Flywheel ups and backup generator green energy saving system and method for data centers

Abstract

This invention relates to the field of flywheel energy storage technology, and particularly to a green energy-saving system and method for flywheel UPS and backup generators used in data centers. The method includes: establishing a flywheel charging and discharging optimization strategy; collecting operating state parameters of the flywheel energy storage system and generator at a preset acquisition frequency, and constructing an operating state parameter set after preprocessing; introducing nonlinear mechanical losses and charging and discharging current characteristic analysis in the flywheel energy storage system into the flywheel charging and discharging optimization strategy to optimize the flywheel charging and discharging strategy and reduce the energy consumption of the flywheel energy storage system; and based on the optimized flywheel charging and discharging optimization strategy, constructing a joint optimization model to synchronously adjust the generator operating state parameters and optimize the overall energy consumption of the flywheel energy storage system and generator combined system.

Description

Technical Field

[0001] This invention relates to the field of flywheel energy storage technology, and in particular to green energy-saving systems and methods for flywheel UPS and backup generators used in data centers. Background Technology

[0002] Flywheel energy storage utilizes a high-speed rotating flywheel to store kinetic energy, converting it into electrical energy when needed. The flywheel can switch from energy storage to discharge in milliseconds, faster than traditional batteries (tens of milliseconds), making it suitable for facilities sensitive to power outages, such as data centers and hospitals. Furthermore, it exhibits no chemical degradation and can withstand tens of thousands of charge-discharge cycles. However, the short power supply time of a flywheel necessitates a high speed of power recovery. When used as an uninterruptible power supply (UPS) in a data center, it needs to be integrated with a backup generator to reduce reliance on batteries and ensure optimal power supply.

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

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

[0005] The technical solution proposed in this invention is: a green energy-saving system and method for flywheel UPS and backup generator for data centers, wherein the method includes:

[0006] Establish flywheel charging and discharging optimization strategies;

[0007] In the flywheel charging and discharging optimization strategy, nonlinear mechanical losses and charging and discharging current characteristics in the flywheel energy storage system are introduced for analysis, and the flywheel charging and discharging strategy is optimized to reduce the energy consumption of the flywheel energy storage system.

[0008] 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 overall energy consumption of the flywheel energy storage system and the generator combination system.

[0009] Preferably, the establishment of the flywheel charging and discharging optimization strategy includes:

[0010] The operating status parameters of the flywheel energy storage system and the generator are collected according to the preset acquisition frequency, and the collected parameters are preprocessed to form a set of operating status parameters.

[0011] Constructing a dynamic optimization model involves obtaining flywheel and generator state parameters from the operating state parameter set, and using these parameters as input variables into the dynamic optimization model; this includes: constructing the dynamic optimization model:

[0012]

[0013] in, Indicates that the generator is Output power at any moment Indicates generator start-stop decision parameters , , and These respectively represent the flywheel in The charging power, discharging power, and load power at any given time; They represent 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 any time; , These respectively represent the flywheel in The status of energy storage and nuclear power at any given time; These represent the generator's minimum and maximum output power, respectively. These represent the charging weight and discharging weight, respectively. Indicates energy consumption cost;

[0014] The dynamic optimization model transforms the problem of minimizing energy consumption costs into a mixed-integer linear programming (MILP) form.

[0015] Use a solver to solve for the control variables. , and .

[0016] Preferably, the analysis of nonlinear mechanical losses and charge / discharge current characteristics in the flywheel energy storage system, and the optimization of the flywheel charge / discharge strategy, includes:

[0017] The nonlinear mechanical losses of the flywheel energy storage system are introduced, and the dynamic optimization model is improved by using the nonlinear mechanical losses.

[0018] Obtain the charging and discharging current of the flywheel energy storage system, analyze the stability of the charging and discharging current, and optimize the improved dynamic optimization model based on the analysis results to obtain the joint optimization model;

[0019] The real-time operating status parameters are input into the flywheel energy storage dynamic optimization model, and the corresponding control variables are output. The operating status of the flywheel energy storage system is adjusted through the corresponding control variables to reduce the energy consumption of the flywheel energy storage system.

[0020] Preferably, the nonlinear mechanical losses introduced into the flywheel energy storage system are used to improve the dynamic optimization model, including:

[0021] Introducing nonlinear mechanical losses into flywheel energy storage systems: ;in, These represent air resistance loss, bearing friction loss, eddy current loss, and vacuum pump energy consumption, respectively.

