考虑状态相依老化特性的电池储能参与调峰投资效益评估方法

doi:10.19595/j.cnki.1000-6753.tces.241171

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (677 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Battery energy storage (BES) is widely used in various applications in the power system as a flexible regulation resource. One of the key applications is peak shaving. BES can charge and discharge to capture the price difference between peak and valley periods in the electricity market. However, due to the inherent operational characteristics of BES, peak shaving behavior results in irreversible aging loss, leading to a reduction in lifespan and available capacity. Establishing an accurate aging model for BES is fundamental for simulating its peak shaving behavior and evaluating investment benefits. The aging characteristics of BES are state-dependent at different stages of its lifespan. However, aging models with fixed parameters fail to consider the variation in aging parameters over time during long-term peak shaving simulations.
A state-dependent aging model for BES is proposed, based on the Arrhenius empirical model, which accounts for the impact of lifespan on aging characteristics. This model can represent the full aging behavior of BES throughout its lifespan. A discrete form of the model is developed to meet the application requirements of power system optimization. The discrete variables are adapted to those of the power system, enabling their integration. Furthermore, the discrete aging model is embedded in a peak shaving optimization model. To address the challenge of solving the highly nonlinear discrete aging model, linearization techniques are employed to facilitate model computation. A dynamic parameter updating mechanism for BES is also designed and incorporated into the discrete aging model, enabling dynamic updates of the aging loss rate and available capacity during long-term peak shaving simulations, thus accurately capturing the full aging process throughout the lifespan.
To validate the effectiveness of the proposed method in investment benefits evaluation, historical locational marginal prices from the Southern Power Pool (SPP) electricity market are used as inputs to simulate the daily peak shaving operations of BES. The results of the daily simulations are used to update BES parameters via the dynamic updating mechanism, and the peak shaving benefits are then calculated. By updating boundary conditions, long-term peak shaving simulations of BES throughout its lifespan are performed, and the cumulative net present value (NPV) of investing in BES for peak shaving is computed. The results show that the proposed method allows for considering aging loss variation over time, reflecting the impact of state-dependent aging characteristics on the peak shaving performance of BES. This method enables accurate evaluation of BES investment benefits.
From the analysis, the following conclusions can be drawn: (1) The state-dependent aging model of BES, based on the Arrhenius empirical model and after discretization and linearization, can accurately capture the aging characteristics throughout the entire lifespan of BES. It also facilitates effective application in power system peak shaving simulations. (2) The state-dependent aging characteristics influence the charge and discharge behavior decisions of BES under electricity price signals. Using the state-dependent aging model allows for precise quantification of the aging costs arising from BES's peak shaving behavior, enabling accurate evaluation of the investment benefits of BES in peak shaving applications.

Key words： power system peak shaving battery energy storage Arrhenius empirical model state-dependent aging model investment benefits evaluating

Received: 04 July 2024

PACS:

TM732

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Guo Fei
	Yong Pei
	Liu Xinyu
	Yu Juan
	Yang Zhifang

Cite this article:

Guo Fei,Yong Pei,Liu Xinyu等. Evaluating the Investment Benefits of Battery Energy Storage Participating in Peak Shaving Considering the State-Dependent Aging Characteristics[J]. Transactions of China Electrotechnical Society, 0, (): 1171-.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241171 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/1171

