计及碳收益的风电场混合储能容量优化配置

doi:10.11930/j.issn.1004-9649.202204093

中国电力 ›› 2022, Vol. 55 ›› Issue (12): 22-33.DOI: 10.11930/j.issn.1004-9649.202204093

• 新型电力系统储能关键技术应用 • 上一篇下一篇

计及碳收益的风电场混合储能容量优化配置

陈崇德¹, 郭强², 宋子秋¹, 胡阳¹

1. 华北电力大学控制与计算机工程学院，北京 102206;
2. 山西省电力公司电力科学研究院，山西太原 030001

收稿日期:2022-04-20 修回日期:2022-08-26 发布日期:2022-12-28
作者简介:陈崇德（1996—），男，硕士研究生，从事储能在新能源发电系统中的应用研究，E-mail：1597317098@qq.com;郭强（1980—），男，硕士，高级工程师，从事新能源并网控制技术研究，E-mail：346526139@qq. com;宋子秋（1996—），男，博士研究生，从事海上风力发电系统建模优化与控制理论研究，E-mail：quson@ncepu.edu.cn;胡阳（1986—），男，通信作者，博士，副教授，从事面向新能源电力系统的控制理论及应用研究，E-mail：hooyoung@ncepu.edu.cn
基金资助:
国家重点研发计划资助项目(基于信息物理融合的新型城镇多能源互联系统协同调控研究，2021YFE 0102400)；国家电网有限公司科技项目(风光水火储多源互补联合调频控制策略及应用，52053020002S)

Optimal Configuration of Hybrid Energy Storage Capacity for Wind Farms Considering Carbon Trading Revenue

CHEN Chongde¹, GUO Qiang², SONG Ziqiu¹, HU Yang¹

1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2. State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China

Received:2022-04-20 Revised:2022-08-26 Published:2022-12-28
Supported by:
This work is supported by National Key R&D Program of China (Research on Coordinated Regulation of New Urban Multi-Energy Interconnected System Based on Information Physical Fusion, No.2021YFE0102400), Science and Technology Project of SGCC (Multi-source Complementary Combined Frequency Modulation Control Strategy and Application for Wind Power, Photovoltaic, Hydropower, Thermal Power and Energy Storage, No.52053020002S ).

摘要/Abstract

摘要： 风电场输出功率波动剧烈，直接并网会影响电力系统的安全稳定运行，需要配置储能平滑风电功率使其满足并网要求。为克服滑动平均法追随性差而导致储能配置容量较大的问题，提出分级滑动平均法用于确定风电功率平滑目标；采用Butterworth低通滤波对风电功率偏移量进行分解，将低频分量和高频分量分别作为锂电池储能和飞轮储能的参考功率；考虑储能初始投资及置换等成本以及售电收益、碳交易收益，建立风电场混合储能容量优化配置模型，并采用自适应混沌粒子群优化算法进行求解；利用某48MW风电场的运行数据进行仿真验证。仿真结果表明：分级滑动平均法更适用于确定风电功率平滑目标，锂电池与飞轮混合储能有利于提高风储系统全生命周期的净收益。

关键词: 碳交易收益, 风电功率平滑, 混合储能, 储能容量配置, 锂电池储能, 飞轮储能

Abstract: The output power of wind farms fluctuates strongly, and direct grid connection will affect the safe and stable operation of the power system. Therefore, it is necessary to optimize energy storage configuration to smooth the wind power, so as to meet the requirements of grid connection. In order to deal with the large energy storage capacity caused by poor tracking of the moving average method, a hierarchical moving average method was proposed to determine the target in smoothing wind power. The wind power offset was decomposed by a Butterworth low-pass filter, and the low-frequency and high-frequency components were used as the reference power of lithium battery energy storage and flywheel energy storage, respectively. In view of the cost of initial investment and replacement of energy storage as well as the revenue from electricity sales and carbon trading, an optimal configuration model of hybrid energy storage capacity for wind farms was established and solved by an adaptive chaotic particle swarm optimization algorithm. Simulation and verification were carried out according to the operation data of a 48MW wind farm. The simulation results show that the hierarchical moving average method is more suitable for determining the target in smoothing wind power, and the hybrid energy storage of lithium battery and flywheel can improve the net income of the energy storage system of wind farms in the whole life cycle.

Key words: carbon trading revenue, wind power smoothing, hybrid energy storage, energy storage capacity configuration, lithium battery energy storage, flywheel energy storage

陈崇德, 郭强, 宋子秋, 胡阳. 计及碳收益的风电场混合储能容量优化配置[J]. 中国电力, 2022, 55(12): 22-33.

