Optimal capacity allocation method of a distributed energy storage system based on greedy algorithm

doi:10.19799/j.cnki.2095-4239.2021.0695

Abstract

Abstract:

Distributed energy storage system (DESS) is very important for peak shaving of the power system. Its location and capacity arrangement has traditionally made it a focus for field study. However, poor economic and technical analyses, as well as DESS's high processing cost, remain issues. In light of this, this research provides a greedy algorithm-based optimum capacity allocation strategy for a DESS. First, a comprehensive DESS economic model and operation constraint model are established. Compared with the traditional defect of only considering investment and operation costs, it increases the economic benefits brought by energy storage operation scheduling. Then, using power loss sensitivity, site selection can lower the dimension of the addressing problem and enhance optimization efficiency. Then, the greedy algorithm is used to divide the DESS into many units and optimize them, and the decision-making process of each unit is simplified into a simple model, which can significantly improve the solution efficiency. The simulation study is conducted in MATLAB R2015b to validate the efficiency of the suggested technique, using load data from a neighborhood in Jiaxing City, Zhejiang Province, as an example. The results show that (1) when compared with evolutionary algorithms, this approach can only find the local optimal solution, and the economic gain is slightly smaller (albeit the difference is modest), but it can greatly improve computing efficiency and (2) the optimization results are identical to the entire optimization, but it excludes the computation of network loss cost, resulting in increased calculation efficiency.

Key words: distributed energy storage system, capacity allocation, location, greedy algorithm, Monte Carlo simulation

CLC Number:

TM 933

Yuhan GUO, Dan YU, Peng YANG, Ziji WANG, Jintao WANG. Optimal capacity allocation method of a distributed energy storage system based on greedy algorithm[J]. Energy Storage Science and Technology, 2022, 11(7): 2295-2304.

