A control method and device of a direct current system, a computer device and a storage medium

Abstract

The present application relates to the technical field of direct current system control, and discloses a control method and device of a direct current system, computer equipment and a storage medium, wherein the direct current system comprises different types of energy storage systems, the method comprises: obtaining operating parameters of the different types of energy storage systems; establishing a load power prediction model of the energy storage system based on the operating parameters; and performing parallel control on the different types of energy storage systems based on the load power prediction model, a preset droop control link and a preset virtual direct current motor control link of a variable damping coefficient. In the control of the different types of energy storage systems, the damping coefficient of the different types of energy storage systems and the load power control speed influencing factors are considered in the control process, the power balance in the direct current system is ensured, the oscillation of the direct current bus voltage is avoided, and the problem that the oscillation of the direct current bus voltage is caused by the damping coefficient and the power control speed in the direct current system without considering the different types of energy storage is solved.

Description

Technical Field

[0001] This invention relates to the field of DC system control technology, and specifically to a control method, device, computer equipment, and storage medium for a DC system. Background Technology

[0002] With the large-scale construction and rapid development of photovoltaic (PV) power generation, energy storage systems have been widely applied to improve PV power utilization efficiency, mitigate power fluctuations, and supply power to system loads. Simultaneously, to meet the power demands of different loads on energy storage systems, these systems have gradually moved beyond a single type and are developing towards diversification. Therefore, in DC systems, the optimized control of various energy storage charging and discharging powers and PV power directly determines whether the DC system can operate safely, reliably, and efficiently.

[0003] When multiple energy storage systems are operating in parallel, a reasonable charging and discharging power control strategy for the energy storage system is an important measure to ensure the stability of the DC bus in the DC system, and also an important technological development direction to improve the utilization efficiency of the energy storage system and the reliability of the DC system.

[0004] In DC systems, the stability of the DC bus voltage is an important indicator of system stability. When the DC bus voltage of a DC system oscillates, it essentially means that the power balance of the DC system has been disrupted. Therefore, higher requirements are placed on the system damping of the DC system and the power control speed of the power electronic equipment in each subsystem. Although current control methods achieve coordinated control of energy storage systems from different perspectives, they do not achieve optimized control of multiple types of energy storage systems from the perspective of system damping and the power control speed of the power electronic equipment in each subsystem. This can easily lead to the problem of DC bus voltage oscillation in DC systems. Summary of the Invention

[0005] In view of this, the present invention provides a control method, device, computer equipment and storage medium for a DC system to solve the problem of DC bus voltage oscillation caused by the damping coefficient and power control speed in DC systems that do not take into account different types of energy storage.

[0006] In a first aspect, the present invention provides a control method for a DC system, the DC system including different types of energy storage systems, the method comprising:

[0007] Obtain operating parameters for different types of energy storage systems;

[0008] Establish a load power prediction model for the energy storage system based on operating parameters;

[0009] Parallel control of different types of energy storage systems is performed based on a load power prediction model, a preset droop control loop, and a virtual DC motor control loop with a preset variable damping coefficient.

[0010] The DC system control method provided by this invention acquires the operating parameters of different types of energy storage systems, establishes a load power prediction model for the energy storage system based on the operating parameters, and finally performs parallel control of different types of energy storage systems based on the load power prediction model, a preset droop control loop, and a virtual DC motor control loop with a preset variable damping coefficient. When controlling different types of energy storage systems, the damping coefficients of different types of energy storage systems and the influence factors of load power control speed are taken into account in the control process, thereby achieving the purpose of variable damping coefficient control and load power prediction, ensuring power balance in the DC system, avoiding DC bus voltage oscillation, and solving the problem of DC bus voltage oscillation caused by not considering the damping coefficients and power control speed of different types of energy storage systems.

[0011] In one alternative implementation, the operating parameters of different types of energy storage systems include the output current, DC-side output voltage, DC-side output capacitance, and gain of the different types of energy storage systems.

[0012] In one optional implementation, establishing a load power prediction model for the energy storage system based on operating parameters includes:

[0013] The derivatives of the auxiliary power prediction variables inside the energy storage system are determined based on the output current, DC-side output voltage, DC-side output capacitance, and gain of different types of energy storage systems.

[0014] The predicted value of the unknown load power of the energy storage system is determined based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0015] A load power prediction model for the energy storage system is established based on the derivative of the auxiliary power prediction variables within the energy storage system and the predicted value of the unknown load power of the energy storage system.

[0016] In one alternative implementation, the energy storage system includes a DC / DC converter, and the load power prediction model of the energy storage system is determined by the following formula:

[0017]

[0018] in: The derivative of the predicted value of unknown load power for different types of energy storage systems. γ represents the predicted value of unknown load power for different types of energy storage systems. n For gain, For predicting auxiliary power within different types of energy storage systems, x 1n The output current i of different types of energy storage systems Ln x 2n The DC-side output voltage u of different types of energy storage systemsCn C n σ represents the DC output capacitor of different types of energy storage systems, and σ represents the control law of the DC / DC converter in different types of energy storage systems.

[0019] The control method for DC systems provided by this invention uses a load power prediction model for energy storage systems to predict the load power of DC systems, thereby enabling different types of energy storage systems in the DC system to quickly track power changes based on their own load power changes, and realizing control over the power response speed of different types of energy storage systems.

[0020] In one optional implementation, the DC system further includes a DC bus, and the virtual DC motor control loop with preset variable damping coefficient is determined by using a virtual DC motor control loop with variable damping coefficient that simulates different types of energy storage systems.

[0021] In one optional implementation, the virtual DC motor control loop simulating the variable damping coefficient of different types of energy storage systems includes:

[0022] Establish equivalent models of different types of energy storage systems and virtual DC motors;

[0023] Obtain the DC bus voltage and calculate the variable damping coefficient of different types of energy storage systems based on the DC bus voltage;

[0024] Set up the control circuit for the virtual DC motor;

[0025] Based on the equivalent models of different types of energy storage systems and virtual DC motors, the variable damping coefficients of different types of energy storage systems are input into the control loop of the virtual DC motor to obtain the virtual DC motor control loop with variable damping coefficients of different types of energy storage systems.

[0026] In one optional implementation, the control mechanism for the virtual DC motor includes:

[0027] Based on the preset armature circuit equations, preset mechanical equations, preset mechanical power, and preset electromagnetic power of the virtual DC motor, the control links of the virtual DC motor are set.

[0028] The DC system control method provided by this invention employs a virtual DC motor control loop with variable damping coefficient to provide additional damping for the DC system, suppressing DC bus voltage oscillations. Furthermore, it forms a parallel control system for different types of energy storage parallel systems together with a preset droop control and load power prediction model, providing a foundation for subsequent coordinated control of different types of energy storage.

[0029] In one optional implementation, calculating the variable damping coefficient of different types of energy storage systems based on the DC bus voltage includes:

[0030] Fit the dynamic response curve of the DC bus voltage;

[0031] The dynamic response curve is segmented according to a preset time sequence;

[0032] Obtain the change status of the DC bus voltage in each segment;

[0033] The variable damping coefficient is calculated based on the change in DC bus voltage in each segment.

