考虑电池储能寿命损耗的风光储微网随机日前调度策略*

doi:10.11985/2022.03.025

电气工程学报 ›› 2022, Vol. 17 ›› Issue (3): 210-218.doi: 10.11985/2022.03.025

• 新能源发电与电能存储 • 上一篇下一篇

扫码分享

考虑电池储能寿命损耗的风光储微网随机日前调度策略^*

赵诣¹(), 方陈²(), 冯冬涵¹(), 李恒杰¹^,³, 时珊珊², 周云¹()

1.电力传输与功率变换控制教育部重点实验室(上海交通大学) 上海 200240
2.华东电力试验研究院有限公司上海 200437
3.兰州理工大学电气工程与信息工程学院兰州 730050

收稿日期:2021-08-22 修回日期:2022-03-24 出版日期:2022-09-25 发布日期:2022-10-28
通讯作者: 周云 E-mail:Yi.Zhao.sjtu@sjtu.edu.cn;fangc02@gmail.com;seed@sjtu.edu.cn;yun.zhou@sjtu.edu.cn
作者简介:赵诣,男,1998年生,硕士研究生。主要研究方向为用电能效管理。E-mail： Yi.Zhao.sjtu@sjtu.edu.cn
方陈,男,1983年生,高级工程师。主要研究方向为能源互联网多能互补规划与运行,分布式能源、储能、电动汽车、微电网规划与运行等。E-mail： fangc02@gmail.com
冯冬涵,男,1981年生,博士,教授。主要研究方向为智能电网中的策略与风险及电力市场理论与设计等。E-mail： seed@sjtu.edu.cn
基金资助:
*国家自然科学基金(51907122);国家自然科学基金(52077139);上海市科委计划(21DZ2204800);国家电网有限公司科技(52094021000F)

Stochastic Day-ahead Scheduling Optimization for Wind-PV-ES Microgrid Considering Battery Life Loss

ZHAO Yi¹(), FANG Chen²(), FENG Donghan¹(), LI Hengjie¹^,³, SHI Shanshan², ZHOU Yun¹()

1. Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240
2. East China Electric Power Test and Research Institute Co., Ltd., Shanghai 200437
3. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050

Received:2021-08-22 Revised:2022-03-24 Online:2022-09-25 Published:2022-10-28
Contact: ZHOU Yun E-mail:Yi.Zhao.sjtu@sjtu.edu.cn;fangc02@gmail.com;seed@sjtu.edu.cn;yun.zhou@sjtu.edu.cn

摘要/Abstract

摘要：

随着我国“碳达峰”和“碳中和”目标的提出,以新能源为主的电力系统已经受到了广泛的关注,风光储微网就是其中的典型场景。研究电池储能的寿命损耗特性,建立精细的分段线性化电池储能寿命损耗模型。将建立的电池储能寿命损耗模型加入到风光储微网场景中,考虑储能技术在提高风光资源消纳率以及减少碳排放量方面的作用,以最小化微网和上级电网交互费用、弃风弃光成本、削负荷成本、碳排放成本以及电池储能寿命损耗成本为目标函数,对整个风光储微网的随机日前调度模型进行线性化求解。对风光储微网的调度模型算例分析表明,所提模型可以通过规范电池储能的充放电动作,有效提高电池的使用寿命以及提高风光储微网的经济性。

关键词: 风光储微网, 风光消纳, 电池储能寿命损耗, 随机调度, 线性化

Abstract:

With the ‘carbon peak’ and ‘carbon neutrality’ goals proposed, the power system based on renewable resources has received extensive attention, and the wind-PV-ES(Energy storage) microgrid is the typical scenario. The piecewise linear life loss model is established by studying the life loss characteristics of batteries. Combined with the role of energy storage technology in improving the wind and solar power consumption and reducing carbon emissions, the economic effect of battery on the microgrid is added to the wind-PV-ES microgrid scenarios. Regarding the interaction cost of the grid, wind and solar abandonment cost, shaving load cost, carbon emissions cost, and battery life loss cost as the objective function, the day-ahead scheduling model of entire wind-PV-ES microgrid is linearized. The case is used to analyze that the introduction of batteries can effectively increase the battery life and improve the economy of the microgrid by regulating the charging and discharging actions of batteries.

Key words: Wind-PV-ES microgrid, wind-PV accommodation, battery life loss, stochastic scheduling, linearization

中图分类号:

TM732

赵诣, 方陈, 冯冬涵, 李恒杰, 时珊珊, 周云. 考虑电池储能寿命损耗的风光储微网随机日前调度策略^*[J]. 电气工程学报, 2022, 17(3): 210-218.

ZHAO Yi, FANG Chen, FENG Donghan, LI Hengjie, SHI Shanshan, ZHOU Yun. Stochastic Day-ahead Scheduling Optimization for Wind-PV-ES Microgrid Considering Battery Life Loss[J]. Journal of Electrical Engineering, 2022, 17(3): 210-218.

