Direct current microgrid system and variable inertia control method thereof

Abstract

A direct current micro-grid system and a variable inertia control method thereof, the direct current micro-grid system comprising one or more groups of energy storage batteries, the energy storage batteries being connected to a bus of the direct current micro-grid system, the method comprising: acquiring working condition parameters of the direct current micro-grid system and battery states of the energy storage batteries; inputting the working condition parameters and the battery states into a preset fuzzy logic controller to obtain corresponding virtual inertia and damping coefficients; and adjusting a bus voltage of the bus according to at least the virtual inertia and the damping coefficients. Through the scheme, voltage impact of the bus caused by load fluctuation can be buffered to improve system stability, power quality of the direct current micro-grid system can be improved, and impact on the energy storage batteries during system operation can be reduced.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically to a DC microgrid system and its variable inertia control method. Background Technology

[0002] With the increasing global energy shortage and environmental pollution, renewable energy has gradually become an inevitable choice for energy development in countries around the world. Currently, wind power and distributed photovoltaic technologies have been widely used. At the same time, distributed loads such as electric vehicles are also growing year by year, but their complexity and flexibility bring huge challenges to the scheduling and control of energy systems.

[0003] To address the aforementioned challenges, DC microgrids are an effective way to solve the problems of distributed load and distributed energy utilization, and can effectively reduce energy losses caused by AC-DC conversion. Compared with centralized control, the distributed control of DC microgrids has the advantages of low computational load, low system communication requirements, and good scalability, and has attracted increasing attention. Droop control, as a commonly used method in DC microgrid systems, achieves power allocation among energy systems in the DC microgrid system by designing droop curve parameters to meet energy demands.

[0004] On the other hand, an increasing number of photovoltaic and wind power devices are replacing traditional synchronous generators, connecting to DC microgrid systems via power electronic devices to provide energy. However, compared to large synchronous machines with mechanical inertia, power electronic devices have extremely low inertia, thus reducing the overall inertia of the DC microgrid system. This reduction in inertia increases voltage frequency fluctuations in the DC microgrid system, decreasing system stability and power quality. Therefore, existing technologies have proposed virtual inertia control methods to address this issue.

[0005] However, existing virtual inertia control methods for DC microgrid systems have many shortcomings, which greatly reduce the optimization effect on system stability and power quality. Summary of the Invention

[0006] The technical problem solved by this invention is how to better improve the stability and power quality of DC microgrid systems.

[0007] To address the aforementioned technical problems, embodiments of the present invention provide a variable inertia control method for a DC microgrid system. The DC microgrid system includes one or more energy storage battery packs connected to the bus of the DC microgrid system. The variable inertia control method includes: acquiring the operating parameters of the DC microgrid system and the battery status of the energy storage battery packs; inputting the operating parameters and battery status into a preset fuzzy logic controller to obtain corresponding virtual inertia and damping coefficients; and adjusting the bus voltage of the bus based at least on the virtual inertia and damping coefficients.

[0008] Optionally, the step of inputting the operating parameters and battery status into a preset fuzzy logic controller to obtain the corresponding virtual inertia and damping coefficient includes: inputting the operating parameters and battery status into a preset fuzzy logic controller associated with the virtual inertia to obtain the virtual inertia; and inputting the operating parameters and battery status into a preset fuzzy logic controller associated with the damping coefficient to obtain the damping coefficient.

[0009] Optionally, the dimensions of the operating parameters and / or battery state of the preset fuzzy logic controller associated with the virtual inertia can be input differently from the dimensions of the operating parameters and / or battery state of the preset fuzzy logic controller associated with the damping coefficient.

[0010] Optionally, the virtual inertia and damping coefficient are associated with different preset fuzzy logic controllers, and the different preset fuzzy logic controllers have different input membership functions, preset operation rules and / or output membership functions.

[0011] Optionally, different dimensions of operating condition parameters can be associated with different input membership functions, and different dimensions of battery status can be associated with different input membership functions.

[0012] Optionally, the dimensions of the operating parameters are selected from: remaining energy storage power and voltage change rate.

[0013] Optionally, the dimensions of the battery state are selected from: the SOC of the energy storage battery pack, the internal resistance of the energy storage battery pack, and the temperature of the energy storage battery pack.

[0014] Optionally, adjusting the bus voltage of the bus based at least on the virtual inertia and damping coefficient includes: determining a reference voltage based on the virtual inertia and damping coefficient; determining a first reference current based on the reference voltage and the bus voltage; determining DC / DC control parameters based at least on the first reference current and the energy storage current of the energy storage battery pack; and adjusting the bus voltage based on the DC / DC control parameters.

