Control method, device, energy storage combined system and storage medium of wind power system

Abstract

The present invention provides a control method, device, energy storage combined system and storage medium for a wind power system. The method includes: obtaining the operating data of the target wind power system; obtaining the output power prediction value of the target wind power system according to the operating data; optimizing and iterating according to the output power prediction value and the operating data of the target wind power system to obtain the charge and discharge power prediction value of the energy storage battery module; determining the cooling efficiency prediction value of the liquid cooling module according to the charge and discharge power prediction value of the energy storage battery module; and performing feedforward control on the target wind power system according to the charge and discharge power prediction value and the cooling efficiency prediction value. The present invention can obtain the optimal charge and discharge power prediction value of the energy storage battery module through optimization and iteration to improve the control accuracy. Based on the obtained charge and discharge power prediction value and cooling efficiency prediction value, it is possible to realize the temperature rise and charge and discharge advance control of the energy storage battery module, thereby improving the stability of the wind power system and the safe operation capability.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power systems, and in particular to a control method, device, energy storage combined system and storage medium for a wind power system. Background Art

[0002] The country is vigorously building green energy with renewable energy as the main body. With the continuous advancement of wind turbine control technology and electrolyzer technology, in remote areas of my country with sufficient wind resources and weak power grid construction, wind turbines can be used to directly produce hydrogen on site, thus eliminating the embarrassing situation of high hydrogen production costs caused by the need to connect the electricity generated by wind turbines to the grid and high investment costs caused by multi-energy complementarity. Wind power systems have certain development prospects.

[0003] There have always been problems with the regulation of energy storage devices in existing wind power systems, especially in large-scale microgrid energy storage devices. Large-scale microgrid energy storage devices designed to meet growing demand usually have difficulty in heat dissipation. Air cooling or water cooling alone cannot solve the heat dissipation problem well, which in turn causes safety hazards. Therefore, an effective regulation method for wind power systems is urgently needed at this stage. Summary of the invention

[0004] The embodiments of the present invention provide a control method, device, energy storage combined system and storage medium for a wind power system to solve the problem that the energy storage device in the wind power system cannot be effectively heat-dissipated at present.

[0005] In a first aspect, an embodiment of the present invention provides a control method for a wind power system, comprising:

[0006] Obtaining the operating data of the target wind power system;

[0007] Obtaining a predicted output power value of a target wind power system according to operating data;

[0008] According to the output power prediction value and operation data of the target wind power system, optimization iteration is performed to obtain the charging and discharging power prediction value of the energy storage battery module;

[0009] Determine the predicted value of the cooling efficiency of the liquid cooling module according to the predicted value of the charging and discharging power of the energy storage battery module;

[0010] According to the predicted values ​​of charging and discharging power and cooling efficiency, feedforward control is performed on the target wind power system.

[0011] In a possible implementation, the operating data includes the operating state of the energy storage battery module, the rated power of the hydrogen production system, the minimum operating power of the hydrogen production system, the total capacity of the energy storage battery module and the remaining capacity at the current moment;

[0012] According to the output power forecast value and operation data of the target wind power system, optimization iteration is performed to obtain the charge and discharge power forecast value of the energy storage battery module, including:

[0013] According to the output power forecast value and operation data of the target wind power system, the constraints of the energy storage battery module are determined; wherein the constraints of the energy storage battery module include the hourly charging and discharging power range of the energy storage battery module and the Upper and lower limits;

[0014] Under the constraints of the energy storage battery module, optimization iterations are performed to obtain multiple sets of energy storage control strategies to determine the predicted values ​​of the charging and discharging power of the energy storage battery module.

[0015] In a possible implementation, under the constraints of the energy storage battery module, optimization iterations are performed to obtain multiple sets of energy storage control strategies to determine the predicted values ​​of the charge and discharge power of the energy storage battery module, including:

[0016] Under the constraints of the energy storage battery module, an intelligent optimization algorithm is used to perform optimization iterations to obtain the first energy storage control strategy of N groups;

[0017] With the goal of maximizing the total daily hydrogen production power, the intelligent optimization algorithm is used to optimize and iterate the first energy storage control strategy of group N to obtain the second energy storage control strategy of group M;

[0018] With the goal of minimizing the cumulative charge and discharge rate of the energy storage equipment in the energy storage battery module, the intelligent optimization algorithm is used to optimize and iterate the second energy storage control strategy of group M to obtain the third energy storage control strategy of group Q;

[0019] In the Q group of third energy storage control strategies, select any group of third energy storage control strategies to obtain the energy storage battery module in the first t The predicted value of charge and discharge power at each moment in the hour;

[0020] Among them, N, M, Q are positive integers, and N≥M≥Q.