[0022] Calculate and obtain the net charge and discharge power of the flywheel energy storage system:

[0023] ;

[0024] By introducing nonlinear mechanical losses and the net charge / discharge power of the flywheel energy storage system into the dynamic optimization model, an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, is obtained, including:

[0025] Add the operating energy consumption term of the vacuum pump to the dynamic optimization model. and startup energy consumption items ;in, These represent the vacuum pump energy consumption coefficient, start-stop energy consumption coefficient, and vacuum pump start-stop decision parameters, respectively. ;

[0026] Using the net charge / discharge power of the flywheel energy storage system in the dynamic optimization model; adding vacuum degree constraints and flywheel angular velocity constraints to the dynamic optimization model: then, the dynamic optimization model of flywheel energy storage is:

[0027] ;in, These represent the minimum and maximum permissible speeds of the flywheel, respectively.

[0028] The flywheel angular velocity is obtained from the set of operating state parameters, and the relationship between flywheel energy storage and flywheel angular velocity is established. ;

[0029] The improved dynamic optimization model is solved using the Nonlinear Model Predictive Control Framework (NMPC) to obtain new control variables. , , , and .

[0030] Preferably, the optimized flywheel charging and discharging optimization strategy, by constructing a joint optimization model, synchronously adjusts the generator operating state parameters, including:

[0031] Calculate and obtain the total energy consumption to be optimized:

[0032] ;in, Indicates the weighting factor;

[0033] use Replacement of flywheel energy storage dynamic optimization model Obtain the joint optimization model; These represent the generator start-up decision parameters, vacuum pump power, vacuum pump start-stop decision parameters, and flywheel angular velocity, respectively, in the joint optimization model. This indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump;

[0034] The joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ;

[0035] use Adjust the operating status of the generator and the vacuum pump.

[0036] Preferably, the process of acquiring the charging and discharging current of the flywheel energy storage system, analyzing the stability of the charging and discharging current, and optimizing the improved dynamic optimization model based on the analysis results, and optimizing the joint optimization model, includes:

[0037] Get The charging and discharging current of the flywheel energy storage system within a given time period;

[0038] Calculate and obtain the change in current and current smoothness ;in, , Indicates the current fluctuation threshold; Indicates the duration of current sampling;

[0039] exist Add current stability penalty item Get the extended ;

[0040] Right now:

[0041] ;in, Indicates the current smoothness weight;

[0042] use Replace the joint optimization model , thus obtaining the extended joint optimization model; where, , This indicates the preset current smoothness threshold.

[0043] Determined through Pareto front analysis ;

[0044] Through prediction models Predict the current value at a future time, where These represent the generator inductance, back electromotive force coefficient, and voltage, respectively.

[0045] The extended joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm within each control cycle, and the corresponding control variables are solved. .

[0046] Preferably, the method further includes calculating the current smoothness based on the current values ​​predicted by the prediction model for a future period of time; when the current smoothness exceeds a preset fluctuation threshold, adjusting the generator start-stop state and the operating state of the flywheel energy storage system to make the current smoothness less than the fluctuation threshold; changing the objective function and constraints of the extended joint optimization model; and obtaining the corresponding control variables. Specifically, this includes the following steps:

[0047] Set generator start-up time and flywheel speed adjustment time ;in, Indicates the original startup time; Indicates the generator start-up offset time; Indicates the flywheel speed adjustment time offset duration ; ;

[0048] By increasing control variables and The joint optimization model is further optimized and extended in a way that dynamically constrains the generator start-up and shutdown time and the flywheel speed adjustment time.

[0049] Preferably, the step of increasing control variables and The joint optimization model is further optimized and extended in a manner that dynamically constrains the generator start-up and shutdown times and flywheel speed adjustment time, including:

[0050] The generator start-up and shutdown times are constrained by the generator start-up time window; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment timing to match the generator start-stop timing involves the following steps:

[0051] In the extended joint optimization model Add a time offset penalty item ,get ,Right now:

[0052] ;

[0053] in, Indicates the time offset penalty coefficient; using replace That is, to obtain the optimized extended joint optimization model; where, These represent the generator start-stop decision parameters, vacuum pump power, vacuum pump start-stop decision parameters, and current change in the optimized extended joint optimization model, respectively.

