Flywheel energy storage system energy management method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a flywheel energy storage system energy management method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring flywheel data, power grid data and maximum allowable charging and discharging power in real time; constructing a multi-objective optimization function according to the flywheel data, the power grid data and the maximum allowable charging and discharging power of the flywheel; and dynamically adjusting a weight coefficient of the multi-objective optimization function according to a power grid response effect, and generating an optimal flywheel charging and discharging power instruction in real time by adopting a model prediction control algorithm. According to the embodiment of the invention, through the model prediction control algorithm, the real-time flywheel state and the power grid demand data are combined, the charging and discharging power instruction is dynamically optimized, the charging and discharging switching loss is reduced, and the comprehensive efficiency of the system is improved; flywheel mechanical stress constraint is carried out in real time, flywheel fatigue damage is reduced, and the service life of the flywheel is prolonged; by adding the power grid frequency deviation and the load tracking penalty term, the frequency modulation demand of the power grid can be quickly responded.

Description

Technical Field

[0001] The embodiments of the present disclosure belong to the technical field of flywheel energy storage system energy management, and specifically relate to a flywheel energy storage system energy management method, system, electronic device, and storage medium. Background Art

[0002] Flywheel energy storage uses an electric motor to drive a flywheel's high-speed rotation, which then drives a generator to generate electricity when needed. A flywheel energy storage system is an electromechanical energy conversion and storage device. It uses a reciprocating motor / generator to convert and store electrical energy and the mechanical kinetic energy of a high-speed rotating flywheel. It offers advantages such as high energy storage density, strong adaptability, wide application range, high efficiency, long life, zero pollution, and low maintenance costs.

[0003] Energy management is an important research topic in the current field of energy storage technology. The goal of energy management in energy storage systems is to achieve efficient energy utilization and storage through energy monitoring, analysis, and optimization.

[0004] Existing flywheel energy storage systems have the following problems in energy management:

[0005] 1. Adopting a static control strategy: Traditional energy management algorithms are based on fixed charge and discharge thresholds and cannot adapt to dynamic load changes, resulting in low efficiency.

[0006] 2. Response delay: When the flywheel speed regulation does not match the grid demand, the charge and discharge modes need to be frequently switched, resulting in energy loss;

[0007] 3. Insufficient multi-objective optimization: Existing algorithms are difficult to take into account system efficiency, flywheel life and grid stability requirements at the same time. Summary of the Invention

[0008] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art and provide an energy management method, system, electronic device and storage medium for a flywheel energy storage system.

[0009] One aspect of the present disclosure provides a flywheel energy storage system energy management method, the method comprising:

[0010] Real-time acquisition of flywheel data, grid data, and maximum allowable charge and discharge power; wherein the flywheel data includes flywheel speed and flywheel temperature, and the grid data includes grid frequency deviation and load power demand;

[0011] Constructing a multi-objective optimization function based on the flywheel data, the grid data, and the maximum allowable charge and discharge power of the flywheel; wherein the multi-objective optimization function includes one or more of an energy loss minimization term, a mechanical wear minimization term, a grid frequency deviation and load tracking penalty term, and a charge and discharge power change smoothness constraint term;

[0012] The weight coefficients of the multi-objective optimization function are dynamically adjusted according to the grid response effect, and the optimal flywheel charging and discharging power instructions are generated in real time using a model predictive control algorithm.

[0013] Optionally, the energy loss minimization term is expressed as follows:

[0014]

[0015] Where, J energy is the energy loss minimization term, N is the prediction time domain, k is the current time series, Q1(k) is the energy loss weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

[0016] Optionally, the mechanical wear minimization term is expressed as follows:

[0017]

[0018] Where, J wear is the mechanical wear minimization term, N is the prediction time domain, k is the current time series, Q2(k) is the flywheel speed change weight coefficient, Δω(k)=ω(k+1)-ω(k) is the flywheel speed change, Q3(k) is the flywheel temperature weight coefficient, T(k) is the flywheel temperature, T opt The optimal operating temperature of the flywheel.

[0019] Optionally, the grid frequency deviation and load tracking penalty term are shown in the following formula:

[0020]

[0021] Where, J wear is the grid frequency deviation and load tracking penalty term, N is the prediction time domain, k is the current time series, k4(k) is the weight coefficient of grid frequency deviation and load power demand, Δf(k) is the grid frequency deviation, P load (k) is the load power demand, P discharge (k) is the flywheel discharge power, P charge (k) is the flywheel charging power.

[0022] Optionally, the charge and discharge power change smoothness constraint term is as follows:

[0023]

[0024] Where, J inputis the charge and discharge power change smoothness constraint term, N is the prediction time domain, k is the current time series, R1 is the flywheel charging power weight coefficient, R2 is the flywheel discharging power weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

[0025] Furthermore, when the flywheel energy storage system includes a flywheel / battery hybrid energy storage unit, the method further includes:

[0026] Assign high-frequency, short-duration tasks to the flywheel, and high-power, long-duration tasks to the battery.

