Flywheel energy storage system vacuum degree and cooling collaborative optimization method and device

Abstract

The invention discloses a vacuum degree and cooling collaborative optimization method and device for a flywheel energy storage system, and relates to the technical field of flywheel energy storage, and the method comprises the steps: collecting the operation parameters of the flywheel energy storage system in real time; based on the operation parameters, fusing air resistance loss, vacuum pump power consumption, cooling power consumption and a temperature penalty term, and constructing a total energy consumption function; based on a multi-objective optimization algorithm, solving an optimal vacuum cavity air pressure value and cooling power which are minimized; the air pressure of the cavity is adjusted through the variable-frequency vacuum pump and the controllable gulp valve, and the liquid cooling system or the fan is linked to execute cooling power. According to the vacuum degree and cooling collaborative optimization method for the flywheel energy storage system, the value alpha is dynamically adjusted according to the fatigue characteristic of a flywheel material through self-adaptive adjustment of a penalty function, and service life reduction caused by high temperature is avoided; the performance, the reliability and the service life of the flywheel system are effectively improved, and the comprehensive energy efficiency of the system is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of flywheel energy storage, and in particular to a method and device for collaboratively optimizing vacuum degree and cooling of a flywheel energy storage system. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, flywheel energy storage technology has attracted widespread attention as a highly efficient and environmentally friendly energy storage method. Flywheel energy storage systems store and release energy through a high-speed rotating flywheel, offering advantages such as fast response, high energy density, and long cycle life.

[0003] However, in practical applications, the vacuum degree optimization and cooling system control of the flywheel energy storage system are usually independent of each other. This separate control method brings a series of problems. First, there is a contradiction between energy consumption and heat dissipation. In order to reduce air resistance, the system often needs to maintain a higher vacuum chamber pressure value, but this will inhibit the natural heat dissipation effect, resulting in additional cooling power consumption to maintain the system temperature; on the contrary, if the vacuum degree is reduced to enhance natural heat dissipation, the air resistance loss will increase, affecting the system efficiency. Secondly, the existing control strategies are all static modes, that is, the vacuum pump and cooling system operate based on fixed thresholds and cannot be adaptively adjusted according to dynamic changes such as flywheel speed and ambient temperature. This lack of flexibility in control methods makes it difficult to meet the complex needs of actual operation.

[0004] In addition, traditional methods lack comprehensive optimization considerations for multiple objectives. In flywheel energy storage systems, multiple factors such as vacuum maintenance energy consumption, cooling power consumption, flywheel temperature rise, and equipment life are interrelated and influence each other. However, existing technologies often only focus on the optimization of a single indicator or a few indicators, and fail to achieve joint modeling and real-time regulation of these key parameters. This not only limits the room for improvement in the overall performance of the system, but also affects the long-term stable operation of the equipment. Therefore, it is particularly important to develop a new control method and device that can effectively coordinate the vacuum degree and the working state of the cooling system, and has intelligent prediction capabilities and adaptive adjustment functions. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to achieve the lowest total energy consumption of the flywheel energy storage system, safe and controllable temperature, and extend the life of key components.

[0007] To solve the above technical problems, the present invention provides the following technical solution: a flywheel energy storage system vacuum degree and cooling coordinated optimization method, comprising:

[0008] Real-time collection of operating parameters of the flywheel energy storage system;

[0009] Based on the operating parameters, the total energy consumption function is constructed by integrating air resistance loss, vacuum pump power consumption, cooling power consumption and temperature penalty terms;

[0010] The total energy consumption function is expressed as,

[0011] E total =P drag +P pump +P cool +α*max(0,TT safe )

[0012] Among them, E total is the total energy consumption of the flywheel energy storage system, P drag is the air resistance loss power, P pump is the power consumption of the vacuum pump; P cool is the power consumption of the cooling system; α is the adaptive temperature rise penalty coefficient; T is the flywheel surface temperature, T safe is the safe value of flywheel surface temperature;

[0013] Based on the multi-objective optimization algorithm, the solution is to make E total Minimize the optimal vacuum chamber pressure value p cav-opt And the optimal cooling power value P cool-opt ;

[0014] The cavity pressure is adjusted to the optimal vacuum cavity pressure value p by the variable frequency vacuum pump and the controllable air supply valve. cav-opt , and the liquid cooling system is linked to execute the optimal cooling power value P cool-opt , in order to maximize the comprehensive energy efficiency of the flywheel energy storage system.

