Key technologies of the evaluation on distributed wind-storage systems' frequency and voltage regulation capacities

doi:10.3969/j.issn.2097-0706.2024.06.009

Abstract

Abstract:

The wide and scattered connection of wind power to medium/high voltage distribution networks is getting popular，resulting in insufficient frequency and voltage support of weak power grids. The evaluation on the frequency and voltage regulation capacities of distributed wind-storage systems is helpful for the scheduling and management of grids，detecting and avoiding potential problems that could destroy the stability of grids timely，but the construction of an evaluation system is thorny due to its complex and variable structure. To fulfill the construction， the influence factors， comprehensive evaluation indicators and comprehensive evaluation method for the systems' frequency and voltage regulation capacities have to be considered comprehensively in analyzing existing technical methods and pointing out the key techniques and challenges. The study focuses on the following contents：multi-time scale high-precision wind power output forecasting technology，adjustment capacities of different wind-storage system configuration schemes under different conditions;calculation and improvement methods of the frequency-voltage regulation capacity and contribution index of a distributed wind-storage system under multi-modal coupling condition; improvement on the robustness of evaluation method under uncertain conditions. Finally， new ideas on frequency and voltage regulation capability evaluation for distributed wind-storage systems are presented， to provide references for the development of wind power in China.

Key words: distributed wind-storage systems, frequency control, voltage control, assessment of regulation capability

CLC Number:

TK01：TM712

ZHAO Changwei, WANG Hui, GU Zhicheng, LIU Xubin, ZHU Guangming, GE Leijiao. Key technologies of the evaluation on distributed wind-storage systems' frequency and voltage regulation capacities[J]. Integrated Intelligent Energy, 2024, 46(6): 78-87.