[0021] In a possible implementation, the operating data also includes an initial temperature of the energy storage battery module;

[0022] According to the predicted value of the charge and discharge power of the energy storage battery module, the predicted value of the cooling efficiency of the liquid cooling module is determined, including:

[0023] According to the energy storage battery module The predicted value of charge and discharge power at each moment in the hour is used to determine the charge and discharge rate corresponding to the energy storage battery module at each moment;

[0024] According to the charge and discharge ratios corresponding to the energy storage battery module at each moment, and the temperature change characteristics under different charge and discharge ratios, the predicted value of the temperature rise rate of the energy storage battery corresponding to the energy storage battery module at each moment is obtained;

[0025] According to the initial temperature of the energy storage battery module and the predicted value of the temperature rise rate of the energy storage battery corresponding to each moment, the predicted value of the temperature of the energy storage battery module corresponding to each moment and the predicted value of the cooling efficiency of the liquid cooling module are obtained.

[0026] In a possible implementation, feedforward control is performed on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency, including:

[0027] The predicted value of the charge and discharge power of the energy storage battery module at each moment is used as input to pre-control the charge and discharge of the energy storage battery module;

[0028] The predicted value of cooling efficiency is used as a feedforward quantity and input into the liquid cooling module to control the liquid cooling module to act in advance and cool down the energy storage battery module in advance.

[0029] In a possible implementation, the operating data also includes the real-time temperature of the energy storage cells in the energy storage battery module;

[0030] After the target wind power system is controlled according to the predicted value of charging and discharging power and the predicted value of cooling efficiency, it also includes:

[0031] According to the real-time temperature of the energy storage cell and the predicted temperature value of the energy storage battery module at each moment, the liquid cooling module is controlled so that the temperature of the energy storage battery module fluctuates within a preset range.

[0032] In a possible implementation, obtaining an output power forecast value of a target wind power system according to operation data includes:

[0033] Determine the output power prediction value of each wind turbine generator in the target wind power system according to the operation data;

[0034] According to the output power prediction value of each wind turbine, the output power prediction value of the target wind power system is obtained.

[0035] In a second aspect, an embodiment of the present invention provides a control device for a wind power system, comprising:

[0036] A collection module, used to obtain the operating data of the target wind power system;

[0037] A prediction module, used to obtain a predicted output power value of a target wind power system according to operating data;

[0038] The prediction module is also used to perform optimization iterations based on the output power prediction value and operation data of the target wind power system to obtain the charge and discharge power prediction value of the energy storage battery module;

[0039] The prediction module is further used to determine the predicted value of the cooling efficiency of the liquid cooling module according to the predicted value of the charging and discharging power of the energy storage battery module;

[0040] The control module is used to perform feedforward control on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency.

[0041] In a third aspect, an embodiment of the present invention provides an energy storage combined system, including a wind power system, an energy storage intelligent linkage system, a water electrolyzer hydrogen production system and a DC bus system; wherein the energy storage intelligent linkage system includes an energy storage intelligent safety manager, an energy storage battery module, a modular temperature measurement optical fiber, a DC / DC converter module and a liquid cooling module; the energy storage intelligent safety manager includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0043] The embodiment of the present invention provides a control method, device, energy storage joint system and storage medium for a wind power system. By predicting and analyzing the operating data of the target risk system, the output power prediction value of the target wind power system is obtained. Then, according to the output power prediction value and operating data of the target wind power system, optimization iteration is performed to obtain the optimal charge and discharge power prediction value of the energy storage battery module to improve the control accuracy. On this basis, the cooling efficiency prediction value of the liquid cooling module is obtained. Afterwards, based on the obtained charge and discharge power prediction value and cooling efficiency prediction value, the heating and charge and discharge of the energy storage battery module can be controlled in advance, thereby improving the stability of the wind power system and the safe operation capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 It is a structural schematic diagram of the energy storage intelligent linkage system provided by an embodiment of the present invention;

[0046] Figure 2a-2hIt is a schematic diagram of the structure of a modular temperature measuring optical fiber provided in an embodiment of the present invention;

[0047] Figure 3 is a flow chart of an implementation of a control method for a wind power system provided by an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of a dynamic curve of the output power prediction value of the target wind power system provided by an embodiment of the present invention;

[0049] Figure 5 is a schematic structural diagram of a control device for a wind power system provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0050] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0052] Figure 1 is a schematic diagram of the structure of the energy storage intelligent linkage system provided by an embodiment of the present invention, such as Figure 1 As shown, since the embodiment of the present invention is mainly aimed at controlling the wind power system, and the control of the wind power system is realized based on the energy storage intelligent linkage system, Figure 1 Only the energy storage intelligent linkage system is shown. Figure 1 The energy storage combined system is described.

