Construction method of micro-grid simulation model and server

Abstract

The invention relates to the field of power grid model simulation, in particular to a micro-grid simulation model construction method and a server. According to the method, a steady-state model and a transient model are divided, and a digital model is constructed for each functional module of the micro-grid; for the actual architecture and parameters of the target micro-grid, selecting the digital model of the corresponding function module, and constructing a micro-grid simulation model; according to various objective functions constructed based on different simulation requirements in the simulation model, switching a transient model and a steady-state model of the functional module, and performing simulation calculation; adjusting a control strategy or an operation parameter of the target micro-grid according to a simulation calculation result; according to the method, the micro-grid simulation model can be constructed and the steady-state model and the transient model of the function module can be selected according to the actual architecture and the parameters of the target micro-grid and various target functions of simulation calculation, so that different analysis requirements can be met, and the effectiveness of digital twin simulation cooperative control is improved.

Description

[0001] This divisional application is based on the invention patent with the application date of September 18, 2024, application number 202411297317.6, and title "A Microgrid Cooperative Control Method and Server" as the parent case. Technical Field

[0002] The present invention relates to the technical field of power grid model simulation, and particularly relates to a method for constructing a microgrid simulation model and a server. Background Art

[0003] The digital twin simulation cooperative control method, that is, constructing a simulation model parallel to the actual microgrid project, obtaining the decision simulation results in advance, guiding the optimized operation of the actual microgrid project, and 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 of physical devices and an objective function of operation indicators, and run the optimal solution in the twin simulation model. Thus, long-term decision-making information can be fed back based on the simulation results later, long-term operation results can be tracked, and microgrid simulation decision-making and data management based on the cloud can be established.

[0004] However, in the existing technology when constructing the model, there are problems such as the physical modeling of the module being either too complex or too simple, not being applicable when dealing with transients (stability analysis), or not being applicable when dealing with medium- and long-term economic analysis. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: to provide a method for constructing a microgrid simulation model and a server to achieve more effective digital twin simulation.

[0006] To solve the above technical problem, the technical solution adopted by the present invention is:

[0007] A microgrid cooperative control method includes the steps of:

[0008] S1. Divide the steady-state model and the transient model, and construct digital models for each functional module of the microgrid;

[0009] The transient model contains the response characteristics of the complete frequency band of the functional module, has the same output response characteristics as the real functional module, and is used to reflect short-term indicators;

[0010] The steady-state model reflects the characteristics after the stable output of the functional model and is used to reflect steady-state indicators;

[0011] S2. Select the digital models of the corresponding functional modules according to the actual architecture and parameters of the target microgrid, and construct a microgrid simulation model;

[0012] S3. According to the objective functions constructed based on different simulation requirements in the simulation model, switch between the transient model and the steady-state model of the functional module, and perform simulation calculations;

[0013] S4. Adjust the control strategy or operating parameters of the target microgrid according to the results of the simulation calculations.

[0014] A method for constructing a microgrid simulation model includes the steps:

[0015] S1. Divide the steady-state model and the transient model, and construct digital models for each functional module of the microgrid;

[0016] The transient model contains the response characteristics of the complete frequency band of the functional module, has the same output response characteristics as the real functional module, and is used to reflect short-term indicators;

[0017] The steady-state model reflects the characteristics of the functional module after stable output, and is used to reflect steady-state indicators;

[0018] S2. Select the digital models of the corresponding functional modules according to the actual architecture and parameters of the target microgrid, and construct a microgrid simulation model.

[0019] To solve the above technical problems, another technical solution adopted by the present invention is:

[0020] 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, it implements the steps in the above-mentioned microgrid collaborative control method.

[0021] A microgrid simulation model construction 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, it implements the steps in the above-mentioned method for constructing a microgrid simulation model.

