A flywheel energy storage and battery hybrid ups coordinated management system and method

Abstract

This invention relates to the field of power supply management technology, and particularly to a coordinated management system and method for a hybrid UPS using flywheel energy storage and batteries. The method includes: acquiring operating status data of the flywheel energy storage unit and the battery unit according to a preset acquisition frequency; predicting the power supply control input of the flywheel energy storage and battery within a future time window based on the acquired operating status data, and generating a corresponding control sequence; acquiring the overall power supply efficiency parameter within the corresponding time window under the corresponding control sequence; and optimizing the control sequence by analyzing the changes in the overall power supply efficiency parameter to coordinate and manage the charging and discharging processes of the flywheel energy storage unit and the battery.

Description

Technical Field

[0001] This invention relates to the field of power supply management technology, and in particular to a coordinated management system and method for a hybrid UPS that combines flywheel energy storage and battery storage. Background Technology

[0002] A flywheel energy storage system consists of a high-speed rotating flywheel, an electric motor / generator, and a power converter. The flywheel stores kinetic energy through electrical acceleration, which is then converted into electrical energy by the generator when power is interrupted. The battery pack, typically a lead-acid or lithium-ion battery, stores chemical energy and releases it as electrical energy via an inverter when needed. An intelligent management system coordinates the charging and discharging of both components to ensure seamless switching; the rectifier, inverter, and static switch are responsible for power conversion. The flywheel energy storage system can quickly release kinetic energy through the motor, with a fast response time (milliseconds), making it suitable for frequent, short-term power supply; the batteries are powered by the inverter and rectifier, suitable for longer-term power supply. Combining these two components to build a UPS power supply allows for complementary advantages.

[0003] After flywheel energy storage and batteries constitute a UPS power supply, the switching process between the two needs to be coordinated and managed. Existing technologies have conducted extensive research on optimizing the switching process between the two during power supply and the load adaptability of flywheel energy storage systems during discharge. However, few studies have investigated the impact of the flywheel's state of charge and the battery's state of discharge on power supply efficiency under nonlinear operating conditions. Therefore, based on nonlinear operating conditions, we optimize the power supply efficiency of the UPS system by controlling the flywheel's state of charge and the battery's state of discharge. Summary of the Invention

[0004] This invention constructs a nonlinear coupling model of the flywheel energy storage unit and the battery unit, and selects corresponding variables as control inputs. At the same time, it defines the overall power supply efficiency parameters, and seeks the optimal control timing under the premise of maximizing the overall power supply efficiency, to coordinate and manage the charging and discharging process of the flywheel energy storage unit and the battery unit, so as to optimize the UPS power supply efficiency.

[0005] The technical solution proposed in this invention is: a coordinated management system and method for a hybrid UPS combining flywheel energy storage and battery storage, the method comprising:

[0006] The operating status data of the flywheel energy storage unit and the battery unit are collected according to the preset collection frequency;

[0007] Based on the acquired operating status data of the flywheel energy storage unit and the battery unit, the power supply control input of the flywheel energy storage and the battery in the future time window is predicted, and the corresponding control timing is generated.

[0008] Under the corresponding control timing, obtain the overall power supply efficiency parameters within the corresponding time window;

[0009] By analyzing the changes in the overall power supply efficiency parameters, the control timing is optimized to coordinate and manage the flywheel energy storage unit and the battery charging and discharging process.

[0010] Preferably, the step of predicting the flywheel energy storage and battery power supply control inputs within a future time window based on the acquired operating status data of the flywheel energy storage unit and the battery unit, and generating corresponding control timing sequences, includes:

[0011] Construct a nonlinear coupling model of the flywheel energy storage unit and the battery unit, including:

[0012] Constructing a flywheel rotation dynamics model for the flywheel energy storage unit: ,in, These represent the moment of inertia, electromagnetic torque of the motor, load torque, coefficient of viscous friction, coefficient of air friction, and angular velocity, respectively.

[0013] The energy state of the flywheel energy storage system is nonlinearly mapped, i.e.:

[0014] ,in, Indicates the eddy current loss coefficient;

[0015] Construct an equivalent circuit model for the battery:

[0016] ;

[0017] in, This represents a function of open-circuit voltage as a function of state of charge (SOC). This indicates the battery's internal resistance with respect to SOC and temperature. Polynomial fitting, , They represent the fitting coefficients, respectively; A function representing the dynamic capacitor discharge characteristics as a function of SOC; Indicates battery current;

[0018] Coupling the flywheel energy storage unit and the battery state includes:

[0019] Constructing state vectors ;in, These represent the flywheel bearing temperature and the battery temperature, respectively.

