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1.电力调度自动化技术研究与系统评价北京市重点实验室(中国电力科学研究院有限公司),北京市 100192
2.国网江苏省电力有限公司电力科学研究院,江苏省南京市 211103
Received:02 January 2024,
Revised:2024-04-03,
Published Online:16 July 2024,
Published:10 September 2024
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吕闫,李理,孙略等.基于抽样概率区间修正的典型源荷时序场景生成方法[J].电力系统自动化,2024,48(17):141-150.
LYU Yan,LI Li,SUN Lüe,et al.Typical Source-Load Temporal Scenario Generation Method Based on Modification of Sampling Probability Interval[J].Automation of Electric Power Systems,2024,48(17):141-150.
吕闫,李理,孙略等.基于抽样概率区间修正的典型源荷时序场景生成方法[J].电力系统自动化,2024,48(17):141-150. DOI: 10.7500/AEPS20240102003.
LYU Yan,LI Li,SUN Lüe,et al.Typical Source-Load Temporal Scenario Generation Method Based on Modification of Sampling Probability Interval[J].Automation of Electric Power Systems,2024,48(17):141-150. DOI: 10.7500/AEPS20240102003.
新型电力系统背景下,构建典型源荷时序场景可为日前、日内电力生产组织提供场景支撑。为进一步提升基于统计学方法生成的时序场景质量,减少典型源荷时序场景数量,提出一种基于抽样概率区间修正的典型源荷时序场景生成方法。首先,考虑新能源历史波动特性对预测误差时序抽样概率区间进行修正,采用首次抽样时刻拉丁超立方抽样和后续时刻修正概率区间随机抽样的形式对时序场景进行抽样生成;其次,基于聚类方法得到典型时序场景;然后,基于源荷“逻辑与”运算,从系统安全角度对典型源荷时序场景组合进行约简;最后,以中国西北某省的实际数据为基础构建IEEE 39节点源荷时序场景算例,验证所提方法的有效性和可行性。
In the background of the new power system
constructing typical source-load temporal scenarios can provide scenario support for the day-ahead or intra-day electricity production organizations. To further improve the quality of temporal scenarios generated by statistical methods
and reduce the number of typical source-load temporal scenarios
a method for generating typical source-load temporal scenarios based on the modification of sampling probability interval is proposed. Firstly
considering the historical fluctuation characteristics of renewable energy
the probability interval of the prediction error temporal sampling is modified
and the temporal scenarios are sampled and generated using Latin hypercube sampling at the first sampling point and using random sampling in modified probability interval at subsequent sampling points. Secondly
typical temporal scenarios are obtained based on clustering method. Then
the typical source-load temporal scenario combinations are reduced from the perspective of system security based on the source-load “AND” operation. Finally
based on the actual data from a province in Northwest China
the source-load temporal scenarios for an IEEE 39-bus system are constructed to verify the effectiveness and feasibility of the proposed method.
郭庆来 , 兰健 , 周艳真 , 等 . 基于混合智能的新型电力系统运行方式分析决策架构及其关键技术 [J]. 中国电力 , 2023 , 56 ( 9 ): 1 - 13 .
GUO Qinglai , LAN Jian , ZHOU Yanzhen , et al . Architecture and key technologies of hybrid-intelligence-based decision-making of operation modes for new type power systems [J]. Electric Power , 2023 , 56 ( 9 ): 1 - 13 .
吴俊 , 薛禹胜 , 舒印彪 , 等 . 大规模可再生能源接入下的电力系统充裕性优化:(三)多场景的备用优化 [J]. 电力系统自动化 , 2019 , 43 ( 11 ): 1 - 7 .
WU Jun , XUE Yusheng , SHU Yinbiao , et al . Adequacy optimization for a large-scale renewable energy integrated power system: Part three reserve optimization in multiple scenarios [J]. Automation of Electric Power Systems , 2019 , 43 ( 11 ): 1 - 7 .
HOU Q C , DU E S , ZHANG N , et al . Impact of high renewable penetration on the power system operation mode: a data-driven approach [J]. IEEE Transactions on Power Systems , 2020 , 35 ( 1 ): 731 - 741 .
姚建国 , 杨胜春 , 王珂 , 等 . “源-网-荷”互动环境下电网调度控制 [M]. 北京 : 中国电力出版社 , 2019 .
YAO Jianguo , YANG Shengchun , WANG Ke , et al . Power grid dispatching control under the interactive environment of source-network-load [M]. Beijing : China Electric Power Press , 2019 .
李明节 , 陈国平 , 董存 , 等 . 新能源电力系统电力电量平衡问题研究 [J]. 电网技术 , 2019 , 43 ( 11 ): 3979 - 3986 .
LI Mingjie , CHEN Guoping , DONG Cun , et al . Research on power balance of high proportion renewable energy system [J]. Power System Technology , 2019 , 43 ( 11 ): 3979 - 3986 .