[0022] The beneficial effects of the present invention are as follows: A method and a server for constructing a microgrid simulation model of the present invention divide the steady-state model and the transient model. Among them, the transient power model includes the response characteristics of the full frequency band of the converter or physical device corresponding to the module, has the same output response characteristics as the real physical device, can reflect short-time indexes such as power transient, short circuit, startup, etc., and has a slow operation speed; the steady-state power model reflects the characteristics after the converter or physical device of the module outputs stably, such as steady-state indexes such as continuous output power, efficiency, heat, etc., and has a high operation speed; various objective functions for simulation calculation constructed based on different simulation requirements are used to construct the microgrid simulation model and select the steady-state model and the transient model of the functional module to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control. Description of the Drawings

[0023] Figure 1 It is an example diagram of the hybrid microgrid architecture of the embodiment of the present invention;

[0024] Figure 2 It is an example diagram of the transient power model of the transformer of the hybrid microgrid architecture example of the embodiment of the present invention;

[0025] Figure 3 It is an example diagram of the steady-state power model of the transformer of the hybrid microgrid architecture example of the embodiment of the present invention;

[0026] Figure 4 It is a schematic diagram of the first load model of a microgrid collaborative control method of the embodiment of the present invention;

[0027] Figure 5 It is a schematic diagram of the second load model of a microgrid collaborative control method of the embodiment of the present invention;

[0028] Figure 6 It is a schematic diagram of the third load model of a microgrid collaborative control method of the embodiment of the present invention;

[0029] Figure 7 It is a schematic diagram of the control architecture formed among the cloud, the project, and the virtual machine of a microgrid collaborative control method of the embodiment of the present invention;

[0030] Figure 8 It is a partial example diagram of a microgrid modeling project of a microgrid collaborative control method of the embodiment of the present invention;

[0031] Figure 9 It is an example diagram of the objective function of a microgrid collaborative control method of the embodiment of the present invention;

[0032] Figure 10 It is a comparison between the simulation result of the virtual machine model and the actual operation result of the physical device of a microgrid collaborative control method of the embodiment of the present invention Figure 1;

[0033] Figure 11 Comparison between the simulation results of the virtual machine model and the actual operation results of physical devices for a microgrid collaborative control method according to an embodiment of the present invention Figure 2 ;

[0034] Figure 12 Comparison between the simulation results of the virtual machine model and the actual operation results of physical devices for a microgrid collaborative control method according to an embodiment of the present invention Figure 3 ;

[0035] Figure 13 Comparison between the simulation results of the virtual machine model and the actual operation results of physical devices for a microgrid collaborative control method according to an embodiment of the present invention Figure 4 ;

[0036] Figure 14 Flowchart of a microgrid collaborative control method according to an embodiment of the present invention;

[0037] Figure 15 Structure diagram of a microgrid collaborative control server according to an embodiment of the present invention;

[0038] Label description:

[0039] 1. A microgrid collaborative control server; 2. A processor; 3. A memory. Specific implementation manner

[0040] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the implementation manners and accompanied by the drawings.

[0041] Please refer to Figure 14 , a microgrid collaborative control method, including the steps:

[0042] S1. Divide the steady-state model and the transient model, and construct digital models for each functional module of the microgrid;

[0043] The transient model contains the response characteristics of the complete frequency band of the functional module, has the same output response characteristics as the real functional module, and is used to reflect short-term indicators;

[0044] The steady-state model reflects the characteristics after the stable output of the functional model and is used to reflect steady-state indicators;

[0045] S2. Select the digital models of the corresponding functional modules according to the actual architecture and parameters of the target microgrid, and construct a microgrid simulation model;

[0046] S3. According to the objective functions constructed based on different simulation requirements in the simulation model, switch between the transient model and the steady-state model of the functional modules, and perform simulation calculations;

[0047] S4. Adjust the control strategy or operating parameters of the target microgrid according to the results of the simulation calculations.

[0048] As can be seen from the above description, the beneficial effects of the present invention are as follows: 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 construction of the microgrid simulation model and the selection of the steady-state model and the transient model of the functional modules can be carried out according to the actual architecture, 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.

[0049] Furthermore, the digital models of the functional modules include a power supply grid model, a transformer model, a photovoltaic model, a 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.

[0050] As can be seen from the above description, the functional modules are divided into six basic functional modules of light (photovoltaic), storage (electrochemical energy storage), charge (electric vehicle charging and discharging), load, grid (distribution network), and road (transmission line), and specifically include the digital models of the above nine functional modules.

