Control method, system and platform of energy storage conversion system

Abstract

The embodiment of the invention provides a control method, system and platform of an energy storage conversion system. Relates to the power technology field. When the intelligent management module monitors that power generation of the renewable energy system is excessive, the renewable energy system is controlled to charge the supercapacitor bank through the bidirectional direct current-direct current converter, and the supercapacitor bank charges the storage battery pack; when the demand peak of the power grid side or insufficient power generation of the renewable energy system is monitored, the super capacitor bank is controlled to discharge to supply power to the load side, and the storage battery pack is controlled to supply power to the load side after the super capacitor bank finishes discharging; monitoring power data of the energy storage conversion system in real time through a power controller, and controlling power distribution of the energy storage conversion system according to the power data; acquiring working parameters of the super energy storage conversion system, and inputting the working parameters into the intelligent algorithm model to output an optimization strategy; and controlling the energy storage conversion system according to the optimization strategy so as to solve the problem of low efficiency of the energy storage and conversion technology of the power system.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a control method, system and platform for an energy storage conversion system. Background Art

[0002] In the power system, renewable energy (such as solar energy) and new energy such as wind energy are increasingly widely used. However, due to the intermittent and unstable characteristics of these new energy sources, effective energy storage and conversion technologies are needed to ensure the stable operation of the power system.

[0003] At present, the energy storage and conversion technologies of the power system have the problem of low efficiency and cannot meet the demand for efficient energy. Summary of the invention

[0004] The embodiments of the present application provide a control method, system and platform for an energy storage and conversion system, which can solve the problem of low efficiency of energy storage and conversion technology in power systems and the technical problem of difficulty in meeting efficient energy needs.

[0005] In a first aspect, an embodiment of the present application provides a control method for an energy storage conversion system, including: the energy storage conversion system includes a supercapacitor group, a battery group, a bidirectional DC-DC converter, a power controller and an intelligent management module, and the;

[0006] The method is applied to an intelligent management module, comprising:

[0007] When it is detected that the renewable energy system generates excess power, the renewable energy system is controlled to charge the supercapacitor bank through a bidirectional DC-DC converter, so that the supercapacitor bank charges the battery bank;

[0008] When it is monitored that the demand on the grid side is at a peak or the renewable energy system generates insufficient power, the supercapacitor group is controlled to discharge to supply power to the load side, and after the supercapacitor group is fully discharged, the battery group is controlled to continue to supply power to the load side;

[0009] The power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data;

[0010] Obtaining operating parameters of the supercapacitor bank, the battery bank, the bidirectional DC-DC converter, and the power controller, and inputting the operating parameters into an intelligent algorithm model to output an optimization strategy;

[0011] The operation of the energy storage conversion system is controlled according to the optimization strategy.

[0012] In an optional embodiment, the working parameters include: voltage value, current value and temperature value; the inputting of the working parameters into the intelligent algorithm model to output the optimization strategy includes: obtaining multiple voltage values ​​of any target device among the supercapacitor group, the battery group, the bidirectional DC-DC converter and the power controller within a first preset time length; and generating a voltage value curve according to the multiple voltage values ​​within the first preset time length; inputting the voltage value curve into the intelligent algorithm model to output the voltage health of the target device; obtaining multiple current values ​​of the target device within a first preset time length; and generating a current value curve according to the multiple current values ​​within the first preset time length; inputting the current value curve into the intelligent algorithm model to output the current health of the target device; obtaining multiple temperature values ​​of the target device within a first preset time length; and generating a temperature value curve according to the multiple temperature values ​​within the first preset time length; inputting the temperature value curve into the intelligent algorithm model to output the temperature health of the target device; and generating the optimization strategy of the target device according to the voltage health, the current health and / or the temperature health.