Figures/Tables 11

Fig. 1

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

Fig. 8

Fig. 9

References 27

1	宋航, 刘友波, 刘俊勇, 等. 考虑用户侧分布式储能交互的售电公司智能化动态定价[J]. 中国电机工程学报, 2020, 40(24): 7959-7972, 8233.
	SONG H, LIU Y B, LIU J Y, et al. Intelligent dynamic pricing of electricity retailers considering distributed energy storage interaction on user side[J]. Proceedings of the CSEE, 2020, 40(24): 7959-7972, 8233.
2	LIANG H J, LIU Y G, LI F Z, et al. Dynamic economic/emission dispatch including PEVs for peak shaving and valley filling[J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 2880-2890.
3	MAHMUD K, SAHOO A. Multistage energy management system using autoregressive moving average and artificial neural network for day-ahead peak shaving[J]. Electronics Letters, 2019, 55(15): 853-855.
4	刘文霞, 杨梦瑶, 王静, 等. 基于运行策略智能生成方法的增量配电系统储能优化配置[J]. 中国电机工程学报, 2021, 41(10): 3317-3329, 3658.
	LIU W X, YANG M Y, WANG J, et al. Energy storage planning for incremental power distribution systems based on the intellectual generating method of operational strategies[J]. Proceedings of the CSEE, 2021, 41(10): 3317-3329, 3658.
5	麻秀范, 陈静, 余思雨, 等. 计及容量市场的用户侧储能优化配置研究[J]. 电工技术学报, 2020, 35(19): 4028-4037.
	MA X F, CHEN J, YU S Y, et al. Research on user side energy storage optimization configuration considering capacity market[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4028-4037.
6	林哲, 胡泽春, 宋永华. 考虑N-1准则的配电网与分布式储能联合规划[J]. 中国电机工程学报, 2021, 41(13): 4390-4403.
	LIN Z, HU Z C, SONG Y H. Joint planning of distribution network expansion and distributed energy storage systems under N-1 criterion[J]. Proceedings of the CSEE, 2021, 41(13): 4390-4403.
7	林嘉伟, 王志新, 张永. 独立直流微电网中考虑不同容量的分布式储能系统能量控制策略[J]. 电力自动化设备, 2020, 40(10): 139-146.
	LIN J W, WANG Z X, ZHANG Y. Energy control strategy of distributed energy storage system considering different capacities in isolated DC microgrid[J]. Electric Power Automation Equipment, 2020, 40(10): 139-146.
8	梁晶, 张楷, 陈曈, 等. 分布式能源储能技术发展研究[J]. 能源与节能, 2020(6): 54-55, 72.
	LIANG J, ZHANG K, CHEN T, et al. Research on development of distributed energy storage technology[J]. Energy and Energy Conservation, 2020(6): 54-55, 72.
9	李振坤, 李一骄, 张代红, 等. 储能设备对有源配电网供电可靠性的影响分析及优化配置[J]. 电网技术, 2018, 42(11): 3676-3683.
	LI Z K, LI Y J, ZHANG D H, et al. Influence analysis of energy storage device on reliability of distribution network and its optimal allocation[J]. Power System Technology, 2018, 42(11): 3676-3683.
10	黄弦超. 计及可控负荷的独立微网分布式电源容量优化[J]. 中国电机工程学报, 2018, 38(7): 1962-1970, 2211.
	HUANG X C. Capacity optimization of distributed generation for stand-alone microgrid considering controllable load[J]. Proceedings of the CSEE, 2018, 38(7): 1962-1970, 2211.
11	马溪原, 郭晓斌, 雷金勇. 面向多能互补的分布式光伏与气电混合容量规划方法[J]. 电力系统自动化, 2018, 42(4): 55-63.
	MA X Y, GUO X B, LEI J Y. Capacity planning method of distributed PV and P2G in multi-energy coupled system[J]. Automation of Electric Power Systems, 2018, 42(4): 55-63.
12	全慧, 杨水丽, 李相俊, 等. 改善配电网末端电压的分布式储能容量配置研究[J]. 电器与能效管理技术, 2020(10): 64-70.
	QUAN H, YANG S L, LI X J, et al. Research on distributed energy storage capacity configuration for improving voltage of distribution network[J]. Electrical & Energy Management Technology, 2020(10): 64-70.
13	郜宁, 张慧媛, 王子琪, 等. 区域电网分布式储能选址定容规划[J]. 高压电器, 2020, 56(8): 52-58.
	GAO N, ZHANG H Y, WANG Z Q, et al. Planning for site selection and capacity determination of distributed energy storage in regional power grid[J]. High Voltage Apparatus, 2020, 56(8): 52-58.
14	丁倩, 曾平良, 孙轶恺, 等. 一种考虑可再生能源不确定性的分布式储能电站选址定容规划方法[J]. 储能科学与技术, 2020, 9(1): 162-169.
	DING Q, ZENG P L, SUN Y K, et al. A planning method for the placement and sizing of distributed energy storage system considering the uncertainty of renewable energy sources[J]. Energy Storage Science and Technology, 2020, 9(1): 162-169.
15	MIRANDA I, LEITE H, SILVA N. Coordination of multifunctional distributed energy storage systems in distribution networks[J]. IET Generation, Transmission & Distribution, 2016, 10(3): 726-735.
16	CARPINELLI G, CELLI G, MOCCI S, et al. Optimal integration of distributed energy storage devices in smart grids[J]. IEEE Transactions on Smart Grid, 2013, 4(2): 985-995.
17	吴峰, 黄绮彦, 史林军, 等. 考虑优化充放电策略的分布式储能容量配置方法[J]. 供用电, 2019, 36(5): 85-92.
	WU F, HUANG Q Y, SHI L J, et al. Distributed energy storage system capacity configuration considering optimized charging and discharging strategy[J]. Distribution & Utilization, 2019, 36(5): 85-92.
18	夏新茂, 关洪浩, 丁鹏飞, 等. 基于改进型量子遗传算法的储能系统容量配置与优化策略[J]. 储能科学与技术, 2019, 8(3): 551-558.
	XIA X M, GUAN H H, DING P F, et al. Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm[J]. Energy Storage Science and Technology, 2019, 8(3): 551-558.
19	王静, 刘文霞, 李守强, 等. 计及机组降损收益的电源侧电池储能调频/调峰经济效益评价方法[J]. 电网技术, 2020, 44(11): 4236-4245.
	WANG J, LIU W X, LI S Q, et al. A method to evaluate economic benefits of power side battery energy storage frequency/peak regulation considering the benefits of reducing thermal power unit losses[J]. Power System Technology, 2020, 44(11): 4236-4245.
20	彭穗, 龚贤夫, 刘新苗, 等. 计及直流调节能力的含风电电力系统储能优化配置[J]. 中国电力, 2022, 55(1): 37-45.
	PENG S, GONG X F, LIU X M, et al. Optimized energy storage configuration of wind power integrated power system considering DC regulation capacity[J]. Electric Power, 2022, 55(1): 37-45.
21	彭春华, 陈露, 张金克, 等. 基于分类概率机会约束IGDT的配网储能多目标优化配置[J]. 中国电机工程学报, 2020, 40(9): 2809-2819.
	PENG C H, CHEN L, ZHANG J K, et al. Multi-objective optimal allocation of energy storage in distribution network based on classified probability chance constraint information gap decision theory[J]. Proceedings of the CSEE, 2020, 40(9): 2809-2819.
22	JIN R Y, SONG J, LIU J, et al. Location and capacity optimization of distributed energy storage system in peak-shaving[J]. Energies, 2020, 13(3): 513.
23	傅旭, 李富春, 杨欣, 等. 基于全寿命周期成本的储能成本分析[J]. 分布式能源, 2020, 5(3): 34-38.
	FU X, LI F C, YANG X, et al. Cost analysis of energy storage based on life cycle cost[J]. Distributed Energy, 2020, 5(3): 34-38.
24	李涛, 许苑, 陈健, 等. 计及全寿命成本和收益的微电网储能优化配置[J]. 电力系统及其自动化学报, 2020, 32(3): 46-51, 58.
	LI T, XU Y, CHEN J, et al. Optimal configuration of energy storage for microgrid considering life cycle cost-benefit[J]. Proceedings of the CSU-EPSA, 2020, 32(3): 46-51, 58.
25	黄际元, 李欣然, 常敏, 等. 考虑储能电池参与一次调频技术经济模型的容量配置方法[J]. 电工技术学报, 2017, 32(21): 112-121.
	HUANG J Y, LI X R, CHANG M, et al. Capacity allocation of BESS in primary frequency regulation considering its technical-economic model[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 112-121.
26	袁铁江, 张昱, 栗磊, 等. 计及功率密度约束含氢储能的预装式多元储能电站容量优化配置研究[J]. 电工技术学报, 2021, 36(3): 496-506.
	YUAN T J, ZHANG Y, LI L, et al. Capacity optimization configuration of pre-installed multi-energy storage power station considering power density constrained hydrogen storage[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 496-506.
27	刘闯, 孙傲, 王艺博, 等. 计及电熔镁负荷与储能联合调峰的电力系统日前-日内联合经济调度方法[J]. 电力自动化设备, 2022, 42(2): 8-15.
	LIU C, SUN A, WANG Y B, et al. Day-ahead and intra-day joint economic dispatching method of electric power system considering combined peak-shaving of fused magnesium load and energy storage[J]. Electric Power Automation Equipment, 2022, 42(2): 8-15.

