Green energy-saving system and method for flywheel UPS (Uninterrupted Power Supply) and standby generator for data center

Abstract

The invention relates to the technical field of flywheel energy storage, in particular to a flywheel UPS and standby generator green energy-saving system and method for a data center, and the method comprises the steps: building a flywheel charging and discharging optimization strategy; working state parameters of the flywheel energy storage system and the generator are collected according to a preset collection frequency, and a working state parameter set is formed after preprocessing; nonlinear mechanical loss and charge-discharge current characteristic analysis in the flywheel energy storage system are introduced into a flywheel charge-discharge optimization strategy, and the flywheel charge-discharge strategy is optimized, so that the energy consumption of the flywheel energy storage system is reduced; and based on the optimized flywheel charging and discharging optimization strategy, by constructing a joint optimization model, synchronously adjusting the working state parameters of the generator, and optimizing the comprehensive energy consumption of the flywheel energy storage system and the generator combination system.

Description

Technical Field

[0001] The present invention relates to the technical field of flywheel energy storage, and in particular to a green energy-saving system and method for a flywheel UPS and a backup generator for a data center. Background Art

[0002] Flywheel energy storage uses a high-speed rotating flywheel to store kinetic energy and convert it into electrical energy when needed. Flywheels can switch from storage to discharge in milliseconds, significantly faster than traditional batteries (tens of milliseconds), making them suitable for facilities sensitive to power outages, such as data centers and hospitals. They exhibit no chemical degradation and can withstand tens of thousands of charge and discharge cycles. However, their short power supply duration requires a high degree of power recovery speed. Therefore, when used as an uninterruptible power supply (UPS) in a data center, they must be integrated with a backup generator to reduce reliance on batteries and ensure efficient power supply.

[0003] Charging the flywheel energy storage system requires electricity, and the generator power supply requires fossil fuels. How to regulate the working status of the flywheel energy storage system and the generator and reduce the system energy consumption while ensuring the basic power supply requirements is an urgent problem that needs to be solved. Summary of the Invention

[0004] The present invention constructs a joint optimization model of the working status of the flywheel and the generator according to different usage scenarios, and adopts multiple methods to adjust the joint optimization model. The working status of the flywheel energy storage system and the generator is dynamically adjusted through the control variables output by the joint optimization model to reduce system energy consumption.

[0005] The technical solution proposed by the present invention is a green energy-saving system and method for a flywheel UPS and a backup generator for a data center, the method comprising: Establish flywheel charging and discharging optimization strategy; In the flywheel charging and discharging optimization strategy, the nonlinear mechanical loss and charging and discharging current characteristics analysis of the flywheel energy storage system are introduced to optimize the flywheel charging and discharging strategy to reduce the energy consumption of the flywheel energy storage system; Based on the optimized flywheel charging and discharging optimization strategy, a joint optimization model is constructed to synchronously adjust the generator operating state parameters and optimize the comprehensive energy consumption of the flywheel energy storage system and generator combination system.

[0006] Preferably, the establishment of a flywheel charging and discharging optimization strategy includes: Collect the working state parameters of the flywheel energy storage system and the generator according to the preset collection frequency, and form a working state parameter set after preprocessing; Constructing a dynamic optimization model, obtaining the flywheel state parameters and the generator state parameters from the working state parameter set, and using the flywheel state parameters and the generator state parameters as input variables, inputting them into the dynamic optimization model; including: Constructing a dynamic optimization model: in, Indicates that the generator is The output power at the moment, Represents the generator start-stop decision parameter , 、 and Respectively represent the flywheel Charging power, discharging power and load power at each moment; Respectively The grid electricity cost coefficient, fuel cost coefficient, charging and discharging time interval, power generation energy consumption coefficient and start-stop energy consumption weight at each moment; , Respectively represent the flywheel Energy storage and nuclear power status at all times; Respectively represent the minimum output power and maximum output power of the generator; Represent the charging weight and discharging weight respectively; represents the energy consumption cost; The dynamic optimization model transforms the energy cost minimization problem into the mixed integer linear programming MILP form; Use the solver to solve and obtain the control variables 、 and .

