Power Secondary Optimization Strategy for Hybrid Energy Storage Systems Based on Fuzzy Control

Abstract

This invention discloses a secondary power optimization strategy for hybrid energy storage systems based on fuzzy control. Specifically, the battery's State of Charge (SOC) is first divided into five operating zones—over-discharge zone, over-discharge warning zone, normal operating zone, overcharge warning zone, and overcharge zone—using six boundary points. Then, a fuzzy controller is designed, taking the battery's SOC and its primary power command changes as inputs and a power correction coefficient as output. Based on the correction coefficient output by the fuzzy controller, the primary power command is adaptively adjusted to generate an optimized secondary power command. This secondary power optimization strategy for hybrid energy storage systems considers overcharge and over-discharge protection. By introducing fuzzy control and an adaptive strategy, the SOC of the energy storage device is maintained within the desired range of 0.3 to 0.8, significantly improving the charge-discharge balance of the hybrid energy storage system, optimizing the depth of charge-discharge, reducing the number of charge-discharge switching cycles, and extending the battery's lifespan.

Description

Technical Field

[0001] This invention belongs to the field of wind power grid-connected power fluctuation mitigation technology, specifically involving a power secondary optimization strategy for hybrid energy storage systems based on fuzzy control. Background Technology

[0002] Wind power generation has seen rapid development due to its clean and renewable characteristics. However, wind energy is highly intermittent and volatile, and large-scale grid connection of wind power poses challenges to the safe and stable operation of the power grid. Energy storage devices can achieve rapid, precise, and bidirectional power throughput, playing a crucial role in smoothing out wind power fluctuations.

[0003] Hybrid energy storage systems combine high-energy-density lithium batteries with high-power-density, fast-response supercapacitors. The complementary advantages of these two technologies enable efficient mitigation of drastic fluctuations in wind power. However, in actual operation, if the battery's state of charge (SOC) frequently approaches its upper or lower limits or fluctuates drastically, it can easily lead to overcharging and over-discharging. This not only accelerates battery aging and shortens its lifespan but may also affect the mitigation effect and the reliability of the energy storage device.

[0004] In scenarios with frequent wind power fluctuations, the output power often exhibits drastic and irregular fluctuations. Traditional power allocation strategies lack real-time adjustments to address these dynamic fluctuations, making it difficult to achieve precise power matching between energy storage units. This limitation easily leads to a dual core problem for battery modules in hybrid energy storage systems: on the one hand, uneven charge and discharge depths result in some batteries experiencing accelerated degradation of active materials due to prolonged overcharging and discharging, while others suffer from insufficient charge and discharge depths leading to idle capacity, affecting the overall charge and discharge efficiency and cycle life of the energy storage unit; on the other hand, frequent switching between charge and discharge states increases the switching losses of the energy storage converter and disrupts the internal electrochemical balance of the battery, accelerating electrode aging and electrolyte loss, further shortening the battery's service life. These problems are coupled, significantly limiting the core operational efficiency of hybrid energy storage systems, such as energy dispatch accuracy, peak smoothing effect, and energy utilization rate. They also significantly shorten the lifespan of batteries and key components, increase system operation and maintenance costs, and restrict their stable application and value release in scenarios such as wind power consumption and grid frequency regulation. Summary of the Invention

[0005] The purpose of this invention is to provide a power secondary optimization strategy for hybrid energy storage systems based on fuzzy control, which solves the problems of charge-discharge imbalance and battery lifespan that may exist in the process of smoothing wind power in hybrid energy storage systems.

[0006] The technical solution adopted in this invention is a power secondary optimization strategy for hybrid energy storage systems based on fuzzy control, which is implemented according to the following steps:

[0007] Step 1: Divide the SOC of the energy storage device into five working ranges using six dividing points. Step 2: Design a two-input single-output fuzzy controller, using the SOC of each energy storage device and the change in its primary power distribution as inputs, and the output as the power regulation coefficient. Step 3: Based on the SOC range defined in Step 1 and the power adjustment coefficient of the fuzzy controller output obtained in Step 2, the primary power command is adaptively adjusted to generate the optimized secondary power command.