[0013] In an optional embodiment, the optimization strategy of the target device is generated according to the voltage health, the current health and / or the temperature health, including: if the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health does not meet the third preset condition, then generate a first optimization strategy for the target device, wherein the first optimization strategy is used to adjust the voltage, current and temperature of the target device; if the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health meets the third preset condition, then generate a second optimization strategy for the target device, wherein the second optimization strategy is used to adjust the voltage and current of the target device; if the voltage health does not meet the first preset condition, the current health meets the second preset condition, and the temperature health does not meet the third preset condition, then generate a third optimization strategy for the target device, wherein the third optimization strategy is used to adjust the voltage and temperature of the target device; if the voltage health meets If the first preset condition is met, the current health does not meet the second preset condition, and the temperature health does not meet the third preset condition, a fourth optimization strategy for the target device is generated, wherein the fourth optimization strategy is used to adjust the current and temperature of the target device; if the voltage health does not meet the first preset condition, the current health meets the second preset condition, and the temperature health meets the third preset condition, a fifth optimization strategy for the target device is generated, wherein the fifth optimization strategy is used to adjust the voltage of the target device; if the voltage health meets the first preset condition, the current health meets the second preset condition, and the temperature health does not meet the third preset condition, a sixth optimization strategy for the target device is generated, wherein the sixth optimization strategy is used to adjust the temperature of the target device; if the voltage health meets the first preset condition, the current health does not meet the second preset condition, and the temperature health meets the third preset condition, a seventh optimization strategy for the target device is generated, wherein the seventh optimization strategy is used to adjust the current of the target device.

[0014] In an optional embodiment, the power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data, including: collecting the first power data of the renewable energy system, the second power data of the battery pack, the third power data of the grid side and the fourth power data of the load side in real time through the power controller; inputting the first power data, the second power data, the third power data and the fourth power data into the trained power distribution model to output the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side; and controlling the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side.

[0015] In an optional embodiment, the energy storage conversion system also includes: a cost management module; the method is used for the cost management module, including: obtaining procurement information and maintenance information of the supercapacitor group, battery group, bidirectional DC-DC converter, power controller and intelligent management module; and outputting the cost management information of the supercapacitor group, battery group, bidirectional DC-DC converter, power controller and intelligent management module according to the procurement information and maintenance information.

[0016] In an optional embodiment, the supercapacitor group includes a plurality of high-performance supercapacitors connected in series or in parallel; and the battery group includes a lithium-ion battery or a lead-acid battery.

[0017] In a second aspect, an embodiment of the present application provides an energy storage conversion system, including: a supercapacitor group, a battery group, a bidirectional DC-DC converter, a power controller and an intelligent management module; the intelligent management module is specifically used for:

[0018] When it is detected that the renewable energy system generates excess power, the renewable energy system is controlled to charge the supercapacitor bank through a bidirectional DC-DC converter, so that the supercapacitor bank charges the battery bank;

[0019] When it is monitored that the demand on the grid side is at a peak or the renewable energy system generates insufficient power, the supercapacitor group is controlled to discharge to supply power to the load side, and after the supercapacitor group is fully discharged, the battery group is controlled to continue to supply power to the load side;

[0020] The power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data;

[0021] Obtaining operating parameters of the supercapacitor bank, the battery bank, the bidirectional DC-DC converter, and the power controller, and inputting the operating parameters into an intelligent algorithm model to output an optimization strategy;

[0022] The operation of the energy storage conversion system is controlled according to the optimization strategy.

[0023] In a third aspect, an embodiment of the present application provides an intelligent management platform, including: a memory, a processor;

[0024] The memory stores computer-executable instructions;

[0025] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various optional implementations of the first aspect.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and / or various optional implementations of the first aspect.

[0027] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various optional implementations of the first aspect.