[0053] In this embodiment, in order to perform joint control, this embodiment constructs an energy storage joint system, which may include a wind power system, an energy storage intelligent linkage system, a water electrolyzer hydrogen production system, and a DC bus system. Among them, the wind power system is connected to the DC bus system through an internal AC / DC converter, and is the power supply unit of the microgrid system; the energy storage intelligent linkage system is connected to the DC bus system through an internal DC / DC converter, and is the coordination unit of the microgrid system; the water electrolyzer hydrogen production system is connected to the DC bus system through an internal DC / DC converter, and is the load unit of the microgrid system.

[0054] like Figure 1As shown in the figure, the energy storage intelligent linkage system consists of an energy storage intelligent safety manager, an energy storage battery module, a modular temperature measurement optical fiber, a DC / DC converter module and a liquid cooling module. The energy storage intelligent safety manager is used to coordinate and control the working status of each module. It can collect real-time information of the wind power system, the water electrolyzer hydrogen production system, the energy storage battery module and the wind power forecast, perform big data analysis, obtain the wind power system power forecast curve, the optimal energy storage charge and discharge ratio, the liquid cooling control mode, etc., and optimize and iterate based on the data information such as wind power forecast to obtain the optimal charge and discharge power forecast value of the energy storage battery module to improve the control accuracy. On this basis, the cooling efficiency forecast value of the liquid cooling module is obtained. Afterwards, based on the obtained charge and discharge power forecast value and cooling efficiency forecast value, the temperature rise and charge and discharge advance control of the energy storage battery module can be realized, and the stability of the wind power system and the safe operation capability can be improved. In addition, the energy storage intelligent linkage system can also control the liquid cooling module according to the battery cell temperature of the energy storage battery module monitored in real time by the modular temperature measurement optical fiber, so that the temperature of the energy storage battery module fluctuates within a preset range.

[0055] The structural diagram of the modular temperature measurement optical fiber is as follows: Figure 2a-2h shown. Figure 2a and Figure 2b This is an isometric view of a modular temperature measurement fiber, where: Figure 2a This is an isometric view of the modular temperature measurement fiber without the battery pack. Figure 2b An isometric view of the modular temperature measurement fiber optic when the battery pack is placed; Figure 2c and Figure 2d This is the top view of the modular temperature measurement optical fiber; Figure 2c This is the top view of the modular temperature measurement optical fiber without the battery pack. Figure 2d Top view of the modular temperature measurement fiber when the battery pack is placed; Figure 2e and Figure 2f This is the front view of the modular temperature measurement optical fiber; Figure 2e This is the front view of the modular temperature measurement optical fiber without the battery pack. Figure 2f This is a front view of the modular temperature measurement optical fiber when the battery pack is placed; Figure 2g and Figure 2h This is the right view of the modular temperature measurement optical fiber; Figure 2g This is the right side view of the modular temperature measurement optical fiber without the battery pack. Figure 2hThe right view of the modular temperature measurement fiber when the battery pack is placed. The modular temperature measurement fiber is integrated inside the battery partition. With PACK as the basic unit, each PACK-level battery pack integrates a modular temperature measurement fiber and is numbered separately. The modular fiber temperature measurement is integrated inside the battery partition, close to the side of the battery cell, and collects real-time temperature information on the side of the single battery cell. Both ends of the fiber extend to the outside of the PACK package. Each modular temperature measurement fiber is serially fused when the energy storage system is grouped, which can greatly improve the monitoring accuracy of the battery cell temperature.

[0056] After introducing the entire system structure, the functions performed by the energy storage intelligent linkage system are described below, that is, the method provided in this embodiment is described in detail.

[0057] Figure 3 is a flow chart of the implementation of the control method of the wind power system provided by the embodiment of the present invention, such as Figure 3 As shown, the method may include:

[0058] Step 110: Acquire operating data of the target wind power system.

[0059] In this embodiment, the operating data of the target wind power system may include the operating status of the energy storage battery module, the rated power of the hydrogen production system, the minimum operating power of the hydrogen production system, the total capacity of the energy storage battery module and the remaining capacity at the current moment, the initial temperature of the energy storage battery module, the real-time temperature of the energy storage cells in the energy storage battery module, wind data, the output power of the energy storage battery module, the output power of the energy storage battery module and other data, which are not specifically limited here.

