Cooperative control method and device of energy storage converter

Abstract

The invention relates to the technical field of energy storage control, and discloses a cooperative control method and device for energy storage converters, and the method comprises the steps: carrying out the recognition and judgment of the strength of a power grid through employing an artificial intelligence algorithm based on the grid-connected point electrical parameters of a target energy storage converter, selecting a target operation mode, controlling each energy storage converter in a field to be switched to the target operation mode, the target operation mode comprises a network following mode or a network construction mode; and for each energy storage converter, if the system is disturbed, issuing a corresponding charging and discharging active power instruction to the energy storage battery according to the output of the energy storage battery in the energy storage converter and the SOC state. According to the invention, the power grid strength is intelligently judged based on the grid-connected electrical parameters, and the operation mode is automatically switched, so that the energy storage system can stably operate under different power grid conditions, the power quality can be improved by adopting a grid following mode in a strong power grid, and the system supporting capability is enhanced by adopting a grid construction mode in a weak power grid, and the overall power grid adaptability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage control, and in particular to a coordinated control method and device for an energy storage converter. Background Art

[0002] With the increasing penetration of renewable energy in the power grid, a large number of power electronic converters with different control methods are being connected to the grid. Energy storage integrated through grid-based control can provide transient frequency support for renewable energy units and increase system moment of inertia. The application of grid-based technology to energy storage is still in its early stages. Scholars at home and abroad have proposed various grid-based control strategies. Droop control and virtual synchronous generator control, which mimic the operating mechanism of synchronous generators, are widely used. In addition, nonlinear control methods such as matching control and virtual oscillator control have also received widespread attention. Grid-based energy storage converters offer autonomous frequency support, while grid-following converters are controlled by upper-level energy management systems. This leads to certain coordination issues between the energy management system and the grid-based and grid-following converters. Because grid-following and grid-based devices have heterogeneous frequency response characteristics, their combined participation in system frequency regulation complicates power distribution and the system's frequency response mechanism. Summary of the Invention

[0003] In view of this, the present invention provides a collaborative control method and device for an energy storage converter to solve the problem of collaborative cooperation between EMS and grid-following and grid-forming energy storage in large-scale shared energy storage sites during frequency / voltage support.

[0004] In a first aspect, the present invention provides a collaborative control method for energy storage converters, comprising: based on the grid-connected electrical parameters of the target energy storage converter, using an artificial intelligence algorithm to identify and judge the strength of the power grid, and selecting a target operating mode, controlling each energy storage converter in the field to switch to the target operating mode, the target operating mode including a grid-following mode or a grid-forming mode; for each energy storage converter, if the system is disturbed, corresponding charging and discharging active power instructions are issued to the energy storage battery based on the output and SOC status of the energy storage battery inside the energy storage converter.

[0005] In the present invention, the strength of the power grid is intelligently judged based on the electrical parameters of the grid connection point and the operating mode is automatically switched, so that the energy storage system can operate stably under different grid conditions. The strong grid adopts the grid-following mode to improve the power quality, and the weak grid adopts the grid-forming mode to enhance the system support capability and improve the overall grid adaptability. At the same time, it solves the problem of coordination between EMS and grid-following and grid-forming energy storage in large-scale shared energy storage sites during frequency / voltage support.

[0006] In an optional embodiment, the artificial intelligence algorithm is a neural network algorithm.

[0007] In an optional embodiment, the process of selecting the target operating mode includes: when the power grid is a strong power grid, the target operating mode is a grid-following mode; when the power grid is a weak power grid, the target operating mode is a grid-building mode.

[0008] In an optional embodiment, the process of issuing corresponding charging and discharging active power instructions to the energy storage battery includes: if the system frequency change exceeds a threshold value, determining whether the absolute value of the current active output of the energy storage battery exceeds a limit value; if the absolute value of the current active output of the energy storage battery exceeds the limit value, selecting a constraint coefficient based on the absolute value of the current active output of the energy storage battery; determining whether the energy storage battery is in a charging or discharging state based on the positive or negative sign of the system frequency change; if the energy storage battery is in a charging or discharging state, calculating the charging and discharging weights of the energy storage battery according to a hierarchical analysis method; and constructing the charging and discharging active power instructions based on the constraint coefficient, the charging and discharging weights of the energy storage battery, the initial charging and discharging power of the energy storage, and the system frequency change.