Figures/Tables 2

References 50

[1]	何可欣, 马速良, 马壮, 等. 储能技术发展态势及政策环境分析[J]. 分布式能源, 2021, 6(6): 45-52.
	HE Kexin, MA Suliang, MA Zhuang, et al. Energy storage technology development trend and policy environment analysis[J]. Distributed Energy, 2021, 6(6): 45-52.
[2]	舒印彪, 陈国平, 贺静波, 等. 构建以新能源为主体的新型电力系统框架研究[J]. 中国工程科学, 2021, 23(6): 61-69.
	SHU Yinbiao, CHEN Guoping, HE Jingbo, et al. Building a new electric power system based on new energy sources[J]. Strategic Study of CAE, 2021, 23(6): 61-69.
[3]	文云峰, 杨游航, 邢鹏翔, 等. 多维因素制约下新能源消纳能力评估方法研究综述[J/OL]. 中国电机工程学报,1-25(2023-05-18)[2024-01-02]. https://doi.org/10.13334/j.0258-8013.pcsee.223390.
	WEN Yunfeng, YANG Youhang, XING Pengxiang, et al. Review on the new energy accommodation capability evaluation methods considering muti-dimensiona factor[J/OL]. Proceedings of the CSEE,1-25(2023-05-18)[2024-01-02]. https://doi.org/10.13334/j.0258-8013.pcsee.223390.
[4]	孙秦峰, 李凤婷, 王森, 等. 提升风电场有功调节能力的风储系统多时间尺度运行策略[J]. 电力系统保护与控制, 2023, 51(9): 21-31.
	SUN Qinfeng, LI Fengting, WANG Sen, et al. Multi-timescale operation strategy of a wind storage system to enhance the active regulation capacity of wind farms[J]. Power System Protection and Control, 2023, 51(9): 21-31.
[5]	葛磊蛟, 范延赫, 来金钢, 等. 面向低碳经济的人工智能赋能微电网优化运行技术[J]. 高电压技术, 2023, 49(6): 2219-2238.
	GE Leijiao, FAN Yanhe, LAI Jingang, et al. Artificial intelligence enabled microgrid optimization technology for low carbon economy[J]. High Voltage Engineering, 2023, 49(6): 2219-2238.
[6]	冯璐, 李斌, 张新敬, 等. 考虑需求侧响应的风储系统容量配置优化分析[J]. 中国电机工程学报, 2020, 40(S1): 222-231.
	FENG Lu, LI Bin, ZHANG Xinjing, et al. Capacity allocation optimization of energy storage system considering demand side response[J]. Proceedings of the CSEE, 2020, 40 (S1): 222-231.
[7]	牛东晓, 纪会争. 风电功率物理预测模型引入误差量化分析方法[J]. 电力系统自动化, 2020, 44(8): 57-65.
	NIU Dongxiao, JI Huizheng. Quantitative analysis method for errors introduced by physical prediction model of wind power[J]. Automation of Electric Power Systems, 2020, 44 (8): 57-65
[8]	姜兆宇, 贾庆山, 管晓宏. 多时空尺度的风力发电预测方法综述[J]. 自动化学报, 2019, 45(1): 51-71.
	JIANG Zhaoyu, JIA Qingshan, GUAN Xiaohong. A Review of multi-temporal-and-spatial-scale wind power forecasting method[J]. Acta Automatica Sinica, 2019, 45(1): 51-71.
[9]	ROUNGKVIST J S, ENEVOLDSEN P. Timescale classification in wind forecasting: A review of the state of the art[J]. Journal of Forecasting, 2020, 39(5): 757-768.
[10]	梁超, 刘永前, 周家慷, 等. 基于卷积循环神经网络的风电场内多点位风速预测方法[J]. 电网技术, 2021, 45(2): 534-541.
	LIANG Chao, LIU Yongqian, ZHOU Jiakang, et al. Wind speed prediction at multi-locations based on combination of recurrent and convolutional neural networks[J]. Power System Technology, 2021, 45(2): 534-541.
[11]	朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802.
	ZHU Qiaomu, LI Hongyi, WANG Ziqi, et al. Short-term wind power forecasting based on LSTM[J]. Power System Technology, 2017, 41(12): 3797-3802.
[12]	殷豪, 欧祖宏, 陈德, 等. 基于二次模式分解和级联式深度学习的超短期风电功率预测[J]. 电网技术, 2020, 44(2):445-453.
	YIN Hao, OU Zuhong, CHEN De, et al. Ultra-short-term wind power prediction based on two-layer mode decomposition and cascaded deep learning[J]. Power System Technology, 2020, 44(2): 445-453.
[13]	王铮, RUI Pestana, 冯双磊, 等. 基于加权系数动态修正的短期风电功率组合预测方法[J]. 电网技术, 2017, 41(2): 500-507.
	WANG Zheng, RUI Pestana, FENG Shuanglei, et al. Short-term wind power combination forecasting method based on dynamic coefficient updating[J]. Power System Technology, 2017, 41(2): 500-507.