[0051] Furthermore, the transformer model includes a transient model and a steady-state model;

[0052] Express the transient model of the transformer model as output U 2 For input U 1 Transfer function of:

[0053]

[0054] where N is the turns ratio, Z L is the load impedance, Z 1 is the primary winding impedance, Z 2 is the secondary winding impedance, Z m is the exciting impedance;

[0055] Equivalent the steady-state model of the transformer model by the DC circuit method as:

[0056]

[0057] where η is the transformer efficiency obtained by the fitting method.

[0058] As described above, the transient model and steady-state model of the transformer model are constructed as shown above. The transient model is applicable to scenarios with short-term indicators and has high requirements for computing power. The output voltage in the steady-state model does not contain the frequency and phase angle parameters of the AC transformer, but only for calculating losses, which is sufficient for evaluating economic indicators such as economy, and the calculation speed will be improved by an order of magnitude.

[0059] Further, 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:

[0060]

[0061] Among them, U a , U b and U c represent the grid port voltage, e a , e b and e c represent the electromotive force expressions containing the nth harmonic. k is the harmonic order, E k is the amplitude of the nth harmonic, α, β, γ are the initial phase angles, R a , R b and R c , as well as L a , L b and L c represent the grid impedance parameters, and I a , I b and I c represent the grid load.

[0062] As described above, it can be seen from the expressions of e a , e b and e c that this model is an instantaneous power model, and the electromotive force parameters E k of each harmonic can be obtained when measuring the harmonic voltage at the initial port.

[0063] Further, 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;

[0064] The maximum output power of the controlled power source is expressed as:

[0065] P max = k a k t I p V p ;

[0066] Among them, k a is a coefficient related to atmospheric conditions for power generation prediction, and k tis a coefficient related to the temperature of the photovoltaic panel, I p , V p are the current and voltage at the output terminal of the photovoltaic panel;

[0067] The secondary converter realizes the transmission of the power of the photovoltaic panel to the bus. The output voltage U of the DC / DC converter is established out For the input voltage U in Transfer function of:

[0068]

[0069] Among them, D is the control duty cycle, and C, R, and L are the parameters of the power loop devices.

[0070] As can be seen from the above description, the photovoltaic model is divided into two series-connected structures. The first stage is a controlled power source for simulating the photovoltaic panel, and the second stage is a DC / DC converter or a DC / AC inverter.

[0071] Furthermore, 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 state of charge of the battery, SOH represents the state of health of the battery, T represents the battery temperature, E m represents the open-circuit voltage of the battery, and R 0 represents the total internal DC resistance of the battery. For SOC, SOH, and T within their respective value ranges, there is a set of E m and R 0 corresponding to it.

[0075] As can be seen from the above description, the electrochemical energy storage battery model is established as shown above.

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

[0077]

[0078] Among them, T s represents the simulation sampling period, L (n+1,n) and R (n+1,n) represent the inductance value and resistance value of the line between node n and node n + 1, and i (n+1,n) represents the current of the line between node n and node n + 1.

[0079] As can be seen from the above description, the transmission line is a passive network. Whether it is a DC or AC bus, the voltage change and current change between any adjacent nodes (node n+1 and n) reflect the impedance characteristics between these two points. Whether it is a DC bus or an AC bus, in a radial or other connection type, the feeder model of any newly added node device 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 conditions are:

[0082]

[0083] where, U 3 is the input voltage of the DC / DC converter, U 4 is the output voltage of the DC / DC converter, P 1 is the DC load demand power, P 2 is the load transmitted between the bus and this load path, I 1_peak is the peak input current of the DC / DC converter, is the control response adjustment time of U 4 , U Bus_min represents the minimum allowable value of the DC bus voltage control, U Bus_max represents the maximum allowable value of the DC bus voltage control, U L_min represents the minimum allowable value of the DC load supply voltage, U L_max represents the maximum allowable value of the DC load supply voltage, P 1_min represents the minimum power demand of the DC load, P 1_max represents the maximum power demand of the DC load, P 2_min represents the minimum allowable value for supplying power to the DC load DC / DC converter, P 2_max represents the maximum allowable value for supplying power to the DC load DC / DC converter, I 1_max represents the maximum allowable value of the peak current for supplying power to the DC load, t s_max represents the maximum allowable value of the control response adjustment time of U 4

[0084] ​As described above, the type of load can be 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 a first load model, a second load model, and a third load model. The first load model has a DC input and a DC load; the second load model has an AC input and a DC load; the third load model has a DC or AC input and an AC load. The construction of the first load model is as shown above, and the second load model and the third load model are constructed in the same way.