[0009] In an optional embodiment, the process of calculating the charging and discharging weights of the energy storage battery according to the hierarchical analysis method includes: dividing the SOC of the energy storage battery into multiple intervals, each interval representing a different working state of the energy storage battery; calculating the charging and discharging weights of the energy storage battery in each interval according to the hierarchical analysis method; and determining the interval in which the current SOC of the energy storage battery is located and its corresponding weight.

[0010] In an optional embodiment, the charge and discharge active power instruction calculation formula is:

[0011] P set =P0+K1|Δf|+K2SOC

[0012] Among them, P set is the charging and discharging active power instruction; P0 is the initial charging and discharging power of the energy storage battery; K1 is the constraint coefficient; Δf is the system frequency change; K2 is the weight of charging and discharging the energy storage battery.

[0013] In a second aspect, the present invention provides a collaborative control device for an energy storage converter, the device comprising: a mode switching module, for identifying and judging the strength of the power grid based on the electrical parameters of the grid connection point of the target energy storage converter, using an artificial intelligence algorithm, and selecting a target operating mode to control each energy storage converter in the field to switch to the target operating mode, the target operating mode including a grid-following mode or a grid-forming mode; an energy storage control module, for sending corresponding charging and discharging active power instructions to the energy storage battery for each energy storage converter, if the system is disturbed, based on the output and SOC status of the energy storage battery inside the energy storage converter.

[0014] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the collaborative control method of the energy storage converter according to the first aspect or any corresponding embodiment thereof.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the collaborative control method of the energy storage converter according to the first aspect or any corresponding embodiment thereof.

[0016] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for coordinated control of an energy storage converter according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of a coordinated control method for an energy storage converter according to an embodiment of the present invention;

[0019] Figure 2 is a block diagram of coordinated control of an energy storage converter according to an embodiment of the present invention;

[0020] Figure 3 is a topology diagram of an energy storage converter according to an embodiment of the present invention;

[0021] Figure 4 is a block diagram of a networked control according to an embodiment of the present invention;

[0022] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0024] As a flexible, high-quality resource, electrochemical energy storage offers significant advantages in supporting grid stability. Shared energy storage, by optimizing resource allocation, reducing investment costs, and improving energy efficiency, provides strong support for the stable operation of power systems and the large-scale integration of renewable energy. Despite this, research on converter control modes in shared energy storage is currently limited, with most adopting a single grid-following control mode. This immature control strategy has hindered the further development of shared energy storage. Furthermore, deploying energy storage converters with different control modes under varying grid strengths can improve system economics and stability. However, how converters with different control modes can coordinate in real time remains to be studied.

[0025] Based on this, according to an embodiment of the present invention, an embodiment of a collaborative control method for an energy storage converter is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] In this embodiment, a coordinated control method for an energy storage converter is provided. Figure 1 FIG. 1 is a flow chart of a coordinated control method for an energy storage converter according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0027] Step S1: Based on the grid-connected electrical parameters of the target energy storage converter, an artificial intelligence algorithm is used to identify and judge the strength of the power grid, select a target operating mode, and control each energy storage converter in the field to switch to the target operating mode. The target operating mode includes a grid-following mode or a grid-forming mode.

[0028] Optionally, the artificial intelligence algorithm is a neural network algorithm. The process of selecting the target operation mode includes: when the power grid is a strong power grid, the target operation mode is a grid-following mode; when the power grid is a weak power grid, the target operation mode is a grid-building mode.

[0029] Specifically, the grid-connected converter adopts the grid-following mode control under strong grid conditions and the grid-building mode control under weak grid conditions, switching according to grid conditions to achieve the complementary advantages of the two modes. The control block diagram of the dual-mode switching control strategy based on the neural network algorithm in the artificial intelligence algorithm is as follows: Figure 2 As shown, it mainly includes the neural network algorithm link, the dual-mode switching link and the network following / network building mode control link. Figure 2 in,i PCC and u PCC are the current and voltage responses of PCC after current perturbation injection; L gis the inductive component of the grid impedance; e g is the remote grid voltage. In this control scheme, the grid-connected converter determines the strength of the grid by identifying the fundamental impedance and adaptively selects the operating mode, effectively improving the adaptability of the grid impedance.

