Method and device for determining power distribution coefficient of alternating current networking output port of multi-source direct current microgrid, computer equipment, storage medium and computer program product

Abstract

The invention relates to a method and a device for determining a power distribution coefficient of an alternating current networking output port of a multi-source direct current microgrid, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring the output power of each new energy power generation system, the residual electric quantity state of a concentrated energy storage system and the load size of a direct-current load; according to the output power of each new energy power generation system and the residual electric quantity state of the centralized energy storage system, determining a power supply capability prediction value of the multi-source direct current microgrid, and according to the load size of the direct current load, determining a power consumption capability prediction value of the multi-source direct current microgrid; according to the power supply capability predicted value and the power consumption capability predicted value, determining a power output capability predicted value supplied by the multi-source DC microgrid to the AC bus; and adjusting a power distribution coefficient based on the power output capability predicted value to obtain an adjusted power distribution coefficient. By adopting the method, power distribution can be accurately carried out when the abrupt-change direct-current load occurs in the power grid.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to a method, device, computer device, storage medium, and computer program product for determining a power distribution coefficient of an AC grid-forming output port of a multi-source DC microgrid. Background Art

[0002] With the continuous development of the new energy field, various renewable energy sources are connected to the system through grid-connected inverters to jointly build a new power system, which undertakes the DC load power as needed and provides frequency support for the power grid.

[0003] Currently, when there is a sudden change in the DC load of the power grid, the frequency support power is often simply distributed according to the fixed capacity of each new energy grid-connected inverter, without considering the dynamic changes of each subsystem of photovoltaic power generation and wind power generation, as well as the various time-varying parameters of the centralized energy storage system and the influence of the DC load bearing capacity. When the system load suddenly changes and causes a frequency mutation, it may cause the subsystems with weak real-time power output capabilities to bear excessive frequency modulation power, and in severe cases, it may even lead to the collapse of the subsystems.

[0004] Therefore, there is a problem in the traditional technology that it is impossible to accurately distribute power when there is a sudden change in the DC load of the power grid. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for determining a power distribution coefficient of an AC grid-forming output port of a multi-source DC microgrid that can accurately distribute power when there is a sudden change in the DC load of the power grid in view of the above technical problems.

[0006] A method for determining a power distribution coefficient of an AC grid-forming output port of a multi-source DC microgrid, which is applied to a multi-source DC microgrid. The multi-source DC microgrid includes each new energy power generation system, a centralized energy storage system, and a DC load. The method includes:

[0007] Obtain the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load;

[0008] Determine a predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and determine a predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load;

[0009] Determine a predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity;

[0010] Based on the predicted value of the power output capability, adjust the power distribution coefficient for the AC grid-forming output port to obtain the adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

[0011] In one embodiment, determining the predicted value of the power supply capability of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system includes:

[0012] Using a grey prediction model, based on the output power information of each new energy power generation system, determine the predicted output power value of each new energy power generation system, and, using a grey prediction model, based on the remaining power state information of the centralized energy storage system, determine the predicted output power value of the centralized energy storage system;

[0013] According to the predicted output power values of each new energy power generation system and the predicted output power value of the centralized energy storage system, determine the predicted value of the power supply capability of the multi-source DC microgrid;

[0014] According to the load information of the DC load, determining the predicted value of the power consumption capability of the multi-source DC microgrid includes:

[0015] Using a grey prediction model, based on the load information of the DC load, determine the predicted value of the power consumption capability of the multi-source DC microgrid.

[0016] In one embodiment, adjusting the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capability to obtain the adjusted power distribution coefficient includes:

[0017] Obtain the maximum frequency offset that the multi-source DC microgrid can tolerate;

[0018] Based on the maximum frequency offset and the predicted value of the power output capability, determine the adjustment amount for the power distribution coefficient;

[0019] Use the adjustment amount to adjust the power distribution coefficient to obtain the adjusted power distribution coefficient.

[0020] In one embodiment, the determination method of the adjusted power distribution coefficient is expressed as:

[0021] ,

[0022] Wherein, represents the adjusted power distribution coefficient, represents the power distribution coefficient, represents the adjustment amount; the adjustment amount is expressed as:

[0023] ,

[0024] Wherein, represents the maximum frequency offset, and ΔP represents the predicted value of power output capability.

