Microgrid multi-mode cooperative control method, system and device and storage medium

Abstract

The invention belongs to the technical field of micro-grids, and particularly relates to a micro-grid multi-mode cooperative control method, system and device and a storage medium. The objective of the invention is to solve the technical problem in the prior art that the rapid adjustment capability of a large amount of distributed photovoltaic power and the supporting capability of energy storage cannot be converted into economic benefits participating in market transactions, so that huge waste of resource values is caused. Comprising the steps of obtaining electricity market data and prediction data; establishing a daily operation plan by taking daily operation income maximization as a target; refining the daily operation plan into a power set point sequence, and generating a dynamic scheduling instruction; a multi-mode cooperative controller is used for collecting state data of a common connection point in real time, and in combination with a current market signal, according to built-in mode switching logic, a working mode which should be operated currently is intelligently decided; converting the dynamic scheduling instruction into a bottom layer control parameter of each inverter, and issuing the bottom layer control parameter to physical layer equipment; and controlling physical layer equipment to operate according to the bottom layer control parameters.

Description

Technical Field

[0001] This invention belongs to the field of microgrid technology, and in particular relates to a multi-modal collaborative control method, system, device and storage medium for microgrids. Background Technology

[0002] The penetration rate of distributed photovoltaic (PV) power in distribution networks, especially at the end of the grid, is experiencing explosive growth. The end of the grid is typically located at the end of long transmission lines, where the grid structure is weak, short-circuit capacity is low, and impedance ratio is high, making it a typical "weak grid" environment. The high proportion of PV power integration brings two challenges: on the one hand, the randomness and volatility of its output can easily lead to problems such as voltage exceeding limits, frequency fluctuations, and power angle instability, threatening the safe operation of the grid; on the other hand, the traditional distributed power generation model of "grid-connected but not connected to the grid" or simply connecting to the grid has failed to fully tap its enormous potential for participating in electricity market transactions and providing ancillary services.

[0003] To address the aforementioned issues, existing technologies utilize microgrid systems based on hybrid control. These systems typically consist of grid-connected photovoltaic (GFL-PV), grid-connected energy storage (GFM-ESS), and loads. The GFM-ESS, acting as the system's primary power source, employs droop control or virtual synchronous generator (VSG) control to establish voltage and frequency references and balance power deficits within the system. The grid-connected photovoltaic system always operates in maximum power point tracking (MPPT) mode and does not participate in system regulation.

[0004] The existing technology has the following technical problems: 1. Existing microgrid systems operate solely with technical safety as their objective, resulting in control strategies that are disconnected from the electricity market. The rapid regulation capabilities of a large number of distributed photovoltaic systems and the supporting capabilities of energy storage cannot be translated into economic benefits from market transactions, leading to a huge waste of resource value.

[0005] 2. Existing microgrid systems can only maintain their own operation and cannot respond to grid dispatch instructions to actively provide auxiliary services such as peak shaving, frequency regulation, reactive power reserve, and inertia response to the main grid, thus failing to fulfill the obligations and responsibilities of the grid-connected entity.

[0006] 3. Grid-connected photovoltaic systems are fixed in MPPT mode. Even in emergency situations where the grid needs to reduce power output, they cannot flexibly adjust their active and reactive power outputs, becoming "bystanders" rather than "participants" in the grid. Summary of the Invention

[0007] This invention provides a multi-modal collaborative control method, system, device, and storage medium for microgrids, aiming to solve the technical problems in the prior art, such as the inability to convert the rapid adjustment capability and energy storage support capability of a large number of distributed photovoltaic systems into economic benefits for market transactions, resulting in a huge waste of resource value; lack of active support capability; and rigid control modes.

[0008] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A multi-modal cooperative control method for microgrids, comprising: Obtain electricity market data and forecast data; Based on the electricity market data and the forecast data, a daily operation plan is established with the goal of maximizing daily operating revenue. The daily operation plan is refined into a power setpoint sequence, and dynamic scheduling instructions are generated. The system utilizes a multimodal collaborative controller to collect real-time status data from common connection points, combines this data with current market signals, and intelligently decides the current operating mode based on built-in mode switching logic. Based on the current operating mode, the dynamic scheduling command is converted into the underlying control parameters of each inverter, and the underlying control parameters are sent down to the physical layer devices; The physical layer devices are controlled to operate according to the underlying control parameters, and the multimodal collaborative controller continuously monitors the status data of the common connection point. Based on the status data and market signals, dynamic mode switching is performed when the working mode needs to be switched.

