A simulation method and device for closed-loop test of micro-grid energy management system

Abstract

The application relates to a simulation method and device for closed-loop testing of a micro-grid energy management system, which comprises the following steps: S1, receiving an energy management system command and a local command configured with a local coverage channel; S2, performing command source arbitration on a device model configured with the local coverage channel, selecting the local command when the local coverage is enabled, selecting the energy management system command when the local coverage is not enabled, and forming an effective command of each device model; S3, inputting the effective command into each device model respectively, and performing corresponding constraint processing according to the operation logic of each device model; S4, mapping and organizing state parameters output by each device model into a device state bus set according to a preset field; and S5, converting the device state bus set into a top-level output in a fixed step simulation cycle, and feeding back to the energy management system, so that the closed-loop testing of the command input, the constrained execution and the actual state feedback is completed.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy management system testing, and more specifically to a simulation method and apparatus for closed-loop testing of microgrid energy management systems. Background Technology

[0002] Microgrid energy management systems typically require coordinated control of the main grid, photovoltaic power generation equipment, wind power generation equipment, energy storage equipment, power converters, generator sets, and loads. To verify the scheduling and control functions of the energy management system, a closed-loop controller testing method is usually adopted. This method connects the energy management system to a simulation model of the controlled object. The controlled object model receives scheduling commands, calculates the operating status of the equipment, and feeds back quantities such as power, voltage, current, frequency, operating status, and fault status to the energy management system.

[0003] Some existing controlled object models employ ideal power execution, directly using power commands issued by the energy management system as the actual power output of the equipment. Inputs, status variables, and fault variables for different equipment models are output as distributed signals. Energy storage devices, power converters, and field smart switches may simultaneously receive commands from both the energy management system and local control commands, requiring the determination of the actual effective command source at the controlled object side. Power converters also have different requirements for voltage and frequency reference sources under different operating modes, such as grid-connected and grid-connected operation.

[0004] In hardware-in-the-loop (HIL) test bench applications, controller closed-loop testing requires connecting the controlled object model, real-time measurement and control system, real-time simulation system, source devices, topology switching units, and the device under test (DUT) into a unified test environment. The power paths, DUT connection relationships, and safety states differ under different test conditions in microgrid testing. Therefore, in addition to consistency within the device model, it is also necessary to clarify the relationships between topology switching, communication interfaces, mode exclusivity, hardware safety interlocks, and model state feedback.

[0005] The aforementioned idealized or distributed modeling methods have at least the following shortcomings: First, power commands are not limited by the device's rated capacity and dynamic response, making it difficult to reflect the actual execution boundaries and response delays of the device. Second, battery charging and discharging power is not correlated with the available capacity corresponding to the state of charge, potentially generating feedback exceeding the current energy storage capacity. Third, when energy management system commands and local commands coexist, the lack of a unified command source arbitration can lead to inconsistencies between test inputs and actual effective inputs. Fourth, when switching the operating mode of the power converter, the selection relationship between external voltage, frequency reference, and common bus feedback may not form a closed loop. Fifth, the dispersed output of device status and branch switch status makes it difficult to ensure that the energy management system obtains complete feedback corresponding to the constrained operating states of each device within the same simulation cycle. For power device models requiring hardware-in-the-loop verification, the lack of limit protection may also introduce test safety risks.

[0006] In different test scenarios, if the power connection and topology switching of the dual power distribution units, the interface communication of the device under test, the mutual exclusion of modes, and the fault and emergency stop signals are not included in the unified coordination, problems such as inconsistency between the model state and the physical branch state, incorrect paralleling of two test modes, or failure to block the power path in time under fault conditions may occur, affecting the test accuracy and constituting a safety issue in the test process. Summary of the Invention

[0007] The purpose of this invention is to provide a simulation method and apparatus for closed-loop testing of microgrid energy management systems to solve the above-mentioned problems.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: According to one aspect of the present invention, a simulation method for closed-loop testing of a microgrid energy management system is provided, comprising: S1, Receive commands from the energy management system and local commands configured for the local coverage channel; S2. Perform command source arbitration on the device model configured with local coverage channel. When local coverage is enabled, select local command; when local coverage is not enabled, select energy management system command to form the effective command for each device model. S3. Input the effective command into each device model respectively, and each device model will execute the corresponding constraint processing according to its own operating logic; S4. Organize the status parameters of each device model into a device status bus set according to the preset field mapping; S5. Within a fixed-step simulation cycle, the device state bus set is converted into top-level output and fed back to the energy management system, thereby completing the closed-loop test of command input, constrained execution, and actual state feedback.