[0025] In one embodiment, the control model of the AC grid-forming output port is expressed as:

[0026] ;

[0027] where P ref represents the reference value of the output power of the multi-source DC microgrid, P represents the system output power of the multi-source DC microgrid, D represents the adjusted power distribution coefficient, represents the virtual rotor speed, and J represents the virtual moment of inertia coefficient, represents the power frequency.

[0028] In one embodiment, the multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filter network unit, so that the multi-source DC microgrid provides a frequency support function for the AC bus.

[0029] A device for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid, which is applied to the multi-source DC microgrid. The multi-source DC microgrid includes each new energy power generation system, a centralized energy storage system, and DC loads. The device includes:

[0030] An acquisition module, configured to acquire the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC loads;

[0031] A determination module, configured to determine the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and determine the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC loads;

[0032] A determination module, configured to determine the predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity;

[0033] An adjustment module, configured to adjust the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capacity to obtain an adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

[0034] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0035] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0036] A computer program product includes a computer program which, when executed by a processor, implements the steps of the above-mentioned method.

[0037] The method, device, computer equipment, storage medium and computer program product for determining the power distribution coefficient of the AC grid-forming output port of the multi-source DC microgrid as described above obtain the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load; determine the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and determine the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load; determine the predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity; adjust the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capacity to obtain the adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port; in this way, it is possible to dynamically adjust the power distribution coefficient of the AC grid-forming output port of the multi-source DC microgrid according to the predicted values of the real-time output power capabilities of each distributed power source in the multi-source DC microgrid, so that the multi-source DC microgrid automatically undertakes a reasonable proportion of the grid load mutation power, and at the same time, this power is also reasonably distributed among the multiple sources within the DC microgrid, thereby ensuring a more accurate contribution of a reasonable proportion of the system load mutation power to the grid and stabilizing the grid frequency. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is an application environment diagram of a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in one embodiment;

[0040] Figure 2 It is a flowchart of a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in one embodiment;

[0041] Figure 3 It is a system topology diagram in one embodiment;

[0042] Figure 4 It is a schematic diagram of a method for dynamically adjusting the power distribution coefficient in one embodiment;

[0043] Figure 5 Schematic diagram of a method for predicting power supply capacity and power consumption capacity through a grey prediction model to adjust the power distribution coefficient in an embodiment;

[0044] Figure 6 Schematic diagram of the small-signal model of the AC grid-forming output port in an embodiment;

[0045] Figure 7 Schematic diagram of the specific structure of the AC grid-forming output port in an embodiment;

[0046] Figure 8 Schematic diagram of the control logic of the AC grid-forming output port in an embodiment;

[0047] Figure 9 Schematic diagram of the flow of a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in another embodiment;

[0048] Figure 10 Structure block diagram of a device for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in an embodiment;

[0049] Figure 11 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0050] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] When a sudden load appears in the power grid, the existing technical solutions only simply distribute the frequency support power according to the fixed capacities of each new energy grid-connected inverter, without considering the dynamic changes of photovoltaic power generation, wind power generation, each subsystem, as well as various time-varying parameters of the centralized energy storage system and the load-bearing capacity. When the system load suddenly changes and causes a frequency mutation, it may cause a subsystem with a weak real-time power output capacity to bear an excessive frequency modulation power, and in severe cases, even lead to the collapse of the subsystem. Further, with the development of DC microgrid technology, multiple new energy power generation systems will be connected to the same DC microgrid, and the power output capacity of the AC grid-forming output port of the DC microgrid, or rather the ratio of its power to bear the sudden change of the grid load, needs to be adjusted according to the time-varying power output capacities of multiple sources in the DC microgrid. At the same time, the sudden change power of the grid load can also be reasonably distributed among multiple sources within the DC microgrid.

[0052] The method for determining the power distribution coefficient of the AC grid-forming output port of the multi-source DC microgrid provided by the embodiments of the present application can be applied to, for example, Figure 1 the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers. The server 104 obtains the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load; the server 104 determines the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and, according to the load information of the DC load, determines the predicted value of the power consumption capacity of the multi-source DC microgrid; the server 104 determines the predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity; the server 104 adjusts the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capacity to obtain the adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0053] In an exemplary embodiment, as Figure 2 shown, a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid is provided, which is applied to the multi-source DC microgrid. The multi-source DC microgrid includes each new energy power generation system, a centralized energy storage system, and a DC load. Taking the method applied to Figure 1 the server 104 in it as an example for description, it includes the following steps S202 to step S208. Among them:

[0054] Step S202, obtain the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load.