[0009] Furthermore, the aforementioned electricity market data includes: real-time electricity prices, ancillary service demand signals, and inter-provincial trading plans; the forecast data includes photovoltaic power generation forecast data and demand-compliant data.

[0010] Furthermore, the aforementioned daily operation plan includes: the charging and discharging plan of the energy storage system, the power generation plan of the photovoltaic system, the bidding strategy for ancillary services, and the daily power transmission plan curve for inter-provincial transactions.

[0011] Furthermore, the above-mentioned daily operation plan is further refined into a power setpoint sequence, and dynamic scheduling instructions are generated as follows: The daily operation plan is received by a multi-timescale scheduler, and rolling optimization and real-time scheduling are performed to obtain a sequence of power setpoints for future preset time windows; Dynamic scheduling instructions are generated based on the power setpoint sequence within a future preset time window and sent to the multimodal cooperative controller.

[0012] Furthermore, the status data of the aforementioned common connection point includes: voltage, frequency, and power, and the operating modes include: market-oriented mode, active support mode, autonomous optimization mode, and island survival mode; The market-oriented mode is triggered when the real-time electricity price is higher than the electricity price threshold, or when the current time is during the execution of an inter-provincial transaction plan. The active support mode is triggered when a superior scheduling control command is received, or when the voltage / frequency of the common connection point is exceeded by local monitoring. The triggering conditions for the autonomous optimization mode are that the grid voltage and frequency are within the normal range, and the real-time electricity price is lower than the electricity price threshold. The island survival mode is triggered when a power grid fault is detected.

[0013] Furthermore, the control strategy for the market-oriented model described above is as follows: Grid-based energy storage: The output power tracks the charge and discharge plan value in the market signal, and discharges when the real-time electricity price is higher than the electricity price threshold. If it is during the period of executing the inter-provincial transaction plan, power escort control is performed. Grid-based photovoltaic: Switch to grid-based mode; its power generation output is optimized and dispatched based on real-time electricity price and local grid loss sensitivity; Grid-connected photovoltaic systems: operating in maximum power point tracking mode; The control strategy for the active support mode is as follows: Grid-based energy storage and grid-based photovoltaics: both operate in grid-based mode and adopt adaptive droop control, with their droop coefficient dynamically adjusted according to the support strength requirements; Grid-connected photovoltaic systems: exit maximum power point tracking mode and enter active power reserve mode; specifically, active power reserve mode involves: proportionally reducing active power based on frequency deviation and dynamically injecting or absorbing reactive power based on voltage deviation. The control strategy for the autonomous optimization mode is as follows: Grid-based energy storage: Switch to charging mode; Grid-connected photovoltaic and grid-linked photovoltaic systems: Enter maximum power point tracking mode; The control strategy for the isolated survival mode is as follows: Grid-based energy storage: As the main power source, it uses VSG control to establish voltage and frequency; Grid-based photovoltaic: Switch to grid-based mode; Grid-connected photovoltaic systems: orderly disconnection or blocking.

[0014] Furthermore, the aforementioned power escort control specifically involves: Obtain the cross-provincial daily power transmission plan power corresponding to the current moment from the multi-timescale scheduler; Obtain the actual power transmitted from the status data of the common connection point; Based on the planned daily power transmission power across provinces and the actual power transmitted, the power deviation is calculated to obtain the deviation signal. The deviation signal is used as an additional control signal and injected into the active power control loop of the grid-type energy storage, specifically: If the deviation signal is greater than 0, the grid-type energy storage will be controlled to discharge; If the deviation signal is less than 0, the grid-type energy storage will be charged.

[0015] Furthermore, the aforementioned underlying control parameters include: voltage, frequency, active power reference value, and reactive power reference value.

[0016] Secondly, to solve the above-mentioned technical problems, the present invention also provides a microgrid multimodal cooperative control system, comprising: The data acquisition module is used to acquire electricity market data and forecast data; The daily operation plan module is used to establish a daily operation plan based on the electricity market data and the forecast data, with the goal of maximizing daily operation revenue. A multi-timescale scheduling module is used to refine the daily operation plan into a power setpoint sequence and generate dynamic scheduling instructions; The multimodal collaborative control module is used to collect the status data of the common connection point in real time using the multimodal collaborative controller, and, combined with the current market signals, intelligently decides the current working mode to be operated based on the built-in mode switching logic. The communication module is used to convert dynamic scheduling commands into low-level control parameters for each inverter according to the current operating mode, and to send the low-level control parameters to the physical layer devices. The mode switching module is used to control the physical layer devices to operate according to the underlying control parameters, and to continuously monitor the status data of the common connection point using a multimodal collaborative controller. Based on the status data and market signals, it performs dynamic mode switching when the working mode needs to be switched.

