A microgrid collaborative control method and server

Abstract

The present invention relates to the field of power grid model simulation, and in particular to a microgrid collaborative control method and server; the present invention divides a steady-state model and a transient model, and constructs a digital model for each functional module of a microgrid; according to the actual architecture and parameters of a target microgrid, the digital model of the corresponding functional module is selected to construct a microgrid simulation model; according to various objective functions constructed based on different simulation requirements in the simulation model, the transient model and the steady-state model of the functional module are switched to perform simulation calculations; the control strategy or operating parameters of the target microgrid are adjusted according to the results of the simulation calculations; the present invention can construct a microgrid simulation model and select a steady-state model and a transient model of the functional module according to the actual architecture and parameters of the target microgrid and various objective functions of the simulation calculations, so as to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid model simulation, and in particular to a microgrid collaborative control method and a server. Background Art

[0002] The digital twin simulation collaborative control method is to build a simulation model in parallel with the real microgrid project, obtain decision-making simulation results in advance, and guide the optimal operation of the real microgrid project, as well as the architecture for implementing this method. It is necessary to establish a digital twin simulation model corresponding to the microgrid and the actual application scenario, obtain the optimal solution of the objective function by establishing a simulation model for the physical equipment and running the indicator objective function, and run the optimal solution in the twin simulation model. In this way, long-term decision information can be fed back based on the simulation results, long-term operation results can be tracked, and cloud-based microgrid simulation decision-making and data management can be established.

[0003] However, when constructing models, existing technologies have problems such as module physical modeling being too complex or too simple, being unsuitable for processing transients (stability analysis), or being unsuitable for processing medium- and long-term economic analysis. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a microgrid collaborative control method and server to achieve more effective digital twin simulation collaborative control.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A microgrid collaborative control method comprises the following steps:

[0007] S1. Divide the steady-state model and transient model, and construct digital models for each functional module of the microgrid;

[0008] The transient model includes the response characteristics of the complete frequency range of the functional module, has the same output response characteristics as the real functional module, and is used to reflect the short-term index;

[0009] The steady-state model reflects the characteristics of the functional model after stable output, and is used to reflect the steady-state index;

[0010] S2. According to the actual architecture and parameters of the target microgrid, select the digital model of the corresponding functional module to build a microgrid simulation model;

[0011] S3. According to various objective functions constructed based on different simulation requirements in the simulation model, the transient model and the steady-state model of the functional module are switched to perform simulation calculations;

[0012] S4. Adjust the control strategy or operating parameters of the target microgrid according to the simulation calculation results.

[0013] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0014] A microgrid collaborative control server comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the microgrid collaborative control method described above when executing the computer program.

[0015] The beneficial effects of the present invention are: a microgrid collaborative control method and server of the present invention divide the steady-state model and the transient model; wherein, the transient power model includes the response characteristics of the complete frequency band of the converter or physical device of the corresponding module, has the same output response characteristics as the real physical device, can reflect short-term indicators such as power transient, short circuit, startup, etc., and has a slow calculation speed; the steady-state power model reflects the characteristics of the converter or physical device of the module after stable output, such as continuous output power, efficiency, heat and other steady-state indicators, and has a high operating speed; based on the various objective functions of the simulation calculation constructed according to different simulation requirements, the microgrid simulation model is constructed and the steady-state model and transient model of the functional module are selected to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an example diagram of a hybrid microgrid architecture according to an embodiment of the present invention;

[0017] Figure 2 An example diagram of a transformer transient power model for a hybrid microgrid architecture example according to an embodiment of the present invention;

[0018] Figure 3 An example diagram of a transformer steady-state power model of a hybrid microgrid architecture example according to an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of a first load model of a microgrid collaborative control method according to an embodiment of the present invention;

[0020] Figure 5 A schematic diagram of a second load model of a microgrid collaborative control method according to an embodiment of the present invention;

[0021] Figure 6 A schematic diagram of a third load model of a microgrid collaborative control method according to an embodiment of the present invention;

[0022] Figure 7 A schematic diagram of a control architecture formed between a cloud, a project, and a virtual machine in a microgrid collaborative control method according to an embodiment of the present invention;

[0023] Figure 8 A partial example diagram of a microgrid modeling project of a microgrid collaborative control method according to an embodiment of the present invention;