[0020] The nonlinear state equation is ;

[0021] in, Indicates control input, ; Indicates the disturbance term. , These represent load torque fluctuation and ambient temperature, respectively.

[0022] The nonlinear state equations are discretized using the fourth-order Runge-Kutta algorithm (RK4), including:

[0023] set up ,in, Indicates the discretization step size. ; ; ; ; These represent the intermediate slope terms, respectively.

[0024] Performing rolling time-domain prediction includes:

[0025] At every moment Based on the current state vector Predict the state sequence for a future time window ,in, Indicates the number of steps in the time window;

[0026] The prediction model is ;in, This indicates that discretization is performed using the RK4 algorithm;

[0027] By setting an objective function and constraints, the control sequence, i.e., the control timing, is obtained through iterative solution using the Sequence Quadratic Programming (SQP) algorithm.

[0028] Preferably, the step of obtaining the control sequence, i.e., the control timing sequence, by setting an objective function and constraints and iteratively solving the problem using the Sequence Quadratic Programming (SQP) algorithm includes:

[0029] The objective function is constructed based on minimizing tracking error and control cost. ;

[0030] in, This represents the state tracking weight matrix, which is a diagonal matrix. ;

[0031] This represents the control input penalty matrix, which is a diagonal matrix. ;

[0032] This represents the terminal state penalty matrix, which is... Integer multiples of;

[0033] This indicates a reference state, which includes the rated speed and the target state of charge (SOC).

[0034] They represent The sum of the state vectors at time t. Time-based control input;

[0035] Set constraints: ;in, express The flywheel bearing temperature at any given time. express Battery current at any given moment; express A battery that lasts for a moment ; These represent the minimum values ​​of the storage battery. and maximum ; These represent the minimum and maximum speeds of the flywheel, respectively.

[0036] Temperature-dependent torque derating function SOC-related current limiting function , ;

[0037] Iterative solution is performed, including:

[0038] At the current operation point to Perform a Taylor expansion, that is:

[0039] ;in, , ; The increment vector representing the state feature vector; This represents the increment vector of the control input;

[0040] Constructing a quadratic programming problem, i.e., constructing a QP problem:

[0041] ;in, Represents the Hessian matrix; Represents the gradient vector;

[0042] To achieve iterative convergence, the QP problem is solved using the interior-point method or the effective set method to obtain the control sequence. .

[0043] Preferably, the step of obtaining the overall power supply efficiency parameters within the corresponding time window under the corresponding control timing includes:

[0044] The system boundary and energy flow of the UPS are determined based on the overall power supply efficiency parameters within the time window, including:

[0045] The input energy within the acquisition time window, i.e., grid energy. ;

[0046] Acquire stored energy within the time window, i.e., flywheel kinetic energy. and battery chemical energy ;

[0047] The output energy within the acquisition time window, i.e., the actual electrical energy consumed by the load. ;in, Indicates load power;

[0048] Construct the overall power supply efficiency parameters within the time window, including:

[0049] Obtain charging efficiency parameters to measure the conversion efficiency from the grid to the energy storage medium:

[0050] The charging efficiency parameter ;in, This indicates the charging circuit losses, including inverter efficiency and cable resistance losses. express Total energy storage of the flywheel energy storage unit and the battery;

[0051] Obtain storage efficiency parameters, which reflect the energy decay during storage;

[0052] The storage efficiency parameters ;in, This represents the flywheel speed decay time constant; express Constantly monitor the battery's self-discharge current. This indicates the idle time of energy storage, which is the interval from the completion of charging to the start of discharging.

[0053] Obtain discharge efficiency parameters, which reflect the energy conversion efficiency from energy storage to load;

[0054] The discharge efficiency parameter ;in This indicates the flywheel's power generation efficiency. ; Indicates the efficiency of the battery-powered DC / AC inverter; express Constant battery output power; express Constant load power; express Output power of the instantaneous flywheel energy storage system;

[0055] Obtain the switching efficiency, which reflects the energy loss during the switching between charging and discharging.

[0056] The switching efficiency ,in They represent Energy loss due to constant switching; Indicates the total transmitted energy;

[0057] but Overall power supply efficiency parameters at all times

[0058] The overall power supply efficiency within the time window is ; Indicates the weight of each time point.

