风光互补系统中混合储能容量优化配置研究

doi:10.12028/j.issn.2095-4239.2019.0014

摘要/Abstract

摘要： 储能系统可以有效解决微电网中分布式可再生能源特别是风光互补发电的间歇性、波动性以及“源”与“荷”错位的问题。不同储能技术在响应时间、容量规模、技术成熟度及成本等方面各有特点，两种或多种储能技术耦合将可以更有效地满足用电系统的技术性和经济性的要求。针对电力用户对分布式可再生能源的利用情况，本文提出一种由压缩空气储能、锂电池和超级电容器组成的混合储能系统，建立了三种储能的数学模型，针对其不同的特性，提出了基于二次移动平均滤波的储能系统功率分配方法和基于连续性运行的容量优化配置方法。基于某个实际的用户负荷进行了案例分析，得到了混合储能系统的功率和容量配置结果，并分析了其运行特性。研究表明，在分布式可再生能源微电网中，多种储能技术耦合既能充分发挥每种储能的优势，又可以通过相互配合弥补各自的劣势，这对于可再生能源的充分利用和满足用电负荷的严苛需求具有重要的作用和意义，在分布式能源利用领域具有较好的工程应用前景。

关键词: 可再生能源, 混合储能, 功率分配, 容量配置

Abstract: The energy storage system can effectively solve problem of intermittence, volatility and source-load misalignment of distributed renewable energy,especially wind-solar generation in the micro-grid system. Different energy storage forms have their own characteristics in terms of response time, power and energy capacity, technological maturity and cost, etc. The hybridization of two or more types of energy storage technology will be a good solution to meet the technical and economic requirements of energy utilization system. In this paper, a hybrid energy storage system consisting of compressed air energy storage, lithium battery and supercapacitor is proposed, and mathematical modellings of three energy storage systems are established. For different characteristics, a power allocation method based on secondary moving average filtering and a capacity optimal allocation method based on continuous operation are proposed. Based on a practical customer load requirement, the power and capacity confguration results of the hybrid energy storage system are obtained, and the operational characteristics are analyzed. The research indicates that in the system of distributed renewable energy micro-grid, the coupling of multiple energy storage technologies can not only fully utilize the advantages of each type of energy storage, but also compensate for their respective disadvantages through mutual cooperation. That has an important role and significance of fully utilize renewable energy and satisfy demand of electric load, and has a good engineering application prospect in the feld of distributed energy utilization.

Key words: renewable energy, hybrid energy storage system, power allocation, capacity confguration

中图分类号:

TM61

李瑞民, 张新敬, 徐玉杰, 孙雯雯, 周学志, 郭丛, 陈海生. 风光互补系统中混合储能容量优化配置研究[J]. 储能科学与技术, 2019, 8(3): 512-522.

LI Ruimin, ZHANG Xinjing, XU Yujie, SUN Wenwen, ZHOU Xuezhi, GUO Cong, CHEN Haisheng. Research on optimal confguration of hybrid energy storage capacity for wind-solar generation system[J]. Energy Storage Science and Technology, 2019, 8(3): 512-522.