[0024] Fig. 9 This is an example diagram of an objective function of a microgrid collaborative control method according to an embodiment of the present invention;

[0025] Fig.10 Comparison of virtual machine model simulation results of a microgrid collaborative control method according to an embodiment of the present invention with actual operation results of physical equipment Figure 1 ;

[0026] Fig.11 Comparison of virtual machine model simulation results of a microgrid collaborative control method according to an embodiment of the present invention with actual operation results of physical equipment Figure 2 ;

[0027] Fig.12 Comparison of virtual machine model simulation results of a microgrid collaborative control method according to an embodiment of the present invention with actual operation results of physical equipment Figure 3 ;

[0028] Fig.13 Comparison of virtual machine model simulation results of a microgrid collaborative control method according to an embodiment of the present invention with actual operation results of physical equipment Figure 4 ;

[0029] Fig.14 A flowchart of a microgrid collaborative control method according to an embodiment of the present invention;

[0030] Fig.15 A structural diagram of a microgrid collaborative control server according to an embodiment of the present invention;

[0031] Description of labels:

[0032] 1. A microgrid collaborative control server; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0033] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0034] Please refer to Fig.14 , a microgrid collaborative control method, comprising the steps of:

[0035] S1. Divide the steady-state model and transient model, and construct digital models for each functional module of the microgrid;

[0036] The transient model includes the response characteristics of the complete frequency range of the functional module, has the same output response characteristics as the real functional module, and is used to reflect the short-term index;

[0037] The steady-state model reflects the characteristics of the functional model after stable output, and is used to reflect the steady-state index;

[0038] S2. According to the actual architecture and parameters of the target microgrid, select the digital model of the corresponding functional module to build a microgrid simulation model;

[0039] S3. According to various objective functions constructed based on different simulation requirements in the simulation model, the transient model and the steady-state model of the functional module are switched to perform simulation calculations;

[0040] S4. Adjust the control strategy or operating parameters of the target microgrid according to the simulation calculation results.

[0041] From the above description, it can be seen that the beneficial effects of the present invention are: a microgrid collaborative control method and server of the present invention divide the steady-state model and the transient model, and construct digital models for each functional module of the microgrid, so that the microgrid simulation model can be constructed and the steady-state model and transient model of the functional module can be selected according to the actual architecture, parameters and various objective functions of the simulation calculation of the target microgrid, so as to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control.

[0042] Furthermore, the digital model of the functional module includes a power supply grid model, a transformer model, a photovoltaic model, an energy storage battery model, a battery converter model, an energy storage converter model, a transmission line model, a load model and a charging and discharging unit model of an electric vehicle.

[0043] From the above description, it can be seen that the functional modules are divided into six basic functional modules: light (photovoltaic), storage (electrochemical energy storage), charging (electric vehicle charging and discharging), load (load), network (distribution network), and road (transmission line), specifically including the digital models of the above nine functional modules.

[0044] Further, the transformer model includes a transient model and a steady-state model;

[0045] The transient model of the transformer model is expressed as the transfer function of the output U2 to the input U1:

[0046] ;

[0047] Where N is the transformation ratio, is the load impedance, is the primary winding impedance, is the secondary winding impedance, is the magnetizing impedance;

[0048] The steady-state model of the transformer model is equivalent to:

[0049] ;

[0050] in, is the transformer efficiency obtained by the fitting method.

[0051] From the above description, it can be seen that the transient model and steady-state model of the transformer model are constructed as shown above. The transient model is suitable for scenarios with short-term indicators and has high requirements on computing power. The output voltage in the steady-state model does not contain the frequency and phase angle parameters of the AC transformer, but is only used to calculate the loss. This is sufficient for evaluating indicators such as economic performance, and the calculation speed will be improved by orders of magnitude.

[0052] Furthermore, the power supply grid model is represented as a general mathematical model of the three-phase voltage at the output terminal reaching the device side:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] in, , and Indicates the grid port voltage, , and It represents the electromotive force expression containing n harmonics, k is the harmonic order, is the subharmonic amplitude, , , is the initial phase angle, , and ,as well as , and represents the grid impedance parameter, , and Indicates the grid load.

