power Generation Technology，2024，45(2)：260-272.
    [10] 戴建军, 王明明, 游云汉,等. 基于时间敏感网络的风电主动支撑和运行控制网络技术研究[J]. 中国电力, 2023, 56(10): 53-61. DAI Jianjun,WANG Mingming,YOU Yunhan,et al. Research on ac-tive support and operation control network of wind turbine based on time-sensitive network[J].Electric Power,2023, 56(10): 53-61.
    [11] ZHENG H，KUSIAK A．Prediction of wind farm power ramp rates:a data-mining approach[J]．Journal of Solar Energy Engineering,2009，131(3)：376-385．
    [12] CUI M，ZHANG J，FLORITA A R，et al．An optimized swinging door algorithm for identifying wind ramping events[J].IEEE Transactions on Sustainable Energy，2015，7(1)：150-162．
    [13] 崔明建,孙元章,柯德平.基于原子稀疏分解和BP神经网络的风电功率爬坡事件预测[J].电力系统自动化,2014,38(12):6-11+26. CUI Mingjian,SUN Yuanzhang,KE Deping.Wind power ramp events forecasting based on atomic sparse decomposition and BP neural networks[J].Automation of Electric Power Systems,2014,38(12):6-11+26.
    [14] 马欢，李常刚，刘玉田.风电爬坡事件对系统运行充裕性的影响 评估[J].电力系统自动化,2017,41(4):41-47. MA Huan,LI Changgang,LIU Yutian.Assessing impact of wind power ramp events on operation adequacy of power systems[J].Automation of Electric Power Systems,2017,41(4):41-47.
    [15] 何川，刘天琪，胡晓通，等．基于超短期风电预测和混合储能的 风电爬坡优化控制[J].电网技术,2017,41(3):782-790. HE Chuan,LIU Tianqi,HU Xiaotong,et al.Optimal control of wind ramp based on very short-term wind forecast and hybrid ESS[J].Power System Technology,2017,41(3):782-790.
    [16] KAMATH C．Understanding wind ramp events through analysis of historical data[C]// IEEE Transmission and Distribution Conference and Exposition,New Orleans，LA，USA，2010．
    [17] 茹瑶, 赵永宁, 叶林, 等. 超短期LSTM风电功率预测模型的混合专家模块化代理解释方法[J]. 电力建设, 2024, 45(11): 114-124． RU Yao, ZHAO Yongning, YE Lin, et al. Modular surrogate inter-pretation method based on decision tree mixture of experts for ultra-short-term LSTM wind power forecasting model[J]. Electric Power Construction, 2024, 45(11): 114-124.
    [18] 夏雪,戚永志,刘玉田.风机爬坡功率的有限度控制策略[J].电力系统自动化,2014,38(20):26-32. XIA Xue,QI Yongzhi,LIU Yutian.Finite control strategy for wind turbine ramping power[J].Automation of Electric Power Systems,2014,38(20):26-32.
    [19] 岳晓宇，彭显刚，林俐.鲸鱼优化支持向量机的短期风电功率预 测[J].电力系统及其自动化学报，2020，32(2)：146-150. YUE Xiaoyu,PENG Xiaogang,LIN Li.Short-term wind power forecasting based on whales optimization algorithm and support vector machine[J].Proceedings of the CSU-EPSA,2020,32（2）：146-150.
    [20] 杨宏，闫玉杰，王瑜.Beta 分布在风电预测误差模型中的适用性[J].电测与仪表，2020，57(11)：37-41. YANG Hong,YAN Yujie,WANG Yu.Applicability of beta distribution on wind power forecast error modeling[J].Electrical Measurement & Instrumentation，2020，57(11)：37-41.
    [21] 张丁予，解佗，马易晨，等.基于爬坡特征识别的短期风电功率集成预测方法[J]. 电网与清洁能源，2024，40(8)：128-133. ZHANG Dingyu，XIE Tuo，MA Yichen，et al.Research on inte-grated short term wind power prediction methods based on climbing feature recognition[J].Power System and Clean Energy，2024，40(8)：128-133.
