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

doi:10.19799/j.cnki.2095-4239.2021.0167

Abstract

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

CLC Number:

TK 9

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.

Figures/Tables 19

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Fig. 2

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 2

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Fig. 9

Table 3

Fig. 10

Table 4

Table 5

Multi-scenario daily income analysis"

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

Table 5

Fig. 11

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Fig. 13

Fig. 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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