[0058] From the above description, we can see that according to , and It can be seen from the expression that this model is an instantaneous power model, and the electromotive force parameters of each harmonic are It can be obtained by measuring the harmonic voltage at the initial port.

[0059] Furthermore, the photovoltaic model is divided into a two-stage series structure, the first stage is a controlled power source, and the second stage is a converter, which is a DC / DC converter or a DC / AC inverter;

[0060] The maximum output power of the controlled power source is expressed as:

[0061] ;

[0062] in, is a coefficient related to atmospheric conditions and is used for power generation prediction. is the coefficient related to the temperature of the photovoltaic panel, , is the current and voltage at the output of the photovoltaic panel;

[0063] The secondary converter realizes the transmission of photovoltaic panel power to the bus and establishes the output voltage for the DC / DC converter. For input voltage The transfer function is:

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] Where D is the control duty cycle, C, R and L are the power circuit device parameters.

[0070] From the above description, it can be seen that the photovoltaic model is divided into a two-stage series structure, the first stage is a controlled power source for simulating photovoltaic panels, and the second stage is a DC / DC converter or a DC / AC inverter.

[0071] Further, the energy storage battery model includes an electrochemical energy storage battery model;

[0072] The electrochemical energy storage battery model is established as the following discrete function:

[0073] ;

[0074] Among them, SOC represents the battery state of charge, SOH represents the battery health state, T represents the battery temperature, Indicates the battery open circuit voltage, Represents the total internal DC resistance of the battery. Within their respective value ranges, there exists a set Corresponding to it.

[0075] From the above description, it can be seen that the establishment of the electrochemical energy storage battery model is as shown above.

[0076] Furthermore, the transmission line model reflects the impedance characteristics by voltage change and current change between two adjacent nodes on the transmission line, and is expressed by a discrete differential equation as follows:

[0077] ;

[0078] in, represents the simulation sampling period, Indicates the inductance and resistance of the line between node n and node n+1. Represents the current in the line between node n and node n+1.

[0079] From the above description, it can be seen that the transmission line is a passive network. Regardless of whether it is a DC or AC bus, the voltage change and current change between any adjacent nodes (nodes n+1 and n) reflect the impedance characteristics between these two points. Regardless of whether it is a DC bus or an AC bus, radial or other connection types, the feeder model of any newly added node equipment is composed of voltage sampling and current sampling between two points, which is easy to implement in physical devices and simulations.

[0080] Furthermore, the load model is divided into a first load model, a second load model and a third load model according to the type of input current and the type of load;

[0081] The first load model indicates that the input current is DC and the load is also DC, and the power control constraint is:

[0082] ;

[0083] in, is the DC / DC converter input voltage, is the DC / DC converter output voltage, is the DC load power requirement, is the load transmitted between the bus and the load on this line, is the DC / DC converter input peak current, yes The control response adjustment time, Indicates the minimum value allowed for DC bus voltage control. Indicates the maximum value allowed by DC bus voltage control. Indicates the minimum allowable DC load supply voltage. Indicates the maximum allowable DC load supply voltage. Indicates the minimum power demand of the DC load, Indicates the maximum power demand of the DC load, Indicates the minimum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum allowable peak current for supplying DC loads. express The maximum value allowed for the control response adjustment time.

[0084] As can be seen from the above description, the type of load is DC or AC, and its power supply may also come from different buses. According to the type of input current and the type of load, it is divided into the first load model, the second load model and the third load model. The first load model is that the input is DC and the load is also DC; the second load model is that the input is AC and the load is DC; the third load model is that the input is DC or AC, and the load is AC. The construction of the first load model is shown above, and the second load model and the third load model are constructed in the same way.

[0085] Further, step S2 comprises the steps of:

[0086] S21, running the digital models of each functional module in a simulation tool to form a virtual machine;

[0087] S22. Establish an objective function based on the actual architecture of the target microgrid, and generate operation control parameters and prediction parameters of the engineering project;

[0088] S23, receiving project information transmitted by the local end of the project, inputting the project information into the virtual machine, and obtaining a microgrid simulation model corresponding to the engineering project of the target microgrid;

[0089] The project information includes parameter information of functional modules contained in actual physical devices in the target microgrid.