[0060] Step 120: Obtaining a predicted output power value of the target wind power system according to the operating data.

[0061] In this embodiment, in order to determine the output power prediction value of the target wind power system, the output power prediction value of each wind turbine in the target wind power system may be determined first, and then the output power prediction value of the target wind power system may be obtained based on the output power prediction value of each wind turbine.

[0062] Accordingly, in order to determine the output power prediction value of each wind turbine in the target wind power system, it is necessary to first construct the constraints of each system:

[0063] In the energy storage combined system, that is, the microgrid system, the wind power system is the power supply unit of the system, responsible for providing energy support to the entire system. The hydrogen production system is the load unit that consumes energy in the system. The energy storage battery module is the coordination unit in the system to ensure the balance of energy in the system. Therefore, the power balance equation in the microgrid system is:

[0064]

[0065] In the formula, is the actual output power of the wind power system, is the output power of the hydrogen production system, is the output power of the energy storage battery module.

[0066] In order to ensure the normal operation and service life of each wind turbine in the wind power system, it is necessary to display the output power of the wind turbine. Correspondingly, the output power constraint of the wind turbine is:

[0067]

[0068] 0< ≤

[0069] In the formula, i is the number of the wind turbine in the wind power system, is the number of wind turbines in the wind power system, is the planned outage factor for wind turbines, is the failure downtime coefficient, is the maintenance factor, For the i The actual output power of the wind turbines, is the maximum output power of the wind turbine.

[0070] in, The operator needs to enter it manually as 0 or 1, 0 means shutdown, and 1 means no shutdown. Automatically entered by the system as 0 or 1, 0 means shutdown, 1 means no shutdown. The system automatically inputs the data and the operator manually inputs the data. For example, the operator manually inputs the estimated maintenance downtime. moment; when the fault is eliminated and the fan restarts, When it changes from 0 to 1, the system automatically enters the update, making Changes from 0 to 1.

[0071] When the water electrolyzer is working normally, the power of the hydrogen production system needs to be limited between the minimum operating power and the rated power. Correspondingly, the output power constraint of the hydrogen production system is:

[0072] < ≤

[0073] In the formula, is the minimum operating power of the hydrogen production system, is the rated power of the hydrogen production system

[0074] The main function of the energy storage intelligent linkage system is to stabilize the DC bus voltage and coordinate control to ensure power balance in the system. It is necessary to control the charging and discharging power of the energy storage battery module in the energy storage intelligent linkage system and monitor the battery's state of charge. Accordingly, the constraints of the charging and discharging power of the energy storage battery module and the battery's state of charge are:

[0075]

[0076]

[0077] In the formula, and Respectively represent the minimum and maximum charging and discharging power of the energy storage battery module; and They respectively represent the minimum and maximum values ​​of the state of charge of the batteries in the energy storage battery module.

[0078] Under the constraints of the above-mentioned constraints, the output power prediction value of each wind turbine is calculated according to the acquired operation data.

[0079] Then, the output power forecast value of the target wind power system is calculated according to the following calculation formula and the output power forecast value of each wind turbine:

[0080]

[0081] in, For the i The predicted power of wind turbines.

[0082] Step 130: performing optimization iterations according to the output power prediction value and operation data of the target wind power system to obtain the charge and discharge power prediction value of the energy storage battery module.

[0083] In this embodiment, the energy storage intelligent safety manager can control the working state of the energy storage battery module according to the output power prediction value of the target wind power system and the battery cell data of the current energy storage battery module. Accordingly, this embodiment can determine the constraint conditions of the energy storage battery module according to the output power prediction value and operation data of the target wind power system, and further determine the charge and discharge power prediction value of the energy storage battery module.

[0084] Correspondingly, the constraints of the energy storage battery module can be expressed as:

[0085] The predicted range of charging and discharging power of the energy storage battery module can be obtained based on the predicted output power of the target wind power system and the output power of the hydrogen production system:

[0086]

[0087] in, Indicates that the energy storage battery module is in charging state. Indicates that the energy storage battery module is in discharge state.