[0017] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the microgrid multimodal cooperative control method of the present application.

[0018] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the microgrid multimodal cooperative control method of the present application.

[0019] Compared with the prior art, the present invention has the following advantages: 1. In a microgrid environment, this invention uses electricity market data and forecast data to formulate a daily operation plan with the goal of maximizing daily operating revenue. This ensures that the microgrid achieves maximum economic benefits while participating in the electricity market, and also rationally arranges the charging and discharging of energy storage systems, the power generation output of photovoltaic systems, and inter-provincial trading strategies.

[0020] 2. In a microgrid environment, this invention, through the coordinated control of grid-connected energy storage, grid-connected photovoltaics, and grid-linked photovoltaics, can not only achieve efficient energy distribution and utilization, but also adjust active / reactive power according to the actual needs of the power grid, thereby ensuring power quality.

[0021] 3. This invention utilizes a multi-timescale scheduler to refine the daily operating plan into a power setpoint sequence and generate dynamic scheduling instructions. Combining the status data of the point of common coupling (PCC) and current market signals, it intelligently determines the operating mode and adjusts the underlying control parameters of each inverter in real time. This allows the microgrid to flexibly switch operating modes according to actual operating conditions, enhancing the system's adaptability and stability.

[0022] 4. When a grid fault is detected, the system can automatically switch to islanded survival mode to ensure the continuity of power supply to critical loads. Especially for grid-connected energy storage and grid-connected photovoltaic systems, they can independently build voltage and provide frequency support in islanded mode, improving the self-healing capability and reliability of the entire microgrid.

[0023] 5. This invention not only considers the coordinated operation between internal devices, but also emphasizes effective interaction with the external power grid, such as responding to superior dispatch instructions and participating in inter-provincial transactions. This helps strengthen the complementary and mutually supportive relationship between the microgrid and the main power grid, and promotes the consumption of new energy sources.

[0024] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a multi-modal cooperative control method for a microgrid according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of the framework of a microgrid multimodal cooperative control method according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of a microgrid multimodal cooperative control system according to an embodiment of the present invention is shown; Figure 4A schematic diagram of an electronic device structure according to an embodiment of the present invention is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Figure 1 A flowchart illustrating a multi-modal cooperative control method for a microgrid according to an embodiment of the present invention is shown, as follows: Figure 1 As shown in the figure, a microgrid multimodal cooperative control method according to an embodiment of the present invention includes: Obtain electricity market data and forecast data; Based on the electricity market data and the forecast data, a daily operation plan is established with the goal of maximizing daily operating revenue. The daily operation plan is refined into a power setpoint sequence, and dynamic scheduling instructions are generated. The system utilizes a multimodal collaborative controller to collect real-time status data from common connection points, combines this data with current market signals, and intelligently decides the current operating mode based on built-in mode switching logic. Based on the current operating mode, the dynamic scheduling command is converted into the underlying control parameters of each inverter, and the underlying control parameters are sent down to the physical layer devices; The physical layer devices are controlled to operate according to the underlying control parameters, and the multimodal collaborative controller continuously monitors the status data of the common connection point. Based on the status data and market signals, dynamic mode switching is performed when the working mode needs to be switched.

[0029] In this embodiment, the multimodal collaborative controller continuously monitors the PCC status and market signals to determine whether it is necessary to switch the operating mode, thereby realizing dynamic priority adjustment and seamless switching among multiple objectives.

[0030] Optionally, the electricity market data includes: real-time electricity prices, ancillary service demand signals, and inter-provincial trading plans; the forecast data includes photovoltaic power generation forecast data and demand-compliant data.

[0031] Optionally, the daily operation plan includes: the charging and discharging plan of the energy storage system, the power generation plan of the photovoltaic system, the ancillary service bidding strategy, and the daily power transmission plan curve for inter-provincial transactions.