[0015] As a preferred solution of the flywheel energy storage system vacuum degree and cooling coordinated optimization method described in the present invention, the operating parameters include the vacuum chamber pressure value, the flywheel surface temperature, and the flywheel speed.

[0016] As a preferred solution of the flywheel energy storage system vacuum degree and cooling coordinated optimization method described in the present invention, the air resistance loss power is expressed as:

[0017]

[0018] Where Pdrag is the air resistance loss power, ρ is the air density, v is the flywheel speed, Cd is the drag coefficient, and A is the frontal area;

[0019] The vacuum pump power consumption is expressed as,

[0020]

[0021] Among them, P pumpis the power consumption of the vacuum pump, V sp is the pumping rate, p atm is atmospheric pressure, p cav is the vacuum chamber pressure value, η pump is the pump efficiency.

[0022] The cooling system power consumption is expressed as,

[0023]

[0024] Among them, Pcool is the cooling power consumption, Q is the heat exchange mass flow rate, Tin is the intake temperature, Tout is the exhaust temperature, and ηcool is the cooling efficiency.

[0025] As a preferred solution of the flywheel energy storage system vacuum and cooling collaborative optimization method described in the present invention, the adaptive temperature rise over-limit penalty coefficient is expressed as:

[0026]

[0027] Where k is the material degradation coefficient and t is the cumulative operating time of the flywheel.

[0028] As a preferred solution of the flywheel energy storage system vacuum and cooling collaborative optimization method described in the present invention, when the flywheel accelerates or the ambient temperature changes by more than a preset threshold value per unit time, the vacuum chamber pressure value is relaxed to the allowable upper limit, and the liquid cooling system is started to the maximum power to suppress the temperature rise rate.

[0029] As a preferred solution of the flywheel energy storage system vacuum and cooling coordinated optimization method described in the present invention, when it is detected that the vacuum leakage rate exceeds a threshold, the air supply valve is triggered to reversely inject dry nitrogen to prevent moisture in the external air from entering the cavity, and the vacuum pump is started at the same time to restore the vacuum degree.

[0030] As a preferred solution for the coordinated optimization of vacuum degree and cooling of the flywheel energy storage system described in the present invention, it includes: a multi-physics field sensing module, which collects vacuum chamber air pressure, temperature and speed data in real time through sensors; an optimization control core, which is an embedded controller with a built-in vacuum cooling coordinated optimization algorithm, for calculating the optimal vacuum chamber air pressure and cooling power; an execution module, including a variable frequency vacuum pump, a controllable air supply valve, a liquid cooling flow control valve and a variable frequency fan, for performing air pressure regulation and cooling control; and a predictive maintenance module, which is used to monitor the degradation trend of the vacuum chamber sealing performance and generate a seal replacement warning when the vacuum maintenance time drops by more than 30%; based on the historical data of the cooling system efficiency, predict filter blockage or pump oil aging and trigger maintenance instructions; the variable frequency vacuum pump is used to adjust the air pressure in the vacuum chamber according to the results of the optimization algorithm; the controllable air supply valve is used to reversely inject dry nitrogen to maintain air pressure balance when vacuum leaks; and the liquid cooling flow control valve is used to adjust the flow of the liquid cooling system according to the results of the optimization algorithm.

[0031] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for coordinated optimization of vacuum degree and cooling of a flywheel energy storage system are implemented.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for collaboratively optimizing the vacuum degree and cooling of a flywheel energy storage system.

[0033] Beneficial effects of the present invention: The flywheel energy storage system vacuum degree and cooling collaborative optimization method provided by the present invention dynamically optimizes the vacuum cavity air pressure value and the cooling system parameters as coupling variables, breaking through the limitations of traditional independent control; through adaptive adjustment of the penalty function, the α value is dynamically adjusted according to the fatigue characteristics of the flywheel material to avoid life loss caused by high temperature; based on the air pressure value, a hybrid heat dissipation strategy dominated by radiation / convection / liquid cooling is automatically selected to realize intelligent switching of heat dissipation modes, effectively improving the performance, reliability and service life of the flywheel system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0035] Figure 1 An overall flow chart of a method for collaboratively optimizing vacuum and cooling of a flywheel energy storage system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Example 1

[0039] Reference Figure 1 , as one embodiment of the present invention, provides a flywheel energy storage system vacuum degree and cooling coordinated optimization method, comprising:

[0040] The operating parameters of the flywheel energy storage system are collected in real time, including the vacuum chamber pressure value (Pcav), flywheel surface temperature (T), and flywheel speed (v). Compared with traditional technologies, more comprehensive operating parameters are taken into account. The comprehensive consideration of these parameters enables subsequent optimization control to adapt more accurately to different operating conditions, further improving the performance and reliability of the system.