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

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

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

[0088] S23. Receive the project information transmitted from the local end of the project, input the project information into the virtual machine, and obtain a microgrid simulation model corresponding to the engineering project of the target microgrid;

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

[0090] As described above, according to the actual architecture and parameters of the target microgrid, constructing a microgrid simulation model can adapt to different target microgrid architectures and improve applicability.

[0091] Please refer to Figure 15 , a microgrid collaborative control server, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned microgrid collaborative control method.

[0092] The microgrid collaborative control method and server of the present invention are applicable to the digital twin simulation of the microgrid.

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

[0094] A microgrid collaborative control method, including the steps of:

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

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

[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 including six basic functional modules of light (photovoltaic), storage (electrochemical energy storage), charging (electric vehicle charging and discharging), load, grid (distribution network), and road (transmission line) is established to simulate the actual physical devices such as power electronic converters, batteries, transmission paths, and control units within each module. The modeling adopts forms such as basic theories, transfer functions, control constraint conditions, and data tables.

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

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

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

[0105]

[0106] Among them, U a , U b and U c represent the grid port voltages, e a , e b and e c represent the electromotive force expressions containing the nth harmonic, k is the harmonic order, E k is the amplitude of the nth harmonic, α, β, γ are the initial phase angles, R a , R b and R c , and L a , L b and L c represent the grid impedance parameters, I a , I b and I c represent the grid load.

[0107] Through e a 、e b and e c 's expression, it can be seen that this model is an instantaneous power model, and the electromotive force parameters E of each harmonic k can be obtained when measuring the harmonic voltage of the initial port and input as the initial parameters into the model.

[0108] The transient model of the distribution network includes frequency and phase angle, which is used when analyzing instantaneous (or short-time) aspects such as reactive power and dynamic response. It is not required when doing energy and power calculations and will be equivalent to a DC model, where U a 、U b 、U c are equivalent to DC according to the effective value, and only the equivalent resistances R a 、R b 、R c are retained, and inductance, phase angle, and frequency are ignored.

[0109] (2) Transformer model

[0110] According to the example of the hybrid microgrid architecture shown as Figure 1 , the AC grid forms an AC bus after passing through the distribution line Line_1 and the transformer, and the transfer function of the output U 2 to the input U 1 is derived through the equivalent circuit method of the transformer.

[0111] The described transformer model includes a transient model and a steady-state model;

[0112] The transient model of the described transformer model is expressed as the transfer function of the output U2 to the input U1:

[0113]

[0114] where N is the turns ratio, Z L is the load impedance, Z 1 is the primary winding impedance, Z 2 is the secondary winding impedance, and Z m is the exciting impedance.

[0115] These parameters can be obtained through transformer tests or specifications and calculations. The expressions of these impedances Z involve parameters related to the transformer's frequency and operating state. When describing short-time indicators mentioned in the following text, this transient power model is necessary, but it has high requirements for the computing power of the calculation server and generally only requires operations with a time length of seconds.

[0116] The steady-state model of the described transformer model is equivalent using the DC circuit method as:

[0117]

[0118] Among them, η is the transformer efficiency obtained by the fitting method.

[0119] The output voltage obtained here will not contain the frequency and phase angle parameters of the AC transformer, but only for calculating losses, which is sufficient for evaluating economic indicators, and the calculation speed will be improved by several orders of magnitude.