[0009] In one embodiment, the energy management system commands include equipment scheduling commands, operating mode commands, fault commands, load shedding level commands, and switch control commands.

[0010] In one embodiment, the equipment model includes a main grid and main grid converter model, a photovoltaic power generation model, a wind power generation model, a battery energy storage and power converter model, a generator set model, a load model, and a smart switch model.

[0011] In one embodiment, the device model equipped with a local coverage channel includes a battery energy storage and power converter model and a smart switch model.

[0012] In one embodiment, the equipment model further includes a common bus model, which receives the actual switching status of each branch and the electrical output of the relevant equipment model, calculates the voltage and frequency of the common bus based on the current branch access relationship or obtains it from the common bus measurement module, and provides the voltage and frequency to the power converter operation mode selection, the wind power generation model and the top-level output respectively.

[0013] In one embodiment, S3 includes: selecting a control reference source according to the power converter operating mode; when the operating mode command is equal to 2, selecting an external voltage reference and a frequency reference; when the operating mode command is not equal to 2, selecting the voltage and frequency feedback of the common bus.

[0014] In one embodiment, the smart switch model is configured with nine smart switches, which are respectively connected to the main grid, photovoltaic, wind power, two sets of battery energy storage, two sets of generator sets, and two load branches. Each smart switch receives opening and closing commands and interlocking inputs, calculates the actual state through a state machine, and outputs the actual switch state and interlocking state. The energy management system provides a nine-element closing command vector, the local control channel provides local closing commands for each channel, and the opening command is formed by the internal logic of the current model.

[0015] In one embodiment, the actual state of the nine-way intelligent switch can serve as the basis for calculating the enabling, branch access, and operating status of the corresponding devices; each switch outputs its actual state and interlocking output based on the opening command, closing command, interlocking input, and related branch status.

[0016] In one embodiment, in S4, the preset field content includes actual power, voltage, current, frequency, operating status, fault status, available capacity, switching status, and interlocking status.

[0017] According to another aspect of the present invention, a simulation apparatus for closed-loop testing of a microgrid energy management system is also provided, comprising: The command interface unit is used to receive commands from the energy management system and local commands configured with local coverage channels; The command source arbitration unit is used to select the effective command for each device model; The multi-object device model constraint processing unit is used to simulate multiple device models. Based on the effective command received from the command source arbitration unit, each device model performs corresponding constraint processing according to its own operating logic. The status bus aggregation unit is used to organize the actual power, voltage, current, frequency, operating status, fault status, available capacity, switching status and interlocking status output by each device model into a device status bus set according to preset field mapping. The output unit is used to convert the device state bus set into top-level output and feed it back to the energy management system within a fixed-step simulation cycle.

[0018] In one embodiment, the simulation device is implemented using the model-based simulation tool Simulink, configured with 41 top-level inputs and 126 outputs, and employs a fixed-step third-order Runge-Kutta solver ode3.

[0019] The present invention, by adopting the above-described technical solution, has the following beneficial effects: Compared with simulation methods that treat devices as ideal execution units, this invention first arbitrates the scheduling commands from the command source, and then performs power limiting, response delay, state of charge capability limitation or start-stop state processing according to the actual operating constraints of different devices. Therefore, the resulting actual power and operating state can reflect the device's capability boundaries and dynamic execution process, avoiding the energy management system from obtaining feedback that is inconsistent with the actual executable state of the controlled object in closed-loop testing.

