Network construction type energy storage converter control method and device and related products

Abstract

The invention relates to the technical field of electric power systems, and discloses a network construction type energy storage converter control method and device and a related product. The method comprises the following steps: constructing a corresponding MPC controller based on a pre-constructed discrete state space equation of a to-be-controlled network-building type energy storage converter; the MPC controller comprises a cost function and a constraint condition; the cost function comprises a state error term; performing optimization solution on the MPC controller by adopting a preset optimization algorithm to obtain an active power reference value and a reactive power reference value when the cost function reaches the minimum; and utilizing a virtual synchronous generator module and a voltage and current double closed-loop control module to generate a pulse width modulation signal based on the active power reference value and the reactive power reference value so as to control the target network-building type energy storage converter. According to the invention, the problems of current impact, power angle instability and the like which are easy to occur in the grid-forming type energy storage converter during voltage drop can be effectively solved, and stable grid-connected operation of the grid-forming type energy storage converter under the working condition of power grid voltage drop is ensured.

Description

Technical Field

[0001] This application relates to the field of power system technology, and more specifically, to a control method, device and related products for a grid-type energy storage converter. Background Technology

[0002] Grid-Forming Converters (GFMCs) are devices that integrate energy storage units and power electronic conversion functions. They can autonomously construct and maintain the voltage amplitude, frequency, and phase of the power grid, independently supporting grid operation in grid scenarios while simultaneously regulating the charging and discharging power of the energy storage system. With the large-scale integration of new energy sources (such as wind and solar power) into the grid, the proportion of traditional synchronous generators is gradually decreasing, significantly reducing the inertia and damping of the power system. This makes the grid more susceptible to frequency fluctuations and power angle instability when subjected to disturbances such as voltage dips and short-circuit faults. Therefore, GFMCs with virtual synchronization characteristics are key equipment for improving grid stability, and related technologies have become an important research direction in the power system field.

[0003] When a voltage drop occurs in the power grid, the energy storage converter needs to be controlled to have a certain low voltage ride-through capability, that is, it can still maintain grid-connected operation and support grid voltage recovery during the voltage dip.

[0004] Currently, existing control methods for energy storage converters include constant power control and VSG control. However, these methods are prone to current surges and power angle instability during voltage dips, affecting the safe operation of the system. Therefore, it is urgent to solve this technical problem. Summary of the Invention

[0005] In view of the above situation, this application provides a grid-type energy storage converter control method, device and related products, which aim to solve the above problems or at least partially solve the above problems.

[0006] In a first aspect, embodiments of this application provide a control method for a grid-type energy storage converter, the method comprising: Based on the pre-constructed discrete state-space equations of the grid-type energy storage converter to be controlled, a corresponding MPC controller is constructed; the MPC controller includes a cost function and constraints; the cost function includes a state error term. A preset optimization algorithm is used to optimize and solve the MPC controller, and the active power reference value and reactive power reference value are obtained when the cost function is minimized. Using a virtual synchronous generator module and a voltage and current dual closed-loop control module, a pulse width modulation signal is generated based on the active power reference value and the reactive power reference value to control the target grid-type energy storage converter.

[0007] Secondly, embodiments of this application also provide a grid-type energy storage converter control device, the device comprising: The model building module is used to construct the corresponding MPC controller based on the pre-built discrete state-space equations of the grid-type energy storage converter to be controlled; the MPC controller includes a cost function and constraints; the cost function includes a state error term; The optimization module is used to optimize the MPC controller using a preset optimization algorithm to obtain the active power reference value and reactive power reference value when the cost function is minimized. The control module is used to generate a pulse width modulation signal based on the active power reference value and the reactive power reference value using a virtual synchronous generator module and a voltage and current dual closed-loop control module, so as to control the target grid-type energy storage converter.

[0008] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the above-described grid-type energy storage converter control method.

[0009] Fourthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the above-described grid-type energy storage converter control method.