[0054] 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. ;

[0055] By adjusting To balance the generator's output power and discharge it to the flywheel:

[0056] The generator's final actual output power:

[0057] ;

[0058] in, This indicates the maximum allowable output power of the generator. Indicates the generator power ramp-up time;

[0059] By adjusting Control the flywheel deceleration rate and suppress sudden current changes;

[0060] The actual speed of the flywheel ,in This indicates the flywheel speed at the point in time when the current smoothness exceeds a preset fluctuation threshold.

[0061] A coordinated management system for a flywheel energy storage and battery hybrid UPS, the system being used to implement the aforementioned green energy-saving method for flywheel UPS and backup generators in data centers.

[0062] A computer-readable storage medium storing a computer program that is executed by a processor to implement the green energy-saving method for flywheel UPS and backup generator for data centers.

[0063] The beneficial effects of this invention are:

[0064] 1. In this invention, when optimizing the energy consumption of a flywheel energy storage system, the nonlinear energy consumption of the flywheel energy storage system (including friction loss, copper loss caused by current fluctuations, and air resistance loss) is considered. The system is optimized through a joint optimization model to adjust the working state of the flywheel energy storage system and the generator, thereby reducing the system energy consumption.

[0065] 2. This invention predicts the current fluctuation at a future moment. When the current fluctuation exceeds a preset current fluctuation threshold, it optimizes the switching time and operating status of the flywheel energy storage system and the generator to cope with the current fluctuation, reduce the losses of the flywheel energy storage system under the current fluctuation (such as increased copper loss and bearing mechanical wear), and reduce the maintenance cost of the equipment. Attached Figure Description

[0066] Figure 1 This is a flowchart of the green energy-saving method for flywheel UPS and backup generator used in data centers according to the present invention. Detailed Implementation

[0067] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0068] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0069] Example 1:

[0070] refer to Figure 1 The technical solution provided by this invention is: a green energy-saving system and method for flywheel UPS and backup generator for data centers, comprising the following steps:

[0071] Step 1: Establish a flywheel charging and discharging optimization strategy; specifically including the following steps:

[0072] The operating status parameters of the flywheel energy storage system and the generator are collected according to the preset acquisition frequency, and the collected parameters are preprocessed to form a set of operating status parameters.

[0073] Constructing a dynamic optimization model involves obtaining flywheel and generator state parameters from the operating state parameter set, and using these parameters as input variables into the dynamic optimization model; this includes: constructing the dynamic optimization model:

[0074] ;

[0075] in, Indicates that the generator is Output power at any moment Indicates generator start-stop decision parameters , , and These respectively represent the flywheel in The charging power, discharging power, and load power at any given time; They represent 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 any time; , These respectively represent the flywheel in The status of energy storage and nuclear power at any given time; These represent the generator's minimum and maximum output power, respectively. These represent the charging weight and discharging weight, respectively. Indicates energy consumption cost;

[0076] The dynamic optimization model transforms the problem of minimizing energy consumption costs into a mixed-integer linear programming (MILP) form.

[0077] Use a solver to solve for the control variables. , and .

[0078] Step 2: In the flywheel charging and discharging optimization strategy, nonlinear mechanical losses and charging and discharging current characteristics in the flywheel energy storage system are introduced for analysis. The flywheel charging and discharging strategy is optimized to reduce the energy consumption of the flywheel energy storage system. Specifically, this includes the following steps:

[0079] Step 2.1: Import the nonlinear mechanical losses of the flywheel energy storage system and use these losses to improve the dynamic optimization model; specifically, this includes the following steps:

[0080] Introducing nonlinear mechanical losses into flywheel energy storage systems:

[0081] ;

[0082] in, These represent air resistance loss, bearing friction loss, eddy current loss, and vacuum pump energy consumption, respectively.

[0083] Calculate and obtain the net charge and discharge power of the flywheel energy storage system:

[0084] ;

[0085] By introducing nonlinear mechanical losses and the net charge / discharge power of the flywheel energy storage system into the dynamic optimization model, an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, is obtained, including:

[0086] Add the operating energy consumption term of the vacuum pump to the dynamic optimization model. and startup energy consumption items ;in, These represent the vacuum pump energy consumption coefficient, start-stop energy consumption coefficient, and vacuum pump start-stop decision parameters, respectively. ;

[0087] The net charge and discharge power of the flywheel energy storage system is used in the dynamic optimization model;

[0088] Add vacuum degree constraints and flywheel angular velocity constraints to the dynamic optimization model:

[0089] Therefore, the flywheel energy storage dynamic optimization model is:

[0090] ;

[0091] in, These represent the minimum and maximum permissible speeds of the flywheel, respectively.