[0034] In one alternative implementation, the variable damping coefficient is determined by the following formula:

[0035]

[0036] Where: D0 and D b The initial values ​​for the first damping coefficient and the first damping coefficient parameter are: A is the amplification factor; Δu is the difference between the expected DC bus voltage and the DC bus voltage; Δu DC The difference between the sampling values ​​of the DC bus voltage before and after the sampling point; k is the threshold value of |Δu|.

[0037] The control method for DC systems provided by this invention provides sufficient damping support through a variable damping coefficient when the DC system is subjected to external disturbances, thereby achieving a rapid dynamic response of the energy storage system, avoiding large fluctuations and changes in the DC bus voltage, reducing the impact on the DC bus voltage, and suppressing oscillations in the DC bus voltage.

[0038] In one optional implementation, parallel control of different types of energy storage systems based on a load power prediction model, a preset droop control loop, and a virtual DC motor control loop with a preset variable damping coefficient includes:

[0039] Based on a preset droop control loop, droop control is performed on different types of energy storage systems to obtain stable DC-side output voltages for different types of energy storage systems.

[0040] The stable DC-side output voltage of different types of energy storage systems is input into the virtual DC motor control loop with a preset variable damping coefficient to perform virtual DC motor control on different types of energy storage systems, thereby obtaining virtual inertial power;

[0041] Virtual inertial power is input into the load power prediction model for parallel control to compensate for power control errors of different types of energy storage systems.

[0042] The DC system control method provided by this invention performs droop control on different types of energy storage systems based on a preset droop control loop to obtain stable DC-side output voltages for each type of energy storage system. The stable DC-side output voltages of the different types of energy storage systems are then input into a virtual DC motor control loop with a preset variable damping coefficient to perform virtual DC motor control on the different types of energy storage systems, obtaining virtual inertial power. This virtual inertial power is then input into a load power prediction model for parallel control to compensate for power control errors in different types of energy storage systems. Through the preset droop control loop, the virtual DC motor control loop with a preset variable damping coefficient, and the load power prediction model, parallel control of different types of energy storage systems is achieved, providing sufficient damping support and enabling tracking of the power control speed of different types of energy storage systems. This method can provide additional damping to suppress DC bus voltage oscillations while simultaneously predicting the system load power, achieving rapid power control in the DC system, and ensuring the stability of the DC bus voltage.

[0043] In one alternative implementation, the DC system further includes a photovoltaic power generation system, and the method further includes:

[0044] Acquire the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems obtained from parallel control;

[0045] The tiered operation sequence of different types of energy storage systems is determined based on power prediction results, DC bus voltage, output power of photovoltaic power generation system and real-time state of charge of different types of energy storage systems.

[0046] Different types of energy storage systems are coordinated and controlled according to their tiered operating sequence.

[0047] The control method for DC systems provided by this invention, when the DC system contains different types of energy storage systems, determines the tiered utilization sequence of different types of energy storage systems when the DC system is unbalanced by predicting the power characteristics of different energy storage systems and considering the real-time state of charge of the energy storage systems, thereby improving the utilization efficiency of different types of energy storage and more effectively achieving power balance of the DC system.

[0048] In a second aspect, the present invention provides a control device for a DC system, the device comprising:

[0049] The acquisition module is used to acquire operating parameters of different types of energy storage systems;

[0050] A module is established to build a load power prediction model for the energy storage system based on operating parameters;

[0051] The control module is used to perform parallel control of different types of energy storage systems based on a load power prediction model, a preset droop control loop, and a preset variable damping coefficient virtual DC motor control loop.

[0052] In one alternative implementation, the establishment module includes:

[0053] The first determining unit is used to determine the auxiliary power prediction variables inside the energy storage system based on the output current, DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0054] The second determining unit is used to determine the predicted value of the load power of the unknown energy storage system based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems;

[0055] Establishment unit, used to build load power prediction model of energy storage system based on the auxiliary power prediction variables inside the energy storage system and the predicted value of unknown load power of the energy storage system.

[0056] Thirdly, 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 perform the control method of the DC system described in the first aspect or any corresponding embodiment thereof.

[0057] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the control method of the DC system described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0058] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating a control method for a DC system according to an embodiment of the present invention;

[0060] Figure 2 This is a flowchart illustrating another control method for a DC system according to an embodiment of the present invention;

[0061] Figure 3 This is a flowchart illustrating a control method for a DC system according to an embodiment of the present invention.

[0062] Figure 4 This is a flowchart illustrating a control method for a recirculating DC system according to an embodiment of the present invention.

[0063] Figure 5 This is a topology diagram of a DC system containing multiple types of energy storage systems according to an embodiment of the present invention;

[0064] Figure 6 This is a structural diagram of parallel control in the control method of a DC system according to an embodiment of the present invention;

[0065] Figure 7(a) is a step response curve of DC bus voltage when the damping coefficient is equal to 5 in the control method of DC system according to an embodiment of the present invention;

[0066] Figure 7(b) is a step response curve of DC bus voltage when the damping coefficient is equal to 10 in the control method of DC system according to an embodiment of the present invention;

[0067] Figure 7(c) is a step response curve of DC bus voltage when the damping coefficient is equal to 15 in the control method of DC system according to an embodiment of the present invention;

[0068] Figure 7(d) is a step response curve of DC bus voltage when the damping coefficient is equal to 20 in the control method of DC system according to an embodiment of the present invention;

[0069] Figure 7(e) is a step response curve of DC bus voltage when the damping coefficient is equal to 25 in the control method of DC system according to an embodiment of the present invention.

[0070] Figure 8 This is a schematic diagram of a typical dynamic response process of DC bus voltage in the control method of a DC system according to an embodiment of the present invention;

[0071] Figure 9 This is a control block diagram of coordinated control in the control method of a DC system according to an embodiment of the present invention;

[0072] Figure 10 This is a structural block diagram of a control device for a DC system according to an embodiment of the present invention;

[0073] Figure 11 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] According to an embodiment of the present invention, a control method for a DC system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0076] This embodiment provides a control method for a DC system, which can be used in the aforementioned DC system, including different types of energy storage systems. Figure 1 This is a flowchart of a control method for a DC system according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0077] Step S101: Obtain the operating parameters of different types of energy storage systems.

[0078] Specifically, Figure 5 This is a topology diagram of a DC system containing multiple types of energy storage systems according to an embodiment of the present invention, such as... Figure 5 As shown, DC systems include different types of energy storage systems. An energy storage system is a device or system that stores energy and releases it when needed. Different types of energy storage systems include photovoltaic arrays, flywheel energy storage systems, supercapacitor energy storage systems, lithium battery energy storage systems, pumped hydro storage systems, compressed air energy storage systems, and thermal energy storage systems. Energy storage systems are classified according to their characteristics into power-type energy storage systems and energy-type energy storage systems. Power-type energy storage systems have a fast response time but a shorter storage duration, while energy-type systems have a slower response time but a longer storage duration.

[0079] Flywheel energy storage systems, supercapacitor energy storage systems, and lithium battery energy storage systems are power-type energy storage systems, while pumped hydro storage systems, compressed air energy storage systems, and thermal energy storage systems are energy-type energy storage systems.

[0080] like Figure 5 As shown, each energy storage system in different types of energy storage systems includes a DC / DC converter. The operating parameters can include the output current, DC-side output voltage, DC-side output capacitance, and amplification gain of each type of energy storage system's DC / DC converter.