[0007] Preferably, the introduction of nonlinear mechanical loss and charge and discharge current characteristic analysis in the flywheel energy storage system to optimize the flywheel charge and discharge strategy includes: Introducing the nonlinear mechanical loss of the flywheel energy storage system and using it to improve the dynamic optimization model; Obtain the charge and discharge current of the flywheel energy storage system, analyze the stability of the charge and discharge current, and optimize the improved dynamic optimization model based on the analysis results to obtain a joint optimization model; The working state parameters obtained in real time are input into the flywheel energy storage dynamic optimization model, and the corresponding control variables are output. The working state of the flywheel energy storage system is adjusted through the corresponding control variables to reduce the energy consumption of the flywheel energy storage system.

[0008] Preferably, the nonlinear mechanical loss of the flywheel energy storage system is introduced and the dynamic optimization model is improved by using the nonlinear mechanical loss, including: Importing nonlinear mechanical losses of flywheel energy storage system: ;in, Represent air resistance loss, bearing friction loss, eddy current loss and vacuum pump energy consumption respectively; Calculate the net charge and discharge power of the flywheel energy storage system: ; Nonlinear mechanical loss and the net charge and discharge power of the flywheel energy storage system are introduced into the dynamic optimization model to obtain an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, which includes: Adding the energy consumption of the vacuum pump to the dynamic optimization model and startup energy consumption items ;in, They represent the vacuum pump energy consumption coefficient, start-stop energy consumption coefficient and vacuum pump start-stop decision parameters respectively. ; The net charge and discharge power of the flywheel energy storage system is used in the dynamic optimization model; vacuum constraints and flywheel angular velocity constraints are added to the dynamic optimization model: Then, the dynamic optimization model of the flywheel energy storage is: ;in, They represent the minimum and maximum speeds allowed by the flywheel respectively; The flywheel angular velocity is obtained from the working state parameter set, and the relationship between flywheel energy storage and flywheel angular velocity is established, that is, ; The improved dynamic optimization model is solved using the nonlinear model predictive control framework NMPC to obtain new control variables. 、 、 、 and .

[0009] Preferably, the optimized flywheel charge and discharge optimization strategy, by constructing a joint optimization model and synchronously adjusting the generator operating state parameters, includes: Calculate the total energy consumption to be optimized: ;in, represents the weight factor; use Replace the flywheel energy storage dynamic optimization model , obtain the joint optimization model; represent the generator starting decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and flywheel angular velocity in the joint optimization model respectively; Indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump; The joint optimization model is solved by the mixed integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ; use , adjust the working status of the generator and the vacuum pump.

[0010] Preferably, the acquiring the charge and discharge current of the flywheel energy storage system, analyzing the stability of the charge and discharge current, and optimizing the improved dynamic optimization model and the joint optimization model according to the analysis results include: Get The charge and discharge current of the flywheel energy storage system within the time period; Calculate the current change and current smoothness ;in, , Indicates the current fluctuation threshold; Indicates the current acquisition time length; exist Add current smoothness penalty term , get the expanded ; Right now: ;in, represents the current smoothing weight; use Instead of the joint optimization model , obtain the extended joint optimization model; where, , Indicates the preset current smoothness threshold; Determined through Pareto front analysis ; Through the prediction model Predict the current value at a future moment, where They represent the generator inductance, back electromotive force coefficient and voltage respectively; In each control cycle, the extended joint optimization model is solved using the mixed integer nonlinear programming (NINLP) algorithm to solve the corresponding control variables. .