[0059] Preferably, the step of optimizing the control timing by analyzing changes in the overall power supply efficiency parameters includes:

[0060] Obtain the state sequence for a future time window ;

[0061] Calculate each moment within the time window of And obtain the overall power supply efficiency parameters at the corresponding time. ;

[0062] Integrate overall power supply efficiency parameters within the time window to construct an efficiency optimization objective function. ;in, Indicates the control value smoothing weight;

[0063] Adjusting the control timing using gradient descent includes:

[0064] Assume the updated control input ;in Indicates the learning rate;

[0065] Calculate the gradient of the objective function with respect to the control variables. ,in Indicates the first Overall power supply efficiency parameters at any given moment;

[0066] The optimized control timing is constructed using the updated control inputs. .

[0067] Preferably, the step of optimizing the control timing by analyzing changes in the overall power supply efficiency parameters further includes:

[0068] Considering electricity costs, a new optimization objective function is constructed:

[0069] ;in, They represent The overall power supply efficiency parameters, grid power, and control input at any given time; Indicates the weight of the grid electricity price; This represents the penalty coefficient for controlling smoothness;

[0070] Construct flywheel speed constraints and adjust the flywheel speed, i.e. Minimum flywheel speed at any given time ;in, Indicates the efficiency threshold;

[0071] Construct battery charge and discharge current constraints to limit high-current battery discharge, i.e. Maximum battery current at any time ,in Indicates the maximum allowable discharge current of the battery;

[0072] Based on flywheel speed constraints and battery charging / discharging current constraints, sequential quadratic programming is used to calculate...

[0073] The optimal control sequence within a future time window is solved iteratively using a method.

[0074] Preferably, based on flywheel speed constraints and battery charging / discharging current constraints, a sequential quadratic programming algorithm is used to iteratively solve for the optimal control sequence within the future time window, including:

[0075] The new optimization objective function is approximated by a second approximation, namely: ;in, This represents the control increment vector. Represents the Hessian matrix; Indicates the preset initial value;

[0076] Constraint linearization includes:

[0077] Linearization of flywheel speed constraints, i.e. ;

[0078] The battery charge and discharge current is preset to be linearized, i.e. ;in This represents the increment of the maximum battery discharge current;

[0079] Maximizing the overall power supply efficiency parameter is transformed into a quadratic programming subproblem of constructing power supply efficiency, i.e. ;in, Let these represent the constraint linearization matrix and the constraint linearization vector, respectively. This indicates that gradient calculation is being performed;

[0080] Solve the quadratic programming subproblem of power supply efficiency using the interior-point method or the effective set method, and update the control input as follows: ;in, To represent the step size, using Constructing the optimal control sequence .

[0081] Preferably, the step of iteratively solving the optimal control sequence within the future time window using a sequential quadratic programming algorithm based on flywheel speed constraints and battery charging / discharging current constraints further includes:

[0082] After obtaining the optimal control sequence, the sensitivity of the overall power supply efficiency parameter to the control input is propagated backward time by time using the chain rule: .

[0083] A coordinated management system for a hybrid UPS combining flywheel energy storage and battery, the system being used to execute the coordinated management method for the hybrid UPS combining flywheel energy storage and battery.

[0084] A computer-readable storage medium storing a computer program that is executed by a processor to implement the aforementioned coordinated management system and method for a flywheel energy storage and battery hybrid UPS.

[0085] The beneficial effects of this invention are:

[0086] This invention predicts the control inputs for flywheel energy storage and battery power supply within a future time window based on the acquired operating status data of the flywheel energy storage unit and battery unit, and generates the corresponding control timing sequence. Based on the flywheel speed constraint and the battery charging and discharging current constraint, the optimal control sequence within the future time window is iteratively solved using a sequential quadratic programming algorithm, and the optimal control sequence is used to achieve coordinated management of the flywheel energy storage unit and battery unit. Attached Figure Description

[0087] Figure 1 This is a flowchart illustrating a coordinated management method for a hybrid UPS combining flywheel energy storage and battery, according to the present invention. Detailed Implementation

[0088] 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.

[0089] 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.

[0090] Example 1:

[0091] refer to Figure 1The technical solution provided by this invention is: a coordinated management system and method for a hybrid UPS combining flywheel energy storage and battery storage, comprising the following steps:

[0092] Step 1: Collect the operating status data of the flywheel energy storage unit and the battery unit according to the preset acquisition frequency. The operating status data of the flywheel energy storage unit includes flywheel angular velocity, motor electromagnetic torque, bearing temperature, and flywheel friction coefficient; the operating status data of the battery unit includes input current, output current, and battery state of charge (SOC).