[0020] This invention also selects appropriate voltage and frequency reference sources based on the power converter's operating mode and calculates the actual branch state through an intelligent switching state machine. Subsequently, it aggregates equipment electrical quantities, equipment states, fault states, available capacity, switching states, and interlocking states according to preset field mappings and feeds them back to the energy management system within the same fixed-step simulation cycle. This improves the consistency between commands, equipment constraints, operating modes, branch states, and feedback data in multi-object closed-loop testing and provides a controlled object model with clear limit protection for real-time or semi-physical simulation in-loop testing. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall architecture of a simulation method for closed-loop testing of a microgrid energy management system according to an embodiment of the present invention. Figure 2 A flowchart illustrating a simulation method for closed-loop testing of a microgrid energy management system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the command source arbitration and device constraint processing flow. Figure 4 A schematic diagram showing the operation mode of a power converter and the selection of voltage and frequency reference sources; Figure 5 A diagram illustrating battery state of charge lookup table and charge / discharge capacity limitations. Figure 6 This is a schematic diagram of the intelligent switch state machine and branch state feedback; Figure 7 A schematic diagram of the device status bus field mapping and top-level input / output; Figure 8 This is a schematic diagram of the overall architecture of a simulation device for closed-loop testing of a microgrid energy management system according to an embodiment of the present invention. Detailed Implementation

[0022] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings to provide a clearer understanding of the purpose, features, and advantages of the present invention. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely illustrative of the essential spirit of the technical solution of the present invention.

[0023] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the art will recognize that embodiments may be practiced without one or more of these specific details. In other instances, well-known apparatuses, structures, and techniques associated with this application may not have been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0024] Unless the context requires otherwise, throughout the specification and claims, the word “comprising” and its variations, such as “including” and “having”, shall be understood to have an open, inclusive meaning, that is, to be interpreted as “including, but not limited to”.

[0025] Throughout this specification, references to "an embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Therefore, the appearance of "in an embodiment" or "an embodiment" in various places throughout the specification does not necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any manner in one or more embodiments.

[0026] The singular forms “a” and “the” used in this specification and the appended claims include plural references unless otherwise expressly stated herein. It should be noted that the term “or” is generally used to mean “and / or” unless otherwise expressly stated herein.

[0027] like Figure 1As shown, this invention employs a simulation architecture combining command source arbitration, device-level constraint processing, operating mode reference selection, intelligent switch state calculation, and device state bus aggregation. The simulation objects include the main grid and its converters, photovoltaic power generation equipment, wind power generation equipment, two sets of battery energy storage and power converters, two sets of generator sets, conventional loads, charging loads, critical loads, and nine intelligent switches. The current embodiment is implemented using the model-based simulation tool Simulink, configured with forty-one top-level inputs and one hundred and twenty-six outputs, employing a fixed-step third-order Runge-Kutta solver (ode3) with a fixed step size of 50 μs. The model can be further configured for real-time operation, enabling semi-physical in-loop simulation verification.

[0028] The 41 top-level inputs include energy management system dispatch commands, local coverage commands, main grid fault commands, load shedding level commands, power converter operating mode commands, and grid voltage and frequency references. The 126 outputs include status fields for the main grid, photovoltaic, wind power, batteries, power converters, generator sets, loads, smart switches, and the common bus. Inputs and outputs are organized using preset signal names and field mappings.

[0029] like Figure 2 As shown, a simulation method for closed-loop testing of a microgrid energy management system according to the present invention includes: S1. Receive commands from the energy management system and local commands configured for the local coverage channel. The energy management system includes dispatch commands, main grid fault commands, load shedding level commands, power converter operation mode commands, and grid voltage and frequency references, etc.

[0030] S2. Perform command source arbitration for device models configured with local coverage channels. When local coverage is enabled, select the local command; when local coverage is not enabled, select the energy management system command, thus generating the effective commands for each device model. The specific process is as follows: Figure 3 As shown.

[0031] In the model, energy management system commands and local commands first enter the command source arbitration unit. For two sets of batteries, two sets of power converters, and nine smart switches configured with local coverage, the command source is determined using the local coverage selection signal; the active power and operating mode of the two sets of batteries, the active power reference and operating mode of the two sets of power converters, and the local closing command of the nine smart switches can all be selected in this way. For objects without a configured local coverage channel, the energy management system command distribution path within the model is used. The output of the arbitration unit serves as the sole effective command for subsequent device constraint processing, preventing the same device from simultaneously receiving conflicting commands from different sources.

