Microgrid control method, control device and microgrid

Abstract

The present application proposes a control method, a control device and a microgrid for a microgrid, and relates to the field of microgrid technology. The control method for the microgrid includes: obtaining the current energy storage data of multiple sub-microgrids in the monitoring area, the current power consumption data of each load and the historical grid load data; determining the real-time power demand corresponding to each sub-microgrid according to the power consumption data; determining the energy storage state corresponding to each sub-microgrid according to the energy storage data; predicting the future grid load trend according to the historical grid load data; generating the corresponding power dispatching parameters and operating constraints of the sub-microgrid according to the real-time power demand, energy storage state, future grid load trend and the preset microgrid power model; and controlling the power output of each sub-microgrid according to the power dispatching parameters and operating constraints. The present application can jointly control multiple sub-microgrids in the area to comprehensively improve the comprehensive benefits of the power system.

Description

Technical Field

[0001] The present application relates to the technical field of microgrids, and in particular to a control method and a control device for a microgrid, and a microgrid. Background Art

[0002] With the continuous evolution of distributed energy technology, microgrids, as a key energy supply mode, continue to expand their application scope in power systems. However, the layout of microgrids in the monitoring area (such as residential areas or industrial parks) is decentralized, which leads to challenges in centralized coordinated control. In addition, in actual operation, voltage fluctuations and power imbalances frequently occur, which have an adverse impact on the overall performance of the power system. Summary of the invention

[0003] The main purpose of the present invention is to provide a microgrid control method, which aims to jointly control multiple sub-microgrids in a region to comprehensively improve the comprehensive benefits of the power system.

[0004] To achieve the above object, the present invention provides a control method for a microgrid, wherein the microgrid includes a plurality of sub-microgrids, and the plurality of sub-microgrids are respectively connected to a corresponding plurality of loads, and the control method includes:

[0005] Obtain the current energy storage data of multiple sub-microgrids in the monitoring area, the current power consumption data of each load, and the historical grid load data;

[0006] Determine the real-time power demand corresponding to each of the sub-microgrids according to the power consumption data;

[0007] Determine the energy storage state corresponding to each of the sub-microgrids according to the energy storage data;

[0008] Predicting future grid load trends based on the historical grid load data;

[0009] Generate power dispatch parameters and operation constraints of the corresponding sub-microgrid according to the real-time power demand, the energy storage state, the future grid load trend and the preset microgrid power model;

[0010] According to the power dispatch parameters and operation constraints, the power output of each sub-microgrid is controlled.

[0011] Optionally, the step of generating power dispatching parameters and operation constraints of the corresponding sub-microgrid according to the real-time power demand, the energy storage state, the future grid load trend and a preset microgrid power model includes:

[0012] Determining the priority ranking of each sub-microgrid according to the real-time power demand;

[0013] Determining a power distribution strategy for each sub-microgrid according to the energy storage state and the priority ranking;

[0014] Determining a possible load peak period according to the future power grid load trend;

[0015] According to the load peak period and the preset microgrid power model, the power distribution strategy is adjusted to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid.

[0016] Optionally, the step of adjusting the power distribution strategy according to the load peak period and the preset microgrid power model to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid includes:

[0017] Determining the backup capacity requirement of the microgrid according to the load peak period;

[0018] Determining a data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model;

[0019] Determining the power deviation allocated to each sub-microgrid according to the data deviation tolerance;

[0020] According to the power deviation, the power allocation strategy is adjusted to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid.

[0021] Optionally, the step of determining the data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model includes:

[0022] According to the backup capacity requirement, the backup capacity ratio required by each sub-microgrid is calculated;

[0023] According to the reserve capacity ratio, the stability of the total output power of the microgrid under different load conditions and the stability of the output power of each sub-microgrid under different load conditions are evaluated to generate a corresponding stability report; the stability report includes the reserve capacity ratio of each sub-microgrid, the stability evaluation result and the recommended power deviation range;

[0024] Determining a maximum power deviation acceptable to each of the sub-microgrids according to the preset microgrid power model and the stability report;

[0025] The maximum power deviation is determined as a data deviation tolerance.

[0026] Optionally, the control method further includes:

[0027] Obtain multiple historical energy storage data and historical electricity consumption data;

[0028] Analyze the relationship between historical energy storage data and historical electricity consumption data to determine the charging and discharging characteristics of the energy storage device and the operating characteristics of the sub-microgrid under different load conditions;

[0029] Establishing a target power model according to the charge-discharge characteristics and the operation characteristics;

[0030] The target power model is trained based on a neural convolution algorithm, and a plurality of current energy storage data and a plurality of current power consumption data of loads are imported into the target power model to generate the preset microgrid power model.

[0031] Optionally, the step of controlling the power output of each sub-microgrid according to the power dispatching parameters and the operating constraints includes:

[0032] Determine the charging and discharging strategy of the energy storage equipment and the grid access strategy of each sub-microgrid according to the power dispatch parameters and the operating constraints;

[0033] According to the charging and discharging strategy and the grid access strategy, the energy storage device status and grid connection status of each sub-microgrid are continuously adjusted to control the power output of each sub-microgrid.

[0034] Optionally, the step of determining the charging and discharging strategy of the energy storage device and the grid access strategy of each sub-microgrid according to the power dispatching parameters and the operating constraints includes:

[0035] Calculate the optimal charging and discharging power of each sub-microgrid and the optimal connection power of the grid according to the power dispatch parameters and the operation constraints;

[0036] Obtain the current operating status of the energy storage devices of each sub-microgrid, wherein the operating status includes remaining capacity, charging and discharging efficiency, and health status;

[0037] Determine the charging and discharging strategy of each sub-microgrid according to the optimal charging and discharging power, the optimal connection power of the power grid and the operating state;

[0038] A grid access strategy is determined according to the power consumption data and the optimal connection power of the grid.