[0055] Among them, the new energy power generation system can be a photovoltaic power generation system, a wind power generation system, etc. in the multi-source DC microgrid.

[0056] Among them, the output power information of the new energy power generation system can refer to the output power of the photovoltaic power generation system and the output power of the wind power generation system.

[0057] Among them, the centralized energy storage system can be a local centralized energy storage module.

[0058] Among them, the remaining power state information of the centralized energy storage system can refer to the SOC parameter of the centralized energy storage system.

[0059] Among them, the DC load can refer to the equipment or circuit that needs DC power supply to work properly.

[0060] Among them, the load information of the DC load can refer to the DC load size of the DC load.

[0061] Optionally, the server obtains the output power of new energy power generation systems such as photovoltaic power generation systems and wind power generation systems, the SOC parameter of the local centralized energy storage system, and the load size of the DC load.

[0062] Step S204, according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, determine the predicted value of the power supply capacity of the multi-source DC microgrid, and, according to the load information of the DC load, determine the predicted value of the power consumption capacity of the multi-source DC microgrid.

[0063] Among them, the predicted value of the power supply capacity can characterize the power supply capacity of the multi-source DC microgrid.

[0064] Among them, the predicted value of the power consumption capacity can characterize the power consumption capacity of the multi-source DC microgrid.

[0065] Optionally, the server determines the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power of new energy power generation systems such as photovoltaic power generation systems and wind power generation systems and the SOC parameter of the local centralized energy storage system, and, the server determines the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load size of the DC load.

[0066] Step S206, according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity, determine the predicted value of the power output capacity of the multi-source DC microgrid to the AC bus.

[0067] Among them, the predicted value of the power output capacity of the multi-source DC microgrid to the AC bus can be expressed as ΔP, and ΔP characterizes the net frequency regulation capacity that the multi-source DC microgrid can supply to the AC bus.

[0068] Optionally, the server subtracts the predicted value of the power consumption capacity from the predicted value of the power supply capacity to obtain the predicted value of the power output capacity ΔP of the multi-source DC microgrid to the AC bus.

[0069] Step S208: Based on the predicted power output capability, adjust the power distribution coefficient for the AC grid-connected output port to obtain the adjusted power distribution coefficient, which is used to control the power output ratio of the AC grid-connected output port.

[0070] Among them, the power distribution coefficient can be the original coefficient used to control the power output ratio of the AC grid-connected output port, which can be expressed as .

[0071] Among them, the adjusted power distribution coefficient can be the new power distribution coefficient obtained after adjusting the original coefficient used to control the power output ratio of the AC grid-connected output port, which can be expressed as .

[0072] Among them, the power output ratio can refer to the ratio of the output power of the AC grid-connected output port to the total power.

[0073] Optionally, the server adjusts the power distribution coefficient for the AC grid-connected output port based on the predicted power output capability ΔP , to obtain the adjusted power distribution coefficient .

[0074] For the convenience of those skilled in the art to understand, Figure 3 Exemplarily, a system topology diagram of the present application is provided. Among them, multiple new energy power generation systems are connected to the common DC bus of the DC microgrid through corresponding power converters to form a multi-source DC microgrid. At the same time, there are certain and fluctuating DC loads in the DC microgrid. The DC microgrid is connected to the AC bus through the AC grid-connected output port realized by an inverter, and thus is incorporated into the large power grid.

[0075] Figure 4 Exemplarily, a schematic diagram of a method for dynamically adjusting the power distribution coefficient is provided. Among them, by collecting the output power of the power generation module (the output power of new energy power generation systems such as photovoltaic power generation systems and wind power generation systems), the SOC of the local centralized energy storage module, and the load of the multi-source DC microgrid system, and then judging whether the local centralized energy storage system should be connected according to the load size of the DC load, and then calculating the power capability of the multi-source DC microgrid system at this time (the ability to bear the sudden change power of the grid load), and further dynamically adjusting the power distribution coefficient of the AC grid-connected output port to achieve grid frequency support.