[0041] Construct a total energy consumption function that incorporates air resistance loss, vacuum pump power consumption, cooling power consumption, and temperature penalty terms;

[0042] E total =P drag +P pump +P cool +α*max(0,TT safe )

[0043] Among them, E total is the total energy consumption of the flywheel energy storage system, P drag is the air resistance loss power, P pump is the power consumption of the vacuum pump; P cool is the power consumption of the cooling system; α is the adaptive temperature rise penalty coefficient; T is the flywheel surface temperature, T safeis the safe value for the flywheel surface temperature. In conventional technologies, vacuum optimization and cooling system control in flywheel energy storage systems are typically independent of each other. This separate control approach presents a series of problems, such as the conflict between energy consumption and heat dissipation. Furthermore, existing control strategies are mostly static and cannot adaptively adjust to dynamic changes such as flywheel speed and ambient temperature. However, the present invention collects the operating parameters of the flywheel energy storage system in real time and constructs a total energy consumption function based on these operating parameters, integrating air resistance loss, vacuum pump power consumption, cooling power consumption, and a temperature penalty term.

[0044] Based on the multi-objective optimization algorithm, the solution is to make E total Minimize the optimal vacuum chamber pressure value p cav-opt And the optimal cooling power value P cool-opt ;

[0045] The cavity pressure is adjusted to the optimal vacuum cavity pressure value p by the variable frequency vacuum pump and the controllable air supply valve. cav-opt , and the liquid cooling system is linked to execute the optimal cooling power value P cool-opt , in order to maximize the comprehensive energy efficiency of the flywheel energy storage system.

[0046] This collaborative optimization method can make dynamic adjustments based on the real-time operating status of the system, overcoming the limitations of independent control and static control in traditional technologies and effectively improving the overall energy efficiency of the system.

[0047] It should be noted that the following four factors are considered: Air resistance loss. Flywheel energy storage systems store and release energy through a high-speed rotating flywheel. During rotation, the interaction between the flywheel and the surrounding air generates air resistance loss. This loss is closely related to factors such as the flywheel's rotational speed, air density, the flywheel's frontal area, and the drag coefficient. Air resistance loss is a significant source of energy loss in flywheel energy storage systems, directly impacting system efficiency and performance.

[0048] Air resistance model: According to the principles of fluid mechanics, air resistance loss is proportional to the square of the flywheel speed, which can be expressed as:

[0049]

[0050] Among them, P drag is the air resistance loss power, ρ is the air density, v is the flywheel speed, C d is the drag coefficient, and A is the frontal area.

[0051] Parameter determination: Determine the air density ρ and drag coefficient C through experimental measurement or theoretical calculation d and frontal area A. These parameters may vary with environmental conditions (such as temperature, pressure) and flywheel conditions (such as surface roughness), so dynamic adjustment is required.

[0052] Furthermore, the method of this embodiment considers the power consumption of the vacuum pump. To reduce air resistance losses, flywheel energy storage systems typically require maintaining a high vacuum level. The vacuum pump is used to maintain the air pressure within the vacuum chamber. Its power consumption is related to factors such as the pumping rate, atmospheric pressure, the vacuum chamber pressure, and pump efficiency. The power consumption of the vacuum pump is a significant component of the system's total energy consumption, especially when high vacuum levels are required.

[0053] Vacuum pump model: The power consumption of a vacuum pump is related to its pumping rate and pressure difference, which can be expressed as:

[0054]

[0055] Among them, P pump is the power consumption of the vacuum pump, V sp is the pumping rate, p atm is atmospheric pressure, p cav is the vacuum chamber pressure value, η pump is the pump efficiency.