[0039] Optionally, the step of continuously adjusting the energy storage device state and grid connection state of each sub-microgrid according to the charging and discharging strategy and the grid access strategy to control the power output of each sub-microgrid includes:

[0040] According to the charging and discharging strategy and the grid access strategy, continuously obtain the current status of the energy storage devices of each sub-microgrid and the real-time status of the grid connection;

[0041] Determining a state deviation according to a current state of the energy storage device, a real-time state of the grid connection, and a preset optimal sub-microgrid state;

[0042] Analyze the difference between the current state of the energy storage device of each sub-microgrid and the preset optimal state to generate a first analysis result, wherein the current state of the energy storage device includes a charge and discharge rate, a remaining capacity, and a health status;

[0043] Analyzing a difference between a real-time state of a grid connection and a preset optimal state to generate a corresponding second analysis result, wherein the real-time state of the grid connection includes voltage stability, frequency stability, and power transmission efficiency;

[0044] Generate energy storage device state deviation data and grid connection state deviation data of each sub-microgrid according to the first analysis result and the second analysis result;

[0045] According to the energy storage device state deviation data and the grid connection state deviation data, the energy storage device state and the grid connection state of each sub-microgrid are adjusted so that the power output of each sub-microgrid meets the power dispatching parameters and the operating constraints.

[0046] In addition, to achieve the above-mentioned purpose, the present invention also provides a control device, which includes: a memory, a processor, and a microgrid control program stored in the memory and executable on the processor, wherein the microgrid control program is configured to implement the microgrid control method as described above.

[0047] In addition, to achieve the above-mentioned purpose, the present invention also provides a microgrid, including the control device as described above.

[0048] The embodiment of the present invention divides the microgrid in the monitoring area into multiple sub-microgrids and connects to the corresponding multiple loads, then obtains the current energy storage data of the multiple sub-microgrids in the monitoring area and the current power consumption data of each load, thereby determining the real-time power demand and the corresponding energy storage status of each sub-microgrid, and then predicts the future grid load trend based on the historical grid load data. Finally, combined with the preset microgrid power model, the power dispatching parameters and operating constraints of the corresponding sub-microgrid are generated, so as to control the power output of each sub-microgrid, so as to jointly control the multiple sub-microgrids in the area, so as to comprehensively improve the comprehensive benefits of the power system, thereby effectively solving problems such as voltage fluctuations and power imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 It is a schematic flow chart of a control method for a microgrid according to an embodiment of the present invention;

[0052] Figure 2 for Figure 1 Schematic diagram of the method flow of step S500;

[0053] Figure 3 for Figure 2 Schematic diagram of the method flow of step S540;

[0054] Figure 4 for Figure 3 Schematic diagram of the method flow of step S542;

[0055] Figure 5 A schematic flow chart of a control method for a microgrid according to another embodiment of the present invention;

[0056] Figure 6 for Figure 1 Schematic diagram of the method flow of step S600;

[0057] Figure 7 for Figure 6 Schematic diagram of the method flow of step S610;

[0058] Figure 8 for Figure 6 Schematic diagram of the method flow of step S620 in FIG.

[0059] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and the well-known modules, units and their connections, links, communications or operations are not shown or described in detail. In addition, the described features, architectures or functions can be combined in any way in one or more embodiments. It should be understood by those skilled in the art that the various embodiments described below are only for illustration and not for limiting the scope of protection of the present invention. It can also be easily understood that the modules or units or processing methods in the various embodiments described herein and shown in the drawings can be combined and designed according to various different configurations. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] The limitations of various nouns or methods referred to in the following embodiments, except for those that are logically untenable, are generally based on the broad concepts that can be implemented under the premise of the disclosure in the embodiments. Under such understanding, the various specific subordinate specific limitations of the nouns or methods should be regarded as the inventive content of the present invention, and should not be understood narrowly or interpreted in a biased manner on the grounds that the specification does not disclose the specific limitations. Similarly, under the premise that it can be implemented logically, the order of the steps in the method is flexible and changeable, and the specific subordinate specific limitations in the broad concepts of various nouns or methods all fall within the scope of protection of the present invention.

[0062] The main solution of the embodiment of the present application is: by dividing the microgrid in the monitoring area into multiple sub-microgrids and connecting corresponding multiple loads, and then obtaining the current energy storage data of the multiple sub-microgrids in the monitoring area and the current power consumption data of each load, so as to determine the real-time power demand and the corresponding energy storage status of each sub-microgrid, and then predict the future grid load trend based on the historical grid load data, and finally combine with the preset microgrid power model to generate the corresponding sub-microgrid power dispatch parameters and operating constraints, so as to control the power output of each sub-microgrid.

[0063] In this embodiment, for the convenience of description, the following description is made with the control device as the execution subject.

[0064] Since the existing technology has a decentralized layout of microgrids in the monitoring area, centralized coordinated control faces challenges. In addition, problems such as voltage fluctuations and power imbalance frequently occur during actual operation, which have an adverse impact on the overall performance of the power system.

[0065] This application provides a solution that can jointly control multiple sub-microgrids in a region to comprehensively improve the overall benefits of the power system, thereby effectively solving problems such as voltage fluctuations and power imbalance.

[0066] To this end, the present invention proposes a control method for a microgrid; it can be understood that a control device for storing and executing the following method is provided in the microgrid, and the control device can be implemented by a main controller, such as MCU (Microcontroller Unit), DSP (Digital Signal Process), FPGA (Field Programmable Gate Array), SOC (System On Chip), etc.

[0067] Reference Figure 1 In one embodiment of the present invention, the control method includes steps S100-S600, wherein:

[0068] S100, obtaining current energy storage data of multiple sub-microgrids in the monitoring area, current power consumption data of each load, and historical grid load data;

[0069] S200, determining the real-time power demand corresponding to each sub-microgrid according to the power consumption data;

[0070] S300, determining the energy storage state corresponding to each sub-microgrid according to the energy storage data;

[0071] S400, predict future grid load trends based on historical grid load data;

[0072] S500, generating power dispatch parameters and operation constraints of the corresponding sub-microgrid according to the real-time power demand, energy storage status, future grid load trend and preset microgrid power model;

[0073] S600: Control the power output of each sub-microgrid according to power dispatch parameters and operation constraints.

[0074] In this embodiment, the monitoring area can be a city, an industrial area, a residential area or any area that requires electricity supply. The microgrid in the monitoring area can be composed of multiple distributed power sources, such as solar photovoltaic panels, wind turbines, small hydropower stations, etc., as well as energy storage devices such as battery packs, supercapacitors, etc. The sub-microgrid in the microgrid is a smaller-scale power system composed of some distributed power sources and energy storage devices. For example, in a residential area, the sub-microgrid can be any of the residents. In an industrial area, a sub-microgrid can be a production workshop or a group of production lines. Each sub-microgrid can operate independently or work in conjunction with other sub-microgrids to achieve power balance and optimization in the entire monitoring area.