[0008] The invention is further characterized in that, Step 1 specifically involves: The State of Charge (SOC) of energy storage devices is divided into five operating ranges using six dividing points. When the SOC of an energy storage device is greater than 90%, it is in an extreme state, corresponding to the over-discharge zone. When the SOC is between 80% and 90%, it corresponds to the overcharge warning zone. When the SOC is between 20% and 80%, it corresponds to the normal operating zone. When the SOC is between 10% and 20%, it corresponds to the over-discharge warning zone. When the SOC is less than 10%, it is in an extreme state, corresponding to the overcharge zone.

[0009] Step 2 specifically involves designing a two-input, single-output fuzzy controller based on the SOC of each energy storage device. x and the change in its primary distribution power As input; the output is the power regulation coefficient k. x When x is b, it represents an energy storage battery; when x is SC, it represents a supercapacitor. The input and output quantities are fuzzified using membership functions, employing a combination of trapezoidal and triangular fuzzification methods. (Power change) We can obtain it using the following formula: (1) In the formula, P ( n+ 1) indicates n Reference power at time +1; P ( k )express n ( The reference power at time ( ).

[0010] For energy storage batteries: Input 1: Assume the SOC of the energy storage battery b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. b Extremely low, low, medium, high, extremely high; VL is extremely low: corresponding to SOC b ≤0.1, at this point the energy storage device is in an extreme state, corresponding to the over-discharge zone; L low: corresponds to 0.1 <SOC b ≤0.2 corresponds to the over-discharge warning zone, indicating that the battery is in a low-charge state; In M: 0.2 <SOC b <0.8 corresponds to the normal operating range, indicating that the battery is in a normal state; H height: corresponds to 0.8≤SOC b <0.9 corresponds to the overcharge warning zone, indicating that the battery is in a high charge state; Extremely high VH: corresponding to SOC b ≥0.9 indicates that the energy storage device is in an extreme state, corresponding to the overcharge zone; Membership function design: NB subset: Using a trapezoidal membership function, the membership degree of VL is 1 in the interval [0,0.1], and decreases linearly to 0 in the interval 0.1~0.2; the membership degree of VH is 1 in the interval [0.9,1], and decreases to 0 in the interval 0.8~0.9.

[0011] Subsets L and H: Using triangular membership functions, L in SOC b When the membership value is 0.2, the membership degree is 1, covering the interval from 0.1 to 0.3; H in SOC b When the membership value is 0.8, the membership degree is 1, covering the interval from 0.7 to 0.9; M subset: Using a trapezoidal membership function, the membership degree is always 1 in the interval of 0.3~0.7, and the intervals of 0.2~0.3 and 0.7~0.8 are linear transition regions; Input 2: Set the change in the reference power of the energy storage battery. The universe of discourse is [-10, 10], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the battery reference power change as negative large, negative small, zero, positive small, and positive large, respectively. When the reference power change is negative, it indicates that the battery is in a charging state; when it is positive, it is in a discharging state. Membership function design: NB subset: The triangular membership function is used in the interval [-10, -6], which corresponds to a significant increase in power and requires the energy storage system to charge quickly; NS subset: The triangular membership function is used in the interval [-8, -2], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power. Z subset: A trapezoidal membership function is used in the interval [-4,4], and the membership degree is always 1 in the interval -2~2, so no significant charge and discharge adjustment is required. The intervals -4~-2 and 2~4 are linear transition regions. PS subset: A triangular membership function is used in the interval [2,8], resulting in a slight decrease in power; PB subset: The triangular membership function is used in the interval [6,10], which corresponds to a significant reduction in power, requiring the energy storage system to discharge quickly; Output: Let the power regulation coefficient be k b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output. In summary, when SOC b When low, if If positive, reduce the discharge power, i.e., k b Small; if If the value is negative, then the charging power is increased, i.e., k b big; When SOC b When it is high, if If it is negative, the charging power is reduced, i.e., k b Small; if If it is positive, then the discharge power increases, i.e., k b big; When SOC b During the medium-term period, normal charging and discharging occurs, i.e., k b Choose the middle option.