[0030] Optionally, in this intelligent control strategy architecture, the neural network algorithm serves as the core decision-making center to build a closed-loop decision-making system from operating parameter perception to control strategy output. Its input layer integrates the real-time operating parameters of the power grid and the state characteristics of the converter, covering multi-dimensional key information such as the fundamental impedance identification results, the voltage amplitude and frequency of the common coupling point, the harmonic distortion rate of the converter output current, the active and reactive power deviation, and the DC bus voltage fluctuation value. The output layer innovatively adopts a dual-channel design: the first is the operating mode decision channel, which uses the Softmax function to perform probability mapping between the grid-following mode and the grid-forming mode, and accurately outputs the confidence level of mode switching; the second is the dynamic parameter adjustment channel, which uses the Tanh activation function to smoothly optimize six core control parameters such as the current loop reference correction and the virtual synchronous machine inertia coefficient.

[0031] This neural network is deeply trained using a supervised learning mechanism, with the optimization objective of minimizing transient overshoot under sudden grid impedance changes. This training mechanism empowers the converter with intelligent perception and autonomous decision-making capabilities, enabling it to adaptively switch control strategies based on grid impedance characteristics. Under strong grid conditions, the converter can efficiently track current source characteristics; when grid conditions shift to weak, the system can quickly switch to voltage source networking mode. This strategy significantly enhances the converter's dynamic stability under wide impedance range operating conditions, effectively improving system power quality and operational reliability.

[0032] Step S2: For each energy storage converter, if the system is disturbed, corresponding charge and discharge active power instructions are sent to the energy storage battery according to the output and SOC status of the energy storage battery inside the energy storage converter.

[0033] Optionally, the process of sending the corresponding charging and discharging active power instructions to the energy storage battery includes: if the system frequency change exceeds a threshold value, determining whether the absolute value of the current active output of the energy storage battery exceeds a limit; if the absolute value of the current active output of the energy storage battery exceeds the limit, selecting a constraint coefficient according to the absolute value of the current active output of the energy storage battery; determining whether the energy storage battery is in a charging or discharging state based on the positive or negative sign of the system frequency change; if the energy storage battery is in a charging or discharging state, calculating the charging and discharging weights of the energy storage battery according to the hierarchical analysis method; and constructing the charging and discharging active power instructions based on the constraint coefficient, the charging and discharging weights of the energy storage battery, the initial charging and discharging power of the energy storage, and the system frequency change.

[0034] Specifically, the state of charge (SOC) and rated capacity of different energy storage batteries are used as constraints, with the rated capacity constraint taking precedence over the SOC. When a power system disturbance causes the frequency variation to exceed a preset threshold, the system initiates the following control process: First, the absolute value of the energy storage battery's current active output, |P|, is monitored in real time to determine whether it exceeds the rated limit. If |P| is within the allowable range, the corresponding constraint coefficient K1 is selected based on its value. After determining K1, the system accurately determines the charge and discharge status of the energy storage battery by detecting the positive and negative signs of the frequency variation. Furthermore, the energy storage battery's SOC is divided into five characteristic intervals: 0-20%, 20%-40%, 40%-60%, 60%-80%, and 80%-100%. Each interval corresponds to a different operating condition and energy reserve state of the energy storage battery, thereby implementing dynamic constraint management of the SOC.

[0035] Optionally, the process of calculating the weights of charging and discharging of the energy storage battery according to the hierarchical analysis method includes: dividing the SOC of the energy storage battery into multiple intervals, each interval representing a different working state of the energy storage battery; calculating the weights of charging and discharging of the energy storage battery in each interval according to the hierarchical analysis method; and determining the interval in which the current SOC of the energy storage battery is located and its corresponding weight.

[0036] Specifically, first, based on the changing characteristics of the energy storage battery's state of charge (SOC), it is divided into multiple continuous and non-overlapping intervals. Each interval corresponds to a different working state of the battery, such as the deep discharge zone, the normal working zone, and the full charge protection zone. Secondly, the hierarchical analysis method is used to construct a judgment matrix. Through expert scoring or historical data statistics, the relative importance of the energy storage battery's charging and discharging behavior in each interval is quantified, and then the corresponding weight coefficient is calculated. Finally, the current SOC value of the energy storage battery is monitored in real time, and the interval it is in is determined according to the division rules. The charging and discharging weight corresponding to the interval is called to provide a decision-making basis for subsequent energy management and control strategies.

[0037] For example, each SOC interval is divided as follows:

[0038]

[0039] The calculation formula for charging and discharging active power command is:

[0040] P set =P0+K1|Δf|+K2SOC

[0041] Among them, P setis the charging and discharging active power instruction; P0 is the initial charging and discharging power of the energy storage battery; K1 is the constraint coefficient; Δf is the system frequency change; K2 is the weight of charging and discharging the energy storage battery.