[0092] The flywheel angular velocity is obtained from the set of operating state parameters, and the relationship between flywheel energy storage and flywheel angular velocity is established. ;

[0093] The improved dynamic optimization model is solved using the Nonlinear Model Predictive Control Framework (NMPC) to obtain new control variables. , , , and .

[0094] Step 2.2: Obtain the charging and discharging current of the flywheel energy storage system, analyze the stability of the charging and discharging current, and optimize the improved dynamic optimization model based on the analysis results to obtain the joint optimization model; specifically, this includes the following steps:

[0095] Get The charging and discharging current of the flywheel energy storage system within a given time period;

[0096] Calculate and obtain the change in current and current smoothness ;in, , Indicates the current fluctuation threshold; Indicates the duration of current sampling;

[0097] exist Add current stability penalty item Get the extended ;Right now:

[0098] ;in, Indicates the current smoothness weight;

[0099] use Replace the joint optimization model , thus obtaining the extended joint optimization model; where, , This indicates the preset current smoothness threshold.

[0100] Determined through Pareto front analysis ;

[0101] Through prediction models Predict the current value at a future time, where These represent the generator inductance, back electromotive force coefficient, and voltage, respectively.

[0102] The extended joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm within each control cycle, and the corresponding control variables are solved. .

[0103] For example, in a scenario where the load power jumps from 200kW to 500kW within 10 seconds:

[0104] Current sudden change 500 ,Right now Due to the sudden change in current, the copper loss in the diesel engine increased by 25%. )). If the current fluctuation is less than the preset fluctuation threshold of 80A, optimization of the start-stop time is not required; optimization is performed using an extended joint optimization model, and the optimized current follows the specified parameters. The ramp-up allows the target current to be reached within 10 seconds, reducing the increase in copper losses and thus reducing generator losses and equipment maintenance costs.

[0105] Step 2.3: Input the real-time acquired working status parameters into the flywheel energy storage dynamic optimization model, output the corresponding control variables, and adjust the working status of the flywheel energy storage system through the corresponding control variables to reduce the energy consumption of the flywheel energy storage system.

[0106] Step 3: Based on the optimized flywheel charging and discharging strategy, a joint optimization model is constructed to synchronously adjust the generator operating state parameters and optimize the overall energy consumption of the flywheel energy storage system and the generator combination system. This includes the following steps:

[0107] The optimized flywheel charging and discharging optimization strategy, by constructing a joint optimization model, synchronously adjusts the generator operating state parameters, including:

[0108] Calculate and obtain the total energy consumption to be optimized:

[0109] ;in, Indicates the weighting factor;

[0110] use Replacement of flywheel energy storage dynamic optimization model Obtain the joint optimization model; These represent the generator start-up decision parameters, vacuum pump power, vacuum pump start-stop decision parameters, and flywheel angular velocity, respectively, in the joint optimization model. This indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump;

[0111] The joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ;

[0112] use Adjust the operating status of the generator and the vacuum pump.

[0113] For example: when the mains power is normal, use the mains power to charge the flywheel energy storage system. Increase the vacuum level to 90%; maintain the vacuum at 0.5 Pa; at this point, keep the diesel engine (generator) off. ;

[0114] When the mains power is interrupted, During this phase, the flywheel is discharged at full power to start the vacuum pump. This will increase the vacuum level to 1 Pa to reduce flywheel friction loss;

[0115] exist During this phase, start the diesel engine and gradually increase its output power to [a certain level]. Control the flywheel speed to ;

[0116] exist During this phase, the diesel engine is powered independently.

[0117] Example 2:

[0118] If the current fluctuation does not exceed the preset fluctuation threshold ( If the current smoothness does not exceed the preset current smoothness threshold, the working state of the diesel engine (generator) and flywheel energy storage system can be controlled by the corresponding control variables through the technical solution of Embodiment 1.

