平滑风电功率波动的混合储能系统容量优化配置

靳雯皓,刘继春

分布式能源 ›› 2017, Vol. 2 ›› Issue (2) : 32-38.

PDF(1126 KB)
PDF(1126 KB)
分布式能源 ›› 2017, Vol. 2 ›› Issue (2) : 32-38. DOI: 10.16513/j.cnki.10-1427/tk.2017.02.005

平滑风电功率波动的混合储能系统容量优化配置

作者信息 +

Capacity Optimization Configuration of Hybrid Energy Storage System for Smoothing Wind Power Fluctuation

Author information +
文章历史 +

摘要

风电输出功率具有波动性,为减小其对电力系统运行的影响,提高电力系统的电能质量,提出了平抑功率波动的混合储能系统容量优化配置方法。采用滑动平均法滤除波动功率,平滑风电输出功率,滑动平均法中窗口长度的选择将影响功率平滑效果及混合储能系统容量配置,依据并网功率波动限制为约束确定窗口长度值,实现其最优选择。采用频谱分析的方法分解波动功率,以年均综合成本最小为目标函数确定超级电容器和蓄电池各自补偿频段,进而确定其实时充放电功率。最后,建立了考虑蓄电池寿命损耗的混合储能系统成本模型,通过案例分析验证本方案的可行性以及经济优越性。

Abstract

Wind power have the characteristic of generation output variability. To reduce the impact of power fluctuations on grids and improve the power quality of power system, this paper puts forward the capacity optimization configuration of hybrid energy storage system for smoothing wind power fluctuation. The paper adopts moving-average method to filter the fluctuation of power and smooth wind power. The window length of moving-average method will affect the smoothing effect of power and the capacity configuration of hybrid energy storage system, so we consider the fluctuation limit of grid power as constraints to determine the window length, then achieve its optimal choice. We use the method of spectrum analysis to decompose the fluctuation of wind power, and take the minimum annual comprehensive cost as the objective function to determine the compensation frequency bands of the super capacitor and the battery respectively, and then determine the charge and discharge power. Finally, we establish a cost model of hybrid energy storage system considering battery life loss, and verify the feasibility and economic superiority of the program through case analysis.

关键词

超级电容器 / 蓄电池 / 混合储能系统 / 寿命损耗 / 滑动平均法 / 频谱分析

Key words

super capacitor / battery / hybrid energy storage system / life loss / moving-average method / spectrum analysis

引用本文

导出引用
靳雯皓, 刘继春. 平滑风电功率波动的混合储能系统容量优化配置[J]. 分布式能源. 2017, 2(2): 32-38 https://doi.org/10.16513/j.cnki.10-1427/tk.2017.02.005
Wenhao JIN, Jichun LIU. Capacity Optimization Configuration of Hybrid Energy Storage System for Smoothing Wind Power Fluctuation[J]. Distributed Energy Resources. 2017, 2(2): 32-38 https://doi.org/10.16513/j.cnki.10-1427/tk.2017.02.005

参考文献

[1]
曹超马玉鑫常悦,等. 基于经验模态分解和模糊机会约束的混合储能容量配置方法[J]. 分布式能源2016, 1(3): 43-48.
CAO Chao, MA Yuxin, CHANG Yue, et al. Capacity allocation method of hybrid energy storage system based on empirical mode decomposition and fuzzy chance constrained programming[J]. Distributed Energy, 2016, 1(3): 43-48.
[2]
陈谦陈霄逸金宇清,等. 基于混合储能的大型风电场优化控制[J]. 电力自动化设备2015, 35(4): 76-70.
CHEN Qian, CHEN Xiaoyi, JIN Yuqing, et al. Optimal control of large-scale wind farm based on hybrid energy storage[J]. Electric Power Automation Equipment, 2015, 35(4): 76-70.
[3]
张蕴昕孙运全. 混合储能在风光互补微网中的控制策略[J]. 电力系统保护与控制2015, 43(21): 93-97.
ZHANG Yunxin, SUN Yunquan. Control strategy of a hybrid energy storage in wind-solar hybrid generation micro-grid[J]. Power System Protection and Control, 2015, 43(21): 93-97.
[4]
刘世林文劲宇孙海顺. 适用于风电功率调控的复合储能系统及其控制策略[J]. 中国电机工程学报2015, 35(1): 95-101.
LIU Shilin, WEN Jinyu, SUN Haishun. Hybrid energy storage system and its control strategies intended for wind power conditioning[J]. Proceedings of the CSEE, 2015, 35(1): 95-101.
[5]
程启明徐聪程尹曼,等. 基于混合储能技术的光储式充电站直流微网系统协调控制[J]. 高电压技术2016, 42(7): 2073-2083.
CHENG Qiming, XU Cong, CHENG Yinman, et al. Coordination control of PV charging station DC microgrid system based on hybrid energy storage technology[J]. High Voltage Engineering, 2016, 42(7): 2073-2083.
[6]
蒋平熊华川. 混合储能系统平抑风力发电输出功率波动控制方法设计[J]. 电力系统自动化2013, 37(1): 122-126.
JIANG Ping, XIONG Huachuan. A control scheme design for smoothing wind power fluctuation with hybrid energy storage system[J]. Automation of Electric Power Systems, 2013, 37(1): 122-126.
[7]
刘方杨秀时珊珊,等. 不同时间尺度下基于混合储能调度的微网能量优化[J]. 电网技术2014, 38(11): 3079-3087.
LIU Fang, YANG Xiu, SHI Shanshan, et al. Hybrid energy storage scheduling based microgrid energy optimization under different time scales[J]. Power System Technology, 2014, 38(11): 3079-3087.
[8]
马速良马会萌蒋小平,等. 基于Bloch球面的量子遗传算法的混合储能系统容量配置[J]. 中国电机工程学报2015, 35(3): 592-597.
MA Suliang, MA Huimeng, JIANG Xiaoping, et al. Capacity configuration of the hybrid energy storage system based on Bloch spherical quantum genetic algorithm[J]. Proceedings of the CSEE, 2015, 35(3): 592-597.
[9]
孙承晨袁越CHOI S S, 等. 基于经验模态分解和神经网络的微网混合储能容量优化配置[J]. 电力系统自动化2015, 39(8): 19-25.
SUN Chengchen, YUAN Yue, CHOI S S, et al. Capacity optimization of hybrid energy storage system in micro-grid using empirical mode decomposition and neural network[J]. Automation of Electric Power Systems, 2015, 39(8): 19-25.
[10]
孙纯军倪春花窦晓波. 基于SOC状态反馈的混合储能功率优化策略[J]. 电测与仪表2016, 53(15): 81-87.
SUN Chunjun, NI Chunhua, DOU Xiaobo. Research on optimal power allocation strategy based on SOC state feedback in hybrid energy storage system[J]. Electrical Measurement & Instrumentation, 2016, 53(15): 81-87.

编辑: 蒋毅恒
PDF(1126 KB)

Accesses

Citation

Detail

段落导航
相关文章

/