[0093] Step 2: Based on the acquired operating status data of the flywheel energy storage unit and battery unit, predict the power supply control input of the flywheel energy storage and battery within the future time window, and generate the corresponding control timing sequence. This specifically includes the following steps:

[0094] Step 2.1: Construct a nonlinear coupling model of the flywheel energy storage unit and the battery unit, including:

[0095] Constructing a flywheel rotation dynamics model for the flywheel energy storage unit: ,in, These represent the moment of inertia, electromagnetic torque of the motor, load torque, coefficient of viscous friction, coefficient of air friction, and angular velocity, respectively.

[0096] Step 2.2: Perform a nonlinear mapping on the energy state of the flywheel energy storage system, i.e.:

[0097] ,in, Indicates the eddy current loss coefficient;

[0098] Construct an equivalent circuit model for the battery:

[0099] ;

[0100] in, This represents a function of open-circuit voltage as a function of state of charge (SOC). This indicates the battery's internal resistance with respect to SOC and temperature. Polynomial fitting, , They represent the fitting coefficients, respectively; A function representing the dynamic capacitor discharge characteristics as a function of SOC; Indicates battery current;

[0101] Step 2.3: Couple the flywheel energy storage unit and the battery state, including:

[0102] Constructing state vectors ;in, These represent the flywheel bearing temperature and the battery temperature, respectively.

[0103] The nonlinear state equation is ;

[0104] in, Indicates control input, ; Indicates the disturbance term. , These represent load torque fluctuation and ambient temperature, respectively.

[0105] In this embodiment, the selected As control input parameters, the magnitude of the motor's electromagnetic torque reflects the motor's speed. During the charging process, knowing the motor speed allows for control of the flywheel's charging process. The battery current reflects the battery's output power, and controlling the battery current manages the battery's discharge process.

[0106] Step 2.4: Discretize the nonlinear state equations using the fourth-order Runge-Kutta algorithm (RK4), including:

[0107] set up ,in, Indicates the discretization step size. ; ; ; ; These represent the intermediate slope terms, respectively.

[0108] Step 2.5: Perform rolling time-domain prediction, including:

[0109] At every moment Based on the current state vector Predict the state sequence for a future time window ,in, This represents the number of steps within the time window; the step size between each step is... ; In this embodiment, the length of the time window is... .

[0110] The prediction model is: ;

[0111] in, This indicates that discretization is performed using the RK4 algorithm.

[0112] By setting an objective function and constraints, the control sequence, i.e., the control timing, is obtained through iterative solution using the Sequence Quadratic Programming (SQP) algorithm.

[0113] In hybrid energy storage UPS systems, the flywheel and battery exhibit nonlinear dynamic characteristics, requiring a predictive model that accurately describes the nonlinear coupling effect. Therefore, this step involves setting up a nonlinear coupling model for the flywheel and battery to predict their states under nonlinear operating conditions.

[0114] The process involves setting an objective function and constraints, then iteratively solving the problem using the Sequence Quadratic Programming (SQP) algorithm to obtain the control sequence, i.e., the control timing. This includes the following steps:

[0115] The objective function is constructed based on minimizing tracking error and control cost. ;

[0116] in, This represents the state tracking weight matrix, which is a diagonal matrix. ;

[0117] This represents the control input penalty matrix, which is a diagonal matrix. ;

[0118] This represents the terminal state penalty matrix, which is... Integer multiples of;

[0119] This indicates a reference state, which includes the rated speed and the target state of charge (SOC).

[0120] They represent The sum of the state vectors at time t. Time-based control input;

[0121] Set constraints: ;in, express The flywheel bearing temperature at any given time. express Battery current at any given moment; express A battery that lasts for a moment ; These represent the minimum values ​​of the storage battery. and maximum ; These represent the minimum and maximum speeds of the flywheel, respectively.

[0122] Temperature-dependent torque derating function SOC-related current limiting function , ;

[0123] Iterative solution is performed, including:

[0124] At the current operation point to Perform a Taylor expansion, that is:

[0125] ;in, , ; The increment vector representing the state feature vector; This represents the increment vector of the control input;

[0126] Constructing a quadratic programming problem, i.e., constructing a QP problem:

[0127] ;in, Represents the Hessian matrix; Represents the gradient vector;

[0128] To achieve iterative convergence, the QP problem is solved using the interior-point method or the effective set method to obtain the control sequence. .

