Coordinated management system and method for flywheel energy storage and storage battery mixed UPS (Uninterrupted Power Supply)

Abstract

The invention relates to the technical field of power supply management, in particular to a flywheel energy storage and storage battery hybrid UPS coordination management system and method, and the method comprises the steps: collecting the operation state data of a flywheel energy storage unit and a storage battery unit according to a preset collection frequency; predicting flywheel energy storage and energy supply control input of a storage battery in a future time window based on the acquired running state data of the flywheel energy storage unit and the storage battery unit, and generating a corresponding control time sequence; acquiring a power supply overall efficiency parameter in the corresponding time window under the corresponding control time sequence; the control time sequence is optimized by analyzing changes of power supply overall efficiency parameters, and the charging and discharging processes of the flywheel energy storage unit and the storage battery are managed in a coordinated mode.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply management, and particularly relates to a coordinated management system and method for a flywheel energy storage and battery hybrid UPS. Background Art

[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 by accelerating with electric energy and converts it into electric energy through a generator during a power outage. The battery pack is usually a lead-acid or lithium-ion battery that stores chemical energy and releases electric energy through an inverter when needed. The intelligent management system coordinates the charging and discharging of both to ensure seamless switching; the rectifier, inverter, and static switch are responsible for electric energy conversion. The flywheel energy storage system can quickly release kinetic energy through an engine, with a fast response time (millisecond level) to adapt to frequent short-term power supply; the battery supplies power through an inverter, rectifier, etc. to adapt to long-term power supply. Combining the two to construct a UPS power supply can achieve complementary advantages.

[0003] After the flywheel energy storage and the battery form a UPS power supply, it is necessary to coordinate and manage the switching process between the two. Existing technologies have conducted a lot of research on optimizing the switching process between the two in power supply, the load adaptability during the discharging process of the flywheel energy storage system, etc. However, few people have studied the influence of the charging state of the flywheel and the discharging state of the battery on the power supply efficiency under non-linear working conditions. Therefore, based on non-linear working conditions, we optimize the power supply efficiency of the UPS system by controlling the charging state of the flywheel and the discharging state of the battery. Summary of the Invention

[0004] In the present invention, a non-linear coupling model of the flywheel energy storage unit and the battery unit is constructed, and corresponding variables are selected as control inputs; at the same time, the overall power supply efficiency parameter is defined, and under the premise of maximizing the overall power supply efficiency, the optimal control timing is sought to coordinate and manage the charging and discharging processes of the flywheel energy storage unit and the battery unit, so as to achieve the purpose of optimizing the UPS power supply efficiency.

[0005] The technical solution proposed by the present invention is: a coordinated management system and method for a flywheel energy storage and battery hybrid UPS, and the method includes: Collect the operation state data of the flywheel energy storage unit and the battery unit according to a preset acquisition frequency; Predict the energy supply control inputs of the flywheel energy storage and the battery within a future time window based on the obtained operation state data of the flywheel energy storage unit and the battery unit, and generate corresponding control timings; Obtain the overall power supply efficiency parameter within the corresponding time window under the corresponding control timing; Optimize the control timing by analyzing the change of the overall power supply efficiency parameter, and coordinate and manage the charging and discharging processes of the flywheel energy storage unit and the battery.

[0006] Preferably, predicting the flywheel energy storage and battery power supply control inputs within a future time window based on the obtained operating state data of the flywheel energy storage unit and the battery unit, and generating corresponding control timings, includes: Constructing a non - linear coupling model of the flywheel energy storage unit and the battery unit, including: Constructing a flywheel rotation dynamics model of the flywheel energy storage unit: , where respectively represent moment of inertia, motor electromagnetic torque, load torque, viscous friction coefficient, air friction coefficient, and angular velocity; Performing a non - linear mapping on the energy state of the flywheel energy storage system, that is: , where represents the eddy current loss coefficient; Constructing a battery equivalent circuit model: ; where represents the function of the open - circuit voltage varying with SOC; represents the polynomial fitting of the battery internal resistance with respect to SOC and temperature , , respectively represent fitting coefficients; represents the function of the dynamic capacitance discharge characteristic varying with SOC; represents the battery current; Coupling the states of the flywheel energy storage unit and the battery, including: Constructing a state vector ; where respectively represent the flywheel bearing temperature and the battery temperature; The non - linear state equation is ; where represents the control input, ; represents the disturbance term, , respectively represent the load torque fluctuation and the ambient temperature; Discretizing the non - linear state equation through the fourth - order Runge - Kutta algorithm RK4, including: Let , where represents the discretization step size, ; ; ; ; respectively represent the intermediate slope terms; Performing rolling - horizon prediction, including: At each moment , based on the current state vector predict the state sequence for a future time window , where represents the number of steps in the time window; The prediction model is ; where represents discretization by the RK4 algorithm; By setting up an objective function and constraint conditions, iterative solution is carried out through the sequential quadratic programming algorithm SQP to obtain the control sequence, that is, the control time series.