[0088] Considering that the output power forecast value of the target wind power system is automatically updated every hour, a new output power forecast value of the target wind power system is formed. That is, the output power forecast value of the target wind power system can be referred to Figure 4 ;exist Figure 4 In the figure, the horizontal axis represents the time point and the vertical axis represents the output power. Figure 4 The corresponding predicted wind power curves (the predicted output power of the wind power system), the lower limit curve of the electrolytic hydrogen production power, the upper limit curve of the electrolytic hydrogen production power, the upper limit curve of the energy storage battery module predicted power, and the lower limit curve of the energy storage battery module predicted power are given. Figure 4 According to the prediction value given in the above, the system can update the output power prediction value and fault shutdown coefficient of each wind turbine accordingly. , maintenance coefficient And other information.

[0089] Correspondingly, based on the predicted range of charge and discharge power of the energy storage battery module and the current state of the energy storage battery module, the hourly charge and discharge power range of the energy storage battery module can be obtained:

[0090]

[0091] Among them, the relevant prediction values ​​of the wind power system are updated once an hour, and the charging and discharging power control strategy of the energy storage battery module is updated every 10 minutes. i Indicates i time, For the t Hourly wind power system forecast updated The output power forecast value of the wind power system at the time, For the i The lower limit of the charging and discharging power of the energy storage battery module at the moment, For the i The upper limit of the charging and discharging power of the energy storage battery module at the moment, For the i The charging and discharging power of the energy storage battery module at each moment.

[0092] Energy storage battery module The upper and lower limits can be expressed as:

[0093]

[0094] in, Indicates the remaining capacity of the energy storage battery module at the current moment. Indicates the total capacity of the energy storage battery module.

[0095] In the hourly charging and discharging power range of the energy storage battery module and the energy storage battery module Under the upper and lower limit constraints, the intelligent optimization algorithm is used for optimization iteration to obtain N groups of first energy storage control strategies that meet the constraints, which can be expressed as:

[0096]

[0097] With the goal of maximizing the total daily hydrogen production power and improving the utilization rate of heavy energy in the microgrid system, the first energy storage control strategy of group N is optimized and iterated using an intelligent optimization algorithm to obtain the second energy storage control strategy of group M; accordingly, the second energy storage control strategy of group M can be expressed as:

[0098]

[0099] With the goal of minimizing the cumulative charge and discharge rate of the energy storage device in the energy storage battery module and improving the service life of the energy storage battery module, the second energy storage control strategy of group M is optimized and iterated using an intelligent optimization algorithm to obtain the third energy storage control strategy of group Q; accordingly, the third energy storage control strategy of group Q can be expressed as:

[0100]

[0101] The obtained Q group of third energy storage control strategies can be regarded as the optimal control strategy. Therefore, a group of third energy storage control strategies can be arbitrarily selected, and the predicted value of the charge and discharge power of the battery at each moment in the selected third energy storage control strategy is used as the energy storage battery module in the first t The predicted charging and discharging power at each time in the hour.

[0102] In addition, it should be noted that N, M, and Q are positive integers, and N≥M≥Q. For example, in the optimization iteration process of obtaining M groups of second energy storage control strategies, each group of first energy storage control strategies in N groups of first energy storage control strategies can be optimized, and the first energy storage control strategy that is finally optimized and meets the conditions is used as the second energy storage control strategy. Correspondingly, the optimization iteration process of the third energy storage control strategy is the same.

[0103] Step 140: Determine a predicted value of cooling efficiency of the liquid cooling module according to the predicted value of charging and discharging power of the energy storage battery module.

[0104] In this embodiment, in order to determine the predicted value of the cooling efficiency of the liquid cooling module, it is necessary to calculate the cooling efficiency of the energy storage battery module according to the following formula: The predicted value of charge and discharge power at each moment in the hour is used to calculate the charge and discharge rate corresponding to the energy storage battery module at each moment. Correspondingly, the charge and discharge rate can be expressed as:

[0105]

[0106] According to the charge and discharge ratios corresponding to the energy storage battery module at each moment, and the temperature change characteristics at different charge and discharge ratios, the predicted value of the temperature rise rate of the energy storage battery module at each moment is obtained. Among them, the temperature change characteristics at different charge and discharge ratios are the inherent properties of the energy storage battery module, and different batteries have different corresponding characteristics.

[0107] According to the initial temperature of the energy storage battery module and the predicted value of the temperature rise rate of the energy storage battery corresponding to each moment, the predicted value of the temperature of the energy storage battery module corresponding to each moment and the predicted value of the cooling efficiency of the liquid cooling module are obtained.

[0108] Correspondingly, the corresponding relationship between the charge and discharge rate, the predicted value of the temperature rise rate, the predicted value of the temperature and the predicted value of the cooling efficiency is shown in Table 1:

[0109] Table 1 Correspondence table

[0110]

[0111] in, is the initial temperature of the energy storage battery module.