[0032] Optionally, the daily operation plan is refined into a power setpoint sequence, and dynamic scheduling instructions are generated as follows: The daily operation plan is received by a multi-timescale scheduler, and rolling optimization and real-time scheduling are performed to obtain a sequence of power setpoints for future preset time windows; Dynamic scheduling instructions are generated based on the power setpoint sequence within a future preset time window and sent to the multimodal cooperative controller.

[0033] like Figure 2 As shown, in this embodiment, the market layer acquires external information and formulates a day-ahead economic optimization plan; the control layer performs multi-timescale scheduling, decomposing the plan into real-time instructions; rolling optimization is set to update every 15 minutes, correcting the plan based on the latest status; real-time scheduling is set to every 1 minute, generating a more refined power setpoint to guide real-time control. The multimodal collaborative controller, combined with the grid status, makes mode decisions and implements millisecond-level control. The physical layer executes instructions to realize energy conversion and grid support; each layer achieves information closure through a communication network, forming a complete chain of "market guidance—control coordination—physical execution".

[0034] Optionally, the status data of the common connection point includes: voltage, frequency, and power, and the operating modes include: market-oriented mode, active support mode, autonomous optimization mode, and island survival mode; The market-oriented mode is triggered when the real-time electricity price is higher than the electricity price threshold, or when the current time is during the execution of an inter-provincial transaction plan. The active support mode is triggered when a superior scheduling control command is received, or when the voltage / frequency of the common connection point is exceeded by local monitoring. The triggering conditions for the autonomous optimization mode are that the grid voltage and frequency are within the normal range, and the real-time electricity price is lower than the electricity price threshold. The island survival mode is triggered when a power grid fault is detected.

[0035] In this embodiment, the control strategy for the market-oriented model is as follows: Grid-based energy storage: The output power tracks the charging and discharging plan value in the market signal, discharges during periods of high electricity price, and performs power protection control during periods of cross-provincial transaction plan execution; Grid-based photovoltaic (PV): Switching to grid-based mode (establishing a stable PCC point voltage amplitude and frequency reference as a voltage source, improving the system's voltage support capability); its power generation output is optimized and dispatched based on real-time electricity prices and local grid loss sensitivity. Achieving optimal economic efficiency while meeting voltage constraints.

[0036] Grid-connected photovoltaic: It operates in maximum power point tracking (MPPT) mode, but its active power output is constrained by the upper limit set by the market plan to prevent over-generation from causing curtailment or deviation from the trading plan, thus achieving "power-limited MPPT operation".

[0037] The control strategy for the active support mode is as follows: Grid-based energy storage and grid-based photovoltaics both operate in grid-forming mode and employ adaptive droop control. Their droop coefficients are dynamically adjusted based on the required support strength. For example, when the frequency deviation is large, the active power-frequency droop coefficient is reduced to increase the power support strength per unit frequency deviation.

[0038] Grid-connected photovoltaic systems: Exit MPPT mode and enter active power reserve mode. Active power is reduced proportionally according to frequency deviation (providing primary frequency regulation), and reactive power is dynamically injected or absorbed according to voltage deviation (providing reactive power support).

[0039] The control strategy for the autonomous optimization mode is as follows: Grid-based energy storage: Switches to charging mode to absorb low-cost electricity from the grid.

[0040] Both grid-connected and grid-linked photovoltaic systems operate in MPPT mode to maximize power generation.

[0041] In this embodiment, under the autonomous optimization mode, as much low-cost electricity as possible is stored, and self-generation and self-consumption are maximized to reduce the cost of purchasing electricity from the grid.

[0042] The control strategy for the island survival mode is as follows: Grid-based energy storage: As the main power source, it uses VSG control to establish voltage and frequency; Grid-based photovoltaic (PV): Switching to grid-based mode, droop control with adjustable maximum power is adopted. Active power output is dynamically adjusted according to system frequency changes: when the frequency is too low, PV output is actively reduced to prevent overload; when the frequency is too high, output is increased to balance load fluctuations.

[0043] Grid-connected photovoltaic systems: Because they rely on external voltage sources and cannot build up voltage independently, they are systematically disconnected or locked to avoid voltage instability or circulating current in isolated systems, thus ensuring the safe operation of the system.

[0044] Optionally, power escort control is performed as follows: Obtain the cross-provincial daily power transmission plan power corresponding to the current moment from the multi-timescale scheduler; Obtain the actual power transmitted from the status data of the common connection point; Based on the planned daily power transmission power across provinces and the actual power transmitted, the power deviation is calculated to obtain the deviation signal. The deviation signal is used as an additional control signal and injected into the active power control loop of the grid-type energy storage, specifically: If the deviation signal is greater than 0, the grid-type energy storage will be controlled to discharge; If the deviation signal is less than 0, the grid-type energy storage will be charged.

