高比例新能源电网运行备用容量需求概率动态评估方法

doi:10.12204/j.issn.1000-7229.2023.06.013

电力建设 ›› 2023, Vol. 44 ›› Issue (6): 126-134.doi: 10.12204/j.issn.1000-7229.2023.06.013

高比例新能源电网运行备用容量需求概率动态评估方法

吴思嘉¹(), 迟方德², 叶希³, 况理¹, 王泽¹, 文云峰¹()

1.湖南大学电气与信息工程学院, 长沙市 410082
2.国网陕西省电力有限公司, 西安市 710048
3.国网四川省电力公司, 成都市 610041

收稿日期:2022-08-22 出版日期:2023-06-01 发布日期:2023-05-25
通讯作者: 文云峰(1986),男,博士,教授,主要研究方向为低惯量电力系统规划、运行与控制,E-mail:yunfeng.8681@163.com。
作者简介:吴思嘉(1999),女,硕士研究生,主要研究方向为电力系统优化运行,E-mail:wusijia_1999@163.com;
迟方德(1979),男,硕士,教授级高级工程师,主要从事电力系统运行控制、调度管理工作;
叶希(1987),女,博士,高级工程师,主要从事电网规划与新能源分析工作;
况理(1996),男,硕士,主要研究方向为电力系统运行备用容量需求评估和优化;
王泽(1997),男,硕士研究生,主要研究方向为高比例新能源电力系统备用需求评估。
基金资助:
国家自然科学基金项目(52077066)

Probabilistic Dynamic Assessment for Operating Reserve Requirements of Power System with High Penetrated Renewables

WU Sijia¹(), CHI Fangde², YE Xi³, KUANG Li¹, WANG Ze¹, WEN Yunfeng¹()

1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2. State Grid Shaanxi Electric Power Company, Xi’an 710048, China
3. State Grid Sichuan Electric Power Company, Chengdu 610041, China

Received:2022-08-22 Online:2023-06-01 Published:2023-05-25
Supported by:
National Natural Science Foundation of China(52077066)

摘要/Abstract

摘要：

我国目前采用基于负荷百分比和最大单机容量的运行备用容量配置准则,在高比例新能源接入场景下存在备用容量不足或过于充裕的问题。针对当前调度运行中面临的如何准确评估高比例新能源电网运行备用容量需求的难题,构建了基于非参数核密度估计的运行备用容量需求概率动态评估方法,并提出考虑新能源渗透率和评估时段的置信度动态选择策略,可精确评价复杂多变场景的运行备用需求量,实现对上调备用容量需求和下调备用容量需求的滚动式差异化评估。在此基础上,构建了考虑动态备用容量需求的常规电源开机容量制定策略,在保障运行备用容量充裕的前提下最大程度提升新能源消纳空间。基于某新能源高占比省级电网开展了应用测试,验证了所提方法的有效性。

关键词: 运行备用, 备用容量, 核密度估计, 开机方式, 新能源

Abstract:

The current criterion for the configuration of operating reserve capacity is based on the load percentage and maximum single-unit capacity, which can result in insufficient or excessively abundant reserve capacity in cases with a high proportion of renewable energy access. To address the challenge of accurately assessing the reserve capacity demand for high proportions of renewable energy grid operation during current dispatching operations, this study proposes a probabilistic dynamic assessment method for operating reserve requirements based on kernel density estimation. Furthermore, a dynamic confidence selection strategy considering the renewable energy penetration rate and evaluation period is proposed, which can facilitate the rolling differential evaluation of the up- and down-regulation of reserve capacity demand. Based on this proposal, a strategy for formulating conventional power supply startup capacity that considers dynamic reserve capacity demands was developed. This strategy maximizes the space for renewable energy consumption while ensuring sufficient operation reserve capacity. An application test was conducted using a provincial power grid with a high proportion of renewable energy sources, and the effectiveness of the proposed method was verified.

Key words: operational reserve, reserve capacity, kernel density estimation, unit commitment, renewable energy

中图分类号:

TM73

吴思嘉, 迟方德, 叶希, 况理, 王泽, 文云峰. 高比例新能源电网运行备用容量需求概率动态评估方法[J]. 电力建设, 2023, 44(6): 126-134.

WU Sijia, CHI Fangde, YE Xi, KUANG Li, WANG Ze, WEN Yunfeng. Probabilistic Dynamic Assessment for Operating Reserve Requirements of Power System with High Penetrated Renewables[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 126-134.