[0120] 2. Photovoltaic model

[0121] 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;

[0122] (1) The maximum output power of the controlled power source is expressed as:

[0123] P max =k a k t I p V p ;

[0124] Among them, k a is a coefficient related to the atmospheric conditions for power generation prediction, k t is a coefficient related to the temperature of the photovoltaic panel, I p , V p are the current and voltage at the output end of the photovoltaic panel.

[0125] In this embodiment, k a is calculated by a server located in the cloud and transmitted to the virtual machine of the digital twin simulation (hereinafter referred to as the virtual machine), k t is collected by the local terminal of the project and transmitted to the virtual machine, k t , I p , V p The curve formed by is determined by the specifications of the battery panel and is input into the photovoltaic model of the virtual machine in advance.

[0126] (2) The secondary converter realizes the transmission of the power of the photovoltaic panel to the bus. The transfer function of the output voltage U out of the DC / DC converter with respect to the input voltage U in is:

[0127]

[0128] Among them, D is the control duty cycle, and C, R, and L are the parameters of the power loop devices.

[0129] Photovoltaic is direct current, with relatively small computational requirements, and there is no need to analyze transient power characteristics, nor to distinguish between transient models and steady-state models.

[0130] 3. Energy storage battery model

[0131] In this embodiment, an electrochemical energy storage battery model is taken as an example. For example, for a lithium-ion battery, the initial battery parameters provided by the lithium-ion battery manufacturer can be used to construct the battery model.

[0132] (1) Furthermore, the electrochemical energy storage battery model is established as the following discrete function:

[0133]

[0134] Among them, SOC represents the state of charge of the battery, SOH represents the state of health of the battery, T represents the battery temperature, and E m represents the open-circuit voltage of the battery, and R 0 represents the total internal DC resistance of the battery. For SOC, SOH, and T within their respective value ranges, there is a set of E m and R 0 corresponding to them.

[0135] (2) The operation of the battery physical device is controlled by the local terminal. The local terminal can obtain the SOC, SOH, and T parameters from the battery management system in real time, and at the same time, through the pulse power method, obtain the E m , R 0 of each battery physical device, constituting a set of function relationships f: (SOC, SOH, T) → (E m , R 0 ) under a certain working condition.

[0136] (3) According to the mapping between the model running in the virtual machine and the battery physical device, the SOC, SOH, T, E m , R 0 of the battery physical device collected by the local terminal are transmitted to the server, and then the server transmits this set of function relationships to the simulation model of the virtual machine.

[0137] (4) Based on a large amount of working condition data of similar devices, the server transmits batches of function relationships f: (SOC, SOH, T) → (E m , R 0 ) under different working conditions to the simulation model of the virtual machine, enabling the simulation to run continuously.

[0138] (5) The electrochemical energy storage battery model established by this method avoids complex theoretical calculations and is completely corresponding to the operating conditions. Through continuous operation and self-learning, complete and accurate battery parameters can be accumulated, and at the same time, support is provided for decision-making such as safety warning.

[0139] S2. Select the digital models of the corresponding functional modules according to the actual architecture and parameters of the target microgrid, and construct a microgrid simulation model;

[0140] 4. Battery Converter Model

[0141] The battery converter forms the connection between the battery and the DC bus, and is a two-way unit for supporting the DC bus and charging the battery. A model is constructed using a typical double closed-loop voltage source and current source converter. The constraints for modeling are as follows:

[0142]

[0143] 5. Energy Storage Converter Model

[0144] When power is transmitted from the AC bus to the DC bus, the energy storage converter is equivalent to a VSR rectifier. The model uses double-loop control to regulate the voltage of the DC bus and control the power input from the AC side. In the reverse direction, it is a current-source inverter with only a current inner loop, controlling the power input from the DC bus to the AC grid.

[0145] 6. Transmission Line Model

[0146] The transmission line is a passive network. For both DC and AC buses, for any adjacent nodes ( Figure 1 nodes n+1 and n), the voltage change and current change between them reflect the impedance characteristics between these two points.

[0147] Therefore, the transmission line model for two adjacent nodes on the transmission line reflects the impedance characteristics through voltage change and current change, and is represented by a discrete differential equation as follows:

[0148]

[0149] where T s represents the simulation sampling period, L (n+1,n) and R (n+1,n) represent the inductance value and resistance value of the line between node n and node n+1, and i (n+1,n) represents the current of the line between node n and node n+1.