[0119] If the current fluctuation exceeds a preset fluctuation threshold, i.e., the current smoothness exceeds a preset current smoothness threshold, how can the operating state of the flywheel and generator be optimized to achieve energy saving? To solve the above problem, we propose the following technical solution based on Embodiment 1:

[0120] Based on the predicted current values ​​for a future period of time from the prediction model, the current smoothness is calculated. 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, and the corresponding control variables are obtained. The specific steps include:

[0121] Set generator start-up time and flywheel speed adjustment time ;in, Indicates the original startup time; Indicates the generator start-up offset time; Indicates the flywheel speed adjustment time offset duration ; ;

[0122] By increasing control variables and The joint optimization model is further optimized and extended in a manner that dynamically constrains the generator start-up and shutdown times and flywheel speed adjustment time. Specifically, this includes the following steps:

[0123] The generator start-up and shutdown times are constrained by the generator start-up time window; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment timing to match the generator start-stop timing involves the following steps:

[0124] In the extended joint optimization model Add a time offset penalty item and remove the constraint on the change in current; obtain ,Right now:

[0125] ;

[0126] in, Indicates the time offset penalty coefficient; using replace That is, to obtain the optimized extended joint optimization model;

[0127] 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. ;

[0128] By adjusting To balance the generator's output power and discharge it to the flywheel:

[0129] The generator's final actual output power:

[0130] ;

[0131] in, This indicates the maximum allowable output power of the generator. Indicates the generator power ramp-up time;

[0132] By adjusting Control the flywheel deceleration rate and suppress sudden current changes;

[0133] The actual speed of the flywheel ,in This indicates the flywheel speed at the point in time when the current smoothness exceeds a preset fluctuation threshold.

[0134] For example, predicting a future moment The current fluctuation is 120A, which exceeds the preset fluctuation threshold of 80A. Optimized control is required.

[0135] The solution obtained , Therefore, the diesel engine starts 3 seconds earlier and reaches full power before the flywheel discharge ends;

[0136] The flywheel slows down by 1 second, extending the existing rotational speed time and suppressing sudden current changes.

[0137] The present invention also provides a coordinated management system for a hybrid UPS of flywheel energy storage and battery, the system being used to implement the green energy-saving method of flywheel UPS and backup generator for data centers.

[0138] The present invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the aforementioned green energy-saving method for flywheel UPS and backup generator for data centers.

[0139] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0141] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A green and energy-saving method for flywheel UPS and backup generators in data centers, characterized in that, The method includes: Establish a flywheel charging and discharging optimization strategy; including: constructing a dynamic optimization model, obtaining flywheel state parameters and generator state parameters from the operating state parameter set, and using the flywheel state parameters and generator state parameters as input variables into the dynamic optimization model; including: constructing the dynamic optimization model: ； in, Indicates that the generator is Output power at any moment Indicates generator start-stop decision parameters , , and These respectively represent the flywheel in The charging power, discharging power, and load power at any given time; They represent 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 any time; , These respectively represent the flywheel in Energy storage and state of charge at any given moment; These represent the generator's minimum and maximum output power, respectively. These represent the charging weight and discharging weight, respectively. Indicates energy consumption cost; In the flywheel charging and discharging optimization strategy, nonlinear mechanical losses and charging and discharging current characteristics in the flywheel energy storage system are introduced for analysis, and the flywheel charging and discharging strategy is optimized 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 overall energy consumption of the flywheel energy storage system and the generator combination system. The flywheel charging and discharging optimization strategy incorporates nonlinear mechanical losses and charging / discharging current characteristic analysis in the flywheel energy storage system to optimize the flywheel charging and discharging strategy, including: The nonlinear mechanical losses of the flywheel energy storage system are introduced, and the dynamic optimization model is improved by using the nonlinear mechanical losses. Obtain the charging and discharging current of the flywheel energy storage system, analyze the stability of the charging and discharging current, and optimize the improved dynamic optimization model based on the analysis results to obtain a joint optimization model; including: Get The charging and discharging current of the flywheel energy storage system within a given time period; Calculate and obtain the change in current and current smoothness ;in, , Indicates the current fluctuation threshold. Indicates the duration of current sampling; The real-time operating status parameters are input into the flywheel energy storage dynamic optimization model, and the corresponding control variables are output. The operating status of the flywheel energy storage system is adjusted through the corresponding control variables to reduce the energy consumption of the flywheel energy storage system.

2. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 1, characterized in that, The established flywheel charging and discharging optimization strategy includes: The operating status parameters of the flywheel energy storage system and the generator are collected according to the preset acquisition frequency, and the collected parameters are preprocessed to form a set of operating status parameters. The dynamic optimization model transforms the problem of minimizing energy consumption costs into a mixed-integer linear programming (MILP) form. Use a solver to solve for the control variables. , and .

3. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 2, characterized in that, The nonlinear mechanical losses introduced into the flywheel energy storage system are used to improve the dynamic optimization model, including: Introducing nonlinear mechanical losses into flywheel energy storage systems: ； in, These represent air resistance loss, bearing friction loss, eddy current loss, and vacuum pump energy consumption, respectively. Calculate and obtain the net charge and discharge power of the flywheel energy storage system: ； By introducing nonlinear mechanical losses and the net charge / discharge power of the flywheel energy storage system into the dynamic optimization model, an improved dynamic optimization model, namely the flywheel energy storage dynamic optimization model, is obtained, including: Add the operating energy consumption term of the vacuum pump to the dynamic optimization model. and startup energy consumption items ;in, These 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 degree constraints and flywheel angular velocity constraints to the dynamic optimization model: Therefore, the flywheel energy storage dynamic optimization model is: ； in, These represent the minimum and maximum permissible speeds of the flywheel, respectively. The flywheel angular velocity is obtained from the set of operating state parameters, and the relationship between flywheel energy storage and flywheel angular velocity is established. ; The improved dynamic optimization model is solved using the Nonlinear Model Predictive Control Framework (NMPC) to obtain new control variables. , , , and .

4. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 3, characterized in that, The optimized flywheel charging and discharging optimization strategy, by constructing a joint optimization model, synchronously adjusts the generator operating state parameters, including: Calculate and obtain the total energy consumption to be optimized: ;in, Indicates the weighting factor; use Replacement of flywheel energy storage dynamic optimization model Obtain the joint optimization model; These represent the generator start-up decision parameters, vacuum pump power, vacuum pump start-stop decision parameters, and flywheel angular velocity, respectively, in the joint optimization model. This indicates the energy consumption coefficient and start-stop energy consumption coefficient of the vacuum pump; The joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm to obtain the control variables that minimize the total energy consumption to be optimized. ; use Adjust the operating status of the generator and the vacuum pump.

5. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 4, characterized in that, The process of acquiring the charging and discharging current of the flywheel energy storage system, analyzing the stability of the charging and discharging current, and optimizing the improved dynamic optimization model and the joint optimization model based on the analysis results also includes: exist Add current stability penalty item Get the extended ;Right now: ;in, Indicates the current smoothness weight; use Replace the joint optimization model , thus obtaining the extended joint optimization model; where, , This indicates the preset current smoothness threshold. These 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 prediction models Predict the current value at a future time, where These represent the generator inductance, back electromotive force coefficient, and voltage, respectively. The extended joint optimization model is solved using the mixed-integer nonlinear programming (NINLP) algorithm within each control cycle, and the corresponding control variables are solved. .

6. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 5, characterized in that, It also includes calculating the current smoothness based on the current values ​​predicted by the prediction model for a future period of time. When the current smoothness exceeds a preset fluctuation threshold, the generator start-stop state and the working 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, and the corresponding control variables are obtained. Specifically, this includes the following steps: Set generator start-up time and flywheel speed adjustment time ;in, Indicates the original startup time; Indicates the generator start-up offset time; Indicates the flywheel speed adjustment time offset duration ; ; By increasing control variables and The joint optimization model is further optimized and extended in a way that dynamically constrains the generator start-up and shutdown time and the flywheel speed adjustment time.

7. The green energy-saving method for flywheel UPS and backup generator for data centers according to claim 6, characterized in that, The method of increasing control variables and The joint optimization model is further optimized and extended in a manner that dynamically constrains the generator start-up and shutdown times and flywheel speed adjustment time, including: The generator start-up and shutdown times are constrained by the generator start-up time window; the time offset duration is adjusted by the speed. Adjusting the flywheel speed adjustment timing to match the generator start-stop timing involves the following steps: In the extended joint optimization model Add a time offset penalty item ,get ,Right now: ； in, Indicates the time offset penalty coefficient; using replace That is, to obtain the optimized extended joint optimization model; where, These represent the generator start-stop decision parameters, vacuum pump power, vacuum pump start-stop decision parameters, and current change 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 generator's output power and discharge it to the flywheel: The generator's final actual output power: ； in, This indicates the maximum allowable output power of the generator. Indicates the generator power ramp-up time; By adjusting Control the flywheel deceleration rate and suppress sudden current changes; The actual speed of the flywheel ,in This indicates the flywheel speed at the point in time when the current smoothness exceeds a preset fluctuation threshold.

8. A coordinated management system for a hybrid UPS combining flywheel energy storage and battery storage, characterized in that, The system is used to execute the green energy-saving method for flywheel UPS and backup generator for data centers as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the green energy-saving method for a flywheel UPS and backup generator for data centers as described in any one of claims 1-7.

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