多场景规划下混合储能对风光耦合出力波动的平抑方法

doi:10.19595/j.cnki.1000-6753.tces.241679

Abstract
Figure/Table
References (0)
Related Citation (12)

Download: PDF (13759 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

The purpose of the wind-solar complementary system (WSCS) is to couple wind power and photovoltaic (PV) in a complementary way to strengthen the ability to generate power continuously in the medium and long term. However, due to the uncertainty of natural resources, the power output of WSCS is still unstable. In recent years, hybrid energy storage systems (HESS) have been used to match the WSCS to reduce the volatility of system output, but there are still some problems leading to the system's economic cost making it difficult to control. For example, the coupling relationship between wind and solar is linear, and the premise of the fluctuation smoothing strategy is to meet the power demand of load-side or grid-connected. In order to solve the mentioned problems, this paper proposes a method that HESS smooths fluctuations of wind-solar coupling power considering multi-scenario planning. By constructing a nonlinear coupling relationship between wind and solar and optimizing the capacity allocation of power source-side hybrid energy storage, the system accommodation characteristics for power fluctuations are improved.
Firstly, the marginal distributions of the two power sources are constructed using KDE based on the historical data of wind power and PV, and the joint distribution is obtained by preferably using the Gumbel-Copula functions. The multi-scenario set obtained by random sampling of the joint distribution is able to reflect the intensity of the fluctuation changes. Secondly, the FFT and its IFFT are used to analyze the spectral analysis of the unstable power in scenarios set to determine the power borne by each energy storage unit. In this part, since the multi-scenario ensemble originates from a joint distribution, the correlation of each scenario in the ensemble is consistent, which means the cut-off frequency that distinguishes battery and super-capacitor does not change with the change of scenario. Finally, an optimization model is established with the objective function of minimum the cycle operating cost of HESS, and the capacity configuration of the HESS is calculated using an improved PSO. The result of capacity configuration provides room to accommodate fluctuations in power source-side output, which reduces the instability of the system.
The results of the simulation example show that the increase in frequency deviation before the HESS configuration is much larger than that after the HESS configuration. The change rate of RMSE for calculating the frequency deviation before and after the configuration of HESS ranges from 60.0% to 83.5%, the change rate gradually increases with the increase of the number of scenarios in the ensemble. This suggests that the role of HESS in regulating frequency increases as the number of scenarios increases. Meanwhile, with the increase in the number of scenarios in the set, the maximum growth in the rated power and rated capacity of the batteries is 77.1% and 54.9%, respectively. And that of the super-capacitors is 40.0% and 42.1%. However, this makes the increase in equipment cost of the super-capacitor more prominent. Further, the configuration of HESS makes the power fluctuation of the system at adjacent moments smoother. The fluctuation accommodation range of the power source-side within the time intervals of 10h, 60h, and 240h is enhanced by 12.9%, 7.4%, and 6%, respectively. The amplification of the fluctuation accommodation range decreases with the longer of the time intervals. Nevertheless, the HESS still has rechargeable power characteristics when the power source-side output is zero.
From the simulation results, the following conclusions can be drawn: (1) Different numbers of scenarios in the set have consistent correlation, so the dividing frequency of the battery and the super-capacitor does not change with the number of scenarios, which makes the HESS can be effective for the fluctuating power to modulate frequency. (2) The more complex the frequency variations of the fluctuating power at the wind-solar coupling output, the more pronounced the capability of HESS modulating frequency. (3) The results of the HESS configuration reflect that super-capacitors and batteries have greater advantages in the rated power and rated capacity, respectively. (4) Even if the wind-solar coupling output is close to 0 or 0 after the configuration of HESS, the system is still able to ensure that there is a certain margin to counteract the fluctuating impact of sudden power changes.

Key words： Wind-solar coupled scenario generation frequency analysis hybrid energy storage configuration fluctuating accommodations

Received: 26 September 2024

PACS:

TM61

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Gao Fan
	Bao Daorina
	Zhao Mingzhi
	Wang Tianbo
	Xu Junming

Cite this article:

Gao Fan,Bao Daorina,Zhao Mingzhi等. Smoothing Method of Wind-solar Coupled Output Fluctuations by Hybrid Energy Storage under Multi-scenario Planning[J]. Transactions of China Electrotechnical Society, 0, (): 1679-11.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241679 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/1679