[0076] In the method for determining the power distribution coefficient of the AC grid-forming output port of the above multi-source DC microgrid, the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load are obtained; according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, the predicted value of the power supply capacity of the multi-source DC microgrid is determined, and, according to the load information of the DC load, the predicted value of the power consumption capacity of the multi-source DC microgrid is determined; according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity, the predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus is determined; based on the predicted value of the power output capacity, the power distribution coefficient for the AC grid-forming output port is adjusted to obtain the adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port; thus, it is possible to dynamically adjust the power distribution coefficient of the AC grid-forming output port of the multi-source DC microgrid according to the predicted values of the real-time output power capabilities of the various distributed power sources in the multi-source DC microgrid, enabling the multi-source DC microgrid to automatically bear a reasonable proportion of the grid load mutation power, and at the same time, reasonably distribute this power among the multiple sources within the DC microgrid, thereby ensuring a more accurate contribution of a reasonable proportion of the system load mutation power to the grid and stabilizing the grid frequency.

[0077] In an exemplary embodiment, determining the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system includes: using a grey prediction model to determine the predicted value of the output power of each new energy power generation system based on the output power information of each new energy power generation system, and, using a grey prediction model to determine the predicted value of the output power of the centralized energy storage system based on the remaining power state information of the centralized energy storage system; according to the predicted value of the output power of each new energy power generation system and the predicted value of the output power of the centralized energy storage system, determining the predicted value of the power supply capacity of the multi-source DC microgrid; determining the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load includes: using a grey prediction model to determine the predicted value of the power consumption capacity of the multi-source DC microgrid based on the load information of the DC load.

[0078] Among them, the grey prediction model can be a prediction method based on grey systems, mainly used to deal with prediction problems of uncertainty and small sample data.

[0079] Among them, the predicted value of the output power of the new energy power generation system can represent the power that the new energy power generation system can output, and the predicted value of the output power of the centralized energy storage system can represent the power that the centralized energy storage system can output.

[0080] Optionally, the server uses a grey prediction model to determine the output power prediction values of each new energy power generation system based on the output power of each new energy power generation system, and uses the grey prediction model to determine the output power prediction value of the centralized energy storage system based on the remaining power state parameters of the centralized energy storage system. Then, the server adds the output power prediction values of each new energy power generation system and the output power prediction value of the centralized energy storage system to obtain the power supply capacity prediction value of the multi-source DC microgrid. The server also uses the grey prediction model to determine the power consumption capacity prediction value of the multi-source DC microgrid based on the load size of the DC load.

[0081] For the convenience of those skilled in the art to understand, the following takes the output power of a photovoltaic power generation system, which is a new energy power generation system, as an example to introduce how the present application uses a grey prediction model to determine the output power prediction value of the new energy power generation system. Among them, the prediction is carried out in the way of cumulative generation, which specifically includes: Taking the output power of a photovoltaic power generation system as an example, this application will introduce how to use the grey prediction model to determine the output power prediction value of the new energy power generation system. The prediction is carried out by cumulative generation, which specifically includes:

[0082] Step 1: Accumulate the initial sequence to obtain the sequence , where n is the total number of data.

[0083] Step 2: Construct the differential equation , where t represents the time variable, u is the endogenous control grey number, and a is the development grey number.

[0084] Step 3: Write the above results as a matrix .

[0085] Step 4: Calculate the vector , where , , .

[0086] Step 5: According to the formula obtain the prediction value, where is the prediction value of the output power of the photovoltaic power generation system at the (i + 1)-th moment. of the output power of the photovoltaic power generation system at the (i + 1)-th moment.

[0087] For the convenience of those skilled in the art to understand, Figure 5 exemplarily provides a schematic diagram of a method for predicting the power supply capacity and power consumption capacity through a grey prediction model to realize the adjustment of the power distribution coefficient.

[0088] Among them, by collecting the output powers of the photovoltaic power generation system and the wind power generation system, since both of them operate near the maximum power point (MPPT), and the output power also fluctuates with the change of the external environment, it is necessary to consider their power fluctuations. At the same time, the SOC parameter of the local centralized energy storage module at this time and the load power consumption of the multi-source DC microgrid system also need to be considered. According to the above parameters, after predicting the power output capabilities of each source respectively through the grey model prediction algorithm and summing them up to obtain the predicted value of the power supply capacity, and then taking the difference with the predicted value of the power consumption energy of the multi-source DC microgrid, the predicted value of the power output capacity that the multi-source DC microgrid system can supply to the AC bus at this time is obtained.