[0012] For supercapacitors: Input 1: Assume the SOC of the supercapacitor SC The universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. SC Extremely low, low, medium, high, extremely high; their corresponding intervals and membership functions are the same as those of energy storage batteries; Input 2: Set the change in the reference power of the supercapacitor. The universe of discourse is [-15, 15], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the supercapacitor reference power change as negative large, negative small, zero, positive small, and positive large, respectively. When the reference power change is negative, it indicates that the battery is in a charging state, and when it is positive, it is in a discharging state. Membership function design: NB subset: The triangular membership function is used in the interval [-15, -9], which corresponds to a significant increase in power and requires the energy storage system to charge quickly; NS subset: The triangular membership function is used in the interval [-12,-3], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power. Z subset: A trapezoidal membership function is used in the interval [-4,4], and the membership degree is always 1 in the interval -2~2, so no significant charge and discharge adjustment is required. The intervals -4~-2 and 2~-4 are linear transition regions. PS subset: A triangular membership function is used in the interval [3,12], resulting in a slight decrease in power; PB subset: The triangular membership function is used in the interval [9,15], which corresponds to a significant reduction in power, requiring the energy storage system to discharge quickly; Output: Let the power regulation coefficient be k SC The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output.

[0013] Step 3 specifically involves: when the SOC of the energy storage device... x In extreme conditions, without charging or discharging, the adjusted power reference value is 0, and the energy storage device does not operate; when the SOC of the energy storage device... x When in the over-discharge warning zone, if the primary reference power is greater than 0, discharge is required, and adjustments need to be made using the power regulation coefficient; when the SOC of the energy storage device... x When in the normal operating range, it maintains primary power command operation and requires no adjustment; when the energy storage device's SOC... x When in the overcharge warning zone, if the primary reference power is less than 0, the energy storage battery needs to be charged, which requires adjustment through the power regulation coefficient; the charging and discharging power adjustment strategy is shown in equation (2): (2) In the formula: SOC x (t) The current SOC of the energy storage device; k This is the power regulation coefficient.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control of the present invention makes the charging and discharging of energy storage batteries more balanced and reduces the number of charge and discharge cycles by dividing the SOC range of energy storage devices, fuzzy logic reasoning, and dynamically correcting power commands. Attached Figure Description

[0015] Figure 1 This is a diagram showing the SOC interval partitioning of the energy storage battery in the power secondary optimization strategy of the hybrid energy storage system based on fuzzy control in this invention. Figure 2 This is a block diagram of the power secondary optimization strategy for a hybrid energy storage system based on fuzzy control according to the present invention. Figure 3 This is a graph of the SOC membership function of the energy storage battery in the power secondary optimization strategy of the hybrid energy storage system based on fuzzy control in this invention. Figure 4 This is a membership function diagram of the power variation of the energy storage battery in the power secondary optimization strategy of the hybrid energy storage system based on fuzzy control in this invention. Figure 5 This is a membership function diagram of the power regulation coefficient of the energy storage battery in the power secondary optimization strategy of the hybrid energy storage system based on fuzzy control in this invention. Figure 6 The membership function diagram of the power change of the supercapacitor in the power secondary optimization strategy of the hybrid energy storage system based on fuzzy control in this invention; Figure 7 The SOC curve of the lithium battery before correction; Figure 8 The corrected SOC curve of the lithium battery; Figure 9 The SOC curve of the supercapacitor before correction; Figure 10 The corrected SOC curve of the supercapacitor; Figure 11 To smooth out the grid-connected power curves before and after. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0017] Example 1 This invention relates to a power secondary optimization strategy for hybrid energy storage systems based on fuzzy control, which is implemented according to the following steps: Step 1: The State of Charge (SOC) of the energy storage device was divided into five operating zones using six dividing points; combined with... Figure 1 Specifically: To optimize the number of battery charge-discharge cycles and extend its service life, the State of Charge (SOC) of the energy storage device was divided into five working ranges using six dividing points.