[0150] For both DC and AC buses, regardless of the radial or other connection types, the feeder model of any newly added node device consists of voltage sampling and current sampling between two points, which is easy to implement in physical devices and simulations.

[0151] 7. Charging and Discharging Unit Model of Electric Vehicles

[0152] The charging (charging and discharging) unit of an electric vehicle includes unidirectional charging (bidirectional charging and discharging) power conversion, and must be an isolated converter. The power transmission model and constraints are as follows:

[0153]

[0154] Among them, n, U 7 、U 8 、f s 、L, and η are the turns ratio of the power converter transformer, the input and output port voltages, the operating frequency, the power transfer inductor, and the efficiency respectively. D is the control signal.

[0155] 8. Load Model

[0156] It can be referred to Figures 1 to 3 the example of the hybrid microgrid architecture shown. The type of the load is DC or AC, and its power supply may also come from different buses. According to the type of the input current and the type of the 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.

[0157] The first load model indicates that the input current is DC and the load is also DC, and the power control constraint conditions are:

[0158]

[0159] Among them, U 3 is the input voltage of the DC / DC converter, U 4 is the output voltage of the DC / DC converter, P 1 is the power demand of the DC load, P 2 is the load transmitted between the bus and this load path, I 1_peak is the peak input current of the DC / DC converter, is the control response adjustment time of U 4 , U Bus_min represents the minimum allowable value of the DC bus voltage control, U Bus_max represents the maximum allowable value of the DC bus voltage control, U L_min represents the minimum allowable value of the DC load supply voltage, U L_max represents the maximum allowable value of the DC load supply voltage, P 1_min represents the minimum power demand of the DC load, P 1_max represents the maximum power demand of the DC load, P 2_min represents the minimum allowable value of the power supply to the DC load DC / DC converter, P 2_max represents the maximum allowable value of the power supply to the DC load DC / DC converter, I 1_max represents the maximum allowable value of the peak current of the power supply to the DC load, t s_max represents U 4 the maximum allowable value of the control response adjustment time.

[0160] The above constraints of the first load model include transient indicators. To enable the model to truly represent the transient response characteristics, a closed-loop control function is established for one of the control parameters U of the DC / DC converter, as shown in 4 , so that this parameter reaches the constraint condition. Similarly, a closed-loop control function is established for I 1_peak to make it meet the constraint condition. Figure 4

[0161] 4 One of them to establish a closed-loop control function, such as Figure 4 shown, so that this parameter reaches the constraint condition. Similarly for I 1_peak to establish a closed-loop control function to make it meet the constraint condition.

[0161] The second load model and the third load model are modeled using the same method.

[0162] These three types of load models contain three different converters, but the principles are the same. The first load model is DC / DC, and the basic control block diagram given in Figure 4 can be referred to. The other two load models are AC / DC and DC / AC (inverter) respectively. The control methods are the same and can be referred to Figure 5 and Figure 6 respectively. Both use U4 and U4_ref to indicate the control object and the reference value of the control object. Essentially, it is to control the voltage U4 at the load end to meet the load demand. Figure 4 Among them, the basic control block diagram given in can be referred to. The other two load models are AC / DC and DC / AC (inverter) respectively. The control methods are the same and can be referred to Figure 5 and Figure 6 respectively. Both use U4 and U4_ref to indicate the control object and the reference value of the control object. Essentially, it is to control the voltage U4 at the load end to meet the load demand.

[0163] S3. According to the various objective functions constructed based on different simulation requirements in the simulation model, switch the transient model and the steady-state model of the functional module, and perform simulation calculations;

[0164] Step S2 includes the steps of:

[0165] S21. Run the digital models of each functional module in a simulation tool to form a virtual machine;

[0166] S22. Establish an objective function according to the actual architecture of the target microgrid, and generate engineering project operation control parameters and prediction parameters;

[0167] S23. Receive the project information transmitted from the local end of the project, input the project information into the virtual machine, and obtain a microgrid simulation model corresponding to the engineering project of the target microgrid;

[0168] The project information includes the parameter information of the functional modules contained in the actual physical devices in the target microgrid.