[0112] Step 150: Perform feedforward control on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency.

[0113] In this embodiment, the energy storage battery module can be placed in the t The predicted charging and discharging power at each moment in the hour is used as input and input into the energy storage safety manager, so that the energy storage safety manager can pre-control the charging and discharging of the energy storage battery module to increase the safety and stability of the system.

[0114] In the i At this moment, the energy storage safety manager uses the predicted value of cooling efficiency as a feedforward quantity and inputs it into the liquid cooling module to control the liquid cooling module to act in advance, cool down the energy storage battery module in advance, and improve the safe operation capability of the system.

[0115] In an optional embodiment, the energy storage safety manager can also receive the real-time temperature of the energy storage cells in the energy storage battery module collected by the modular temperature measurement optical fiber.

[0116] According to the real-time temperature of the energy storage cell and the corresponding temperature prediction value of the energy storage battery module at each moment, the liquid cooling module is controlled to realize the dynamic adjustment of the cooling efficiency prediction value and the charge and discharge rate, so that the temperature of the energy storage battery module fluctuates within the preset range. The preset range can be 25℃±2℃.

[0117] In summary, the embodiment of the present invention provides an energy storage joint system, which links various energy systems and can balance the operation of various modules between microgrids. In addition, the embodiment of the present invention also provides an energy storage intelligent linkage system, which includes modular temperature measurement optical fiber. By integrating the temperature measurement optical fiber inside the PACK package battery cell partition, the multi-point temperature of the battery cell is detected in real time, the monitoring range and accuracy of the battery cell temperature are expanded, and the safety warning capability is improved. The embodiment of the present invention formulates a predicted power dynamic curve of the wind power system and real-time information of the battery cell, and the energy storage intelligent safety manager calculates and analyzes to obtain the optimal control strategy for energy storage and improve energy utilization. The embodiment of the present invention also formulates a predicted power dynamic curve of the wind power system, collects real-time information of the battery cell and the actual working conditions of the liquid cooling module, and the energy storage intelligent safety manager calculates and analyzes to give a liquid cooling control feedforward amount, and performs feedforward pre-control on the liquid cooling module to improve the system's safe operation capability.

[0118] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0119] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0120] Figure 5 The structure diagram of the control device of the wind power system provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0121] like Figure 5 As shown, the control device 5 of the wind power system includes:

[0122] The acquisition module 51 is used to obtain the operating data of the target wind power system;

[0123] A prediction module 52, used to obtain a predicted output power value of a target wind power system according to the operation data;

[0124] The prediction module 52 is further used to perform optimization iterations according to the output power prediction value and operation data of the target wind power system to obtain the charge and discharge power prediction value of the energy storage battery module;

[0125] The prediction module 52 is further used to determine the cooling efficiency prediction value of the liquid cooling module according to the charging and discharging power prediction value of the energy storage battery module;

[0126] The control module 53 is used to perform feedforward control on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency.

[0127] In a possible implementation, the operating data includes the operating state of the energy storage battery module, the rated power of the hydrogen production system, the minimum operating power of the hydrogen production system, the total capacity of the energy storage battery module and the remaining capacity at the current moment;

[0128] The prediction module 52 is specifically used for:

[0129] According to the output power forecast value and operation data of the target wind power system, the constraints of the energy storage battery module are determined; wherein the constraints of the energy storage battery module include the hourly charging and discharging power range of the energy storage battery module and the Upper and lower limits;

[0130] Under the constraints of the energy storage battery module, optimization iterations are performed to obtain multiple sets of energy storage control strategies to determine the predicted values ​​of the charging and discharging power of the energy storage battery module.

[0131] In a possible implementation, the prediction module 52 is specifically configured to:

[0132] Under the constraints of the energy storage battery module, an intelligent optimization algorithm is used to perform optimization iterations to obtain the first energy storage control strategy of N groups;

[0133] With the goal of maximizing the total daily hydrogen production power, the intelligent optimization algorithm is used to optimize and iterate the first energy storage control strategy of group N to obtain the second energy storage control strategy of group M;

[0134] With the goal of minimizing the cumulative charge and discharge rate of the energy storage equipment in the energy storage battery module, the intelligent optimization algorithm is used to optimize and iterate the second energy storage control strategy of group M to obtain the third energy storage control strategy of group Q;

[0135] In the Q group of third energy storage control strategies, select any group of third energy storage control strategies to obtain the energy storage battery module in the first The predicted value of charge and discharge power at each moment in the hour;

[0136] Among them, N, M, Q are positive integers, and N≥M≥Q.