[14]	孙伟卿, 罗静, 张婕. 高比例风电接入的电力系统储能容量配置及影响因素分析[J]. 电力系统保护与控制, 2021, 49(15): 9-18.
	SUN Weiqing, LUO Jing, ZHANG Jie. Energy storage capacity allocation and influence factor analysis of a power system with a high proportion of wind power[J]. Power System Protection and Control, 2021, 49(15): 9-18.
[15]	徐雁飞, 宋天昊, 袁铁江, 等. 风-储联合发电系统容量优化配置及其影响因素分析[J]. 电力电容器与无功补偿, 2021, 42(1): 173-180.
	XU Yanfei, SONG Tianhao, YUAN Tiejiang, et al. Capacity optimization configuration of wind-storage power generation system and analysis of its influencing factors[J]. Power Capacitor & Reactive Power Compensation, 2021, 42(1): 173-180.
[16]	谢桦, 滕晓斐, 张艳杰, 等. 风/光/储微网规划经济性影响因素分析[J]. 电力系统自动化, 2019, 43(6): 70-76,115.
	XIE Hua, TENG Xiaofei, ZHANG Yanjie, et al. Analysis of economic influence factors in wind photovoltaic storage microgrid[J]. Automation of Electric Power Systems, 2019, 43(6): 70-76,115.
[17]	丁明, 刘新宇, 朱乾龙, 等. 电网风电接纳能力建模及影响因素分析[J]. 电气工程学报, 2016, 11(7): 1-8,17.
	DING Ming, LIU Xinyu, ZHU Qianlong, et al. Research on the model and influences of power grid's ability of admitting wind power[J]. Journal of Electrical Engineering, 2016, 11(7): 1-8,17.
[18]	ZHU W J, SONG K X, GU Y L, et al. Allocating the capacity of shared energy storage for wind farm groups based on the over-limit power export risk[J]. Frontiers in Energy Research, 2023, 10: 1099262.
[19]	YIN W Q, LI Y J, HOU J Z, et al. Coordinated planning of wind power generation and energy storage with decision-dependent uncertainty induced by spatial correlation[J]. IEEE Systems Journal, 2023, 17(2): 2247-2258.
[20]	LIU S, WANG L, JIANG H L, et al. Wind farm energy storage system based on cat swarm optimization-backpropagation neural network wind power prediction[J]. Frontiers in Energy Research, 2022, 10.DOI:10.3389/fenrg.2022.850295.
[21]	SUN D Y, ZHAO F Q, GUO Y F, et al. Research on multi-energy cooperative participation of grid frequency inertia response control strategy for energy storage type doubly-fed wind turbine considering wind speed disturbance[J]. Frontiers in Energy Research, 2023, 11.DOI:10.3389/fenrg.2023.1068080.
[22]	LI L, LU G Z, LIANG Z C, et al. Optimal control strategy of wind-storage combined system participating in frequency regulation based on Bollinger bands[J]. Journal of Electrical Engineering & Technology, 2023, 18:3361-3373.
[23]	LI J L, XIN D X, LIU C A, et al. Research on the frequency regulation characteristics and control strategy of wind power generation with energy storage synergy[J]. Batteries, 2023, 9(2),117.DOI:10.3390/batteries9020117.
[24]	GHOLAMI M, SHAHRYARI O, REZAEI N, et al. Optimum storage sizing in a hybrid wind-battery energy system considering power fluctuation characteristics[J]. Journal of Energy Storage, 2022, 52:104634.
[25]	BALDI F, CORADDU A, KALIKATZARAKIS M, et al. Optimisation-based system designs for deep offshore wind farms including power to gas technologies[J]. Applied Energy, 2022, 310:118540.
[26]	孙振球. 医学综合评价方法及其应用[M]. 北京: 化学工业出版社, 2006.
[27]	陶顺. 现代电力系统电能质量评估体系的研究[D]. 北京: 华北电力大学, 2008.
	TAO Shun. Research on power quality assessment system of modern power system[D]. Beijing: North China Electric Power University, 2008.
[28]	肖白, 赵雪纯, 董光德. 电能质量综合评估方法综述与展望[J/OL]. 发电技术,1-18(2023-12-28)[2024-01-02]. http://kns.cnki.net/kcms/detail/33.1405.tk.20231227.1124.002.html.