[0028] The control method, system and platform of the energy storage conversion system provided in the embodiments of the present application charge a supercapacitor group through a bidirectional DC-DC converter through a renewable energy system, and the supercapacitor group charges the battery group; discharges the supercapacitor group to power the load side, and controls the battery group to continue to power the load side; controls the power distribution of the renewable energy system, the battery group, the grid side and the load side through a power controller; and outputs an optimization strategy through an intelligent algorithm model, and controls the operation of the energy storage conversion system according to the optimization strategy, which can solve the problem of low efficiency of energy storage and conversion technology in the power system and the technical problem of difficulty in meeting efficient energy needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0030] Figure 1 A schematic diagram of the structure of the energy storage conversion system provided in the embodiment of the present application;

[0031] Figure 2 A schematic diagram of a control method for an energy storage conversion system provided in an embodiment of the present application;

[0032] Figure 3 A schematic diagram of the structure of the intelligent management platform provided in the embodiment of the present application.

[0033] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0034] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0035] In order to solve the problem of low efficiency of energy storage and conversion technology in power systems, it is difficult to meet the technical problem of efficient energy demand. This application proposes a control method for an energy storage and conversion system, and its technical concept is: when the renewable energy system generates excess power, the supercapacitor group is charged, and the supercapacitor group is used to charge the battery group; when the demand on the grid side peaks or the renewable energy system generates insufficient power, the supercapacitor group is used to discharge to power the load side, and the battery group is used to continue to power the load side after the supercapacitor group is discharged; by real-time monitoring of the power data of the renewable energy system, the battery group, the grid side and the load side, and adjusting the power distribution of the renewable energy system, the battery group, the grid side and the load side according to the power data; obtaining the working parameters of the supercapacitor group, the battery group, the bidirectional DC-DC converter, and the power controller, and outputting the optimization strategy according to the working parameters; and then improving the efficiency of the operation of the energy storage and conversion system according to the optimization strategy.

[0036] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0037] Figure 1 This is a schematic diagram of the structure of the energy storage conversion system provided in the embodiment of the present application. Figure 1 As shown, the energy storage conversion system 10 of the present application includes: a supercapacitor group 101, a battery group 102, a bidirectional DC-DC converter 103, a power controller 104 and an intelligent management module 105; wherein the intelligent management module 105 is specifically used for: when it is detected that the renewable energy system generates excess power, controlling the renewable energy system to charge the supercapacitor group through the bidirectional DC-DC converter so that the supercapacitor group charges the battery group; when it is detected that the demand on the grid side is at a peak or the renewable energy system generates insufficient power, controlling the supercapacitor group to discharge to supply power to the load side, and controlling the battery group to continue to supply power to the load side after the supercapacitor group is discharged; monitoring the power data of the renewable energy system, the battery group, the grid side and the load side in real time through the power controller, and controlling the power distribution of the renewable energy system, the battery group, the grid side and the load side according to the power data; obtaining the working parameters of the supercapacitor group, the battery group, the bidirectional DC-DC converter and the power controller, and inputting them into the intelligent algorithm model according to the working parameters to output the optimization strategy; and controlling the operation of the energy storage conversion system according to the optimization strategy.

[0038] Figure 2 A flow chart of a control method for an energy storage conversion system provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method is applied to the above-mentioned intelligent management module 105, and the method is described in detail as follows:

[0039] S201: When it is monitored that the renewable energy system generates excess power, the renewable energy system is controlled to charge the supercapacitor group through a bidirectional DC-DC converter, so that the supercapacitor group charges the battery group.

[0040] In the embodiments of the present application, the renewable energy system may be a photovoltaic power generation system or a wind power generation system.

[0041] Among them, the bidirectional DC-DC converter is a bidirectional DC-DC converter.

[0042] Among them, the supercapacitor group includes multiple high-performance supercapacitors connected in series or in parallel. The working principle of the supercapacitor group is: based on the principles of double-layer capacitance and pseudocapacitance, at the interface between the electrode and the electrolyte of the supercapacitor, the charge is stored in the form of electrostatic adsorption to form a double-layer capacitance. At the same time, the electrode material may also store charge through a fast and reversible redox reaction to produce pseudocapacitance. When charging, electrons flow from the power supply to the electrodes of the supercapacitor, and the ions in the electrolyte migrate and adsorb on the electrode surface to form charge storage. During discharge, the adsorbed ions and electrons are released again to form current output. Because the electrode material of the supercapacitor has a high specific surface area and good conductivity, as well as an extremely thin double-layer thickness, it can achieve a fast charging and discharging process in a short time and has an extremely high power density.