[0045] In this embodiment, the compensation process is a second-level dynamic response, ensuring that the transmitted power is smooth and accurately tracks the daily power transmission plan curve.

[0046] Optionally, the underlying control parameters include: voltage, frequency, active power reference value, and reactive power reference value.

[0047] In this embodiment, the physical layer includes grid-connected energy storage (GFM-ESS), grid-connected photovoltaic (GFM-PV), grid-connected photovoltaic (GFL-PV), and local loads. It issues voltage amplitude, frequency reference, and active / reactive power targets to the grid-connected energy storage; issues active power output settings and reactive power support commands to the grid-connected photovoltaic; issues current injection commands to the grid-connected photovoltaic; and coordinates local controllable loads for flexible adjustment.

[0048] Based on and Figure 1 Using the same principle as the method shown, this embodiment of the invention also provides a microgrid multimodal cooperative control system, such as... Figure 3 As shown, it includes: The data acquisition module is used to acquire electricity market data and forecast data; The daily operation plan module is used to establish a daily operation plan based on the electricity market data and the forecast data, with the goal of maximizing daily operation revenue. A multi-timescale scheduling module is used to refine the daily operation plan into a power setpoint sequence and generate dynamic scheduling instructions; The multimodal collaborative control module is used to collect the status data of the common connection point in real time using the multimodal collaborative controller, and, combined with the current market signals, intelligently decides the current working mode to be operated based on the built-in mode switching logic. The communication module is used to convert dynamic scheduling commands into low-level control parameters for each inverter according to the current operating mode, and to send the low-level control parameters to the physical layer devices. The mode switching module is used to control the physical layer devices to operate according to the underlying control parameters, and to continuously monitor the status data of the common connection point using a multimodal collaborative controller. Based on the status data and market signals, it performs dynamic mode switching when the working mode needs to be switched.

[0049] The microgrid multimodal cooperative control system of this invention can execute the microgrid multimodal cooperative control method provided in this invention. The implementation principle is similar. The actions performed by each module and unit in the microgrid multimodal cooperative control system of each embodiment of this invention correspond to the steps in the microgrid multimodal cooperative control method of each embodiment of this invention. For detailed functional descriptions of each module of the microgrid multimodal cooperative control system, please refer to the descriptions in the corresponding microgrid multimodal cooperative control methods shown above, which will not be repeated here.

[0050] The aforementioned microgrid multimodal cooperative control system can be a computer program (including program code) running on a computer device. For example, the microgrid multimodal cooperative control system is an application software. The application software can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0051] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0052] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0053] In one alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the electronic device may also include a transceiver, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this electronic device does not constitute a limitation on the embodiments of the present invention.

[0054] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0055] Among these, electronic devices can also be terminal devices. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0056] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0057] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0058] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0059] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A microgrid multi-modal collaborative control method, characterized in that, The method comprises: acquiring power market data and prediction data; establishing a daily operation plan based on the power market data and the prediction data, with the goal of maximizing daily operation revenue; refining the daily operation plan into a power set point sequence and generating dynamic scheduling instructions; using a multi-modal collaborative controller to collect state data of a point of common coupling in real time, combining current market signals, and intelligently deciding the current working mode according to the built-in mode switching logic; according to the current working mode, converting the dynamic scheduling instructions into bottom-layer control parameters of each inverter, and issuing the bottom-layer control parameters to physical layer devices; controlling the physical layer devices to operate according to the bottom-layer control parameters, and using the multi-modal collaborative controller to continuously monitor the state data of the point of common coupling, and dynamically switching the working mode according to the state data and market signals; wherein the physical layer includes grid-forming energy storage, grid-forming photovoltaic, and grid-following photovoltaic. 2.The microgrid multi-modal coordinated control method of claim 1, wherein, The power market data includes real-time electricity price, auxiliary service demand signal, and inter-provincial transaction plan, and the prediction data includes photovoltaic power generation prediction data and demand conforming data. 3.The microgrid multi-modal coordinated control method of claim 1, wherein, The daily operation plan includes the charge-discharge plan of the energy storage system, the power generation output plan of the photovoltaic system, the auxiliary service bidding strategy, and the daily power transmission power plan curve of the inter-provincial transaction. 4.The microgrid multi-modal collaborative control method of claim 1, wherein, Refining the daily operation plan into a power set point sequence and generating dynamic scheduling instructions specifically comprises: using a multi-time scale scheduler to receive the daily operation plan, performing rolling optimization and real-time scheduling, and obtaining a power set point sequence of a future preset time window; generating dynamic scheduling instructions according to the power set point sequence of the future preset time window, and issuing the dynamic scheduling instructions to the multi-modal collaborative controller.