[0081] Step S102: Establish a load power prediction model for the energy storage system based on the operating parameters.

[0082] Specifically, the load power prediction model is used to predict the load power of different types of energy storage systems, enabling each type of energy storage system in a DC system to quickly track power changes based on its own load power. The load power prediction model for an energy storage system can be established based on the output current, DC-side output voltage, DC-side output capacitance, and amplification gain of each type of energy storage system.

[0083] Step S103: Parallel control of different types of energy storage systems is performed based on the load power prediction model, the preset droop control loop, and the virtual DC motor control loop with the preset variable damping coefficient.

[0084] For example, Figure 6 This is a structural diagram of parallel control in the control method of a DC system according to an embodiment of the present invention, as shown below. Figure 6 As shown, the droop control circuit controls the stability of the DC system by detecting the frequency and voltage of different types of energy storage systems within the DC system. When the frequency or voltage of different types of energy storage systems in the DC system changes, the droop control circuit automatically adjusts the droop control coefficient of the DC / DC converter for each type of energy storage system, so that the DC system returns to a normal stable state and obtains the DC-side output voltage. However, the DC system will lose output power, resulting in power control errors.

[0085] like Figure 6 As shown, the stable DC-side output voltage obtained from the droop control loop is input to the virtual DC motor control loop with a preset variable damping coefficient. During the control process of the virtual DC motor control loop, different damping coefficients are adopted according to the response speed of the DC bus voltage, and finally a virtual power is output.

[0086] like Figure 6 As shown, the virtual power obtained by the virtual DC motor control loop with preset variable damping coefficient is input into the load power prediction model to perform parallel control of different types of energy storage systems. This can compensate for the power control error caused by the DC system in the droop control loop and predict the load power of different types of energy storage systems.

[0087] The DC system control method provided in this embodiment obtains the operating parameters of different types of energy storage systems, establishes a load power prediction model for the energy storage system based on the operating parameters, and finally performs parallel control of different types of energy storage systems based on the load power prediction model, a preset droop control loop, and a virtual DC motor control loop with a preset variable damping coefficient. When controlling different types of energy storage systems, the damping coefficients of different types of energy storage systems and the influence factors of load power control speed are taken into account in the control process, thereby achieving the purpose of variable damping coefficient control and load power prediction, ensuring power balance in the DC system, avoiding DC bus voltage oscillation, and solving the problem of DC bus voltage oscillation caused by not considering the damping coefficients and power control speed of different types of energy storage in DC systems.

[0088] This embodiment provides a control method for a DC system, which can be used in DC systems, including different types of energy storage systems. Figure 2 This is a flowchart of a control method for a DC system according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0089] Step S201: Obtain the operating parameters of different types of energy storage systems.

[0090] Specifically, the operating parameters of different types of energy storage systems include the output current, DC-side output voltage, DC-side output capacitance, and gain. DC / DC converters include a sampling module and a core controller, such as... Figure 5 As shown, in a DC system, DC / DC converters of different types of energy storage systems acquire parameters such as input voltage and current, DC-side output voltage, and DC-side capacitance of each DC / DC converter through a sampling module, and send the above operating parameters to the core controller (e.g., DSP control module) of each DC / DC converter.

[0091] Step S202: Establish a load power prediction model for the energy storage system based on the operating parameters.

[0092] Specifically, step S202 includes:

[0093] Step S2021: Determine the auxiliary power prediction variables inside the energy storage system based on the output current, DC-side output voltage, DC-side output capacitance, and gain of different types of energy storage systems.

[0094] Specifically, the derivative of the auxiliary power prediction variable within the energy storage system is determined using the following formula:

[0095]

[0096] Step S2022: Determine the predicted value of the unknown load power of the energy storage system based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0097] Specifically, the predicted value of the unknown load power of the energy storage system is determined using the following formula:

[0098]

[0099] Step S2023: Establish a load power prediction model for the energy storage system based on the derivative of the auxiliary power prediction variable inside the energy storage system and the predicted value of the unknown load power of the energy storage system.

[0100] Specifically, the load power prediction model for the energy storage system is determined by the following formula:

[0101]

[0102] in: The derivative of the predicted value of unknown load power for different types of energy storage systems. γ represents the predicted value of unknown load power for different types of energy storage systems. n Gain, or magnification factor. For predicting auxiliary power within different types of energy storage systems, x 1n The output current i of different types of energy storage systems Ln x 2n The DC-side output voltage u of different types of energy storage systems Cn C n σ represents the DC output capacitor of different types of energy storage systems, and σ represents the control law of the DC / DC converter in different types of energy storage systems.

[0103] Further, from equation (3), we can obtain

[0104]

[0105] In the formula, P e Let P represent the load power prediction error for different types of energy storage systems, and P represent the real-time load power of different types of energy storage systems. It is the derivative of the load power prediction error, P e (0) is P that changes over time. e Value, γ n For gain, i.e., amplification factor. Equation (4) shows that P e It approaches 0 exponentially, and the rate of approaching 0 depends on the gain γ. n Gain γ n The larger the value, the faster it approaches zero, thus achieving... Therefore, the gain γn of the DC / DC converter of various types of energy storage systems can be adjusted to achieve the adjustment of the load power prediction speed of different types of energy storage systems.

[0106] like Figure 5 As shown, the DC system also includes a DC bus, and the virtual DC motor control loop with preset variable damping coefficient is determined by simulating the virtual DC motor control loop with variable damping coefficient of different types of energy storage systems.

[0107] Step S203: Simulate the virtual DC motor control loop with variable damping coefficients for different types of energy storage systems.

[0108] Specifically, step S203 includes:

[0109] Step S2031: Establish equivalent models of different types of energy storage systems and virtual DC motors.

[0110] Specifically, a virtual DC motor is a software-simulated motor that can mimic the operation of a generator in a real DC system. By introducing the mechanical rotation equations of a DC motor, it provides inertia to the DC side, improving the voltage stability of the DC system. In this embodiment, the internal circuits of different types of energy storage systems are equivalent to virtual DC motor circuits, providing a foundation for subsequent virtual DC motor control.

[0111] Step S2032: Obtain the DC bus voltage and calculate the variable damping coefficient of different types of energy storage systems based on the DC bus voltage.

[0112] In some optional implementations, step S2032 above includes:

[0113] Step a1: Fit the dynamic response curve of the DC bus voltage.

[0114] Specifically, such as Figure 8 As shown, the horizontal axis represents the dynamic response time of the DC bus voltage, and the vertical axis represents the dynamic response voltage value of the DC bus voltage. A dynamic response curve is fitted based on the dynamic response time and dynamic response voltage value of the DC bus voltage.

[0115] Step a2: Segment the dynamic response curve according to a preset time sequence.

[0116] Specifically, such as Figure 8 As shown, the dynamic response curve of the DC bus voltage can be approximately divided into 5 stages: 0~t0, t0~t1, t1~t2, t2~t3, t3~t4.

[0117] Step a3: Obtain the change status of the DC bus voltage in each segment.

