Energy Storage Capacity Allocation Scheme of Wind Farm Considering Multi-Objective Optimization Model

doi:10.12096/j.2096-4528.pgt.22020

Abstract

Abstract:

A new scheme of lithium ion battery and super capacitor hybrid energy storage system in wind farm was proposed. The original wind power was decomposed by the wavelet packet frequency division technology to obtain the compensation power of the hybrid energy storage system. Taking the hybrid energy storage configuration scheme as the optimization variable, the improved life cycle cost was introduced, and a multi-objective energy storage system configuration model of net income volatility waste air volume was established. The model was solved by the improved probabilistic mutation particle swarm optimization (PMPSO) to obtain the Pareto solution set. The solution set was normalized to obtain the optimal allocation scheme of entropy weight. Compared with the single objective optimal scheme, it shows that the optimal allocation scheme of entropy weight takes into account both economy and function. Compared with the adaptive quantum particle swarm optimization (AQPSO), it is verified that the optimal allocation scheme of entropy weight obtained by improved PMPSO algorithm not only ensures the small waste air volume and volatility, but also configures the energy storage with smaller capacity to obtain greater benefits and meet the multi-objective allocation requirements.

Key words: energy storage, wind farm, multi-objective solution, optimal allocation, lithium ion battery, super capacitor

CLC Number:

TK 02

Xiaoguang CHEN, Xiuyuan YANG, Zhenlin WANG, Haoyang WANG. Energy Storage Capacity Allocation Scheme of Wind Farm Considering Multi-Objective Optimization Model[J]. Power Generation Technology, 2022, 43(5): 718-730.

Figures/Tables 16

Fig. 1 Schematic diagram of probability variation

Fig. 2 Flow chart of energy storage optimization allocation

Tab. 1 Energy storage economic parameters

参数	锂离子电池	超级电容
$C p p u r$ /(元/kW)	9 000	1 860
$C e p u r$ /[元/(kW⋅h)]	9 600	12 400
$C p a u$ /(元/kW)	620	620
$C p o m$ /[元/(kW⋅a)]	62	80.6
$C e o m$ /[元/(kW⋅h⋅a)]	0.014 07	0.013 4
$C p e x$ /(元/kW)	2 472.3	1 860
$C p s c$ /(元/kW)	465	93
$C e s c$ /[元/(kW⋅h)]	0	0
$α m u l$ /[元/(kW⋅h)]	0.55	0.55
$α s u b$ /[元/(kW⋅h)]	0.89	0.89
$α l a c k$ /[元/(MW⋅h)]	0.55	0.55
$α a b$ /[元/(MW⋅h)]	0.55	0.55

Tab. 1 Energy storage economic parameters

参数	锂离子电池	超级电容
$C p p u r$ /(元/kW)	9 000	1 860
$C e p u r$ /[元/(kW⋅h)]	9 600	12 400
$C p a u$ /(元/kW)	620	620
$C p o m$ /[元/(kW⋅a)]	62	80.6
$C e o m$ /[元/(kW⋅h⋅a)]	0.014 07	0.013 4
$C p e x$ /(元/kW)	2 472.3	1 860
$C p s c$ /(元/kW)	465	93
$C e s c$ /[元/(kW⋅h)]	0	0
$α m u l$ /[元/(kW⋅h)]	0.55	0.55
$α s u b$ /[元/(kW⋅h)]	0.89	0.89
$α l a c k$ /[元/(MW⋅h)]	0.55	0.55
$α a b$ /[元/(MW⋅h)]	0.55	0.55

Tab. 2 Relevant technical parameters

参数	取值
$δ r e s$ /%	4
$k p a s s$ /(元/kW)	1 500
i₀/%	10

Tab. 2 Relevant technical parameters

参数	取值
$δ r e s$ /%	4
$k p a s s$ /(元/kW)	1 500
i₀/%	10

Fig. 3 Pareto solution set of improved PMPSO algorithm

Tab. 3 Partial solution set of improved PMPSO Pareto

编号	$P l i_r a t e$ /MW	$E l i_r a t e$ /(MW·h)	$P s c_r a t e$ /MW	$E s c_r a t e$ /(MW⋅h)	$f 2$ /(10⁵MW)	$f 1$ /(10⁷元)	$f 3$ /(MW⋅h)	$χ$ /MW
1	7.545	13.486	2.544	1.505	2.310	-6.71	11 457	4.15
2	6.878	12.156	2.321	1.416	2.449	-7.081	12 952	3.94
3	6.654	11.637	2.085	1.320	2.656	-7.28	14 941	3.25
4	5.989	10.021	1.346	1.103	2.837	-7.368	16 950	2.91
5	5.825	9.298	1.276	0.847	3.075	-7.447	19 072	1.85
6	4.985	8.521	0.996	0.740	3.299	-7.486	21 654	1.06
7	4.035	7.024	0.968	0.535	3.380	-7.500	22 456	0.09