[0015] Optionally, determining the DC / DC control parameters based at least on the first reference current and the energy storage current of the energy storage battery pack includes: determining the DC / DC control parameters based on the first reference current, the energy storage current of the energy storage battery pack, and the feedforward term.

[0016] Optionally, the feedforward term is the result of processing the output current or bus voltage of the DC / DC converter through a feedforward function.

[0017] Optionally, determining the DC / DC control parameters based on the first reference current, the energy storage current of the energy storage battery pack, and the feedforward term includes: performing PI control on the difference between the first reference current, the feedforward term, and the energy storage current to obtain the DC / DC control parameters.

[0018] Optionally, determining the reference voltage based on the virtual inertia and damping coefficient includes: calculating a first control current based on a preset droop intercept, a preset droop coefficient, and the bus voltage; calculating a second control current based on the reference voltage, the bus voltage, and the damping coefficient; and calculating the reference voltage based on the first control current, the second control current, the output current of the DC / DC converter, and the virtual inertia.

[0019] Optionally, the preset droop intercept and the preset droop coefficient are obtained from the energy management system of the DC microgrid system, wherein the energy management system is used to control the operation of the DC microgrid system.

[0020] Optionally, the energy storage battery pack is connected to the bus via a DC / DC converter, and the adjustment of the bus voltage based on the DC / DC control parameters includes: generating PWM waves for each switch in the DC / DC converter according to the DC / DC control parameters; and controlling the output of the DC / DC converter based on the PWM waves of each switch to adjust the bus voltage.

[0021] Optionally, when there are multiple energy storage battery packs, the virtual inertia and damping coefficient are determined for each energy storage battery pack.

[0022] To address the aforementioned technical problems, this invention also provides a DC microgrid system, comprising: one or more sets of energy storage battery packs connected to the bus of the DC microgrid system; and a controller for executing the above method to adjust the bus voltage of the bus according to the operating parameters and battery status.

[0023] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0024] This invention provides a variable inertia control method for a DC microgrid system. The DC microgrid system includes one or more energy storage battery packs connected to the bus of the DC microgrid system. The variable inertia control method includes: acquiring the operating parameters of the DC microgrid system and the battery status of the energy storage battery packs; inputting the operating parameters and battery status into a preset fuzzy logic controller to obtain corresponding virtual inertia and damping coefficients; and adjusting the bus voltage of the bus based at least on the virtual inertia and damping coefficients.

[0025] Compared to existing technical solutions that only consider the impact of voltage or fluctuations when designing virtual inertia control methods, this implementation scheme can buffer the voltage surges on the bus caused by load fluctuations to improve system stability. It also improves the power quality of the DC microgrid system and reduces the impact on energy storage batteries during system operation. Specifically, based on the distributed droop control of the DC microgrid system, virtual inertia control is used to buffer the voltage surges on the DC bus caused by load fluctuations. Furthermore, based on the operating conditions of the DC microgrid system and the current state of the energy storage battery packs, the virtual inertia and damping coefficient in the inertia control are obtained through a preset fuzzy logic controller, thereby adjusting the control parameters according to the real-time system conditions. This improves the system's voltage recovery capability, reduces DC bus voltage fluctuations, improves the system's power quality, rationally distributes the current surges caused by power fluctuations among the energy storage battery packs, and reduces the adverse effects on the energy storage batteries during system operation.

[0026] Furthermore, when there are multiple energy storage battery packs, a virtual inertia and damping coefficient are determined for each energy storage battery pack. Therefore, when multiple energy storage battery packs are connected in parallel to a DC microgrid system, the virtual inertia can be allocated differently according to the power state of each battery pack, achieving distributed control. Attached Figure Description

[0027] Figure 1 This is a flowchart of a variable inertia control method for a DC microgrid system according to an embodiment of the present invention;

[0028] Figure 2 yes Figure 1 The diagram shows the principle of variable inertia control method.

[0029] Figure 3 This is a schematic diagram of the P membership function of a preset fuzzy logic controller for virtual inertia association in a typical application scenario.

[0030] Figure 4 This is a schematic diagram of the SOC membership function of a pre-defined fuzzy logic controller with virtual inertia association in a typical application scenario.

[0031] Figure 5 This is a schematic diagram of the output membership function of a preset fuzzy logic controller for virtual inertia association in a typical application scenario.

[0032] Figure 6 This is a schematic diagram of the P membership function of a preset fuzzy logic controller associated with damping coefficients in a typical application scenario.

[0033] Figure 7 This is a schematic diagram of the SOC membership function of a preset fuzzy logic controller associated with damping coefficients in a typical application scenario.

