考虑风能不确定性的压缩空气储能容量配置及经济性评估

doi:10.19799/j.cnki.2095-4239.2021.0167

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (5): 1614-1623.doi: 10.19799/j.cnki.2095-4239.2021.0167

• 物理储能十年专刊·压缩空气 • 上一篇下一篇

考虑风能不确定性的压缩空气储能容量配置及经济性评估

虞启辉^1,³(), 田利¹, 李晓飞¹, 李晓东¹, 谭心¹, 张业明^2,⁴()

^1.内蒙古科技大学，内蒙古包头 014010
^2.流体动力与机电系统国家重点实验室，浙江杭州 310027
^3.气动热力储能与供能技术北京市重点实验室，北京 100191
^4.河南理工大学机械与动力工程学院，河南焦作 454000

收稿日期:2021-04-21 修回日期:2021-05-17 出版日期:2021-09-05 发布日期:2021-09-08
作者简介:虞启辉（1983—），男，副教授，从事新能源利用技术研究，E-mail：2016988@imust.edu.cn|张业明，副教授，从事气动系统控制及节能研究，E-mail：yqhhxq@163.com。
基金资助:
国家自然科学基金地区基金项目(52065054);流体动力与机电系统国家重点实验室开放基金课题(GZKF-202016);北京高等学校卓越青年科学家计划(BJJWZYJH01201910006021┫项目)

Compressed air energy storage capacity configuration and economic evaluation considering the uncertainty of wind energy

Qihui YU^1,³(), Li TIAN¹, Xiaofei LI¹, Xiaodong LI¹, Xin TAN¹, Yeming ZHANG^2,⁴()

^1.Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
^2.State Key Laboratory of Fluid Power and Electromechanical Systems, Hangzhou 310027, Zhejiang, China
^3.Beijing Key Laboratory of Pneumatic Thermal Energy Storage and Energy Supply Technology, Beijing 100191, China
^4.School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, China

Received:2021-04-21 Revised:2021-05-17 Online:2021-09-05 Published:2021-09-08

摘要/Abstract

摘要：

风能的随机特性是造成风电场弃风现象严重的重要原因，配置压缩空气储能系统(CAES)可以有效平衡风力发电随机特性，减少风电场弃风量，但CAES存储规模配置不当会造成经济利益的损失。因此，为了提高风能利用率，基于风能不确定性条件下，对压缩空气储能系统容量配置进行研究。首先，利用历史数据获得风力发电典型小时功率分布；然后，考虑用户负荷需求、电网分时电价、系统投资成本、电力不足成本和电力销售收入等因素，构建以CAES系统充放电功率和储气容量为约束条件、以最大效益为目标的模型，并采用遗传算法进行求解；最后，利用所建立的模型对多场景运行案例进行优化。仿真结果表明，对于典型小时负荷功率需求3.241 MW的工厂用户，风电场保持每日4台风机运行，并配置额定功率1 MW、额定容量6.5 MW·h的CAES系统经济效益最佳，可减少弃风量3.84 MW·h，节约购电成本4208.9元，实现日最大净收益699.86元。

关键词: 风能不确定性, 压缩空气储能, 遗传算法, 经济性

Abstract:

The randomness of wind energy is an important reason for the abandonment of wind farms. The configuration of compressed-air energy storage (CAES) systems can effectively balance the randomness of wind power generation and lead to a reduction in wind farms. However, the improper configuration of CAES storage scales causes the loss of economic benefits. Therefore, to improve the rate of wind energy usage, this study examines the capacity configuration of CAES based on the uncertainty of wind energy. First, historical data are used to obtain the typical hourly power distribution of wind power generation. Then, factors, such as user load demand, grid time-of-use electricity price, system investment cost, power shortage cost, and power sales revenue, are considered to develop a CAES system for charging and discharging power and gas storage capacity. The developed CAES system is a model with constraints and maximum benefit as the aim; it is solved using the genetic algorithm. Finally, the established model is used to optimize multiscene operation cases. The simulation results demonstrate that for factory users with a typical hourly load power demand of 3.241 MW, the wind farm maintains four wind turbines running daily, and it is equipped with a CAES system with rated power and capacity of 1 MW and 6.5 MW·h, respectively. The economic benefits are the best, and the amount of wind curtailed can be reduced by 3.84 MWh, thus saving 4208.9 yuan in power purchase costs and realizing the largest daily net income of 699.86 yuan.