[0027] Furthermore, the method further comprises:

[0028] When an abnormal flywheel state or an abnormal grid state signal is monitored, a backup energy management strategy is triggered; wherein, the backup energy management strategy at least includes power reduction operation and switching to backup energy storage.

[0029] Another aspect of the present disclosure provides a flywheel energy storage system energy management system, the system comprising:

[0030] A data acquisition module, configured to acquire flywheel data, grid data, and maximum allowable charge and discharge power in real time; wherein the flywheel data includes flywheel speed and flywheel temperature, and the grid data includes grid frequency deviation and load power demand;

[0031] An objective function module, configured to construct a multi-objective optimization function based on the flywheel data, the grid data, and the maximum allowable charge and discharge power of the flywheel;

[0032] The instruction generation module is used to dynamically adjust the weight coefficient of the multi-objective optimization function according to the grid response effect, and use the model predictive control algorithm to generate the optimal flywheel charging and discharging power instructions in real time.

[0033] Another aspect of the present disclosure provides an electronic device, comprising:

[0034] at least one processor; and,

[0035] The memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the flywheel energy storage system energy management method described above.

[0036] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned flywheel energy storage system energy management method.

[0037] The disclosed embodiments of a flywheel energy storage system energy management method, system, electronic device, and storage medium utilize a multi-objective optimization model based on real-time data on its own state and the real-time needs of the power grid to achieve efficient and stable energy storage and release. A model predictive control algorithm, combined with real-time flywheel state and power grid demand data, dynamically optimizes charge and discharge power instructions, reducing charge and discharge switching losses and improving the overall efficiency of the system. Real-time flywheel mechanical stress constraints are applied to reduce flywheel fatigue damage and extend flywheel life. The inclusion of power grid frequency deviation and load tracking penalty terms enables rapid response to the power grid's frequency regulation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic flow chart of an energy management method for a flywheel energy storage system according to an embodiment of the present disclosure;

[0039] Figure 2 This is a structural diagram of an energy management system for a flywheel energy storage system according to another embodiment of the present disclosure;

[0040] Figure 3 This is a schematic structural diagram of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0042] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0043] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0044] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of this disclosure. As used in this disclosure, the term "and / or" includes any one of the associated listed items and all combinations of one or more of them.

[0045] Those skilled in the art will understand that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the scope of protection of the present disclosure.

[0046] like Figure 1 As shown, an embodiment of the present disclosure provides an energy management method for a flywheel energy storage system, the method comprising:

[0047] Step S1: acquiring flywheel data, grid data, and maximum allowable charge and discharge power in real time.

[0048] Specifically, a multi-source sensor set up in the flywheel energy storage unit is used to collect flywheel data, including flywheel speed ω(k) and flywheel temperature t(k); grid data is collected from the grid interface of the flywheel energy storage unit, including grid frequency deviation Δf(k) and load power demand P load (k); Calculate the maximum allowable charge and discharge power in the current flywheel state based on the flywheel dynamics model, including the maximum allowable charging power P max,charge (k) and the maximum allowable discharge power P max,discharge (k). The above k is the current time sequence.

[0049] Step S2: constructing a multi-objective optimization function based on the flywheel data, the grid data and the maximum allowable charge and discharge power of the flywheel.

[0050] Specifically, the multi-objective optimization function includes one or more of an energy loss minimization term, a mechanical wear minimization term, a grid frequency deviation and load tracking penalty term, and a charge and discharge power change smoothness constraint term.

[0051] Energy loss is mainly related to the power loss during the charging and discharging process. The energy loss minimization term is shown in the following formula:

[0052]

[0053] Where, J energy is the energy loss minimization term, N is the prediction time domain, k is the current time series, Q1(k) is the energy loss weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

[0054] Mechanical wear is related to the change in flywheel speed and flywheel temperature. The mechanical wear minimization term is shown below:

[0055]

[0056] Where, J wear is the mechanical wear minimization term, N is the prediction time domain, k is the current time series, Q2(k) is the flywheel speed change weight coefficient, Δω(k)=ω(k+1)-ω(k) is the flywheel speed change, Q3(k) is the flywheel temperature weight coefficient, T(k) is the flywheel temperature, T opt The optimal operating temperature of the flywheel.

[0057] In order to ensure the stable operation of the power grid, it is necessary to minimize the grid frequency deviation while meeting the load power demand. The grid frequency deviation and load tracking penalty term are shown in the following formula:

[0058]

[0059] Where, J wear is the grid frequency deviation and load tracking penalty term, N is the prediction time domain, k is the current time series, k4(k) is the weight coefficient of grid frequency deviation and load power demand, Δf(k) is the grid frequency deviation, P load (k) is the load power demand, P discharge (k) is the flywheel discharge power, P charge (k) is the flywheel charging power.