	XIAO Bai, ZHAO Xuechun, DONG Guangde. Review and prospect of power quality comprehensive evaluation methods[J/OL]. Power Generation Technology,1-18(2023-12-28)[2024-01-02]. http://kns.cnki.net/kcms/detail/33.1405.tk.20231227.1124.002.html.
[29]	李强年, 赵巧妮, 聂龑. 基于层次分析法(AHP)的风电产业发展影响因素探析——以甘肃省酒泉风力发电基地为例[J]. 电网与清洁能源, 2019, 35(10):75-81.
	LI Qiangnian, ZHAO Qiaoni, NIE Yan. Research on the factors affecting the development of wind power industry based on Analytic Hierarchy Process (AHP)—Taking Jiuquan wind power generation base in Gansu province as an example[J]. Power System and Clean Energy, 2019, 35(10):75-81.
[30]	LIU Z W, XIE Q Y, DAI L, et al. Research on comprehensive evaluation method of distribution network based on AHP-entropy weighting method[J]. Frontiers in Energy Research, 2022, 10.DOI:10.3389/fenrg.2022.975462.
[31]	苏开元, 董文凯, 邱银锋, 等. 分散式风储一体化系统提升海上油田群电网频率稳定性研究[J]. 中国电力, 2023, 56(5):163-171.
	SU Kaiyuan, DONG Wenkai, QIU Yinfeng, et al. Study on using distributed wind-storage integrated system to improve frequency stability of offshore oilfield power systems[J]. Electric Power, 2023, 56(5): 163-171.
[32]	蒋平, 袁铁江, 车勇, 等. 分散式风储一体化系统容量规划研究[J]. 电力电容器与无功补偿, 2018, 39(1):138-146.
	JIANG Ping, YUAN Tiejiang, CHE Yong, et al. Study on capacity planning of distributed wind-storage integration system[J]. Power Capacitor & Reactive Power Compensation, 2018, 39(1):138-146.
[33]	张洋, 丘东元, 张波, 等. DC-DC变换器分层级构造方法[J]. 电工技术学报, 2023, 38(20): 5473-5487.
	ZHANG Yang, QIU Dongyuan, ZHANG Bo, et al. Hierarchical construction method of DC-DC converter[J]. Transactions of China Electrotechnical Society, 2023, 38(20): 5473-5487.
[34]	李成, 张婕, 石轲, 等. 面向风电场的主动支撑电网型分散式储能控制策略与优化配置[J]. 中国电力, 2023, 56(12):238-247.
	LI Cheng, ZHANG Jie, SHI Ke, et al. Distributed energy storage control strategy and optimal configuration of active support grid for wind farms[J]. Electric Power, 2023, 56(12):238-247.
[35]	王力, 胡佳成, 曾祥君, 等. 基于混合储能的交直流混联微电网功率分级协调控制策略[J/OL]. 电工技术学报,1-14(2023-12-13)[2024-01-02]. https://doi.org/10.19595/j.cnki.1000-6753.tces.231426.
	WANG Li, HU Jiacheng, ZENG Xiangjun, et al. Power hierarchical coordinated control strategy for AC-DC hybrid microgrid based on hybrid energy storage[J/OL]. Transactions of China Electrotechnical Society,1-14(2023-12-13)[2024-01-02]. https://doi.org/10.19595/j.cnki.1000-6753.tces.231426.
[36]	王永生, 张哲, 刘利民, 等. 基于改进熵权法和SECEEMD的短期风电功率预测[J]. 科学技术与工程, 2023, 23(27): 11688-11697.
	WANG Yongsheng, ZHANG Zhe, LIU Limin, et al. Short term wind power prediction based on improved entropy weight method and SECEEMD[J]. Science Technology and Engineering, 2023, 23(27): 11688-11697.
[37]	王晓晨, 金艳鸣, 张晋芳, 等. 考虑新能源大规模发展的差异化综合效益评价模型[J]. 中国电力, 2018, 51(10): 178-184.
	WANG Xiaochen, JIN Yanming, ZHANG Jinfang, et al. A comprehensive evaluation model for power system with large-scale new energy considering regional differences[J]. Electric Power, 2018, 51(10): 178-184.
[38]	初芯言, 郑祥. 基于改进相对强度熵法的风电场电能质量评估方法研究[J]. 电器与能效管理技术, 2023(6):75-79.
	CHU Xinyan, ZHENG Xiang. Research on wind farm power quality evaluation method based on improved relative intensity entropy method[J]. Electrical & Energy Management Technology, 2023(6): 75-79.
[39]	丁志康, 王维俊, 米红菊, 等. 基于层次分析-熵权法的最优储能方案评估[J]. 电子设计工程, 2020, 28(21): 1-4.
	DING Zhikang, WANG Weijun, MI Hongju, et al. Evaluation of optimal energy storage scheme based on AHP and EWM[J]. Electronic Design Engineering, 2020, 28 (21): 1-4
[40]	赵源上, 林伟芳. 基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究[J]. 中国电力, 2023, 56(5): 193-202.