[0034] Figure 8 This is a schematic diagram of the output membership function of a preset fuzzy logic controller associated with damping coefficients in a typical application scenario. Detailed Implementation

[0035] As mentioned in the background section, existing virtual inertia control methods for DC microgrid systems have many shortcomings, which greatly reduce the optimization effect on system stability and power quality.

[0036] The inventors of this application discovered through analysis that one of the reasons for the aforementioned problems is that existing virtual inertia control methods only consider the effects of voltage or fluctuations in inertia design, lacking consideration of the state of the energy storage system providing the inertia. This results in existing virtual inertia control methods failing to meet the varying requirements of DC microgrid systems for fast response and large inertia under different operating conditions, thereby affecting system stability and power quality.

[0037] To address the aforementioned technical problems, embodiments of the present invention provide a variable inertia control method for a DC microgrid system. The DC microgrid system includes one or more energy storage battery packs connected to the bus of the DC microgrid system. The variable inertia control method includes: acquiring the operating parameters of the DC microgrid system and the battery status of the energy storage battery packs; inputting the operating parameters and battery status into a preset fuzzy logic controller to obtain corresponding virtual inertia and damping coefficients; and adjusting the bus voltage of the bus based at least on the virtual inertia and damping coefficients.

[0038] This implementation scheme can buffer voltage surges on the bus caused by load fluctuations to improve system stability, enhance the power quality of the DC microgrid system, and reduce the impact on energy storage batteries during system operation. Specifically, based on the distributed droop control of the DC microgrid system, virtual inertia control is used to buffer voltage surges on the DC bus (i.e., the busbar) caused by load fluctuations. Furthermore, based on the operating conditions of the DC microgrid system and the current state of the energy storage battery packs, the virtual inertia and damping coefficient in the inertia control are obtained through a preset fuzzy logic controller, making it possible to adjust the control parameters according to the real-time system conditions. This improves the system's voltage recovery capability, reduces DC bus voltage fluctuations, enhances the system's power quality, rationally distributes current surges caused by power fluctuations among the energy storage battery packs, and reduces the adverse effects on the energy storage batteries during system operation.

[0039] To make the above-mentioned objectives, features and beneficial effects of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Figure 1This is a flowchart of a variable inertia control method for a DC microgrid system according to an embodiment of the present invention.

[0041] Specifically, the bus of the DC microgrid system can be connected to one or more energy storage battery packs to provide charging and discharging power and inertia to the DC microgrid system through the energy storage battery packs.

[0042] Furthermore, when there are multiple energy storage battery packs, virtual inertia and damping coefficients can be determined separately for each energy storage battery pack. For example, this implementation scheme can be executed separately for each energy storage battery pack to determine the most suitable virtual inertia for that energy storage battery pack based on the operating conditions of the DC microgrid system and the battery state of the energy storage battery pack. This enables distributed control of the energy storage battery packs, allowing each energy storage battery pack to adaptively determine the fluctuations it needs to withstand based on its own capabilities and system operating conditions.

[0043] The variable inertia control method described in this implementation plan will be elaborated in detail from the perspective of a specific energy storage battery pack in a DC microgrid system.

[0044] Specifically, refer to Figure 1 The variable inertia control method for DC microgrid systems described in this embodiment may include the following steps:

[0045] Step S101: Obtain the operating parameters of the DC microgrid system and the battery status of the energy storage battery pack.

[0046] Step S102: Input the operating parameters and battery status into a preset fuzzy logic controller to obtain the corresponding virtual inertia and damping coefficient;

[0047] Step S103: Adjust the bus voltage of the bus based at least on the virtual inertia and damping coefficient.

[0048] More specifically, the operating parameters can be used to characterize the operating conditions of the DC microgrid system. For example, the dimensions for measuring the operating parameters may include the remaining energy storage power (P), which is the difference between the maximum power that the energy storage side of the DC microgrid system can provide and the actual power. Another example is the voltage change rate (unit: dV / dt), which is the degree of voltage fluctuation at the bus. In practical applications, other dimensions can also be used to characterize the operating parameters.

[0049] Furthermore, the battery state can be used to characterize the state parameters of the energy storage battery pack. For example, dimensions for measuring the battery state may include the state of charge (SOC) of the energy storage battery pack, the internal resistance (R) of the energy storage battery pack, and the temperature (T) of the energy storage battery pack. In practical applications, other dimensions can also be used to characterize the battery state.

[0050] In practical applications, the operating parameters or battery status can be measured from other dimensions as needed, which will not be elaborated here.

[0051] In one specific implementation, in step S101, monitoring equipment can be used to monitor and obtain the required operating parameters and battery status. For example, the temperature of the energy storage battery pack can be collected based on a temperature sensor.