[0018] When the SOC of an energy storage device is greater than 90%, the energy storage device is in an extreme state, corresponding to the over-discharge zone. When the SOC of an energy storage device is between 80% and 90%, it corresponds to the overcharge warning zone. When the SOC of an energy storage device is between 20% and 80%, it corresponds to the normal operating range. When the SOC of an energy storage device is between 10% and 20%, it corresponds to the over-discharge warning zone. When the SOC of an energy storage device is less than 10%, the energy storage device is in an extreme state, corresponding to the overcharge zone. Step 2: Design a two-input, single-output fuzzy controller, using the SOC of each energy storage device and the changes in its primary power distribution as inputs, and the output as the power regulation coefficient; combined with... Figure 2 And Table 1, specifically: Design a two-input, single-output fuzzy controller to achieve the SOC of each energy storage device. x (When x is b, it represents the energy storage battery; when x is SC, it represents the supercapacitor 0) and the change in its primary distributed power. (When x is b, it represents an energy storage battery; when x is SC, it represents a supercapacitor) as input; output is the power regulation coefficient k. x (When x is b, it represents an energy storage battery; when x is SC, it represents a supercapacitor 0). The input and output quantities are fuzzified using membership functions, employing a combination of trapezoidal and triangular fuzzification methods. Change in reference power (Unit: MW) can be obtained using the following formula: (1) In the formula, P ( n+ 1) indicates n Reference power at time +1; P ( k )express n ( Reference power at time ( ); Table 1 Fuzzy Control Rules

[0019] For energy storage batteries: Input 1: Assume the SOC of the energy storage battery b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. b Extremely low, low, medium, high, extremely high. Their physical meanings are as follows: VL (Very Low): Corresponds to SOC b ≤0.1 indicates that the energy storage device is in an extreme state, corresponding to the over-discharge region, which represents the deep discharge state of the battery and requires the triggering of over-discharge protection.

[0020] L (low): corresponds to 0.1 <SOC b ≤0.2 corresponds to the over-discharge warning zone, indicating that the battery is in a low-charge state and discharge needs to be limited.

[0021] M (middle): corresponds to 0.2 <SOC b <0.8 corresponds to the normal operating range, indicating that the battery is in a normal state and can be charged and discharged at full power.

[0022] H (High): Corresponds to 0.8 ≤ SOC b A value less than 0.9 corresponds to the overcharge warning zone, indicating that the battery is in a high-charge state and charging needs to be limited.

[0023] VH (Very High): Corresponds to SOC b ≥0.9 indicates that the energy storage device is in an extreme state, corresponding to the overcharge zone, which represents the deep charging state of the battery and requires the triggering of overcharge protection.

[0024] Membership function design: NB subset: Using a trapezoidal membership function, the membership degree of VL is 1 in the interval [0,0.1], and decreases linearly to 0 in the interval 0.1~0.2; the membership degree of VH is 1 in the interval [0.9,1], and decreases to 0 in the interval 0.8~0.9.

[0025] Subsets L and H: Using triangular membership functions, L in SOC b When the membership value is 0.2, the membership degree is 1, covering the interval from 0.1 to 0.3; H in SOC b When the membership value is 0.8, the membership degree is 1, covering the interval from 0.7 to 0.9.

[0026] M subset: Employs a trapezoidal membership function, with a membership degree of 1 always present in the range of 0.3 to 0.7, and linear transition regions in the ranges of 0.2 to 0.3 and 0.7 to 0.8. This trapezoidal design ensures that the power command does not need to be frequently adjusted when the battery is operating within its normal range.

[0027] Input 2: Set the change in the reference power of the energy storage battery. The universe of discourse is [-10, 10], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the battery reference power change as negative large, negative small, zero, positive small, and positive large, respectively. The larger the value, the more drastic the power change. When the reference power change is negative, it indicates that the battery is in a charging state, and the smaller the value, the stronger the charging demand; when it is positive, it is in a discharging state, and the larger the value, the higher the discharging demand.

[0028] Membership function design: NB subset: The triangular membership function is used in the interval [-10, -6], which corresponds to a significant increase in power. The energy storage system needs to charge quickly to reduce the magnitude of the power increase.

[0029] NS subset: The triangular membership function is used in the interval [-8, -2], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power.

[0030] Z subset: A trapezoidal membership function is used in the interval [-4,4]. The membership degree is always 1 in the interval -2 to 2, so no significant charge and discharge adjustment is required. The intervals -4 to -2 and 2 to 4 are linear transition regions to avoid frequent adjustment.

[0031] PS subset: The triangular membership function is used in the interval [2,8], and the power is reduced slightly accordingly.

[0032] PB subset: A triangular membership function is used in the interval [6,10], which corresponds to a significant reduction in power and requires the energy storage system to discharge quickly.

[0033] Output: Let the power regulation coefficient be k b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output.

[0034] In summary, when SOC b When it is very low, if If the value is positive (requiring discharge), then to protect the battery, the discharge power should be reduced, i.e., k. b It should be small; if If the value is negative (charging), then the charging power can be increased, i.e., k. b It can be large, because charging is beneficial for batteries with low SOC, but you should also be careful not to make it too large.

[0035] When SOC b When it is very high, if To avoid overcharging and protect the battery, the charging power should be reduced, i.e., k. b It should be small; if If the value is positive (discharge), then the discharge power can be increased, i.e., k b Take the larger one.

[0036] When SOC b At medium (M) conditions, normal charging and discharging can proceed, i.e., k b Choose the middle option.

[0037] For supercapacitors: Input 1: Assume the SOC of the supercapacitor SCThe universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. SC Extremely low, low, medium, high, extremely high. The corresponding intervals and membership functions are the same as those for energy storage batteries.

[0038] Input 2: Set the change in the reference power of the supercapacitor. The universe of discourse is [-15, 15], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the supercapacitor's reference power change as negative large, negative small, zero, positive small, and positive large, respectively. The larger the value, the more drastic the power change. When the reference power change is negative, it indicates that the battery is in a charging state, and the smaller the value, the stronger the charging demand; when it is positive, it is in a discharging state, and the larger the value, the higher the discharging demand.

[0039] Membership function design: NB subset: The triangular membership function is used in the interval [-15, -9], which corresponds to a significant increase in power. The energy storage system needs to charge quickly to reduce the magnitude of the power increase.

[0040] NS subset: The triangular membership function is used in the interval [-12,-3], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power.

[0041] Z subset: A trapezoidal membership function is used in the interval [-4,4]. The membership degree is always 1 in the interval -2 to 2, so no significant charge and discharge adjustment is required. The intervals -4 to -2 and 2 to -4 are linear transition regions to avoid frequent adjustment.

[0042] PS subset: A triangular membership function is used in the interval [3,12], resulting in a slight reduction in power.

[0043] PB subset: A triangular membership function is used in the interval [9,15], which corresponds to a significant reduction in power and requires the energy storage system to discharge quickly.

[0044] Output: Let the power regulation coefficient be k SC The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output.

[0045] In summary, the membership function of the SOC of the energy storage battery is established as follows: Figure 3 As shown; the membership function of the energy storage battery power change is as follows: Figure 4As shown; establish the membership function of the energy storage battery power regulation coefficient as Figure 5 As shown; establish the membership function of the supercapacitor power change amount as Figure 6 As shown; the membership function of the SOC of the supercapacitor and the membership function of its power change amount are the same as Figure 3 and Figure 5 ; Step 3: According to the SOC interval divided in Step 1 and the correction coefficient output by the fuzzy controller obtained in Step 2, adaptively adjust the primary power command to generate an optimized secondary power command.

