高比例新能源电力系统调节资源灵活性不足风险分析

doi:10.11930/j.issn.1004-9649.202401117

中国电力 ›› 2024, Vol. 57 ›› Issue (11): 129-138.DOI: 10.11930/j.issn.1004-9649.202401117

高比例新能源电力系统调节资源灵活性不足风险分析

许竞¹(), 赵铁军¹(), 高小刚¹, 叶鞠¹, 孙玲玲²()

1. 国网冀北电力有限公司秦皇岛供电公司，河北秦皇岛　066000
2. 河北省电力电子节能与传动控制重点实验室（燕山大学），河北秦皇岛　066004

收稿日期:2024-01-26 出版日期:2024-11-28 发布日期:2024-11-27
作者简介:许竞（1977—），男，硕士，高级工程师，从事电力系统及其自动化研究，E-mail：302623537@qq.com
赵铁军（1976—），男，硕士，高级工程师，从事电力系统及其自动化研究，E-mail：1499092353@qq.com
孙玲玲（1987—），女，通信作者，博士，实验师，从事电力系统规划与调度研究，E-mail：linglingwudi@126.com
基金资助:
国家自然科学基金资助项目（主动配电网电能质量治理资源分区协同优化配置策略研究，51877186）；国网冀北电力有限公司科技项目（5201041900VX）。

Risk Analysis of Insufficient Flexibility from Regulation Resources in High Proportion Renewable Energy Power Systems

Jing XU¹(), Tiejun ZHAO¹(), Xiaogang GAO¹, Ju YE¹, Lingling SUN²()

1. Qinhuangdao Power Supply Company, State Grid Jibei Electric Power Company Limited, Qinhuangdao 066000, China
2. Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province (Yanshan University), Qinhuangdao 066004, China

Received:2024-01-26 Online:2024-11-28 Published:2024-11-27
Supported by:
This work is supported by the National Natural Science Foundation of China (Research on Collaborative Optimization Allocation Strategy of power quality management resource partition in Active Distribution Network, No.51877186), Science and Technology Project of State Grid Jibei Electric Power Limited (No.5201041900VX).

摘要/Abstract

摘要：

高比例新能源接入电力系统带来了显著的波动性和不确定性，电力系统面临灵活性不足的运行风险。不确定状态下灵活性不足风险评判对控制电力系统运行风险水平、评价规划方案的性能优劣具有重要意义。研究新能源电力系统调节资源灵活性不足风险量化评估方法，提出了新能源电力系统调节资源灵活性不足风险评价指标体系。首先，基于核密度估计与序优化理论，提出源荷不确定性数据驱动建模方法；为提高电力系统源荷样本数据的充裕度，提出基于云模型的电力系统小概率风险样本集重构方法，实现训练样本的无成本、灵活获取；然后，从爬坡能力和调节深度两方面提出新能源电力系统调节资源灵活性不足的风险量化评估方法。最后，算例分析验证了所提方法的有效性和可行性。

关键词: 高比例新能源电力系统, 调节资源灵活性, 风险评估指标, 数据驱动建模, 风险样本重构

Abstract:

High-penetration renewable energy power systems introduce significant volatility and uncertainty, exposing the power system to operational risks associated with inadequate flexibility. Assessing the risk of insufficient flexibility under uncertain conditions is crucial for controlling the operational risk levels of power systems and evaluating the merits of planning scenarios. This study explores quantitative assessment methods for the risk of inadequate flexibility in renewable energy power system regulation resources and proposes a risk evaluation index system for this risk. Firstly, a data-driven modeling approach for source-load uncertainty is introduced based on kernel density estimation and order optimization theory. To enhance the adequacy of source-load sample data in power systems, a reconstruction method for low-probability risk sample sets in power systems based on cloud modeling is proposed, enabling cost-free and flexible acquisition of training samples. Secondly, a quantitative assessment method for the risk of inadequate flexibility in renewable energy power system regulation resources is developed from two aspects: ramping capability and regulation depth. Finally, case studies validate the effectiveness and feasibility of the proposed methods.

Key words: high penetration renewable energy grid, regulate resource flexibility, risk assessment indicators, data-driven modeling, reconstruction of risk samples

许竞, 赵铁军, 高小刚, 叶鞠, 孙玲玲. 高比例新能源电力系统调节资源灵活性不足风险分析[J]. 中国电力, 2024, 57(11): 129-138.