[0032] S3. Input the effective command into each device model respectively, and each device model will execute the corresponding constraint processing according to its own operating logic.

[0033] The equipment models include the main grid and main grid converter model, photovoltaic power generation model, wind power generation model, battery energy storage and power converter model, generator set model, load model, and smart switch model. The structure and constraint handling of each model are described in detail below.

[0034] The main grid model includes three-phase power supply, line impedance, three-phase voltage and current measurements, grid-connected circuit breakers, fault sources, and main grid converters. The model receives main grid fault commands, active power references, reactive power references, and enable status of the main grid converters, and outputs three-phase voltage, main grid frequency, main grid availability, actual active power of the converters, actual reactive power, and converter status. The main grid converters limit the active power reference to -30 to 30 kW and the reactive power reference to -15 to 15 Var, and perform response delay processing on the limited power commands. The main grid fault section is configured with three-phase voltage dips and B-phase ground fault branches.

[0035] The photovoltaic power generation model includes a photovoltaic array, maximum power point tracking load control, DC-side output constraints, a grid-connected inverter, and AC-side measurement components. It outputs DC voltage, DC current, DC power, maximum power point tracking status, current available active power, AC voltage amplitude, AC frequency, inverter active power, maximum power under current irradiance conditions, and inverter reactive power. Photovoltaic irradiance, temperature, and some power control parameters are used as internal scene signals within the model in this embodiment.

[0036] The wind power generation model includes a wind turbine model and frequency and voltage amplitude fault models. Outputs include available active power, AC active power, AC reactive power, AC voltage, AC current, operating status, fault status, AC frequency, frequency fault status, voltage amplitude fault status, rotor speed, pitch angle, power factor, and low-voltage ride-through status. Current wind speed, wind power active power limit, and wind power fault commands are given by internal scenario signals within the model. The top-level input of the energy management system includes wind power reactive power references.

[0037] The battery energy storage and power converter model is configured with two identical battery and power converter sets. Each battery model receives active power commands and operating modes, and internally includes battery management, electrothermal and power limiting, current command generation, state-of-charge (POC) limit memory, and charge / discharge capacity lookup tables. It outputs POC, terminal voltage, actual current, actual power, maximum allowable charging power, maximum allowable discharging power, highest single-cell temperature, health status, and fault status. The POC breakpoints for maximum charging power lookup are [0, 10, 20, 70, 85, 95, 100], and the POC breakpoints for maximum discharging power lookup are [0, 5, 10, 20, 80, 100]. The constraint processing unit limits the current maximum charging power and maximum discharging power based on the lookup results, ensuring that the actual power does not exceed the capacity boundary corresponding to the current POC. The process for limiting the battery POC capacity is shown in Figure 5. The maximum charging power and maximum discharging power are determined by looking up a table based on the battery's state of charge, and the actual power is constrained to ensure that the charging and discharging power is consistent with the battery's current available capacity, preventing the output from exceeding the capacity range corresponding to the state of charge.

[0038] Each power converter receives active power reference, reactive power reference, operating mode, DC side voltage, grid voltage, and frequency reference. Its active power reference is limited to -100 to 100 kW, and its reactive power reference is limited to -60 to 60 kVar. Response delay processing is applied to the limited active and reactive power references respectively. The power converter efficiency is calculated by looking up the load factor in a table.

[0039] The generator set model is configured with two identical generator sets. Each set receives start / stop commands, active power reference, and reactive power reference. It uses a start / stop state machine to process the operating status, performs amplitude limiting and response delay processing on the active power reference and reactive power reference, and outputs the actual frequency, actual voltage, actual active power, actual reactive power, fuel consumption rate, cumulative running time, engine speed, operating status, fault codes, coolant temperature, oil pressure, and fuel balance.