[0046] Embodiment 2 Different from Embodiment 1, further, Step 3 is specifically as follows: When the SOC of the energy storage device x is in an extreme state, in order to prevent the life attenuation or damage of the energy storage device, it is not suitable to charge or discharge, and the adjusted power reference value is 0, and the energy storage device does not operate. When the SOC of the energy storage device x is in the over-discharge warning area, if the primary reference power is greater than 0, that is, discharge is required. In order to avoid over-discharge, it needs to be adjusted by the power regulation coefficient k (0 < k < 1). When the SOC of the energy storage device x is in the normal working area, keep the primary power command running without adjustment. When the SOC of the energy storage device x is in the over-charge warning area, it means that the energy storage battery capacity is close to full. If the primary reference power is less than 0 at this time, that is, the energy storage battery needs to be charged. In order to avoid over-charging, it needs to be adjusted by the power regulation coefficient k (0 < k < 1) to reduce its value. The adaptive charge-discharge power adjustment strategy is shown in Equation (2): (2) In the formula: SOC x (t) is the current SOC of the energy storage device; k is the power regulation coefficient. indicates charging when indicates discharging when

[0047] Embodiment 3 This invention presents a fuzzy control-based power secondary optimization strategy for hybrid energy storage systems, aiming to address the charge-discharge imbalance problem in hybrid energy storage systems when smoothing wind power, thereby improving the smoothing effect and extending battery life. Considering overcharge and over-discharge protection, by introducing fuzzy control and an adaptive strategy, the SOC of the energy storage battery is maintained within the desired range of 0.3~0.8, significantly improving the charge-discharge balance of the hybrid energy storage system, optimizing the depth of charge and discharge, and reducing the number of charge-discharge switching cycles. Simulation results show that this strategy can effectively extend battery life by approximately 6.91%.

[0048] Example 4 The secondary optimization control strategy of the present invention is verified and analyzed through simulation, combined with Figures 7-10 The details are as follows: Taking the actual output power data of a 50MW wind farm in China as an example, the simulation duration is 24 hours, and the sampling period is 1 minute. A simulation model is built based on MATLAB / Simulink. The SOC of the energy storage battery before and after power correction is analyzed. b The curve shows that the SOC of the battery before correction... b The curve fluctuated wildly, repeatedly reaching both the upper and lower limits, indicating that the battery frequently underwent overcharging and over-discharging, which would severely impact battery life; while the corrected SOC... b The curve fluctuation amplitude decreased, consistently remaining within the expected range of 0.3 to 0.8, fluctuating only slightly with system power demand, thus avoiding overcharging and over-discharging, and significantly reducing the number of charge-discharge cycles. This is reflected in the SOC of the supercapacitor before and after correction. SC The change curves show that before correction, the SOC was between 2000s and 2500s. SC The value is high and is about to reach SOC. SC The upper limit is that if wind power fluctuates significantly at this point, the supercapacitor will lose its ability to smooth out wind power fluctuations. The corrected supercapacitor SOC... SC The curve is between 2000s and 2500s, SOC SC The value is significantly reduced, avoiding extreme operating conditions due to insufficient charging capacity.

[0049] Example 5 Furthermore, the lifetime of the hybrid energy storage system was estimated using the rainflow counting method. In this invention, the lifetime of the supercapacitor in the hybrid energy storage system was extended by approximately 6.37%, and the lifetime of the energy storage battery was extended by approximately 6.91%. This verifies that this scheme can improve the charge-discharge balance of the hybrid energy storage system, optimize the depth of charge-discharge of the battery pack, reduce the number of charge-discharge switching cycles, and extend the battery's lifespan.