	ZHAO Yuanshang, LIN Weifang. Research on typical scenarios based on fusion density peak value and entropy weight method of Pearson's correlation coefficient[J]. Electric Power, 2023, 56(5): 193-202.
[41]	马丽叶, 张涛, 卢志刚, 等. 基于变权可拓云模型的区域综合能源系统综合评价[J]. 电工技术学报, 2022, 37(11): 2789-2799.
	MA Liye, ZHANG Tao, LU Zhigang, et al. Comprehensive evaluation of regional integrated energy system based on variable weight extension cloud model[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2789-2799.
[42]	张文朝, 顾雪平. 应用变异系数法和逼近理想解排序法的风电场综合评价[J]. 电网技术, 2014, 38(10): 2741-2746.
	ZHANG Wenchao, GU Xueping. Comprehensive evaluation of wind farms using variation coefficient method and technique for order preference by similarity to ideal solution[J]. Power System Technology, 2014, 38(10): 2741-2746.
[43]	郝艳捧, 张磊, 刘远鹤, 等. 基于改进灰色关联分析的GIS质量评价[J]. 电力自动化设备, 2017, 37(7): 161-165.
	HAO Yanpeng, ZHANG Lei, LIU Yuanhe, et al. GIS quality evaluation based on improved grey relational analysis[J]. Electric Power Automation Equipment, 2017, 37(7): 161-165.
[44]	徐长宝, 王玉磊, 赵立进, 等. 基于信息趋势预测和组合赋权的智能变电站继电保护系统状态模糊综合评价[J]. 电力自动化设备, 2018, 38(1): 162-168.
	XU Changbao, WANG Yulei, ZHAO Lijin, et al. Fuzzy comprehensive evaluation of intelligent substation relay protection system state based on information trend prediction and combination weighting[J]. Electric Power Automation Equipment, 2018, 38(1): 162-168.
[45]	李进友, 李媛, 冯冰, 等. 基于随机组合赋权模糊评价的风电机组健康状态评估[J]. 太阳能学报, 2022, 43(8): 340-351. doi: 10.19912/j.0254-0096.tynxb.2020-1416
	LI Jinyou, LI Yuan, FENG Bing, et al. Wind turbine health state assessment based on stochasticcombination weighting fuzzy evaluation[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 340-351. doi: 10.19912/j.0254-0096.tynxb.2020-1416
[46]	赵玉林, 周航, 王余阳, 等. 基于模糊综合评判的风电并网系统电能质量研究[J]. 可再生能源, 2023, 41(10): 1368-1375.
	ZHAO Yulin, ZHOU Hang, WANG Yuyang, et al. Research on power quality of wind power grid connected systems based on fuzzy comprehensive evaluation[J]. Renewable Energy Resources, 2023, 41(10): 1368-1375.
[47]	沈阳武, 祖文静, 梁利清, 等. 基于组合赋权法的风电场无功电压控制能力综合评估[J]. 电力系统保护与控制, 2020, 48(14): 18-24.
	SHEN Yangwu, ZU Wenjing, LIANG Liqing, et al. Comprehensive evaluation of reactive power and voltage control capability of wind farms based on combination weighting method[J]. Power System Protection and Control, 2020, 48(14): 18-24.
[48]	张全明, 兰泉妮, 刘柳君, 等. 基于组合赋权-云模型的广义需求侧资源网荷互动水平评估[J]. 武汉大学学报(工学版), 2023, 56(6): 717-724.
	ZHANG Quanming, LAN Quanni, LIU Liujun, et al. Evaluation of generalized demand side resource network load interaction level based on combination weighting cloud model[J]. Engineering Journal of Wuhan University, 2023, 56(6): 717-724.
[49]	罗宁, 贺墨琳, 高华, 等. 基于改进的AHP-CRITIC组合赋权与可拓评估模型的配电网综合评价方法[J]. 电力系统保护与控制, 2021, 49(16): 86-96.
	LUO Ning, HE Molin, GAO Hua, et al. A comprehensive evaluation method for distribution networks based on an improved AHP-CRITIC combination weighting and extension evaluation model[J]. Power System Protection and Control, 2021, 49(16): 86-96.
[50]	李德鑫, 田春光, 吕项羽, 等. 基于AHP和CRITIC的电网调峰调频储能系统规划[J]. 电源学报, 2021, 19(2): 136-141. doi: 10.13234/j.issn.2095-2805.2021.2.136
	LI Dexin, TIAN Chunguang, LV Xiangyu, et al. Planning of energy storage system for power grid peaking shaving and frequency regulation based AHP and CRITIC[J] Journal of Power Supply, 2021, 19(2): 136-141. doi: 10.13234/j.issn.2095-2805.2021.2.136