CHEN Chongde, GUO Qiang, SONG Ziqiu, HU Yang. Optimal Configuration of Hybrid Energy Storage Capacity for Wind Farms Considering Carbon Trading Revenue[J]. Electric Power, 2022, 55(12): 22-33.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202204093

https://www.electricpower.com.cn/CN/Y2022/V55/I12/22

参考文献

[1] 李建林, 袁晓冬, 郁正纲, 等. 利用储能系统提升电网电能质量研究综述[J]. 电力系统自动化, 2019, 43(08): 15–24
LI Jianlin, YUAN Xiaodong, YU Zhenggang, et al. Comments on power quality enhancement research for power grid by energy storage system[J]. Automation of Electric Power Systems, 2019, 43(08): 15–24
[2] 杨蕾,王智超,周鑫,等. 大规模双馈风电机组并网频率稳定控制策略[J]. 中国电力, 2021, 54(5): 186–194
YANG Lei, WANG Zhichao, ZHOU Xin, et al. Frequency stability control strategy for large-scale grid connections with DFIG units[J]. Electric Power, 2021, 54(5): 186–194
[3] 徐国栋, 程浩忠, 马紫峰, 等. 用于平滑风电出力的储能系统运行与配置综述[J]. 电网技术, 2017, 41(11): 3470–3479
XU Guodong, CHENG Haozhong, MA Zifeng, et al. An overview of operation and configuration of energy storage systems for smoothing wind power outputs[J]. Power System Technology, 2017, 41(11): 3470–3479
[4] 刘宗浩, 张华民, 高素军, 马相坤, 刘玉峰. 风场配套用全球最大全钒液流电池储能系统[J]. 储能科学与技术, 2014, 3(1): 71–77
LIU Zonghao, ZHANG Huamin, GAO Sujun, et al. The world’s largest all-vanadium redox flow battery energy storage system for a wind farm[J]. Energy Storage Science and Technology, 2014, 3(1): 71–77
[5] KENJI Y, KAZUAKI K. Connecting wind power generation to a power system[J]. Fuji Electric Review, 2006, 52(3): 99–106.
[6] 汪海蛟, 江全元. 应用于平抑风电功率波动的储能系统控制与配置综述[J]. 电力系统自动化, 2014, 38(19): 126–135
WANG Haijiao, JIANG Quanyuan. An overview of control and configuration of energy storage system used for wind power fluctuation mitigation[J]. Automation of Electric Power Systems, 2014, 38(19): 126–135
[7] 涂伟超, 李文艳, 张强, 等. 飞轮储能在电力系统的工程应用[J]. 储能科学与技术, 2020, 9(3): 869–877
TU Weichao, LI Wenyan, ZHANG Qiang, et al. Engineering application of flywheel energy storage in power system[J]. Energy Storage Science and Technology, 2020, 9(3): 869–877
[8] 李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148–4160
LI Junhui, ZHANG Jiahui, LI Cuiping, et al. Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148–4160
[9] 张新松, 顾菊平, 袁越, 等. 基于电池储能系统的风功率波动平抑策略[J]. 中国电机工程学报, 2014, 34(28): 4752–4760
ZHANG Xinsong, GU Juping, YUAN Yue, et al. Strategy of smoothing wind power fluctuation based on battery energy storage system[J]. Proceedings of the CSEE, 2014, 34(28): 4752–4760
[10] 丁明, 吴建锋, 朱承治, 等. 具备荷电状态调节功能的储能系统实时平滑控制策略[J]. 中国电机工程学报, 2013, 33(1): 22–29
DING Ming, WU Jianfeng, ZHU Chengzhi, et al. A real-time smoothing control strategy with SOC adjustment function of storage systems[J]. Proceedings of the CSEE, 2013, 33(1): 22–29
[11] 王桂松, 肖碧涛, 赖晓路, 等. 计及风功率波动和电池荷电状态的储能系统平滑控制策略[J]. 太阳能学报, 2021, 42(11): 280–286
WANG Guisong, XIAO Bitao, LAI Xiaolu, et al. Smoothing control strategy of energy storage system considering wind power fluctuations and battery SOC[J]. Acta Energiae Solaris Sinica, 2021, 42(11): 280–286
[12] 甘伟, 郭剑波, 艾小猛, 等. 应用于风电场出力平滑的多尺度多指标储能配置[J]. 电力系统自动化, 2019, 43(9): 92–98
GAN Wei, GUO Jianbo, AI Xiaomeng, et al. Multi-scale multi -index sizing of energy storage applied to fluctuation mitigation of wind farm[J]. Automation of Electric Power Systems, 2019, 43(9): 92–98
[13] 张峰, 梁军, 张利, 等. 考虑最佳期望输出与荷电状态的风电场储能容量优化方法[J]. 电力系统自动化, 2014, 38(24): 12–19
ZHANG Feng, LIANG Jun, ZHANG Li, et al. Energy storage capacity optimization for wind farms considering optimal expected output and SOC[J]. Automation of Electric Power Systems, 2014, 38(24): 12–19
[14] 马兰, 谢丽蓉, 叶林, 等. 基于混合储能双层规划模型的风电波动平抑策略[J]. 电网技术, 2022, 46(3): 1016–1029
MA Lan, XIE Lirong, YE Lin, et al. Wind power fluctuation suppression strategy based on hybrid energy storage bi-level programming model[J]. Power System Technology, 2022, 46(3): 1016–1029