[0075] In this embodiment, the energy storage data may include information such as the current power of the energy storage device, the charging and discharging status, and the charging and discharging rate. Real-time power demand reflects the demand for electricity of each load at a specific moment, which may be affected by various factors such as the production process, residents' electricity usage habits, and weather changes. Historical grid load data provides the changing trend of grid load over a period of time in the past, and then uses advanced prediction algorithms such as time series analysis and machine learning to predict future grid load trends to improve the accuracy of the prediction. These data are helpful in predicting future changes in grid load.

[0076] After obtaining the above data, the control device can determine the amount of power that each sub-microgrid needs to provide at the current moment according to the real-time power demand. Then, according to the energy storage data, the corresponding energy storage status is determined to help the control device understand the current charging and discharging status of the energy storage equipment of each sub-microgrid, ensure that the performance limit of the energy storage equipment is not exceeded during the power dispatch process, and evaluate whether the energy storage equipment of each sub-microgrid can meet these power demands and whether it is necessary to obtain additional power resources from other sub-microgrids or the main grid, thereby extending the service life of the equipment and improving the stability of the system. By analyzing the historical grid load data, the control device can predict the grid load trend in the future, so as to prepare for power dispatch in advance.

[0077] The preset microgrid power model can integrate the microgrid topology, equipment characteristics, environmental factors and other information. The control device will generate the corresponding sub-microgrid power dispatch according to the real-time power demand, energy storage status, future grid load trend and the preset microgrid power model.

[0078] By combining the preset microgrid power model, the control device can generate the power dispatch parameters and operation constraints of each sub-microgrid. Among them, the power dispatch parameters can include the power generation of each sub-microgrid, the charging and discharging plan of the energy storage equipment, etc., while the operation constraints include restrictions such as voltage, frequency, and power factor. These parameters and conditions will ensure that each sub-microgrid can maintain the stable operation of the grid while meeting the real-time power demand.

[0079] The control device will control the power output of each sub-microgrid based on the generated power dispatch parameters and operating constraints. This may include adjusting the power generation of distributed power sources, the charging and discharging status of energy storage devices, and obtaining or transmitting power from the main grid when necessary. Through this joint control, the microgrid system can effectively deal with problems such as voltage fluctuations and power imbalance, and improve the overall performance and reliability of the power system.

[0080] In practical applications, the control device can communicate with each sub-microgrid in real time through the communication network to obtain and transmit data. The control device can also dynamically adjust the power dispatch parameters and operating constraints based on real-time data to cope with grid load fluctuations and emergencies. For example, during peak power consumption periods, the control device can prioritize the discharge of energy storage devices to relieve grid load pressure; and during low power consumption periods, it can prioritize charging of energy storage devices to prepare for peak periods in case of emergency.

[0081] In addition, the control device can also optimize the distribution of power resources according to the geographical location, weather conditions and other factors of each sub-microgrid. For example, in areas with abundant solar energy resources, the control device can prioritize the power generation of solar photovoltaic panels to make full use of renewable energy. In areas with abundant wind resources, the power generation of wind turbines can be prioritized, which can effectively improve the operating efficiency and reliability of the microgrid, reduce operating costs, and improve the utilization rate of renewable energy.

[0082] In addition, the control device can also realize fault detection and isolation functions. When a sub-microgrid or load is detected to have a fault, the control device can quickly adjust the dispatch strategy to isolate the faulty area from the power grid to prevent the fault from spreading to the entire system, while ensuring that the power supply in other areas is not affected.

[0083] In practical applications, assuming that the monitoring area is a residential area, the control device can be installed in the central control room of the residential area. The control device acquires energy storage data, power consumption data, and historical load data by communicating in real time with the sub-microgrids of each household. These data are transmitted through a dedicated communication network to ensure the real-time and accuracy of the information. The real-time power demand of the corresponding sub-microgrid is determined by the acquired power consumption data. For example, Family A needs to turn on the air conditioner to cool down the room, turn on the induction cooker to cook, and turn on the lamps for indoor lighting. At this time, the real-time power demand of resident A is the real-time power of the air conditioner plus the power of the induction cooker plus the power of the lamp, and a preset threshold is reserved to avoid impact overload, such as a 20% margin. Assuming that the three devices of Family A add up to 4000W, the real-time power demand is set to 4800W at this time. Then obtain the energy storage data corresponding to the energy storage equipment in Family A, for example, the current power of the energy storage battery is 80%, the charging and discharging state is charging, and the charging and discharging rate is 500W. Based on this information, the control device can calculate how long the energy storage device can provide power support while meeting the real-time power demand. If the remaining power and charge and discharge rate of the energy storage battery cannot meet the real-time power demand of Family A, the control device will activate the dispatch strategy to obtain additional power resources from other residents' sub-microgrids or the main grid.

[0084] The control device can also predict the power load trend of the community in the future period based on historical load data and real-time power demand. For example, it is 7 o'clock in the evening, and most of the residents in the community have returned home. At this time, the air conditioner is turned on at the peak period. The control device predicts that the power load of the community will continue to rise in the next few hours through historical load data and real-time power demand analysis to generate the corresponding future grid load trend. At this time, the trend is a load increase trend. These load increase trends and the real-time power demand of Family A and the real-time power demand of other residents except Family A, as well as the energy storage status of each resident are input into the preset microgrid power model to generate the overall power dispatching plan of the community and the operation constraints during the dispatching process to avoid overload or underload of the energy storage equipment of any resident in the community. The control device will adjust the power output of each sub-microgrid according to the generated dispatching plan to ensure stable and efficient power supply for the entire community.

[0085] The control device will comprehensively consider the real-time power demand, energy storage status and future load trends of each household, and optimize the power generation of each sub-microgrid and the charging and discharging plan of the energy storage equipment. For example, for those households whose energy storage equipment has sufficient power and whose current load is low, such as the energy storage status of the energy storage equipment in Family B is fully charged, but Family B is only lighting at this time, the control device can arrange for Family B to output power to Family A to relieve the pressure on the power grid. At the same time, the control device can also predict weather changes in the future based on weather forecast data, so as to adjust the power dispatch strategy in advance to cope with the impact of possible extreme weather on power demand.