图/表 8

图1 新能源日前出力预测误差概率拟合

Fig.1 Probability fitting of day-ahead renewable generation prediction error

图2 日前运行备用容量需求概率动态评估框架

Fig.2 Day-ahead operational reserve capacity requirement assessment framework

图3 核密度估计方法中置信水平动态选取流程

Fig.3 Confidence level dynamic selection process in Kernel density estimation method

图4 常规电源开机容量对新能源消纳空间的影响

Fig.4 Impact of traditional unit online capacity on the accommodation range of renewable generation

图5 大负荷方式下运行备用容量需求评估结果

Fig.5 Evaluation results of different methods for testing grid operation reserve capacity requirements

表1 运行备用容量需求评估方法适应性分析

Table 1 Adaptability analysis of different assessment methods on operational reserve capacity requirement

图6 小负荷方式下运行备用容量需求评估结果

Fig.6 Evaluation results of different methods for testing grid operation reserve capacity requirements

图7 不同方法下次日常规电源开机容量

Fig.7 The online capacity of the conventional power supply of the grid on the next day tested by different methods

参考文献 31

[1]	文云峰, 杨伟峰, 汪荣华, 等. 构建100%可再生能源电力系统述评与展望[J]. 中国电机工程学报, 2020, 40(6): 1843-1856.
	WEN Yunfeng, YANG Weifeng, WANG Ronghua, et al. Review and prospect of toward 100% renewable energy power systems[J]. Proceedings of the CSEE, 2020, 40(6): 1843-1856.
[2]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.
[3]	温佳鑫, 卜思齐, 陈麒宇, 等. 基于数据学习的新能源高渗透电网频率风险评估[J]. 发电技术, 2021, 42(1):40-47. doi: 10.12096/j.2096-4528.pgt.20105
	WEN Jiaxin, BU Siqi, CHEN Qiyu, et al. Data learning-based frequency risk assessment in a high-penetrated renewable power system[J]. Power Generation Technology, 2021, 42(1):40-47. doi: 10.12096/j.2096-4528.pgt.20105
[4]	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[5]	LIU L K, HU Z C. Data-driven regulation reserve capacity determination based on Bayes theorem[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1646-1649. doi: 10.1109/TPWRS.59 URL
[6]	电力系统技术导则: GB/T 38969—2020[S]. 北京: 中国标准出版社, 2020.
	Guide on technology for power system:GB/T 38969—2020[S]. Beijing: Standards Press of China, 2020.
[7]	于洋, 陈琳, 甘德强, 等. 国内外备用容量评估方法比较[J]. 电力系统自动化, 2005, 29(18): 19-23.
	YU Yang, CHEN Lin, GAN Deqiang, et al. Comparison between foreign and domestic experiences in reserve assessment[J]. Automation of Electric Power Systems, 2005, 29(18): 19-23.
[8]	杨肖虎, 罗剑波, 郁琛, 等. 适应大规模新能源并网的电力系统备用配置及优化综述[J]. 电力工程技术, 2020, 39(1): 10-20, 63.
	YANG Xiaohu, LUO Jianbo, YU Chen, et al. Review of power system reserve configuration and optimization for large-scale renewable energy integration[J]. Electric Power Engineering Technology, 2020, 39(1): 10-20, 63.
[9]	MOUSAVI AGAH S M, FLYNN D. Impact of modelling non-normality and stochastic dependence of variables on operating reserve determination of power systems with high penetration of wind power[J]. International Journal of Electrical Power & Energy Systems, 2018, 97: 146-154. doi: 10.1016/j.ijepes.2017.11.002 URL
[10]	李晓萌, 张忠, 赵华, 等. 能源互联网背景下电网备用问题探索[J]. 电力建设, 2021, 42(5): 57-68. doi: 10.12204/j.issn.1000-7229.2021.05.007
	LI Xiaomeng, ZHANG Zhong, ZHAO Hua, et al. Research on reserve services of the power grid under the background of energy Internet[J]. Electric Power Construction, 2021, 42(5): 57-68. doi: 10.12204/j.issn.1000-7229.2021.05.007
[11]	ELA E, MILLIGAN M, KIRBY B. Operating reserves and variable generation[R]. National Renewable Energy Lab, Golden, CO(United States), 2011.
[12]	ZHANG G Y, MCCALLEY J D. Estimation of regulation reserve requirement based on control performance standard[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1173-1183. doi: 10.1109/TPWRS.2017.2734654 URL
[13]	ORTEGA-VAZQUEZ M A, KIRSCHEN D S. Optimizing the spinning reserve requirements using a cost/benefit analysis[J]. IEEE Transactions on Power Systems, 2007, 22(1): 24-33. doi: 10.1109/TPWRS.2006.888951 URL
[14]	黄鹏翔, 周云海, 徐飞, 等. 基于负荷与风电出力场景集的运行备用动态调度方法[J]. 可再生能源, 2021, 39(5): 658-665.
	HUANG Pengxiang, ZHOU Yunhai, XU Fei, et al. Operating reserve dynamic dispatching method based on load and wind power scenario set[J]. Renewable Energy Resources, 2021, 39(5): 658-665.