[0090] From the above description, it can be seen that the construction of the microgrid simulation model according to the actual architecture and parameters of the target microgrid can adapt to different target microgrid architectures and improve applicability.

[0091] Please refer to Fig.15 A microgrid collaborative control server includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the microgrid collaborative control method described above are implemented.

[0092] A microgrid collaborative control method and server of the present invention are suitable for digital twin simulation of microgrids.

[0093] Please refer to Figures 1 to 14 , Embodiment 1 of the present invention is:

[0094] A microgrid collaborative control method comprises the following steps:

[0095] S1. Divide the steady-state model and transient model, and construct digital models for each functional module of the microgrid;

[0096] The transient model includes the response characteristics of the complete frequency range of the functional module, has the same output response characteristics as the real functional module, and is used to reflect the short-term index;

[0097] The steady-state model reflects the characteristics of the functional model after stable output, and is used to reflect steady-state indicators.

[0098] In this embodiment, a digital model containing six basic functional modules, namely, photoelectric, storage (electrochemical energy storage), charging (electric vehicle charging and discharging), load, network (distribution network), and path (transmission line), is established to simulate the actual physical equipment in each module, including power electronic converters, batteries, transmission paths, control units, etc. The modeling adopts basic theories, transfer functions, control constraints, data tables, etc.

[0099] The digital model of the functional module includes a power supply grid model, a transformer model, a photovoltaic model, an energy storage battery model, a battery converter model, an energy storage converter model, a transmission line model, a load model and a charging and discharging unit model of an electric vehicle.

[0100] 1. Distribution network model

[0101] In this embodiment, the distribution network is divided into two parts: an AC power grid (power supply grid) and a transformer.

[0102] (1) Power supply grid model

[0103] When considering energy efficiency and response characteristics, a non-ideal model of the grid power supply should be established.

[0104] The power supply grid model is represented by a general mathematical model of the three-phase voltage at the output terminal reaching the device side:

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] in, , and Indicates the grid port voltage, , and It represents the electromotive force expression containing n harmonics, k is the harmonic order, is the subharmonic amplitude, , , is the initial phase angle, , and ,as well as , and represents the grid impedance parameter, , and Indicates the grid load.

[0110] pass , and It can be seen from the expression that this model is an instantaneous power model, and the electromotive force parameters of each harmonic are It can be obtained by measuring the harmonic voltage at the initial port and input into the model as the initial parameter.

[0111] The transient model of the distribution network includes frequency and phase angle, which is used in analyzing reactive power, dynamic response and other transients (or short moments). It is not needed when calculating energy and power. It is equivalent to a DC model, and Ua, Ub, and Uc are equivalent to DC according to their effective values. Only the equivalent resistances Ra, Rb, and Rc are retained, and the inductance, phase angle, and frequency are ignored.

[0112] (2) Transformer model

[0113] According to Figure 1 From the example of the hybrid microgrid architecture shown, it can be seen that the AC grid forms an AC bus after passing through the distribution line Line_1 and the transformer. The transfer function of the output U2 to the input U1 is derived by the transformer equivalent circuit method.

[0114] The transformer model includes a transient model and a steady-state model;

[0115] The transient model of the transformer model is expressed as the transfer function of the output U2 to the input U1:

[0116] ;

[0117] Where N is the transformation ratio, is the load impedance, is the primary winding impedance, is the secondary winding impedance, is the magnetizing impedance.

[0118] These parameters can be obtained through transformer testing or calculation in the specification. The expression involves transformer parameters related to frequency and working status. This transient power model is necessary when describing short-term indicators mentioned below, but it has high requirements on the computing power of the computing server, and generally only calculations of a time length of seconds are required.

[0119] The steady-state model of the transformer model is equivalent to:

[0120] ;

[0121] in, is the transformer efficiency obtained by the fitting method.

[0122] The output voltage obtained here will not contain the frequency and phase angle parameters of the AC transformer, but only for calculating the loss, which is sufficient for evaluating economic indicators, and the calculation speed will be improved by orders of magnitude.