[0007] Preferably, the iterative solution through the sequential quadratic programming algorithm SQP by setting up an objective function and constraint conditions to obtain the control sequence, that is, the control time series, includes: Construct an objective function based on minimizing the tracking error and control cost ; where represents the state tracking weight matrix, and the state tracking weight matrix is a diagonal matrix, that is ; represents the control input penalty matrix, and the control input penalty matrix is a diagonal matrix, that is ; represents the terminal state penalty matrix, and the terminal state penalty matrix is an integer multiple of; represents the reference state, and the reference state includes the rated speed and the target SOC; respectively represent the state vector at time and the control input at time; Set the constraint conditions: ; where represents the flywheel bearing temperature at time, represents the battery current at time; represents the of the storage battery at time; respectively represent the minimum and maximum of the storage battery; respectively represent the minimum speed and the maximum speed of the flywheel; Temperature-related torque derating function ; SOC-related current limit function , ; Perform iterative solution, including: At the current operating point Perform Taylor expansion on That is: ; where , ; Represents the incremental vector of the state feature vector; Represents the incremental vector of the control input; Construct a quadratic programming problem, that is, construct a QP problem: ; where Represents the Hessian matrix; Represents the gradient vector; Perform iterative convergence, that is, solve the QP problem by the interior point method or the active set method to obtain the control sequence .

[0008] Preferably, obtaining the overall power supply efficiency parameter within the corresponding time window under the corresponding control timing includes: Determine the system boundary and energy flow of the UPS based on the overall power supply efficiency parameter within the time window, including: Obtain the input energy within the time window, that is, the grid energy ; Obtain the stored energy within the time window, that is, the flywheel kinetic energy and the chemical energy of the battery ; Obtain the output energy within the time window, that is, the electrical energy actually consumed by the load ; where Represents the load power; Construct the overall power supply efficiency parameter within the time window, including: Obtain the charging efficiency parameter, and measure the conversion efficiency from the grid to the energy storage medium through the charging efficiency parameter: The charging efficiency parameter ; where Represents the charging circuit loss, including the inverter efficiency and the cable resistance loss; Represents The total energy storage of the flywheel energy storage unit and the battery at time Obtain the storage efficiency parameter, and reflect the attenuation of energy during storage through the storage efficiency parameter; The storage efficiency parameter ; where Represents the flywheel speed decay time constant; Represents The self-discharge current of the battery at time Indicates the energy storage idle time, i.e., the interval from the completion of charging to the start of discharging; Obtain the discharge efficiency parameter, and reflect the energy conversion efficiency from the energy storage to the load through the discharge efficiency parameter; The said discharge efficiency parameter ; where Indicates the flywheel power generation efficiency, ; Indicates the battery DC / AC inverter efficiency; Indicates The output power of the storage battery at time Indicates The load power at time; Indicates The output power of the flywheel energy storage system at time; Obtain the switching efficiency, and reflect the energy loss during the charging and discharging switching through the switching efficiency; The said switching efficiency , where Respectively indicate The loss energy of the switching at time; Indicates the total transmission energy; Then The overall power supply efficiency parameter at time The overall power supply efficiency within the time window is ; Indicates the time point weight.