[0118] Specifically, during the 0–t0 stage, the DC bus voltage is in the initial steady-state operation stage, and the DC bus voltage is at the initial steady-state value; during the t0–t1 stage, the DC bus voltage starts to drop from the initial steady-state value until it drops to the trough value u. A Up to that point. According to Figure 8 It can be seen that at this time, a smaller damping coefficient is used to reduce the DC bus voltage trough value, and at the same time, the dynamic response time of this stage is reduced, so as to achieve a faster dynamic response of the energy storage system; in the t1 to t2 stage, the DC bus voltage drops from the trough value u A Recovery begins. During the DC bus voltage recovery phase, a larger damping coefficient can provide greater damping support for the energy storage system, suppressing DC bus voltage fluctuations; in the t2 to t3 phase, the voltage rises from the initial value to the peak value u. B Similar to the t0~t1 stage, this is a stage where the voltage error increases, so a smaller damping coefficient is used; t3~t4 is from the peak value u B The voltage recovers to its final steady-state value, similar to t1 to t2, which is the voltage recovery stage, and a larger damping coefficient is used.

[0119] Step a4: Calculate the variable damping coefficient based on the change in DC bus voltage in each segment.

[0120] Specifically, based on the DC bus voltage from step a3, from the initial steady-state value to the trough value u A -Voltage recovery -Peak peak value -Final steady-state value, calculate the variable damping coefficient for each stage. The final variable damping coefficient is determined by the following formula:

[0121]

[0122] Where: D0 and D b The initial values ​​for the first damping coefficient and the first damping coefficient parameter are: A is the amplification factor; Δu is the difference between the expected DC bus voltage and the DC bus voltage; Δu DC The difference between the sampling values ​​of the DC bus voltage before and after the sampling point; k is the threshold value of |Δu|. The DC bus voltage step response curves with different damping coefficients are shown in Figures 7(a), 7(b), 7(c), 7(d), and 7(e). It can be seen from Figures 7(a), 7(b), 7(c), 7(d), and 7(e) that the smaller the damping coefficient, the faster the response speed, and the larger the damping coefficient, the slower the response speed.

[0123] Based on the typical dynamic response process of DC bus voltage when a DC system is subjected to external disturbances, this embodiment of the invention adopts a variable damping coefficient method to provide sufficient damping support for the energy storage system, avoid large changes and fluctuations in DC bus voltage, and reduce the impact on DC bus voltage.

[0124] Step S2033: Set the control loop of the virtual DC motor. Specifically, based on the preset armature circuit equations, preset mechanical equations, preset mechanical power, and preset electromagnetic power of the virtual DC motor, the control loop of the virtual DC motor is set. The control loop of the virtual DC motor is determined using the following formula:

[0125]

[0126]

[0127]

[0128] Wherein: Equation (6) is the armature circuit equation of the preset virtual DC motor, Equation (7) is the mechanical equation of the preset virtual DC motor, and Equation (8) is the mechanical power and electromagnetic power of the preset virtual DC motor. In Equation (6), C T This is the virtual DC motor torque coefficient. E represents the flux of the virtual DC motor, ω represents the actual mechanical angular velocity of the virtual DC motor, and E represents the flux of the virtual DC motor. a U is the armature electromotive force of the virtual DC motor, and U is the terminal voltage of the virtual DC motor; a L represents the armature current of the virtual DC motor. a R is the armature inductance of a virtual DC motor. a T is the equivalent resistance of the armature circuit; in equation (7), T m and T e These represent the mechanical torque and electromagnetic torque of the virtual DC motor, respectively; J is the moment of inertia; D is the damping coefficient; ω o This is the rated mechanical angular velocity of the virtual DC motor. Therefore, when a DC / DC converter uses virtual DC motor control, if the DC-side output voltage equals the terminal voltage of the virtual DC motor, i.e., u... Cn =U, the DC-side output current is equal to the virtual DC motor armature current, i.e., i Ln =i a At this time, the DC / DC converter has external characteristics consistent with the virtual DC motor, which enhances the inertia and damping of the system and improves the stability of the DC bus voltage of the DC microgrid; in equation (8), P m and P e These represent the mechanical power and electromagnetic power of the virtual DC motor, respectively.

[0129] Step S2034: Based on the equivalent models of different types of energy storage systems and virtual DC motors, the variable damping coefficients of different types of energy storage systems are input into the control loop of the virtual DC motor to obtain the virtual DC motor control loop with variable damping coefficients of different types of energy storage systems.

[0130] Specifically, by substituting the variable damping coefficient obtained from formula (5) into formula (7), the virtual DC motor control loop with variable damping coefficients for different types of energy storage systems is obtained.

[0131] Step S204 involves parallel control of different types of energy storage systems based on a load power prediction model, a preset droop control loop, and a virtual DC motor control loop with a preset variable damping coefficient. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0132] The DC system control method provided in this embodiment uses an energy storage system load power prediction model to predict the load power of the DC system. This allows different types of energy storage systems in the DC system to quickly track power changes based on their own load power, achieving control over the power response speed of different types of energy storage systems. When the DC system is subjected to external disturbances, sufficient damping support is provided through a variable damping coefficient, enabling rapid dynamic response of the energy storage system, avoiding large fluctuations and amplitudes in the DC bus voltage, reducing the impact on the DC bus voltage, and suppressing DC bus voltage oscillations. A virtual DC motor control loop with a variable damping coefficient provides additional damping to the DC system, suppressing DC bus voltage oscillations. Furthermore, it, together with the preset droop control and load power prediction model, constitutes parallel control of different types of parallel energy storage systems, providing a foundation for subsequent coordinated control of different types of energy storage.

[0133] This embodiment provides a control method for a DC system, which can be used in DC systems, including different types of energy storage systems. Figure 3 This is a flowchart of a control method for a DC system according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0134] Step S301: Obtain the operating parameters of different types of energy storage systems. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0135] Step S302: Establish a load power prediction model for the energy storage system based on operating parameters. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0136] Step S303: Parallel control of different types of energy storage systems is performed based on the load power prediction model, the preset droop control loop, and the virtual DC motor control loop with the preset variable damping coefficient.

[0137] Specifically, step S303 includes:

[0138] Step S3031: Based on the preset droop control loop, droop control is performed on different types of energy storage systems to obtain stable DC-side output voltages for different types of energy storage systems.

[0139] Specifically, such as Figure 6 As shown, in the droop control loop, the droop control coefficient d is based on the DC-side output current and the DC / DC converter of different types of energy storage systems. n The superposition, as an input to the droop control loop, also includes the reference voltage U of the DC / DC converter in different types of energy storage systems. ref The DC-side output voltage is used as two other inputs to perform droop control on different types of energy storage systems, resulting in a stable DC-side output voltage.

[0140] Step S3012: Input the stable DC-side output voltage of different types of energy storage systems into the virtual DC motor control loop with preset variable damping coefficient to perform virtual DC motor control on different types of energy storage systems and obtain virtual inertial power.