[0052] In one specific implementation, the preset fuzzy logic controller is a control algorithm integrated into the controller of the DC microgrid system. Specifically, the virtual inertia and damping coefficient can be associated with different preset fuzzy logic controllers to obtain the corresponding virtual inertia and damping coefficient based on the input operating parameters and battery state, respectively.

[0053] For example, step S102 may include the step of: inputting the operating condition parameters and battery status into a preset fuzzy logic controller associated with the virtual inertia to obtain the virtual inertia (Cv).

[0054] For example, step S102 may also include the step of: inputting the operating parameters and battery status into a preset fuzzy logic controller associated with the damping coefficient to obtain the damping coefficient (kd).

[0055] Furthermore, the preset fuzzy logic controller can pre-store input membership functions, preset operation rules, and output membership functions.

[0056] The operating parameters and battery status collected in step S101 are used as input parameters to the input membership function, which transforms them into fuzzy logic values ​​for each fuzzy subset of the input membership function. Then, defuzzification is performed using preset operation rules and the output membership function, ultimately calculating the output value of the preset fuzzy logic controller, i.e., the virtual inertia or damping coefficient. Furthermore, operating parameters of different dimensions can be associated with different input membership functions; that is, a corresponding input membership function can be set for each dimension of the operating parameters. For example, the remaining energy storage power P can correspond to the P membership function. Similarly, the voltage change rate can correspond to the voltage change rate membership function. Furthermore, the P membership function can be different from the voltage change rate membership function.

[0057] Similarly, different dimensions of battery state can be associated with different input membership functions; that is, a corresponding input membership function can be set for each dimension of the battery state. For example, the internal resistance of an energy storage battery pack can correspond to an internal resistance membership function. As another example, the state of charge (SOC) of an energy storage battery pack can correspond to a state of charge (SOC) membership function. Furthermore, the internal resistance membership function can be different from the SOC membership function.

[0058] In a specific implementation, different preset fuzzy logic controllers can have different input membership functions.

[0059] For example, in preset fuzzy logic controllers associated with virtual inertia and damping coefficient, the horizontal axis of the input membership function corresponding to the same input parameter can differ in the number of divisions and / or the range of values ​​for each division segment when partitioning the fuzzy set. For instance, the P membership function in the preset fuzzy logic controller associated with virtual inertia can differ from the P membership function in the preset fuzzy logic controller associated with damping coefficient.

[0060] For example, the dimensions of the operating parameters received by different preset fuzzy logic controllers can be different, and different dimensions of operating parameters can correspond to different input membership functions. Consequently, the preset input membership functions in different preset fuzzy logic controllers are also different.

[0061] In a specific implementation, different preset fuzzy logic controllers can have different preset operation rules.

[0062] For example, the number of fuzzy subsets included in the preset operation rules of the preset fuzzy logic controller associated with virtual inertia may be different from the number of fuzzy subsets included in the preset operation rules of the preset fuzzy logic controller associated with damping coefficient.

[0063] For example, different preset fuzzy logic controllers may have different numbers of dimensions for their input parameters, which will result in differences in the constructed operation rule tables.

[0064] In a specific implementation, different preset fuzzy logic controllers can have different output membership functions. For example, the horizontal axis of the output membership function associated with the virtual inertia and damping coefficient can have different values ​​for the number of divisions and / or the range of values ​​for each division segment when partitioning the fuzzy set.

[0065] In one specific implementation, the dimension of the operating parameters of the preset fuzzy logic controller associated with the virtual inertia can be different from the dimension of the operating parameters of the preset fuzzy logic controller associated with the damping coefficient. Alternatively, the dimensions of the operating parameters of the two preset fuzzy logic controllers can be the same.

[0066] In one specific implementation, the dimension of the battery state of the preset fuzzy logic controller associated with the virtual inertia can be different from the dimension of the battery state of the preset fuzzy logic controller associated with the damping coefficient. Alternatively, the dimensions of the battery states of the two preset fuzzy logic controllers can be the same.

[0067] In one specific implementation, the dimensions of the operating parameters and battery state of the preset fuzzy logic controller associated with the virtual inertia can differ from those of the preset fuzzy logic controller associated with the damping coefficient. Alternatively, the operating parameters and battery state dimensions of the two preset fuzzy logic controllers can be the same.

[0068] In one specific implementation, the number of dimensions of the operating parameters and battery state of the preset fuzzy logic controller associated with the virtual inertia can be the same as or different from the number of dimensions of the operating parameters and battery state of the preset fuzzy logic controller associated with the damping coefficient.

[0069] In one specific implementation, the operating parameters and battery status can be selectively input into the preset fuzzy logic controller to obtain the corresponding virtual inertia or damping coefficient.