刘纯 , 屈姬贤 , 石文辉 . 基于随机生产模拟的新能源消纳能力评估方法 [J]. 中国电机工程学报 , 2020 , 40 ( 10 ): 3134 - 3144 .
LIU Chun , QU Jixian , SHI Wenhui . Evaluating method of ability of accommodating renewable energy based on probabilistic production simulation [J]. Proceedings of the CSEE , 2020 , 40 ( 10 ): 3134 - 3144 .
钱韦廷 , 赵长飞 , 万灿 , 等 . 基于概率预测的混合储能平抑风电波动随机优化调控方法 [J]. 电力系统自动化 , 2021 , 45 ( 18 ): 18 - 27 .
QIAN Weiting , ZHAO Changfei , WAN Can , et al . Probabilistic forecasting based stochastic optimal dispatch and control method of hybrid energy storage for smoothing wind power fluctuations [J]. Automation of Electric Power Systems , 2021 , 45 ( 18 ): 18 - 27 .
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 Jinming , ZHAO Shuqiang , WEI Ziyu , 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 .
董骁翀 , 张姝 , 李烨 , 等 . 电力系统中时序场景生成和约简方法研究综述 [J]. 电网技术 , 2023 , 47 ( 2 ): 709 - 721 .
DONG Xiaochong , ZHANG Shu , LI Ye , et al . Review of power system temporal scenario generation and reduction methods [J]. Power System Technology , 2023 , 47 ( 2 ): 709 - 721 .
马骞 , 辛阔 , 王子强 , 等 . 光伏出力序列场景生成新方法 [J/OL]. 电力系统及其自动化学报 [ 2023-12-10 ]. https://doi.org/10.19635/j.cnki.csu-epsa.001370 https://doi.org/10.19635/j.cnki.csu-epsa.001370 .
MA Qian , XIN Kuo , WANG Ziqiang , et al . A new method for scenario generation of daily photovoltaic output sequence [J/OL]. Proceedings of the CSU-EPSA [ 2023-12-10 ]. https://doi.org/10.19635/j.cnki.csu-epsa.001370 https://doi.org/10.19635/j.cnki.csu-epsa.001370 .
黎静华 , 文劲宇 , 程时杰 , 等 . 考虑多风电场出力Copula相关关系的场景生成方法 [J]. 中国电机工程学报 , 2013 , 33 ( 16 ): 30 - 36 .
LI Jinghua , WEN Jinyu , CHENG Shijie , et al . A scene generation method considering Copula correlation relationship of multi-wind farms power [J]. Proceedings of the CSEE , 2013 , 33 ( 16 ): 30 - 36 .
汤向华 , 李秋实 , 侯丽钢 , 等 . 基于Copula函数的风电时序联合出力典型场景生成 [J]. 电力工程技术 , 2020 , 39 ( 5 ): 152 - 161 .
TANG Xianghua , LI Qiushi , HOU Ligang , et al . Generation of typical sequential joint output scenarios of wind power based on Copula function [J]. Electric Power Engineering Technology , 2020 , 39 ( 5 ): 152 - 161 .
唐锦 , 张书怡 , 吴秋伟 , 等 . 基于Copula函数与等概率逆变换的风电出力场景生成方法 [J]. 电力工程技术 , 2021 , 40 ( 6 ): 86 - 94 .
TANG Jin , ZHANG Shuyi , WU Qiuwei , et al . Wind power output scenario generation method based on Copula function and equal probability inverse transformation [J]. Electric Power Engineering Technology , 2021 , 40 ( 6 ): 86 - 94 .
孙骁强 , 马晓伟 , 张小奇 , 等 . 基于相依关系的新能源功率预测场景生成及调度应用 [J]. 电力系统自动化 , 2019 , 43 ( 15 ): 10 - 17 .
SUN Xiaoqiang , MA Xiaowei , ZHANG Xiaoqi , et al . Dependence relationship based scenario generation of power prediction for renewable energy and its application in dispatch [J]. Automation of Electric Power Systems , 2019 , 43 ( 15 ): 10 - 17 .
董雷 , 孟天骄 , 陈乃仕 , 等 . 采用马尔可夫链-多场景技术的交直流主动配电网优化调度 [J]. 电力系统自动化 , 2018 , 42 ( 5 ): 147 - 153 .
DONG Lei , MENG Tianjiao , CHEN Naishi , et al . Optimized scheduling of AC/DC hybrid active distribution network using Markov chains and multiple scenarios technique [J]. Automation of Electric Power Systems , 2018 , 42 ( 5 ): 147 - 153 .
杨辉明 , 雷勇 . 基于改进马尔科夫链的风电日前不确定性建模方法 [J]. 南方电网技术 , 2021 , 15 ( 7 ): 54 - 60 .
YANG Huiming , LEI Yong . Day-ahead uncertainty simulation method of wind power based on improved Markov chain [J]. Southern Power System Technology , 2021 , 15 ( 7 ): 54 - 60 .