[0089] In this embodiment, the grey prediction model is used to determine the predicted value of the output power of each new energy power generation system and the predicted value of the output power of the centralized energy storage system to determine the predicted value of the power supply capacity of the multi-source DC microgrid. Moreover, the grey prediction model is used to determine the predicted value of the power consumption capacity of the multi-source DC microgrid, which can accurately perform dynamic prediction on the load mutation power bearing capacity of the multi-source DC microgrid system, is beneficial to accurately adjusting the power distribution coefficient of the AC grid-forming output port, and finally realizes that the multi-source DC microgrid automatically bears a reasonable proportion of the grid load mutation power and at the same time reasonably distributes this power among multiple sources inside the DC microgrid.

[0090] In an exemplary embodiment, based on the predicted value of the power output capacity, adjusting the power distribution coefficient for the AC grid-forming output port to obtain the adjusted power distribution coefficient includes: obtaining the maximum frequency offset that the multi-source DC microgrid can tolerate; determining the adjustment amount for the power distribution coefficient based on the maximum frequency offset and the predicted value of the power output capacity; and adjusting the power distribution coefficient with the adjustment amount to obtain the adjusted power distribution coefficient.

[0091] Among them, the maximum frequency offset may refer to the maximum deviation range allowed for the DC bus frequency when the multi-source DC microgrid operates stably.

[0092] Among them, the adjustment amount of the power distribution coefficient may be the adjustment amplitude of the power distribution coefficient.

[0093] Optionally, the server obtains the maximum frequency offset that the multi-source DC microgrid can tolerate, determines the adjustment amount for the power distribution coefficient based on the maximum frequency offset and the predicted value of the power output capacity, and then adjusts the power distribution coefficient with the adjustment amount to obtain the adjusted power distribution coefficient.

[0094] In this embodiment, the maximum frequency offset that the multi-source DC microgrid can tolerate is obtained; based on the maximum frequency offset and the predicted power output capacity, the adjustment amount for the power distribution coefficient is determined; the adjustment amount is used to adjust the power distribution coefficient to obtain the adjusted power distribution coefficient; in this way, the power distribution coefficient can be accurately adjusted.

[0095] In an exemplary embodiment, the determination method of the adjusted power distribution coefficient is expressed as:

[0096] ,

[0097] where, represents the adjusted power distribution coefficient, represents the power distribution coefficient, represents the adjustment amount; the adjustment amount is expressed as:

[0098] ,

[0099] where, represents the maximum frequency offset, and ΔP represents the predicted power output capacity.

[0100] Optionally, the server first needs to solve the predicted power output capacity ΔP through the above steps S202 - S208, and then, based on the maximum frequency offset and the predicted power output capacity ΔP, determine the power distribution coefficient adjustment amount , and then, add the adjustment amount to the power distribution coefficient to obtain the adjusted power distribution coefficient .

[0101] In this embodiment, through the determination method of the adjusted power distribution coefficient, the power distribution coefficient can be accurately adjusted.

[0102] In an exemplary embodiment, the control model of the AC grid-forming output port is expressed as:

[0103] ;

[0104] where, P ref represents the output power reference value of the multi-source DC microgrid, P represents the system output power of the multi-source DC microgrid, D represents the adjusted power distribution coefficient, represents the virtual rotor speed, J represents the virtual moment of inertia coefficient, represents the power frequency.

[0105] For the active part, its equation is: , where Δδ is the power angle difference between the port of the grid-forming inverter and the point of common coupling (PCC) of the public grid; for the reactive part, the voltage amplitude can be controlled by the voltage-reactive droop control algorithm, and the formula is: , where Q ref is the reference value of the reactive power output by the system, Q is the reactive power output by the system, D q is the reactive droop coefficient, and E0 is the reference voltage amplitude. In the implementation of this application, only the active part can be considered.

[0106] For easy understanding, Figure 6 a small-signal model of the AC grid-forming output port is also provided. According to the derivation, its output power is proportional to the power distribution coefficient at steady state. Specifically: its output active power is related to both the reference power and the power distribution coefficient. In the case of system dynamic changes, the following relationship exists between the change in the output active power of the system and the change in the reference power:

[0107] ,

[0108] where E is the output voltage amplitude of the grid-forming output port, V is the AC bus voltage amplitude, J is the virtual inertia, X is the equivalent output impedance, and s is the Laplace operator.

[0109] In this embodiment, through the control model of the AC grid-forming output port, the power distribution of the AC grid-forming output port can be accurately and reasonably performed.

[0110] In an exemplary embodiment, the multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filter network unit, so that the multi-source DC microgrid provides a frequency support function for the AC bus.