[0123] 2. Photovoltaic model

[0124] The photovoltaic model is divided into a two-stage series structure, the first stage is a controlled power source, and the second stage is a converter, which is a DC / DC converter or a DC / AC inverter;

[0125] (1) The maximum output power of the controlled power source is expressed as:

[0126] ;

[0127] in, is a coefficient related to atmospheric conditions and is used for power generation prediction. is the coefficient related to the temperature of the photovoltaic panel, , are the current and voltage at the output of the photovoltaic panel.

[0128] In this embodiment, The data is calculated by a server in the cloud and transmitted to the virtual machine (hereinafter referred to as the virtual machine) of the digital twin simulation. Collected by the project local terminal and transmitted to the virtual machine, The constructed curve is determined by the panel specifications and is input into the virtual machine photovoltaic model in advance.

[0129] (2) The secondary converter realizes the transmission of photovoltaic panel power to the bus and establishes the output voltage for the DC / DC converter For input voltage The transfer function is:

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] Where D is the control duty cycle, C, R and L are the power circuit device parameters.

[0136] Photovoltaic power is direct current, so the amount of calculation is relatively small, and there is no need to analyze transient power characteristics, and no distinction is made between transient models and steady-state models.

[0137] 3. Energy storage battery model

[0138] In this embodiment, an electrochemical energy storage battery model is taken as an example. For example, for a lithium-ion battery, the battery model can be constructed using initial battery parameters provided by a lithium-ion battery manufacturer.

[0139] (1) The electrochemical energy storage battery model is then established as the following discrete function:

[0140] ;

[0141] Among them, SOC represents the battery state of charge, SOH represents the battery health state, T represents the battery temperature, Indicates the battery open circuit voltage, Represents the total internal DC resistance of the battery. Within their respective value ranges, there exists a set Corresponding to it.

[0142] (2) The operation of the battery physical equipment is controlled by the local terminal, which can obtain real-time information from the battery management system. parameters, and at the same time, the pulse power method is used to obtain the physical device of each battery , forming a group under a certain working condition Functional relationship.

[0143] (3) According to the mapping between the model running in the virtual machine and the battery physical device, the collected data of the battery physical device is transmitted through the local terminal. , The data is transmitted to the server, and then the server transmits the set of functional relationships to the simulation model of the virtual machine.

[0144] (4) The server transmits batch data of different working conditions to the simulation model of the virtual machine based on a large amount of working condition data of similar equipment. Functional relationships enable the simulation to run continuously.

[0145] (5) The electrochemical energy storage battery model established by this method avoids complex theoretical calculations and fully corresponds to the operating conditions. Through continuous operation and self-learning, complete and accurate battery parameters can be accumulated, while providing support for decisions such as safety warnings.

[0146] S2. According to the actual architecture and parameters of the target microgrid, select the digital model of the corresponding functional module to build a microgrid simulation model;

[0147] 4. Battery inverter model

[0148] The battery converter forms the connection between the battery and the DC bus. It is a bidirectional unit that supports the DC bus and charges the battery. The model is constructed using a typical dual closed-loop voltage source and current source converter. The modeling constraints are:

[0149] .

[0150] 5. Energy storage converter model

[0151] When transmitting power from the AC bus to the DC bus, the energy storage converter is equivalent to a VSR rectifier. The model uses dual-loop control to stabilize the DC bus and control the power input from the AC. In the reverse direction, it is a current-type inverter, which only contains the current inner loop and controls the power input from the DC bus to the AC grid.

[0152] 6. Transmission line model

[0153] The transmission line is a passive network. Regardless of whether it is a DC or AC bus, any adjacent node ( Figure 1 The voltage change and current change between nodes n+1 and n) reflect the impedance characteristics between these two points.

[0154] Therefore, the transmission line model reflects the impedance characteristics by voltage change and current change between two adjacent nodes on the transmission line, and is expressed by a discrete differential equation as follows:

[0155] ;

[0156] in, represents the simulation sampling period, Indicates the inductance and resistance of the line between node n and node n+1. Represents the current in the line between node n and node n+1.

[0157] Regardless of DC bus or AC bus, radial or other connection types, the feeder model of any newly added node equipment is composed of voltage sampling and current sampling between two points, which is easy to implement in physical equipment and simulation.

[0158] 7.Charging and discharging unit model of electric vehicle

[0159] The electric vehicle charging (charging and discharging) unit includes unidirectional charging (bidirectional charging and discharging) power conversion, which must be an isolated converter. The power transmission model and constraints are:

[0160] ;

[0161] in, They are the transformer ratio, input port and output port voltage, operating frequency, power transmission inductance and efficiency of the power converter. D is the control signal.