5. The microgrid multi-modal coordinated control method of claim 1, wherein, The state data of the point of common coupling includes voltage, frequency, and power, and the working mode includes market-oriented mode, active support mode, autonomous optimization mode, and island survival mode; The trigger condition of the market-oriented mode is that the real-time electricity price is higher than the electricity price threshold, or the current time is in the period of executing the inter-provincial transaction plan; The trigger condition of the active support mode is that the superior dispatching control instruction is received, or the voltage / frequency of the point of common coupling is out of limit locally monitored; The trigger condition of the autonomous optimization mode is that the grid voltage and frequency are in the normal range, and the real-time electricity price is lower than the electricity price threshold; The trigger condition of the island survival mode is that the grid is detected to be in a fault state.

6. The microgrid multi-modal coordinated control method of claim 5, wherein, The control strategy of the market-oriented mode is: grid-forming energy storage: output power tracks the charge-discharge plan value in the market signal, discharges during the period when the real-time electricity price is higher than the electricity price threshold, and performs power escort control if in the period of executing the inter-provincial transaction plan; grid-forming photovoltaic: switches to the grid-forming mode; its power generation output is optimally scheduled according to the real-time electricity price and local network loss sensitivity; grid-following photovoltaic: operates in the maximum power point tracking mode; The control strategy of the active support mode is: grid-forming energy storage and grid-forming photovoltaic: both work in the grid-forming mode, and adopt adaptive droop control, with the droop coefficient dynamically adjusted according to the support intensity demand; Grid-connected photovoltaic systems: exit maximum power point tracking mode and enter active power reserve mode; specifically, active power reserve mode involves: proportionally reducing active power based on frequency deviation and dynamically injecting or absorbing reactive power based on voltage deviation. The control strategy for the autonomous optimization mode is as follows: Grid-based energy storage: Switch to charging mode; Grid-connected photovoltaic and grid-linked photovoltaic systems: Enter maximum power point tracking mode; The control strategy for the isolated survival mode is as follows: Grid-based energy storage: As the main power source, it uses VSG control to establish voltage and frequency; Grid-based photovoltaic: Switch to grid-based mode; Grid-connected photovoltaic systems: orderly disconnection or blocking.

7. The microgrid multi-modal coordinated control method of claim 6, wherein, The specific steps for power escort control are as follows: Obtain the cross-provincial daily power transmission plan power corresponding to the current moment from the multi-timescale scheduler; Obtain the actual power transmitted from the status data of the common connection point; Based on the planned daily power transmission power across provinces and the actual power transmitted, the power deviation is calculated to obtain the deviation signal. The deviation signal is used as an additional control signal and injected into the active power control loop of the grid-type energy storage, specifically: If the deviation signal is greater than 0, the grid-type energy storage will be controlled to discharge; If the deviation signal is less than 0, the grid-type energy storage will be charged.

8. A microgrid multi-modal coordinated control system, characterized in that, include: The data acquisition module is used to acquire electricity market data and forecast data; The daily operation plan module is used to establish a daily operation plan based on the electricity market data and the forecast data, with the goal of maximizing daily operation revenue. A multi-timescale scheduling module is used to refine the daily operation plan into a power setpoint sequence and generate dynamic scheduling instructions; The multimodal collaborative control module is used to collect the status data of the common connection point in real time using the multimodal collaborative controller, and, combined with the current market signals, intelligently decides the current working mode to be operated based on the built-in mode switching logic. The communication module is used to convert dynamic scheduling commands into low-level control parameters for each inverter according to the current operating mode, and to send the low-level control parameters to the physical layer devices. The mode switching module is used to control the physical layer devices to operate according to the underlying control parameters, and to continuously monitor the status data of the common connection point using a multimodal collaborative controller. Based on the status data and market signals, it performs dynamic mode switching when the working mode needs to be switched.

9. An electronic device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the method of any one of claims 1-7.

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