[0009] Preferably, by analyzing the change of the overall power supply efficiency parameter, optimizing the control timing includes: Obtain the state sequence of the next time window ; Calculate each moment within the time window of ; and obtain the overall power supply efficiency parameter at the corresponding moment ; Integrate the overall power supply efficiency parameters within the time window to construct an efficiency optimization objective function ; where, Indicates the control value smoothing weight; Adjust the control timing by the gradient descent method, including: Set the updated control input ; where Indicates the learning rate; Calculate the gradient of the optimization objective function with respect to the control variable , where Indicates the th overall power supply efficiency parameter at time; Construct an optimized control timing using the updated control input .

[0010] Preferably, optimizing the control timing by analyzing the change of the overall power supply efficiency parameter further includes: Considering the electricity cost, construct a new optimization objective function: ; where respectively represent the overall power supply efficiency parameter, grid power, and control input at time represents the grid electricity price weight; represents the control smoothness penalty coefficient; Construct a flywheel speed constraint and adjust the flywheel speed, i.e., the minimum flywheel speed at time ; where represents the efficiency threshold; Construct a battery charge and discharge current constraint to limit the large current discharge of the battery, i.e., the maximum battery current at time , where represents the maximum allowable discharge current of the storage battery; Based on the flywheel speed constraint and the battery charge and discharge current constraint, use the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window.

[0011] Preferably, based on the flywheel speed constraint and the battery charge and discharge current constraint, using the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window includes: Quadratically approximate the new optimization objective function, i.e.: ; where represents the control quantity increment vector, represents the Hessian matrix; represents the preset initial value; Linearize the constraint conditions, including: Linearize the flywheel speed constraint, i.e., ; Linearize the preset battery charge and discharge current, i.e., ; where represents the increment of the maximum battery discharge current; Convert the maximization of the overall power supply efficiency parameter into a quadratic programming sub-problem for constructing the power supply efficiency, i.e., ; where respectively represent the constraint linearization matrix and the constraint linearization vector; represents performing a gradient operation; Solve the quadratic programming sub-problem of the power supply efficiency using the interior point method or the active set method, and update the control input to ; where represents the step size, and use to form the optimal control sequence .

[0012] Preferably, based on the flywheel speed constraint and the battery charge and discharge current constraint, using the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window, further includes: After obtaining the optimal control sequence, through the chain rule, backpropagate the sensitivity of the overall power supply efficiency parameter to the control input moment by moment: .

[0013] A coordinated management system for a flywheel energy storage and battery hybrid UPS, the system is used to execute the coordinated management method of a flywheel energy storage and battery hybrid UPS described above.

[0014] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the coordinated management system and method of a flywheel energy storage and battery hybrid UPS described above.

[0015] Advantages of the present invention: In the present invention, based on the obtained operation state data of the flywheel energy storage unit and the battery unit, predict the control inputs of the flywheel energy storage and the battery energy supply within the future time window, and generate corresponding control timings; based on the flywheel speed constraint and the battery charge and discharge current constraint, use the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window, and use the optimal control sequence to realize the coordinated management of the flywheel energy storage unit and the battery unit. Description of the drawings

[0016] Figure 1 is a flowchart of a coordinated management method for a flywheel energy storage and battery hybrid UPS of the present invention. Detailed implementation manners

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious deformations. The basic principles defined in the following description can be applied to other implementation manners, deformation schemes, improvement schemes, equivalent schemes, and other technical schemes that do not depart from the spirit and scope of the present invention.

[0018] It can be 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 other embodiments, the number of the element can be multiple. The term "a" should not be understood as a limitation on the quantity.

[0019] Embodiment 1: Reference Figure 1 , the technical solution provided by the present invention is: a coordinated management system and method for a flywheel energy storage and battery hybrid UPS, including the following steps: Step 1: Collect the operation status data of the flywheel energy storage unit and the battery unit according to a preset acquisition frequency. The operation status data of the flywheel energy storage unit includes flywheel angular velocity, motor electromagnetic torque, bearing temperature, and flywheel friction coefficient; the operation status data of the battery unit includes input current, output current, and battery state of charge (SOC).