[0043] S202: When it is monitored that the demand on the grid side reaches a peak or the renewable energy system generates insufficient power, the supercapacitor group is controlled to discharge to supply power to the load side, and after the supercapacitor group is discharged, the battery group is controlled to continue to supply power to the load side.

[0044] In the embodiment of the present application, the load side is powered by the renewable energy system. When the grid demand peaks or the renewable energy system generates insufficient power and cannot meet the load demand, the supercapacitor bank is discharged to supply power to the load side.

[0045] Optionally, when the supercapacitor group is completely discharged, the battery group is controlled to discharge until the supercapacitor continues to supply power to the load side.

[0046] S203: Monitor the power data of the renewable energy system, the battery pack, the grid side and the load side in real time through the power controller, and control the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data.

[0047] Specifically, step S203 includes S2031-2033:

[0048] S2031: Collecting in real time, through the power controller, first power data of the renewable energy system, second power data of the battery pack, third power data of the grid side, and fourth power data of the load side.

[0049] In an embodiment of the present application, the first power data, the second power data, the third power data and the fourth power data may be actual working rates of the renewable energy system, the battery pack, the grid side and the load side within a first preset time period.

[0050] S2032: Input the first power data, the second power data, the third power data and the fourth power data into the trained power distribution model to output the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side.

[0051] In an embodiment of the present application, the actual working rates of the renewable energy system, the battery pack, the grid side and the load side within a first preset time period are input into a trained power distribution model to output the power adjustment value of the renewable energy system, the power adjustment value of the battery pack, the power adjustment value of the grid side and the power adjustment value of the load side; according to the power adjustment value of the renewable energy system, the power adjustment value of the battery pack, the power adjustment value of the grid side and the power adjustment value of the load side, the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side are determined.

[0052] S2033: Controlling power distribution among the renewable energy system, the battery pack, the grid side and the load side according to the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side.

[0053] In an embodiment of the present application, controlling the power distribution of a renewable energy system, a battery pack, a grid side and a load side includes: controlling the input or output distribution of power and the storage of power of the renewable energy system, a battery pack, a grid side and a load side.

[0054] S204: Obtain operating parameters of the supercapacitor group, the battery group, the bidirectional DC-DC converter, and the power controller, and input the operating parameters into the intelligent algorithm model to output an optimization strategy.

[0055] Specifically, the operating parameters include: voltage value, current value and temperature value; step S204 specifically includes S2041-2047:

[0056] S2041: Acquire multiple voltage values ​​of any target device among the supercapacitor group, the battery group, the bidirectional DC-DC converter, and the power controller within a first preset time period; and generate a voltage value curve according to the multiple voltage values ​​within the first preset time period.

[0057] S2042: Input the voltage value curve into the intelligent algorithm model to output the voltage health of the target device.

[0058] S2043: Acquire multiple current values ​​of the target device within a first preset time period; and generate a current value curve according to the multiple current values ​​within the first preset time period.

[0059] S2044: Input the current value curve into the intelligent algorithm model to output the current health of the target device.

[0060] S2045: Acquire multiple temperature values ​​of the target device within a first preset time period; and generate a temperature value curve according to the multiple temperature values ​​within the first preset time period.

[0061] S2046: Input the temperature value curve into the intelligent algorithm model to output the temperature health of the target device.

[0062] S2047: Generate an optimization strategy for the target device according to the voltage health, the current health, and / or the temperature health.

[0063] Optionally, the optimization strategy of the target device includes the following situations:

[0064] a. If the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health does not meet the third preset condition, a first optimization strategy for the target device is generated, wherein the first optimization strategy is used to adjust the voltage, current, and temperature of the target device.