[0141] Specifically, such as Figure 6 As shown, the stable DC-side output voltage obtained from the droop control loop is input to the virtual DC motor control loop with a preset variable damping coefficient, and is compared with the reference voltage U of the DC / DC converter in different types of energy storage systems. ref The superimposed input is fed to the first PI controller of different virtual DC motor controls to obtain the output power P. mn The output power P mn Divide by the rated mechanical angular velocity ω of the virtual DC motor o Obtain the mechanical torque T of different virtual motor controls mn The mechanical torque T controlled by different virtual motors mn Substituting these values ​​into the preset virtual DC motor mechanical equation, i.e., formula (7), the difference between the mechanical torque and electromagnetic torque of different virtual DC motors is obtained using the variable damping coefficient. Then, the preset virtual DC motor mechanical power and preset DC motor electromagnetic power are calculated according to formulas (3), (4), and (8), ultimately yielding the virtual inertial power P. VDCM . Figure 6 In the middle, C Tn and Φ n The torque coefficient and magnetic flux of different virtual DC motor controls are ω. n The actual mechanical angular velocity of different virtual DC motors.

[0142] Virtual inertial power P VDCM Determined by the following formula:

[0143]

[0144] Where: Ea U is the armature electromotive force of the virtual DC motor, and U is the terminal voltage of the virtual DC motor. ref R is the reference voltage for DC / DC converters in different types of energy storage systems. a This is the equivalent resistance of the armature circuit of the virtual DC motor.

[0145] Step S3013: Input the virtual inertial power into the load power prediction model for parallel control to compensate for the power control errors of different types of energy storage systems.

[0146] Specifically, such as Figure 6 As shown, the virtual inertial power obtained from the virtual DC motor control loop with a preset variable damping coefficient is input into the load power prediction model to perform parallel control of different types of energy storage systems. This compensates for the power control errors of different types of energy storage systems, specifically by incorporating the predicted values ​​of the unknown load power of each system into the virtual inertial power. Dividing the DC-side output voltage by the virtual inertial power yields the reference current I of the DC / DC converter in each type of energy storage system. refn Calculate the reference current I of the DC / DC converter in different types of energy storage systems. refn and DC side output current i Ln The difference is input to the second PI controllers that control different virtual DC motors. The second PI controllers that control different virtual DC motors calculate the duty cycle of the DC / DC converters of different types of energy storage systems, which provides a basis for the subsequent coordinated control of different types of energy storage systems.

[0147] like Figure 5 As shown, the DC system also includes a photovoltaic (PV) power generation system. The PV power generation system includes a PV array and a DC / DC converter. Different types of energy storage systems are each connected to a DC / DC converter, and all DC / DC converters are connected to both the positive and negative terminals of the DC bus. Simultaneously, at least three converters are connected to the AC grid and AC / DC loads, respectively. These at least three converters are a first DC / AC converter, a second DC / AC converter, and a DC / DC converter. The first DC / AC converter is connected to the AC grid, the second DC / AC converter is connected to the AC load, and the DC / DC converter is connected to the DC load.

[0148] Step S304: Obtain the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems obtained from parallel control; determine the tiered operation sequence of different types of energy storage systems based on the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems; and coordinate the control of different types of energy storage systems according to the tiered operation sequence.

[0149] In DC systems, energy storage systems primarily operate under two conditions. First, when the load in the DC system remains constant or changes only slightly, the DC bus voltage fluctuation is small and does not exceed the voltage fluctuation thresholds for each energy storage system. In this case, the coordination controller in the DC system controls the charging and discharging of the energy storage systems based on the state of charge (SOC) of different types of energy storage systems and the output power of the photovoltaic power generation system. Second, when distributed power generation equipment is switched on or off in the DC system, or when there are significant changes in the load within the DC system, the DC bus voltage fluctuates considerably and exceeds the voltage fluctuation thresholds for each energy storage system. In this situation, the power balance of the DC system is disrupted. The coordination controller in the DC system then coordinates the control of the energy storage systems based on the power characteristics of different types of energy storage, the real-time SOC of different types of energy storage systems, and the DC bus voltage. The power characteristics refer to the power prediction results of different types of energy storage systems obtained through parallel control.

[0150] by Figure 5 The coordinated control block diagram of the DC system shown is as follows: Figure 9 As shown, the DC system also includes a coordination controller, which is mainly responsible for collecting data from the energy storage system, the photovoltaic power generation system, and the DC bus voltage, and issuing relevant control commands. The sequential operation of different types of energy storage systems is determined based on power prediction results, DC bus voltage, photovoltaic power generation system output power, and the real-time state of charge of different types of energy storage systems.

[0151] Specifically, when the DC system bus voltage fluctuation is small and does not exceed the voltage fluctuation threshold, the photovoltaic system charges the energy storage system and supplies power to the load based on the real-time state of charge of different types of energy storage systems. If the photovoltaic system power cannot meet the needs of the energy storage system and the load, it is connected to the AC grid. When the DC system bus voltage is detected to exceed the fluctuation voltage threshold, the tiered operation sequence of different types of energy storage systems is determined according to the power characteristics of the energy storage system and the real-time state of charge of different types of energy storage systems. Different types of energy storage systems are put into operation in tiers according to the tiered operation sequence. When the output power of the photovoltaic power generation system and the real-time state of charge of the energy storage system cannot meet the load demand of the DC system at this time, it is connected to the AC grid so that the AC grid supplies power to the DC system and satisfies the power balance of the DC system.

[0152] For example, the sequential deployment of different types of energy storage systems according to their operating sequence can also be considered from the following perspectives: If a DC system experiences instantaneous power fluctuations, the power-type energy storage system, due to its fast response speed, can quickly smooth out the fluctuations; similarly, if the DC system requires the energy storage system to provide power support for a longer period of time, the power-type energy storage system is difficult to handle and can generally only support it for a short time. Therefore, the power-type energy storage system needs to provide rapid power support first, followed by the energy-type energy storage system, and then the power-type energy storage system is deactivated.

[0153] The DC system control method provided in this embodiment performs droop control on different types of energy storage systems based on a preset droop control loop to obtain the DC-side output voltage of different types of energy storage systems. The DC-side output voltage of different types of energy storage systems is then input into a virtual DC motor control loop with a preset variable damping coefficient to perform virtual DC motor control on different types of energy storage systems, obtaining virtual inertial power. This virtual inertial power is then input into a load power prediction model for parallel control to compensate for the power errors of different types of energy storage systems. Through the preset droop control loop, the virtual DC motor control loop with a preset variable damping coefficient, and the load power prediction model, parallel control of different types of energy storage systems is achieved, providing sufficient damping support and enabling tracking of the power control speed of different types of energy storage systems. This method can provide additional damping to suppress DC bus voltage oscillations while simultaneously predicting the system load power, achieving rapid power control in the DC system, and ensuring the stability of the DC bus voltage. When a DC system contains different types of energy storage systems, by predicting the power characteristics of different energy storage systems and considering the real-time state of charge of the energy storage systems, the tiered utilization sequence of different types of energy storage systems when the DC system is unbalanced can be determined, thereby improving the utilization efficiency of different types of energy storage and more effectively achieving the power balance of the DC system.

[0154] As one or more specific application embodiments of the present invention, such as Figure 4 As shown, the control method for a DC system can be implemented using the following process:

[0155] Step S401: Obtain the operating parameters of each energy storage system in the DC system. The operating parameters of different types of energy storage systems include the output current, DC-side output voltage, DC-side output capacitance, and gain of each type of energy storage system.

[0156] A DC / DC converter includes a sampling module and a core controller, such as Figure 5 As shown, in a DC system, DC / DC converters of different types of energy storage systems acquire parameters such as input voltage and current, DC-side output voltage, and DC-side capacitance of each DC / DC converter through a sampling module, and send the above operating parameters to the core controller (e.g., DSP control module) of each DC / DC converter.