[0070] In one variation, a neural network can be used instead of the preset fuzzy logic controller. For example, by inputting the operating parameters and battery status into the neural network, the corresponding virtual inertia and damping coefficient can be obtained.

[0071] In one specific implementation, step S103 may include the steps of: determining a reference voltage based on the virtual inertia and damping coefficient; determining a first reference current based on the reference voltage and the bus voltage; determining DC / DC control parameters based at least on the first reference current and the energy storage current of the energy storage battery pack; and adjusting the bus voltage based on the DC / DC control parameters.

[0072] For example, the DC / DC control parameter can be the duty cycle.

[0073] Next, combined Figure 1 and Figure 2 The specific control principle of the variable inertia control method described in this implementation plan is explained in detail.

[0074] Specifically, refer to Figure 2In the DC microgrid system described in this embodiment, the energy storage battery packs (not shown) can be connected to the DC bus (not shown) of the DC microgrid system via a DC / DC converter 10 (identified as DC / DC in the figure). For example, the DC / DC converter 10 corresponds one-to-one with the energy storage battery packs, and the output U of the DC / DC converter 10 can be adjusted... dc It can regulate the bus voltage.

[0075] Furthermore, the DC microgrid system may include a controller (not shown), which performs the above-described actions. Figure 1 The method shown is used to adjust the bus voltage of the bus based on input operating parameters and battery status. For example, the controller can control the output of the DC / DC converter 10 to achieve the effect of adjusting the bus voltage.

[0076] By executing the above Figure 1 The method shown allows the controller to execute a variable inertia control algorithm and fuzzy logic control to determine the virtual inertia and damping coefficient of the energy storage battery pack.

[0077] In a typical application scenario, continue to refer to Figure 1 and Figure 2 The energy storage voltage U of the energy storage battery pack (not shown) connected to the DC / DC converter 10 can be obtained. b and energy storage current I b And obtain the bus voltage U on the DC bus side connected to the energy storage battery pack. dc and the output current I of the DC / DC converter 10 dc The four parameters obtained can be used as input parameters for subsequent variable inertia control. For example, the energy storage voltage U b It can affect the values ​​of the preset droop coefficient R and the preset droop intercept U0.

[0078] The energy storage voltage U can be obtained by monitoring the battery status of the energy storage battery pack. b and energy storage current I b Bus voltage U dc That is, the DC terminal voltage of the output terminal of the connected DC / DC converter 10, and the output current I of the DC / DC converter 10. dc This refers to the DC terminal current at the output of the connected DC / DC converter 10. The bus voltage U can be obtained by monitoring the output of the DC / DC converter 10. dc and the output current I of the DC / DC converter 10 dc Monitoring equipment may include voltage sensors and current sensors.

[0079] Furthermore, the droop control algorithm in the controller controls the steady-state output of the system. Specifically, the droop control algorithm determines the steady-state output of the system through droop control parameters. For example, the droop control parameters may include a preset droop coefficient R and a preset droop intercept U0.

[0080] refer to Figure 2 The specific process of the droop control algorithm may include calculating the preset droop intercept U0 and the bus voltage U based on the input of the first calculation unit 11. dc The difference between (U0-U) dc The difference obtained from the calculation, together with the preset droop coefficient R, is used to calculate the first control current I. c1 .

[0081] For example, the preset droop intercept U0 and the preset droop coefficient R can be obtained from the energy management system, which (not shown) is used to control the operation of the DC microgrid system. Alternatively, the preset droop intercept U0 and the preset droop coefficient R can be pre-set values. In practical applications, the preset droop intercept U0 and the preset droop coefficient R can also be calculated using other methods.

[0082] Furthermore, the algorithm flow for the controller to perform fuzzy logic control may include obtaining the operating parameters of the DC microgrid system and the battery status of the energy storage battery pack, and inputting these two input parameters into the preset fuzzy logic controller 13 associated with the virtual inertia to obtain the virtual inertia Cv.

[0083] In addition, the operating parameters of the DC microgrid system and the battery status of the energy storage battery pack are obtained, and these two input parameters are input into the preset fuzzy logic controller 14 associated with the damping coefficient to obtain the damping coefficient kd.

[0084] Taking the preset fuzzy logic controller 13 associated with input virtual inertia as an example, the operating parameters are the remaining energy storage power P of the DC microgrid system and the battery state is the SOC of the energy storage battery pack.