[15] 马速良, 马会萌, 蒋小平, 等. 基于Bloch球面的量子遗传算法的混合储能系统容量配置[J]. 中国电机工程学报, 2015, 35(3): 592–599
MA Suliang, MA Huimeng, JIANG Xiaoping, et al. Capacity configuration of the hybrid energy storage system based on bloch spherical quantum genetic algorithm[J]. Proceedings of the CSEE, 2015, 35(3): 592–599
[16] 张晴, 李欣然, 杨明, 等. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40–48
ZHANG Qing, LI Xinran, YANG Ming, et al. Capacity determination of hybrid energy storage system for smoothing wind power fluctuations with maximum net benefit[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 40–48
[17] 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
[18] 李学斌, 刘建伟. 采用二阶滤波的混合储能系统实时功率分配方法[J]. 电网技术, 2019, 43(5): 1650–1657
LI Xuebin, LIU Jianwei. Real-time power distribution method adopting second-order filtering for hybrid energy storage system[J]. Power System Technology, 2019, 43(5): 1650–1657
[19] 蒋华婷. 储能系统参与自动发电控制的控制策略和容量配置[D]. 北京: 华北电力大学(北京), 2019.
JIANG Huating. The control strategy and capacity configuration of energy storage system participating in automatic generation control[D]. Beijing: North China Electric Power University, 2019.
[20] 何林轩, 李文艳. 飞轮储能辅助火电机组一次调频过程仿真分析[J]. 储能科学与技术, 2021, 10(5): 1679–1686
HE Linxuan, LI Wenyan. Simulation of the primary frequency modulation process of thermal power units with auxiliary of flywheel energy storage[J]. Energy Storage Science and Technology, 2021, 10(5): 1679–1686
[21] 刘永前, 梁超, 阎洁, 等. 风-光电站中储能系统混合最优配置及其经济性研究[J]. 中国电力, 2020, 53(12): 143–150
LIU Yongqian, LIANG Chao, YAN Jie, et al. Optimal configuration and economic study of hybrid energy storage system in wind and solar power plants[J]. Electric Power, 2020, 53(12): 143–150
[22] 魏震波, 马新如, 郭毅, 等. 碳交易机制下考虑需求响应的综合能源系统优化运行[J]. 电力建设, 2022, 43(1): 1–9
WEI Zhenbo, MA Xinru, GUO Yi, et al. Optimized operation of integrated energy system considering demand response under carbon trading mechanism[J]. Electric Power Construction, 2022, 43(1): 1–9
[23] 赵文会, 李阮. 考虑碳交易收益的风电项目价值评估[J]. 生态经济, 2018, 34(8): 19–24
ZHAO Wenhui, LI Ruan. Research on value evaluation of wind power project considering carbon transaction revenue[J]. Ecological Economy., 2018, 34(8): 19–24
[24] 王明, 李欣然, 谭绍杰, 等. 考虑经济性的风储联合双应用的储容配置方法[J]. 电力系统及其自动化学报, 2017, 29(2): 7–13
WANG Ming, LI Xinran, TAN Shaojie, et al. Capacity configuration method of energy storage in wind-ESS coordination considering economic efficiency[J]. Proceedings of the CSU-EPSA, 2017, 29(2): 7–13
[25] 丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报, 2019, 40(3): 593–599
DING Ming, WU Jie, ZHANG Jingjing. Capacity optimization method of hybrid energy storage system for wind power smoothing[J]. Acta Energiae Solaris Sinica, 2019, 40(3): 593–599
[26] 沃克. 风—储调频系统的控制与容量配置策略研究[D]. 济南: 山东大学, 2020.
WO Ke. Research on operation control and capacity configuration strategy of wind-energy storage frequency regulation system[D]. Jinan: Shandong University, 2020.
[27] 陈玉龙, 武鑫, 滕伟, 等. 用于风电功率平抑的飞轮储能阵列功率协调控制策略[J]. 储能科学与技术, 2022, 11(2): 600–608
CHEN Yulong, WU Xin, TENG Wei, et al. Power coordinated control strategy of flywheel energy storage array for wind power smoothing[J]. Energy Storage Science and Technology, 2022, 11(2): 600–608
[28] 杨锡运, 任杰, 李相俊, 等. 储能系统平滑光伏电站功率波动的变参数斜率控制方法[J]. 电力系统自动化, 2016, 40(24): 56–63
YANG Xiyun, REN Jie, LI Xiangjun, et al. Slope control method with variable coefficients of battery energy storage system for smoothing photovoltaic power fluctuation[J]. Automation of Electric Power Systems, 2016, 40(24): 56–63
[29] 陈厚合, 杜欢欢, 张儒峰, 等. 考虑风电不确定性的混合储能容量优化配置及运行策略研究[J]. 电力自动化设备, 2018, 38(8): 174–182, 188
CHEN Houhe, DU Huanhuan, ZHANG Rufeng, et al. Optimal capacity configuration and operation strategy of hybrid energy storage considering uncertainty of wind power[J]. Electric Power Automation Equipment, 2018, 38(8): 174–182, 188