[0056] Parameter determination: Determine the pumping speed V through the performance curve of the vacuum pump or experimental measurement sp and pump efficiency η pump Atmospheric pressure P atm The cavity pressure P is measured by the environmental parameter sensor. cav Provided by the cavity air pressure sensor.

[0057] Furthermore, the method in this embodiment considers cooling power consumption. The flywheel generates heat during high-speed rotation, causing the temperature to rise. To maintain the flywheel surface temperature within a safe range, a cooling system is required to dissipate the heat. The cooling system's power consumption is related to factors such as heat transfer mass flow rate, intake air temperature, exhaust air temperature, and cooling efficiency. Cooling power consumption is another important component of the system's total energy consumption, especially under high-load operating conditions.

[0058] Cooling system model: Cooling power consumption is related to heat transfer mass flow and temperature difference, which can be expressed as:

[0059]

[0060] Among them, P cool is the cooling power consumption, Q is the heat exchange mass flow rate, T in is the intake air temperature, T out is the exhaust temperature, η cool For cooling efficiency.

[0061] Parameter determination: The heat transfer mass flow rate Q and cooling efficiency ηcool are determined through cooling system design parameters and performance testing. The intake and exhaust temperatures Tin and Tout are measured by temperature sensors.

[0062] It should be noted that the method of this embodiment also takes into account the temperature penalty term. Excessive surface temperature of the flywheel will accelerate the degradation of the material and affect the life and reliability of the flywheel. When considering the impact of temperature on the flywheel energy storage system, traditional technologies often lack in-depth analysis and dynamic adjustment mechanisms of the fatigue characteristics of the flywheel material. In order to ensure that the surface temperature of the flywheel is within a safe range, the present invention introduces a temperature penalty term. The temperature penalty term is dynamically adjusted through an adaptive temperature rise over-limit penalty coefficient, reflecting the fatigue cumulative effect of the flywheel material at different temperatures. This helps to avoid life loss caused by high temperature and ensure the long-term stable operation of the system.

[0063] The adaptive temperature rise penalty coefficient α is dynamically adjusted according to the fatigue accumulation data of the flywheel material. The specific formula is:

[0064]

[0065] Where k is the material degradation coefficient, t is the cumulative operating time of the flywheel. T(τ) is the temperature of the flywheel surface at time τ, T safe It is the safety value of flywheel surface temperature.

[0066] Specifically, the material degradation coefficient k is obtained by the following steps:

[0067] 1. Design an experiment to test the fatigue life of the flywheel material at different temperatures, setting multiple different temperature points to cover the temperature range in which the flywheel may operate.

[0068] 2. Conduct experimental tests and collect data, including temperature, time, material strength, and crack growth rate.

[0069] 3. Establish a mathematical model and use the Arrhenius equation to relate different temperatures and times to material strength and crack growth rate, and quantify the effects of temperature and time on material degradation.

[0070] 4. Fit the collected data through regression analysis method to determine the specific value of the material degradation coefficient k.

[0071] These four factors encompass the primary energy losses and control parameters of a flywheel energy storage system during operation. Air resistance loss and vacuum pump power consumption are directly related to maintaining vacuum levels, while cooling power consumption and temperature penalties are related to system heat dissipation and temperature control. A comprehensive consideration of these factors provides a comprehensive picture of the system's energy consumption. These factors directly impact the system's energy efficiency and performance. By optimizing these key parameters, the system's overall energy efficiency can be significantly improved, extending the life of key components.

[0072] Furthermore, the control parameters of the four factors can be collected in real time by sensors and dynamically adjusted using optimization algorithms. This enables the system to perform adaptive control based on real-time operating conditions, achieving optimal energy efficiency.

[0073] The multi-objective optimization algorithm is NSGAII genetic algorithm or gradient descent method, and the optimization variables include the vacuum chamber pressure value p cav and cooling power consumption P cool , the constraint condition is the flywheel surface temperature (T≤T safe ).

[0074] It should be noted that these are processing strategies for different situations. 1 is when the flywheel is accelerated or the ambient temperature changes suddenly, and 2 is when the vacuum leakage rate is detected to exceed the threshold. These are other operating strategies after optimization to ensure that the flywheel energy storage system can operate safely, stably and efficiently under various working conditions, while extending the service life of the system.