[0085] Assuming the input membership function pre-stored in the preset fuzzy logic controller 13 associated with the virtual inertia is as follows: Figure 3 and Figure 4 As shown. Among them, Figure 3 The input membership function (i.e., P membership function) is the remaining energy storage power P. The horizontal axis represents the remaining energy storage power P, and the vertical axis represents the fuzzy logic value µ. Figure 4 The input membership function (i.e., SOC membership function) of the energy storage battery pack's SOC is shown on the horizontal axis, and the fuzzy logic value µ is shown on the vertical axis. Figure 3 and Figure 4The horizontal axis is divided into four fuzzy sets, denoted as NL, NS, PS and PL respectively. The fuzzy logic value µ of each fuzzy set ranges from 0 to 1.

[0086] Assume that Table 1 is the preset operation rule of the preset fuzzy logic controller 13 for virtual inertia association, where S, M and L are fuzzy subsets.

[0087] Table 1

[0088]

[0089] Assuming the output membership function pre-stored in the virtual inertia-associated preset fuzzy logic controller 13 is as follows: Figure 5 As shown, the horizontal axis represents the output parameter, i.e., the virtual inertia Cv, and the vertical axis represents the fuzzy logic value µ. Figure 3 and Figure 4 Similarly, Figure 5 The horizontal axis is also divided into four fuzzy sets, denoted as NL, NS, PS, and PL. The fuzzy logic value µ for each fuzzy set ranges from 0 to 1.

[0090] Based on the current input of the remaining energy storage power P and the SOC of the energy storage battery pack, using Figure 3 and Figure 4 The input membership functions shown can be used to calculate the corresponding fuzzy logic values ​​µ, according to Table 1 and Figure 5 The output membership function shown is used to calculate the virtual inertia Cv using the centroid method.

[0091] Taking the operating parameters of the preset fuzzy logic controller 14 associated with the input damping coefficient as the remaining energy storage power P of the DC microgrid system and the battery state as the SOC of the energy storage battery pack as an example.

[0092] Assuming the input membership function pre-stored in the preset fuzzy logic controller 14 associated with the damping coefficient is as follows: Figure 6 and Figure 7 As shown. Among them, Figure 6 The input membership function (i.e., P membership function) is the remaining energy storage power P. The horizontal axis represents the remaining energy storage power P, and the vertical axis represents the fuzzy logic value µ. Figure 7 The input membership function (i.e., SOC membership function) of the energy storage battery pack's SOC is shown on the horizontal axis, and the fuzzy logic value µ is shown on the vertical axis. Figure 6 and Figure 7 The horizontal axis is divided into four fuzzy sets, denoted as NL, NS, PS and PL respectively. The fuzzy logic value µ of each fuzzy set ranges from 0 to 1.

[0093] Assume that the preset operation rules of the virtual inertia-associated preset fuzzy logic controller 13 are as shown in Table 2, where S, M and L are fuzzy subsets.

[0094] Table 2

[0095]

[0096] Assuming the output membership function pre-stored in the preset fuzzy logic controller 14 associated with the virtual inertia is as follows: Figure 8 As shown, the horizontal axis represents the output parameter, i.e., the damping coefficient kd, and the vertical axis represents the fuzzy logic value µ. Figure 6 and Figure 7 Similarly, Figure 8 The horizontal axis is also divided into four fuzzy sets, denoted as NL, NS, PS and PL respectively. The fuzzy logic value µ of each fuzzy set ranges from 0 to 1.

[0097] Based on the current input of the remaining energy storage power P and the SOC of the energy storage battery pack, using Figure 6 and Figure 7 The input membership functions shown can be used to calculate the corresponding fuzzy logic values ​​µ, according to Table 2 and Figure 8 The output membership function shown is used to calculate the damping coefficient kd using the centroid method.

[0098] Therefore, based on the real-time acquired operating parameters and battery status, the preset fuzzy logic controller can determine the most suitable virtual inertia Cv and damping coefficient kd for the current system operating state. Furthermore, when performing variable inertia control, the controller integrates the virtual inertia Cv and damping coefficient kd obtained in real-time by the preset fuzzy logic controller with droop control. This allows for timely adjustment of control parameters based on the voltage and power conditions of the DC microgrid system and the status of the energy storage battery pack. On the one hand, it buffers the voltage surges caused by the load; on the other hand, it rationally distributes the surge current among the energy storage battery packs, thereby improving the power quality of the DC microgrid system and reducing the impact on the energy storage battery packs.

[0099] Further reference Figure 2 The algorithm flow for the controller to perform variable inertia control may include, based on the reference voltage U ref The bus voltage U dc The second control current I is calculated from the damping coefficient kd. c2 Based on Formula I c2 =(U ref -U dc The second control current I is obtained by calculating )×kd. c2 .