[0010] By employing the above technical solutions, the grid-type energy storage converter control method, device, and related products provided in this application embodiment can first construct a cost function for a model predictive control algorithm based on the discrete state-space equations of the grid-type energy storage converter to be controlled. The cost function includes a state error term, which can accurately match the dynamic operating characteristics of the converter itself and quantify the control deviation. Then, the control optimization model composed of the cost function and constraints is optimized and solved by a preset optimization algorithm. Under the premise of satisfying the system operating constraints, the active and reactive power reference values ​​that minimize the cost function can be solved. Finally, using a virtual synchronous generator module and a voltage and current dual closed-loop control module, a pulse width modulation signal is generated based on the optimal power reference value to achieve precise control of the target converter. The solution provided in this application embodiment can effectively solve the current surge and power angle instability problems that are prone to occur in traditional power control methods and VSG control methods when voltage drops occur. It ensures the stable grid-connected operation of the grid-type energy storage converter under grid voltage drop conditions, while improving the accuracy and optimization of converter control, enhancing the system's ability to cope with grid voltage disturbances, and maintaining the operational safety of the power system.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This paper illustrates a flowchart of the grid-type energy storage converter control method provided in an embodiment of this application. Figure 1 ; Figure 2 The power angle curves of the grid-type energy storage converter under different voltage sags provided in the embodiments of this application are shown. Figure 3 The diagram shows the relationship between disturbance current and grid voltage and converter output voltage provided in the embodiments of this application. Figure 4 This paper illustrates a flowchart of the grid-type energy storage converter control method provided in an embodiment of this application. Figure 2 ; Figure 5 This paper shows a schematic diagram of the structure of the grid-type energy storage converter control device provided in an embodiment of this application; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0016] As mentioned earlier, existing energy storage converter control methods, such as power control and VSG control, are prone to current surges and power angle instability during voltage dips, affecting the safe operation of the system. Based on this, this invention proposes a grid-type energy storage converter control method, device, and related products. The following detailed description uses specific embodiments to illustrate this application.

[0017] To facilitate understanding of this embodiment, a detailed description of the grid-type energy storage converter control method disclosed in this application embodiment will be provided first. The execution entity of the grid-type energy storage converter control method provided in this application embodiment is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), a mobile device, a user terminal, or a terminal, etc. In some possible implementations, the grid-type energy storage converter control method can be implemented by a processor calling computer-readable instructions stored in memory.

[0018] Figure 1 This paper illustrates a flowchart of a grid-type energy storage converter control method provided in an embodiment of this application. Figure 1 It can be seen that the embodiments of this application include at least steps S101-S103: S101: Based on the pre-constructed discrete state-space equations of the grid-type energy storage converter to be controlled, a corresponding MPC controller is constructed; the MPC controller includes a cost function and constraints; the cost function includes a state error term; S102: Using a preset optimization algorithm, the MPC controller is optimized and solved to obtain the active power reference value and reactive power reference value when the cost function is minimized; S103: Using a virtual synchronous generator module and a voltage and current dual closed-loop control module, a pulse width modulation signal is generated based on the active power reference value and the reactive power reference value to control the target grid-type energy storage converter.

[0019] As can be seen, this embodiment first constructs a cost function for the model predictive control algorithm based on the discrete state-space equations of the grid-type energy storage converter to be controlled. The cost function includes a state error term, which can accurately match the dynamic operating characteristics of the converter and quantify the control deviation. Then, a preset optimization algorithm is used to optimize and solve the control optimization model composed of the cost function and constraints. Under the premise of satisfying the system operating constraints, the active and reactive power reference values ​​that minimize the cost function can be solved. Finally, using a virtual synchronous generator module and a voltage and current dual closed-loop control module, a pulse width modulation signal is generated based on the optimal power reference value to achieve precise control of the target converter. The solution provided by this embodiment can effectively solve the current surge and power angle instability problems that are prone to occur in traditional power control methods and VSG control methods when voltage drops occur. It ensures the stable grid-connected operation of the grid-type energy storage converter under grid voltage drop conditions, while improving the accuracy and optimization of converter control, enhancing the system's ability to cope with grid voltage disturbances, and maintaining the operational safety of the power system.

[0020] The following provides a detailed explanation of S101-S103.

[0021] Regarding the above S101: In practice, firstly, the grid-type energy storage converter to be controlled is taken as the research object. Based on its power equation, voltage equation, etc., the corresponding nonlinear discrete state-space equation is established. This model can accurately reflect the influence of the dynamic characteristics of the grid-type energy storage converter to be controlled on the output power angle, voltage, etc.