[0065] b. If the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health meets the third preset condition, a second optimization strategy for the target device is generated, wherein the second optimization strategy is used to adjust the voltage and current of the target device.

[0066] c. If the voltage health does not meet the first preset condition, the current health meets the second preset condition, and the temperature health does not meet the third preset condition, a third optimization strategy for the target device is generated, wherein the third optimization strategy is used to adjust the voltage and temperature of the target device.

[0067] d. If the voltage health satisfies the first preset condition, the current health does not meet the second preset condition, and the temperature health does not meet the third preset condition, a fourth optimization strategy for the target device is generated, wherein the fourth optimization strategy is used to adjust the current and temperature of the target device.

[0068] e. If the voltage health does not meet the first preset condition, the current health meets the second preset condition, and the temperature health meets the third preset condition, a fifth optimization strategy for the target device is generated, wherein the fifth optimization strategy is used to adjust the voltage of the target device.

[0069] f. If the voltage health satisfies the first preset condition, the current health satisfies the second preset condition, and the temperature health does not satisfy the third preset condition, a sixth optimization strategy for the target device is generated, wherein the sixth optimization strategy is used to adjust the temperature of the target device.

[0070] g. If the voltage health satisfies the first preset condition, the current health does not satisfy the second preset condition, and the temperature health satisfies the third preset condition, a seventh optimization strategy for the target device is generated, wherein the seventh optimization strategy is used to adjust the current of the target device.

[0071] In this embodiment, the first preset condition is that the voltage health is ±10% of the preset voltage health value; the second preset condition is that the preset current health value is ±10%; and the third preset condition is that the preset temperature health value is ±10%.

[0072] S205: Control the operation of the energy storage conversion system according to the optimization strategy.

[0073] In an embodiment of the present application, the voltage, current and / or temperature of a target device of the energy storage conversion system is controlled according to an optimization strategy.

[0074] In summary, the renewable energy system charges the supercapacitor group through a bidirectional DC-DC converter, and the supercapacitor group charges the battery group; the supercapacitor group discharges to power the load side, and the battery group continues to power the load side by controlling the battery group; the power controller controls the power distribution of the renewable energy system, the battery group, the grid side and the load side; and the optimization strategy is output through an intelligent algorithm model, and the operation of the energy storage and conversion system is controlled according to the optimization strategy, which can solve the problem of low efficiency of energy storage and conversion technology in the power system and the technical problem of difficulty in meeting efficient energy needs.

[0075] Based on the above embodiment, in this embodiment, the energy storage conversion system further includes: a cost management module 106 (refer to Figure 1 );

[0076] The above method is used in the cost management module, including:

[0077] Obtain purchase and maintenance information for supercapacitor banks, battery banks, bidirectional DC-DC converters, power controllers, and intelligent management modules;

[0078] According to the purchase information and maintenance information, the cost management information of the supercapacitor group, the battery group, the bidirectional DC-DC converter, the power controller and the intelligent management module is output.

[0079] In an embodiment of the present application, the purchase information includes a purchase price; and the maintenance information includes a maintenance price.

[0080] Specifically, the cost price is calculated based on the purchase price and the maintenance price; the cost price is determined as the cost management information; and the cost management information is displayed on a display screen.

[0081] In summary, by real-time monitoring and analysis of the procurement cost, operation and maintenance cost of each equipment in the energy storage conversion system, and through reasonable configuration and optimized operation mode, the overall cost of the system can be reduced.

[0082] It should be noted that a supercapacitor group is a group of high-performance supercapacitors connected in series or in parallel. It can quickly absorb and release high-power pulses to meet high-power requirements in a short period of time.

[0083] The battery pack includes lithium-ion batteries or lead-acid batteries. Lithium-ion batteries or lead-acid batteries with high energy density and long cycle life are selected to store a large amount of electrical energy to meet the long-term stable power supply needs.