[0162] 8. Load model

[0163] Please refer to Figures 1 to 3 In the example of hybrid microgrid architecture shown, the load type is DC or AC, and its power supply may also come from different buses. According to the type of input current and the type of load, it is divided into the first load model, the second load model and the third load model. The first load model is that the input is DC and the load is also DC; the second load model is that the input is AC and the load is DC; the third load model is that the input is DC or AC, and the load is AC.

[0164] The first load model indicates that the input current is DC and the load is also DC, and the power control constraint is:

[0165] ;

[0166] in, is the DC / DC converter input voltage, is the DC / DC converter output voltage, is the DC load power requirement, is the load transmitted between the bus and the load on this line, is the DC / DC converter input peak current, yes The control response adjustment time, Indicates the minimum value allowed for DC bus voltage control. Indicates the maximum value allowed by DC bus voltage control. Indicates the minimum allowable DC load supply voltage. Indicates the maximum allowable DC load supply voltage. Indicates the minimum power demand of the DC load, Indicates the maximum power demand of the DC load, Indicates the minimum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum allowable peak current for supplying DC loads. express The maximum value allowed for the control response adjustment time.

[0167] The above constraints of the first load model include transient indicators. In order to make the model truly express the transient response characteristics, the control parameters of the DC / DC converter are One of the closed-loop control functions is established, such as Figure 4 As shown, the parameter reaches Constraints. Similarly, Establish a closed-loop control function to satisfy the constraints.

[0168] The second load model and the third load model are modeled using the same method.

[0169] These three types of load models contain three different converters, but the principles are the same. The first load model is DC / DC, which can be referred to Figure 4 The basic control block diagram is given in the figure. The other two load models are AC / DC and DC / AC (inverter). The control methods are the same and can be referred to separately. Figure 5 and Figure 6 , U4 and U4_ref are used to indicate the control object and the reference value of the control object, which is essentially to control the voltage U4 at the load end to meet the load demand.

[0170] S3. According to various objective functions constructed based on different simulation requirements in the simulation model, the transient model and the steady-state model of the functional module are switched to perform simulation calculations;

[0171] Step S2 comprises the steps of:

[0172] S21, running the digital models of each functional module in a simulation tool to form a virtual machine;

[0173] S22. Establish an objective function based on the actual architecture of the target microgrid, and generate operation control parameters and prediction parameters of the engineering project;

[0174] S23, receiving project information transmitted by the local end of the project, inputting the project information into the virtual machine, and obtaining a microgrid simulation model corresponding to the engineering project of the target microgrid;

[0175] The project information includes parameter information of functional modules contained in actual physical devices in the target microgrid.

[0176] S4. Adjust the control strategy or operating parameters of the target microgrid according to the simulation calculation results.

[0177] In this embodiment, the above-mentioned models can be run on SIMULINK / MATLAB, or other simulation tools; corresponding functions are developed so that the tool running the model has three functions: simulation calculation, data storage, and communication; the program containing the three functions runs on the server to form a virtual machine.

[0178] Define the module interface to adapt it to the splicing form; and form product forms for different applications, such as photovoltaic-storage; photovoltaic-storage-load and other combinations. Figure 8 A partial capture of a microgrid modeling project shows that each model is modular. Module interfaces with the same definition can be combined into the system, and the data can be aggregated to the interface and transmitted to the database. Among them, the number of electrochemical energy storage battery units can be reduced according to the actual project; the data interface is used to transmit simulation data to the virtual machine database and receive simulation instructions; and each model is modular and has a DC+ / DC- port, which can be connected to achieve electrical connection. The model runs on Figure 7 on the virtual machine shown.

[0179] In this embodiment, a transient power model and a steady-state power model are created for each of the six types of modules of the photovoltaic storage charging microgrid model for simulating different time scales.

[0180] Among them, the transient power model contains the response characteristics of the complete frequency range of the converter or physical device of the corresponding module, has the same output response characteristics as the real physical device, and can reflect short-term indicators such as power transients, short circuits, and startups, but the operation speed is slow. The steady-state power model reflects the characteristics of the converter or physical device of the module after stable output, such as continuous output power, efficiency, heat and other steady-state indicators, and has a high operating speed.