[0075] When the flywheel accelerates or the ambient temperature changes by more than a preset threshold per unit time, the vacuum chamber pressure is first relaxed to the upper limit and the liquid cooling system is activated to its maximum power to suppress the temperature rise rate.

[0076] When it is detected that the vacuum leakage rate exceeds the threshold, the air supply valve is triggered to reversely inject dry nitrogen to prevent moisture in the external air from entering the cavity, and the vacuum pump is started to restore the vacuum degree.

[0077] When faced with special situations such as flywheel acceleration or sudden changes in ambient temperature, traditional technologies often lack effective response strategies, which may cause the flywheel surface temperature to rise rapidly, affecting system performance and safety. The present invention stipulates that when the flywheel accelerates or the ambient temperature changes by more than a preset threshold per unit time, the vacuum chamber air pressure value is preferentially relaxed to the allowable upper limit, and the liquid cooling system is started to maximum power to suppress the temperature rise rate. This targeted processing strategy can quickly respond to changes in working conditions, take timely measures to protect the flywheel, and avoid safety hazards and performance degradation problems caused by excessive temperatures. Compared with traditional technologies, it improves the adaptability and reliability of the system under complex working conditions.

[0078] The dry nitrogen injected by the air supply valve prevents moisture from the outside air from entering the cavity, thereby preventing moisture from corroding the flywheel surface and affecting its performance. The synergistic effect of the variable frequency vacuum pump and the controllable air supply valve ensures that the gas and vacuum level in the cavity are maintained within the optimal range.

[0079] In this invention, the calculation and application of cooling power are closely linked to the regulation of the vacuum chamber's air pressure. Although air pressure is primarily used in model switching, adjusting cooling power is a crucial means of maximizing the system's overall energy efficiency. Cooling power adjustment and air pressure adjustment work in tandem, ensuring efficient and safe system operation under varying operating conditions through the use of an optimization algorithm.

[0080] In a simulation experiment of the present invention, when the flywheel is discharged at 20000RPM and the ambient temperature is 30℃, a multi-objective optimization algorithm (NSGAII genetic algorithm) is used to solve the optimal vacuum chamber pressure value p that minimizes the total energy consumption function Etotal. cav-opt and cooling power P cool-opt :

[0081] Optimal vacuum chamber pressure value p cav-opt :5×10 -2 Pa

[0082] Cooling power consumption P cool-opt : 50% traffic

[0083] Total energy consumption after optimization E total =3.41×108W

[0084] The total energy consumption E of conventional single vacuum control solution single =4.37×108W

[0085] Calculate the energy consumption reduction ratio:

[0086]

[0087] Example 2

[0088] One embodiment of the present invention provides a flywheel energy storage system vacuum and cooling coordinated optimization system, comprising:

[0089] Multi-physics field sensing module: including air pressure sensor, temperature sensor, and speed sensor, used to collect vacuum chamber air pressure, temperature, and speed data in real time;

[0090] Optimization control core: embedded controller with built-in vacuum-cooling collaborative optimization algorithm to calculate the optimal vacuum chamber pressure and cooling power;

[0091] Execution module: includes variable frequency vacuum pump, controllable air supply valve, liquid cooling flow control valve and variable frequency fan, used to perform air pressure regulation and cooling control.

[0092] The predictive maintenance module is used to monitor the degradation trend of the vacuum chamber sealing performance and generate an early warning for seal replacement when the vacuum maintenance time drops by more than 30%;

[0093] Based on historical cooling system efficiency data, filter clogging or pump oil aging can be predicted and maintenance instructions can be triggered.

[0094] The variable frequency vacuum pump is used to adjust the air pressure in the vacuum chamber according to the result of the optimization algorithm;

[0095] The controllable air supply valve is used to reversely inject dry nitrogen to maintain air pressure balance when vacuum leaks;

[0096] The liquid cooling flow regulating valve is used to regulate the flow of the liquid cooling system according to the result of the optimization algorithm.