[0042] In some optional embodiments, the control strategy of the grid-following energy storage converter is the simplest energy storage converter topology, which is an energy storage system that only includes a single-stage DC / AC converter. Figure 3 The energy storage is connected in parallel to both ends of the DC capacitor, and the AC side of the energy storage converter is connected to the AC grid at the AC bus common coupling point through a transformer after passing through an LC filter.

[0043] The control structure of the grid-following converter covers important parts such as the phase-locked loop, outer loop power control, inner loop current control and PWM generator. In the specific operation process, the phase-locked loop controls the input voltage u ia 、u ib and u ic Perform Park transform to convert it into u id and u iq Then, through the synergy of PI regulation and feedback control, u iq The phase angle of the AC bus voltage is accurately obtained, and the synchronous operation between the converter and the power grid is finally achieved, ensuring the stable and reliable operation of the system.

[0044] The power outer loop of the energy storage converter can be expressed as:

[0045]

[0046] Among them, i dref 、i qref P is the active and reactive current command; ref 、P e and Q ref , Q e are the active power command value, actual output value, reactive power command value, and actual output value of the energy storage converter respectively; k pp 、k pi 、k qp 、k qi are the proportional-integral coefficients of the PI controller of the active and reactive control loops respectively.

[0047] The inner loop adopts the dq axis decoupling control strategy based on Park transformation. By controlling the current, the dq axis AC current command is converted into the dq axis AC voltage command and sent to the trigger pulse generation link, as shown in the following formula:

[0048]

[0049] Where: e dref 、e qrefThe output voltage reference value generated for the current inner loop control of the energy storage converter, i d 、i q The dq-axis components of the grid current are injected into the energy storage converter.

[0050] The energy storage converter adopting grid-following control generates an inner loop current reference value through PI control by the power outer loop, and generates an output voltage reference value of the energy storage converter through PI control by the current inner loop, which can realize the tracking of active power and reactive power, namely PQ control.

[0051] In some optional implementations, in order to improve the frequency support capability of the system, the grid-type energy storage converter port adopts virtual synchronous generator control (VSG). The grid-type control structure based on virtual synchronous generator control is as follows: Figure 4 shown.

[0052] Figure 4 In the equation, J is the virtual moment of inertia, P set is the active power setting value, P ref is the input active power of the inverter power supply, P is the measured value of the active power output by the VSG unit, θ ref is the phase angle, ω0 is the angular frequency reference value, ω is the actual angular velocity, D p is the damping coefficient, s represents the complex frequency of Laplace transform, k ω is the active power droop coefficient, M f represents the virtual mutual inductance of VSG, ||V dq || is the actual voltage RMS value, V0 is the reference voltage, E is the reference voltage amplitude, u * odq is the outer loop dq decoupling voltage reference value, u odq is the dq outer loop decoupling voltage measurement value, i * dqi is the inner loop dq decoupling current reference value, i dqi is the inner loop dq decoupling current measurement value, u * idq is the inner loop dq decoupling voltage reference value, u * iabc is the inner loop phase voltage reference value.

[0053] For active power-frequency control, the following relationship exists between the electromagnetic power Pe output by the synchronous generator and the mechanical power Pm input:

[0054]

[0055] Where ω is the mechanical angular velocity; J is the moment of inertia; T m and T eare the mechanical torque and electromagnetic torque of the synchronous generator respectively.

[0056] Considering the damping characteristics of the generator, we have:

[0057]

[0058] Where D is the damping coefficient; ω N is the rated angular velocity.

[0059] The above equation is the rotor motion equation, from which the active power-frequency control equation of the virtual synchronous generator can be obtained:

[0060]

[0061] Among them, P ref 、P e are the simulated values ​​of mechanical power and electromagnetic power respectively; θ is the electrical angle.

[0062] In addition, in order to simulate the primary frequency regulation characteristics of the synchronous generator, the active power-frequency droop control equation is added:

[0063] P ref =P set +k ω (ω N -ω)

[0064] Among them, P set is the set value of active power.

[0065] For reactive power-voltage control, traditional synchronous generators maintain terminal voltage stability through the automatic voltage regulator in the excitation control system. Grid-type control uses a PI controller to continuously adjust the excitation current to reduce the error in the inverter output voltage, thereby simulating the automatic voltage regulation function of the synchronous generator. The specific expression of the excitation current is as follows:

[0066]

[0067] Where u dref is the AC output voltage d-axis reference value; u od is the d-axis measurement value of the AC output voltage; k p 、k i are the proportional and integral coefficients of the voltage regulation link respectively; M f is the virtual excitation mutual inductance.