[0020] Step 2: Predict the energy supply control inputs of the flywheel energy storage and the battery within a future time window based on the obtained operation status data of the flywheel energy storage unit and the battery unit, and generate corresponding control timings. Specifically, it includes the following steps: Step 2.1: Construct a non-linear coupling model of the flywheel energy storage unit and the battery unit, including: Construct a flywheel rotation dynamics model of the flywheel energy storage unit: , where respectively represent moment of inertia, motor electromagnetic torque, load torque, viscous friction coefficient, air friction coefficient, and angular velocity; Step 2.2: Perform a non-linear mapping on the energy state of the flywheel energy storage system, that is: , where represents the eddy current loss coefficient; Construct an equivalent circuit model of the battery: ; where represents a function of the open-circuit voltage varying with SOC; represents a polynomial fitting of the battery internal resistance with respect to SOC and temperature , , respectively represent fitting coefficients; represents a function of the dynamic capacitance discharge characteristic varying with SOC; represents the battery current; Step 2.3: Couple the states of the flywheel energy storage unit and the battery, including: Construct a state vector ; where respectively represent the flywheel bearing temperature and the battery temperature; The non - linear state equation is ; wherein, represents the control input, ; represents the disturbance term, , respectively represent the load torque fluctuation and the ambient temperature.

[0021] In this embodiment, is selected as the control input parameter. Because the magnitude of the electromagnetic torque of the motor can reflect the rotational speed of the motor, during the charging process, the charging process of the flywheel can be controlled by knowing the motor speed; the battery current reflects the output power of the battery, and the discharging process of the battery can be managed by controlling the battery current.

[0022] Step 2.4: Discretize the non - linear state equation by the fourth - order Runge - Kutta algorithm RK4, including: Let , wherein, represents the discretization step size, ; ; ; ; respectively represent the intermediate slope terms; Step 2.5: Conduct rolling - horizon prediction, including: At each moment , based on the current state vector predict the state sequence in the future time window, where represents the number of steps in the time window; the step size between each step is ; is the length of the time window. In this embodiment,

[0023] The prediction model is: ; wherein, represents discretization by the RK4 algorithm.

[0024] By setting up the objective function and constraint conditions, iterative solution is carried out through the sequential quadratic programming algorithm SQP to obtain the control sequence, that is, the control time series.

[0025] In the hybrid energy storage UPS system, the flywheel and the battery have non - linear dynamic characteristics, and the prediction model is required to accurately describe the non - linear coupling effect. Therefore, in this step, by setting up the non - linear coupling model of the flywheel and the battery, the state under non - linear conditions of the flywheel and the battery can be predicted.

[0026] Among them, by setting up an objective function and constraint conditions, iterative solution is carried out through the sequential quadratic programming algorithm SQP to obtain the control sequence, that is, the control time series, including the following steps: Construct an objective function based on minimizing the tracking error and control cost ; Among them, represents the state tracking weight matrix, and the state tracking weight matrix is a diagonal matrix, that is ; represents the control input penalty matrix, and the control input penalty matrix is a diagonal matrix, that is ; represents the terminal state penalty matrix, and the terminal state penalty matrix is times of an integer; represents the reference state, and the reference state includes the rated speed and the target SOC; respectively represent the state vector at time and the control input at time Set the constraint conditions: ; Among them, represents the temperature of the flywheel bearing at time represents the battery current at time represents the of the storage battery at time respectively represent the minimum and the maximum of the storage battery; respectively represent the minimum speed and the maximum speed of the flywheel; The temperature-related torque derating function ; The SOC-related current limit function , ; Carry out iterative solution, including: Perform Taylor expansion on at the current operating point , that is: ; Among them, , ; represents the increment vector of the state feature vector; represents the increment vector of the control input; Construct a quadratic programming problem, that is, construct a QP problem: ; Among them, represents the Hessian matrix; represents the gradient vector; Perform iterative convergence, that is, solve the QP problem through the interior point method or the active set method to obtain the control sequence .

