An Aggregation Model and Evaluation Method of Distributed Energy Storage Based on Adaptive Equalization Technology

doi:10.16183/j.cnki.jsjtu.2021.322

Abstract

Abstract:

Aimed at the problems of wide area distribution, resource dispersion, and inefficient aggregation of distributed energy storage, this paper proposes an aggregation model and evaluation method of distributed energy storage based on the adaptive equalization technology. First, this paper establishes an adaptive equalization function model based on dynamic characteristic parameters such as energy storage capacity, power, and state of charge. Then, based on the adaptive equalization function model, it establishes the aggregation model and evaluation method of distributed energy storage, which takes the power regulation rate, adaptive equalization rate, and capacity contribution rate as the dynamic parameters of aggregation degree. The example simulation verifies that the model can realize the fact that each energy storage unit can complete the aggregation from energy storage unit to energy storage aggregate with a smaller internal difference and a higher external aggregation rate. It can be applied to a large number of distributed energy storage aggregation participating in grid auxiliary services, and realize the efficient utilization of energy storage resources.

Key words: adaptive equalization technology, aggregation model of distributed energy storage, energy storage unit, aggregate, energy storage aggregation

CLC Number:

TM762

YE Peng, LIU Siqi, GUAN Duojiao, JIANG Zhunan, SUN Feng, GU Haifei. An Aggregation Model and Evaluation Method of Distributed Energy Storage Based on Adaptive Equalization Technology[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1689-1699.