[0086] In order to improve the reliability and flexibility of the microgrid, the control device can also be linked with smart home appliances in the community. For example, during peak hours, the control device can send instructions to high-power-consuming devices such as smart air conditioners and washing machines to adjust their working hours or reduce power consumption, thereby reducing the load on the power grid. At the same time, the control device can also adjust the charging and discharging plan of the energy storage device in advance according to weather forecast information to cope with the impact of possible weather changes on electricity demand.

[0087] In the microgrid system of the community, the control device can also realize the energy sharing function. When the energy storage device of a sub-microgrid has sufficient power, it can provide power support to other sub-microgrids with insufficient power. This energy sharing mechanism can not only improve energy utilization efficiency, but also enhance the risk resistance of the community microgrid system.

[0088] In addition, the control device can also optimize the scheduling of renewable energy equipment in the community. For example, when it is predicted that there will be strong sunlight in the future, the control device can prioritize the power generation of solar photovoltaic panels to make full use of renewable energy. At the same time, the control device can also adjust the output of wind power generation according to the real-time data of wind power generation equipment to ensure the stable operation of the power system.

[0089] In actual operation, the control device can further optimize the power dispatching plan through intelligent algorithms, such as genetic algorithms, particle swarm optimization, etc. These algorithms can find the optimal power dispatching strategy under the premise of meeting real-time power demand and operation constraints, so as to reduce the overall power cost and improve the economy and reliability of the system.

[0090] In practical applications, assuming that the monitoring area is an industrial park, the control device can be installed in the central control center of the industrial park. The control device communicates with the sub-microgrids of each factory in real time to obtain the power demand, energy storage equipment status and historical load data in the production process. These data are transmitted through a dedicated communication network to ensure the real-time and accuracy of the information. The real-time power demand of the corresponding sub-microgrid is determined by the obtained production power demand. For example, Factory A needs to start large-scale mechanical equipment for production at this time, and turn on lighting and air-conditioning equipment at the same time. At this time, the real-time power demand of the factory is the power of the mechanical equipment plus the lighting and air-conditioning power, and a preset threshold is reserved to avoid shock overload.

[0091] The control device can calculate how long the energy storage device can provide power support while meeting the real-time power demand. If the remaining power and charge and discharge rate of the energy storage device cannot meet the real-time power demand of the factory, the control device will start the dispatch strategy to obtain additional power resources from other factory sub-microgrids or the main grid.

[0092] The control device can also predict the power load trend of the industrial park in the future based on historical load data and real-time power demand. For example, it is 9 am on a weekday and most factories have started production. The control device predicts that the power load of the industrial park will continue to rise in the next few hours through historical load data and real-time power demand analysis to generate the corresponding future grid load trend, which is a load increase trend. These load increase trends, the real-time power demand of each factory, and the energy storage status of each factory are then input into the preset microgrid power model to generate the overall power dispatching plan for the industrial park and the operating constraints during the dispatching process to avoid overloading or underloading of the energy storage equipment in any factory.

[0093] The control device will adjust the power output of each sub-microgrid according to the generated dispatch plan to ensure stable and efficient power supply for the entire industrial park. The control device will comprehensively consider the real-time power demand, energy storage status and future load trends of each factory to optimize the power generation of each sub-microgrid and the charging and discharging plan of the energy storage equipment. For example, for those factories with sufficient energy storage equipment and low current load, the control device can arrange for these factories to output power to factories with higher loads to relieve the pressure on the power grid.

[0094] In addition, the control device can also optimize the dispatch of renewable energy equipment in the industrial park. For example, when it is predicted that there will be strong winds in the future, the control device can prioritize the power generation of wind power generation equipment to make full use of renewable energy. At the same time, the control device can also adjust the output of photovoltaic power generation according to the real-time data of solar photovoltaic panels to ensure the stable operation of the power system.

[0095] Optionally, refer to Figure 2 Another embodiment of the present invention provides a microgrid control method based on the above Figure 1 In the embodiment shown, the steps of generating the power dispatching parameters and operation constraints of the corresponding sub-microgrid according to the real-time power demand, energy storage status, future grid load trend and preset microgrid power model include S510-S540, wherein:

[0096] S510, determining the priority ranking of each sub-microgrid according to the real-time power demand;

[0097] S520, determining the power distribution strategy of each sub-microgrid according to the energy storage status and priority ranking;

[0098] S530, determining a possible load peak period according to future power grid load trends;

[0099] S540: According to the load peak period and the preset microgrid power model, the power distribution strategy is adjusted to generate the corresponding power dispatching parameters and operation constraints of the sub-microgrid.

[0100] In this embodiment, the real-time power demands of the corresponding sub-microgrids are different, and their priorities are also different. Combined with the different energy storage states, a more refined power distribution strategy can be formulated. For example, for those factories with sufficient energy storage equipment and low current load, the control device can arrange for these factories to output power to factories with higher loads to relieve the pressure on the power grid.

[0101] In this embodiment, by identifying and judging the future load trend of the power grid, the time node and duration of the peak load in the future can be determined, so as to prepare countermeasures in advance. For example, when it is predicted that the industrial park will usher in a peak load at around 2 pm, the control device can adjust the charging and discharging plan of the energy storage device in advance to ensure that there is sufficient power reserve during the peak load period. In addition, the control device can also adjust the operation strategy of wind and solar power generation equipment according to real-time weather forecast information to make full use of renewable energy and reduce dependence on the main power grid.

[0102] In this embodiment, when the load peak period is determined, it is imported into the preset microgrid power model to determine the power dispatch parameters and operating constraints of each sub-microgrid in the preset time period after the current time node. The preset microgrid power model can be an optimization model containing multiple variables and constraints, and the optimal power dispatch scheme can be obtained by solving the model. For example, the preset microgrid power model can take into account factors such as the charge and discharge rate limit of the energy storage device, the maximum output power of the power generation equipment, and the fluctuation range of the load demand to ensure that the power supply of the entire industrial park is stable and efficient during the load peak period.