图/表 10

图1

图2

图3

表1

表2

图4

表3

图5

表4

图6

参考文献 16

[1]	米剑锋, 马晓芳. 中国CCUS技术发展趋势分析[J]. 中国电机工程学报, 2019, 39(9):2537-2544.
	MI Jianfeng, MA Xiaofang. Development trend analysis of carbon capture,utilization and storage technology in China[J]. Proceedings of the CSEE, 2019, 39(9):2537-2544.
[2]	苏适, 周立栋, 陆海, 等. 基于改进混沌粒子群算法的多源独立微网多目标优化方法[J]. 电力系统保护与控制, 2017, 45(23):34-41.
	SU Shi, ZHOU Lidong, LU Hai, et al. Multi-objective optimization method of multi-source independent microgrid based on modified CPSO[J]. Power System Protection and Control, 2017, 45(23):34-41.
[3]	谭颖, 吕智林, 李捷. 基于改进ELM的风/光/柴/储独立微网分布式电源多目标容量优化配置[J]. 电力系统保护与控制, 2016, 44(8):63-70.
	TAN Ying, LÜ Zhilin, LI Jie. Multi-objective optimal sizing method for distributed power of wind-solar-diesel-battery independent microgrid based on improved electromagnetism-like mechanism[J]. Power System Protection and Control, 2016, 44(8):63-70.
[4]	叶亮, 吕智林, 王蒙, 等. 基于最优潮流的含多微网的主动配电网双层优化调度[J]. 电力系统保护与控制, 2020, 48(18):27-37.
	YE Liang, LÜ Zhilin, WANG Meng, et al. Bi-level programming optimal scheduling of ADN with a multi-microgrid based on optimal power flow[J]. Power System Protection and Control, 2020, 48(18):27-37.
[5]	ZHANG Cuo, XU Yan, DONG Zhaoyang, et al. Robust operation of microgrids via two-stage coordinated energy storage and direct load control[J]. IEEE Transactions on Power Systems, 2017, 32(4):2858-2868. doi: 10.1109/TPWRS.2016.2627583
[6]	丁宇劼. 基于可再生能源与储能系统的微电网模型及其优化运行仿真研究[J]. 电气工程学报, 2020, 15(1):36-40.
	DING Yujie. Microgrid model with renewable energy and energy storage system and its optimal operation simulation[J]. Journal of Electrical Engineering, 2020, 15(1):36-40.
[7]	WANG D, GUAN X, WU J, et al. Integrated energy exchange scheduling for multi-microgrid system with electric vehicles[J]. IEEE Transactions on Smart Grid, 2016, 7(4):1762-1774. doi: 10.1109/TSG.2015.2438852
[8]	SHI Y, XU B, WANG D, et al. Using battery storage for peak shaving and frequency regulation:Joint optimization for superlinear gains[J]. IEEE Transactions on Power Systems, 2018, 33(3):2882-2894. doi: 10.1109/TPWRS.2017.2749512
[9]	WU K, ZHOU H, AN S, et al. Optimal coordinate operation control for wind-photovoltaic-battery storage power-generation units[J]. Energy Conversion and Management, 2015, 90(9):466-475. doi: 10.1016/j.enconman.2014.11.038
[10]	王开科, 南东亮, 李勇, 等. 虚拟电厂参与大规模新能源系统的储能侧双层优化运行策略[J]. 电气工程学报, 2020, 15(2):24-33.
	WANG Kaike, NAN Dongliang, LI Yong, et al. Bi-level optimal operation strategy of energy storage with large-scale new energy system under the participation of virtual power plant[J]. Journal of Electrical Engineering, 2020, 15(2):24-33.
[11]	孙运志, 蒋德玉, 张盛林, 等. 计及电池寿命损耗的多能源微网储能优化配置[J]. 电力系统及其自动化学报, 2021, 33(5):128-133.
	SUN Yunzhi, JIANG Deyu, ZHANG Shenglin, et al. Optimal energy-storage configuration of multi-energy microgrid considering battery life degradation[J]. Proceedings of the CSU-EPSA, 2021, 33(5):128-133.
[12]	吴江. 考虑风电光伏出力不确定性的微网经济优化调度[D]. 南京: 南京邮电大学, 2020.
	WU Jiang. Economic optimal scheduling of microgrid considering uncertainty of wind power photovoltaic output[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2020.
[13]	Featherweight lithium ion battery[EB/OL]. [2020-11-05]. http://www.cdtechno.com/pdf/lit/12_1093_0217.pdf.
[14]	段建民, 王志新, 王承民, 等. 考虑碳减排效益的可再生电源规划[J]. 电网技术, 2015, 39(1):11-15.
	DUAN Jianmin, WANG Zhixin, WANG Chengmin, et al. Renewable power planning considering carbon emission reduction benefits[J]. Power System Technology, 2015, 39(1):11-15.
[15]	LIU G, TOMSOVIC K. Quantifying spinning reserve in systems with significant wind power penetration[J]. IEEE Transactions on Power Systems, 2012, 27(4):2385-2393. doi: 10.1109/TPWRS.2012.2207465
[16]	DING T, BO R, SUN H, et al. Big-M based MIQP method for economic dispatch with disjoint prohibited zones[J]. IEEE Transactions on Power System, 2014, 29(2):976-977. doi: 10.1109/TPWRS.2013.2287993