计及碳收益的风电场混合储能容量优化配置

Optimal Configuration of Hybrid Energy Storage Capacity for Wind Farms Considering Carbon Trading Revenue

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics

[1]	刘抒睿, 李培强, 陈家煜, 郭雅诗. 基于VMD分解下的皮尔逊相关性分析及T-tFD的混合储能容量配置[J]. 中国电力, 2024, 57(7): 82-97.
[2]	徐峰亮, 王克谦, 王文豪, 王鹏, 王文烨, 张帅, 赵凤展. 计及激励型需求响应的低压配电网混合储能优化配置[J]. 中国电力, 2024, 57(6): 90-101.
[3]	曹宇, 胡鹏飞, 蔡婉琪, 王曦, 江道灼, 梁一桥. 基于MMC的超级电容与蓄电池混合储能系统及其混合同步控制策略[J]. 中国电力, 2024, 57(6): 78-89.
[4]	王海燕, 钱林宇. 基于NGO-VMD的混合储能功率分配策略[J]. 中国电力, 2024, 57(11): 119-128.
[5]	孙志媛, 彭博雅, 孙艳. 考虑多能互补的电力电量平衡优化调度策略[J]. 中国电力, 2024, 57(1): 115-122.
[6]	钱国明, 孟杰, 朱海东, 丁泉, 陈孝煜. 基于调频服务的新型光-储电站容量规划及运行策略[J]. 中国电力, 2023, 56(6): 132-138,147.
[7]	蒋棹骏, 向月, 谈竹奎, 郭咏涛, 王扬, 周科. 计及需求响应的高比例清洁能源园区储能容量优化配置[J]. 中国电力, 2023, 56(12): 147-155, 163.
[8]	徐衍会, 徐宜佳. 平抑风电波动的混合储能容量配置及控制策略[J]. 中国电力, 2022, 55(6): 186-193.
[9]	代倩, 张健, 吴俊玲, 张立波, 秦晓辉, 张彦涛, 牛彦芹. 基于多分区时序生产模拟的省级电网差异化储能规划方法[J]. 中国电力, 2022, 55(11): 21-28.
[10]	张晓宇, 张建成, 王宁, 王冠. 基于变分模态分解的混合储能系统协调控制[J]. 中国电力, 2018, 51(9): 165-173.
[11]	李想, 张建成, 王宁. 超级电容器-飞轮-蓄电池混合储能系统容量配置方法研究[J]. 中国电力, 2018, 51(11): 117-124.
[12]	李辉, 黄瑶妹, 马飞. 基于荷电状态的混合储能系统协调控制策略[J]. 中国电力, 2017, 50(1): 158-163.
[13]	梁适春, 张晓冬, 林培峰, 牛萌. 一种混合储能光伏发电系统的功率预测算法[J]. 中国电力, 2014, 47(3): 24-27.

模态框（Modal）标题

计及碳收益的风电场混合储能容量优化配置

Optimal Configuration of Hybrid Energy Storage Capacity for Wind Farms Considering Carbon Trading Revenue

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics