Intelligent operation control method applied to wind power energy storage

Abstract

The invention discloses an intelligent operation control method applied to wind power energy storage. The intelligent operation control method comprises the steps of S1, establishing a control system, S2, calculating an attenuation coefficient beta value, S3, estimating a residual capacity SOC, S4, performing charging control, S5, performing discharging control and S6, performing grid-connected control. According to the intelligent operation control method applied to wind power energy storage, charging and discharging control is carried out on the energy storage equipment by judging the change rate of the SOC of the energy storage equipment, a good effect is achieved on charging and discharging control of the wind power energy storage equipment, the energy storage equipment is protected, and the charging time of the energy storage equipment is effectively shortened; the distribution of energy storage power is adjusted in a self-adaptive mode according to the power requirement, starting from the mutual relation of wind power grid-connected power and energy storage SOC, when energy storage of energy storage equipment has enough large output margin, the fluctuation of wind power is stabilized; and when the energy storage output margin of the energy storage equipment is small, the standard of wind power fluctuation stabilization is reduced to recover the energy storage output margin, so that the service life loss of the energy storage equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power energy storage, and in particular to an intelligent operation control method applied to wind power energy storage. Background Art

[0002] Wind energy varies greatly with the weather, resulting in large changes in the output power of the wind-solar hybrid power generation system. Therefore, the wind-solar hybrid power generation system mainly considers two aspects in controlling the charging and discharging of the battery: ① When the system output power is sufficient, control the battery to stop when it is full; ② When the system output power is insufficient, discharge to the load in time, and stop discharging when the battery capacity is less than 30% of the rated capacity.

[0003] At present, the commonly used battery charging and discharging methods mainly include constant current and constant voltage charging method, four-stage charging method, pulse charging method, trickle charging method, current decreasing charging method, constant current discharge method, and power constant discharge method. Based on the analysis and research of the above-mentioned charging and discharging methods, this scheme charges the battery by improving the current decreasing charging method. The principle of improvement is to shorten the charging time of the battery under the premise of protecting the battery. The fundamental of this improved charging method is to judge the change rate of the remaining capacity (SOC). The higher the change rate of SOC, the shorter the charging time.

[0004] Under the "dual carbon" goal, wind power generation has become the most developed and commercially promising renewable energy generation method due to its relatively mature technology. However, due to the intermittent, volatile and random characteristics of wind itself, as its grid-connected penetration level increases, it will have a serious impact on the safe and stable operation of the power system. Energy storage systems have a quick response and flexible operation. Through control, they can better absorb and release electrical energy. When dealing with complex wind power fluctuations, they can greatly improve the stability of wind power grid-connected power, thereby reducing the adverse effects of direct wind power grid connection on the power grid.

[0005] Therefore, it is also necessary to restrict the grid-connected regulation of wind power energy storage, otherwise the service life of the energy storage equipment will be seriously affected. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent operation control method applied to wind power energy storage to solve the problems raised in the above background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A smart operation control method for wind power energy storage, specifically comprising the following steps: S1. Establish a control system: including intelligent controller, temperature sensor, current sensor, and voltage sensor; The intelligent controller has a built-in central processing unit, which detects the ambient temperature through a temperature sensor, and detects the current and voltage of the energy storage device charging and discharging through a current sensor and a voltage sensor. The central processing unit has a built-in clock circuit, an attenuation coefficient β value calculation module, a remaining capacity SOC estimation module, a charge and discharge control module, and a grid-connected control module. S2, calculate the attenuation coefficient β value; S3, estimate the remaining capacity SOC; S4, charging control: performing current decreasing charging control on the energy storage device, which calculates the value of the initial charging rate C of the energy storage device according to the estimated value of SOC; Then, the maximum allowable charging voltage Vbat,max of the energy storage device is calculated by measuring the ambient temperature Ta of the energy storage device. S5. Discharge control: Discharge control is based on the principle of protecting energy storage equipment and saving electric energy. It determines whether the bus voltage U is lower than its lower limit of 0.8U. If U is lower than 0.8U and the energy storage equipment SOC ≥ 30%, the energy storage equipment is controlled to discharge; if 25% < SOC < 30%, the circuit system starts to alarm and the energy storage equipment stops discharging; if SOC < 25%, the energy storage equipment starts to charge; S6. Grid-connected control: When the energy storage output margin of the energy storage device is small, the standard for smoothing wind power fluctuations can be appropriately reduced to restore the output margin of the energy storage and reduce the life loss of the energy storage device.

[0008] Preferably, in S1, establishing a control system, the central processing unit is electrically connected to a USB interface and a wireless communication module.

[0009] Preferably, in S2, the attenuation coefficient β value is calculated: when a constant current is used to charge the energy storage device, the capacity of the energy storage device will be limited and can only reach a certain value. If the charging continues to exceed this certain value, a large amount of gas will be generated inside the energy storage device, thereby rapidly increasing the temperature of the energy storage device. However, if the charging current is gradually reduced in an exponential form during the charging process, the gas generated by the energy storage device will be greatly reduced, and the temperature will not rise too quickly. The relationship between the charging current and the charging time of the energy storage device is:

[0010] i is the charging current received by the energy storage device at a certain time t; Io is the acceptable current for the energy storage device to start charging; α is the charge acceptance ratio, which is the ratio of the initial charging current to the chargeable capacity; During the charging process, if the energy storage device does not have reverse discharge and follows the charge acceptance characteristic law, the charge acceptance ratio α can be calculated by the following formula:

[0011] Where Ct is the charging capacity of the battery at time t; When the energy storage device is charged at a constant current of 0.2C, its capacity can only reach 80%. If it continues to be charged, the temperature of the energy storage device will rise rapidly due to the generation of a large amount of gas. The initial constant charging current of this system is set to 20A. When the rated capacity is charged to 80%, it will enter a new charging process. The starting current of the new charging process is I0′, the input capacity C′ is 0.2C, and the new charging acceptance ratio α 1 for:

[0012] During the new charging process, the charging current i′ is:

[0013] Assume t k The energy storage device is charged from 0.8C to t k1 When the capacity reaches 0.9C, the capacity of the energy storage device increases by 0.1C. From the above formula, we can get t k1 The energy storage device can accept charging current i′ at the moment k1 for:

[0014] So the capacity C will be charged 3 The charging current is set to 10A, then the attenuation coefficient β of the discharge rate C is: By analogy, the charging current of the energy storage device to charge capacity C2 is 5A.

[0015] Preferably, in S3, the remaining capacity SOC is estimated: when the energy storage device is charging, its charging efficiency η c It is related to the remaining capacity SOC of the energy storage device. c =1; when charging:

[0016] During discharge: In the formula is the time variation, is the temperature change; It can be seen from the above formula that during the charging and discharging process of the energy storage device, the SOC can be estimated by measuring the working current of the energy storage device.

[0017] Preferably, in S6, grid connection control: starting from the relationship between wind power grid connection power and energy storage SOC, when the energy storage device has a sufficiently large output margin, the fluctuation of wind power should be smoothed; when the energy storage device has a small output margin, the standard for smoothing wind power fluctuations can be appropriately reduced to restore the output margin of energy storage, so as to reduce the life loss of energy storage equipment; based on the above considerations, the charge and discharge saturation capacity function ρ(SOC) is introduced into the MPC optimization objective function in the form of a weight coefficient:

[0018] In the formula, ρ(SOC ES ) is the charge and discharge saturation capacity function of the energy storage device; P w_rate for wind power installed capacity; ΔPg(k) is the grid-connected power increment of wind power, ΔPg(k)=Pg(k)−Pg(k−1); p is the prediction time domain; k is the controlled time node.

[0019] Preferably, the MPC optimization objective function in the formula includes adjusting the energy storage SOC and adjusting the grid-connected power increment. By changing the weight, that is, the charge-discharge saturation capacity function ρ(SOC ES ) to achieve the adjustment of the two control objectives.

[0020] Preferably, when ρ(SOC ES ) is small, the charge and discharge saturation of the energy storage system is low, and the control target is mainly to restore the energy storage SOC, so that the energy storage SOC is in area 1 as much as possible and adjusted around 0.5.

[0021] Preferably, when ρ(SOC ES ) is large, the energy storage system charge and discharge saturation is high, the energy storage SOC is in a good operating range, and the control target is mainly to minimize the wind power grid-connected power increment to improve the smoothness of the wind power grid-connected power; therefore, by adjusting the parameters of the charge and discharge saturation capacity function ESρ(SOC), the control proportion of the energy storage SOC and the grid-connected power increment can be flexibly adjusted.

[0022] Preferably, in addition to satisfying the MPC optimization objective function, the grid-connected control also needs to satisfy the energy storage SOC constraint of the energy storage device, the charge and discharge power constraint, and the grid-connected power constraint, that is,

[0023] In the formula, SOC ES_min , SOC ES_max They are the minimum and maximum SOC limits of the energy storage device; PES_max is the maximum power of the energy storage device; δ is the fluctuation limit per unit time.

[0024] Preferably, δ is limited to between 1.5 and 3.

[0025] Compared with the prior art, the intelligent operation control method for wind power storage provided by the present invention has the following advantages: The intelligent operation control method applied to wind power energy storage controls the charging and discharging of the energy storage device by judging the change rate of the remaining capacity (SOC) of the energy storage device, which has a good effect on the charging and discharging control of the wind power energy storage device, not only protecting the energy storage device, but also effectively shortening the charging time of the energy storage device; It can adaptively adjust the distribution of energy storage power according to power demand. Starting from the relationship between wind power grid-connected power and energy storage SOC, when the energy storage equipment has a large enough output margin, it can smooth out wind power fluctuations. When the energy storage equipment has a small output margin, it can reduce the standard for smoothing wind power fluctuations to restore the output margin of energy storage and reduce the life loss of energy storage equipment. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 A control system block diagram of the intelligent operation control method applied to wind power energy storage according to the present invention; Figure 2 A charging flow chart of an energy storage device for the intelligent operation control method for wind power energy storage of the present invention; Figure 3 A discharge flow chart of an energy storage device for the intelligent operation control method for wind power energy storage of the present invention; Figure 4 The circuit diagram of the charging and discharging of the intelligent operation control method applied to wind power energy storage of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] Example 1 like Figure 1-3 , a smart operation control method applied to wind power energy storage, specifically comprising the following steps: S1. Establish a control system: intelligent controller, temperature sensor, current sensor, voltage sensor. The intelligent controller has a built-in central processor. The central processor detects the ambient temperature through the temperature sensor, and detects the current and voltage of the energy storage device charging and discharging through the current sensor and voltage sensor. The central processor has a built-in clock circuit. The central processor is electrically connected to a USB interface and a wireless communication module to facilitate information transmission and remote control. The central processor has a built-in attenuation coefficient β value calculation module, a remaining capacity SOC estimation module, a charge and discharge control module, and a grid-connected control module. S2. Calculate the attenuation coefficient β value: When a constant current is used to charge the energy storage device, the capacity of the energy storage device will be limited and can only reach a certain value. If the charging continues to exceed this specific value, a large amount of gas will be generated inside the energy storage device, which will quickly increase the temperature of the energy storage device. However, if the charging current is gradually reduced exponentially during the charging process, the gas generated by the energy storage device will be greatly reduced, and the temperature will not rise too quickly. The relationship between the charging current and the charging time of the energy storage device is:

[0029] i is the charging current received by the energy storage device at a certain time t; Io is the acceptable current for the energy storage device to start charging; α is the charge acceptance ratio, which is the ratio of the initial charging current to the chargeable capacity; During the charging process, if the energy storage device does not have reverse discharge and follows the charge acceptance characteristic law, the charge acceptance ratio α can be calculated by the following formula:

[0030] Where Ct is the charging capacity of the battery at time t; When the energy storage device is charged at a constant current of 0.2C, its capacity can only reach 80%. If it continues to be charged, the temperature of the energy storage device will rise rapidly due to the generation of a large amount of gas. The initial constant charging current of this system is set to 20A. When the rated capacity is charged to 80%, it will enter a new charging process. The starting current of the new charging process is I0′, the input capacity C′ is 0.2C, and the new charging acceptance ratio α 1 for:

[0031] During the new charging process, the charging current i′ is:

[0032] Assume t k The energy storage device is charged from 0.8C to t k1 When the capacity reaches 0.9C, the capacity of the energy storage device increases by 0.1C. From the above formula, we can get t k1 The energy storage device can accept charging current i′ at the moment k1 for:

[0033] So the capacity C will be charged 3 The charging current is set to 10A, then the attenuation coefficient β of the discharge rate C is: By analogy, the charging current of the energy storage device to charge capacity C2 is 5A; S3. Estimate the remaining capacity SOC: When the energy storage device is charging, its charging efficiency η c It is related to the remaining capacity SOC of the energy storage device. c =1; when charging:

[0034] During discharge:

[0035] In the formula is the time variation, is the temperature change; It can be seen from the above formula that during the charging and discharging process of the energy storage device, the SOC can be estimated by measuring the working current of the energy storage device; S4, charging control: perform current decreasing charging control on the energy storage device, calculate the value of the initial charging rate C of the energy storage device according to the estimated value of SOC, and then calculate the current maximum allowable charging current Vbat,max of the energy storage device by measuring the ambient temperature Ta of the energy storage device; S5. Discharge control: Discharge control is based on the principle of protecting energy storage equipment and saving electric energy. It determines whether the bus voltage U is lower than its lower limit of 0.8U. If U is lower than 0.8U and the energy storage equipment SOC ≥ 30%, the energy storage equipment is controlled to discharge; if 25% < SOC < 30%, the circuit system starts to alarm and the energy storage equipment stops discharging; if SOC < 25%, the energy storage equipment starts to charge; Taking a certain battery as an example, the relationship between its open circuit voltage and SOC is as follows: Open circuit voltage (V) 11.96 12.20 12.31 12.37 12.48 12.52 12.67 SOC (%) 30 50 60 70 80 90 100 After testing, when the time t=0.6s, the charging current is 20A; when the time t=0.4s, the charging current changes from 20A to 10A, the SOC value is 0.8; when the time t=0.35s, the charging current changes from 10A to 5A, the SOC value is 0.9. When the battery is discharged, the simulation results show that when t>0.5s, the bus voltage is 400V and remains basically unchanged, the discharge current is 25A, and the discharge time t is about 3.9h.

[0036] S6. Grid connection control: Based on the relationship between wind power grid connection power and energy storage SOC, when the energy storage equipment has a large enough output margin, the fluctuation of wind power should be smoothed; when the energy storage equipment has a small output margin, the standard for smoothing wind power fluctuations can be appropriately reduced to restore the output margin of energy storage, so as to reduce the life loss of energy storage equipment; Based on the above considerations, the charge and discharge saturation capacity function ρ(SOC) is introduced into the MPC optimization objective function in the form of a weight coefficient:

[0037] In the formula, ρ(SOC ES ) is the charge and discharge saturation capacity function of the energy storage device; P w_rate for wind power installed capacity; ΔPg(k) is the grid-connected power increment of wind power, ΔPg(k)=Pg(k)−Pg(k−1); p is the prediction time domain; k is the control time node; The MPC optimization objective function in the formula includes adjusting the energy storage SOC and adjusting the grid-connected power increment. By changing the weight, that is, the charge and discharge saturation capacity function ρ(SOC ES ) to achieve the adjustment of the two control objectives; when ρ(SOC ES ) is small, the energy storage system charge and discharge saturation is low, and the control target is mainly to restore the energy storage SOC, so that the energy storage SOC is in area 1 as much as possible and adjusted around 0.5; when ρ(SOC ES ) is large, the energy storage system charge and discharge saturation is high, the energy storage SOC is in a good operating range, and the control target is mainly to minimize the wind power grid-connected power increment to improve the smoothness of wind power grid-connected power; therefore, by adjusting the parameters of the charge and discharge saturation capacity function ESρ(SOC), the control proportion of the energy storage SOC and the grid-connected power increment can be flexibly adjusted; In addition to satisfying the MPC optimization objective function, the grid-connected control must also satisfy the energy storage SOC constraints of the energy storage equipment, the charging and discharging power constraints, and the grid-connected power constraints, that is,