[0011] Preferably, the method further includes calculating current smoothness based on the current value of a future period predicted by the prediction model. When the current smoothness exceeds a preset fluctuation threshold, adjusting the start and stop state of the generator and the working state of the flywheel energy storage system so that the current smoothness is less than the fluctuation threshold, changing the objective function and constraint conditions of the extended joint optimization model, and obtaining corresponding control variables. Specifically, the method includes the following steps: Set the generator start time Adjust the time with the flywheel speed ;in, Indicates the original startup time; Indicates the generator start offset duration; Indicates the flywheel speed adjustment time offset duration ; ; By increasing the control variables and The joint optimization model is further optimized and extended in a way to dynamically constrain the start and stop time of the generator and the flywheel speed adjustment time.

[0012] Preferably, the control variable is increased and The joint optimization model is further optimized and extended to dynamically constrain the generator start and stop time and flywheel speed adjustment time, including: The generator start time window is used to constrain the generator start and stop time; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment time point so that the flywheel speed adjustment time matches the generator start and stop time specifically includes the following steps: In the extended joint optimization model Add a time offset penalty term to ,get ,Right now: ; in, Indicates the time offset penalty coefficient; use replace , that is, the optimized extended joint optimization model is obtained; among them, They represent the generator start-stop decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and current variation in the optimized extended joint optimization model respectively; Determined through Pareto front analysis By using the nonlinear model predictive control framework NMPC to solve the optimized extended joint optimization model, the corresponding control variables are obtained ; By adjusting To balance the output power of the generator and discharge it to the flywheel: The final actual output power of the generator: ; in, Indicates the maximum power that the generator is allowed to output. Indicates the generator power ramp time; By adjusting Control the flywheel deceleration rate and suppress current mutation; Actual flywheel speed ,in Indicates the flywheel speed at the time point when the current smoothness is detected to exceed the preset fluctuation threshold.

[0013] A coordinated management system for flywheel energy storage and battery hybrid UPS, the system being used to implement the green energy-saving method for flywheel UPS and backup generators used in data centers.

[0014] A computer-readable storage medium stores a computer program, which is executed by a processor to implement the green energy-saving method for a flywheel UPS and a backup generator for a data center.

[0015] Beneficial effects of the present invention: 1. When optimizing the energy consumption of a flywheel energy storage system, the present invention takes into account the nonlinear energy consumption of the flywheel energy storage system (including friction loss, copper loss caused by current fluctuations, and air resistance loss). By optimizing the flywheel energy storage system and adjusting the operating states of the generator through a joint optimization model, the energy consumption of the system is reduced.

[0016] 2. The present invention predicts the current fluctuation at a certain moment in the future. When the current fluctuation exceeds a preset current fluctuation threshold, the flywheel energy storage system and the generator switching time and operating state are optimized to cope with the current fluctuation, thereby reducing the losses of the flywheel energy storage system under current fluctuation (such as copper loss and increased mechanical wear of bearings), and reducing the maintenance cost of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the green energy-saving method for a flywheel UPS and a backup generator for a data center according to the present invention. DETAILED DESCRIPTION

[0018] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0019] It is understandable that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0020] Example 1: refer to Figure 1 The technical solution provided by the present invention is: a green energy-saving system and method for a flywheel UPS and a backup generator for a data center, comprising the following steps: Step 1: Establish a flywheel charging and discharging optimization strategy; specifically, the following steps are included: Collect the working state parameters of the flywheel energy storage system and the generator according to the preset collection frequency, and form a working state parameter set after preprocessing; Constructing a dynamic optimization model, obtaining the flywheel state parameters and the generator state parameters from the working state parameter set, and using the flywheel state parameters and the generator state parameters as input variables, inputting them into the dynamic optimization model; including: Constructing a dynamic optimization model: ; in, Indicates that the generator is The output power at the moment, Represents the generator start-stop decision parameter , 、 and Respectively represent the flywheel Charging power, discharging power and load power at each moment; Respectively The grid electricity cost coefficient, fuel cost coefficient, charging and discharging time interval, power generation energy consumption coefficient and start-stop energy consumption weight at each moment; , Respectively represent the flywheel Energy storage and nuclear power status at all times; Respectively represent the minimum output power and maximum output power of the generator; Represent the charging weight and discharging weight respectively; represents the energy consumption cost; The dynamic optimization model transforms the energy cost minimization problem into the mixed integer linear programming MILP form; Use the solver to solve and obtain the control variables 、 and .