[0169] S4. Adjust the control strategy or operation parameters of the target microgrid according to the results of the simulation calculation.

[0170] In this embodiment, the above models can be run on SIMULINK / MATLAB or other simulation tools; develop corresponding functions so that the tool running the model has three functions: simulation calculation, data storage, and communication; the program including the three functions runs on the server to form a virtual machine.

[0171] Define the module interface to adapt to the splicing form; and different application product forms can be formed, such as the combination of photovoltaic and energy storage; photovoltaic - energy storage - load, etc. Figure 8 A part of a certain micro - grid modeling project is intercepted. It can be seen that each model is modular. Modules with the same defined interface can be combined into the system, and the data is 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 database of the virtual machine and receive simulation instructions; and each model is modular and has DC + / DC - ports, and electrical connection can be achieved by accessing. This model runs on Figure 7 the virtual machine shown.

[0172] In this embodiment, transient power models and steady - state power models are created for each of the six types of modules of the photovoltaic - energy storage - charging - load micro - grid model to simulate different time scales.

[0173] Among them, the transient power model contains the response characteristics of the full frequency band of the converter or physical device corresponding to the module, and has the same output response characteristics as the real physical device, which can reflect short - moment indicators such as power transient, short - circuit, start - up, etc., 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 steady - state indicators such as continuous output power, efficiency, heat, etc., and the operation speed is high.

[0174] The switching operation between the transient power model and the 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.

[0175] The output result of the virtual machine simulation operation can be stored in the server database.

[0176] The virtual machine can communicate with the server, receive simulation calculation instructions, operation parameters, and send calculation results.

[0177] In this embodiment, reference can be made to Figure 9 , for the actually operating photovoltaic - energy storage - charging - load micro - grid, establish an economic evaluation objective function, a prediction model, safety evaluation indicators, etc. Generate operation control parameters and prediction parameters for engineering projects, etc.

[0178] According to the engineering project specifications, project information is entered into the decision - making server through the project local terminal. The information includes the parameters of the converters, electrochemical energy storage, transmission media, and protection unit modules contained in the actual physical equipment of the project. One set of servers can execute several engineering projects.

[0179] The server establishes communication with the virtual machine and transmits the obtained parameters to the virtual machine.

[0180] The functional modules included in the virtual machine enabled project input corresponding parameters into the simulation model to make the model correspond to the engineering project.

[0181] The decision-making server controls the virtual machine to run the simulation.

[0182] (1) Judge the short-term simulation requirements. When various transient response indexes need to be simulated before the operation of microgrid equipment, such as but not limited to grid connection and disconnection switching, load switching, power quality, harmonic limit, surge current, transient voltage drop, short-circuit current, lightning transient overvoltage, etc. The virtual machine controlled by the decision-making server accesses the required transient power model, sets the simulation step size and simulation time, and runs the simulation.

[0183] (2) The virtual machine stores the simulation data in the computing server and transmits the results back to the decision-making server in a communication form for joint security decision-making to determine that the microgrid equipment can be put into operation.

[0184] (3) After the equipment is put into operation, according to the preset operation program in the decision-making server, through the local terminal, control the operation of each physical function module.

[0185] (4) The decision-making server switches the simulation module of the virtual machine to the steady-state power module to greatly improve the simulation speed, and issues a policy period (24 hours or longer) according to the objective function, and the virtual machine executes the simulation, and the speed is much faster than the real operation speed.

[0186] (5) Synchronous with the simulation, the virtual machine transmits the simulation results of a policy period stored in the database back to the decision-making server in real time, compares them with the results of the expected objective function, and adjusts the control strategy or operation parameters of the physical device.

[0187] (6) At a certain time interval, the decision-making server updates the control strategy or operation parameters, updates the simulation parameters of the virtual machine, and iterates the simulation results. Repeat the above steps to achieve the twin iterative control of the objective function.

[0188] In this embodiment, the designed architecture is that the virtual machine provides cloud-edge collaborative support for the local terminal of the project.