Figures/Tables 12

Fig.1

Fig.2

Tab.1

Tab.2

Tab.3

Fig.3

Fig.4

Fig.5

Fig.6

Tab.4

Fig.7

Fig.8

References 18

[1]	MA Y W, ZHOU Y M, ZHANG J A, et al. Economic dispatch of islanded microgrid considering a cooperative strategy between diesel generator and battery energy storage system[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5):593-599.
[2]	BARONE G, MENNITI D, PINNARELLI A, et al. A profitability analysis of a consumers aggregation with storage system in dispatching market[C]// 2019 16th International Conference on the European Energy Market (EEM). Piscataway, NJ, USA: IEEE, 2019: 1-5.
[3]	MORSTYN T, SAVKIN A V, HREDZAK B, et al. Scalable energy management for low voltage microgrids using multi-agent storage system aggregation[J]. IEEE Transactions on Power Systems, 2018, 33(2):1614-1623. doi: 10.1109/TPWRS.2017.2734850 URL
[4]	WOGRIN S, TEJADA-ARANGO D A, PINEDA S, et al. What time-period aggregation method works best for power system operation models with renewables and storage?[C]// 2019 International Conference on Smart Energy Systems and Technologies (SEST). Piscataway, NJ, USA: IEEE, 2019: 1-6.
[5]	HU J J, YANG G Y, BINDNER H W, et al. Application of network-constrained transactive control to electric vehicle charging for secure grid operation[J]. IEEE Transactions on Sustainable Energy, 2017, 8(2):505-515. doi: 10.1109/TSTE.2016.2608840 URL
[6]	吴志伟, 张建龙, 吴红杰, 等. 低速电动汽车混合能源存储系统效率分析[J]. 上海交通大学学报, 2012, 46(8):1304-1309.
	WU Zhiwei, ZHANG Jianlong, WU Hongjie, et al. Efficiency analysis of hybrid energy storage system in light electric vehicles[J]. Journal of Shanghai Jiao Tong University, 2012, 46(8):1304-1309.
[7]	HANNAN M A, HOQUE M M, MOHAMED A, et al. Review of energy storage systems for electric vehicle applications: Issues and challenges[J]. Renewable and Sustainable Energy Reviews, 2017, 69:771-789. doi: 10.1016/j.rser.2016.11.171 URL
[8]	PERTL M, CARDUCCI F, TABONE M, et al. An equivalent time-variant storage model to harness EV flexibility: Forecast and aggregation[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4):1899-1910. doi: 10.1109/TII.9424 URL
[9]	靳文涛, 牛萌, 吕洪章, 等. 客户侧分布式储能汇聚潜力评估方法[J]. 电力建设, 2019, 40(4):34-41.
	JIN Wentao, NIU Meng, LÜ Hongzhang, et al. Evaluation method for convergence potential of distributed energy storage on customer side[J]. Electric Power Construction, 2019, 40(4):34-41.
[10]	尹渠凯, 米增强, 贾雨龙, 等. 基于改进K-means聚类的电力市场下分布式储能系统经济性调控模型[J]. 电力建设, 2019, 40(5):20-27.
	YIN Qukai, MI Zengqiang, JIA Yulong, et al. Economy regulation method for distributed energy storage in distribution network according to K-means clustering[J]. Electric Power Construction, 2019, 40(5):20-27.
[11]	赵显秋, 秦立军, 段惠. 基于聚合效应的配电网分布式储能优化调度[J]. 电力电容器与无功补偿, 2020, 41(4):228-234.
	ZHAO Xianqiu, QIN Lijun, DUAN Hui. Distributed energy storage optimal scheduling of distribution network based on aggregation effect[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(4):228-234.
[12]	刘根才, 陆志欣, 杨智诚, 等. 考虑SOC均衡的分布式储能聚合控制方法[J]. 电力电容器与无功补偿, 2020, 41(3):174-181.
	LIU Gencai, LU Zhixin, YANG Zhicheng, et al. Distributed energy storage aggregation control method considering SOC equalization[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(3):174-181.
[13]	FU Q, MONTOYA L F, SOLANKI A, et al. Microgrid generation capacity design with renewables and energy storage addressing power quality and surety[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2019-2027. doi: 10.1109/TSG.2012.2223245 URL
[14]	李欣然, 邓涛, 黄际元, 等. 储能电池参与电网快速调频的自适应控制策略[J]. 高电压技术, 2017, 43(7):2362-2369.
	LI Xinran, DENG Tao, HUANG Jiyuan, et al. Battery energy storage systemsê self-adaptation control strategy in fast frequency regulation[J]. High Voltage Engineering, 2017, 43(7):2362-2369.
[15]	李欣然, 崔曦文, 黄际元, 等. 电池储能电源参与电网一次调频的自适应控制策略[J]. 电工技术学报, 2019, 34(18):3897-3908.
	LI Xinran, CUI Xiwen, HUANG Jiyuan, et al. The self-adaption control strategy of energy storage batteries participating in the primary frequency regulation[J]. Transactions of China Electrotechnical Society, 2019, 34(18):3897-3908.
[16]	李若, 李欣然, 谭庄熙, 等. 考虑储能电池参与二次调频的综合控制策略[J]. 电力系统自动化, 2018, 42(8):74-82.
	LI Ruo, LI Xinran, TAN Zhuangxi, et al. Integrated control strategy considering energy storage battery participating in secondary frequency regulation[J]. Automation of Electric Power Systems, 2018, 42(8):74-82.
[17]	李金武. 基于自适应分段聚合的云模型序列相似度评价方法[J]. 计算机应用与软件, 2020, 37(11):239-245.
	LI Jinwu. Similarity evaluation method of cloud model series based on self-adaption piecewise aggregate[J]. Computer Applications and Software, 2020, 37(11):239-245.
[18]	GU H F, JIE Y, LI Y, et al. Optimal economic dispatch for an industrial park with consideration of an elastic energy cloud model with integrated demand response uncertainty[J]. IEEE Access, 2020, 9:52485-52508.

s_x	取值
s_min	0.1
s_low	0.2
s_shc	0.4
s_mid	0.5
s_shd	0.6
s_high	0.8
s_max	0.9

a_x	取值
a_min	0.10
a_low	0.15
a_mid	0.50
a_high	0.85
a_max	0.90

储能配置	功率/ MW	容量/ (MW·h^-1)	d_i	A_i₀	A_i
铅酸电池	4	10	20	1.00	1
全钒液流电池1	50	100	15	0.01	0.001
镍镉电池	8	20	18	0.01	0.005
全钒液流电池2	25	80	13	0.50	0.0625
锂离子电池1	15	40	18	0.50	0.125
锂离子电池2	30	60	13	0.01	0.00167

类别	残差
储能1	7.0202
储能2	7.3653
储能3	7.0002
储能4	4.8672
储能5	3.0025
储能6	3.5995