[0038] In the formula, SOC ES_min , SOC ES_max They are the minimum and maximum SOC limits of the energy storage device; PES_max is the maximum power of the energy storage device; δ is the fluctuation limit per unit time, and δ is limited between 1.5 and 3.

[0039] In summary: The intelligent operation control method applied to wind power energy storage controls the charging and discharging of the energy storage device by judging the change rate of the remaining capacity (SOC) of the energy storage device, which has a good effect on the charging and discharging control of the wind power energy storage device, not only protecting the energy storage device, but also effectively shortening the charging time of the energy storage device; It can adaptively adjust the distribution of energy storage power according to power demand. Starting from the relationship between wind power grid-connected power and energy storage SOC, when the energy storage equipment has a large enough output margin, it can smooth out wind power fluctuations. When the energy storage equipment has a small output margin, it can reduce the standard for smoothing wind power fluctuations to restore the output margin of energy storage and reduce the life loss of energy storage equipment.

[0040] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart operation control method applied to wind power energy storage, characterized in that: The specific steps include: S1. Establish a control system: including intelligent controller, temperature sensor, current sensor, and voltage sensor; The intelligent controller has a built-in central processing unit, which detects the ambient temperature through a temperature sensor, and detects the current and voltage of the energy storage device charging and discharging through a current sensor and a voltage sensor. The central processing unit has a built-in clock circuit, an attenuation coefficient β value calculation module, a remaining capacity SOC estimation module, a charge and discharge control module, and a grid-connected control module. S2, calculate the attenuation coefficient β value; S3, estimate the remaining capacity SOC; S4, charging control: performing current decreasing charging control on the energy storage device, which calculates the value of the initial charging rate C of the energy storage device according to the estimated value of SOC; Then, the maximum allowable charging voltage Vbat,max of the energy storage device is calculated by measuring the ambient temperature Ta of the energy storage device. S5. Discharge control: Discharge control is based on the principle of protecting energy storage equipment and saving electric energy. It determines whether the bus voltage U is lower than its lower limit of 0.8U. If U is lower than 0.8U and the energy storage equipment SOC ≥ 30%, the energy storage equipment is controlled to discharge; if 25% < SOC < 30%, the circuit system starts to alarm and the energy storage equipment stops discharging; if SOC < 25%, the energy storage equipment starts to charge; S6. Grid-connected control: When the energy storage output margin of the energy storage device is small, the standard for smoothing wind power fluctuations can be appropriately reduced to restore the output margin of the energy storage and reduce the life loss of the energy storage device.

2. The intelligent operation control method for wind power storage according to claim 1 is characterized in that: In S1, establishing a control system, the central processing unit is electrically connected to a USB interface and a wireless communication module.