[0100] Furthermore, the algorithm flow for the controller to perform variable inertia control may include, based on the first control current I...c1 Second control current I c2 The output current I of the DC / DC converter 10 is obtained. dc The reference voltage U is calculated from the virtual inertia Cv. ref For example, the second calculation unit 15 calculates the input first control current I. c1 Second control current I c2 and the output current I of the DC / DC converter 10 dc The difference between (I) c1 -I dc -I c2 The difference obtained from the calculation, together with the virtual inertia Cv, is used to calculate the reference voltage U. ref In the figure, 1 / s represents the integral.

[0101] Furthermore, the algorithm flow for the controller to perform variable inertia control may include, based on the reference voltage U ref With the bus voltage U dc The first reference current I was calculated. r1 For example, the third calculation unit 12 calculates the input reference voltage U. ref With the bus voltage U dc The difference between (U) ref -U dc The calculated difference is input into the first PI (proportional integral) controller 16 to obtain the first reference current I. r1 .

[0102] Furthermore, the algorithm flow for the controller to perform variable inertia control may include, based on the first reference current I... r1 With the energy storage current I b Determine the duty cycle d. For example, the fourth calculation unit 17 calculates the input first reference current I. r1 With energy storage current I b The difference between (I) r1 -I b The calculated difference is input into the second PI controller 18 to obtain the duty cycle d.

[0103] Furthermore, when the controller performs variable inertia control, it can adjust the bus voltage U based on the duty cycle d. dc For example, PWM waves of each switching transistor in the DC / DC converter 10 can be generated according to the duty cycle d, and then the output of the DC / DC converter 10 can be controlled based on the PWM waves of each switching transistor to adjust the bus voltage U. dc .

[0104] In a variation, when calculating the duty cycle d, the input parameters received by the fourth calculation unit 17, in addition to the first reference current I, are... r1 With the energy storage current I b In addition, it may include a second reference current I. r2 .

[0105] Specifically, the second reference current I r2 The output current I of the DC / DC converter 10 dc The result after processing by the feedforward function.

[0106] Furthermore, the fourth calculation unit 17 can calculate the first reference current I. r1 The second reference current I r2 With the energy storage current I b The difference (I) r1 +I r2 -I b The calculated difference is input to the second PI controller 18 to obtain the duty cycle d. That is, in addition to the first reference current I... r1 and energy storage current I b In addition, the duty cycle d can be determined by combining the feedforward term. The feedforward term, besides... Figure 2 In addition to the output current of the DC / DC converter 10 used in the process, the bus voltage can also be used.

[0107] In a specific implementation, when constructing the input membership function, preset operation rules, and output membership function, it is ensured that the larger the remaining energy storage power P, the larger the corresponding virtual inertia, and the better the effect of suppressing voltage fluctuations.

[0108] In one specific implementation, when constructing the input membership function, preset operation rules, and output membership function, it is ensured that the greater the voltage change rate, the greater the corresponding virtual inertia.

[0109] Therefore, this implementation scheme can buffer the voltage surges on the bus caused by load fluctuations to improve system stability, enhance the power quality of the DC microgrid system, and reduce the impact on the energy storage battery during system operation. Specifically, based on the distributed droop control of the DC microgrid system, virtual inertia control is used to buffer the voltage surges on the DC bus caused by load fluctuations.

[0110] Furthermore, based on the operating conditions of the DC microgrid system and the current state of the energy storage battery pack, a virtual inertia and damping coefficient in inertia control are obtained through a preset fuzzy logic controller, thereby adjusting the control parameters according to the real-time system conditions. The operating parameters can provide the required inertia of the system, and combined with the battery characteristics, a virtual inertia more suitable for the current state of the battery is provided.

[0111] Therefore, this implementation scheme can improve the voltage recovery capability of the system, reduce DC bus voltage fluctuations, improve the power quality of the system, rationally distribute the current impact caused by power fluctuations among the energy storage battery packs, and reduce the adverse effects on the energy storage batteries during system operation.

[0112] Furthermore, by implementing this embodiment, the distribution of inertia among multiple energy storage battery packs can be reasonably determined when multiple energy storage battery packs are simultaneously connected to the DC microgrid system. Specifically, in this embodiment, the inertia can be distributed non-uniformly among the energy storage battery packs. For example, by using the battery state of the energy storage battery packs as input parameters of a preset fuzzy logic controller, the energy storage battery packs with higher power states can receive more inertia.

[0113] Furthermore, this implementation scheme can also decouple the design of dynamic control parameters from steady-state control parameters.

[0114] Furthermore, the feedforward function can better improve the performance of the DC microgrid system and further suppress fluctuations.