[0068] It can be seen from the above formula that when the system is disturbed, the output voltage will stabilize at the set value, which can effectively provide a guarantee for the stability of the system voltage amplitude.

[0069] In this embodiment, a coordinated control device for an energy storage converter is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments. Details that have already been described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0070] This embodiment provides a coordinated control device for an energy storage converter, the device comprising:

[0071] The mode switching module is used to identify the strength of the power grid based on the grid-connected electrical parameters of the target energy storage converter and adopt artificial intelligence algorithms to select the target operating mode and control each energy storage converter in the field to switch to the target operating mode, which includes the grid-following mode or the grid-forming mode.

[0072] The energy storage control module is used to send corresponding charging and discharging active power instructions to the energy storage battery for each energy storage converter if the system is disturbed, based on the output and SOC status of the energy storage battery inside the energy storage converter.

[0073] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0074] The collaborative control device of the energy storage converter in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0075] An embodiment of the present invention further provides a computer device having the above-mentioned coordinated control device for the energy storage converter.

[0076] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0077] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0078] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0079] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0080] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0081] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0082] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0083] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0084] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0085] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A coordinated control method for an energy storage converter, characterized in that: include: Based on the electrical parameters of the target energy storage converter's grid connection point, an artificial intelligence algorithm is used to identify and judge the strength of the grid, select a target operating mode, and control each energy storage converter in the field to switch to the target operating mode, which includes a grid-following mode or a grid-building mode. For each energy storage converter, if the system is disturbed, the corresponding charge and discharge active power instructions are sent to the energy storage battery according to the output and SOC status of the energy storage battery inside the energy storage converter.

2. The coordinated control method of the energy storage converter according to claim 1, characterized in that: The artificial intelligence algorithm is a neural network algorithm.

3. The coordinated control method of the energy storage converter according to claim 1, characterized in that: The process of selecting the target operating mode includes: When the power grid is a strong power grid, the target operation mode is the grid-following mode; When the power grid is a weak power grid, the target operation mode is the grid construction mode.

4. The coordinated control method of the energy storage converter according to claim 1, characterized in that: The process of sending the corresponding charge and discharge active power instructions to the energy storage battery includes: If the system frequency change exceeds the threshold, it is determined whether the absolute value of the current active output of the energy storage battery exceeds the limit; If the absolute value of the current active output of the energy storage battery exceeds the limit, the constraint coefficient is selected according to the absolute value of the current active output of the energy storage battery; Based on the positive or negative value of the system frequency change, determine whether the energy storage battery is in the charging or discharging state; If the energy storage battery is in a charging or discharging state, the weights of charging and discharging of the energy storage battery are calculated according to the analytic hierarchy process; Based on the constraint coefficient, the weight of charging and discharging of the energy storage battery, the initial charging and discharging power of the energy storage, and the change in system frequency, a charging and discharging active power instruction is constructed.

5. The coordinated control method of the energy storage converter according to claim 4, characterized in that: The process of calculating the weights of charging and discharging of energy storage batteries according to the analytic hierarchy process includes: The SOC of the energy storage battery is divided into multiple intervals, each interval represents a different working state of the energy storage battery; According to the hierarchical analysis method, the weight of charging and discharging of energy storage batteries in each interval is calculated; Determine the current SOC range of the energy storage battery and its corresponding weight.

6. The coordinated control method of the energy storage converter according to claim 4, characterized in that: The calculation formula for charging and discharging active power command is: P set =P0+K1|Δf|+K2SOC Among them, P set is the charging and discharging active power instruction; P0 is the initial charging and discharging power of the energy storage battery; K1 is the constraint coefficient; Δf is the system frequency change; K2 is the weight of charging and discharging the energy storage battery.

7. A coordinated control device for an energy storage converter, characterized in that: The device comprises: A mode switching module is used to identify and judge the strength of the power grid based on the electrical parameters of the grid connection point of the target energy storage converter, using an artificial intelligence algorithm, and select a target operating mode to control each energy storage converter in the field to switch to the target operating mode, which includes a grid-following mode or a grid-forming mode. The energy storage control module is used to send corresponding charge and discharge active power instructions to each energy storage converter based on the output and SOC status of the energy storage battery inside the energy storage converter if the system is disturbed.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the collaborative control method of the energy storage converter according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the coordinated control method of the energy storage converter according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the coordinated control method of the energy storage converter according to any one of claims 1 to 6.

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