3. The intelligent operation control method for wind power storage according to claim 1 is characterized in that: In S2, the attenuation coefficient β is calculated: when a constant current is used to charge the energy storage device, the capacity of the energy storage device will be limited and can only reach a certain value. If the charging continues to exceed this specific value, a large amount of gas will be generated inside the energy storage device, which will quickly increase the temperature of the energy storage device. However, if the charging current is gradually reduced in an exponential form during the charging process, the gas generated by the energy storage device will be greatly reduced, and the temperature will not rise too quickly; the relationship between the charging current and the charging time of the energy storage device is: ； i is the charging current received by the energy storage device at a certain time t; Io is the acceptable current for the energy storage device to start charging; α is the charge acceptance ratio, which is the ratio of the initial charging current to the chargeable capacity; During the charging process, if the energy storage device does not have reverse discharge and follows the charge acceptance characteristic law, the charge acceptance ratio α can be calculated by the following formula: ； Where Ct is the charging capacity of the battery at time t; When the energy storage device is charged at a constant current of 0.2C, its capacity can only reach 80%. If it continues to be charged, the temperature of the energy storage device will rise rapidly due to the generation of a large amount of gas. The initial constant charging current of this system is set to 20A. When it is charged to 80% of the rated capacity, it turns to a new charging process. The starting current of the new charging process is I0′, the input capacity C′ is 0.2C, and the new charging acceptance ratio α1 is: ； During the new charging process, the charging current i′ is: ； Assume t k The energy storage device is charged from 0.8C to t k1 When the capacity reaches 0.9C, the capacity of the energy storage device increases by 0.1C. From the above formula, we can get t k1 The energy storage device can accept charging current i′ at the moment k1 for: ； Therefore, if the charging current of the charging capacity C3 is set to 10A, the attenuation coefficient β of the discharge rate C is: ; By analogy, the charging current of the energy storage device to charge capacity C2 is 5A.

4. The intelligent operation control method for wind power storage according to claim 1 is characterized in that: In S3, the remaining capacity SOC is estimated: when the energy storage device is charging, its charging efficiency η c It is related to the remaining capacity SOC of the energy storage device. c =1; when charging: ； During discharge: In the formula is the time variation, is the temperature change; It can be seen from the above formula that during the charging and discharging process of the energy storage device, the SOC can be estimated by measuring the working current of the energy storage device.

5. The intelligent operation control method for wind power storage according to claim 1 is characterized in that: In S6, grid-connected control: starting from the relationship between wind power grid-connected power and energy storage SOC, when the energy storage device has a large enough output margin, the fluctuation of wind power should be smoothed; when the energy storage device has a small output margin, the standard for smoothing wind power fluctuations can be appropriately reduced to restore the output margin of energy storage, so as to reduce the life loss of energy storage equipment; based on the above considerations, the charge and discharge saturation capacity function ρ(SOC) is introduced into the MPC optimization objective function in the form of a weight coefficient: ； In the formula, ρ(SOC ES ) is the charge and discharge saturation capacity function of the energy storage device; P w_rate for wind power installed capacity; ΔPg(k) is the grid-connected power increment of wind power, ΔPg(k)=Pg(k)−Pg(k−1); p is the prediction time domain; k is the controlled time node.

6. The intelligent operation control method for wind power storage according to claim 5 is characterized in that: The MPC optimization objective function in the formula includes adjusting the energy storage SOC and adjusting the grid-connected power increment. By changing the weight, that is, the charge-discharge saturation capacity function ρ(SOC ES ) to achieve the adjustment of the two control objectives.

7. The intelligent operation control method for wind power storage according to claim 6 is characterized in that: When ρ(SOC ES ) is small, the charge and discharge saturation of the energy storage system is low, and the control target is mainly to restore the energy storage SOC, so that the energy storage SOC is in area 1 as much as possible and adjusted around 0.

5.

8. The intelligent operation control method for wind power storage according to claim 7 is characterized in that: When ρ(SOC ES ) is large, the energy storage system charge and discharge saturation is high, the energy storage SOC is in a good operating range, and the control target is mainly to minimize the wind power grid-connected power increment to improve the smoothness of the wind power grid-connected power; therefore, by adjusting the parameters of the charge and discharge saturation capacity function ESρ(SOC), the control proportion of the energy storage SOC and the grid-connected power increment can be flexibly adjusted.

9. The intelligent operation control method for wind power storage according to claim 5 is characterized in that: In addition to satisfying the MPC optimization objective function, the grid-connected control must also satisfy the energy storage SOC constraints of the energy storage equipment, the charging and discharging power constraints, and the grid-connected power constraints, that is, ； In the formula, SOC ES_min , SOC ES_max They are the minimum and maximum SOC limits of the energy storage device; PES_max is the maximum power of the energy storage device; δ is the fluctuation limit per unit time.

10. The intelligent operation control method for wind power storage according to claim 9 is characterized in that: δ is limited to between 1.5 and 3.

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