[0189] (1) During the control process of the physical device by the local terminal of the project, the complex calculations that need to be processed in real time or the operation data for preprocessing are transmitted to the virtual machine.

[0190] (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.

[0191] A control architecture is formed among the cloud, the project, and the virtual machine as Figure 7 shown.

[0192] Comparison between the simulation results of the virtual machine model and the actual operation results of the physical device Figures 10 to 13 。

[0193] Please refer to Figure 15 , the second embodiment of the present invention is as follows:

[0194] 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 above-mentioned microgrid collaborative control method are implemented.

[0195] In summary, the microgrid collaborative control method and server provided by the present invention divide the steady-state model and the transient model, construct digital models for each functional module of the microgrid, and thus can construct the microgrid simulation model and select the steady-state model and transient model of the functional module according to the actual architecture, parameters of the target microgrid and various objective functions of the simulation calculation to adapt to different analysis requirements and improve the effectiveness of digital twin simulation collaborative control.

[0196] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in the related technical field, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for constructing a microgrid simulation model, 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; S2. According to the actual architecture and parameters of the target microgrid, the digital model of the corresponding functional module is selected to construct a microgrid simulation model.

2. The method for constructing a microgrid simulation model according to claim 1, characterized in that: 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.

3. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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: Among them, N is the transformation ratio, Z L is the load impedance, Z1 is the primary winding impedance, Z2 is the secondary winding impedance, and Z m is the magnetizing impedance; The steady-state model of the transformer model is equivalent to: Where η is the transformer efficiency obtained by fitting method.

4. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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: Among them, U a , U b and U c Indicates the grid port voltage, e a 、e b and e c It represents the electromotive force expression containing n harmonics, k is the harmonic order, E k is the subharmonic amplitude, α, β, γ are the initial phase angles, R a , R b and R c , and L a , L b and L c Represents the grid impedance parameter, I a ,I b and I c Indicates the grid load.

5. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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: P max =k a k t I p V p ; Among them, k a is a coefficient related to atmospheric conditions, used for power generation prediction, k t is the coefficient related to the temperature of the photovoltaic panel, I p , V p is the current and voltage at the output of the photovoltaic panel; The secondary converter realizes the transmission of photovoltaic panel power to the busbar and establishes the output voltage U for the DC / DC converter. out For input voltage U in The transfer function is: Where D is the control duty cycle, and C, R and L are the power circuit device parameters.

6. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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, and E m represents the open circuit voltage of the battery, R0 represents the total internal DC resistance of the battery, and for SOC, SOH, and T within their respective value ranges, there is a set of E m And R0 corresponds to it.

7. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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: Among them, T s represents the simulation sampling period, L (n+1,n) and R (n+1,n) represents the inductance and resistance of the line between node n and node n+1, i (n+1,n) Represents the current in the line between node n and node n+1.

8. The method for constructing a microgrid simulation model according to claim 2, characterized in that: 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: Among them, U3 is the input voltage of DC / DC converter, U4 is the output voltage of DC / DC converter, P1 is the required power of DC load, P2 is the load transmitted between bus and load, I 1_peak is the DC / DC converter input peak current, t s_u4 is the control response adjustment time of U4, U Bus_min Indicates the minimum allowable value of DC bus voltage control, U Bus_max Indicates the maximum value allowed by DC bus voltage control, U L_min Indicates the minimum allowable DC load supply voltage, U L_max Indicates the maximum allowable DC load supply voltage, P 1_min Indicates the minimum power demand of the DC load, P 1_max Indicates the maximum power demand of the DC load, P 2_min Indicates the minimum value allowed for the DC / DC converter to supply power to a DC load, P 2_max Indicates the maximum value allowed for the DC / DC converter to supply power to a DC load, I 1_max Indicates the maximum allowable peak current for supplying DC loads, t s_max Indicates the maximum value allowed for the U4 control response adjustment time.

9. The method for constructing a microgrid simulation model according to claim 1, characterized in that: 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 actual physical devices in the target microgrid.

10. A server for constructing a microgrid simulation model, 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 method for constructing a microgrid simulation model described in any one of claims 1 to 9 are implemented.

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