[15]	GONG Y Z, JIANG Q Y, BALDICK R. Ramp event forecast based wind power ramp control with energy storage system[J]. IEEE Transactions on Power Systems, 2016, 31(3): 1831-1844. doi: 10.1109/TPWRS.2015.2445382 URL
[16]	CHATTOPADHYAY D, BALDICK R. Unit commitment with probabilistic reserve[C]// 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309). IEEE, 2002: 280-285.
[17]	刘建涛, 朱炳铨, 马经纬, 等. 计及可靠性的日前旋转备用容量评估指标[J]. 电网技术, 2019, 43(6): 2147-2154.
	LIU Jiantao, ZHU Bingquan, MA Jingwei, et al. Study on evaluation indexes of spinning reserve capacity considering reliability in day-ahead schedule[J]. Power System Technology, 2019, 43(6): 2147-2154.
[18]	吴杰康, 史美娟, 陈国通, 等. 区域电力系统最优备用容量模型与算法[J]. 中国电机工程学报, 2009, 29(1): 14-20.
	WU Jiekang, SHI Meijuan, CHEN Guotong, et al. Immune genetic algorithms for modeling optimal reserve capacity of interconnected regional power systems[J]. Proceedings of the CSEE, 2009, 29(1): 14-20.
[19]	张静, 李生虎. 考虑风电机组旋转备用的风电系统备用容量计算[J]. 合肥工业大学学报(自然科学版), 2013, 36(10): 1179-1184.
	ZHANG Jing, LI Shenghu. Calculation of reserve capacity of wind power system with spinning reserve of wind turbine generators[J]. Journal of Hefei University of Technology (Natural Science), 2013, 36(10): 1179-1184.
[20]	张楠, 黄越辉, 刘德伟, 等. 考虑风电接入的电力系统备用容量计算方法[J]. 电力系统及其自动化学报, 2016, 28(3): 6-10.
	ZHANG Nan, HUANG Yuehui, LIU Dewei, et al. Reserve capacity calculation of power system considering wind power integration[J]. Proceedings of the CSU-EPSA, 2016, 28(3): 6-10.
[21]	WANG Y, BAYEM H, GIRALT-DEVANT M, et al. Methods for assessing available wind primary power reserve[J]. IEEE Transactions on Sustainable Energy, 2015, 6(1): 272-280. doi: 10.1109/TSTE.2014.2369235 URL
[22]	陶诗洋, 洪沅伸, 张天辰, 等. 计及源-荷多灵活备用资源的随机优化调度[J]. 电力建设, 2021, 42(12): 39-48. doi: 10.12204/j.issn.1000-7229.2021.12.005
	TAO Shiyang, HONG Yuanshen, ZHANG Tianchen, et al. Stochastic optimal scheduling considering multiple flexible reserve resources on both source and load sides[J]. Electric Power Construction, 2021, 42(12): 39-48. doi: 10.12204/j.issn.1000-7229.2021.12.005
[23]	李冉, 王明强, 杨明, 等. 考虑故障概率和净负荷不确定性的鲁棒随机备用优化[J]. 电力系统自动化, 2022, 46(6): 20-29.
	LI Ran, WANG Mingqiang, YANG Ming, et al. Robust-stochastic reserve optimization considering uncertainties of failure probability and net load[J]. Automation of Electric Power Systems, 2022, 46(6): 20-29.
[24]	蔡乾, 王晶, 耿天翔, 等. 考虑新能源资源及出力特性的全局备用容量优化方法[J]. 中国电力, 2021, 54(2): 90-97.
	CAI Qian, WANG Jing, GENG Tianxiang, et al. Global reserve optimization method considering resources and output characteristics of renewable energy[J]. Electric Power, 2021, 54(2): 90-97.
[25]	FAHIMAN F, DISANO S, ERFANI S M, et al. Data-driven dynamic probabilistic reserve sizing based on dynamic Bayesian belief networks[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2281-2291. doi: 10.1109/TPWRS.59 URL
[26]	PARKER K, BAROOAH P. A probabilistic method for reserve sizing in power grids with high renewable penetration[J]. IEEE Transactions on Power Systems, 2021, 36(3): 2473-2480. doi: 10.1109/TPWRS.2020.3030041 URL
[27]	徐询, 谢丽蓉, 叶林, 等. 基于非参数核密度估计的风电场有功功率双层优化模型[J]. 电力系统自动化, 2022, 46(2): 43-55.
	XU Xun, XIE Lirong, YE Lin, et al. Bi-level optimization model of active power for wind farm based on nonparametric kernel density estimation[J]. Automation of Electric Power Systems, 2022, 46(2): 43-55.
[28]	ZHAO C F, WAN C, SONG Y H. Operating reserve quantification using prediction intervals of wind power: an integrated probabilistic forecasting and decision methodology[J]. IEEE Transactions on Power Systems, 2021, 36(4): 3701-3714. doi: 10.1109/TPWRS.2021.3053847 URL
[29]	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.
[30]	肖心园, 江冰, 任其文, 等. 基于插值法和皮尔逊相关的光伏数据清洗[J]. 信息技术, 2019, 43(5): 19-22, 28.
	XIAO Xinyuan, JIANG Bing, REN Qiwen, et al. Photovoltaic data cleaning based on interpolation and Pearson correlation[J]. Information Technology, 2019, 43(5): 19-22, 28.
[31]	张振宇, 孙骁强, 万筱钟, 等. 基于统计学特征的新能源纳入西北电网备用研究[J]. 电网技术, 2018, 42(7): 2047-2054.
	ZHANG Zhenyu, SUN Xiaoqiang, WAN Xiaozhong, et al. Research on reserve of northwest power grid considering renewable energy based on statistical characteristics[J]. Power System Technology, 2018, 42(7): 2047-2054.