参数	数值
额定容量${{E}_{\text{rated}}}$/(kW·h)	400
额定功率${{P}_{\text{rated}}}$/kW	100
充、放电效率${{\eta }_{\text{c}}}$,${{\eta }_{\text{d}}}$	0.95
荷电状态最大值$S_{\text{OC}}^{\text{max}}$	0.9
荷电状态最小值$S_{\text{OC}}^{\text{min}}$	0.2
单位容量投资成本${{c}_{\text{E}}}$/[元/(kW·h)]	1 000
单位功率变流器投资成本${{c}_{\text{p}}}$/(元/kW)	1 500

运行成本	模式
运行成本	含电池储能	无电池储能
和电网交互费用/元	38.49	55.45
储能寿命损耗成本/元	84.88	0
削负荷成本/元	540.58	2 559.90
弃风弃光成本/元	7.61	302.63
碳排放成本/元	30.97	31.51
总运行成本/元	702.53	2 949.49

运行成本	模式
运行成本	计及电池寿命损耗	不计及电池寿命损耗
和电网交互成本/元	38.49	34.91
储能寿命损耗成本/元	84.88	0
削负荷成本/元	540.58	539.88
弃风弃光成本/元	7.61	7.45
碳排放成本/元	30.97	35.08
总运行成本/元	702.53	617.32

电池储能配置容量/(kW·h)	和电网交互费用/元	储能寿命损耗成本/元	削负荷成本/元	弃风弃光成本/元	碳排放成本/元	总运行成本/元
300	59.43	69.43	768.58	81.32	32.83	1 011.59
350	49.02	77.35	654.70	44.18	31.90	857.15
400	38.49	84.88	540.58	7.61	30.97	702.53
450	37.40	92.79	426.56	0	30.06	586.81
500	40.51	98.76	312.48	0	29.11	480.86

考虑电池储能寿命损耗的风光储微网随机日前调度策略^*

Stochastic Day-ahead Scheduling Optimization for Wind-PV-ES Microgrid Considering Battery Life Loss

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 16

相关文章 15

编辑推荐

Metrics

本文评价

[1]	梁捷. 基于Edgeworth级数和多重线性化的概率潮流算法[J]. 电气工程学报, 2018, 13(2): 36-40.
[2]	张胜, 方向, 赵志成, 刘冠华. 基于反馈线性化的软壳体球形机器人纵向运动控制^*[J]. 机械工程学报, 2017, 53(3): 43-50.
[3]	周锋, 顾临怡, 陈宗恒. 比例减压阀控桨系统的模型线性化及稳定性分析[J]. 机械工程学报, 2017, 53(14): 187-194.
[4]	孔凡, 李书进. 非线性随机动力系统时频子域上的统计线性化方法[J]. 机械工程学报, 2015, 51(9): 90-96.
[5]	吕学勤;张;轲;吴毅雄. 轮式移动焊接机器人输出反馈线性化控制[J]. , 2014, 50(6): 48-54.
[6]	刘志华;唐晓强;邵珠峰;王立平. 6自由度索并联机构的振动特性[J]. , 2013, 49(3): 49-55.
[7]	黄用华;廖启征;魏世民;郭磊. 45°车把转角下前轮驱动自行车机器人的定车运动[J]. , 2012, 48(7): 16-22.
[8]	褚明;贾庆轩;叶平;孙汉旭. 关节驱动柔性臂非最小相位系统的全局终端滑模控制[J]. , 2012, 48(3): 41-49.
[9]	母东杰;李长春;延皓;孙萌. 双喷嘴挡板伺服阀非线性建模及其线性化[J]. , 2012, 48(2): 193-198.
[10]	高辉;徐龙祥. 主动磁悬浮轴承系统拍振现象分析[J]. , 2011, 47(13): 104-112.
[11]	常英杰;陆宪忠;王世龙;王志明. 基于偏最小二乘回归的发动机排气分析仪线性化研究[J]. , 2011, 47(10): 76-81.
[12]	魏彤;房建成;刘珠荣. 双框架磁悬浮控制力矩陀螺动框架效应补偿方法[J]. , 2010, 46(2): 159-165.
[13]	毛新涛;包钢;杨庆俊;王祖温. 3自由度气动串联机械手的关节控制[J]. , 2008, 44(12): 254-260.
[14]	魏彤;房建成. 磁悬浮控制力矩陀螺磁轴承的变工作点线性化自适应控制方法[J]. , 2007, 43(6): 110-115.
[15]	杨军宏;尹自强;李圣怡. 阀控非对称缸的非线性建模及其反馈线性化[J]. , 2006, 42(5): 203-207.