[0022] In some embodiments, the discrete state-space equation of the grid-type energy storage converter to be controlled is:

[0023] in, This represents the system state vector during the (k+1)th sampling period. This represents the system state vector during the k-th sampling period. This represents the control input vector in the k-th sampling period. Including the kth sampling period , , , , Indicates the system's operating power angle. Indicates the angular frequency of the power grid. This represents the output angular frequency of the grid-type energy storage converter to be controlled. This represents the output voltage of the grid-type energy storage converter to be controlled. Including the kth period and , , , Indicates the active power reference value. Indicates the reactive power reference value. , , , , Represents the identity matrix. Indicates the preset control cycle. Indicates the virtual damping coefficient. Indicates the rated angular frequency of the power grid. Indicates the frequency droop factor. Represents the virtual moment of inertia. This represents the reactive power-voltage droop factor. This indicates the rated capacity reference for grid-type energy storage converters. This indicates the rated reference voltage.

[0024] In this embodiment, the system state vector includes the state vector under the kth sampling period. , , , In practice, the converter output voltage, grid voltage, and converter output angular frequency can be monitored in real time. Combined with the fixed-parameter equivalent inductance of the line and the converter's rated current, the system operating power angle can be calculated using the following formula:

[0025] in, Indicates the grid voltage. Indicates the equivalent inductance of the line. This indicates the rated current of the converter.

[0026] It can be calculated based on the real-time detected grid angular frequency and the converter's output angular frequency. The converter output voltage can be detected in real time through the PCC point, thus obtaining... .

[0027] The following analysis explains the principle behind selecting active and reactive power reference values ​​as output control quantities in the embodiments of this application.

[0028] Figure 2 The diagram illustrates the power angle curves of the grid-type energy storage converter under different voltage sags according to embodiments of this application. See also... Figure 2As shown, the system operates normally at equilibrium point a. During a slight voltage drop (curve II), the power angle curve still intersects with the active power reference value, and the system state transitions from a to b, eventually stabilizing at point c. The power angle increases from δa to δc, increasing the output current and potentially causing overcurrent. During a severe voltage drop (curve III), the power angle curve does not intersect with the active power reference value, and the system state drops from a to d. Because the output active power remains consistently lower than the reference value, the power angle continuously increases, eventually leading to system instability. Therefore, whether a system can operate stably during large disturbances depends on whether the system's power angle curve intersects with the active power reference value and whether the power angle remains stable. Thus, adjusting the active power reference value can ensure that the power angle remains constant, achieving transient stability of the system.

[0029] Figure 3 The diagram illustrates the relationship between disturbance current, grid voltage, and converter output voltage, as provided in an embodiment of this application. Figure 3 It can be seen that when the grid voltage drop is constant, the disturbance current increases with the increase of the difference between the grid voltage and the converter output voltage. Therefore, the magnitude of the disturbance current is positively correlated with the difference between the converter output voltage and the grid voltage. During disturbances, the disturbance current can be limited by controlling the converter output voltage. Adjusting the reactive power reference value can change the magnitude of the converter output voltage, thereby limiting the disturbance current (however, it is important to ensure that reactive power is output to the maximum extent possible to provide voltage support to the system while limiting the disturbance current), avoiding overcurrent surges under low voltage conditions, and providing maximum voltage support while meeting the 1.1 times current limiting requirement.

[0030] In practical implementation, the following state-space equations can be established first:

[0031] The above equation can be written as:

[0032] in, , .

[0033] Since the method provided in this application embodiment is implemented using a digital controller, it needs to be discretized. Let the control cycle be... Then the above state-space equation can be discretized as:

[0034] In some embodiments, the discrete state-space equation of the grid-type energy storage converter to be controlled is:

[0035] in, This represents the total disturbance term in the k-th sampling period. Including frequency-dependent perturbations in the kth sampling period Voltage-related disturbances ,in , , This represents the actual active power output to the grid by the grid-connected energy storage converter under control via electromagnetic coupling. This represents the actual reactive power output of the grid-connected energy storage converter to the power grid. This represents the rated output voltage of the grid-type energy storage converter to be controlled. , .

[0036] In this embodiment, during implementation, the following state-space equation can be established first. This model can accurately reflect the dynamic characteristics of the grid-type energy storage converter to be controlled and the influence of external disturbances on the output power angle and voltage:

[0037] The above equation can be written as:

[0038] in, , , In practice, frequency-dependent disturbances Voltage-related disturbances It can be observed using existing extended state observers. The extended state observer (ESO) does not rely on precise system model parameters, can estimate and compensate for the disturbance term d in real time, and maintains observation accuracy even under the combined effects of modeling errors and external disturbances.