[1] 程曹阳, 杨知方, 余娟, 等. 面向全场景安全的储能投资高效规划方法[J]. 电工技术学报, 2025, 40(1): 63-78.
Cheng Caoyang, Yang Zhifang, Yu Juan, et al.An efficient method for energy storage planning considering full-scenario security[J]. Transactions of China Electrotechnical Society, 2025, 40(1): 63-78.
[2] 朱宗耀, 王秀丽, 吴雄, 等. 复合储能参与电能量及辅助服务市场的运行策略[J]. 电力系统自动化, 2023, 47(18): 80-90.
Zhu Zongyao, Wang Xiuli, Wu Xiong, et al.Operation strategy for composite energy storage participating in electric energy and ancillary service markets[J]. Automation of Electric Power Systems, 2023, 47(18): 80-90.
[3] 王灿, 张羽, 田福银, 等. 基于双向主从博弈的储能电站与综合能源系统经济运行策略[J]. 电工技术学报, 2023, 38(13): 3436-3446, 3472. Wang Can, Zhang Yu, Tian Fuyin, et al. Economic operation of energy storage power stations and integrated energy systems based on bidirectional master-slave game[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3436-3446, 3472.
[4] 郝艺, 周瑀涵, 刘晨曦, 等. 含跟网型储能的新能源多馈入系统小扰动电压支撑强度分析[J]. 电工技术学报, 2024, 39(11): 3569-3580.
Hao Yi, Zhou Yuhan, Liu Chenxi, et al.Small-disturbance voltage support strength analysis for renewable multi-infeed system with grid-following energy storage[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3569-3580.
[5] 李军徽, 安晨宇, 李翠萍, 等. 计及调峰市场交易的储能-新能源-火电多目标优化调度[J]. 电工技术学报, 2023, 38(23): 6391-6406.
Li Junhui, An Chenyu, Li Cuiping, et al.Multi-objective optimization scheduling method considering peak regulating market transactions for energy storage-new energy-thermal power[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6391-6406.
[6] 夏向阳, 陈贵全, 刘俊翔, 等. 储能系统直流侧纹波电流对锂离子电池寿命影响分析及优化控制策略[J]. 电工技术学报, 2023, 38(22): 6218-6229.
Xia Xiangyang, Chen Guiquan, Liu Junxiang, et al.Analysis of the impact of DC-side ripple current on lithium-ion battery life in energy storage systems and optimal control strategies[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 6218-6229.
[7] 刘萍, 李泽文, 蔡雨思, 等. 基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[J]. 电工技术学报, 2024, 39(10): 3232-3243.
Liu Ping, Li Zewen, Cai Yusi, et al.Joint estimation method of SOC and SOH based on fusion of equivalent circuit model and data-driven model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243.
[8] 姜欣, 郑雪媛, 胡国宝, 等. 市场机制下面向电网的储能系统优化配置[J]. 电工技术学报, 2019, 34(21): 4601-4610.
Jiang Xin, Zheng Xueyuan, Hu Guobao, et al.Optimization of battery energy storage system locating and sizing for the grid under the market mechanism[J]. Transactions of China Electrotechnical Society, 2019, 34(21): 4601-4610.
[9] Fallahifar R, Kalantar M.Optimal planning of lithium ion battery energy storage for microgrid applications: Considering capacity degradation[J]. Journal of Energy Storage, 2023, 57: 106103.
[10]Collath N, Winner H, Frank A, et al. Suitability of late-life lithium-ion cells for battery energy storage systems[J]. Journal of Energy Storage, 2024, 87: 111508.
[11] Collath N, Cornejo M, Engwerth V, et al.Increasing the lifetime profitability of battery energy storage systems through aging aware operation[J]. Applied Energy, 2023, 348: 121531.
[12] Alic A, Trovato V, De Paola A.Revenue maximization for a battery storage with optimal capacity revamping due to cyclic fade[J]. IEEE Transactions on Smart Grid, 2024, 15(5): 4779-4791.
[13] Bahloul M, Daoud M, Khadem S K.Optimal dispatch of battery energy storage for multi-service provision in a collocated PV power plant considering battery ageing[J]. Energy, 2024, 293: 130744.
[14] Collath N, Tepe B, Englberger S, et al.Aging aware operation of lithium-ion battery energy storage systems: a review[J]. Journal of Energy Storage, 2022, 55: 105634.
[15] Muqbel A, Al-Awami A T, Parvania M. Optimal planning of distributed battery energy storage systems in unbalanced distribution networks[J]. IEEE Systems Journal, 2022, 16(1): 1194-1205.
[16] Cardoso G, Brouhard T, DeForest N, et al. Battery aging in multi-energy microgrid design using mixed integer linear programming[J]. Applied Energy, 2018, 231: 1059-1069.
[17] Kwon K B, Zhu Hao.Reinforcement learning-based optimal battery control under cycle-based degradation cost[J]. IEEE Transactions on Smart Grid, 2022, 13(6): 4909-4917.
[18] 商立群, 张建涛. 计及储能电池寿命衰减的居民小区光储优化配置[J]. 电气技术, 2024, 25(2): 1-11, 20.
Shang Liqun, Zhang Jiantao.Optimal configuration of photovoltaic energy storage systems in residential communities taking into account energy storage battery life decay[J]. Electrical Engineering, 2024, 25(2): 1-11, 20.
[19] Fagerström J, Das S, Klyve Ø S, et al.Profitability of battery storage in hybrid hydropower-solar photovoltaic plants[J]. Journal of Energy Storage, 2024, 77: 109827.
[20] Amini M, Sanjareh M B, Nazari M H, et al.A novel model for battery optimal sizing in microgrid planning considering battery capacity degradation process and thermal impact[J]. IEEE Transactions on Sustainable Energy, 2024, 15(3): 1435-1449.
