An Advanced Fuzzy Control Strategy for Hybrid Energy Storage Systems Considering Smoothing of Wind Power Fluctuations at Future Moments

doi:10.12096/j.2096-4528.pgt.23153

Abstract

Abstract:

Objectives The existing hybrid energy storage system control strategy finds it difficult to maintain the state of charge (SOC) within a reasonable range while also meeting the advanced charging and discharging needs due to future wind power fluctuations. Therefore, a new advanced fuzzy control strategy for hybrid energy storage systems was proposed, which takes into account the smoothing of future wind power fluctuations. Methods Firstly, the wind power needing to be smoothed by different types of energy storage devices was decomposed using the ensemble empirical mode decomposition (EEMD) method. Secondly, the power correction parameter was adjusted according to the SOC and power saturation level of the hybrid energy storage system to correct its output power. Thirdly, the wind power prediction algorithm was used to obtain the predicted value of wind power in the forward-looking cycle. The advance charging and discharging parameters were adjusted to correct the output power of the energy storage system based on the wind power fluctuations in the forward-looking cycle and the over-advance control theory. Finally, taking the actual data of a wind farm as an example, the validity of the proposed forward-looking fuzzy control strategy was validated through simulation. Results The proposed control strategy can not only reduce the over-limit probability of wind power grid-connected fluctuation, significantly reduce the deviation between the total output power and the target power, but also maintain the SOC of the hybrid energy storage system within a reasonable range. Conclusions This strategy can provide a useful reference for research related to smoothing wind power fluctuations.

Key words: wind power, hybrid energy storage system, fuzzy control, advanced control

CLC Number:

TK 89

Dan ZHOU, Zhi YUAN, Ji LI, Wei FAN. An Advanced Fuzzy Control Strategy for Hybrid Energy Storage Systems Considering Smoothing of Wind Power Fluctuations at Future Moments[J]. Power Generation Technology, 2024, 45(3): 412-422.

Figures/Tables 17

Fig. 1 Schematic diagram of wind power and hybrid energy storage system

Fig. 2 Calculation step diagram based on EEMD algorithm

Fig. 3 Equivalent circuit model of supercapacitor

Fig. 4 Equivalent circuit model of the battery

Fig. 5 Membership function of input for the adjustment of αx

Tab. 1 Fuzzy logic rules for the adjustment of αx

$ε x (t)$	$η x (t)$
$ε x (t)$	NB	NS	ZO	PS	PB
NB	ZE	ZE	ZE	NM	NB
NS	ZE	ZE	ZE	NS	NM
ZO	ZE	ZE	ZE	ZE	ZE
PS	PM	PS	ZE	ZE	ZE
PB	PB	PM	ZE	ZE	ZE

Tab. 1 Fuzzy logic rules for the adjustment of αx

$ε x (t)$	$η x (t)$
$ε x (t)$	NB	NS	ZO	PS	PB
NB	ZE	ZE	ZE	NM	NB
NS	ZE	ZE	ZE	NS	NM
ZO	ZE	ZE	ZE	ZE	ZE
PS	PM	PS	ZE	ZE	ZE
PB	PB	PM	ZE	ZE	ZE

Fig. 6 Membership function of output for the adjustment of αx

Fig. 7 Membership function of input for the adjustment of β

Fig. 8 Membership function of output for the adjustment of β

Tab. 2 Fuzzy logic rules for the adjustment of β

$Δ P 2$	$Δ P 1$
$Δ P 2$	NB	NS	ZO	PS	PB
NB	VB	VB	B	B	B
NS	B	B	M	M	S
ZO	S	VS	VS	VS	S
PS	S	M	M	B	B
PB	B	B	B	VB	VB

Tab. 2 Fuzzy logic rules for the adjustment of β

$Δ P 2$	$Δ P 1$
$Δ P 2$	NB	NS	ZO	PS	PB
NB	VB	VB	B	B	B
NS	B	B	M	M	S
ZO	S	VS	VS	VS	S
PS	S	M	M	B	B
PB	B	B	B	VB	VB

Fig. 9 Flow chart of fuzzy control and look-ahead control strategy

Fig. 10 Comparison of the synthetic output power of wind power and energy storage

Fig. 11 Comparison of grid-connected power fluctuations

Tab. 3 Statistics on fluctuation value overruns

控制策略	越限次数	越限概率/%
EEMD控制	487	16.23
单层模糊控制	116	3.87
双层模糊控制	81	2.70
超前模糊控制	62	2.07

Fig. 12 Total deviation of the final output power from the desired target power value

Fig. 13 SOC comparison of super capacitor

Fig. 14 SOC comparison of four methods of battery

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