[0111] For the convenience of those skilled in the art to understand, Figure 7 exemplarily provides the specific structure of the AC grid-forming output port of this application. Among them, the multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filter network module, so that the multi-source DC microgrid can provide a frequency support function for the AC bus.

[0112] Figure 8 The control logic diagram of the AC grid-forming output port of this application is also provided. Among them, "first, through power calculation, the input of the power loop is obtained and input into the grid-forming power control module, and then the reference voltage is obtained. The reference voltage is input into the voltage control module. After being adjusted, the voltage signal is compared with the carrier signal and input into the PWM generation module, and then the PWM signal for controlling the three-phase bridge circuit can be obtained, and then the grid-forming function of the AC grid-forming output port is controlled.

[0113] In this embodiment, the multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filtering network unit, enabling the multi-source DC microgrid to provide frequency support for the AC bus.

[0114] In another embodiment, as Figure 9 shown, a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid is provided. Taking the server 104 in Figure 1 as an example, the method includes the following steps:

[0115] Step S902, obtain the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load.

[0116] Step S904, determine the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and determine the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load.

[0117] Step S906, determine the predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity.

[0118] Step S908, obtain the maximum frequency offset that the multi-source DC microgrid can tolerate.

[0119] Step S910, determine the adjustment amount for the power distribution coefficient based on the maximum frequency offset and the predicted value of the power output capacity.

[0120] Step S912, adjust the power distribution coefficient using the adjustment amount to obtain the adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

[0121] It should be noted that the specific limitations of the above steps can refer to the specific limitations of the method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid described above.

[0122] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0123] Based on the same inventive concept, an embodiment of the present application further provides a device for determining the power distribution coefficient of the AC grid-connected output port of the multi-source DC microgrid involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the power distribution coefficient of the AC grid-connected output port of the multi-source DC microgrid provided below can refer to the limitations on the method for determining the power distribution coefficient of the AC grid-connected output port of the multi-source DC microgrid in the above text, and will not be repeated here.

[0124] In an exemplary embodiment, as Figure 10 shown, a device for determining the power distribution coefficient of the AC grid-connected output port of a multi-source DC microgrid is provided, including: an acquisition module 1002, a determination module 1004, a prediction module 1006, and an adjustment module 1008, where:

[0125] The acquisition module 1002 is configured to acquire the output power information of each new energy power generation system, the remaining power state information of the centralized energy storage system, and the load information of the DC load;

[0126] The determination module 1004 is configured to determine a predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each new energy power generation system and the remaining power state information of the centralized energy storage system, and determine a predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load;

[0127] The prediction module 1006 is configured to determine a predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity;

[0128] An adjustment module 1008 is configured to adjust a power distribution coefficient for an AC grid-forming output port based on a predicted power output capacity value to obtain an adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

[0129] In one embodiment, the determination module 1004 is specifically configured to use a grey prediction model to determine the predicted output power values of each new energy power generation system based on the output power information of each new energy power generation system, and use the grey prediction model to determine the predicted output power value of the centralized energy storage system based on the remaining power state information of the centralized energy storage system; determine the predicted power supply capacity value of the multi-source DC microgrid according to the predicted output power values of each new energy power generation system and the predicted output power value of the centralized energy storage system; use the grey prediction model to determine the predicted power consumption capacity value of the multi-source DC microgrid based on the load information of the DC load.

[0130] In one embodiment, the adjustment module 1008 is specifically configured to obtain the maximum frequency offset that the multi-source DC microgrid can tolerate; determine an adjustment amount for the power distribution coefficient based on the maximum frequency offset and the predicted power output capacity value; adjust the power distribution coefficient using the adjustment amount to obtain an adjusted power distribution coefficient.

[0131] In one embodiment, the determination method of the adjusted power distribution coefficient is expressed as: , where represents the adjusted power distribution coefficient, represents the power distribution coefficient, represents the adjustment amount; the adjustment amount is expressed as: , where represents the maximum frequency offset, and ΔP represents the predicted power output capacity value.

[0132] In one embodiment, the control model of the AC grid-forming output port is expressed as: ; where P ref represents the reference output power value of the multi-source DC microgrid, P represents the system output power of the multi-source DC microgrid, D represents the adjusted power distribution coefficient, represents the virtual rotor speed, J represents the virtual moment of inertia coefficient, represents the power frequency.

[0133] In one embodiment, the multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filter network unit, so that the multi-source DC microgrid provides a frequency support function for the AC bus.