[0084] Bidirectional DC-DC converter: It adopts advanced topology and control strategy to achieve efficient conversion of electric energy between supercapacitor bank, battery bank and external power grid, reducing energy loss.

[0085] Power controller: Based on a high-performance microprocessor, it monitors the grid load demand, renewable energy generation and the status of the energy storage system in real time, dynamically adjusts the output and storage of electrical energy, and ensures the stability and reliability of power supply.

[0086] Intelligent management module: collects operating data of various parts of the system through sensors, including voltage, current, temperature, etc., uses intelligent algorithms to analyze and process data, optimizes the system's operating strategy, and improves energy efficiency

[0087] Cost management module: Real-time monitoring and analysis of the purchase cost, operation and maintenance cost of each component of the system, and reducing the overall cost of the system through reasonable configuration and optimized operation mode.

[0088] Figure 3 This is a schematic diagram of the structure of the intelligent management platform provided in the embodiment of the present application. Figure 3 As shown, the intelligent management platform provided in this embodiment includes: at least one processor 301 and a memory 302. Optionally, the intelligent management platform also includes a communication component 303. The processor 301, the memory 302 and the communication component 303 are connected via a bus 304.

[0089] In a specific implementation process, at least one processor 301 executes the computer-executable instructions stored in the memory 302, so that at least one processor 301 executes the above method.

[0090] The specific implementation process of the processor 301 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0091] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0092] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0093] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0094] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the control method of the above-mentioned energy storage conversion system is implemented.

[0095] The present application also provides a computer program product, including a computer program, which implements the above-mentioned control method of the energy storage conversion system when executed by a processor.

[0096] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0097] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0098] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0101] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0102] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0103] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A control method for an energy storage conversion system, characterized in that: The energy storage conversion system includes a supercapacitor group, a battery group, a bidirectional DC-DC converter, a power controller and an intelligent management module; the method is applied to the intelligent management module, including: When it is detected that the renewable energy system generates excess power, the renewable energy system is controlled to charge the supercapacitor bank through a bidirectional DC-DC converter, so that the supercapacitor bank charges the battery bank; When it is monitored that the demand on the grid side is at a peak or the renewable energy system generates insufficient power, the supercapacitor group is controlled to discharge to supply power to the load side, and after the supercapacitor group is fully discharged, the battery group is controlled to continue to supply power to the load side; The power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data; Obtaining operating parameters of the supercapacitor bank, the battery bank, the bidirectional DC-DC converter, and the power controller, and inputting the operating parameters into an intelligent algorithm model to output an optimization strategy; The operation of the energy storage conversion system is controlled according to the optimization strategy.

2. The method according to claim 1, characterized in that The working parameters include: voltage value, current value and temperature value; The step of inputting the working parameters into the intelligent algorithm model to output an optimization strategy includes: Acquire multiple voltage values ​​of any target device among a supercapacitor bank, a battery bank, a bidirectional DC-DC converter, and a power controller within a first preset time period; and generate a voltage value curve according to the multiple voltage values ​​within the first preset time period; Inputting the voltage value curve into an intelligent algorithm model to output the voltage health of the target device; Acquire multiple current values ​​of the target device within a first preset time period; and generate a current value curve according to the multiple current values ​​within the first preset time period; Inputting the current value curve into an intelligent algorithm model to output the current health of the target device; Acquire multiple temperature values ​​of the target device within a first preset time period; and generate a temperature value curve according to the multiple temperature values ​​within the first preset time period; Inputting the temperature value curve into an intelligent algorithm model to output the temperature health of the target device; An optimization strategy for the target device is generated according to the voltage health, the current health, and / or the temperature health.