[0157] Step S402: Determine the derivative of the auxiliary power prediction variable inside the energy storage system based on the output current, DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0158] The derivative of the auxiliary power prediction variable within the energy storage system is determined using the following formula:

[0159]

[0160] Step S403: Determine the predicted value of the unknown load power of the energy storage system based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0161] Specifically, the predicted value of the unknown load power of the energy storage system is determined using the following formula:

[0162]

[0163] Step S404: Establish a load power prediction model for the energy storage system based on the derivative of the auxiliary power prediction variable inside the energy storage system and the predicted value of the unknown load power of the energy storage system.

[0164] Specifically, the load power prediction model for the energy storage system is determined by the following formula:

[0165]

[0166] in: The derivative of the predicted value of unknown load power for different types of energy storage systems. γ represents the predicted value of unknown load power for different types of energy storage systems. n Gain, or magnification factor. For predicting auxiliary power within different types of energy storage systems, x 1n The output current i of different types of energy storage systems Ln x 2n The DC-side output voltage u of different types of energy storage systems Cn C n σ represents the DC output capacitor of different types of energy storage systems, and σ represents the control law of the DC / DC converter in different types of energy storage systems.

[0167] Further, from equation (3), we can obtain:

[0168]

[0169] In the formula, P e Let P represent the load power prediction error for different types of energy storage systems, and P represent the real-time load power of different types of energy storage systems. It is the derivative of the load power prediction error, P e (0) is P that changes over time. e Value, γ n For gain, i.e., amplification factor. Equation (4) shows that P e It approaches 0 exponentially, and the rate of approaching 0 depends on the gain γ. n Gain γ n The larger the value, the faster it approaches zero, thus achieving... Therefore, the gain γn of the DC / DC converter of various types of energy storage systems can be adjusted to achieve the adjustment of the load power prediction speed of different types of energy storage systems.

[0170] Step S405: Establish equivalent models of different types of energy storage systems and virtual DC motors.

[0171] Specifically, a virtual DC motor is a software-simulated motor that can mimic the operation of a generator in a real DC system. By introducing the mechanical rotation equations of a DC motor, it provides inertia to the DC side, improving the voltage stability of the DC system. In this embodiment, the internal circuits of different types of energy storage systems are equivalent to virtual DC motor circuits, providing a foundation for subsequent virtual DC motor control.

[0172] Step S406: Obtain the DC bus voltage and calculate the variable damping coefficient of different types of energy storage systems based on the DC bus voltage.

[0173] Specifically, such as Figure 8 As shown, the horizontal axis represents the dynamic response time of the DC bus voltage, and the vertical axis represents the dynamic response voltage value of the DC bus voltage. A dynamic response curve is fitted based on the dynamic response time and dynamic response voltage value of the DC bus voltage. The dynamic response curve of the DC bus voltage can be approximately divided into 5 stages: 0~t0, t0~t1, t1~t2, t2~t3, and t3~t4.

[0174] Specifically: during the period from 0 to t0, the DC bus voltage belongs to the initial steady-state operation stage, and the DC bus voltage is the initial steady-state value; during the period from t0 to t1, the DC bus voltage starts to drop from the initial steady-state value until it drops to the trough value u. A Up to that point. According to Figure 8 It can be seen that at this time, a smaller damping coefficient is used to reduce the DC bus voltage trough value, and at the same time, the dynamic response time of this stage is reduced, so as to achieve a faster dynamic response of the energy storage system; in the t1 to t2 stage, the DC bus voltage drops from the trough value u A Recovery begins. During the DC bus voltage recovery phase, a larger damping coefficient can provide greater damping support for the energy storage system, suppressing DC bus voltage fluctuations; in the t2 to t3 phase, the voltage rises from the initial value to the peak value u. B Similar to the t0~t1 stage, this is a stage where the voltage error increases, so a smaller damping coefficient is used; t3~t4 is from the peak value u B The voltage recovers to its final steady-state value, similar to t1 to t2, which is the voltage recovery stage, and a larger damping coefficient is used.

[0175] Based on the DC bus voltage from the initial steady-state value to the trough value u A-Voltage recovery -Peak peak value -Final steady-state value, calculate the variable damping coefficient for each stage. The final variable damping coefficient is determined by the following formula:

[0176]

[0177] Where: D0 and D b The initial values ​​for the first damping coefficient and the first damping coefficient parameter are: A is the amplification factor; Δu is the difference between the expected DC bus voltage and the DC bus voltage; Δu DC The difference between the sampling values ​​of the DC bus voltage before and after the sampling point; k is the threshold value of |Δu|. The DC bus voltage step response curves with different damping coefficients are shown in Figures 7(a), 7(b), 7(c), 7(d), and 7(e). It can be seen from Figures 7(a), 7(b), 7(c), 7(d), and 7(e) that the smaller the damping coefficient, the faster the response speed, and the larger the damping coefficient, the slower the response speed.

[0178] Step S407: Set the control loop of the virtual DC motor. Specifically, based on the preset armature circuit equations, preset mechanical equations, preset mechanical power, and preset electromagnetic power of the virtual DC motor, the control loop of the virtual DC motor is set. The control loop of the virtual DC motor is determined using the following formula:

[0179]

[0180]

[0181]

[0182] Wherein: Equation (6) is the armature circuit equation of the preset virtual DC motor, Equation (7) is the mechanical equation of the preset virtual DC motor, and Equation (8) is the mechanical power and electromagnetic power of the preset virtual DC motor. In Equation (6), C T This is the virtual DC motor torque coefficient. E represents the flux of the virtual DC motor, ω represents the actual mechanical angular velocity of the virtual DC motor, and E represents the flux of the virtual DC motor. a U is the armature electromotive force of the virtual DC motor, and U is the terminal voltage of the virtual DC motor; a For the virtual DC motor armature current; L a R is the armature inductance of a virtual DC motor. a T is the equivalent resistance of the armature circuit; in equation (7), T m and T e These represent the mechanical torque and electromagnetic torque of the virtual DC motor, respectively; J is the moment of inertia; D is the damping coefficient; ω oThis is the rated mechanical angular velocity of the virtual DC motor. Therefore, when a DC / DC converter uses virtual DC motor control, if the DC-side output voltage equals the terminal voltage of the virtual DC motor, i.e., u... Cn =U, the DC-side output current is equal to the virtual DC motor armature current, i.e., i Ln =i a At this time, the DC / DC converter has external characteristics consistent with the virtual DC motor, which enhances the inertia and damping of the system and improves the stability of the DC bus voltage of the DC microgrid; in equation (8), P m and P e These represent the mechanical power and electromagnetic power of the virtual DC motor, respectively.

[0183] Step S408: Based on the equivalent models of different types of energy storage systems and virtual DC motors, the variable damping coefficients of different types of energy storage systems are input into the control loop of the virtual DC motor to obtain the virtual DC motor control loop with variable damping coefficients of different types of energy storage systems.

[0184] Specifically, by substituting the variable damping coefficient obtained from formula (5) into formula (7), the virtual DC motor control loop with variable damping coefficients for different types of energy storage systems is obtained.