[0039] Since the method provided in this application embodiment is implemented using a digital controller, it needs to be discretized. Let the control cycle be... Then the above state-space equation can be discretized as:

[0040] This embodiment incorporates the influence of total external disturbances into the state-space equations, which can effectively improve the prediction accuracy of model predictive control for the future state of the system. This allows model predictive control to achieve optimal power allocation that is more in line with actual operating conditions under constraints, greatly improving the converter's adaptability to various external disturbances and system parameter uncertainties. In turn, it enhances the dynamic performance and robustness of the system, increases the stability margin of the system, and ensures the stable operation of the grid-type energy storage converter during low-voltage ride-through.

[0041] After obtaining the discrete state-space equations of the grid-type energy storage converter to be controlled, an MPC controller corresponding to the converter is constructed based on these equations. Firstly, regarding the cost function in the MPC controller, to avoid the increased solution complexity caused by directly introducing nonlinear constraints in Model Predictive Control (MPC), this application indirectly achieves the constraint objective by designing a process for the system state variables to track the reference signal. Therefore, the state variables in the cost function are defined in the form of state error, i.e., the deviation between the actual state and the reference state, thereby reflecting the constraint effect and enhancing the dynamic response capability of the system during the optimization process. In some embodiments, the cost function is:

[0042] in, The weighted matrix for state errors. Let N be the system state error vector predicted at time k+j from time k, where N represents the prediction time domain.

[0043] Let the model of the reference signal be:

[0044] in, This is a constant, and can be set according to the actual situation during implementation. , .here, The derivation of the calculation formula is as follows: The output current of the energy storage converter is:

[0045] Due to the voltage drop in the grid, the amplitude of the three-phase voltage on the grid side decreases, but the phase remains symmetrical. The expression for the amplitude of the disturbance current in phase a can be obtained from the formula for calculating the output current of the energy storage converter:

[0046] The power angle can be kept stable by adjusting the active power reference value, that is, the power angle remains stable during disturbances, as shown in the following formula:

[0047] According to GB / T34120-2023 "General Technical Specification for Grid-type Energy Storage Converters", the maximum overcurrent of a grid-type energy storage converter is 1.1 times the rated output current, as shown in the following formula:

[0048] Based on the expression for the amplitude of the phase a disturbance current and the two formulas that follow, the reference value of the converter output voltage corresponding to the maximum disturbance current can be obtained. V ref ,Right now:

[0049] The discrete form of the reference signal model is:

[0050] For example, if the actual state vector is:

[0051] Then the first Step state The tracking error is:

[0052] satisfy:

[0053] in, For the total disturbance, , .

[0054] Here, U d Incorporating the total disturbance is to convert the original constraint objective acting on the control variable into an equivalent state-level deviation, so that MPC can achieve current limiting and stable control through state error optimization without introducing nonlinear control constraints.

[0055] In some embodiments, the cost function further includes a control increment term; the cost function is:

[0056] in, To control the incremental weighted matrix, This represents the incremental control vector at time k for the optimization control at time k+j.

[0057] The cost function provided in this embodiment, after incorporating the control increment term, forms a dual-term optimization. It ensures that the core states of the converter, such as the power angle and output voltage, accurately track the ideal reference value through the state error term, guaranteeing the stability of the power angle and current limiting when the grid voltage drops. It also constrains the sudden changes in active and reactive power reference values ​​through the control increment term, avoiding problems such as current surges and sudden changes in power angle, thus achieving smooth power regulation and ensuring the feasibility of control quantities. At the same time, different weighting matrices can flexibly adjust the weights to balance the state tracking accuracy and the smoothness of control actions, improving the stability and robustness of the controller during low voltage ride-through and making it more suitable for actual engineering operation requirements.

[0058] In other embodiments, the cost function further includes an end-state error term; the cost function is:

[0059] Where P is the end weight, This represents the system state error vector predicted at time k for time k+N at the end of the prediction time domain.