[0027] Step 3: Obtain the overall power supply efficiency parameters within the corresponding time window at the corresponding control timing; including the following steps: Determine the system boundary and energy flow of the UPS based on the overall power supply efficiency parameters within the time window, including: Obtain the input energy within the time window, that is, the grid energy ; Obtain the stored energy within the time window, that is, the flywheel kinetic energy and the chemical energy of the battery ; Obtain the output energy within the time window, that is, the electrical energy actually consumed by the load ; where, represents the load power; Construct the overall power supply efficiency parameters within the time window, including: Obtain the charging efficiency parameter, and measure the conversion efficiency from the grid to the energy storage medium through the charging efficiency parameter: The charging efficiency parameter is: ; where, represents the charging circuit loss, including the inverter efficiency and the cable resistance loss; represents the total energy storage of the flywheel energy storage unit and the battery at time. The charging efficiency parameter includes the flywheel charging electrical energy and the battery charging electrical energy, reflecting the influence of the charging processes of both on the charging efficiency.

[0028] Obtain the storage efficiency parameter, and reflect the attenuation of energy during storage through the storage efficiency parameter; The storage efficiency parameter ; where, represents the flywheel speed decay time constant; represents the self-discharge current of the battery at time, represents the energy storage idle time, that is, the interval from the end of charging to the start of discharging; Obtain the discharge efficiency parameter, and reflect the energy conversion efficiency from the energy storage to the load through the discharge efficiency parameter; The discharge efficiency parameter is: ; where represents the flywheel power generation efficiency, ; represents the battery DC / AC inverter efficiency; represents the output power of the storage battery at a moment; represents the load power at a moment; represents the output power of the flywheel energy storage system at a moment; Obtain the switching efficiency, and reflect the energy loss during charge and discharge switching through the switching efficiency; The switching efficiency , where respectively represent the loss energy of switching at a moment; represents the total transmission energy; Then the overall power supply efficiency parameter at a moment The overall power supply efficiency within the time window is ; represents the time point weight.

[0029] Step 4, by analyzing the change of the overall power supply efficiency parameter, optimize the control timing, and coordinate the management of the flywheel energy storage unit and the charge and discharge process of the storage battery, including the following steps: Obtain the state sequence of the next time window ; Calculate each moment within the time window of ; and obtain the overall power supply efficiency parameter at the corresponding moment ; Integrate the overall power supply efficiency parameters within the time window to construct an efficiency optimization objective function ; where represents the control value smoothing weight; Adjust the control timing by the gradient descent method, including: Set the updated control input ; where represents the learning rate; Calculate the gradient of the optimization objective function with respect to the control variable , where represents the th overall power supply efficiency parameter; Use the updated control input to form the optimized control timing .

[0030] Example 2: In the first embodiment, a method for optimizing the control timing of the operating state characteristics within a future time window based on prediction has been given. In some cases, it is still necessary to consider the power grid electricity cost and manage the flywheel charging process to improve the overall efficiency while ensuring the optimal power supply efficiency.

[0031] Therefore, we propose the following solution based on the first embodiment: Considering the electricity cost, construct a new optimization objective function: ; where respectively represent the overall power supply efficiency parameter, grid power, and control input at time represents the grid electricity price weight; represents the control smoothness penalty coefficient; Construct a flywheel speed constraint and adjust the flywheel speed, that is the minimum flywheel speed at time ; where represents the efficiency threshold; in the case where the power supply efficiency requirement cannot be met even when reducing to the minimum speed, through this step, the minimum flywheel speed constraint can be dynamically adjusted.

[0032] Construct a battery charge and discharge current constraint to limit the large current discharge of the battery, that is the maximum battery current at time , where represents the maximum allowable discharge current of the storage battery. In the case where the power supply efficiency still cannot be met when reaching the existing maximum current, through this step, the maximum current constraint of the battery current can be dynamically adjusted.

[0033] Based on the flywheel speed constraint and the battery charge and discharge current constraint, use the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window. The steps include: Quadratically approximate the new optimization objective function, that is: ; where represents the control variable increment vector, represents the Hessian matrix (which can be approximated by the quasi-Newton method, such as BFGS update); represents the preset initial value; Linearize the constraint conditions, including: Linearize the flywheel speed constraint, that is ; Linearize the preset battery charge and discharge current, that is ; where represents the increment of the maximum battery discharge current; Convert the maximization of the overall power supply efficiency parameter into a quadratic programming sub-problem for constructing the power supply efficiency, that is: ; wherein, respectively represent the constrained linearization matrix and the constrained linearization vector, which are generated by the linearized constraint conditions; represents performing a gradient operation; Use the interior point method or the active set method to solve the quadratic programming sub-problem of the power supply efficiency, and update the control input to ; wherein, represents the step size, which is determined by a linear search to ensure satisfaction of the constraints.