兰健 , 郭庆来 , 周艳真 , 等 . 基于生成对抗网络和模型迁移的电力系统典型运行方式样本生成 [J]. 中国电机工程学报 , 2022 , 42 ( 8 ): 2889 - 2900 .
LAN Jian , GUO Qinglai , ZHOU Yanzhen , et al . Generation of power system typical operation mode samples: a generation adversarial network and model-based transfer learning approach [J]. Proceedings of the CSEE , 2022 , 42 ( 8 ): 2889 - 2900 .
陈继林 , 陈勇 , 田芳 , 等 . 基于LSTM算法的电网仿真样本生成方法 [J]. 中国电机工程学报 , 2019 , 39 ( 14 ): 4129 - 4135 .
CHEN Jilin , CHEN Yong , TIAN Fang , et al . The method of sample generation for power grid simulation based on LSTM [J]. Proceedings of the CSEE , 2019 , 39 ( 14 ): 4129 - 4135 .
庄颖睿 , 程林 , 齐宁 , 等 . 基于深度时间聚类的微电网典型场景生成方法 [J]. 电力系统自动化 , 2023 , 47 ( 20 ): 95 - 103 .
ZHUANG Yingrui , CHENG Lin , QI Ning , et al . Typical scenario generation algorithm for microgrid based on deep temporal clustering [J]. Automation of Electric Power Systems , 2023 , 47 ( 20 ): 95 - 103 .
王新迎 , 李烨 , 董骁翀 , 等 . 基于变分自编码器的主动配电网多源-荷场景生成方法 [J]. 电网技术 , 2021 , 45 ( 8 ): 2962 - 2969 .
WANG Xinying , LI Ye , DONG Xiaochong , et al . Multi-source-load scenario generation of active distribution network based on variational autoencoder [J]. Power System Technology , 2021 , 45 ( 8 ): 2962 - 2969 .
HEITSCH H , RÖMISCH W . Scenario reduction algorithms in stochastic programming [J]. Computational Optimization and Applications , 2003 , 24 ( 2 ): 187 - 206 .
辛阔 , 马骞 , 许琴 , 等 . 基于月份划分与指定日类型的风电出力序列场景生成方法 [J]. 电力系统自动化 , 2023 , 47 ( 15 ): 151 - 161 .
XIN Kuo , MA Qian , XU Qin , et al . Wind power output sequence scenario generation method based on monthly division and specified day type [J]. Automation of Electric Power Systems , 2023 , 47 ( 15 ): 151 - 161 .
季玉琦 , 严亚帮 , 和萍 , 等 . 基于 k -medoids聚类与栅格法提取负荷曲线特征的CNN-LSTM短期负荷预测 [J]. 电力系统保护与控制 , 2023 , 51 ( 18 ): 81 - 93 .
JI Yuqi , YAN Yabang , HE Ping , et al . CNN-LSTM short-term load forecasting based on the k -medoids clustering and grid method to extract load curve features [J]. Power System Protection and Control , 2023 , 51 ( 18 ): 81 - 93 .
赵书强 , 要金铭 , 李志伟 . 基于改进K-means聚类和SBR算法的风电场景缩减方法研究 [J]. 电网技术 , 2021 , 45 ( 10 ): 3947 - 3954 .
ZHAO Shuqiang , YAO Jinming , LI Zhiwei . Wind power scenario reduction based on improved K-means clustering and SBR algorithm [J]. Power System Technology , 2021 , 45 ( 10 ): 3947 - 3954 .
白斌 , 韩明亮 , 林江 , 等 . 含风电和光伏的可再生能源场景削减方法 [J]. 电力系统保护与控制 , 2021 , 49 ( 15 ): 141 - 149 .
BAI Bin , HAN Mingliang , LIN Jiang , et al . Scenario reduction method of renewable energy including wind power and photovoltaic [J]. Power System Protection and Control , 2021 , 49 ( 15 ): 141 - 149 .
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 Chenyu , XU Gang . Distributed multivariate random dynamic scenario generation and fast & accurate scenario simplified algorithm [J]. Power System Technology , 2022 , 46 ( 2 ): 671 - 681 .
徐野驰 , 颜云松 , 张俊芳 , 等 . 考虑预测误差与频率响应的随机优化调度 [J]. 电网技术 , 2020 , 44 ( 10 ): 3663 - 3671 .
XU Yechi , YAN Yunsong , ZHANG Junfang , et al . Stochastic optimal dispatching considering prediction error and frequency response [J]. Power System Technology , 2020 , 44 ( 10 ): 3663 - 3671 .
黄越辉 , 曲凯 , 李驰 , 等 . 基于K-means MCMC算法的中长期风电时间序列建模方法研究 [J]. 电网技术 , 2019 , 43 ( 7 ): 2469 - 2476 .
HUANG Yuehui , QU Kai , LI Chi , et al . Research on modeling method of medium- and long-term wind power time series based on K-means MCMC algorithm [J]. Power System Technology , 2019 , 43 ( 7 ): 2469 - 2476 .
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