3. The method according to claim 2, characterized in that The generating an optimization strategy for the target device according to the voltage health, the current health and / or the temperature health includes: If the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health does not meet the third preset condition, generating a first optimization strategy for the target device, wherein the first optimization strategy is used to adjust the voltage, current, and temperature of the target device; If the voltage health does not meet the first preset condition, the current health does not meet the second preset condition, and the temperature health meets the third preset condition, then generating a second optimization strategy for the target device, wherein the second optimization strategy is used to adjust the voltage and current of the target device; If the voltage health does not meet the first preset condition, the current health meets the second preset condition, and the temperature health does not meet the third preset condition, generating a third optimization strategy for the target device, wherein the third optimization strategy is used to adjust the voltage and temperature of the target device; If the voltage health satisfies a first preset condition, the current health does not satisfy a second preset condition, and the temperature health does not satisfy a third preset condition, generating a fourth optimization strategy for the target device, wherein the fourth optimization strategy is used to adjust the current and temperature of the target device; If the voltage health does not satisfy the first preset condition, the current health satisfies the second preset condition, and the temperature health satisfies the third preset condition, generating a fifth optimization strategy for the target device, wherein the fifth optimization strategy is used to adjust the voltage of the target device; If the voltage health satisfies a first preset condition, the current health satisfies a second preset condition, and the temperature health does not satisfy a third preset condition, generating a sixth optimization strategy for the target device, wherein the sixth optimization strategy is used to adjust the temperature of the target device; If the voltage health satisfies the first preset condition, the current health does not satisfy the second preset condition, and the temperature health satisfies the third preset condition, a seventh optimization strategy for the target device is generated, wherein the seventh optimization strategy is used to adjust the current of the target device.

4. The method according to claim 1, characterized in that: The power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data, including: The power controller collects in real time the first power data of the renewable energy system, the second power data of the battery pack, the third power data of the grid side and the fourth power data of the load side; Inputting the first power data, the second power data, the third power data and the fourth power data into a trained power distribution model to output a current adjustment value of the renewable energy system, a current adjustment value of the battery pack, a current adjustment value of the grid side and a current adjustment value of the load side; According to the current adjustment value of the renewable energy system, the current adjustment value of the battery pack, the current adjustment value of the grid side and the current adjustment value of the load side, the power distribution of the renewable energy system, the battery pack, the grid side and the load side is controlled.

5. The method according to any one of claims 1 to 4, characterized in that: The energy storage conversion system further includes: a cost management module; The method is used in the cost management module, comprising: Obtaining purchase information and maintenance information of the supercapacitor bank, battery bank, bidirectional DC-DC converter, power controller, and intelligent management module; According to the purchase information and maintenance information, cost management information of the supercapacitor group, the battery group, the bidirectional DC-DC converter, the power controller and the intelligent management module is output.

6. The method according to any one of claims 1 to 4, characterized in that: The supercapacitor group includes a plurality of high-performance supercapacitors connected in series or in parallel; the battery group includes a lithium-ion battery or a lead-acid battery.

7. An energy storage conversion system, characterized in that: include: Supercapacitor group, battery group, bidirectional DC-DC converter, power controller and intelligent management module; the intelligent management module is specifically used for: When it is detected that the renewable energy system generates excess power, the renewable energy system is controlled to charge the supercapacitor bank through a bidirectional DC-DC converter, so that the supercapacitor bank charges the battery bank; When it is monitored that the demand on the grid side is at a peak or the renewable energy system generates insufficient power, the supercapacitor group is controlled to discharge to supply power to the load side, and after the supercapacitor group is fully discharged, the battery group is controlled to continue to supply power to the load side; The power controller monitors the power data of the renewable energy system, the battery pack, the grid side and the load side in real time, and controls the power distribution of the renewable energy system, the battery pack, the grid side and the load side according to the power data; Obtaining operating parameters of the supercapacitor bank, the battery bank, the bidirectional DC-DC converter, and the power controller, and inputting the operating parameters into an intelligent algorithm model to output an optimization strategy; The operation of the energy storage conversion system is controlled according to the optimization strategy.

8. An intelligent management platform, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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