[0181] Switching between transient power model and steady-state power model. For different simulation requirements, the virtual machine selects one of the two models for calculation; Figures 1 to 3 Taking the transformer model as an example, two types of models and their switching are shown.

[0182] The output results of the virtual machine simulation operation can be stored in the server database.

[0183] The virtual machine can communicate with the server, receive simulation calculation instructions, operation parameters, and send calculation results.

[0184] In this embodiment, refer to Fig. 9, establish economic evaluation objective functions, prediction models, safety evaluation indicators, etc. for the actual photovoltaic storage charging microgrid. Generate engineering project operation control parameters and prediction parameters, etc.

[0185] According to the project specifications, project information is entered into the decision server through the project local terminal. The information includes the parameters of the converter, electrochemical energy storage, transmission medium, and protection unit modules contained in the actual physical equipment of the project. One server can execute several projects.

[0186] The server establishes communication with the virtual machine and transmits the acquired parameters to the virtual machine.

[0187] The virtual machine enables the functional modules contained in the project and inputs the corresponding parameters into the simulation model to make the model correspond to the engineering project.

[0188] The decision server controls the virtual machine to run the simulation.

[0189] (1) Determine the short-cycle simulation requirements. Before the microgrid equipment is put into operation, it is necessary to simulate various transient response indicators, such as but not limited to grid-connected and off-grid switching, load switching, power quality, harmonic limit, surge current, transient voltage drop, short-circuit current, lightning transient overvoltage, etc. The decision server controls the virtual machine to connect the required transient power model, set the simulation step size and simulation time, and run the simulation.

[0190] (2) The virtual machine stores the simulation data on the computing server and transmits the results back to the decision server in the form of communication for joint safety decision-making to determine whether the microgrid equipment can be put into operation.

[0191] (3) After the equipment is put into operation, the operation of each physical functional module is controlled through the local terminal using the operating program preset in the decision server.

[0192] (4) The decision server switches the simulation module of the virtual machine to the steady-state power mode to significantly increase the simulation speed. Based on the objective function, it issues a policy cycle (24 hours or longer) for the virtual machine to perform the simulation at a speed much faster than the actual running speed.

[0193] (5) In synchronization with the simulation, the virtual machine transmits the simulation results of a strategy cycle stored in the database back to the decision server in real time, compares them with the expected objective function results, and adjusts the control strategy or operating parameters of the physical device.

[0194] (6) At certain time intervals, the decision server will update the control strategy or operating parameters, update the virtual machine simulation parameters, and iterate the simulation results. Repeat the above steps to achieve twin iterative control of the objective function.

[0195] In this embodiment, an architecture is designed in which a virtual machine provides cloud-edge collaborative support for the local terminal of the project.

[0196] (1) During the control of the physical equipment by the local terminal of the project, the running data of complex calculations or preprocessing that requires real-time processing is transmitted to the virtual machine.

[0197] (2) The computing server of the virtual machine runs the computing program or performs data preprocessing and transmits the results back to the local terminal of the project.

[0198] The control architecture between cloud, project and virtual machine is as follows: Figure 7 shown.

[0199] The comparison between the simulation results of the virtual machine model and the actual operation results of the physical device is as follows: Figures 10 to 13 .

[0200] Please refer to Fig.15 , Embodiment 2 of the present invention is:

[0201] A microgrid collaborative control server 1 includes a processor 2, a memory 3, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the microgrid collaborative control method described above are implemented.

[0202] To summarize, the present invention provides a microgrid collaborative control method and server, which divides the steady-state model and the transient model, and constructs digital models for each functional module of the microgrid, so that the microgrid simulation model can be constructed and the steady-state model and transient model of the functional module can be selected according to the actual architecture, parameters and various objective functions of the simulation calculation of the target microgrid, so as to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control.