[0021] Step 2: In the flywheel charge and discharge optimization strategy, the nonlinear mechanical loss and charge and discharge current characteristic analysis in the flywheel energy storage system are introduced to optimize the flywheel charge and discharge strategy to reduce the energy consumption of the flywheel energy storage system. Specifically, the following steps are included: Step 2.1: Import the nonlinear mechanical loss of the flywheel energy storage system and use the nonlinear mechanical loss to improve the dynamic optimization model; specifically, the following steps are included: Importing nonlinear mechanical losses of flywheel energy storage system: ; in, Represent air resistance loss, bearing friction loss, eddy current loss and vacuum pump energy consumption respectively; Calculate the net charge and discharge power of the flywheel energy storage system: ; Nonlinear mechanical loss and the net charge and discharge power of the flywheel energy storage system are introduced into the dynamic optimization model to obtain an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, which includes: Adding the energy consumption of the vacuum pump to the dynamic optimization model and startup energy consumption items ;in, They represent the vacuum pump energy consumption coefficient, start-stop energy consumption coefficient and vacuum pump start-stop decision parameters respectively. ; The net charge and discharge power of the flywheel energy storage system is used in the dynamic optimization model; Add vacuum constraints and flywheel angular velocity constraints to the dynamic optimization model: Then, the dynamic optimization model of flywheel energy storage is: ; in, They represent the minimum and maximum speeds allowed by the flywheel respectively; The flywheel angular velocity is obtained from the working state parameter set, and the relationship between flywheel energy storage and flywheel angular velocity is established, that is, ; The improved dynamic optimization model is solved using the nonlinear model predictive control framework NMPC to obtain new control variables. 、 、 、 and .

[0022] Step 2.2: Obtain the charge and discharge current of the flywheel energy storage system, analyze the stability of the charge and discharge current, and optimize the improved dynamic optimization model based on the analysis results to obtain a joint optimization model; specifically, the following steps are included: Get The charge and discharge current of the flywheel energy storage system within the time period; Calculate the current change and current smoothness ;in, , Indicates the current fluctuation threshold; Indicates the current acquisition time length; exist Add current smoothness penalty term , get the expanded ;Right now: ;in, represents the current smoothing weight; use Instead of the joint optimization model , obtain the extended joint optimization model; where, , Indicates the preset current smoothness threshold; Determined through Pareto front analysis ; Through the prediction model Predict the current value at a future moment, where They represent the generator inductance, back electromotive force coefficient and voltage respectively; In each control cycle, the extended joint optimization model is solved using the mixed integer nonlinear programming (NINLP) algorithm to solve the corresponding control variables. .

[0023] For example, if the load power increases from 200kW to 500kW within 10 seconds: Current mutation 500 ,Right now ,Due to the sudden change of current, the copper loss of diesel engine increases by 25%, (copper loss )). The fluctuation threshold is less than 80A, so there is no need to optimize the start and stop time. The extended joint optimization model is used for optimization, and the current is optimized according to The ramp-up reaches the target current within 10 seconds, reducing the increase in copper loss, thereby reducing the loss of the generator and reducing the maintenance cost of the equipment.

[0024] Step 2.3: Input the working state parameters obtained in real time into the flywheel energy storage dynamic optimization model, output the corresponding control variables, adjust the working state of the flywheel energy storage system through the corresponding control variables, and reduce the energy consumption of the flywheel energy storage system.