[0060] In this embodiment, P is used to ensure stability, and P satisfies:

[0061] The cost function provided in this embodiment adds a state error term at the end of the prediction time domain to the original accumulated state error of the next N steps. This term ensures the tracking accuracy of the core states of the grid-connected energy storage converter, such as the power angle and voltage, from time k to k+N-1 through the preceding summation term, guaranteeing the basic stability control requirements when the grid voltage drops. At the same time, the end state error term constrains the state deviation of the last prediction step, avoiding system instability caused by the optimization process focusing only on the preceding time steps and resulting in the end state deviating too much from the ideal value. In addition, the end weight P can be flexibly adjusted to constrain the end error, allowing the optimization effect of model predictive control to cover the entire prediction time domain, further improving the stability of the controller closed-loop system and ensuring the state stability of the grid-connected energy storage converter throughout the low voltage ride-through process.

[0062] In some other embodiments, the cost function further includes a control increment term and an end-state error term; the cost function is:

[0063] In some embodiments, the constraints include: initial state error constraints, control increment vector constraints, and weight matrix constraints.

[0064] In this embodiment, the specific initial state error constraint condition is as follows:

[0065] The constraint optimizes the measured value of the state error at the start time (time k) to be equal to the initial error value set in the optimization problem, thus avoiding the optimization from deviating from the actual operating conditions (for example, the power grid has experienced a slight drop, but the initial error of the normal voltage is used for calculation).

[0066] The control increment vector constraint is:

[0067] in, It represents a 2N-dimensional real vector.

[0068] The constraints on the weight matrix are:

[0069] This constraint is used to limit the "size" of the weight matrix in the cost function, ensuring that matrix multiplication can be performed.

[0070] The following uses Lyapunov function analysis to prove that the MPC controller in the embodiments of this application can still guarantee the input-to-state stability (ISS) of the closed-loop system under bounded disturbances.

[0071] Assuming total disturbance Bounded, that is, existing Makes all All .

[0072] Choosing Lyapunov functions A closed-loop system is one where the input is stable in the state (ISS).

[0073] Specifically, there exist KL-like functions. and class function This makes it possible for all :

[0074] prove: 1. When the total disturbance At that time, based on the standard MPC stability theory, the terminal cost and prediction time domain The design can ensure Decreasing, that is Thus, the error asymptotically converges to zero.

[0075] 2. When the total disturbance At that time, there exists a class function Make:

[0076] This indicates The system satisfies the ISS Lyapunov condition, therefore it is ISS.

[0077] Regarding S102-S103 above: Figure 4 This application illustrates a schematic flowchart of a grid-type energy storage converter control method provided in an embodiment of the present application. Figure 2 .

[0078] See Figure 4 In practice, optimization algorithms such as quadratic programming and sequential quadratic programming can be used to optimize the MPC controller in each sampling period to obtain the active power reference value and reactive power reference value when the cost function is minimized.

[0079] After obtaining the active power reference value and the reactive power reference value, the virtual synchronous generator module (VSG control loop) is first used to receive the active power reference value and the reactive power reference value. By introducing virtual inertia and damping elements, the electromagnetic torque and power angle dynamic characteristics of the traditional synchronous generator are simulated to generate the reference phase and reference voltage. Then, the voltage and current dual closed-loop control module is used to generate a pulse width modulation signal based on the reference voltage and reference phase to control the target grid-type energy storage converter.

[0080] Those skilled in the art will understand that in the above-described method of the specific embodiments, the order in which the steps are written does not imply a strict execution order, but constitutes no limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0081] It should be noted that in practical applications, all the above-described possible implementation methods can be combined in any way to form possible embodiments of this application, and will not be described in detail here. The information (including but not limited to device information, user information, etc.) and data (including but not limited to data used for analysis, storage, and display) involved in this application are all information and data authorized by the user or fully authorized by all parties. The software tools or components appearing in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0082] Based on the same concept, this application also provides a grid-type energy storage converter control device, which corresponds one-to-one with the grid-type energy storage converter control method in the above embodiments. Figure 5 A schematic diagram of the structure of the grid-type energy storage converter control device provided in an embodiment of this application is shown. See also: Figure 5 As shown, the grid-type energy storage converter control device 500 provided in this application embodiment includes: The model building module 501 is used to build a corresponding MPC controller based on the pre-built discrete state-space equations of the grid-type energy storage converter to be controlled; the MPC controller includes a cost function and constraints; the cost function includes a state error term; The optimization module 502 is used to optimize the MPC controller using a preset optimization algorithm to obtain the active power reference value and reactive power reference value when the cost function is minimized. The control module 503 is used to generate a pulse width modulation signal based on the active power reference value and the reactive power reference value using a virtual synchronous generator module and a voltage and current dual closed-loop control module, so as to control the target grid-type energy storage converter.