[0103] The control device will adjust the power generation of each sub-microgrid and the charging and discharging plan of the energy storage equipment according to the calculation results of the optimization model. For example, for those factories with sufficient energy storage equipment and low current load, the control device can arrange these factories to output power to factories with higher loads to relieve the pressure on the power grid. At the same time, the control device can also adjust the operation strategy of wind and solar power generation equipment according to real-time weather forecast information to make full use of renewable energy and reduce dependence on the main power grid.

[0104] In addition, the control device can also optimize the dispatch of renewable energy equipment in the industrial park. For example, when it is predicted that there will be strong winds in the future, the control device can prioritize the power generation of wind power generation equipment to make full use of renewable energy. At the same time, the control device can also adjust the output of photovoltaic power generation according to the real-time data of solar photovoltaic panels to ensure the stable operation of the power system.

[0105] Optionally, refer to Figure 3 Another embodiment of the present invention provides a microgrid control method based on the above Figure 2 In the embodiment shown, according to the load peak period and the preset microgrid power model, the power distribution strategy is adjusted to generate the power dispatch parameters and operation constraints of the corresponding sub-microgrid, including S541-S544, wherein:

[0106] S541. Determine the backup capacity requirement of the microgrid according to the load peak period;

[0107] S542, determining the data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model;

[0108] S543. Determine the power deviation allocated to each sub-microgrid according to the data deviation tolerance;

[0109] S544. According to the power deviation, the power distribution strategy is adjusted to generate the corresponding power dispatching parameters and operation constraints of the sub-microgrid.

[0110] In this embodiment, the backup capacity requirement may include the additional power reserve required by the microgrid during the peak load period. In order to ensure the stability and reliability of the power system, the control device needs to pre-calculate the required backup capacity based on the predicted peak load period. This backup capacity requirement will directly affect the power distribution strategy of each sub-microgrid, ensuring that the power demand of all sub-microgrids can be met during high load periods.

[0111] After determining the backup capacity requirement, the control device inputs the backup capacity requirement into the preset microgrid power model to determine the data deviation tolerance in the power distribution strategy. Data deviation tolerance refers to the maximum deviation value allowed between the power output of each sub-microgrid and the predetermined plan during actual operation. By setting a reasonable deviation tolerance, it can be ensured that the entire microgrid can still maintain stable operation when the load fluctuates greatly.

[0112] Next, the control device will determine the power deviation allocated to each sub-microgrid based on the data deviation tolerance. Power deviation refers to the difference between the actual output power of each sub-microgrid and the planned power during actual operation. By reasonably allocating power deviation, it can be ensured that when the load fluctuates, each sub-microgrid can coordinate with each other and jointly cope with high load demand.

[0113] Finally, the control device will determine the actual value of the power currently used by each sub-microgrid based on the power deviation, so as to adjust the power distribution strategy and generate the corresponding power dispatching parameters and operating constraints of the sub-microgrid. This process ensures that in actual operation, each sub-microgrid can flexibly adjust its power generation and power consumption plan according to the real-time load situation and energy storage status to achieve efficient and stable operation of the entire microgrid. These power dispatching parameters and operating constraints will guide each sub-microgrid on how to adjust the power generation and charging and discharging plans of energy storage equipment during actual operation to ensure stable and efficient power supply of the entire microgrid, optimize the power output of each sub-microgrid, make full use of renewable energy, and reduce dependence on the main power grid.

[0114] Optionally, refer to Figure 4 Another embodiment of the present invention provides a microgrid control method based on the above Figure 3 In the illustrated embodiment, the step of determining the data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model includes S5421-S5424, wherein:

[0115] S5421. Calculate the required reserve capacity ratio of each sub-microgrid according to the reserve capacity demand;

[0116] S5422. Evaluate the stability of the total output power of the microgrid under different load conditions and the stability of the output power of each sub-microgrid under different load conditions according to the reserve capacity ratio, so as to generate a corresponding stability report;

[0117] S5423. Determine the maximum acceptable power deviation of each sub-microgrid according to the preset microgrid power model and stability report;

[0118] S5424. Determine the maximum power deviation as the data deviation tolerance.

[0119] In this embodiment, the reserve capacity ratio is determined by the reserve capacity demand, wherein the reserve capacity ratio can represent the ratio of the reserve power required to be provided by each sub-microgrid during the load peak period to its total power generation. After calculating the reserve capacity ratio, the control device will evaluate the stability of the total output power of the microgrid under different load conditions, and the stability of the output power of each sub-microgrid under different load conditions. This evaluation process will generate a detailed stability report, wherein the stability report includes the reserve capacity ratio of each sub-microgrid, the stability evaluation results, and the recommended power deviation range.

[0120] The generation of stability reports is crucial because it provides key information to the control device to ensure that the entire microgrid can maintain stable operation when the load fluctuates. The recommended power deviation range in the report will guide the control device on how to reasonably allocate power deviation during actual operation to cope with the needs under different load conditions.

[0121] Next, the control device will combine the preset microgrid power model and stability report to determine the maximum acceptable power deviation of each sub-microgrid. The maximum power deviation refers to the maximum difference between the actual output power of each sub-microgrid and the predetermined plan during actual operation. After determining the maximum power deviation, the control device sets it as the data deviation tolerance to ensure that the entire microgrid can maintain stable operation when the load fluctuates greatly.

[0122] By setting a reasonable tolerance for data deviation, the control device can flexibly respond to load fluctuations, while ensuring that each sub-microgrid can coordinate with each other during high-load periods to jointly respond to power demand. Ultimately, this process will generate the power dispatch parameters and operating constraints of the corresponding sub-microgrid, and guide each sub-microgrid in how to adjust the power generation and charging and discharging plans of energy storage equipment during actual operation, so as to achieve efficient and stable operation of the entire microgrid.

[0123] Optionally, refer to Figure 5 Another embodiment of the present invention provides a microgrid control method based on the above Figure 4 In the embodiment shown, the control method further includes S700-S1000, wherein:

[0124] S700, obtaining a plurality of historical energy storage data and historical electricity consumption data;

[0125] S800, analyzing the relationship between historical energy storage data and historical electricity consumption data, determining the charging and discharging characteristics of the energy storage device and the operating characteristics of the sub-microgrid under different load conditions;

[0126] S900, establishing a target power model according to charging and discharging characteristics and operating characteristics;

[0127] S1000, training a target power model based on a neural convolution algorithm, importing multiple current energy storage data and multiple current power consumption data of loads into the target power model to generate a preset microgrid power model.