[0115] This invention also provides a DC microgrid system, comprising: one or more sets of energy storage battery packs connected to the bus of the DC microgrid system; and a controller for executing the above-described... Figures 1 to 8 The method adjusts the bus voltage of the bus based on the operating parameters and battery status.

[0116] It should be noted that the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0117] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0118] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A variable inertia control method for a DC microgrid system, characterized in that, The DC microgrid system includes one or more sets of energy storage battery packs, which are connected to the bus of the DC microgrid system. The variable inertia control method includes: Obtain the operating parameters of the DC microgrid system and the battery status of the energy storage battery pack; The operating parameters and battery status are input into a preset fuzzy logic controller to obtain the corresponding virtual inertia and damping coefficient. Adjusting the bus voltage of the bus based at least on the virtual inertia and damping coefficient includes: determining a reference voltage based on the virtual inertia and damping coefficient; determining a first reference current based on the reference voltage and the bus voltage; determining DC / DC control parameters based at least on the first reference current and the energy storage current of the energy storage battery pack; and adjusting the bus voltage based on the DC / DC control parameters. The step of determining the reference voltage based on the virtual inertia and damping coefficient includes: The first control current is calculated based on the preset droop intercept, the preset droop coefficient, and the bus voltage. The second control current is calculated based on the reference voltage, the bus voltage, and the damping coefficient. The reference voltage is calculated based on the first control current, the second control current, the output current of the DC / DC converter, and the virtual inertia.

2. The variable inertia control method according to claim 1, characterized in that, The step of inputting the operating parameters and battery status into a preset fuzzy logic controller to obtain the corresponding virtual inertia and damping coefficient includes: The operating parameters and battery status are input into a preset fuzzy logic controller associated with the virtual inertia to obtain the virtual inertia; The operating parameters and battery status are input into a preset fuzzy logic controller associated with the damping coefficient to obtain the damping coefficient.

3. The variable inertia control method according to claim 2, characterized in that, The dimensions of the operating parameters and / or battery state of the preset fuzzy logic controller associated with the virtual inertia are different from the dimensions of the operating parameters and / or battery state of the preset fuzzy logic controller associated with the damping coefficient.

4. The variable inertia control method according to claim 1 or 2, characterized in that, The virtual inertia and damping coefficient are associated with different preset fuzzy logic controllers, and the different preset fuzzy logic controllers have different input membership functions, preset operation rules and / or output membership functions.

5. The variable inertia control method according to claim 1 or 2, characterized in that, Different dimensions of operating parameters are associated with different input membership functions, and different dimensions of battery status are associated with different input membership functions.

6. The variable inertia control method according to claim 5, characterized in that, The dimensions of the operating parameters are selected from: the remaining power of the energy storage and the voltage change rate of the bus.

7. The variable inertia control method according to claim 5, characterized in that, The dimensions of the battery state are selected from: the SOC of the energy storage battery pack, the internal resistance of the energy storage battery pack, and the temperature of the energy storage battery pack.

8. The variable inertia control method according to claim 1, characterized in that, Determining the DC / DC control parameters based at least on the first reference current and the energy storage current of the energy storage battery pack includes: The DC / DC control parameters are determined based on the first reference current, the energy storage current of the energy storage battery pack, and the feedforward term.

9. The variable inertia control method according to claim 8, characterized in that, The feedforward term is the result of processing the output current or bus voltage of the DC / DC converter through a feedforward function.

10. The variable inertia control method according to claim 8 or 9, characterized in that, The step of determining the DC / DC control parameters based on the first reference current, the energy storage current of the energy storage battery pack, and the feedforward term includes: The difference between the first reference current, the feedforward term, and the energy storage current is subjected to PI control to obtain the DC / DC control parameters.

11. The variable inertia control method according to claim 1, characterized in that, The preset droop intercept and the preset droop coefficient are obtained from the energy management system of the DC microgrid system, wherein the energy management system is used to control the operation of the DC microgrid system.

12. The variable inertia control method according to claim 1, characterized in that, The energy storage battery pack is connected to the bus via a DC / DC converter, and the adjustment of the bus voltage based on the DC / DC control parameters includes: The PWM wave of each switching transistor in the DC / DC converter is generated according to the DC / DC control parameters. The output of the DC / DC converter is controlled by the PWM wave of each switch to regulate the bus voltage.

13. The variable inertia control method according to claim 1, characterized in that, When there are multiple energy storage battery packs, the virtual inertia and damping coefficient are determined for each energy storage battery pack.

14. A DC microgrid system, characterized in that, include: One or more energy storage battery packs are connected to the bus of the DC microgrid system; A controller is configured to perform the method described in any one of claims 1 to 13 to adjust the bus voltage of the bus according to the operating parameters and the battery status.

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