Jing XU, Tiejun ZHAO, Xiaogang GAO, Ju YE, Lingling SUN. Risk Analysis of Insufficient Flexibility from Regulation Resources in High Proportion Renewable Energy Power Systems[J]. Electric Power, 2024, 57(11): 129-138.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401117

https://www.electricpower.com.cn/CN/Y2024/V57/I11/129

图/表 16

参考文献 20

1	周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38 (7): 1893- 1904, 2205.
	ZHOU Xiaoxin, CHEN Shuyong, LU Zongxiang, et al. Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38 (7): 1893- 1904, 2205.
2	CRUZ M R M, FITIWI D Z, SANTOS S F, et al. A comprehensive survey of flexibility options for supporting the low-carbon energy future[J]. Renewable and Sustainable Energy Reviews, 2018, 97, 338- 353. DOI
3	罗毅, 李昱龙. 基于熵权法和灰色关联分析法的输电网规划方案综合决策[J]. 电网技术, 2013, 37 (1): 77- 81.
	LUO Yi, LI Yulong. Comprehensive decision-making of transmission network planning based on entropy weight and grey relational analysis[J]. Power System Technology, 2013, 37 (1): 77- 81.
4	张建平, 胡建绩. 面向智能电网的多适应性规划体系设计[J]. 电力系统自动化, 2011, 35 (10): 1- 7.
	ZHANG Jianping, HU Jianji. Design of smart grid oriented adaptive planning system[J]. Automation of Electric Power Systems, 2011, 35 (10): 1- 7.
5	马欢, 王柏公, 蔡亮, 等. 冷端耦合相变储热罐的间接空冷机组热力特性及热经济性评价[J]. 中国电机工程学报, 2023, 43 (24): 9616- 9629.
	MA Huan, WANG Baigong, CAI Liang, et al. Thermal characteristic and thermo-economic evaluation on the indirect air-cooled power generating unit with a cold-end system integrating a phase-change heat storage tank[J]. Proceedings of the CSEE, 2023, 43 (24): 9616- 9629.
6	马玉锴, 田亮. 基于证据理论与云模型的火电机组节能减排绩效综合评价[J]. 华北电力大学学报(自然科学版), 2022, 49 (3): 96- 103, 126.
	MA Yukai, TIAN Liang. Energy saving and emission reduction evaluation of thermal power units based on evidence theory and cloud model[J]. Journal of North China Electric Power University (Natural Science Edition), 2022, 49 (3): 96- 103, 126.
7	李响, 牛赛. 双碳目标下源–网–荷多层评价体系研究[J]. 中国电机工程学报, 2021, 41 (S1): 178- 184.
	LI Xiang, NIU Sai. Study on multi-layer evaluation system of source-grid-load under carbon-neutral goal[J]. Proceedings of the CSEE, 2021, 41 (S1): 178- 184.
8	白小元, 吴有兵, 林勇, 等. 含间歇性电源的能源基地源网协调发展评价[J]. 电网与清洁能源, 2023, 39 (3): 109- 115. DOI
	BAI Xiaoyuan, WU Youbing, LIN Yong, et al. Evaluation of source network coordination development for energy base containing intermittent power[J]. Power System and Clean Energy, 2023, 39 (3): 109- 115. DOI
9	赵娟, 王雷, 吴磊, 等. 基于年费用的电源结构评价[J]. 电网与清洁能源, 2019, 35 (12): 7- 14.
	ZHAO Juan, WANG Lei, WU Lei, et al. Power structure evaluation based on annual cost[J]. Power System and Clean Energy, 2019, 35 (12): 7- 14.
10	吕游, 吴斌, 秦瑞钧, 等. 燃煤机组灵活运行性能评价及成本分析[J]. 热力发电, 2023, 52 (12): 20- 28.
	LYU You, WU Bin, QIN Ruijun, et al. Flexible operation performance evaluation and cost analysis of coal-fired units[J]. Thermal Power Generation, 2023, 52 (12): 20- 28.
11	王志杰, 陈文, 朱晓星, 等. 基于数据驱动的火电机组灵活性综合评价方法及应用[J]. 现代电力, 2024, 41 (4): 667- 672.
	WANG Zhijie, CHEN Wen, ZHU Xiaoxing, et al. Comprehensive Evaluation Method for Thermal Power Unit Flexibility Based on Data-driven and its Application[J]. Modern Electric Power, 2024, 41 (4): 667- 672.
12	肖定垚, 王承民, 曾平良, 等. 考虑可再生能源电源功率不确定性的电源灵活性评价[J]. 电力自动化设备, 2015, 35 (7): 120- 125, 139.
	XIAO Dingyao, WANG Chengmin, ZENG Pingliang, et al. Power source flexibility evaluation considering renewable energy generation uncertainty[J]. Electric Power Automation Equipment, 2015, 35 (7): 120- 125, 139.
13	TANG H L, WU J K, CHEN L M, et al. A control optimization model for CVaR risk of distribution systems with PVs/DSs/EVs using Q-learning powered adaptive differential evolution algorithm[J]. International Journal of Electrical Power & Energy Systems, 2021, 132, 107209.
14	JIA J C, YIN G L, SUN L L, et al. Schedule strategy considering the overload violation risk to the security region in distribution networks[J]. Energies, 2022, 15 (23): 8781. DOI
15	JIA J C, YIN G L, SUN L L, et al. Uncertainty risk assessment of overloading violation based on security region and risk scheduling of active distribution networks[J]. International Journal of Electrical Power & Energy Systems, 2024, 155, 109498.
16	ROSEWATER D, BALDICK R, SANTOSO S. Risk-averse model predictive control design for battery energy storage systems[J]. IEEE Transactions on Smart Grid, 2020, 11 (3): 2014- 2022. DOI
17	JU L W, ZHAO R, TAN Q L, et al. A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response[J]. Applied Energy, 2019, 250, 1336- 1355. DOI
18	杨楠, 黄禹, 叶迪, 等. 基于自适应多变量非参数核密度估计的多风电场出力相关性建模[J]. 中国电机工程学报, 2018, 38 (13): 3805- 3812, 4021.
	YANG Nan, HUANG Yu, YE Di, et al. Modeling of output correlation of multiple wind farms based on adaptive multivariable nonparametric kernel density estimation[J]. Proceedings of the CSEE, 2018, 38 (13): 3805- 3812, 4021.
19	COWLING A, HALL P. On pseudodata methods for removing boundary effects in kernel density estimation[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996, 58 (3): 551- 563. DOI
20	李德毅, 刘常昱. 论正态云模型的普适性[J]. 中国工程科学, 2004, 6 (8): 28- 34.
	LI Deyi, LIU Changyu. Study on the universality of the normal cloud model[J]. Engineering Science, 2004, 6 (8): 28- 34.