[0097] Example 3

[0098] An embodiment of the present invention is different from the previous two embodiments in that:

[0099] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0100] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0101] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0102] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A flywheel energy storage system vacuum degree and cooling coordinated optimization method, characterized in that: include: Real-time collection of operating parameters of the flywheel energy storage system; Based on the operating parameters, the total energy consumption function is constructed by integrating air resistance loss, vacuum pump power consumption, cooling power consumption and temperature penalty terms; The total energy consumption function is expressed as, E total =P drag +P pump +P cool +α*max(0,T-T safe ) Among them, E total is the total energy consumption of the flywheel energy storage system, P drag is the air resistance loss power, P pump is the power consumption of the vacuum pump; P cool is the power consumption of the cooling system; α is the adaptive temperature rise penalty coefficient; T is the flywheel surface temperature, T safe is the safe value of flywheel surface temperature; Based on the multi-objective optimization algorithm, the solution is to make E total Minimize the optimal vacuum chamber pressure value p cav-opt And the optimal cooling power value P cool-opt ; The cavity pressure is adjusted to the optimal vacuum cavity pressure value p by the variable frequency vacuum pump and the controllable air supply valve. cav-opt , and the liquid cooling system is linked to execute the optimal cooling power value P cool-opt , in order to maximize the comprehensive energy efficiency of the flywheel energy storage system.

2. The flywheel energy storage system vacuum and cooling coordinated optimization method according to claim 1, characterized in that: The operating parameters include the vacuum chamber pressure value, the flywheel surface temperature, and the flywheel speed.

3. The flywheel energy storage system vacuum degree and cooling coordinated optimization method according to claim 2, characterized in that: The air resistance loss power is expressed as, Where Pdrag is the air resistance loss power, ρ is the air density, v is the flywheel speed, Cd is the drag coefficient, and A is the frontal area; The vacuum pump power consumption is expressed as, Among them, P pump is the power consumption of the vacuum pump, V sp is the pumping rate, p atm is atmospheric pressure, p cav is the vacuum chamber pressure value, η pump is the pump efficiency; The cooling system power consumption is expressed as, Among them, Pcool is the cooling power consumption, Q is the heat exchange mass flow rate, Tin is the intake temperature, Tout is the exhaust temperature, and ηcool is the cooling efficiency.

4. The flywheel energy storage system vacuum and cooling coordinated optimization method according to claim 3, characterized in that: The adaptive temperature rise over-limit penalty coefficient is expressed as: Where k is the material degradation coefficient and t is the cumulative operating time of the flywheel.

5. The flywheel energy storage system vacuum degree and cooling coordinated optimization method according to claim 4, characterized in that: When the flywheel accelerates or the ambient temperature changes by more than a preset threshold per unit time, the vacuum chamber pressure is relaxed to the upper limit and the liquid cooling system is started to the maximum power to suppress the temperature rise rate.

6. The flywheel energy storage system vacuum degree and cooling coordinated optimization method according to claim 5, characterized in that: When it is detected that the vacuum leakage rate exceeds the threshold, the air supply valve is triggered to reversely inject dry nitrogen to prevent moisture in the external air from entering the cavity, and the vacuum pump is started to restore the vacuum degree.

7. A device using the flywheel energy storage system vacuum and cooling coordinated optimization method according to any one of claims 1 to 6, characterized in that: include: Multi-physics field sensing module, which collects vacuum chamber pressure, temperature and speed data in real time through sensors; The optimization control core is an embedded controller with a built-in vacuum cooling collaborative optimization algorithm to calculate the optimal vacuum chamber pressure and cooling power; The execution module includes a variable frequency vacuum pump, a controllable air supply valve, a liquid cooling flow control valve and a variable frequency fan, which are used to perform air pressure regulation and cooling control; as well as, A predictive maintenance module monitors the degradation trend of the vacuum chamber's sealing performance and generates a seal replacement warning when the vacuum maintenance time drops by more than 30%. Based on historical cooling system efficiency data, filter clogging or pump oil aging can be predicted and maintenance instructions can be triggered.

8. The flywheel energy storage system vacuum and cooling coordinated optimization device according to claim 7, characterized in that: include: The variable frequency vacuum pump is used to adjust the air pressure in the vacuum chamber according to the result of the optimization algorithm; The controllable air supply valve is used to reversely inject dry nitrogen to maintain air pressure balance when vacuum leaks; The liquid cooling flow regulating valve is used to regulate the flow of the liquid cooling system according to the result of the optimization algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the flywheel energy storage system vacuum degree and cooling coordinated optimization method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the flywheel energy storage system vacuum degree and cooling coordinated optimization method according to any one of claims 1 to 6 are implemented.

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