[0050] Example 6 Figure 11To smooth out the grid-connected power curves before and after, it can be seen from the figure that the power secondary optimization strategy proposed in this invention can effectively smooth out the random fluctuations of wind power and control the actual grid-connected power within the target stable range, based on optimizing the working state of the energy storage system.

Claims

1. A power secondary optimization strategy for hybrid energy storage systems based on fuzzy control, characterized in that, The specific steps are as follows: Step 1: Divide the SOC of the energy storage device into five working ranges using six dividing points. Step 2: Design a two-input single-output fuzzy controller, using the SOC of each energy storage device and the change in its primary power distribution as inputs, and the output as the power regulation coefficient. Step 3: Based on the SOC range defined in Step 1 and the power adjustment coefficient of the fuzzy controller output obtained in Step 2, the primary power command is adaptively adjusted to generate the optimized secondary power command.

2. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 1, characterized in that, In step 1, specifically: The State of Charge (SOC) of energy storage devices is divided into five operating ranges using six dividing points. When the SOC of an energy storage device is greater than 90%, it is in an extreme state, corresponding to the over-discharge zone. When the SOC is between 80% and 90%, it corresponds to the overcharge warning zone. When the SOC is between 20% and 80%, it corresponds to the normal operating zone. When the SOC is between 10% and 20%, it corresponds to the over-discharge warning zone. When the SOC is less than 10%, it is in an extreme state, corresponding to the overcharge zone.

3. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 2, characterized in that, Step 2 specifically involves designing a two-input, single-output fuzzy controller based on the SOC of each energy storage device. x and the change in its primary distribution power As input; the output is the power regulation coefficient k. x When x is b, it represents an energy storage battery; when x is SC, it represents a supercapacitor 0. The input and output quantities are fuzzified using a membership function, and a combination of trapezoidal and triangular fuzzification methods are used for fuzzification.

4. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 3, characterized in that, Change in power We can obtain it using the following formula: （1） In the formula, P ( n+ 1) indicates n Reference power at time +1; P ( k )express n ( The reference power at time ( ).

5. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 3, characterized in that, For energy storage batteries: Input 1: Assume the SOC of the energy storage battery b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. b Extremely low, low, medium, high, extremely high; VL is extremely low: corresponding to SOC b ≤0.1, at this point the energy storage device is in an extreme state, corresponding to the over-discharge zone; L low: corresponds to 0.1 <SOC b ≤0.2 corresponds to the over-discharge warning zone, indicating that the battery is in a low-charge state; In M: 0.2 <SOC b <0.8 corresponds to the normal operating range, indicating that the battery is in a normal state; H height: corresponds to 0.8≤SOC b <0.9 corresponds to the overcharge warning zone, indicating that the battery is in a high charge state; Extremely high VH: corresponding to SOC b ≥0.9 indicates that the energy storage device is in an extreme state, corresponding to the overcharge zone; Membership function design: NB subset: Using a trapezoidal membership function, VL has a membership of 1 in the interval [0,0.1], and linearly decreases to 0 in the interval 0.1~0.2; VH has a membership of 1 in the interval [0.9,1], and decreases to 0 in the interval 0.8~0.

