Optimal configuration for energy storage system capacity of wind-solar-storage microgrid

doi:10.3969/j.issn.2097-0706.2022.12.008

Abstract

Abstract:

Due to the intermittent and fluctuating characteristics of distributed energies such as solar energy and wind energy， energy storage technology is taken to effectively reduce their output fluctuations and improve their controllability. Based on the analysis on the characteristics of a wind-solar-storage microgrid's output， an optimal capacity allocation model taking the minimum total investment cost， lowest annual load power shortage rate and optimal wind and solar energy abandonment rate of the microgrid system as the optimization objectives is established. The model's optimal capacity allocation schemes under different constraints and operation strategies are obtained by non-dominated sequencing genetic algorithm （NSGA）. The results show that the wind-solar-storage microgrid system optimized by the optimal energy storage capacity allocation scheme and NSGA is of a lower total investment cost， and higher power-supply reliability and energy utilization rate.

Key words: distributed energy, micro-grid, energy storage system, NSGA, optimal energy storage capacity allocation, capacity optimization configuration

CLC Number:

TK019：TM61

ZHOU Chengwei, LI Peng, YU Bin, YU Tianyang, MENG Wei. Optimal configuration for energy storage system capacity of wind-solar-storage microgrid[J]. Integrated Intelligent Energy, 2022, 44(12): 56-61.