[0128] In this embodiment, the historical energy storage data may include the charge and discharge records of the energy storage device in the past period of time, while the historical power consumption data covers the power consumption of each sub-microgrid in the corresponding period of time. By analyzing these historical energy storage data and historical power consumption data, the charge and discharge rules of the energy storage device and the operating characteristics of the sub-microgrid under different load conditions can be determined.

[0129] First, the control device will obtain multiple historical energy storage data and historical power consumption data. These data can come from the monitoring system of the microgrid, which records the charging and discharging status of the energy storage equipment and the power consumption of each sub-microgrid in the past period of time. By analyzing these data, the charging and discharging rules of the energy storage equipment and the operating characteristics of the sub-microgrid under different load conditions can be revealed.

[0130] For example, in a residential area, the charging and discharging characteristics of the energy storage device may be different in three different situations: when family A uses only lighting, when using lighting and induction cooker, and when using air conditioning equipment, induction cooker and lighting. By analyzing these historical data, it can be found that the discharge speed of the energy storage device of family A is faster when using air conditioning equipment, and the discharge speed is slower when only lighting is used. In addition, the operating characteristics of the sub-microgrid of family A will also be different under these three different load conditions. For example, under high load conditions, the energy storage device may need to be charged and discharged frequently to meet the power demand, while under low load conditions, the energy storage device can maintain a stable charging and discharging state for a long time. And under high load conditions, the voltage fluctuation of the sub-microgrid may be greater, while it is relatively stable under low load conditions. For example, under normal circumstances, the power supply voltage output by the sub-microgrid is stable at 220V, and the fluctuation does not exceed 0.5%, but when multiple electrical appliances such as air conditioning equipment, induction cooker and lighting are used at the same time, the voltage may drop to 215V, and the fluctuation range may reach 1%. When only lighting appliances are used, the voltage fluctuation is small, and the power supply voltage may remain at 220V, with a fluctuation of no more than 0.6%. By analyzing the charging and discharging characteristics and operating characteristics, the control device can establish a target power model that can accurately reflect the charging and discharging behavior of the energy storage device under different load conditions and the operating characteristics of the sub-microgrid. Similarly, the operating characteristics of other household sub-microgrids under different combinations of electrical equipment can also be analyzed through historical data.

[0131] Next, the target power model is trained based on the neural convolution algorithm. The neural convolution algorithm is an advanced machine learning technology that can process and analyze large amounts of complex data to discover potential relationships between the data. By importing multiple current energy storage data and multiple load current power consumption data into the target power model, the target power model is trained to screen out the most effective parameters and features to generate a preset microgrid power model. The training process involves repeatedly iterating the model to continuously optimize its prediction accuracy until the predetermined performance standard is met. During the training process, the control device will use the deep learning capabilities of the neural convolution algorithm to identify the complex relationship between the charging and discharging behavior of the energy storage device and the operating characteristics of the sub-microgrid. In this way, the target power model can more accurately predict the power demand and supply under different load conditions, thereby providing strong support for the efficient operation of the microgrid.

[0132] After the training is completed, the control device will use the generated preset microgrid power model to analyze and predict the real-time operation data of the microgrid. This will help to timely discover potential power imbalance problems and take corresponding adjustment measures to ensure the stable operation of the microgrid.

[0133] In actual applications, the control device will adjust the power generation of each sub-microgrid and the charging and discharging plan of the energy storage device in real time according to the preset microgrid power model. For example, during peak load periods, the control device can prioritize the energy storage device for discharge to relieve grid pressure; and during low load periods, the energy storage device can be prioritized for charging in case of emergency. In this way, the microgrid can use renewable energy more efficiently, reduce dependence on the main grid, and improve the stability and reliability of the entire system.

[0134] Optionally, refer to Figure 6 Another embodiment of the present invention provides a microgrid control method based on the above Figures 1 to 5 In any of the illustrated embodiments, the step of controlling the power output of each sub-microgrid according to the power dispatch parameters and the operating constraints includes S610-S620, wherein:

[0135] S610, determining the charging and discharging strategy of the energy storage equipment and the grid access strategy of each sub-microgrid according to the power dispatch parameters and operation constraints;

[0136] S620: Continuously adjust the energy storage device status and grid connection status of each sub-microgrid according to the charging and discharging strategy and the grid access strategy to control the power output of each sub-microgrid.

[0137] In this embodiment, the stability and economy of the microgrid under different operating conditions are ensured by combining the operating constraints and the power dispatching parameters. The operating constraints may include, but are not limited to, the maximum charge and discharge power of the energy storage device, the voltage and frequency limits of the power grid, the volatility of renewable energy generation, etc. The power dispatching parameters may involve load forecasting, electricity prices, the current status of the energy storage device, etc.

[0138] First, the control device will determine the charging and discharging strategies of the energy storage devices of each sub-microgrid and the grid access strategy based on the power dispatch parameters and operating constraints. For example, during periods of low electricity prices, the control device can prioritize the charging of energy storage devices to reduce operating costs by taking advantage of lower electricity prices. During periods of high electricity prices, energy storage devices can be prioritized for discharging to reduce reliance on high-priced electricity.

[0139] Secondly, the control device will continuously adjust the energy storage device status and grid connection status of each sub-microgrid according to the determined charging and discharging strategy and grid access strategy to control the power output of each sub-microgrid. For example, when the load of a sub-microgrid suddenly increases, the control device can quickly adjust the discharge power of the energy storage device to meet the load demand. For example, in normal operation, only lighting appliances are turned on. At this time, the air-conditioning equipment is turned on, and the load suddenly increases. The control device will respond immediately and increase the discharge power of the energy storage device to meet the total demand of the air-conditioning equipment and lighting appliances. At the same time, the control device will monitor the voltage and frequency of the grid in real time to ensure that the operating limits of the grid will not be exceeded during the adjustment process. In addition, the control device can also decide whether to disconnect the sub-microgrid from the main grid according to the real-time status of the grid to avoid grid overload.