参考文献

[1] CUI M, JIE Z, CONG F, et al. Characterizing and analyzing ramping events in wind power, solar power, load, and netload[J]. Renewable Energy, 2017, 111:227-244.
[2] 李翠萍, 卓君武, 李军徽, 等. 光伏发电与风光联合发电系统输出特性分析[J]. 电网与清洁能源, 2017, 33(1):95-102. LI C P, ZHUO J W, LI J H, et al. Characteristic analysis of photovoltaic power generation and wind-photovoltaic joint power generation system[J]. Power System and Clean Energy, 2017, 33(1):95-102.
[3] 孟明, 陈世超, 赵树军, 等. 新能源微电网研究综述[J]. 现代电力, 2017, 34(1):1-7. MENG M, CHEN S C, ZHAO S J, et al.Overview on research of renewable energy microgrid[J]. Modern Electric Power, 2017, 34(1):1-7.
[4] GHAZAL R, FEI G, RUSSELL N, et al. A generic microgrid controller:Concept, testing, and insights[J]. Applied Energy, 2018, 229:660-671.
[5] RUI X, DUAN Y, CAO J, et al. Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an allclimate electric vehicle[J]. Applied Energy, 2018, 217(1):153-165.
[6] 朱熀秋, 汤延祺. 飞轮储能关键技术及应用发展趋势[J]. 机械设计与制造, 2017(1):265-268. ZHU H Q, TANG Y Q. Key technologies and application trends of flywheel energy storage system[J]. Machinery Design & Manufacture, 2017(1):265-268.
[7] SREEDHARAN P, FARBES J, CUTTER E, et al. Microgrid and renewable generation integration:University of California, San Diego[J]. Applied Energy, 2016, 169:709-720.
[8] 陈海生, 刘金超, 郭欢, 等. 压缩空气储能技术原理[J]. 储能科学与技术, 2013, 2(2):146-151. CHEN H S, LIU J C, GUO H, et al. Technical principle of compressed air energy storage system[J]. Energy Storage Science and Technology, 2013, 2(2):146-151.
[9] 张国驹, 唐西胜, 齐智平. 超级电容器与蓄电池混合储能系统在微网中的应用[J]. 电力系统自动化, 2010, 34(12):85-89. ZHANG G J, TANG X S, QI Zhiping. Application of hybrid energy storage system of super-capacitors and batteries in a microgrid[J]. Automation of Electric Power Systems, 2010, 34(12):85-89.
[10] JIAN C, EMADI A. A new battery/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles[J]. IEEE Transactions on Power Electronics, 2011, 27(1):122-132.
[11] 王利猛, 刘久成, 田春光, 等. 基于统计学方法的微网混合储能容量优化配置[J]. 电网技术, 2018(1):doi:10.13335/j.1000-3673. pst.2017.0852. WANG L, LIU J, TIAN C, et al. Capacity optimization of hybrid energy storage in microgrid based on statistic method[J]. Power System Technology, 2018 doi:10.13335/j.1000-3673.pst.2017.0852.
[12] WANG G, CIOBOTARU M, AGELIDIS V G. Optimal capacity design for hybrid energy storage system supporting dispatch of large-scale photovoltaic power plant[J]. Journal of Energy Storage, 2015, 3:25-35.
[13] 李龙云, 胡博, 谢开贵, 等. 基于离散傅里叶变换的孤岛型微电网混合储能优化配置[J]. 电力系统自动化, 2016, 40(12):108-116. LI L Y, HU B, XIE K G, et al.Capacity optimization of hybrid energy storage systems in isolated microgrids based on discrete fourier transform[J]. Automation of Electric Power Systems, 2016, 40(12):108-116.
[14] 罗鹏, 杨天蒙, 娄素华, 等. 基于频谱分析的微网混合储能容量优化配置[J]. 电网技术, 2016, 40(2):376-381. LUO P, YANG T M, LOU S H, et al.Spectrum analysis based capacity confguration of hybrid energy storage in microgrid[J]. Power System Technology, 2016, 40(2):376-381.
[15] MESBAHI T, RIZOUG N, BARTHOLOMEUS P, et al. Optimal energy management for a Li-ion battery/supercapacitor hybrid energy storage system based on particle swarm optimization incorporating nelder-mead simplex approach[J]. IEEE Transactions on Intelligent Vehicles, 2017, PP(99):doi:10.1109/TIV.2017.2720464.
[16] 路小娟, 郭琦, 董海鹰. 基于CMOPSO的混合储能微电网多目标优化研究[J]. 太阳能学报, 2017, 38(1):279-286. LU X J, GUO Q, DONG H Y. Multi objective optimization of hybrid energy storage micro grid based on CMOPSO algorithm[J]. Acta Energiae Solaris Sinica, 2017, 38(1):279-286.
[17] 杨国华, 朱向芬, 周鑫, 等. 基于遗传算法的风电混合储能容量优化配置[J]. 电气传动, 2015, 45(2):50-53. YANG G H, ZHU X F, ZHOU X, et al. Hybrid energy storage capacity optimization for independent wind generation system based on genetic algorithm[J]. Electric Drive, 2015, 45(2):50-53.
[18] ABBASSI A, DAMI M A, JEMLI M. A statistical approach for hybrid energy storage system sizing based on capacity distributions in an autonomous PV/Wind power generation system[J]. Renewable Energy, 2017, 103(Complete):81-93.
[19] 王利猛, 刘久成, 田春光, 等. 基于统计学方法的微网混合储能容量优化配置[J]. 电网技术, 2018, 42(1):187-194. WANG L M,LIU J C,TIAN C G, et al. Capacity optimization of hybrid energy storage in microgrid based on statistic method[J]. Power System Technology 2018, 42(1):187-194.
[20] ZHAO P, WANG M, WANG J, et al. A preliminary dynamic behaviors analysis of a hybrid energy storage system based on adiabatic compressed air energy storage and flywheel energy storage system for wind power application[J]. Energy, 2015, 84:825-839.
