Optimal Control Strategy of Peak Shaving of Flow Battery Energy Storage System Under High Wind Power Permeability

doi:10.12096/j.2096-4528.pgt.23025

Abstract

Abstract:

Objectives Under the background of the “dual carbon” target, the safety and economic problems of peak shaving caused by the construction of high wind power penetration system are urgent to be solved. Methods By using the solution of peak shaving and valley filling of battery energy storage system, a peak shaving optimization control method for zinc-bromine flow battery (ZBB) energy storage taking into account both technology and economy was proposed. According to the actual battery device, the structure analysis and the mathematical model construction of ZBB energy storage were carried out. Considering the technical effects of peak shaving, and taking the minimum standard deviation of load curve after peak shaving as the objective function, a bidirectional optimization control strategy for energy storage considering peak shaving effects was proposed. On this basis, according to the time of use (TOU) policy of the power grid, taking the technical and economic optimization as the objective function, an economic model of energy storage peak shaving based on the TOU price mechanism was proposed, and the optimal power timing results for energy storage were obtained. Finally, taking the load and wind power data of a certain area in Northeast China as an example, the effectiveness of the proposed strategy was verified by comparison. Results Compared with the original load, the proposed strategy reduces the daily average load peak-valley difference and peak-valley difference rate by 35.973% and 34.205%, respectively, and improves the peak shaving economic optimization by 5.582%. In addition, the problem of wind curtailment in the power grid is alleviated. Conclusions The proposed strategy achieves a certain peak shaving effect while maintaining a good peak shaving economy throughout its life cycle.

Key words: energy storage, wind power, flow battery, peak shaving, bidirectional optimization, optimal control

CLC Number:

TK 02

Junhui LI, Guohang CHEN, Teng MA, Cuiping LI, Xingxu ZHU, Chen JIA. Optimal Control Strategy of Peak Shaving of Flow Battery Energy Storage System Under High Wind Power Permeability[J]. Power Generation Technology, 2024, 45(3): 434-447.

Figures/Tables 17

Fig. 1 Impact of wind power reverse peak shaving on peak shaving demand of power grid

Fig. 2 Structure of ZBB energy storage system in power grid

Tab. 1 Time division criteria for peak load, flat load and valley load

峰值时段	谷值时段	平值时段
T_p，1：09:00—12:00	T_v，1：23:00—06:00	T_f，1：06:00—09:00
T_p，2：17:00—21:00	—	T_f，2：12:00—17:00
—	—	T_f，3：21:00—23:00

Tab. 2 BESS calculation parameters

参数	数值
循环次数(100% DOD)	14 000
$p e n$ /[元/(MW⋅h)]	230
$p s c$ /[元/(MW⋅h)]	205
$C E$ /[元/(kW⋅h)]	5 000
$C P$ /(元/kW)	2 000
$C M$ /[元/(kW⋅h)]	0.1
$p m, i$ /(万元/t)	1.6
$α m, i$ /t	0.15
$p h$ /(万元/t)	3.5
$θ b e s s$ /[t/(MW⋅h)]	33

Tab. 2 BESS calculation parameters

参数	数值
循环次数(100% DOD)	14 000
$p e n$ /[元/(MW⋅h)]	230
$p s c$ /[元/(MW⋅h)]	205
$C E$ /[元/(kW⋅h)]	5 000
$C P$ /(元/kW)	2 000
$C M$ /[元/(kW⋅h)]	0.1
$p m, i$ /(万元/t)	1.6
$α m, i$ /t	0.15
$p h$ /(万元/t)	3.5
$θ b e s s$ /[t/(MW⋅h)]	33

Fig. 4 Three-day wind power and load output curves

Fig. 5 ZBB cycle number and DOD relationship curve

Fig. 6 ZBB energy storage capacity and efficiency operating attenuation situation

Fig. 7 Load curves after energy storage peak shaving optimization under different strategies

Fig. 8 Comparison of energy storage charge and discharge power under different strategies

Fig. 9 Comparison of energy storage SOC under different strategies

Fig. 10 Comparison of energy storage charging and wind curtailment under strategy 2 or 3

Fig. 11 Comparison of energy storage discharge and wind curtailment under strategy 2 or 3

Tab. 3 Time-of-use electricity price parameters

分时电价	单价/[元/(kW⋅h)]	时段
谷值电价	0.414	23:00—06:00
平值电价	0.782	06:00—09:00，12:00—17:00，21:00—23:00
峰值电价	1.149	09:00—12:00，17:00—21:00

Tab. 4 Evaluation indicators of each control strategy

控制策略	日均负荷峰谷差/MW	日均负荷峰谷差率/%	日均负荷标准差改善量/MW	储能净收益率/%	日均弃风电量/(MW⋅h)
无储能调节	972.311	16.287	—	—	6 216.138
策略1	760.814	13.105	69.124	10.445	4 964.578
策略2	603.470	10.397	73.537	10.626	4 942.015
策略3	622.537	10.716	73.164	11.028	4 936.140

Tab. 5 Energy storage benefits of each control strategy

控制策略	日均运行收益	日均补偿收益	日均环境收益
策略1	159.556	156.569	55.837
策略2	163.029	158.875	57.317
策略3	177.780	158.875	57.703

Tab. 6 Comparison of ZBB and lithium battery evaluation index under strategy 3

类型	日均负荷峰谷差/MW	日均负荷峰谷差率/%	日均负荷标准差改善量/MW	储能净收益率/%	日均弃风电量/(MW⋅h)
无储能调节	972.311	16.287	—	—	6 216.138
ZBB	622.537	10.716	73.164	11.028	4 936.140
锂离子电池	596.620	10.314	74.784	7.710	5 029.286

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