[0140] In addition, the control device can also dynamically adjust the power generation plan of each sub-microgrid based on real-time data and forecast information. For example, during periods of high wind power generation, the control device can give priority to dispatching wind power generation equipment to make full use of renewable energy. During periods of low wind power generation, other types of power generation equipment can be given priority to ensure the stability of power supply. Similar strategies can also be adopted for the dispatching of solar power generation equipment. When the weather is clear and solar power generation is sufficient, the control device can give priority to the use of solar power generation equipment and reduce the operation of other power generation equipment to reduce operating costs and improve energy utilization efficiency. On the contrary, on cloudy days or at night, when solar power generation is insufficient, the control device can adjust the power generation plan and give priority to the use of energy storage equipment or connection with the main grid to ensure the continuity of power supply.

[0141] Optionally, refer to Figure 7 Another embodiment of the present invention provides a microgrid control method based on the above Figure 6 In the illustrated embodiment, the steps of determining the charging and discharging strategies of the energy storage devices of each sub-microgrid and the grid access strategy according to the power dispatching parameters and the operating constraints include S611-S614, wherein:

[0142] S611. Calculate the optimal charging and discharging power of each sub-microgrid and the optimal grid connection power according to the power dispatch parameters and operation constraints;

[0143] S612, obtaining the current operating status of the energy storage devices of each sub-microgrid, where the operating status includes the remaining capacity, charging and discharging efficiency, and health status;

[0144] S613, determining the charging and discharging strategy of each sub-microgrid according to the optimal charging and discharging power, the optimal connection power of the grid and the operating status;

[0145] S614: Determine a grid access strategy based on the power consumption data and the optimal grid connection power.

[0146] In this embodiment, the optimal charge and discharge power may include the optimal charge power and the optimal discharge power, which are calculated based on the current state of the energy storage device and the expected load demand. During the calculation process, the control device will consider factors such as the maximum charge and discharge power limit of the energy storage device, the current remaining capacity, and the charge and discharge efficiency to ensure that while meeting the load demand, the energy storage device will not be excessively damaged.

[0147] The optimal power connection of the grid refers to the optimal power value of each microgrid connected to the main grid under specific load conditions. The determination of this value needs to take into account factors such as the voltage and frequency limits of the grid, the power supply capacity of the main grid, and the power generation capacity of the microgrid. By calculating the optimal power connection, the control device can ensure that when connected to the main grid, it will not cause overload or instability to the grid.

[0148] Obtaining the current operating status of the energy storage devices in each sub-microgrid is a crucial step. This status information includes remaining capacity, charging and discharging efficiency, and health status, which will directly affect the formulation of charging and discharging strategies. For example, if the health of the energy storage device is poor, the control device may reduce its charging and discharging frequency to extend the service life of the device.

[0149] According to the optimal charging and discharging power, the optimal grid connection power and the operating status, the control device will determine the charging and discharging strategies of each sub-microgrid. These strategies will ensure that the capacity of the energy storage equipment is maximized while meeting the load demand, reduce dependence on the main grid, and improve the economy and reliability of the entire microgrid system.

[0150] Based on the power consumption data and the optimal grid connection power, the control device will determine the grid access strategy. This includes deciding when to connect or disconnect the sub-microgrid from the main grid to cope with load fluctuations and uncertainties in renewable energy generation. For example, during periods of high wind power generation, the control device may choose to disconnect from the main grid to make full use of wind power generation. During periods of low wind power generation, the main grid may be reconnected to ensure the stability of power supply. The microgrid can respond more flexibly to various operating conditions and achieve efficient, stable and economical power supply.

[0151] Optionally, refer to Figure 8 Another embodiment of the present invention provides a microgrid control method based on the above Figure 6 In the embodiment shown, the steps of continuously adjusting the energy storage device status and the grid connection status of each sub-microgrid according to the charging and discharging strategy and the grid access strategy to control the power output of each sub-microgrid include S621-S626, wherein:

[0152] S621. Continuously obtain the current status of the energy storage devices of each sub-microgrid and the real-time status of the grid connection according to the charging and discharging strategy and the grid access strategy;

[0153] S622, determining a state deviation according to the current state of the energy storage device, the real-time state of the grid connection, and the preset optimal sub-microgrid state;

[0154] S623, analyzing the difference between the current state of the energy storage device of each sub-microgrid and the preset optimal state to generate a first analysis result, where the current state of the energy storage device includes the charge and discharge rate, the remaining capacity and the health status;

[0155] S624, analyzing the difference between the real-time state of the grid connection and the preset optimal state to generate a corresponding second analysis result, where the real-time state of the grid connection includes voltage stability, frequency stability, and power transmission efficiency;

[0156] S625. Generate energy storage device state deviation data and grid connection state deviation data of each sub-microgrid according to the first analysis result and the second analysis result;

[0157] S626. According to the energy storage device state deviation data and the grid connection state deviation data, adjust the energy storage device state and the grid connection state of each sub-microgrid so that the power output of each sub-microgrid meets the power dispatching parameters and operation constraints.

[0158] In this embodiment, it is critical to continuously obtain the current status of the energy storage devices of each sub-microgrid and the real-time status of the grid connection. This data provides real-time feedback to the control device, enabling it to adjust its strategy in time to cope with various changes in grid operation. For example, if the charge and discharge rate of the energy storage device suddenly drops, the control device needs to quickly identify the cause and take action, which may be due to reduced efficiency caused by aging of the equipment or excessive ambient temperature.

[0159] Determining the state deviation based on the current state of the energy storage device, the real-time state of the grid connection, and the preset optimal sub-microgrid state is a key step in achieving precise control. The control device will analyze the difference between the actual state of the energy storage device and the grid connection and the ideal state to generate deviation data. These deviation data will be used to guide subsequent adjustment measures. Among them, the preset optimal sub-microgrid is set by comprehensively considering factors such as the operating efficiency, economy, and reliability of the microgrid. The setting of the optimal state needs to be based on historical data, equipment performance parameters, and actual operating experience to ensure that the microgrid can achieve the optimal operating state under various conditions.

[0160] Analyzing the difference between the current state of the energy storage equipment of each sub-microgrid and the preset optimal state to generate the first analysis result is an important step in ensuring the efficient operation of the energy storage equipment. By comparing the actual state with the optimal state, the control device can identify the shortcomings of the energy storage equipment and whether there are potential risks, such as low charging and discharging rates, insufficient remaining capacity, or poor health. In response to these problems, the control device can formulate corresponding adjustment measures to improve the operating efficiency and life of the energy storage equipment.