[0137] In a possible implementation, the operating data also includes an initial temperature of the energy storage battery module;

[0138] The prediction module 52 is specifically used for:

[0139] According to the energy storage battery module t The predicted value of charge and discharge power at each moment in the hour is used to determine the charge and discharge rate corresponding to the energy storage battery module at each moment;

[0140] According to the charge and discharge ratios corresponding to the energy storage battery module at each moment, and the temperature change characteristics under different charge and discharge ratios, the predicted value of the temperature rise rate of the energy storage battery corresponding to the energy storage battery module at each moment is obtained;

[0141] According to the initial temperature of the energy storage battery module and the predicted value of the temperature rise rate of the energy storage battery corresponding to each moment, the predicted value of the temperature of the energy storage battery module corresponding to each moment and the predicted value of the cooling efficiency of the liquid cooling module are obtained.

[0142] In a possible implementation, the control module 53 is specifically configured to:

[0143] The predicted value of the charge and discharge power of the energy storage battery module at each moment is used as input to pre-control the charge and discharge of the energy storage battery module;

[0144] The predicted value of cooling efficiency is used as a feedforward quantity and input into the liquid cooling module to control the liquid cooling module to act in advance and cool down the energy storage battery module in advance.

[0145] In a possible implementation, the operating data also includes the real-time temperature of the energy storage cells in the energy storage battery module;

[0146] The control module 53 is further used for:

[0147] According to the real-time temperature of the energy storage cell and the predicted temperature value of the energy storage battery module at each moment, the liquid cooling module is controlled so that the temperature of the energy storage battery module fluctuates within a preset range.

[0148] In a possible implementation, the prediction module is specifically used to:

[0149] Determine the output power prediction value of each wind turbine generator according to the operation data;

[0150] According to the output power prediction value of each wind turbine, the output power prediction value of the target wind power system is obtained.

[0151] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0152] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0153] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0154] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned control method embodiments of each wind power system can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0158] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A control method for a wind power system, characterized in that: include: Acquire the operating data of the target wind power system; wherein the operating data includes the operating status of the energy storage battery module, the rated power of the hydrogen production system, the minimum operating power of the hydrogen production system, the total capacity of the energy storage battery module and the remaining capacity at the current moment; Obtaining a predicted output power value of the target wind power system according to the operating data; According to the output power prediction value of the target wind power system and the operation data, optimization iteration is performed to obtain the charge and discharge power prediction value of the energy storage battery module; Determining a predicted value of refrigeration efficiency of a liquid cooling module according to a predicted value of charge and discharge power of the energy storage battery module; performing feedforward control on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency; The optimization iteration is performed according to the output power prediction value of the target wind power system and the operation data to obtain the charge and discharge power prediction value of the energy storage battery module, including: According to the output power forecast value of the target wind power system and the operating data, the constraint conditions of the energy storage battery module are determined; wherein the constraint conditions of the energy storage battery module include the charging and discharging power range of the energy storage battery module per hour and the Upper and lower limits; Under the constraint conditions of the energy storage battery module, optimization iterations are performed to obtain multiple sets of energy storage control strategies to determine the predicted value of the charge and discharge power of the energy storage battery module; Wherein, under the constraint conditions of the energy storage battery module, optimization iteration is performed to obtain multiple sets of energy storage control strategies to determine the predicted value of the charge and discharge power of the energy storage battery module, including: Under the constraints of the energy storage battery module, an intelligent optimization algorithm is used to perform optimization iteration to obtain N groups of first energy storage control strategies; Taking the maximum daily total hydrogen production power as the goal, the N groups of first energy storage control strategies are optimized and iterated using an intelligent optimization algorithm to obtain the M groups of second energy storage control strategies; Taking the lowest cumulative charge and discharge rate of the energy storage device in the energy storage battery module as the goal, the second energy storage control strategy of the M group is optimized and iterated by using an intelligent optimization algorithm to obtain the third energy storage control strategy of the Q group; In the Q group of third energy storage control strategies, any group of third energy storage control strategies is selected to obtain the energy storage battery module in the first t The predicted value of charge and discharge power at each moment in the hour; Among them, N, M, Q are positive integers, and N≥M≥Q.