[0129] Step 3: Obtain the overall power supply efficiency parameters within the corresponding time window under the appropriate control timing; including the following steps:

[0130] The system boundary and energy flow of the UPS are determined based on the overall power supply efficiency parameters within the time window, including:

[0131] The input energy within the acquisition time window, i.e., grid energy. ;

[0132] Acquire stored energy within the time window, i.e., flywheel kinetic energy. and battery chemical energy ;

[0133] The output energy within the acquisition time window, i.e., the actual electrical energy consumed by the load. ;in, Indicates load power;

[0134] Construct the overall power supply efficiency parameters within the time window, including:

[0135] Obtain charging efficiency parameters to measure the conversion efficiency from the grid to the energy storage medium:

[0136] The charging efficiency parameter is:

[0137] ;

[0138] in, This indicates the charging circuit losses, including inverter efficiency and cable resistance losses. express The total energy stored in the flywheel energy storage unit and the battery at any given time. The charging efficiency parameter includes the energy consumed by the flywheel during charging and the energy consumed by the battery during charging, reflecting the impact of the charging process of both on the charging efficiency.

[0139] Obtain storage efficiency parameters, which reflect the energy decay during storage;

[0140] The storage efficiency parameters ;in, This represents the flywheel speed decay time constant; express Constantly monitor the battery's self-discharge current. This indicates the idle time of energy storage, which is the interval from the completion of charging to the start of discharging.

[0141] Obtain discharge efficiency parameters, which reflect the energy conversion efficiency from energy storage to load;

[0142] The discharge efficiency parameter is:

[0143] ;

[0144] in This indicates the flywheel's power generation efficiency. ; Indicates the efficiency of the battery-powered DC / AC inverter; express Constant battery output power; express Constant load power; express Output power of the instantaneous flywheel energy storage system;

[0145] Obtain the switching efficiency, which reflects the energy loss during the switching between charging and discharging.

[0146] The switching efficiency ,in They represent Energy loss due to constant switching; Indicates the total transmitted energy;

[0147] but Overall power supply efficiency parameters at all times

[0148] The overall power supply efficiency within the time window is ; Indicates the weight of each time point.

[0149] Step 4: By analyzing the changes in overall power supply efficiency parameters, optimize the control timing and coordinate the management of the flywheel energy storage unit and the battery charging and discharging process, including the following steps:

[0150] Obtain the state sequence for a future time window ;

[0151] Calculate each moment within the time window of And obtain the overall power supply efficiency parameters at the corresponding time. ;

[0152] Integrate overall power supply efficiency parameters within the time window to construct an efficiency optimization objective function. ;in, Indicates the control value smoothing weight;

[0153] Adjusting the control timing using gradient descent includes:

[0154] Assume the updated control input ;in Indicates the learning rate;

[0155] Calculate the gradient of the objective function with respect to the control variables. ,in Indicates the first Overall power supply efficiency parameters at any given moment;

[0156] The optimized control timing is constructed using the updated control inputs. .

[0157] Example 2:

[0158] In Example 1, a method for optimizing control timing based on predicted operating state characteristics within a future time window was presented. In some cases, it is still necessary to consider grid electricity costs and manage the flywheel charging process to improve overall efficiency while ensuring optimal power supply efficiency.

[0159] Therefore, based on Example 1, we propose the following solution:

[0160] Considering electricity costs, a new optimization objective function is constructed:

[0161] ;in, They represent The overall power supply efficiency parameters, grid power, and control input at any given time; Indicates the weight of the grid electricity price; This represents the penalty coefficient for controlling smoothness;

[0162] Construct flywheel speed constraints and adjust the flywheel speed, i.e. Minimum flywheel speed at any given time ;in, This indicates the efficiency threshold; if the power supply efficiency requirements cannot be met even when the speed is reduced to the minimum, this step allows for dynamic adjustment of the flywheel's minimum speed constraint.

[0163] Construct battery charge and discharge current constraints to limit high-current battery discharge, i.e. Maximum battery current at any time ,in This indicates the maximum allowable discharge current of the battery. If the power supply efficiency cannot be met even when the existing maximum current is reached, this step allows for dynamic adjustment of the battery's maximum current constraint.