[0040] The load model includes conventional loads, charging loads, and critical loads. The active and reactive power of conventional and charging loads are given by the model's internal scenario parameters. The top-level input of the energy management system includes the load shedding level. Based on the load shedding level, the model outputs the total active load, total reactive load, three-phase load current, active and reactive power of conventional loads, active and reactive power of charging loads, the two types of load states, critical load power, shedding load power, and the current load reduction level.

[0041] The smart switch model is configured with nine smart switches, connecting to the main grid, photovoltaic, wind power, two sets of battery energy storage, two sets of generator sets, and two load branches. Each smart switch receives opening / closing commands and interlocking inputs, calculates the actual state through a state machine, and outputs the actual switch state and interlocking state. Feedback on the actual branch connectivity state, rather than just the command state, ensures that the equipment operating quantities correspond to the electrical topology state. The energy management system provides a nine-element closing command vector, the local control channel provides local closing commands for each branch, and the opening command is generated by the internal logic of the current model. In this embodiment, some upstream interlocking inputs are given by internal constants; therefore, only the interlocking interface and interlocking state output are considered as implemented content. The constraint processing flow of the smart switch model is as follows: Figure 6 As shown.

[0042] In an optional implementation, the actual state of the nine-way intelligent switches can serve as the basis for calculating the enabling, branch access, and operating status of the corresponding devices. Each switch outputs its actual state and interlocking output based on the opening command, closing command, interlocking input, and relevant branch status. For operating conditions requiring verification of the topology switching sequence, cascaded interlocking logic can be configured between the switches to restrict unreasonable switching-in and switching-out sequences. The specific judgment rules for cascaded interlocking can be based on the actual electrical topology configuration of the main grid, new energy sources, energy storage, generator sets, and load branches.

[0043] The equipment model may also include a common bus model, which receives the actual switching status of each branch and the electrical output of the relevant equipment model. It calculates the voltage and frequency of the common bus based on the current branch connection relationship or obtains them from the common bus measurement module, and provides the voltage and frequency to the power converter operation mode selection, the wind power generation model and the top-level output, respectively.

[0044] The control reference source is selected based on the power converter's operating mode. When the operating mode command equals 2, an external voltage reference and frequency reference are selected; when the operating mode command does not equal 2, the voltage and frequency feedback of the common bus are selected. This ensures the power converter uses a reference source matching the mode in different operating modes and feeds back the actual reference information to the upper-level system. The specific process is as follows: Figure 4 As shown. The operating mode reference source selection can be implemented using a mode selection switch or a state machine.

[0045] S4. Organize the status parameters output by each device model into a device status bus set according to the preset field mapping.

[0046] like Figure 7As shown, the state bus aggregation unit writes the outputs of the main grid, photovoltaic, wind power, energy storage, power converters, generator sets, loads, and smart switches into the corresponding device status fields according to the device name and field name. The field content includes actual power, voltage, current, frequency, operating status, fault status, available capacity, switch status, and interlocking status. After being selected and expanded at the top level, each field forms 126 outputs, which are fed back to the energy management system within the same fixed-step simulation cycle. This ensures that the feedback data corresponds to the effective commands, device constraints, and branch status within that cycle. The actual power feedback simultaneously reflects the device's capacity boundaries and execution dynamics, avoiding distortion caused by ideal power pass-through and the risks of semi-physical testing.

[0047] As an optional extension, the common bus model receives the actual switching states of each branch and the electrical outputs of related equipment models. It calculates the voltage and frequency of the common bus based on the current branch connection relationships or obtains them from the common bus measurement module, and then provides these voltage and frequency to the power converter operating mode selection, the wind power generation model, and the top-level output, respectively. This allows the common bus feedback, the actual branch topology, and the equipment operating states to correspond within the same simulation cycle.

[0048] S5. Within a fixed-step simulation cycle, the device state bus set is converted into top-level output and fed back to the energy management system, thereby completing the closed-loop test of command input, constrained execution, and actual state feedback. State bus aggregation and fixed-step feedback enable the energy management system to synchronously obtain complete and structurally consistent multi-object states, improving the consistency and scalability of closed-loop test feedback.