2. The control method of the wind power system according to claim 1, characterized in that: The operating data also includes the initial temperature of the energy storage battery module; The step of determining the predicted value of the cooling efficiency of the liquid cooling module according to the predicted value of the charge and discharge power of the energy storage battery module comprises: According to the energy storage battery module The predicted value of charge and discharge power at each moment in the hour is used to determine the charge and discharge rate corresponding to each moment of the energy storage battery module; According to the charge and discharge ratios corresponding to the energy storage battery module at each moment, and the temperature change characteristics under different charge and discharge ratios, a predicted value of the temperature rise rate of the energy storage battery corresponding to each moment of the energy storage battery module is obtained; According to the initial temperature of the energy storage battery module and the predicted value of the temperature rise rate of the energy storage battery corresponding to each moment, the predicted value of the temperature of the energy storage battery module corresponding to each moment and the predicted value of the cooling efficiency of the liquid cooling module are obtained.

3. The control method of the wind power system according to claim 1, characterized in that: The feedforward control of the target wind power system according to the charge and discharge power prediction value and the cooling efficiency prediction value includes: Taking the predicted value of the charge and discharge power of the energy storage battery module at each moment as input to pre-control the charge and discharge of the energy storage battery module; The predicted value of refrigeration efficiency is used as a feedforward quantity and input into the liquid cooling module to control the liquid cooling module to act in advance and cool down the energy storage battery module in advance.

4. The control method of the wind power system according to claim 1, characterized in that: The operating data also includes the real-time temperature of the energy storage cells in the energy storage battery module; After the target wind power system is controlled according to the charge and discharge power prediction value and the cooling efficiency prediction value, the method further includes: According to the real-time temperature of the energy storage cell and the predicted temperature value of the energy storage battery module at each moment, the liquid cooling module is controlled so that the temperature of the energy storage battery module fluctuates within a preset range.

5. The control method of the wind power system according to claim 1, characterized in that: The obtaining the output power prediction value of the target wind power system according to the operating data includes: Determine the output power prediction value of each wind turbine generator in the target wind power system according to the operating data; The output power prediction value of the target wind power system is obtained according to the output power prediction value of each wind turbine.

6. A control device for a wind power system, characterized in that: include: A collection module, used to obtain the operating data of the target wind power system; wherein the operating data includes the operating status of the energy storage battery module, the rated power of the hydrogen production system, the minimum operating power of the hydrogen production system, the total capacity of the energy storage battery module and the remaining capacity at the current moment; A prediction module, used for obtaining an output power prediction value of the target wind power system according to the operation data; The prediction module is further used to perform optimization iteration according to the output power prediction value of the target wind power system and the operation data to obtain the charge and discharge power prediction value of the energy storage battery module; The prediction module is further used to determine the predicted value of the cooling efficiency of the liquid cooling module according to the predicted value of the charge and discharge power of the energy storage battery module; A control module, configured to perform feedforward control on the target wind power system according to the predicted value of charging and discharging power and the predicted value of cooling efficiency; Wherein, the prediction module is specifically used for: According to the output power forecast value of the target wind power system and the operating data, the constraint conditions of the energy storage battery module are determined; wherein the constraint conditions of the energy storage battery module include the charging and discharging power range of the energy storage battery module per hour and the Upper and lower limits; Under the constraint conditions of the energy storage battery module, optimization iterations are performed to obtain multiple sets of energy storage control strategies to determine the predicted value of the charge and discharge power of the energy storage battery module; Wherein, the prediction module is specifically used for: Under the constraints of the energy storage battery module, an intelligent optimization algorithm is used to perform optimization iteration to obtain N groups of first energy storage control strategies; Taking the maximum daily total hydrogen production power as the goal, the N groups of first energy storage control strategies are optimized and iterated using an intelligent optimization algorithm to obtain the M groups of second energy storage control strategies; Taking the lowest cumulative charge and discharge rate of the energy storage device in the energy storage battery module as the goal, the second energy storage control strategy of the M group is optimized and iterated by using an intelligent optimization algorithm to obtain the third energy storage control strategy of the Q group; In the Q group of third energy storage control strategies, any group of third energy storage control strategies is selected to obtain the energy storage battery module in the first t The predicted value of charge and discharge power at each moment in the hour; Among them, N, M, Q are positive integers, and N≥M≥Q.

7. An energy storage combined system, comprising a wind power system, an energy storage intelligent linkage system, a water electrolyzer hydrogen production system and a DC bus system; wherein: The energy storage intelligent linkage system includes an energy storage intelligent safety manager, an energy storage battery module, a modular temperature measurement optical fiber, a DC / DC converter module and a liquid cooling module; the energy storage intelligent safety manager includes a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the steps of the method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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