[0060] In order to avoid drastic changes in charge and discharge power, a charge and discharge power change smoothness constraint term can be added to the objective function, as shown in the following formula:

[0061]

[0062] Where, J input is the charge and discharge power change smoothness constraint term, N is the prediction time domain, k is the current time series, R1 is the flywheel charging power weight coefficient, R2 is the flywheel discharging power weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

[0063] Combining the above items, we can get the final multi-objective optimization function:

[0064] J=J energy +J wear +J grid +J input

[0065] Step S3: dynamically adjust the weight coefficients of the multi-objective optimization function according to the grid response effect, and use the model predictive control algorithm to generate the optimal flywheel charging and discharging power instructions in real time.

[0066] Specifically, the weight coefficients Q1(k), Q2(k), Q3(k), and Q4(k) in the multi-objective optimization function are adaptively and dynamically adjusted based on the grid response, thereby dynamically balancing the flywheel energy storage system's efficiency, lifespan, and grid service requirements within the optimization objectives. For example, during a grid emergency, the value of Q4(k) is increased to prioritize grid frequency stability and load power requirements; while under normal conditions, the values ​​of Q1(k) and Q2(k) are increased to prioritize protecting the flywheel's energy loss and mechanical lifespan.

[0067] Based on the above objective function and the constraints of the maximum allowable charge and discharge power, the Model Predictive Control (MPC) algorithm can be used to optimize the flywheel charge and discharge power in the future time window, and the optimal flywheel charge and discharge power at each time k can be solved, including the flywheel charging power P charge (k) and flywheel discharge power P discharge (k) Based on this, corresponding energy management instructions are generated to control the charging and discharging of the flywheel in the flywheel energy storage system in real time.

[0068] Exemplarily, when the flywheel energy storage system includes a flywheel / battery hybrid energy storage unit, the method further includes: allocating high-frequency short-duration tasks to the flywheel, and allocating high-power long-duration tasks to the battery.

[0069] Specifically, if the flywheel energy storage system of this embodiment is a hybrid energy storage system (such as flywheel + battery), different types of charging and discharging tasks are allocated, with high-frequency, short-term tasks being undertaken by the flywheel and high-power, long-term tasks being undertaken by the battery, thereby forming coordinated management of hybrid energy storage.

[0070] Exemplarily, the method further includes: triggering a backup energy management strategy when a flywheel abnormality or grid abnormality signal is detected, wherein the backup energy management strategy at least includes power reduction operation and switching to backup energy storage.

[0071] Specifically, the safe operation boundary under fault conditions is injected into the MPC constraints, and the flywheel mechanical abnormality or grid overload signal is monitored in real time. When the flywheel mechanical abnormality is detected, the backup energy storage is switched, and when the grid overload signal is detected, the power reduction operation is triggered.

[0072] The disclosed embodiments of a flywheel energy storage system energy management method utilize a multi-objective optimization model based on real-time data on its own state and the real-time needs of the power grid, achieving efficient and stable energy storage and release. Through a model predictive control algorithm, combined with real-time flywheel state and grid demand data, the system dynamically optimizes charge and discharge power instructions, reducing charge and discharge switching losses and improving the overall efficiency of the system. Real-time flywheel mechanical stress constraints are applied to reduce flywheel fatigue damage and extend flywheel life. The inclusion of grid frequency deviation and load tracking penalty terms enables rapid response to the grid's frequency regulation needs.

[0073] like Figure 2 As shown, another embodiment of the present disclosure provides an energy management system for a flywheel energy storage system, the system comprising:

[0074] Data acquisition module 210, for acquiring flywheel data, grid data, and maximum allowable charge and discharge power in real time; wherein the flywheel data includes flywheel speed and flywheel temperature, and the grid data includes grid frequency deviation and load power demand;

[0075] An objective function module 220 is configured to construct a multi-objective optimization function based on the flywheel data, the grid data, and the maximum allowable charge and discharge power of the flywheel;

[0076] The instruction generation module 230 is used to dynamically adjust the weight coefficients of the multi-objective optimization function according to the grid response effect, and use the model predictive control algorithm to generate the optimal flywheel charging and discharging power instructions in real time.

[0077] Specifically, an energy management system for a flywheel energy storage system in an embodiment of the present disclosure is used to implement the energy management method for a flywheel energy storage system described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0078] The energy management system for a flywheel energy storage system disclosed in this disclosure utilizes a multi-objective optimization model based on real-time data on its own state and the real-time needs of the power grid, achieving efficient and stable energy storage and release. Through a model predictive control algorithm, combined with real-time flywheel state and grid demand data, it dynamically optimizes charge and discharge power instructions, reducing charge and discharge switching losses and improving the overall system efficiency. Real-time flywheel mechanical stress constraints reduce flywheel fatigue damage and extend flywheel life. The inclusion of grid frequency deviation and load tracking penalty terms enables rapid response to the grid's frequency regulation needs.