Tab. 3 Partial solution set of improved PMPSO Pareto

编号	$P l i_r a t e$ /MW	$E l i_r a t e$ /(MW·h)	$P s c_r a t e$ /MW	$E s c_r a t e$ /(MW⋅h)	$f 2$ /(10⁵MW)	$f 1$ /(10⁷元)	$f 3$ /(MW⋅h)	$χ$ /MW
1	7.545	13.486	2.544	1.505	2.310	-6.71	11 457	4.15
2	6.878	12.156	2.321	1.416	2.449	-7.081	12 952	3.94
3	6.654	11.637	2.085	1.320	2.656	-7.28	14 941	3.25
4	5.989	10.021	1.346	1.103	2.837	-7.368	16 950	2.91
5	5.825	9.298	1.276	0.847	3.075	-7.447	19 072	1.85
6	4.985	8.521	0.996	0.740	3.299	-7.486	21 654	1.06
7	4.035	7.024	0.968	0.535	3.380	-7.500	22 456	0.09

Fig. 4 Objective function values of Pareto typical allocation schemes

Fig. 5 Cost and total benefit curves of different schemes

Tab. 4 Capacity configuration of scheme 1, 7, 8

方案

P l i_r a t e

/MW

$E l i_r a t e$ /

(MW⋅h)

P s c_r a t e

/MW

$E s c_r a t e$ /

(MW⋅h)

Tab. 4 Capacity configuration of scheme 1, 7, 8

方案

P l i_r a t e

/MW

$E l i_r a t e$ /

(MW⋅h)

P s c_r a t e

/MW

$E s c_r a t e$ /

(MW⋅h)

7.545

13.486

2.544

1.505

4.035

7.024

0.968

0.535

6.075

10.084

1.538

1.245

Tab. 5 Evaluation parameters of scheme 1, 7, 8

评价参数	1号方案	7号方案	8号方案
最大波动量/MW	0.85	4.91	2.02
$f 2$ /(10¹¹W)	2.310	3.380	2.814
$f 3$ /(MW⋅h)	11 457	22 456	16 740
成本/(10⁷元)	15.084	13.918	14.146
净收益/(10⁷元)	6.71	7.5	7.354
$ε$ /%	Li：1.25 Sc：1.59	Li：12.67 Sc：11.25	Li：5.67 Sc：4.63
储能年限/a	10.3	9.52	10.32
熵权拟合度	0.498	0.502	0.995

Tab. 5 Evaluation parameters of scheme 1, 7, 8

评价参数	1号方案	7号方案	8号方案
最大波动量/MW	0.85	4.91	2.02
$f 2$ /(10¹¹W)	2.310	3.380	2.814
$f 3$ /(MW⋅h)	11 457	22 456	16 740
成本/(10⁷元)	15.084	13.918	14.146
净收益/(10⁷元)	6.71	7.5	7.354
$ε$ /%	Li：1.25 Sc：1.59	Li：12.67 Sc：11.25	Li：5.67 Sc：4.63
储能年限/a	10.3	9.52	10.32
熵权拟合度	0.498	0.502	0.995

Fig. 6 Daily operation curves of scheme 1, 7, 8

Fig. 7 Energy storage system power of scheme 1, 7, 8

Fig. 8 Pareto optimal solution sets of different algorithms

Tab. 6 Capacity configuration of scheme 8, 9

方案	$P l i_r a t e$ /MW	$E l i_r a t e$ /(MW⋅h)	$P s c_r a t e$ /MW	$E s c_r a t e$ /(MW⋅h)
8	6.075	10.084	1.538	1.245
9	6.512	11.865	1.542	1.547

Tab. 6 Capacity configuration of scheme 8, 9

方案	$P l i_r a t e$ /MW	$E l i_r a t e$ /(MW⋅h)	$P s c_r a t e$ /MW	$E s c_r a t e$ /(MW⋅h)
8	6.075	10.084	1.538	1.245
9	6.512	11.865	1.542	1.547

Tab. 7 Evaluation parameters of scheme 8, 9

评价参数	8号方案	9号方案
最大波动量/MW	2.02	1.82
$f 2$ /(10¹¹W)	2.814	2.821
$f 3$ /(MW⋅h)	16 740	16 905
成本/(10⁷元)	14.146	16.365
净收益/(10⁷元)	7.354	7.031
$ε$ /%	Li：5.67 Sc：4.63	Li：1.25 Sc：1.59
储能年限/a	10.32	10.75

Tab. 7 Evaluation parameters of scheme 8, 9

评价参数	8号方案	9号方案
最大波动量/MW	2.02	1.82
$f 2$ /(10¹¹W)	2.814	2.821
$f 3$ /(MW⋅h)	16 740	16 905
成本/(10⁷元)	14.146	16.365
净收益/(10⁷元)	7.354	7.031
$ε$ /%	Li：5.67 Sc：4.63	Li：1.25 Sc：1.59
储能年限/a	10.32	10.75

Fig. 9 Daily operation diagram of wind power and energy storage compensation power of scheme 8, 9

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