[0134] Each module in the power distribution coefficient determination device for the AC grid-forming output port of the multi-source DC microgrid described above can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0135] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 11 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the power distribution coefficient determination data for the AC grid-forming output port of the multi-source DC microgrid. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid.

[0136] Those skilled in the art can understand that Figure 11 the structure shown in

[0137] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0138] In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to execute the steps of the above method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid. The steps of the method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid herein may be the steps in the method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in each of the above embodiments.

[0139] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, causes the processor to execute the steps of the above method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid. The steps of the method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid herein may be the steps in the method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid in each of the above embodiments.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0141] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0142] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid, characterized in that, Applied to a multi-source DC microgrid, the multi-source DC microgrid includes various new energy power generation systems, a centralized energy storage system, and DC loads, and the method includes: Obtain the output power information of each of the new energy power generation systems, the remaining power state information of the centralized energy storage system, and the load information of the DC loads; Determine a predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each of the new energy power generation systems and the remaining power state information of the centralized energy storage system, and determine a predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC loads; Determine a predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity; Based on the predicted value of the power output capacity, adjust the power distribution coefficient for the AC grid-forming output port to obtain an adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

2. The method according to claim 1, wherein The determining the predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each of the new energy power generation systems and the remaining power state information of the centralized energy storage system includes: Use a grey prediction model to determine the predicted output power value of each of the new energy power generation systems based on the output power information of each of the new energy power generation systems, and use the grey prediction model to determine the predicted output power value of the centralized energy storage system based on the remaining power state information of the centralized energy storage system; Determine the predicted value of the power supply capacity of the multi-source DC microgrid according to the predicted output power values of each of the new energy power generation systems and the predicted output power value of the centralized energy storage system; The determining the predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC loads includes: Use a grey prediction model to determine the predicted value of the power consumption capacity of the multi-source DC microgrid based on the load information of the DC loads.

3. The method according to claim 1, characterized in that, The adjusting the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capacity to obtain an adjusted power distribution coefficient includes: Obtain the maximum frequency offset that the multi-source DC microgrid can tolerate; Determine the adjustment amount for the power distribution coefficient based on the maximum frequency offset and the predicted value of the power output capacity; Adjust the power distribution coefficient using the adjustment amount to obtain the adjusted power distribution coefficient.

4. The method according to claim 3, wherein The determination method of the adjusted power distribution coefficient is expressed as: ， Among them, characterizes the adjusted power distribution coefficient, characterizes the power distribution coefficient, characterizes the adjustment amount; the adjustment amount is expressed as: ， Among them, represents the maximum frequency offset, and ΔP represents the predicted value of the power output capability.

5. The method according to claim 1, characterized in that, The control model of the AC grid-forming output port is expressed as: ； where, P ref represents the output power reference value of the multi-source DC microgrid, P represents the system output power of the multi-source DC microgrid, and D represents the adjusted power distribution coefficient. represents the virtual rotor speed, and J represents the virtual moment of inertia coefficient. represents the power frequency.

6. The method according to claim 1, wherein The multi-source DC microgrid is connected to the AC bus through a three-phase bridge circuit and a filter network unit, so that the multi-source DC microgrid provides a frequency support function for the AC bus.

7. A device for determining the power distribution coefficient of the AC grid-forming output port of a multi-source DC microgrid, characterized in that, Applied to a multi-source DC microgrid, the multi-source DC microgrid includes various new energy power generation systems, a centralized energy storage system, and DC loads, and the device includes: An acquisition module for acquiring the output power information of each of the new energy power generation systems, the remaining power state information of the centralized energy storage system, and the load information of the DC loads; A determination module, configured to determine a predicted value of the power supply capacity of the multi-source DC microgrid according to the output power information of each of the new energy power generation systems and the remaining power state information of the centralized energy storage system, and determine a predicted value of the power consumption capacity of the multi-source DC microgrid according to the load information of the DC load; A determination module, configured to determine a predicted value of the power output capacity supplied by the multi-source DC microgrid to the AC bus according to the predicted value of the power supply capacity and the predicted value of the power consumption capacity; An adjustment module, configured to adjust the power distribution coefficient for the AC grid-forming output port based on the predicted value of the power output capacity to obtain an adjusted power distribution coefficient; the adjusted power distribution coefficient is used to control the power output ratio of the AC grid-forming output port.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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