[0079] like Figure 3 As shown, another embodiment of the present disclosure provides an electronic device, including:

[0080] At least one processor 301; and a memory 302 in communication with the at least one processor 301, for storing one or more programs, which, when executed by the at least one processor 301, enable the at least one processor 301 to implement the flywheel energy storage system energy management method described above.

[0081] The memory 302 and processor 301 are connected using a bus. The bus can include any number of interconnected buses and bridges, connecting various circuits of one or more processors 301 and memory 302. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor 301 is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor 301.

[0082] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 302 can be used to store data used by the processor 301 when performing operations.

[0083] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the flywheel energy storage system energy management method described above is implemented.

[0084] The computer-readable storage medium may be included in the system or electronic device of the present disclosure, or may exist independently.

[0085] Computer-readable storage media may be any tangible medium that contains or stores a program, which may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, optical fiber, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0086] The computer-readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, which carries the computer-readable program code. Specific examples include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0087] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A flywheel energy storage system energy management method, characterized in that: The method comprises: Real-time acquisition of flywheel data, grid data, and maximum allowable charge and discharge power; wherein the flywheel data includes flywheel speed and flywheel temperature, and the grid data includes grid frequency deviation and load power demand; Constructing a multi-objective optimization function based on the flywheel data, the grid data, and the maximum allowable charge and discharge power of the flywheel; wherein the multi-objective optimization function includes one or more of an energy loss minimization term, a mechanical wear minimization term, a grid frequency deviation and load tracking penalty term, and a charge and discharge power change smoothness constraint term; The weight coefficients of the multi-objective optimization function are dynamically adjusted according to the grid response effect, and the optimal flywheel charging and discharging power instructions are generated in real time using a model predictive control algorithm.

2. The method according to claim 1, characterized in that The energy loss minimization term is shown as follows: Where, J energy is the energy loss minimization term, N is the prediction time domain, k is the current time series, Q1(k) is the energy loss weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

3. The method according to claim 1, characterized in that The mechanical wear minimization term is expressed as follows: Where, J wear is the mechanical wear minimization term, N is the prediction time domain, k is the current time series, Q2(k) is the flywheel speed change weight coefficient, Δω(k)=ω(k+1)-ω(k) is the flywheel speed change, Q3(k) is the flywheel temperature weight coefficient, T(k) is the flywheel temperature, T opt The optimal operating temperature of the flywheel.

4. The method according to claim 1, wherein The grid frequency deviation and load tracking penalty term are shown in the following formula: Where, J wear is the grid frequency deviation and load tracking penalty term, N is the prediction time domain, k is the current time series, Q4(k) is the weight coefficient of grid frequency deviation and load power demand, Δf(k) is the grid frequency deviation, P load (k) is the load power demand, P discharge (k) is the flywheel discharge power, P charge (k) is the flywheel charging power.

5. The method according to claim 1, characterized in that The charge and discharge power change smoothness constraint term is as follows: Where, J input is the charge and discharge power change smoothness constraint term, N is the prediction time domain, k is the current time series, R1 is the flywheel charging power weight coefficient, R2 is the flywheel discharging power weight coefficient, P charge (k) is the flywheel charging power, P discharge (k) is the flywheel discharge power.

6. The method according to any one of claims 1 to 5, characterized in that When the flywheel energy storage system includes a flywheel / battery hybrid energy storage unit, the method further includes: Assign high-frequency, short-duration tasks to the flywheel, and high-power, long-duration tasks to the battery.

7. The method according to any one of claims 1 to 5, characterized in that The method further comprises: When an abnormal flywheel state or an abnormal grid state signal is monitored, a backup energy management strategy is triggered; wherein, the backup energy management strategy at least includes power reduction operation and switching to backup energy storage.

8. A flywheel energy storage system energy management system, characterized in that: The system comprises: A data acquisition module, configured to acquire flywheel data, grid data, and maximum allowable charge and discharge power in real time; wherein the flywheel data includes flywheel speed and flywheel temperature, and the grid data includes grid frequency deviation and load power demand; An objective function module, configured to construct a multi-objective optimization function based on the flywheel data, the grid data, and the maximum allowable charge and discharge power of the flywheel; The instruction generation module is used to dynamically adjust the weight coefficient of the multi-objective optimization function according to the grid response effect, and use the model predictive control algorithm to generate the optimal flywheel charging and discharging power instructions in real time.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor, for storing one or more programs, which, when executed by the at least one processor, enables the at least one processor to implement the flywheel energy storage system energy management method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the flywheel energy storage system energy management method according to any one of claims 1 to 7 is implemented.

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