Optimal configuration of liquid flow battery energy storage in photovoltaic system

doi:10.19799/j.cnki.2095-4239.2022.0707

Abstract

Abstract:

A liquid flow battery has low long-term energy storage cost and high system security, and thus, it is suitable for large-scale long-term energy storage application scenarios. The intermittency and fluctuation of the new energy power generation system can be suppressed through reasonable planning and configuration of the liquid flow battery system; this is of great significance for ensuring the safe and stable operation of the power grid. At present, there is a lack of research on the new energy system and the optimization configuration method of liquid flow battery based on their technical characteristics and operation characteristics. With the rapid transformation of energy in China, giving importance to the technology and operation characteristics of liquid flow battery and optimizing their capacity in the new energy system play a positive role in establishing a new power system. Thus, this paper examines the local area network (LAN) of photovoltaic and liquid flow battery joint power generation and proposes the optimal configuration method of liquid flow battery energy storage for photovoltaic system. The initial investment, full life operation, maintenance costs of each module system in the liquid flow battery system were assessed through in-depth research and analysis of operation characteristics, based on the characteristics of liquid flow battery system that power and capacity modules are separated. The power and capacity ratio of large-scale liquid flow battery system were optimized. Our results show that the operation strategy of the LAN system for photovoltaic and liquid flow battery combined power generation is optimized. While ensuring the stable operation of the LAN power supply, the optimal configuration scheme of the liquid flow battery was obtained by taking the minimum sum of the initial investment of the liquid flow battery system and the full life operation cost as the objective function. This shows that the proposed method can obtain the optimal solution of the liquid flow battery energy storage configuration of the photovoltaic system, and the sum of the initial investment and the life-cycle operation and maintenance cost is the minimum. The most economical megawatt liquid flow battery module design is when the power and capacity configuration of large-scale liquid flow battery system is 1 MW/8 MWh, and the LCOE for 25 years of operation is 0.292 yuan/kWh. The objective function of energy storage optimization configuration in the LAN applied in this paper achieves the optimal solution when the energy storage configuration is 20 MW/160 MWh.

Key words: photovoltaic energy storage system, liquid flow battery, energy storage configuration, new energy LAN

CLC Number:

TK 513.5

Xiaoyu GUO, Hao YU, Xin ZHENG, Yujia LIU, Yuanjie ZUO, Miaomiao ZHANG. Optimal configuration of liquid flow battery energy storage in photovoltaic system[J]. Energy Storage Science and Technology, 2023, 12(4): 1158-1167.