节点编号	贪婪算法		遗传算法
节点编号	容量/kWh	净效益/元	容量/kWh	净效益/元
16	80.4	109	80.3	111
19	187.5	109	187.9	111
27	91.0	计算时间	93.8	计算时间
28	107.1	370.8 ms	106.5	2.7 s
29	133.9	370.8 ms	131.5	2.7 s

[1]	Peiping YU, Liang XU, Bingyun MA, Qintao SUN, Hao YANG, Yue LIU, Tao CHENG. Multiscale simulation of a solid electrolyte interphase [J]. Energy Storage Science and Technology, 2022, 11(3): 921-928.
[2]	Jinping LIU, Bowei PU, Zheyi ZOU, Mingqing LI, Yuqing DING, Yuan REN, Yaqiao LUO, Jie LI, Yajie LI, Da WANG, Bing HE, Siqi SHI. Investigating thermodynamic and kinetic properties of ionic conductors via Monte Carlo simulation [J]. Energy Storage Science and Technology, 2022, 11(3): 878-896.
[3]	Jundong DUAN, Gaoshang LI, Yishi LI, Ziheng FU, Hongye HUANG. Coordinated charging control for EV charging stations considering wind power accommodation [J]. Energy Storage Science and Technology, 2021, 10(2): 630-637.
[4]	YAO Qingcheng, YUAN Xiaoling. Optimal configuration of independent microgrid based on Monte Carlo processing of source and load uncertainty [J]. Energy Storage Science and Technology, 2020, 9(1): 186-194.
[5]	ZHANG Mengtian, TIAN Shu, ZENG Zhihui. Optimal allocation of hybrid energy storage capacity based on variational mode decomposition [J]. Energy Storage Science and Technology, 2020, 9(1): 170-177.
[6]	ZHANG Baoge, LI Ping, ZHANG Zhen, WANG Yu, RONG Yao. Energy management strategy of hybrid energy storage system for urban rail trains [J]. Energy Storage Science and Technology, 2020, 9(1): 204-210.
[7]	HOU Shaopan, Lü Xin, MENG Xiangfei, FAN Hualong, HE Jiajia, CHEN Jie, ZHANG Jie, HAI Jianping. Operation strategy of distributed energy storage system with multi-battery under output smoothing scenario [J]. Energy Storage Science and Technology, 2019, 8(S1): 74-84.
[8]	SHI Changli, WEI Tongzheng, HUO Qunhai, HE Junqiang, ZHANG Tongshuo. A control strategy for the distributed energy storage system for a DC distribution power network [J]. Energy Storage Science and Technology, 2019, 8(4): 654-658.
[9]	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.
[10]	HAN Xiaojuan1, ZHANG Hua1, XIU Xiaoqing2, LI Jianlin2. Economic evaluation of fast charging electric vehicle station with second-use batteries energy storage system [J]. Energy Storage Science and Technology, 2016, 5(4): 514-521.