[0034] Utilize to form the optimal control sequence . In reality, only the optimal control quantity at the current moment can be implemented, and the prediction and optimization process are re-performed in the next cycle to achieve the rolling optimization of the control input.

[0035] For example, if the UPS system needs to respond to load fluctuations within 30 seconds and ensure the overall power supply efficiency parameter is optimal.

[0036] The parameters at this time include the moment of inertia of the flywheel, the maximum allowable rotational speed; The maximum output current of the battery is ; Adopt the following control sequence to coordinate the charging and discharging process, specifically: In the range of 0 to 10 seconds (i.e., N = 1, the first step), the control input is that the electromagnetic torque of the motor takes the maximum value, that is, control the flywheel to run at full torque, and the battery output current is 0, that is, control the battery to be in the standby state; the overall power supply efficiency parameter is optimal at this time. Convert the excess power caused by the load fluctuation (load reduction) into the kinetic energy of the flywheel, that is, charge the flywheel.

[0037] In the range of 10 to 20 seconds (i.e., N = 2, the second step), the control input is that the electromagnetic torque of the motor is reduced to 80% of the maximum value, and the battery output current is 80% of the maximum value, that is, control the flywheel to reduce the rotational speed and reduce the charging rate, and discharge through the battery to supplement the power. The overall power supply efficiency parameter is optimal at this time. Supplement the power through the battery to adapt to the load recovery and ensure the load power consumption.

[0038] In the range of 20 to 30 seconds (i.e., N = 3, the third step), the control input is that the electromagnetic torque of the motor is reduced to 0, that is, stop the charging process, and the battery output current is the maximum value, and provide electrical energy through the battery. The overall power supply efficiency parameter is optimal at this time.

[0039] In this embodiment, the electromagnetic torque of the motor refers to the electromagnetic torque of the motor.

[0040] After obtaining the optimal control sequence, the sensitivity of the overall power supply efficiency parameter to the control input is propagated backward moment by moment through the chain rule: .

[0041] Control input , and the influence path of the electromagnetic torque of the motor on the overall power supply efficiency parameter includes: Direct influence: → → → . Indirect influence: Further affects the rotational speed at the subsequent moment moment.

[0042] The above process reflects the influence of the flywheel charging process and the battery discharging process on the overall power supply efficiency parameter. In the UPS system composed of a hybrid energy storage method, the influence of the charging process and the discharging process of different energy storage methods on the system power supply efficiency is established. Under the condition of ensuring the optimal power supply efficiency (which can be obtained through historical data analysis or calibrated through experiments), the corresponding control input is inversely deduced, and the corresponding charging process and discharging process are coordinated through the control input to optimize the system power supply efficiency.

[0043] In some preferred embodiments, different parameters can be selected as the control input to coordinate different states. For example, taking the battery current and the self-discharge current of the battery as the control input (which can be achieved by switching different types of batteries), the energy storage state of the system and the discharging state of the battery can be coordinated to ensure the optimality of the overall power supply efficiency parameter.

[0044] The present invention also provides a coordination management system for a flywheel energy storage and battery hybrid UPS, and the system is used to execute the coordination management method for a flywheel energy storage and battery hybrid UPS described above.

[0045] The present invention also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the coordination management method for a flywheel energy storage and battery hybrid UPS described above.

[0046] Embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. Embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium can, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program codes. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program codes contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: a wireless segment, a wire segment, an optical cable, RF, etc., or any suitable combination of the above.

[0047] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0048] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Without departing from the said principles, any changes or modifications may be made to the embodiments of the present invention.

Claims

1. A coordinated management method for a flywheel energy storage and battery hybrid UPS, characterized in that, The method includes: Collecting the operation status data of the flywheel energy storage unit and the battery unit according to a preset acquisition frequency; Predicting the energy supply control inputs of the flywheel energy storage and the battery within a future time window based on the obtained operation status data of the flywheel energy storage unit and the battery unit, and generating corresponding control timings; Obtaining the overall power supply efficiency parameter within the corresponding time window under the corresponding control timings; Optimizing the control timings by analyzing the changes in the overall power supply efficiency parameter, and coordinating and managing the charging and discharging processes of the flywheel energy storage unit and the battery.