[21] 黄先进, 郝瑞祥, 张立伟, 等. 压缩空气与超级电容混合储能系统能量控制策略[J]. 北京交通大学学报, 2014, 38(4):56-62. HUANG X J, HAO R X, ZHANG L W, et al. Energy management of the hybrid energy storage system based on super-capacitors and compressed air[J]. Journal of Beijing Jiaotong University, 2014, 38(4):56-62.
[22] 王成山, 武震, 杨献莘, 等. 基于微型压缩空气储能的混合储能系统建模与实验验证[J]. 电力系统自动化, 2014, (23):22-6. WANG C S, WU Z, YANG X, et al. Modeling and verification of hybrid energy storage system based on micro compressed air energy storage[J]. Automation of Electric Power Systems, 2014(23):22-6.
[23] JACOB A S, BANERJEE R, GHOSH P C. Sizing of hybrid energy storage system for a PV based microgrid through design space approach[J]. Applied Energy, 2018, 212:640-653.
[24] SHARMA R K, MISHRA S. Dynamic power management and control of PV PEM fuel cell based standalone AC/DC microgrid using hybrid energy storage[J]. IEEE Transactions on Industry Applications, 2017, PP(99):1-1.
[25] THOUNTHONG P, SIKKABUT S, MUNGPORN P, et al. Performance investigation of high-energy high-power densities storage devices by Li-ion battery and supercapacitor for fuel cell/photovoltaic hybrid power plant for autonomous system applications[C]//Proceedings of the Industry Applications Society Meeting, USA, 2015.
[26] 田崇翼, 李珂, 严毅, 等. 基于经验模式分解的风电场多时间尺度复合储能控制策略[J]. 电网技术, 2015, 39(8):2167-2172. TIAN C Y,LI K,YAN Y,et al. A multi-time scale control strategy of hybrid energy storage system in wind farm based on empirical mode decomposition[J]. Power System Technology, 2015, 39(8):2167-2172.
[27] 田崇翼, 张承慧, 李珂, 等. 含压缩空气储能的微网复合储能技术及其成本分析[J]. 电力系统自动化, 2015, 10:36-41. TIAN C Y, ZHANG C H, LI K, et al. Composite energy storage technology with compressed air energy storage in microgrid and its cost analysis[J]. Automation of Electric Power Systems, 2015, 10:36-41.
[28] 张远, 杨科, 李雪梅, 等. 基于先进绝热压缩空气储能的冷热电联产系统[J]. 工程热物理学报, 2013, 34(11):1991-1996. ZHANG Y, YANG K, LI X M, et al.[J]. A combined cooling, heating and power system based on advanced adiabatic compressed air energy storage technology[J]. Journal of Engineering Thermophysics, 2013, 34(11):1991-1996.
[29] 韩中合, 刘士名, 周权, 等. 恒壁温储气模型下先进绝热压缩空气储能系统性能分析[J]. 中国电机工程学报, 2016, 36(12):3373-3380.HAN Z H, LIU S M, ZHOU Q, et al. Performance analysis of AACAES system with constant wall-temperature air storage model[J]. Proceedings of the CSEE, 2016, 36(12):3373-3380.
[30] LI S, CHENG X. A comparative study on RC models of lithium-ion battery[J]. Proceedings of the Transportation Electrification AsiaPacifc, F, 2014:1-4.
[31] 李百华, 郭灿彬, 钟其水, 等. 电动汽车锂电池戴维南等效电路模型参数辨识研究[J]. 微型机与应用, 2017, 36(1):83-85. LI B H, GUO C B, ZHONG Q S, et al. Research on parameter identification of Thevinin equivalent circuit model about electric vehicle lithium-ion battery[J]. Microcomputer & Its Applications, 2017, 36(1):83-85.
[32] DANG X, LI Y, KAI X, et al. Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model[J]. Electrochimica Acta, 2016, 188:356-366.
[33] HAO M, RUI X. Chapter 1-Modeling, evaluation, and state estimation for batteries[J]. Modeling Dynamics & Control of Electrifed Vehicles, 2018,1-38.
[34] 单金生, 吴立峰, 关永, 等. 超级电容建模现状及展望[J]. 电子元件与材料, 2013, 32(8):9-14. SHAN J S, WU L F, GUAN Y, et al. Review and expectation of modeling research on supercapacitor[J]. Electronic Components & Materials, 2013, 32(8):9-14.
[35] ZHANG X, XUE H, XU Y, et al. An investigation of an uninterruptible power supply (UPS) based on supercapacitor and liquid nitrogen hybridization system[J]. Energy Conversion & Management, 2014, 85(9):784-792.
[36] 金英华. 超级电容器的性能研究与状态分析[D].大连:大连理工大学, 2013. JIN Y H. Performance research and state analysis of supercapacitors[D]. Dalian:Dalian University of Technology, 2013.
[37] GENG S, LIU Y, CHAI R, et al. Online model parameter identifcation for supercapacitor based on weighting bat algorithm[J]. AEUInternational Journal of Electronics and Communications, 2018, 87:113-118.
[38] 陈跃燕, 李相俊, 韩晓娟, 等. 基于移动平均法和风电波动率约束的电池储能系统平滑风电出力控制策略[J]. 电力建设, 2013, 34(7):1-5. CHEN Y Y, LI X J, HAN X J, et al. Control strategy of smoothing wind power output using battery energy storage based on moving average method and wind power volatility rate constraint[J]. Electric Power Construction, 2013, 34(7):1-5.
[39] 裴益轩, 郭民. 滑动平均法的基本原理及应用[J]. 火炮发射与控制学报, 2001(1):21-23. PEI Y X, GUO M. The fundamental principle and application of sliding average method[J]. Journal of Gun Launch & Control, 2001(1):21-23.
[40] 徐林, 阮新波, 张步涵, 等. 风光蓄互补发电系统容量的改进优化配置方法[J]. 中国电机工程学报, 2012, 32(25):88-98. XU L, RUAN X B, ZHANG B H, et al. An improved optimal sizing method for wind-solar-battery hybrid power system[J]. Proceedings of the CSEE, 2012, 32(25):88-98.