Figures/Tables 10

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References 17

1	李鑫, 王娟, 邱亚, 等. 基于VMD的混合储能容量优化配置[J]. 太阳能学报, 2022, 43(2): 88-96.
	LI X, WANG J, QIU Y, et al. Optimal allocation of hybrid energy storage capacity based on variational mode decomposition[J]. Acta Energiae Solaris Sinica, 2022, 43(2): 88-96.
2	李建林, 李雅欣, 周喜超. 电网侧储能技术研究综述[J]. 电力建设, 2020, 41(6): 77-84.
	LI J L, LI Y X, ZHOU X C. Summary of research on grid-side energy storage technology[J]. Electric Power Construction, 2020, 41(6): 77-84.
3	袁治章, 刘宗浩, 李先锋. 液流电池储能技术研究进展[J]. 储能科学与技术, 2022, 11(9): 2944-2958.
	YUAN Z Z, LIU Z H, LI X F. Research progress of flow battery technologies[J]. Energy Storage Science and Technology, 2022, 11(9): 2944-2958.
4	房茂霖, 张英, 乔琳, 等. 铁铬液流电池技术的研究进展[J]. 储能科学与技术, 2022, 11(5): 1358-1367.
	FANG M L, ZHANG Y, QIAO L, et al. Research progress of iron-chromium flow batteries technology[J]. Energy Storage Science and Technology, 2022, 11(5): 1358-1367.
5	宋子琛, 张宝锋, 童博, 等. 液流电池商业化进展及其在电力系统的应用前景[J]. 热力发电, 2022, 51(3): 9-20.
	SONG Z C, ZHANG B F, TONG B, et al. Commercialization progress of flow battery and its application prospects in electric power system[J]. Thermal Power Generation, 2022, 51(3): 9-20.
6	陈伟, 刘成, 储欣, 等. 基于遗传-梯度法的配电网储能最优配置方法的研究[J]. 电工技术, 2019(18): 100-102, 106.
	CHEN W, LIU C, CHU X, et al. Research on optimal allocation method for energy storage in dis-tribution network based on genetic-gradient method[J]. Electric Engineering, 2019(18): 100-102, 106.
7	郑恩泽, 顾洁, 刘波, 等. 基于双层优化模型的多能互补微网储能优化配置[J]. 电气自动化, 2017, 39(3): 45-48.
	ZHENG E Z, GU J, LIU B, et al. Optimal configuration of energy storage for multi-energy complementary micro-grids based on a Bi-level optimization model[J]. Electrical Automation, 2017, 39(3): 45-48.
8	李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
	LI J H, ZHANG J H, LI C P, et al. Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
9	席星璇, 熊敏鹏, 袁家海. 风电场发电侧配置储能系统的经济性研究[J]. 智慧电力, 2020, 48(11): 16-21, 47.
	XI X X, XIONG M P, YUAN J H. Economy analysis of energy storage system in wind farm generation side[J]. Smart Power, 2020, 48(11): 16-21, 47.
10	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.
11	刘舒, 李正力, 王翼, 等. 含分布式发电的微电网中储能装置容量优化配置[J]. 电力系统保护与控制, 2016, 44(3): 78-84.
	LIU S, LI Z L, WANG Y, et al. Optimal capacity allocation of energy storage in micro-grid with distributed generation[J]. Power System Protection and Control, 2016, 44(3): 78-84.
12	方金涛, 龚庆武. 考虑需求响应并计及液流电池动态特性的主动配电网系统储能优化配置[J]. 智慧电力, 2019, 47(11): 1-8.
	FANG J T, GONG Q W. Optimal allocation of energy storage system in active distribution network considering demand response and dynamic characteristics of VRB[J]. Smart Power, 2019, 47(11): 1-8.
13	胡枭, 徐国栋, 尚策, 等. 工业园区参与调峰的电池储能-需求响应联合规划[J]. 电力系统自动化, 2019, 43(15): 116-123.
	HU X, XU G D, SHANG C, et al. Joint planning of battery energy storage and demand response for industrial park participating in peak shaving[J]. Automation of Electric Power Systems, 2019, 43(15): 116-123.
14	方金涛, 龚庆武. 考虑需求响应并计及液流电池动态特性的主动配电网系统储能优化配置[J]. 智慧电力, 2019, 47(11): 1-8.
	FANG J T, GONG Q W. Optimal allocation of energy storage system in active distribution network considering demand response and dynamic characteristics of VRB[J]. Smart Power, 2019, 47(11): 1-8.
15	张华民, 王晓丽. 全钒液流电池技术最新研究进展[J]. 储能科学与技术, 2013, 2(3): 281-288.
	ZHANG H M, WANG X L. Recent progress on vanadium flow battery technologies[J]. Energy Storage Science and Technology, 2013, 2(3): 281-288.
16	韩冬冬. 风电场储能电池的容量优化配置[D]. 淄博: 山东理工大学, 2016.
	HAN D D. Wind farm energy storage capacity of the battery optimization[D]. Zibo: Shandong University of Technology, 2016.
17	于浩, 秦文萍, 魏斌, 等. 考虑预测误差的交直流混合微电网经济调度策略[J]. 电网技术, 2019, 43(11): 3987-3996.
	YU H, QIN W P, WEI B, et al. Economic dispatch of hybrid AC/DC microgrid considering prediction error[J]. Power System Technology, 2019, 43(11): 3987-3996.

功率等级/kW	单位造价/(元/kW)	电堆电流/A
20	3000	320
50	2000	450
75	1500	450

储能功率/MW	储能时长/h	平准化度电成本/(元/kWh)
1	4	0.512
1	6	0.396
1	8	0.292

指标	未配置储能	10 MW/80 MWh	20 MW/160 MWh	30 MW/240 MWh
储能系统初投资/万元	0	24984	40498	57221
全寿期运行成本/万元	0	32171	47556	49528
外购电成本/万元	97606	78135	28220	25343
电解液残值/万元	—	3990	7980	11970
合计/万元	97606	134112	113917	128557
年均弃光率/%	13.19	6.59	5.25	0

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