[0083] In some embodiments, in the above-described device, the discrete state-space equation of the grid-type energy storage converter to be controlled is:

[0084] in, This represents the system state vector during the (k+1)th sampling period. This represents the system state vector during the k-th sampling period. This represents the control input vector in the k-th sampling period. Including the kth sampling period , , , , Indicates the system's operating power angle. Indicates the angular frequency of the power grid. This represents the output angular frequency of the grid-type energy storage converter to be controlled. This represents the output voltage of the grid-type energy storage converter to be controlled. Including the kth period and , , , Indicates the active power reference value. Indicates the reactive power reference value. , , , , Represents the identity matrix. Indicates the preset control cycle. Indicates the virtual damping coefficient. Indicates the rated angular frequency of the power grid. Indicates the frequency droop factor. Represents the virtual moment of inertia. This represents the reactive power-voltage droop factor. This indicates the rated capacity reference for grid-type energy storage converters. This indicates the rated reference voltage.

[0085] In some embodiments, in the above-described device, the discrete state-space equation of the grid-type energy storage converter to be controlled is:

[0086] in, This represents the total disturbance term in the k-th sampling period. Including frequency-dependent perturbations in the kth sampling period Voltage-related disturbances ,in , , This represents the actual active power output to the grid by the grid-connected energy storage converter under control via electromagnetic coupling. This represents the actual reactive power output of the grid-connected energy storage converter to the power grid. This represents the rated output voltage of the grid-type energy storage converter to be controlled. , .

[0087] In some embodiments, in the above-described apparatus, the cost function further includes a control increment term; the cost function is:

[0088]

[0089] Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let k be the system state error vector predicted at time k+j. To control the incremental weighted matrix, This represents the control increment vector predicted at time k+j from time k.

[0090] In some embodiments, in the above-described apparatus, the cost function further includes an end-state error term; the cost function is:

[0091]

[0092] Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let P be the system state error vector predicted at time k+j from time k, and let P be the terminal weight. This represents the system state error vector predicted at time k for time k+N at the end of the prediction time domain.

[0093] In some embodiments, in the above-described apparatus, the cost function further includes a control increment term and an end-state error term; the cost function is:

[0094]

[0095] Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let k be the system state error vector predicted at time k+j. To control the incremental weighted matrix, This represents the optimization control increment vector from time k to time k+j, where P is the terminal weight. This represents the system state error vector predicted at time k for time k+N at the end of the prediction time domain.

[0096] In some embodiments, the constraints in the above-described apparatus include: initial state error constraints, control increment vector constraints, and weight matrix constraints.

[0097] This invention provides a control device for a grid-connected energy storage converter. First, based on the discrete state-space equations of the grid-connected energy storage converter to be controlled, a cost function for a model predictive control algorithm is constructed. This cost function includes a state error term, which can accurately match the converter's dynamic operating characteristics and quantify control deviations. Then, a preset optimization algorithm is used to optimize and solve the control optimization model composed of this cost function and constraints. Under the premise of satisfying system operating constraints, the active and reactive power reference values ​​that minimize the cost function are obtained. Finally, using a virtual synchronous generator module and a voltage-current dual closed-loop control module, a pulse width modulation signal is generated based on the optimal power reference value to achieve precise control of the target converter. The solution provided in this application effectively solves the current surge and power angle instability problems that easily occur in traditional power control methods and VSG control methods during voltage dips. It ensures the stable grid-connected operation of the grid-connected energy storage converter under grid voltage dip conditions, while improving the accuracy and optimality of converter control, enhancing the system's ability to cope with grid voltage disturbances, and maintaining the operational safety of the power system.

[0098] Specific limitations regarding the control device for grid-type energy storage converters can be found in the limitations of the control method for grid-type energy storage converters mentioned above, and will not be repeated here. Each module in the aforementioned grid-type energy storage converter control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0099] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 6As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0100] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0101] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0102] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a grid-type energy storage converter control device at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0103] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0104] This electronic device can execute the grid-type energy storage converter control method provided in several embodiments of this application, and realize the grid-type energy storage converter control device in Figure 5 The functions of the embodiments shown are not described in detail here.