2. The coordinated management method of a flywheel energy storage and battery hybrid UPS according to claim 1, wherein, The predicting the energy supply control inputs of the flywheel energy storage and the battery within a future time window based on the obtained operation status data of the flywheel energy storage unit and the battery unit, and generating corresponding control timings includes: Constructing a non-linear coupling model of the flywheel energy storage unit and the battery unit, including: Build a flywheel rotation dynamics model for the flywheel energy storage unit: , where represent the moment of inertia, motor electromagnetic torque, load torque, viscous friction coefficient, air friction coefficient, and angular velocity, respectively; Performing a non-linear mapping on the energy state of the flywheel energy storage system, i.e.: , where represents the eddy current loss coefficient; Constructing an equivalent circuit model of the battery: ； wherein, represents a function of the open-circuit voltage varying with the SOC; represents the polynomial fitting of the internal resistance of the storage battery with respect to the SOC and temperature , , respectively represent the fitting coefficients; represents a function of the dynamic capacitance discharge characteristic varying with the SOC; represents the battery current; Coupling the states of the flywheel energy storage unit and the battery, including: Construct the state vector ; wherein respectively represent the flywheel bearing temperature and the battery temperature; The non-linear state equation is ; Among them, represents the control input, ; represents the disturbance term, , respectively represent the load torque fluctuation and the ambient temperature; Discretizing the non-linear state equation by the fourth-order Runge-Kutta algorithm RK4, including: Let , where represents the discretization step size, ; ; ; ; respectively represent the intermediate slope terms; Performing rolling horizon prediction, including: At each moment , based on the current state vector predict the state sequence for a future time window , where represents the number of steps in the time window; The prediction model is ; where represents discretization by the RK4 algorithm By setting up an objective function and constraint conditions, and performing iterative solution through the sequential quadratic programming algorithm SQP to obtain the control sequence, i.e., the control timings.

3. A coordination management method for a flywheel energy storage and battery hybrid UPS according to claim 2, characterized in that, The obtaining the control sequence, i.e., the control timings, by setting up an objective function and constraint conditions and performing iterative solution through the sequential quadratic programming algorithm SQP includes: Construct an objective function based on minimizing the tracking error and control cost ; Among them, represents a state tracking weight matrix, and the state tracking weight matrix is a diagonal matrix, that is ; Denote the control input penalty matrix, and the control input penalty matrix is a diagonal matrix, i.e., ; Indicates the terminal state penalty matrix, and the terminal state penalty matrix is an integer multiple of; Indicates a reference state, the reference state including a rated speed and a target SOC; respectively represent the state vectors at the moments and the control inputs at the moments; Set constraint conditions: ; Among them, represents the temperature of the flywheel bearing at time represents the battery current at time represents at time of the storage battery; respectively represent the minimum and maximum ; respectively represent the lowest speed and the highest speed of the flywheel; Temperature-related torque derating function ; SOC-related current limit function , ; Performing iterative solution, including: At the current operating point perform a Taylor expansion on as follows: ; wherein, , ; represents the incremental vector of the state feature vector; represents the incremental vector of the control input; Constructing a quadratic programming problem, i.e., constructing a QP problem: ; among which, represents the Hessian matrix; represents the gradient vector; Perform iterative convergence, that is, solve the QP problem by the interior point method or the active set method to obtain the control sequence .