[0164] Based on flywheel speed constraints and battery charging / discharging current constraints, a sequential quadratic programming algorithm is used to iteratively solve for the optimal control sequence within a future time window. The steps include:

[0165] The new optimization objective function is approximated by a second approximation, namely: ;in, This represents the control increment vector. The Hessian matrix can be represented (it can be approximated by a quasi-Newton method, such as BFGS update). Indicates the preset initial value;

[0166] Constraint linearization includes:

[0167] Linearization of flywheel speed constraints, i.e. ;

[0168] The battery charge and discharge current is preset to be linearized, i.e. ;in This represents the increment of the maximum battery discharge current;

[0169] Maximizing the overall power supply efficiency parameter is transformed into a quadratic programming subproblem of constructing power supply efficiency, namely: ;

[0170] in, These represent the constraint linearization matrix and constraint linearization vector, respectively, which are generated from the linearization constraints. This indicates that gradient calculation is being performed;

[0171] Solve the quadratic programming subproblem of power supply efficiency using the interior-point method or the effective set method, and update the control input as follows: ;in, The step size is determined through a linear search to ensure that the constraints are met.

[0172] use Constructing the optimal control sequence In reality, it is possible to implement only the optimal control quantity at the current moment. The prediction and optimization process is repeated in the next cycle to achieve rolling optimization of the control input.

[0173] For example, if the UPS system needs to respond to load fluctuations within 30 seconds and ensure optimal overall power supply efficiency parameters.

[0174] The parameters at this point include the flywheel's moment of inertia. Maximum permissible speed ;

[0175] The battery's maximum output current is ;

[0176] The following control sequence is used to coordinate the charging and discharging process, specifically:

[0177] During the first 0-10 seconds (i.e., N=1, step 1), the control input is the maximum value of the motor's electromagnetic torque, meaning the flywheel operates at full torque, and the battery output current is 0, meaning the battery is in standby mode; the overall power supply efficiency parameters are optimal during this period. Excess power caused by load fluctuations (load reduction) is converted into flywheel kinetic energy, which is to charge the flywheel.

[0178] During the first 10-20 seconds (i.e., N=2, step 2), the control input is that the motor's electromagnetic torque decreases to 80% of its maximum value, and the battery output current is also reduced to 80% of its maximum value. This controls the flywheel to reduce its speed and charging rate, allowing the battery to discharge and replenish power. At this point, the overall power supply efficiency parameters are optimal. By replenishing power through the battery, the system adapts to load recovery and ensures power supply to the load.

[0179] Between 20 and 30 seconds (i.e., N=3, step 3), the control input is that the motor's electromagnetic torque decreases to 0, thus stopping the charging process. The battery output current reaches its maximum value, providing electrical energy through the battery. At this point, the overall power supply efficiency parameters are optimal.

[0180] In this embodiment, the electromagnetic torque of the motor refers to the electromagnetic torque of the electric motor.

[0181] After obtaining the optimal control sequence, the sensitivity of the overall power supply efficiency parameter to the control input is propagated backward time by time using the chain rule: .

[0182] Control input Motor electromagnetic torque The impact paths on overall power supply efficiency parameters include: Direct impact: → → → Indirect impact: Further impact on subsequent moments Rotation speed at any given moment.

[0183] The above process demonstrates the impact of flywheel charging and battery discharging on the overall power supply efficiency parameters. In a UPS system composed of hybrid energy storage methods, the impact of charging and discharging processes of different energy storage methods on system power supply efficiency was established. While ensuring optimal power supply efficiency (which can be obtained through historical data analysis or experimental calibration), the corresponding control inputs were derived in reverse. These control inputs were then used to coordinate the charging and discharging processes to optimize system power supply efficiency.

[0184] In some preferred embodiments, different parameters can be selected as control inputs to coordinate different states. For example, using battery current and battery self-discharge current as control inputs (which can be achieved by switching between different types of batteries) can coordinate the energy storage state of the system and the discharge state of the battery, ensuring optimal overall power supply efficiency parameters.

[0185] The present invention also provides a coordinated management system for a flywheel energy storage and battery hybrid UPS, the system being used to execute the aforementioned coordinated management method for a flywheel energy storage and battery hybrid UPS.

[0186] The present invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the aforementioned coordinated management method for a flywheel energy storage and battery hybrid UPS.

[0187] 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.

[0188] 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.