[1] 黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
Li Bo, Chen Minyou, Zhong Haiwang, et al.A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[2] 肖先勇, 郑子萱. “双碳” 目标下新能源为主体的新型电力系统: 贡献、关键技术与挑战[J]. 工程科学与技术, 2022, 54(1): 47-59.
Xiao Xianyong, Zheng Zixuan.New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality: contribution, key techniques, and challenges[J]. Advanced Engineering Sciences, 2022, 54(1): 47-59.
[3] 张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
Zhang Zhigang, Kang Chongqing.Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[4] 张宇涵, 杜贵平, 雷雁雄, 等. 直流微网混合储能系统控制策略现状及展望[J]. 电力系统保护与控制, 2021, 49(3): 177-187.
Zhang Yuhan, Du Guiping, Lei Yanxiong, et al.Current status and prospects of control strategy for a DC micro grid hybrid energy storage system[J]. Power System Protection and Control, 2021, 49(3): 177-187.
[5] 申建建, 王月, 程春田, 等. 水风光多能互补发电调度问题研究现状及展望[J]. 中国电机工程学报, 2022, 42(11): 3871-3885.
Shen Jianjian, Wang Yue, Cheng Chuntian, et al.Research status and prospect of generation scheduling for hydropower-wind-solar energy complementary system[J]. Proceedings of the CSEE, 2022, 42(11): 3871-3885.
[6] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
Wu Mengxue, Fang Fang.Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[7] 吕海鹏, 希望•阿不都瓦依提, 孟令鹏. 计及源-荷预测不确定性的微电网双级随机优化调度[J]. 电力自动化设备, 2022, 42(9): 70-78.
Lü Haipeng, Xiwang•Abuduwayiti; Meng Lingpeng. Two-level stochastic optimal scheduling of microgrid considering uncertainty of source-load prediction[J]. Electric Power Automation Equipment, 2022, 42(9): 70-78.
[8] 程杉, 张芮嘉, 钟仕凌, 等. 计及需求响应柔性调节的可再生能源优化配置[J]. 电力系统及其自动化学报, 2023, 35(2): 94-102.
Cheng Shan, Zhang Ruijia, Zhong Shiling, et al.Optimal allocation of distributed renewable generations considering flexible regulation of demand-side response[J]. Proceedings of the CSU-EPSA, 2023, 35(2): 94-102.
[9] 陈泽西, 孙玉树, 张妍, 等. 考虑风光互补的储能优化配置研究[J]. 电工技术学报, 2021, 36(增刊1): 145-153.
Chen Zexi, Sun Yushu, Zhang Yan, et al.Research on energy storage optimal allocation considering complementarity of wind power and PV[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 145-153.
[10] Hajiaghasi S, Salemnia A, Hamzeh M.Hybrid energy storage system for microgrids applications: a review[J]. Journal of Energy Storage, 2019, 21: 543-570.
[11] 李建林, 郭斌琪, 牛萌, 等. 风光储系统储能容量优化配置策略[J]. 电工技术学报, 2018, 33(6): 1189-1196.
Li Jianlin, Guo Binqi, Niu Meng, et al.Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system[J]. Transactions of China Electrotechnical Society, 2018, 33(6): 1189-1196.
[12] Tang Rongchuan, Xu Qingshan, Fang Jicheng, et al.Optimal configuration strategy of hybrid energy storage system on industrial load side based on frequency division algorithm[J]. Journal of Energy Storage, 2022, 50: 104645.
[13] 张宇华, 李青松, 王丛, 等. 区域风光低频相关性互补与高频混合储能平抑的并网方法研究[J]. 中国电机工程学报, 2023, 43(4): 1492-1504.
Zhang Yuhua, Li Qingsong, Wang Cong, et al.Grid-connected method of regional wind-solar low-frequency correlation complementation and high-frequency hybrid energy storage stabilization[J]. Proceedings of the CSEE, 2023, 43(4): 1492-1504.
[14] 田博文, 张志禹, 杨梦飞. 基于多次滑动均值滤波的混合储能功率分配与定容研究[J]. 电工技术学报, 2024, 39(5): 1548-1564.
Tian Bowen, Zhang Zhiyu, Yang Mengfei.Research on hybrid energy storage power allocation and capacity determination based on multiple moving average filtering[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1548-1564.
[15] 汪凯琳, 许仪勋, 潘瑞媛, 等. 考虑风光可靠性的微电网混合储能优化配置[J]. 电测与仪表, 2023, 60(5): 39-44, 50.
Wang Kailin, Xu Yixun, Pan Ruiyuan, et al.Optimal configuration of hybrid energy storage systems in micro-grid considering wind-solar reliability[J]. Electrical Measurement & Instrumentation, 2023, 60(5): 39-44, 50.
[16] 孟旭瑶, 付立军, 赖心怡, 等. 计及源荷互补特性的“源网荷储一体化项目” 优化配置[J]. 电力自动化设备, 2024, 44(7): 123-131.
Meng Xuyao, Fu Lijun, Lai Xinyi, et al.Optimal configuration of “source-network-load-storage integrated project” considering source-load complementarity characteristics[J]. Electric Power Automation Equipment, 2024, 44(7): 123-131.
[17] 汪惟源, 窦飞, 程锦闽, 等. 一种风光联合出力概率模型建模方法[J]. 电力系统保护与控制, 2020, 48(10): 22-29.
Wang Weiyuan, Dou Fei, Cheng Jinmin, et al.A modeling method for a wind and photovoltaic joint power probability model[J]. Power System Protection and Control, 2020, 48(10): 22-29.
[18] Karimi H, Jadid S.A strategy-based coalition formation model for hybrid wind/PV/FC/MT/DG/battery multi-microgrid systems considering demand response programs[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107642.
[19] Wahbah M, Mohandes B, EL-Fouly T H M, et al. Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling[J]. Energy Conversion and Management, 2022, 266: 115811.
[20] Nelsen R B.An Introduction to Copulas[M]. New York: Springer, 2006.
[21] Sreekumar S, Khan N U, Rana A S, et al.Aggregated Net-load Forecasting using Markov-Chain Monte-Carlo Regression and C-vine copula[J]. Applied Energy, 2022, 328: 120171.
[22] 贾彦, 李文雄, 赵萌, 等. 罚函数改进粒子群算法的风光储系统优化配置[J]. 太阳能学报, 2019, 40(7): 2071-2077.
Jia Yan, Li Wenxiong, Zhao Meng, et al.Optimal configeration for solar-wind-battery hybrid power system based on penalty function improved particle swarm optimization[J]. Acta Energiae Solaris Sinica, 2019, 40(7): 2071-2077.
[23] 李军徽, 侯涛, 穆钢, 等. 基于权重因子和荷电状态恢复的储能系统参与一次调频策略[J]. 电力系统自动化, 2020, 44(19): 63-72.
Li Junhui, Hou Tao, Mu Gang, et al.Primary frequency regulation strategy with energy storage system based on weight factors and state of charge recovery[J]. Automation of Electric Power Systems, 2020, 44(19): 63-72.