[0049] like Figure 8 As shown, a simulation device for closed-loop testing of a microgrid energy management system according to an embodiment of the present invention includes a command interface unit 1, a command source arbitration unit 2, a multi-object device model constraint processing unit 3, a state bus aggregation unit 4, and an output unit 5.

[0050] Command interface unit 1 is used to receive commands from the energy management system (e.g., scheduling commands, fault commands, operating mode commands, and switching commands) and local commands with configured local coverage channels.

[0051] Command source arbitration unit 2 is used to select the effective commands for each device. Energy management system commands and local commands in the model first enter the command source arbitration unit. For two sets of batteries, two sets of power converters, and nine smart switches with local coverage functionality, the command source is determined using the local coverage selection signal; the active power and operating mode of the two sets of batteries, the active power reference and operating mode of the two sets of power converters, and the local closing command of the nine smart switches can all be selected in this way. For objects without local coverage channels, the energy management system command distribution path within the model is used. The output of the arbitration unit serves as the sole effective command for subsequent device constraint processing, avoiding the simultaneous receipt of conflicting commands from different sources by the same device. Command source arbitration can be implemented using a selector, a priority state machine, or functionally equivalent software logic. The multi-object device model constraint processing unit 3 is used to simulate device models such as the main grid, photovoltaic, wind power, energy storage, power converters, generator sets, and loads. Based on the effective commands received from the command source arbitration unit 2, it performs constraint processing such as power limiting, response delay, state-of-charge charging and discharging capacity limits, and start / stop states. The multi-object device model constraint processing unit 3 is also used to select external references or common bus feedback according to the operating mode and to calculate the actual switching and interlocking states of branches. The structure and constraint processing of each device model have been described in detail above and will not be repeated here.

[0052] The status bus aggregation unit 4 is used to organize the actual power, voltage, current, frequency, operating status, fault status, available capacity, switching status and interlocking status output by each device model into a device status bus set according to preset field mapping.

[0053] Output unit 5 is used to convert the device state bus set into top-level output and feed it back to the energy management system within a fixed-step simulation cycle.

[0054] The current embodiment is implemented using the model-based simulation tool Simulink, configured with 41 top-level inputs and 126 outputs, employing a fixed-step third-order Runge-Kutta solver ode3 with a fixed step size of 50 μs. The model can be further configured for real-time operation, enabling hardware-in-the-loop simulation verification. For hardware-in-the-loop deployment, an even smaller fixed step size can be determined based on the CPU and FPGA resources of the real-time simulator and the model partitioning.

[0055] Typical verification scenarios may include: microgrid grid connection and islanded steady-state operation; photovoltaic and wind power output fluctuations and load changes; PCC point grid connection, islanded switching and segmented bus tie switch switching; deep charging and discharging of energy storage; fault and emergency conditions such as bus or line short circuit, main grid power failure and islanded operation and black start; and simulation of multi-objective optimization scheduling and demand response strategies.

[0056] It should be understood that the number of device models, the number of switch circuits, the number of top-level inputs and outputs, the power limit, the lookup table breakpoints, and the fixed step size can all be configured according to the specific microgrid object, and the specific values ​​in the current embodiment should not be construed as the sole limitation on the scope of protection of this invention.

[0057] Compared with simulation methods that treat devices as ideal execution units, this invention first arbitrates the scheduling commands from the command source, and then performs power limiting, response delay, state of charge capability limitation or start-stop state processing according to the actual operating constraints of different devices. Therefore, the resulting actual power and operating state can reflect the device's capability boundaries and dynamic execution process, avoiding the energy management system from obtaining feedback that is inconsistent with the actual executable state of the controlled object in closed-loop testing.

[0058] This invention also selects appropriate voltage and frequency reference sources based on the power converter's operating mode and calculates the actual branch state through an intelligent switching state machine. Subsequently, it aggregates equipment electrical quantities, equipment states, fault states, available capacity, switching states, and interlocking states according to preset field mappings and feeds them back to the energy management system within the same fixed-step simulation cycle. This improves the consistency between commands, equipment constraints, operating modes, branch states, and feedback data in multi-object closed-loop testing and provides a controlled object model with clear limit protection for real-time or semi-physical simulation in-loop testing.