[0189] 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 coordinated management method for a hybrid UPS combining flywheel energy storage and battery storage, characterized in that, The method includes: The operating status data of the flywheel energy storage unit and the battery unit are collected according to the preset collection frequency; Based on the acquired operating status data of the flywheel energy storage unit and battery unit, the power supply control input of the flywheel energy storage and battery within the future time window is predicted, and the corresponding control timing is generated; including: constructing a nonlinear coupling model of the flywheel energy storage unit and battery unit; performing nonlinear mapping on the energy state of the flywheel energy storage system; coupling the states of the flywheel energy storage unit and battery; and performing rolling time-domain prediction; Under the corresponding control timing, obtain the overall power supply efficiency parameters within the corresponding time window; By analyzing changes in overall power supply efficiency parameters, the control timing is optimized to coordinate and manage the flywheel energy storage unit and the battery charging and discharging process; including: Obtain the state sequence for a future time window ; Calculate each moment within the time window Charging efficiency parameters, storage efficiency parameters, discharging efficiency parameters, and switching efficiency: And obtain the overall power supply efficiency parameters at the corresponding time. ; Integrate overall power supply efficiency parameters within the time window to construct an efficiency optimization objective function. ;in, Indicates the control value smoothing weight; Overall power supply efficiency parameters at all times ; Indicates the weight of time points; express Time-based control input; Adjusting the control timing using gradient descent includes: Assume the updated control input ;in Indicates the learning rate; Calculate the gradient of the objective function with respect to the control variables. ,in Indicates the first Overall power supply efficiency parameters at any given moment; The optimized control timing is constructed using the updated control inputs. .

2. The coordinated management method for a hybrid UPS combining flywheel energy storage and battery as described in claim 1, characterized in that, The construction of the nonlinear coupling model of the flywheel energy storage unit and the battery unit includes: Constructing a flywheel rotation dynamics model for the flywheel energy storage unit: ,in, These represent the moment of inertia, electromagnetic torque of the motor, load torque, coefficient of viscous friction, coefficient of air friction, and angular velocity, respectively. The nonlinear mapping of the energy state of the flywheel energy storage system is as follows: ,in, Indicates the eddy current loss coefficient; Construct an equivalent circuit model for the battery: ； in, This represents a function of open-circuit voltage as a function of state of charge (SOC). This indicates the battery's internal resistance with respect to SOC and temperature. Polynomial fitting, , They represent the fitting coefficients, respectively; A function representing the dynamic capacitor discharge characteristics as a function of SOC; Indicates battery current; The coupling of the flywheel energy storage unit and the battery status includes: Constructing state vectors ;in, These represent the flywheel bearing temperature and the battery temperature, respectively. The nonlinear state equation is ; in, Indicates control input, ; Indicates the disturbance term. , These represent load torque fluctuation and ambient temperature, respectively. The nonlinear state equations are discretized using the fourth-order Runge-Kutta algorithm (RK4), including: set up ,in, Indicates the discretization step size. ; ; ; ; These represent the intermediate slope terms, respectively. The rolling time-domain prediction includes: At every moment Based on the current state vector Predict the state sequence for a future time window ,in, Indicates the number of steps in the time window; The prediction model is ;in, This indicates that discretization is performed using the RK4 algorithm; By setting an objective function and constraints, the control sequence, i.e., the control timing, is obtained through iterative solution using the Sequence Quadratic Programming (SQP) algorithm.

3. The coordinated management method for a hybrid UPS combining flywheel energy storage and battery as described in claim 2, characterized in that, The process involves establishing an objective function and constraints, and then iteratively solving the problem using the Sequence Quadratic Programming (SQP) algorithm to obtain the control sequence, i.e., the control timing. This includes: The objective function is constructed based on minimizing tracking error and control cost. ； in, This represents the state tracking weight matrix, which is a diagonal matrix. ; This represents the control input penalty matrix, which is a diagonal matrix. ; This represents the terminal state penalty matrix, which is... Integer multiples of; This indicates a reference state, which includes the rated speed and the target state of charge (SOC). They represent The sum of the state vectors at time t. Time-based control input; Set constraints: ;in, express The flywheel bearing temperature at any given time. express Battery current at any given moment; express A battery that lasts for a moment ; These represent the minimum values ​​of the storage battery. and maximum ; These represent the minimum and maximum speeds of the flywheel, respectively. Temperature-dependent torque derating function SOC-related current limiting function , ; Iterative solution is performed, including: At the current operation point to Perform a Taylor expansion, that is: ;in, , ; The increment vector representing the state feature vector; This represents the increment vector of the control input; Constructing a quadratic programming problem, i.e., constructing a QP problem: ;in, Represents the Hessian matrix; Represents the gradient vector; To achieve iterative convergence, the QP problem is solved using the interior-point method or the effective set method to obtain the control sequence. .