    [22] 张 野,李凤婷,张高航,等.考虑风电爬坡备用需求的风电高渗透电力系统优化调度方法[J].电力系统保护与控制,2024,52(23):95-106. ZHANG Ye,LI Fengting,ZHANG Gaohang,et al.Optimization and scheduling methods for wind power high-penetration power system-sconsidering wind power ramping reserve requirements[J].Power System Protection and Control,2024,52(23):95-106.
    [23] 陈天阳,钱政,荆博,等.基于K-means++与ELM的短期风电功率预测模型研究[J].电测与仪表,2024,61(6):45-50. CHEN Tianyang,QIAN Zheng,JING Bo,et al.Short-term wind power forecasting based on K-means++ and ELM[J].Electrical Measurement & Instrumentation,2024,61(6):45-50.
    [24] 杨茂，董昊.基于数值天气预报风速和蒙特卡洛法的短期风电功率区间预测[J].电力系统自动化，2021，45(5)：79-85. YANG Mao,DONG Hao.Short-term wind power interval prediction based on wind speed of numerical weather prediction and monte carlo method[J].Automation of Electric Power Systems，2021，45(5)：79-85.
    [25] 杨健，徐思卿，姜尚光，等.基于动态时间规整的风电功率爬坡滚动修正模型[J].电力系统自动化，2021，45(16)：152-159. YANG Jian,XU Siqin,JIANG Shangguang,et al.Rolling correction model of ramp for wind power based on dynamic time warping[J].Automation of Electric Power Systems，2021，45(16)：152-159.
    [26] 李彬,彭曙蓉,彭君哲,等.基于深度学习分位数回归模型的风电功率概率密度预测[J].电力自动化设备,2018,38(9):15-20. LI Bin,PENG Shurong,PENG Junzhe,et al.Wind power probability density forecasting based on deep learning quantile regression model[J].Electric Power Automation Equipment,2018,38(9):15-20.
    [27] 施进炜,张程,原冬芸.基于数据修正的概率稀疏自注意短期风电功率预测[J].智慧电力,2023,51(10):54-61. SHI Jinwei,ZHANG Cheng,YUAN Dongyun.Short-term wind power prediction based on data correction with probabilistic sparse self-attention[J].Smart Power,2023,51(10):54-61.
    [28] 何旭辉，段泉成，严磊.基于DeepAR的短期风速概率预测[J].铁道学报，2023，45(7):152-160. HE Xuhui,DUAN Quancheng,YAN Lei.Short-term wind speed probabilistic prediction model using DeepAR[J].Journal of the China Railway Society，2023，45(7):152-160.
    [29] 朱刚,李文,杜守国,等.基于深度学习模型DeepAR的时间序列预测及应用实例[J].电子商务,2020(7):83-86. ZHU Gang,LI Wen,DU Shouguo,et al.Time series forecasting and application examples based on deep learning model DeepAR[J].E-Business Journal,2020(7):83-86.
    [30] 史永胜,任嘉睿,李锦,等.基于DeepAR与特征选择的锂离子电池在线状态估计[J].电源学报，2023,21(2):163-171. SHI Yongsheng,REN Jiarui,LI Jin,et al.Online state estimation of lithium-ion batteries based on DeepAR and feature selection[J].Journal of Power Supply，2023,21(2):163-171.
    [31] 李天玉.基于sDTW-DeepAR的地面沉降预测研究[D].广州：广东工业大学,2022. LI Tianyu.Research on ground subsidence prediction based on sDTW-DeepAR[D].Guangzhou:Guangdong University of Technology,2022.
    [32] 闫龙川,李妍,宋浒,等.基于Prophet-DeepAR模型的Web流量预测[J].广西师范大学学报(自然科学版),2022,40(3):172-184. YAN Longchuan,LI Yan,SON Hu,et al.Web traffic prediction based on Prophet-DeepAR model[J].Journal of Guangxi Normal University (Natural Science Edition),2022,40(3):172-184.
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张 勇,孙雁斌,颜 融,等.考虑极端天气的新型电力系统智能化调度方法[J].电力科学与技术学报,2025,40(1):163-172.
ZHANG Yong, SUN Yanbin, YAN Rong, et al. Intelligent dispatching of new power system considering extreme climates[J]. Journal of Electric Power Science and Technology,2025,40(1):163-172.

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