9. Subsets L and H: Using triangular membership functions, L in SOC b When the membership value is 0.2, the membership degree is 1, covering the interval from 0.1 to 0.3; H in SOC b When the membership value is 0.8, the membership degree is 1, covering the interval from 0.7 to 0.9; M subset: Using a trapezoidal membership function, the membership degree is always 1 in the interval of 0.3~0.7, and the intervals of 0.2~0.3 and 0.7~0.8 are linear transition regions; Input 2: Set the change in the reference power of the energy storage battery. The universe of discourse is [-10, 10], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the battery reference power change as negative large, negative small, zero, positive small, and positive large, respectively. When the reference power change is negative, it indicates that the battery is in a charging state; when it is positive, it is in a discharging state. Membership function design: NB subset: The triangular membership function is used in the interval [-10, -6], which corresponds to a significant increase in power and requires the energy storage system to charge quickly; NS subset: The triangular membership function is used in the interval [-8, -2], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power. Z subset: A trapezoidal membership function is used in the interval [-4,4], and the membership degree is always 1 in the interval -2~2, so no significant charge and discharge adjustment is required. The intervals -4~-2 and 2~4 are linear transition regions. PS subset: A triangular membership function is used in the interval [2,8], resulting in a slight decrease in power; PB subset: The triangular membership function is used in the interval [6,10], which corresponds to a significant reduction in power, requiring the energy storage system to discharge quickly; Output: Let the power regulation coefficient be k b The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output. In summary, when SOC b When low, if If positive, reduce the discharge power, i.e., k b Small; if If the value is negative, then the charging power is increased, i.e., k b big; When SOC b When it is high, if If it is negative, the charging power is reduced, i.e., k b Small; if If it is positive, then the discharge power increases, i.e., k b big; When SOC b During the medium-term period, normal charging and discharging occurs, i.e., k b Choose the middle option.

6. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 5, characterized in that, For supercapacitors: Input 1: Assume the SOC of the supercapacitor SC The universe of discourse is [0,1], and the corresponding fuzzy sets are {VL, L, M, H, VH}, which represent the state of charge (SOC) of the energy storage battery, respectively. SC Extremely low, low, medium, high, extremely high; their corresponding intervals and membership functions are the same as those of energy storage batteries; Input 2: Set the change in the reference power of the supercapacitor. The universe of discourse is [-15, 15], and the corresponding fuzzy sets are {NB, NS, Z, PS, PB}, which represent the supercapacitor reference power change as negative large, negative small, zero, positive small, and positive large, respectively. When the change in reference power is negative, it indicates that the battery is in a charging state; when it is positive, it is in a discharging state. Membership function design: NB subset: The triangular membership function is used in the interval [-15, -9], which corresponds to a significant increase in power and requires the energy storage system to charge quickly; NS subset: The triangular membership function is used in the interval [-12,-3], which corresponds to a slight increase in power and requires the energy storage system to charge at a low power. Z subset: A trapezoidal membership function is used in the interval [-4,4], and the membership degree is always 1 in the interval -2~2, so no significant charge and discharge adjustment is required. The intervals -4~-2 and 2~-4 are linear transition regions. PS subset: A triangular membership function is used in the interval [3,12], resulting in a slight decrease in power; PB subset: The triangular membership function is used in the interval [9,15], which corresponds to a significant reduction in power, requiring the energy storage system to discharge quickly; Output: Let the power regulation coefficient be k SC The universe of discourse is [0,1], and the corresponding fuzzy sets are {VS, S, M, B, VB}, which represent the scaling factors as very small, small, medium, large, and very large, respectively, corresponding to the intervals [0,0.25], [0,0.5], [0.25,0.75], [0,0.25], [0.5,1], and [0.75,1], respectively. Their values ​​represent the scaling of the power value, and all use triangular membership functions to ensure a smooth transition of the output.

7. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 6, characterized in that, In step 3, specifically: when the SOC of the energy storage device... x In extreme conditions, without charging or discharging, the adjusted power reference value is 0, and the energy storage device does not operate; when the SOC of the energy storage device... x When in the over-discharge warning zone, if the primary reference power is greater than 0, discharge is required, and adjustments need to be made using the power regulation coefficient; when the SOC of the energy storage device... x When in the normal operating range, it maintains primary power command operation and requires no adjustment; when the energy storage device's SOC... x If the primary reference power is less than 0 when the battery is in the overcharge warning zone, the energy storage battery needs to be charged, which requires adjustment through the power regulation coefficient.

8. The power secondary optimization strategy for hybrid energy storage systems based on fuzzy control as described in claim 7, characterized in that, The charging and discharging power adjustment strategy is shown in equation (2): （2） In the formula: SOC x (t) The current SOC of the energy storage device; k This is the power regulation coefficient.

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