[0185] Step S409: Based on the preset droop control loop, droop control is performed on different types of energy storage systems to obtain stable DC-side output voltages for different types of energy storage systems.

[0186] Specifically, such as Figure 6 As shown, in the droop control loop, the droop control coefficient d is based on the DC-side output current and the DC / DC converter of different types of energy storage systems. n The superposition, as an input to the droop control loop, also includes the reference voltage U of the DC / DC converter in different types of energy storage systems. ref The DC-side output voltage is used as two other inputs to perform droop control on different types of energy storage systems, resulting in a stable DC-side output voltage.

[0187] Step S410: Input the stable DC-side output voltage of different types of energy storage systems into the virtual DC motor control loop with preset variable damping coefficient to perform virtual DC motor control on different types of energy storage systems and obtain virtual inertial power.

[0188] Specifically, such as Figure 6 As shown, the stable DC-side output voltage obtained from the droop control loop is input to the virtual DC motor control loop with a preset variable damping coefficient, and is compared with the reference voltage U of the DC / DC converter in different types of energy storage systems. ref The superimposed input is fed to the first PI controller of different virtual DC motor controls to obtain the output power P. mn The output power Pmn Divide by the rated mechanical angular velocity ω of the virtual DC motor o Obtain the mechanical torque T of different virtual motor controls mn The mechanical torque T controlled by different virtual motors mn Substituting these values ​​into the preset virtual DC motor mechanical equation, i.e., formula (7), the difference between the mechanical torque and electromagnetic torque of different virtual DC motors is obtained using the variable damping coefficient. Then, the preset virtual DC motor mechanical power and preset DC motor electromagnetic power are calculated according to formulas (3), (4), and (8), ultimately yielding the virtual inertial power P. VDCM . Figure 6 In the middle, C Tn and Φ n The torque coefficient and magnetic flux of different virtual DC motor controls are ω. n The actual mechanical angular velocity of different virtual DC motors.

[0189] Virtual inertial power P VDCM Determined by the following formula:

[0190]

[0191] Where: E a U is the armature electromotive force of the virtual DC motor, and U is the terminal voltage of the virtual DC motor. ref The reference voltage U for DC / DC converters in different types of energy storage systems ref R a This is the equivalent resistance of the armature circuit of the virtual DC motor.

[0192] Step S411: Input the virtual inertial power into the load power prediction model for parallel control to compensate for the power control errors of different types of energy storage systems.

[0193] Specifically, such as Figure 6 As shown, the virtual inertial power obtained from the virtual DC motor control loop with a preset variable damping coefficient is input into the load power prediction model to perform parallel control of different types of energy storage systems. This compensates for the power control errors of different types of energy storage systems, specifically by incorporating the predicted values ​​of the unknown load power of each system into the virtual inertial power. Dividing the DC-side output voltage by the virtual inertial power yields the reference current I of the DC / DC converter in each type of energy storage system. refn Calculate the reference current I of the DC / DC converter in different types of energy storage systems. refn and DC side output current i LnThe difference is input to the second PI controllers that control different virtual DC motors. The second PI controllers that control different virtual DC motors calculate the duty cycle of the DC / DC converters of different types of energy storage systems, which provides a basis for the subsequent coordinated control of different types of energy storage systems.

[0194] like Figure 5 As shown, the DC system also includes a photovoltaic (PV) power generation system. The PV power generation system includes a PV array and a DC / DC converter. Different types of energy storage systems are each connected to a DC / DC converter, and all DC / DC converters are connected to both the positive and negative terminals of the DC bus. Simultaneously, at least three converters are connected to the AC grid and AC / DC loads, respectively. These at least three converters are a first DC / AC converter, a second DC / AC converter, and a DC / DC converter. The first DC / AC converter is connected to the AC grid, the second DC / AC converter is connected to the AC load, and the DC / DC converter is connected to the DC load.

[0195] Step S412: Obtain the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems obtained from parallel control; determine the tiered operation sequence of different types of energy storage systems based on the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems; and coordinate the control of different types of energy storage systems according to the tiered operation sequence.

[0196] The DC system control method provided in this invention analyzes the characteristics of different types of energy storage, classifies energy storage systems from two perspectives: power-type and energy-type. Considering the real-time state of charge (SOC), voltage step response with variable damping coefficient, and dynamic response of the DC bus voltage, it clarifies the damping and power control methods for different types of energy storage systems. A power prediction model is used to predict the load power of the DC system, enabling each energy storage system in the DC system to quickly track power changes based on load power variations. Simultaneously, a virtual DC motor control loop with a preset variable damping coefficient provides additional damping to the DC system, suppressing DC bus voltage oscillations. A photovoltaic power generation system is added to the DC system, enabling charging of the energy storage system and power supply to the DC system load. This optimizes the real-time SOC of the energy storage system, achieves coordinated control of different types of energy storage systems, and improves the utilization efficiency and lifespan of the energy storage system.

[0197] This embodiment also provides a control device for a DC system, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0198] This embodiment provides a control device for a DC system, such as... Figure 10 As shown, it includes:

[0199] Module 1001 is used to acquire operating parameters of different types of energy storage systems;

[0200] Module 1002 is established to build a load power prediction model for the energy storage system based on operating parameters.

[0201] The control module 1003 is used to perform parallel control of different types of energy storage systems based on a load power prediction model, a preset droop control loop, and a preset variable damping coefficient virtual DC motor control loop.

[0202] In some alternative implementations, the establishment module 1002 includes:

[0203] The first determining unit is used to determine the auxiliary power prediction variables inside the energy storage system based on the output current, DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems.

[0204] The second determining unit is used to determine the predicted value of the load power of the unknown energy storage system based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems;

[0205] Establishment unit, used to build load power prediction model of energy storage system based on the auxiliary power prediction variables inside the energy storage system and the predicted value of unknown load power of the energy storage system.

[0206] In some alternative implementations, the control module 1003 includes:

[0207] The droop control unit is used to perform droop control on different types of energy storage systems based on preset droop control loops, so as to obtain a stable DC-side output voltage for different types of energy storage systems.

[0208] The virtual control unit is used to input the stable DC-side output voltage of different types of energy storage systems into the virtual DC motor control loop with a preset variable damping coefficient to perform virtual DC motor control on different types of energy storage systems and obtain virtual inertial power.

[0209] The power prediction unit is used to input virtual inertial power into the load power prediction model for parallel control, in order to compensate for the power control errors of different types of energy storage systems.

[0210] The control device for the DC system also includes:

[0211] The simulation module is used to simulate the virtual DC motor control loop with varying damping coefficients for different types of energy storage systems.

[0212] The coordination control module is used to acquire the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems obtained from parallel control; determine the tiered operation sequence of different types of energy storage systems based on the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems; and coordinate the control of different types of energy storage systems according to the tiered operation sequence.

[0213] In some alternative implementations, the simulation module further includes:

[0214] Establishment units are used to create equivalent models of different types of energy storage systems and virtual DC motors;

[0215] The calculation unit is used to obtain the DC bus voltage and calculate the variable damping coefficient of different types of energy storage systems based on the DC bus voltage.