[0025] Step 3: Based on the optimized flywheel charge and discharge optimization strategy, a joint optimization model is constructed to synchronously adjust the generator operating state parameters to optimize the comprehensive energy consumption of the flywheel energy storage system and the generator combination system, including the following steps: The optimized flywheel charge and discharge optimization strategy is based on building a joint optimization model to synchronously adjust the generator operating state parameters, including: Calculate the total energy consumption to be optimized: ;in, represents the weight factor; use Replace the flywheel energy storage dynamic optimization model , obtain the joint optimization model; represent the generator starting decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and flywheel angular velocity in the joint optimization model respectively; Indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump; The joint optimization model is solved by the mixed integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ; use , adjust the working status of the generator and the vacuum pump.

[0026] For example: When the mains power is normal, the flywheel energy storage system is charged with mains power. Increase to 90%; maintain the vacuum degree at 0.5Pa; at this time, keep the diesel engine (generator) stopped, that is ; When the mains power is interrupted, Stage, use the flywheel to discharge at full power and start the vacuum pump ( ), increase the vacuum degree to 1Pa to reduce the flywheel friction loss; exist In the stage, start the diesel engine and gradually increase its output power to , control the flywheel speed to ; exist In this stage, the diesel engine is independently powered.

[0027] Example 2: If the current fluctuation does not exceed the preset fluctuation threshold ( ), that is, the current smoothness does not exceed the preset current smoothness threshold, then the working state of the diesel engine (generator) and the flywheel energy storage system can be controlled using the corresponding control variables through the technical solution of embodiment one; If the current fluctuation exceeds the preset fluctuation threshold, that is, the current smoothness exceeds the preset current smoothness threshold, how to optimize the operating state of the flywheel and generator to achieve energy saving. To solve the above problem, we propose the following technical solution based on the first embodiment: Based on the current values predicted by the prediction model for a period of time in the future, the current smoothness is calculated. When the current smoothness exceeds the preset fluctuation threshold, the generator start-stop state and the operating state of the flywheel energy storage system are adjusted to make the current smoothness less than the fluctuation threshold. The objective function and constraints of the extended joint optimization model are changed to obtain the corresponding control variables. The specific steps include: Set the generator start time Adjust the time with the flywheel speed ;in, Indicates the original startup time; Indicates the generator start offset duration; Indicates the flywheel speed adjustment time offset duration ; ; By increasing the control variables and The joint optimization model is further optimized and extended to dynamically constrain the generator start and stop time and the flywheel speed adjustment time. The specific steps include: The generator start time window is used to constrain the generator start and stop time; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment time point so that the flywheel speed adjustment time matches the generator start and stop time specifically includes the following steps: In the extended joint optimization model Add a time offset penalty term to , and remove the constraint on the current change; obtain ,Right now: ; in, Indicates the time offset penalty coefficient; use replace , that is, the optimized extended joint optimization model is obtained; Determined through Pareto front analysis By using the nonlinear model predictive control framework NMPC to solve the optimized extended joint optimization model, the corresponding control variables are obtained ; By adjusting To balance the output power of the generator and discharge it to the flywheel: The final actual output power of the generator: ; in, Indicates the maximum power that the generator is allowed to output. Indicates the generator power ramp time; By adjusting Control the flywheel deceleration rate and suppress current mutation; Actual flywheel speed ,in Indicates the flywheel speed at the time point when the current smoothness is detected to exceed the preset fluctuation threshold.

[0028] For example, predicting a future moment The current fluctuation is 120A, which is greater than the preset fluctuation threshold of 80A. Optimization control is required.

[0029] The solution obtained , Then, the diesel engine starts 3s in advance and reaches full power before the flywheel discharge ends; The flywheel slows down with a delay of 1s, prolonging the current speed and suppressing sudden changes in current.

[0030] The present invention also provides a coordinated management system for flywheel energy storage and battery hybrid UPS, which is used to implement the green energy-saving method for flywheel UPS and backup generator for data centers.

[0031] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the green energy-saving method for a flywheel UPS and a backup generator for a data center.

[0032] In the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a propagated data signal, either in baseband or as part of a carrier wave, embodying computer-readable program code. Such a propagated data signal may take various forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical fiber cable, RF, etc., or any suitable combination thereof.