[0105] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the grid-type energy storage converter control method provided in several embodiments of this application.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0111] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0112] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0113] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0114] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A control method for a grid-type energy storage converter, characterized in that, The method includes: Based on the pre-constructed discrete state-space equations of the grid-type energy storage converter to be controlled, a corresponding MPC controller is constructed; the MPC controller includes a cost function and constraints; the cost function includes a state error term. A preset optimization algorithm is used to optimize and solve the MPC controller, and the active power reference value and reactive power reference value are obtained when the cost function is minimized. Using a virtual synchronous generator module and a voltage and current dual closed-loop control module, a pulse width modulation signal is generated based on the active power reference value and the reactive power reference value to control the target grid-type energy storage converter.

2. The control method for a grid-type energy storage converter according to claim 1, characterized in that, The discrete state-space equation of the grid-type energy storage converter to be controlled is: in, This represents the system state vector during the (k+1)th sampling period. This represents the system state vector during the k-th sampling period. This represents the control input vector in the k-th sampling period. Including the kth sampling period , , , , Indicates the system's operating power angle. Indicates the angular frequency of the power grid. This represents the output angular frequency of the grid-type energy storage converter to be controlled. This represents the output voltage of the grid-type energy storage converter to be controlled. Including the kth period and , , , Indicates the active power reference value. Indicates the reactive power reference value. , , , , Represents the identity matrix. Indicates the preset control cycle. Indicates the virtual damping coefficient. Indicates the rated angular frequency of the power grid. Indicates the frequency droop factor. Represents the virtual moment of inertia. This represents the reactive power-voltage droop factor. This indicates the rated capacity reference for grid-type energy storage converters. This indicates the rated reference voltage.

3. The control method for a grid-type energy storage converter according to claim 2, characterized in that, The discrete state-space equation of the grid-type energy storage converter to be controlled is: in, This represents the total disturbance term in the k-th sampling period. Including frequency-dependent perturbations in the kth sampling period Voltage-related disturbances ,in , , This represents the actual active power output to the grid by the grid-connected energy storage converter under control via electromagnetic coupling. This represents the actual reactive power output of the grid-connected energy storage converter to the power grid. This represents the rated output voltage of the grid-type energy storage converter to be controlled. , .

4. The control method for a grid-type energy storage converter according to claim 2, characterized in that, The cost function further includes a control increment term; the cost function is: Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let k be the system state error vector predicted at time k+j. To control the incremental weighted matrix, This represents the control increment vector predicted at time k+j from time k.

5. The control method for a grid-type energy storage converter according to claim 2, characterized in that, The cost function further includes a terminal state error term; the cost function is: Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let P be the system state error vector predicted at time k+j from time k, and let P be the terminal weight. This represents the system state error vector predicted at time k for time k+N at the end of the prediction time domain.

6. The control method for a grid-type energy storage converter according to claim 2, characterized in that, The cost function further includes a control increment term and an end-state error term; the cost function is: Where N represents the prediction time domain, The system state vector reference signal at time k+j is represented. The weighted matrix for state errors. Let k be the system state error vector predicted at time k+j. To control the incremental weighted matrix, This represents the optimization control increment vector from time k to time k+j, where P is the terminal weight. This represents the system state error vector predicted at time k for time k+N at the end of the prediction time domain.

7. The control method for a grid-type energy storage converter according to any one of claims 1-6, characterized in that, The constraints include: initial state error constraints, control increment vector constraints, and weight matrix constraints.

8. A grid-type energy storage converter control device, characterized in that, The device includes: The model building module is used to construct the corresponding MPC controller based on the pre-built discrete state-space equations of the grid-type energy storage converter to be controlled; the MPC controller includes a cost function and constraints; the cost function includes a state error term; The optimization module is used to optimize the MPC controller using a preset optimization algorithm to obtain the active power reference value and reactive power reference value when the cost function is minimized. The control module is used to generate a pulse width modulation signal based on the active power reference value and the reactive power reference value using a virtual synchronous generator module and a voltage and current dual closed-loop control module, so as to control the target grid-type energy storage converter.

9. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, characterized in that, when executed, the executable instructions cause the processor to perform the steps of the grid-type energy storage converter control method as described in any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs, characterized in that, When the one or more programs are executed by an electronic device including multiple applications, the electronic device performs the steps of the grid-type energy storage converter control method as described in any one of claims 1-7.

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