Figures/Tables 7

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References 21

[1]	徐超, 柏筱飞. 分布式电源并网特性分析[J]. 大众用电, 2022, 37(5):51-53.
	XU Chao, BAI Xiaofei. Analysison characteristics of distributed power grid connection[J]. Popular Utilization of Electricity, 2022, 37(5): 51-53.
[2]	李琼慧, 叶小宁, 胡静, 等. 分布式能源规模化发展前景及关键问题[J]. 分布式能源, 2020, 5(2):1-7.
	LI Qionghui, YE Xiaoning, HU Jing, et al. Outlook and critical issues of large-scale development on distributed energy resources[J]. Distributed Energy, 2020, 5(2): 1-7.
[3]	田军强. 新能源发电与分布式发电及其对电力系统的影响[J]. 通信电源技术, 2019, 36(12):131-132.
	TIAN Junqiang. New energy generation and distributed generation and their influence on power system[J]. Telecom Power Technology, 2019, 36 (12): 131-132.
[4]	王晗雯, 鲁胜, 周照宇. 光伏-混合储能微电网协调控制及经济性分析[J]. 华电技术, 2020, 42(4): 31-36.
	WANG Hanwen, LU Sheng, ZHOU Zhaoyu. Coordinated control and economic analysis on a PV-hybrid energy storage micro-grid system[J]. Huadian Technology, 2020, 42(4): 31-36.
[5]	王俊杰, 孙嘉, 徐猛, 等. 电源侧储能参与电网调频的容量配置研究[J]. 电器与能效管理技术, 2020(10):84-89.
	WANG Junjie, SUN Jia, XU Meng, et al. Research on capacity design of power-side energy storage participating in grid frequency modulation[J]. Electrical & Energy Management Technology, 2020 (10): 84-89.
[6]	胡之荣. 微电网与分布式电源的现状及趋势分析[C]// 第八届云南省科协学术年会论文集——专题六: 工业与信息科技, 2018:178-184.
[7]	陈沛光, 胡维国, 程永生. 基于源荷协同的新能源消纳综合效益测度指标体系构建[J]. 中国市场, 2020(4):61-62,96.
[8]	王鑫, 陈祖翠, 卞在平, 等. 基于粒子群优化算法的智慧微电网风光储容量优化配置[J]. 综合智慧能源, 2022, 44(6): 52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
	WANG Xin, CHEN Zucui, BIAN Zaiping, et al. Optimal allocation of a wind-PV-battery hybrid system in smart microgrid based on particle swarm optimization algorithm[J]. Integrated Intelligent Energy, 2022, 44(6): 52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
[9]	蒋文坤, 韩颖慧, 薛智文, 等. 多能互补能源系统中储能原理及其应用[J]. 综合智慧能源, 2022, 44(1): 63-71. doi: 10.3969/j.issn.2097-0706.2022.01.009
	JIANG Wenkun, HAN Yinghui, XUE Zhiwen, et al. Energy storage technologies and their applications in multi-energy complementary power system[J]. Integrated Intelligent Energy, 2022, 44(1): 63-71. doi: 10.3969/j.issn.2097-0706.2022.01.009
[10]	张兴科, 魏朝阳, 王康平, 等. 面向高比例光伏并网的火电爬坡压力缓解策略[J]. 综合智慧能源, 2022, 44(1): 1-8. doi: 10.3969/j.issn.2097-0706.2022.01.001
	ZHANG Xingke, WEI Chaoyang, WANG Kangping, et al. Strategies for relieving ramp pressure of thermal power units with high-proportion photovoltaic power connecting to the grid[J]. Integrated Intelligent Energy, 2022, 44(1): 1-8. doi: 10.3969/j.issn.2097-0706.2022.01.001
[11]	HU X, SHU Z, MING N. Microgrid development in China: A method for renewable energy and energy storage capacity configuration in a megawatt-level isolated microgrid[J]. IEEE Electrification Magazine, 2017, 5(2):28-35.
[12]	周喜超, 孟凡强, 李娜, 等. 电池储能系统参与电网削峰填谷控制策略[J]. 热力发电, 2021, 50(4):44-50.
	ZHOU Xichao, MENG Fanqiang, LI Na, et al. Control strategies of battery energy storage system participating in peak load regulation of power grid[J]. Thermal Power Generation, 2021, 50 (4): 44-50.
[13]	吴倩, 王洋, 王琳媛, 等. 计及波动平抑与经济性的风光储系统中混合储能容量优化配置[J]. 电测与仪表, 2020, 59(4):112-119.
	WU Qian, WANG Yang, WANG Linyuan, et al. Optimal capacity allocation of hybrid energy storage system in wind-solar-battery system considering fluctuation smoothing and economy[J]. Electric Measurement and Instrumentation, 2020, 59 (4): 112-119.
[14]	甘霖, 陈瑜玮, 刘育权, 等. 含可再生能源的微网冷-热-电多能流协同优化与案例分析[J]. 电力自动化设备, 2017, 37(6):275-281.
	GAN Lin, CHEN Yuwei, LIU Yuquan, et al. Coordinative optimization of multiple energy flows for microgrid with renewable energy resources and case study[J]. Electric Power Automation Equipment, 2017, 37 (6): 275-281.
[15]	高强, 周洪青, 高骞, 等. 基于源荷和光储特征的小区多微电网系统工程设计[J]. 电子测量技术, 2020, 43(2):64-67.
	GAO Qiang, ZHOU Hongqing, GAO Qian, et al. Based on the characteristics of source load and light storage, the multi-micro grid system engineering design of residential area[J]. Electronic Measurement Technology, 2020, 43 (2): 64-67.
[16]	MAO M, JIN P, CHANG L, et al. Economic analysis and optimal design on microgrids with SS-PVs for industries[J]. IEEE Transactions on Sustainable Energy, 2014, 5(4):1328-1336. doi: 10.1109/TSTE.2014.2327067
[17]	陈若奇, 钟建伟, 罗春景, 等. 基于混合储能的光伏微电网控制策略研究[J]. 电工材料, 2021(4):26-28.
	CHEN Ruoqi, ZHONG Jianwei, LUO Chunjing, et al. Thunderstorm cloud prediction with DBSCAN and LS-SVM algorithm[J]. Electrical Engineering Materials, 2021 (4): 26-28.
[18]	窦晓波, 袁简, 吴在军, 等. 并网型风光储微电网容量改进优化配置方法[J]. 电力自动化设备, 2016, 36(3):26-32.
	DOU Xiaobo, YUAN Jian, WU Zaijun, et al. Improved configuration optimization of PV-wind-storage capacities for grid-connected microgrid[J]. Electric Power Automation Equipment, 2016, 36 (3): 26-32.
[19]	常超, 史阳. 基于超级电容储能的直流风电机组协调控制[J]. 现代电力, 2017, 34(6):65-70.
	CHANG Chao, SHI Yang. Coordinated control of DC wind power generator based on super capacitor energy storage[J]. Modern Electric Power, 2017, 34 (6): 65-70.
[20]	严海波, 康林贤, 周冬. 考虑随机性的微电网日前调度与储能优化模型[J]. 电网与清洁能源, 2019, 35(11):61-65.
	YAN Haibo, KANG Linxian, ZHOU Dong. Optimal model of day-ahead dispatching and energy storage for micro-grid considering randomness[J]. Power System and Clean Energy, 2019, 35 (11): 61-65.
[21]	MA X, ZHANG C H, LI K, et al. Optimal dispatching strategy of regional micro energy system with compressed air energy storage[J]. Energy, 2020, 212:118557. doi: 10.1016/j.energy.2020.118557

方案	光伏电池板/块	风力发电机/台	储能电池/块	固定成本/万元	维护成本/万元	购电成本/万元	售电收益/万元	总投资成本/万元	负荷缺电率/%	弃风弃光率/%
1	10	1	0	4.40	0.15	1.50	0.20	5.85	10	30
2	10	1	5	5.40	0.23	0.80	0.80	5.63	8	15
3	15	1	10	6.60	0.34	0.10	1.50	5.54	5	10