[0033] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or portion of code that contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0034] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be subject to any changes or modifications.

Claims

1. A green energy-saving method for using a flywheel UPS and a backup generator in a data center, characterized in that: The method comprises: Establish flywheel charging and discharging optimization strategy; In the flywheel charging and discharging optimization strategy, the nonlinear mechanical loss and charging and discharging current characteristics analysis of the flywheel energy storage system are introduced to optimize the flywheel charging and discharging strategy to reduce the energy consumption of the flywheel energy storage system; Based on the optimized flywheel charging and discharging optimization strategy, a joint optimization model is constructed to synchronously adjust the generator operating state parameters and optimize the comprehensive energy consumption of the flywheel energy storage system and generator combination system.

2. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 1, characterized in that: The flywheel charging and discharging optimization strategy is established, including: Collect the working state parameters of the flywheel energy storage system and the generator according to the preset collection frequency, and form a working state parameter set after preprocessing; Constructing a dynamic optimization model, obtaining the flywheel state parameters and the generator state parameters from the working state parameter set, and using the flywheel state parameters and the generator state parameters as input variables, inputting them into the dynamic optimization model; including: Constructing a dynamic optimization model: ； in, Indicates that the generator is The output power at the moment, Represents the generator start-stop decision parameter , 、 and Respectively represent the flywheel Charging power, discharging power and load power at each moment; Respectively The grid electricity cost coefficient, fuel cost coefficient, charging and discharging time interval, power generation energy consumption coefficient and start-stop energy consumption weight at each moment; , Respectively represent the flywheel Energy storage and nuclear power status at all times; Respectively represent the minimum output power and maximum output power of the generator; Represent the charging weight and discharging weight respectively; represents the energy consumption cost; The dynamic optimization model transforms the energy cost minimization problem into the mixed integer linear programming MILP form; Use the solver to solve and obtain the control variables 、 and .

3. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 2, characterized in that: The introduction of nonlinear mechanical loss and charge and discharge current characteristic analysis in the flywheel energy storage system to optimize the flywheel charge and discharge strategy includes: Introducing the nonlinear mechanical loss of the flywheel energy storage system and using it to improve the dynamic optimization model; Obtain the charge and discharge current of the flywheel energy storage system, analyze the stability of the charge and discharge current, and optimize the improved dynamic optimization model based on the analysis results to obtain a joint optimization model; The working state parameters obtained in real time are input into the flywheel energy storage dynamic optimization model, and the corresponding control variables are output. The working state of the flywheel energy storage system is adjusted through the corresponding control variables to reduce the energy consumption of the flywheel energy storage system.

4. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 3, characterized in that: The method of introducing nonlinear mechanical loss of the flywheel energy storage system and improving the dynamic optimization model by utilizing the nonlinear mechanical loss includes: Importing nonlinear mechanical losses of flywheel energy storage system: ； in, Represent air resistance loss, bearing friction loss, eddy current loss and vacuum pump energy consumption respectively; Calculate the net charge and discharge power of the flywheel energy storage system: ； Nonlinear mechanical loss and the net charge and discharge power of the flywheel energy storage system are introduced into the dynamic optimization model to obtain an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, which includes: Adding the energy consumption of the vacuum pump to the dynamic optimization model and startup energy consumption items ;in, They represent the vacuum pump energy consumption coefficient, start-stop energy consumption coefficient and vacuum pump start-stop decision parameters respectively. ; The net charge and discharge power of the flywheel energy storage system is used in the dynamic optimization model; Add vacuum constraints and flywheel angular velocity constraints to the dynamic optimization model: Then, the dynamic optimization model of flywheel energy storage is: ； in, They represent the minimum and maximum speeds allowed by the flywheel respectively; The flywheel angular velocity is obtained from the working state parameter set, and the relationship between flywheel energy storage and flywheel angular velocity is established, that is, ; The improved dynamic optimization model is solved using the nonlinear model predictive control framework NMPC to obtain new control variables. 、 、 、 and .

5. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 4, characterized in that: The optimized flywheel charge and discharge optimization strategy is based on building a joint optimization model to synchronously adjust the generator operating state parameters, including: Calculate the total energy consumption to be optimized: ;in, represents the weight factor; use Replace the flywheel energy storage dynamic optimization model , obtain the joint optimization model; represent the generator starting decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and flywheel angular velocity in the joint optimization model respectively; Indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump; The joint optimization model is solved by the mixed integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ; use , adjust the working status of the generator and the vacuum pump.

6. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 5, characterized in that: The method of obtaining the charge and discharge current of the flywheel energy storage system, analyzing the stability of the charge and discharge current, and optimizing the improved dynamic optimization model and the joint optimization model according to the analysis results includes: Get The charge and discharge current of the flywheel energy storage system within the time period; Calculate the current change and current smoothness ;in, , Indicates the current fluctuation threshold, Indicates the current acquisition time length; exist Add current smoothness penalty term , get the expanded ;Right now: ;in, represents the current smoothing weight; use Instead of the joint optimization model , obtain the extended joint optimization model; where, , Indicates the preset current smoothness threshold; They represent the motor start-stop decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and flywheel angular velocity in the extended joint model respectively; Determined through Pareto front analysis ; Through the prediction model Predict the current value at a future moment, where They represent the generator inductance, back electromotive force coefficient and voltage respectively; In each control cycle, the extended joint optimization model is solved using the mixed integer nonlinear programming (NINLP) algorithm to solve the corresponding control variables. .

7. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 6, characterized in that: The method further includes calculating current smoothness based on the current value for a period of time in the future predicted by the prediction model. When the current smoothness exceeds a preset fluctuation threshold, the generator start / stop state and the operating state of the flywheel energy storage system are adjusted to make the current smoothness less than the fluctuation threshold. The objective function and constraints of the extended joint optimization model are changed to obtain corresponding control variables. The method specifically includes the following steps: Set the generator start time Adjust the time with the flywheel speed ;in, Indicates the original startup time; Indicates the generator start offset duration; Indicates the flywheel speed adjustment time offset duration ; ; By increasing the control variables and The joint optimization model is further optimized and extended in a way to dynamically constrain the start and stop time of the generator and the flywheel speed adjustment time.

8. The green energy-saving method for flywheel UPS and backup generator for data center according to claim 7, characterized in that: By increasing the control variables and The joint optimization model is further optimized and extended to dynamically constrain the generator start and stop time and flywheel speed adjustment time, including: The generator start time window is used to constrain the generator start and stop time; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment time point so that the flywheel speed adjustment time matches the generator start and stop time specifically includes the following steps: In the extended joint optimization model Add a time offset penalty term to ,get ,Right now: ； in, Indicates the time offset penalty coefficient; use replace , that is, the optimized extended joint optimization model is obtained; among them, They represent the generator start-stop decision parameters, vacuum pump power, vacuum pump start-stop decision parameters and current variation in the optimized extended joint optimization model respectively; Determined through Pareto front analysis By using the nonlinear model predictive control framework NMPC to solve the optimized extended joint optimization model, the corresponding control variables are obtained ; By adjusting To balance the output power of the generator and discharge it to the flywheel: The final actual output power of the generator: ； in, Indicates the maximum power that the generator is allowed to output. Indicates the generator power ramp time; By adjusting Control the flywheel deceleration rate and suppress current mutation; Actual flywheel speed ,in Indicates the flywheel speed at the time point when the current smoothness is detected to exceed the preset fluctuation threshold.

9. The coordinated management system of flywheel energy storage and battery hybrid UPS is characterized by: The system is used to implement the green energy-saving method for flywheel UPS and backup generator for data centers as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the green energy-saving method for a flywheel UPS and a backup generator for a data center as described in any one of claims 1 to 8.

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