[0203] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A microgrid collaborative control method, characterized in that: Includes steps: S1. Divide the steady-state model and transient model, and construct digital models for each functional module of the microgrid; The transient model includes the response characteristics of the complete frequency range of the functional module, has the same output response characteristics as the real functional module, and is used to reflect the short-term index; The steady-state model reflects the characteristics of the functional module after stable output, and is used to reflect the steady-state index; The digital models of the functional modules include a power supply grid model, a transformer model, a photovoltaic model, an energy storage battery model, a battery converter model, an energy storage converter model, a transmission line model, a load model, and a charging and discharging unit model of an electric vehicle; The transformer model includes a transient model and a steady-state model; The transient model of the transformer model is expressed as the transfer function of the output U2 to the input U1: ； Where N is the transformation ratio, is the load impedance, is the primary winding impedance, is the secondary winding impedance, is the magnetizing impedance; The steady-state model of the transformer model is equivalent to: ； in, is the transformer efficiency obtained by the fitting method; The power supply grid model is represented by a general mathematical model of the three-phase voltage at the output terminal reaching the device side: ； ； ； ； in, , and Indicates the grid port voltage, , and It represents the electromotive force expression containing n harmonics, k is the harmonic order, is the subharmonic amplitude, , , is the initial phase angle, , and as well as , and represents the grid impedance parameter, , and Indicates the grid load; The photovoltaic model is divided into a two-stage series structure, the first stage is a controlled power source, and the second stage is a converter, which is a DC / DC converter or a DC / AC inverter; The maximum output power of the controlled power source is expressed as: ； in, is a coefficient related to atmospheric conditions and is used for power generation prediction. is the coefficient related to the temperature of the photovoltaic panel, , is the current and voltage at the output of the photovoltaic panel; The secondary converter realizes the transmission of photovoltaic panel power to the bus and establishes the output voltage for the DC / DC converter. For input voltage The transfer function is: ； ； ； ； ； Where D is the control duty cycle, C, R and L are the power circuit device parameters; The energy storage battery model includes an electrochemical energy storage battery model; The electrochemical energy storage battery model is established as the following discrete function: ； Among them, SOC represents the battery state of charge, SOH represents the battery health state, T represents the battery temperature, Indicates the battery open circuit voltage, Represents the total internal DC resistance of the battery. Within their respective value ranges, there exists a set Corresponding to it; The transmission line model reflects the impedance characteristics between two adjacent nodes on the transmission line by using voltage changes and current changes, and is expressed by a discrete differential equation as follows: ； in, represents the simulation sampling period, L (x+1,x)、 represents the inductance and resistance of the line between node x and node x+1, i (x+1,x) Represents the current of the line between node x and node x+1; The load model is divided into a first load model, a second load model and a third load model according to the type of input current and the type of load; The first load model indicates that the input current is DC and the load is also DC, and the power control constraint is: ； in, is the DC / DC converter input voltage, is the DC / DC converter output voltage, is the DC load power requirement, is the load transmitted between the bus and the load on this line, is the DC / DC converter input peak current, yes The control response adjustment time, Indicates the minimum value allowed for DC bus voltage control. Indicates the maximum value allowed by DC bus voltage control. Indicates the minimum allowable DC load supply voltage. Indicates the maximum allowable DC load supply voltage. Indicates the minimum power demand of the DC load, Indicates the maximum power demand of the DC load, Indicates the minimum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum value allowed for the DC / DC converter to supply power to a DC load. Indicates the maximum allowable peak current for supplying DC loads. express The maximum value allowed for the control response adjustment time; S2. According to the actual architecture and parameters of the target microgrid, select the digital model of the corresponding functional module to build a microgrid simulation model; Step S2 comprises the steps of: S21, running the digital models of each functional module in a simulation tool to form a virtual machine; S22. Establish an objective function based on the actual architecture of the target microgrid, and generate operation control parameters and prediction parameters of the engineering project; S23, receiving project information transmitted by the local end of the project, inputting the project information into the virtual machine, and obtaining a microgrid simulation model corresponding to the engineering project of the target microgrid; The project information includes parameter information of functional modules contained in the actual physical equipment in the target microgrid; S3. According to various objective functions constructed based on different simulation requirements in the simulation model, the transient model and the steady-state model of the functional module are switched to perform simulation calculations; S4. Adjust the control strategy or operating parameters of the target microgrid according to the simulation calculation results.

2. A microgrid collaborative control server, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps in the microgrid collaborative control method described in claim 1 are implemented.

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