高比例新能源电网运行备用容量需求概率动态评估方法

Probabilistic Dynamic Assessment for Operating Reserve Requirements of Power System with High Penetrated Renewables

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李国庆, 张斌, 肖桂莲, 刘大贵, 范慧静, 甄钊, 任惠. 基于多尺度特征集的高占比新能源电网连锁故障数据驱动辨识方法[J]. 电力建设, 2023, 44(6): 91-100.
[2]	李德鑫, 郑涛岳, 姜齐荣, 何斓珂, 董洪达, 李成钢, 刘座铭. 计及消纳惩罚的日前市场优化出清方法[J]. 电力建设, 2023, 44(4): 29-36.
[3]	于琳琳, 王泽, 郝元钊, 晏昕童, 张丽华, 严格, 文云峰. 基于XGBoost的电力系统动态频率响应曲线预测方法[J]. 电力建设, 2023, 44(4): 74-81.
[4]	杨德友, 孟振, 王博, 段方维. 暂态频率约束下考虑新能源最优减载的机组组合双层优化策略[J]. 电力建设, 2023, 44(2): 74-82.
[5]	张尧翔, 刘文颖, 庞清仑, 李亚楼, 安宁, 李芳. 计及综合需求响应参与消纳受阻新能源的多时间尺度优化调度策略[J]. 电力建设, 2023, 44(1): 1-11.
[6]	任景, 周鑫, 程松, 王茁宇, 张小东, 唐早, 刘继春. 源荷双边参与的高比例新能源电力系统能量与备用市场联合出清方法[J]. 电力建设, 2023, 44(1): 30-38.
[7]	鲍海波, 吴阳晨, 张国应, 李江伟, 郭小璇, 黎静华. 基于特征加权Stacking集成学习的净负荷预测方法[J]. 电力建设, 2022, 43(9): 104-116.
[8]	刁利, 李光, 宋雪莹, 德格吉日夫, 谭忠富. 新能源分比例渗透下基于博弈的多主体效益综合评估方法[J]. 电力建设, 2022, 43(6): 43-55.
[9]	张兴平, 何澍, 王泽嘉, 张浩楠, 张又中. 不同新能源渗透率下燃煤机组行为策略分析[J]. 电力建设, 2022, 43(5): 9-17.
[10]	刘飞, 赵澄颢, 王世斌, 田旭, 刘联涛, 朱晓荣. 新能源直流汇集分群综合协调控制策略[J]. 电力建设, 2022, 43(5): 127-136.
[11]	杨立滨, 张磊, 刘艳章, 李正曦, 宗鸣. 基于分布式框架的新能源场站并网性能评估[J]. 电力建设, 2022, 43(5): 137-144.
[12]	康思伟, 董文凯, 郭诗然, 孙红军, 谢小荣. 基于虚拟同步机控制的新能源发电并网系统小干扰稳定临界短路比[J]. 电力建设, 2022, 43(3): 131-140.
[13]	李建宜, 李鹏, 徐晓春, 施儒昱, 曾平良, 夏辉. 基于综合概率模型与深度学习的智能电网功率-电压映射方法[J]. 电力建设, 2022, 43(2): 37-44.
[14]	邢超, 奚鑫泽, 何廷一, 李胜男, 刘明群. 计及风电调频的备用容量滚动优化方法[J]. 电力建设, 2022, 43(2): 117-125.
[15]	严欢, 胡俊杰, 黄旦莉, 张展宇, 岳园园. 考虑电动汽车虚拟电厂灵活性和高比例光伏接入的配电网规划[J]. 电力建设, 2022, 43(11): 14-23.