[21] Rouholamini M, Wang Caisheng, Nehrir H, et al.A review of modeling, management, and applications of grid-connected Li-ion battery storage systems[J]. IEEE Transactions on Smart Grid, 2022, 13(6): 4505-4524.
[22] Shi Yuanyuan, Xu Bolun, Tan Yushi, et al.Optimal battery control under cycle aging mechanisms in pay for performance settings[J]. IEEE Transactions on Automatic Control, 2019, 64(6): 2324-2339.
[23] Diao Rui, Hu Zechun, Song Yonghua.Subgradient of cycle-based aging cost function and its application in optimal operation of battery energy storage system with multiple subsystems[J]. IEEE Transactions on Energy Conversion, 2024, 39(1): 625-643.
[24] Severson K A, Attia P M, Jin N, et al.Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4: 383-391.
[25]Bloom I, Cole B W, Sohn J J, et al. An accelerated calendar and cycle life study of Li-ion cells[J]. Journal of Power Sources, 2001, 101(2): 238-247.
[26] Wang J, Liu Ping, Hicks-Garner J, et al.Cycle-life model for graphite-LiFePO₄ cells[J]. Journal of Power Sources, 2011, 196(8): 3942-3948.
[27] Song Ziyou, Nazir M S, Cui Xiaofan, et al.Benefit assessment of second-life electric vehicle lithium-ion batteries in distributed power grid applications[J]. Journal of Energy Storage, 2022, 56: 105939.
[28] Wang Shuoqi, Guo Dongxu, Han Xuebing, et al.Impact of battery degradation models on energy management of a grid-connected DC microgrid[J]. Energy, 2020, 207: 118228.
[29] Pulazza G, Zhang Ning, Kang Chongqing, et al.Transmission planning with battery-based energy storage transportation for power systems with high penetration of renewable energy[J]. IEEE Transactions on Power Systems, 2021, 36(6): 4928-4940.
[30] 卞一帆, 谢丽蓉, 鲁宗相, 等. 基于多主体投资的双储能系统分层优化配置方案[J]. 电力系统自动化, 2023, 47(1): 63-73.
Bian Yifan, Xie Lirong, Lu Zongxiang, et al.Multi-agent investment based hierarchical optimal configuration scheme for dual energy storage system[J]. Automation of Electric Power Systems, 2023, 47(1): 63-73.
[31] Yang Zhifang, Yong Pei, Xiang Mingxu.Revisit power system dispatch: Concepts, models, and solutions[J]. iEnergy, 2023, 2(1): 43-62.
[32] Huang Wujing, Zhang Xi, Li Kangping, et al.Resilience oriented planning of urban multi-energy systems with generalized energy storage sources[J]. IEEE Transactions on Power Systems, 2022, 37(4): 2906-2918.
[33] 朱睿, 胡博, 谢开贵, 等. 含风电-光伏-光热-水电-火电-储能的多能源电力系统时序随机生产模拟[J]. 电网技术, 2020, 44(9): 3246-3253.
Zhu Rui, Hu Bo, Xie Kaigui, et al.Sequential probabilistic production simulation of multi-energy power system with wind power, photovoltaics, concentrated solar power, cascading hydro power, thermal power and battery energy storage[J]. Power System Technology, 2020, 44(9): 3246-3253.
[34] 李建林, 牛萌, 周喜超, 等. 能源互联网中微能源系统储能容量规划及投资效益分析[J]. 电工技术学报, 2020, 35(4): 874-884.
Li Jianlin, Niu Meng, Zhou Xichao, et al.Energy storage capacity planning and investment benefit analysis of micro-energy system in energy interconnection[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 874-884.
[35] Preger Y, Barkholtz H M, Fresquez A, et al.Degradation of commercial lithium-ion cells as a function of chemistry and cycling conditions[J]. Journal of the Electrochemical Society, 2020, 167(12): 120532.
[36] Xu Bolun, Zhao Jinye, Zheng Tongxin, et al.Factoring the cycle aging cost of batteries participating in electricity markets[J]. IEEE Transactions on Power Systems, 2018, 33(2): 2248-2259.
[37] 王荔妍, 陈启鑫, 何冠楠, 等. 考虑电池储能寿命模型的发电计划优化[J]. 电力系统自动化, 2019, 43(8): 93-100.
Wang Liyan, Chen Qixin, He Guannan, et al.Optimization of generation scheduling considering battery energy storage life model[J]. Automation of Electric Power Systems, 2019, 43(8): 93-100.
[38] Li Jianlin, Fang Zhijin, Wang Qian, et al.Optimal operation with dynamic partitioning strategy for centralized shared energy storage station with integration of large-scale renewable energy[J]. Journal of Modern Power Systems and Clean Energy, 2024, 12(2): 359-370.
[39] Liu Jichun, Chen Xue, Xiang Yue, et al.Optimal planning and investment benefit analysis of shared energy storage for electricity retailers[J]. International Journal of Electrical Power & Energy Systems, 2021, 126: 106561.
[40] Yong Pei, Zhang Ning, Hou Qingchun, et al.Evaluating the dispatchable capacity of base station backup batteries in distribution networks[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 3966-3979.
[41] Grimaldi A, Minuto F D, Brouwer J, et al.Profitability of energy arbitrage net profit for grid-scale battery energy storage considering dynamic efficiency and degradation using a linear, mixed-integer linear, and mixed-integer non-linear optimization approach[J]. Journal of Energy Storage, 2024, 95: 112380.