[0216] The setting unit is used to set the control links of the virtual DC motor;

[0217] The equivalent unit is used to input the variable damping coefficients of different types of energy storage systems into the control loop of the virtual DC motor based on the equivalent models of different types of energy storage systems and virtual DC motors, so as to obtain the virtual DC motor control loop with variable damping coefficients of different types of energy storage systems.

[0218] In some alternative implementations, the computing unit includes:

[0219] The fitting sub-unit is used to fit the dynamic response curve of the DC bus voltage.

[0220] The segmented sub-unit is used to segment the dynamic response curve according to a preset time sequence.

[0221] The acquisition sub-unit is used to acquire the change status of the DC bus voltage in each segment.

[0222] The calculation sub-unit is used to calculate the variable damping coefficient based on the change in DC bus voltage in each segment.

[0223] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0224] In this embodiment, the control device of the DC system is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0225] This invention also provides a computer device having the above-described features. Figure 10 The control device for the DC system shown.

[0226] Please see Figure 11 , Figure 11 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 11 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 11 Take a processor 10 as an example.

[0227] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0228] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0229] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0230] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0231] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 11 Taking the example of a connection between China and Israel via a bus.

[0232] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0233] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0234] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A control method for a DC system, characterized in that, The DC system includes different types of energy storage systems, and the method includes: Obtain operating parameters for different types of energy storage systems; the operating parameters for different types of energy storage systems include the output current, DC-side output voltage, DC-side output capacitance, and gain of the different types of energy storage systems; A load power prediction model for the energy storage system is established based on the aforementioned operating parameters; the establishment of the load power prediction model for the energy storage system based on the operating parameters includes: The derivatives of the auxiliary power prediction variables inside the energy storage system are determined based on the output current, DC-side output voltage, DC-side output capacitance, and gain of the different types of energy storage systems. The predicted value of the unknown load power of the energy storage system is determined based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems. A load power prediction model for the energy storage system is established based on the derivative of the auxiliary power prediction variable inside the energy storage system and the predicted value of the unknown load power of the energy storage system. The energy storage system includes a DC / DC converter, and the load power prediction model of the energy storage system is determined by the following formula: in: The derivative of the predicted value of unknown load power for different types of energy storage systems. These are predicted values ​​for unknown load power in different types of energy storage systems. γ n For gain, For predicting auxiliary power variables within different types of energy storage systems, x 1n Output current for different types of energy storage systems i Ln , x 2n DC-side output voltage for different types of energy storage systems u Cn , C n σ represents the DC output capacitor of different types of energy storage systems, and σ represents the control law of the DC / DC converter in different types of energy storage systems. The different types of energy storage systems are controlled in parallel based on the load power prediction model, the preset droop control loop, and the virtual DC motor control loop with the preset variable damping coefficient.

2. The method according to claim 1, characterized in that, The DC system also includes a DC bus, and the virtual DC motor control loop with preset variable damping coefficient is determined by simulating the virtual DC motor control loop with variable damping coefficient of different types of energy storage systems.

3. The method according to claim 2, characterized in that, The virtual DC motor control loop for simulating the variable damping coefficient of different types of energy storage systems includes: Establish equivalent models of different types of energy storage systems and virtual DC motors; Obtain the DC bus voltage, and calculate the variable damping coefficient of different types of energy storage systems based on the DC bus voltage; Configure the control circuit for the virtual DC motor; Based on the equivalent models of the different types of energy storage systems and the virtual DC motor, the variable damping coefficients of the different types of energy storage systems are input into the control loop of the virtual DC motor to obtain the virtual DC motor control loop with variable damping coefficients of the different types of energy storage systems.

4. The method according to claim 3, characterized in that, The control mechanism for setting the virtual DC motor includes: The control loop of the virtual DC motor is set according to the preset armature circuit equation, preset mechanical equation, preset mechanical power, and preset electromagnetic power of the virtual DC motor.

5. The method according to claim 3, characterized in that, The calculation of the variable damping coefficient for different types of energy storage systems based on the DC bus voltage includes: Fit the dynamic response curve of the DC bus voltage; The dynamic response curve is segmented according to a preset time sequence; Obtain the change status of the DC bus voltage in each segment; The variable damping coefficient is calculated based on the change in DC bus voltage in each segment.

6. The method according to claim 5, characterized in that, The variable damping coefficient is determined by the following formula: in: D 0 and D b The initial values ​​for the first damping coefficient parameter and the first damping coefficient parameter; A This is the magnification factor; u This represents the difference between the expected value of the DC bus voltage and the DC bus voltage. u DC This represents the sampling difference of the DC bus voltage at different times. k For | u | threshold.

7. The method according to claim 1, characterized in that, The parallel control of the different types of energy storage systems based on the load power prediction model, the preset droop control loop, and the preset variable damping coefficient includes: Based on the preset droop control loop, droop control is performed on the different types of energy storage systems to obtain a stable DC-side output voltage for the different types of energy storage systems. The stable DC-side output voltage of the different types of energy storage systems is input to the virtual DC motor control loop with the preset variable damping coefficient to perform virtual DC motor control on the different types of energy storage systems, thereby obtaining virtual inertial power; The virtual inertial power is input into the load power prediction model for parallel control to compensate for the power control errors of the different types of energy storage systems.

8. The method according to claim 1, characterized in that, The DC system also includes a photovoltaic power generation system, and the method further includes: Acquire the power prediction results, DC bus voltage, photovoltaic power generation system output power, and real-time state of charge of different types of energy storage systems obtained from parallel control; Based on the power prediction results, DC bus voltage, photovoltaic power generation system output power and real-time state of charge of different types of energy storage systems, the tiered operation sequence of different types of energy storage systems is determined. The different types of energy storage systems are coordinated and controlled according to the tiered operating sequence.

9. A control device for a DC system, characterized in that, The device includes: The acquisition module is used to acquire the operating parameters of different types of energy storage systems; the operating parameters of different types of energy storage systems include the output current, DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems; A module is established to build a load power prediction model for the energy storage system based on the operating parameters. The control module is used to perform parallel control of the different types of energy storage systems based on the load power prediction model, the preset droop control link, and the virtual DC motor control link with the preset variable damping coefficient. The establishment module includes: The first determining unit is used to determine the auxiliary power prediction variables inside the energy storage system based on the output current, DC-side output voltage, DC-side output capacitance and gain of the different types of energy storage systems. The second determining unit is used to determine the predicted value of the load power of the unknown energy storage system based on the auxiliary power prediction variables inside the energy storage system, the DC-side output voltage, DC-side output capacitance and gain of different types of energy storage systems; The unit is used to establish a load power prediction model for the energy storage system based on the auxiliary power prediction variables inside the energy storage system and the predicted value of the unknown load power of the energy storage system. The energy storage system includes a DC / DC converter, and the load power prediction model of the energy storage system is determined by the following formula: in: The derivative of the predicted value of unknown load power for different types of energy storage systems. These are predicted values ​​for unknown load power in different types of energy storage systems. γ n For gain, For predicting auxiliary power variables within different types of energy storage systems, x 1n Output current for different types of energy storage systems i Ln , x 2n DC-side output voltage for different types of energy storage systems u Cn , C n σ represents the DC output capacitor of different types of energy storage systems, and σ represents the control law of the DC / DC converter in different types of energy storage systems.

10. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the control method of the DC system according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the control method of the DC system according to any one of claims 1 to 8.

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