情况	风力装机容量/ MW	光伏装机容量/ MW	最大负荷容量/ MW
1	0	33.74	34.79
2	25.89	0	31.24
3	20.58	0	44.37

情况	风力装机容量/ MW	光伏装机容量/ MW	最大负荷容量/ MW
1	0	33.74	34.79
2	25.89	0	31.24
3	20.58	0	44.37

情况	新能源			负荷
情况	$E_{\text{x}}$	$E_{\text{n}}$	$H_{\text{e}}$	$E_{\rm x}$	$E_{\text{n}}$	$H_{\text{e}}$
1	0	12.54	5.41	22.42	1.18	3.64
2	18.39	3.10	0.61	21.04	0.49	3.51
3	15.24	3.34	1.24	24.53	2.45	5.33

情况	新能源			负荷
情况	$E_{\text{x}}$	$E_{\text{n}}$	$H_{\text{e}}$	$E_{\rm x}$	$E_{\text{n}}$	$H_{\text{e}}$
1	0	12.54	5.41	22.42	1.18	3.64
2	18.39	3.10	0.61	21.04	0.49	3.51
3	15.24	3.34	1.24	24.53	2.45	5.33

情况	新能源出力/MW	负荷/MW
1	[0，34.31]	[15.75，28.27]
2	[12.24，24.04]	[15.04，26.52]
3	[8.94，20.63]	[10.07，33.86]

高比例新能源电力系统调节资源灵活性不足风险分析

Risk Analysis of Insufficient Flexibility from Regulation Resources in High Proportion Renewable Energy Power Systems

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 20

相关文章 1

编辑推荐

Metrics

情况	新能源出力	负荷
1	97.84	55.84
2	64.17	54.56
3	76.71	96.98

情况	上爬坡能力不足最大风险	上爬坡能力不足最大风险概率/%	上爬坡能力不足总风险	下爬坡能力不足最大风险	下爬坡能力不足最大风险概率/%	下爬坡能力不足的总风险
1	0.26	3.15	1.40	0.18	2.27	0.86
2	0.06	1.26	0.38	0.05	0.81	0.33
3	0.32	2.53	1.48	0.27	3.07	1.52

情况	上调节深度不足最大风险	上调节深度不足最大风险概率/%	年上调节深度不足总风险	下调节深度不足最大风险	下调节深度不足最大风险概率/%	年下调节深度不足总风险
1	0.05	1.86	0.18	0.92	7.68	4.24
2	0.09	2.59	0.24	0.10	2.84	0.35
3	1.58	9.78	6.97	0	0	0

情况	新能源出力/MW	负荷/MW
1	[0，30.58]	[16.68，26.94]
2	[13.24，23.37]	[16.36，24.48]
3	[10.16，18.86]	[14.24，31.57]

情况	新能源出力/MW	负荷/MW
1	[0，30.58]	[16.68，26.94]
2	[13.24，23.37]	[16.36，24.48]
3	[10.16，18.86]	[14.24，31.57]

模态框（Modal）标题

高比例新能源电力系统调节资源灵活性不足风险分析

Risk Analysis of Insufficient Flexibility from Regulation Resources in High Proportion Renewable Energy Power Systems

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 20

相关文章 1

编辑推荐

Metrics