[0059] The preferred embodiments of the present invention have been described in detail above. However, it should be understood that after reading the above teachings, those skilled in the art can make various alterations or modifications to the present invention. These equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A simulation method for closed-loop testing of a microgrid energy management system, characterized in that, include: S1, Receive commands from the energy management system and local commands configured for the local coverage channel; S2. Perform command source arbitration on the device model configured with local coverage channel. When local coverage is enabled, select local command; when local coverage is not enabled, select energy management system command to form the effective command for each device model. S3. Input the effective command into each device model respectively, and each device model will execute the corresponding constraint processing according to its own operating logic; S4. Organize the status parameters output by each device model into a device status bus set according to the preset field mapping; S5. Within a fixed-step simulation cycle, the device state bus set is converted into top-level output and fed back to the energy management system, thereby completing the closed-loop test of command input, constrained execution, and actual state feedback.

2. The simulation method for closed-loop testing of a microgrid energy management system according to claim 1, characterized in that, The energy management system commands include equipment scheduling commands, operating mode commands, fault commands, load shedding level commands, and switch control commands.

3. The simulation method for closed-loop testing of a microgrid energy management system according to claim 1, characterized in that, The equipment models include the main grid and main grid converter model, photovoltaic power generation model, wind power generation model, battery energy storage and power converter model, generator set model, load model, and smart switch model.

4. The simulation method for closed-loop testing of a microgrid energy management system according to claim 1, characterized in that, Equipment models equipped with local coverage channels include battery storage and power converter models and smart switch models.

5. The simulation method for closed-loop testing of a microgrid energy management system according to claim 4, characterized in that, The equipment model also includes a common bus model, which receives the actual switching status of each branch and the electrical output of the relevant equipment model. It calculates the voltage and frequency of the common bus based on the current branch connection relationship or obtains them from the common bus measurement module, and provides the voltage and frequency to the power converter operation mode selection, the wind power generation model and the top-level output, respectively.

6. The simulation method for closed-loop testing of a microgrid energy management system according to claim 5, characterized in that, S3 includes: selecting the control reference source according to the power converter operating mode; when the operating mode command is equal to 2, selecting the external voltage reference and frequency reference; when the operating mode command is not equal to 2, selecting the voltage and frequency feedback of the common bus.

7. The simulation method for closed-loop testing of a microgrid energy management system according to claim 3 or 4, characterized in that, The intelligent switch model is configured with nine intelligent switches, which are respectively connected to the main grid, photovoltaic, wind power, two sets of battery energy storage, two sets of generator sets and two load branches. Each intelligent switch receives opening and closing commands and interlocking inputs, calculates the actual state through a state machine, and outputs the actual switch state and interlocking state. The energy management system provides a nine-element closing command vector, the local control channel provides local closing commands for each channel, and the opening command is formed by the internal logic of the current model.

8. The simulation method for closed-loop testing of a microgrid energy management system according to claim 7, characterized in that, The actual status of the nine-way intelligent switch can serve as the basis for enabling corresponding equipment, branch access, and calculating operating status; each switch outputs its actual status and interlocking output based on the opening command, closing command, interlocking input, and related branch status.

9. The simulation method for closed-loop testing of a microgrid energy management system according to claim 1, characterized in that, In S4, the preset fields include actual power, voltage, current, frequency, operating status, fault status, available capacity, switch status, and interlocking status.

10. A simulation device for closed-loop testing of a microgrid energy management system, characterized in that, include: The command interface unit is used to receive commands from the energy management system and local commands configured with local overlay channels; The command source arbitration unit is used to select the effective command for each device model; The multi-object device model constraint processing unit is used to simulate multiple device models. Based on the effective command received from the command source arbitration unit, each device model performs corresponding constraint processing according to its own operating logic. The status bus aggregation unit is used to organize the status parameters output by each device model into a device status bus set according to preset field mapping. The output unit is used to convert the device state bus set into top-level output and feed it back to the energy management system within a fixed-step simulation cycle.

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