4. The coordinated management method of a flywheel energy storage and battery hybrid UPS according to claim 3, wherein, The obtaining the overall power supply efficiency parameter within the corresponding time window under the corresponding control timings includes: Determining the system boundary and energy flow of the UPS based on the overall power supply efficiency parameter within the time window, including: Obtain the input energy within the time window, i.e., the grid energy ; Obtain the stored energy within the time window, i.e., the kinetic energy of the flywheel and the chemical energy of the battery ; Obtain the output energy within the time window, that is, the electrical energy actually consumed by the load ; where represents the load power Constructing the overall power supply efficiency parameter within the time window, including: Obtaining the charging efficiency parameter to measure the conversion efficiency from the power grid to the energy storage medium through the charging efficiency parameter: The charging efficiency parameter ; wherein represents the charging circuit loss, including the inverter efficiency and cable resistance loss; represents the total energy storage of the flywheel energy storage unit and the battery at the moment; Obtaining the storage efficiency parameter to reflect the attenuation of energy during storage through the storage efficiency parameter; The storage efficiency parameter ; wherein represents the flywheel speed decay time constant; represents the self-discharge current of the battery at time represents the energy storage idle time, i.e., the interval from the completion of charging to the start of discharging; Obtaining the discharge efficiency parameter to reflect the energy conversion efficiency from the energy storage to the load through the discharge efficiency parameter; The discharge efficiency parameter ; where represents the flywheel power generation efficiency, ; represents the battery DC / AC inverter efficiency; represents the output power of the storage battery at time represents the load power at time represents the output power of the flywheel energy storage system at time Obtaining the switching efficiency to reflect the energy loss during the charging and discharging switching through the switching efficiency; The switching efficiency , where respectively represent the energy loss during switching at a certain moment; represents the total transmission energy; Then Overall efficiency parameter for power supply at all times The overall power supply efficiency within the time window is ; indicating the weight of the time point.

5. A coordinated management method for a flywheel energy storage and battery hybrid UPS according to claim 4, characterized in that, The optimizing the control timings by analyzing the changes in the overall power supply efficiency parameter includes: Obtain the state sequence for a future time window ; Calculate each moment within the time window of ; and obtain the overall power supply efficiency parameter at the corresponding moment ; Construct an efficiency optimization objective function based on the overall power supply efficiency parameters within the integrated time window ; among which, represents the control value smoothing weight; Adjusting the control timings by the gradient descent method, including: Let the updated control input ; where represents the learning rate; Calculate the gradient of the optimization objective function with respect to the control variable , where represents the overall power supply efficiency parameter at the -th moment; Construct an optimized control timing sequence using the updated control input 。 6. The coordinated management method of a flywheel energy storage and battery hybrid UPS according to claim 5, characterized in that, The optimizing the control timings by analyzing the changes in the overall power supply efficiency parameter further includes: Considering the electricity cost and constructing a new optimization objective function: ; among which, respectively represent the overall power supply efficiency parameter, grid power and control input at the moment; represents the grid electricity price weight; represents the control smoothness penalty coefficient; Construct the flywheel speed constraint and adjust the flywheel speed, that is The minimum flywheel speed at the moment ; where represents the efficiency threshold; Construct the battery charge and discharge current constraint to limit the large current discharge of the battery, that is The maximum current of the battery at a moment , where represents the maximum allowable discharge current of the storage battery; Based on the flywheel speed constraint and the battery charging and discharging current constraint, using the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window.

7. A coordination management method for a flywheel energy storage and battery hybrid UPS according to claim 6, characterized in that, The using the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window based on the flywheel speed constraint and the battery charging and discharging current constraint includes: Quadratically approximate the new optimization objective function, i.e.: ; where represents the control quantity increment vector represents the Hessian matrix; represents the preset initial value; Linearizing the constraint conditions, including: Linearize the flywheel speed constraint, i.e., ; Preset linearization for the battery charging and discharging current, that is ; where represents the increment of the maximum battery discharge current; Convert the parameter of maximizing the overall power supply efficiency into a quadratic programming sub-problem for constructing the power supply efficiency, that is ; where respectively represent the constraint linearization matrix and the constraint linearization vector; represents performing a gradient operation; Solve the quadratic programming sub-problem of the power supply efficiency using the interior point method or the active set method, and update the control input to ; where represents the step size, and use to form the optimal control sequence .

8. A coordinated management method for a flywheel energy storage and battery hybrid UPS according to claim 7, characterized in that, The using the sequential quadratic programming algorithm to iteratively solve the optimal control sequence within the future time window based on the flywheel speed constraint and the battery charging and discharging current constraint further includes: After obtaining the optimal control sequence, the sensitivity of the overall power supply efficiency parameter to the control input is backpropagated moment by moment through the chain rule: .

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

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a coordination management method for a flywheel energy storage and battery hybrid UPS according to any one of claims 1-8 above.

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