4. The coordinated management method for a hybrid UPS combining flywheel energy storage and battery as described in claim 3, characterized in that, The step of obtaining the overall power supply efficiency parameters within the corresponding time window under the corresponding control timing includes: The system boundary and energy flow of the UPS are determined based on the overall power supply efficiency parameters within the time window, including: The input energy within the acquisition time window, i.e., grid energy. ; Acquire stored energy within the time window, i.e., flywheel kinetic energy. and battery chemical energy ; The output energy within the acquisition time window, i.e., the actual electrical energy consumed by the load. ;in, Indicates load power; Construct the overall power supply efficiency parameters within the time window, including: Obtain charging efficiency parameters to measure the conversion efficiency from the grid to the energy storage medium: The charging efficiency parameter ;in, This indicates the charging circuit losses, including inverter efficiency and cable resistance losses. express Total energy storage of the flywheel energy storage unit and the battery; Obtain storage efficiency parameters, which reflect the energy decay during storage; The storage efficiency parameters ;in, This represents the flywheel speed decay time constant; express Constantly monitor the battery's self-discharge current. This indicates the idle time of energy storage, which is the interval from the completion of charging to the start of discharging. Obtain discharge efficiency parameters, which reflect the energy conversion efficiency from energy storage to load; The discharge efficiency parameter ;in This indicates the flywheel's power generation efficiency. ; Indicates the efficiency of the battery-powered DC / AC inverter; express Constant battery output power; express Constant load power; express Output power of the instantaneous flywheel energy storage system; Obtain the switching efficiency, which reflects the energy loss during the switching between charging and discharging. The switching efficiency ,in They represent Energy loss due to constant switching; Indicates the total transmitted energy; The overall power supply efficiency within the time window is ; Indicates the weight of each time point.

5. The coordinated management method for a flywheel energy storage and battery hybrid UPS according to claim 4, characterized in that, The optimization of the control timing by analyzing changes in the overall power supply efficiency parameters also includes: Considering electricity costs, a new optimization objective function is constructed: ;in, They represent The overall power supply efficiency parameters, grid power, and control input at any given time; Indicates the weight of the grid electricity price; This represents the penalty coefficient for controlling smoothness; Construct flywheel speed constraints and adjust the flywheel speed, i.e. Minimum flywheel speed at any given time ;in, Indicates the efficiency threshold; Construct battery charge and discharge current constraints to limit high-current battery discharge, i.e. Maximum battery current at any time ,in Indicates the maximum allowable discharge current of the battery; Based on flywheel speed constraints and battery charging / discharging current constraints, a sequential quadratic programming algorithm is used to iteratively solve for the optimal control sequence within a future time window.

6. The coordinated management method for a hybrid UPS combining flywheel energy storage and battery as described in claim 5, characterized in that, Based on flywheel speed constraints and battery charging / discharging current constraints, a sequential quadratic programming algorithm is used to iteratively solve for the optimal control sequence within a future time window, including: The new optimization objective function is approximated by a second approximation, namely: ;in, This represents the control increment vector. Represents the Hessian matrix; Indicates the preset initial value; Constraint linearization includes: Linearization of flywheel speed constraints, i.e. ; The battery charge and discharge current is preset to be linearized, i.e. ;in This represents the increment of the maximum battery discharge current; Maximizing the overall power supply efficiency parameter is transformed into a quadratic programming subproblem of constructing power supply efficiency, i.e. ;in, Let them represent the constraint linearization matrix and the constraint linearization vector, respectively. This indicates that gradient calculation is being performed; Solve the quadratic programming subproblem of power supply efficiency using the interior-point method or the effective set method, and update the control input as follows: ;in, To represent the step size, using Constructing the optimal control sequence .

7. The coordinated management method for a hybrid UPS combining flywheel energy storage and battery as described in claim 6, characterized in that, The method of using a sequential quadratic programming algorithm to iteratively solve for the optimal control sequence within a future time window, based on flywheel speed constraints and battery charging / discharging current constraints, also includes: After obtaining the optimal control sequence, the sensitivity of the overall power supply efficiency parameter to the control input is propagated backward time by time using the chain rule: .

8. A coordinated management system for a hybrid UPS combining flywheel energy storage and battery storage, characterized in that, The system is used to perform a coordinated management method for a flywheel energy storage and battery hybrid UPS 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 a coordinated management method for a flywheel energy storage and battery hybrid UPS as described in any one of claims 1-7.

Images