Key words: wind energy uncertainty, compressed air energy storage, genetic algorithm, economic analysis

中图分类号:

TK 9

虞启辉, 田利, 李晓飞, 李晓东, 谭心, 张业明. 考虑风能不确定性的压缩空气储能容量配置及经济性评估[J]. 储能科学与技术, 2021, 10(5): 1614-1623.

Qihui YU, Li TIAN, Xiaofei LI, Xiaodong LI, Xin TAN, Yeming ZHANG. Compressed air energy storage capacity configuration and economic evaluation considering the uncertainty of wind energy[J]. Energy Storage Science and Technology, 2021, 10(5): 1614-1623.

图/表 19

图1

图2

表1

图3

图4

图5

图6

图7

表2

图8

图9

表3

图10

表4

表5

多场景每日收益分析"

场景	配置CAES		弃风量/MW·h	购电成本/元	净收益/元
场景	$P C A E S r a t e d$ /MW	$C C A E S r a t e d$ /MW·h	弃风量/MW·h	购电成本/元	净收益/元
1	0	0	0	5001.42
1	0.5	5	0	2712.94	-789.6
2	0	0	6.22	4532.02
2	1	6.5	2.37	323.12	699.86
3	0	0	20.52	1711.58
3	1	8	16.04	281.61	204.21
4	0	0	36.67	255.11
4	1	9	30.75	0	-548.37

表5

图11

图12

图13

图14

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规格参数	FD70b风力涡轮机
额定功率/kW	1500
切入风速/(m·s^-1)	3.5
额定速度/(m·s^-1)	13
切出风速/(m·s^-1)	20
叶片长度/m	34

时间/h	P_W/kW	Weibull		P_L/kW	时间/h	P_W/kW	Weibull		P_L/kW
时间/h	P_W/kW	λ	k	P_L/kW	时间/h	P_W/kW	λ	k	P_L/kW
1	978	9.5066	3.7573	3214	13	515	6.9250	3.0024	3224
2	960	9.5738	4.2971	3223	14	495	6.8958	2.7920	3230
3	924	9.3518	4.0170	3236	15	547	7.3842	2.9834	3252
4	865	8.7569	4.3823	3259	16	573	7.5216	2.8032	3235
5	775	8.2620	3.8390	3237	17	638	7.5176	2.8227	3223
6	831	8.7103	3.6736	3263	18	714	7.9414	3.0703	3250
7	843	9.1729	3.3510	3240	19	824	8.5255	3.4291	3259
8	880	9.1128	3.4454	3257	20	904	8.8638	3.2778	3247
9	850	8.8900	3.2251	3215	21	983	9.5070	3.5320	3279
10	726	8.4639	3.2821	3204	22	1038	10.0147	3.9381	3244
11	567	7.5206	2.8135	3228	23	1048	10.3256	4.1200	3283
12	524	6.9715	3.0194	3251	24	990	9.8302	3.9504	3235

类别	CAES
安装成本Inv_p/(USD?kW^-1)	700
安装成本Inv_c/(USD?kW?h^-1)	5
运营OM_c/(USD?MW^-1?a^-1)	200
充放电效率	0.7
寿命/年	20

场景	风机/台	实际输出功率/MW	风机理论发电量/(MW?h)
1	3	2.373	4.5
2	4	3.164	6
3	5	3.955	7.5
4	6	4.746	9

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考虑风能不确定性的压缩空气储能容量配置及经济性评估

Compressed air energy storage capacity configuration and economic evaluation considering the uncertainty of wind energy

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