[0161] Similarly, analyzing the difference between the real-time state of the grid connection and the preset optimal state to generate the corresponding second analysis result is the key to ensuring the stable operation of the grid. The analysis of the grid connection state includes aspects such as voltage stability, frequency stability, and power transmission efficiency. If a deviation in the grid connection state is found, the control device will take corresponding measures, such as adjusting the output of the power conditioner or changing the connection method between the sub-microgrid and the main grid, to ensure the stability and reliability of the grid. For example, if the voltage stability is poor, the control device may reduce the power supply to certain non-critical loads to reduce the pressure on the grid.

[0162] Generating the energy storage device state deviation data and grid connection state deviation data of each sub-microgrid based on the first analysis result and the second analysis result is a key step to achieve precise control. These deviation data will be used to guide subsequent adjustment measures to ensure that the power output meets the power dispatch parameters and operation constraints. The control device will formulate corresponding adjustment strategies based on the deviation data, such as adjusting the charging and discharging power of the energy storage device, changing the connection time between the sub-microgrid and the main grid, etc.

[0163] Among them, the state deviation data of the energy storage device may include charge and discharge rate deviation, remaining capacity deviation, health status deviation and charge and discharge efficiency deviation, wherein the charge and discharge rate deviation means that by comparing the actual charge and discharge rate with the preset optimal charge and discharge rate, the control device can determine whether it is necessary to adjust the charge and discharge strategy of the energy storage device to ensure that it operates within the optimal rate range. The remaining capacity deviation means that the deviation data of the remaining capacity will help the control device evaluate whether the energy storage device can meet the load demand in the future. If the remaining capacity is lower than expected, the control device may reduce the discharge depth of the energy storage device to avoid equipment damage caused by excessive discharge. The health status deviation means that by analyzing the health status deviation data of the energy storage device, the control device can promptly discover potential faults or performance degradation problems. For example, in the case of health status deviations such as increased battery internal resistance and abnormal temperature, the control device can take preventive maintenance measures to avoid equipment failure. The charge and discharge efficiency deviation means that the deviation data of the charge and discharge efficiency will help the control device evaluate the actual operating efficiency of the energy storage device. If the efficiency is found to be lower than expected, the control device can adjust the charge and discharge strategy to improve the energy utilization of the overall system.

[0164] In this embodiment, the control device will adjust the energy storage device status and grid connection status of each sub-microgrid according to the energy storage device status deviation data and the grid connection status deviation data. For example, if the health of the energy storage device is poor, the control device may reduce its charging and discharging frequency to extend the service life of the device. At the same time, if the real-time status of the grid connection shows that the voltage stability is poor, the control device may adjust the output of the power conditioner to ensure the stable operation of the grid.

[0165] By continuously adjusting the energy storage device status and grid connection status of each sub-microgrid, the control device can ensure the economy and reliability of the entire microgrid system. This dynamic adjustment mechanism enables the microgrid to respond more flexibly to various operating conditions and achieve efficient, stable and economical power supply. Ultimately, the microgrid system can maximize the capacity of energy storage equipment while meeting load demand, reduce dependence on the main grid, and improve the operating efficiency and stability of the entire system.

[0166] The present invention also proposes a control device, which includes: the control device includes: a memory, a processor, and a microgrid control program stored in the memory and executable on the processor, the microgrid control program being configured to implement the above microgrid control method.

[0167] It is worth noting that since the control device of the present invention is based on the above-mentioned microgrid control method, the embodiments of the control device of the present invention include all technical solutions of all embodiments of the above-mentioned microgrid control method, and the technical effects achieved are exactly the same, which will not be repeated here.

[0168] The present invention also provides a microgrid, which includes the control device as described in the above embodiment.

[0169] It is worth noting that since the microgrid of the present invention is based on the above-mentioned control device, the embodiments of the microgrid of the present invention include all technical solutions of all embodiments of the above-mentioned control device, and the technical effects achieved are also exactly the same, which will not be repeated here.

[0170] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0171] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0173] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control method for a microgrid, wherein the microgrid comprises a plurality of sub-microgrids, wherein the plurality of sub-microgrids are respectively connected to a plurality of corresponding loads, characterized in that: The control method comprises: Obtain the current energy storage data of multiple sub-microgrids in the monitoring area, the current power consumption data of each load, and the historical grid load data; Determine the real-time power demand corresponding to each of the sub-microgrids according to the power consumption data; Determine the energy storage state corresponding to each of the sub-microgrids according to the energy storage data; Predicting future grid load trends based on the historical grid load data; Generate power dispatch parameters and operation constraints of the corresponding sub-microgrid according to the real-time power demand, the energy storage state, the future grid load trend and the preset microgrid power model; Controlling the power output of each sub-microgrid according to the power dispatch parameters and operation constraints; The step of generating the corresponding power dispatching parameters and operation constraints of the sub-microgrid according to the real-time power demand, the energy storage state, the future grid load trend and the preset microgrid power model comprises: Determining the priority ranking of each sub-microgrid according to the real-time power demand; Determining a power distribution strategy for each sub-microgrid according to the energy storage state and the priority ranking; Determining a possible load peak period according to the future power grid load trend; According to the load peak period and the preset microgrid power model, the power distribution strategy is adjusted to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid; The step of adjusting the power distribution strategy according to the load peak period and the preset microgrid power model to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid includes: Determining the backup capacity requirement of the microgrid according to the load peak period; Determining a data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model; Determining the power deviation allocated to each sub-microgrid according to the data deviation tolerance; According to the power deviation, the power allocation strategy is adjusted to generate power dispatch parameters and operation constraints of the corresponding sub-microgrid; The step of determining the data deviation tolerance in the power distribution strategy according to the backup capacity requirement and the preset microgrid power model comprises: According to the backup capacity requirement, the backup capacity ratio required by each sub-microgrid is calculated; According to the reserve capacity ratio, the stability of the total output power of the microgrid under different load conditions and the stability of the output power of each sub-microgrid under different load conditions are evaluated to generate a corresponding stability report; the stability report includes the reserve capacity ratio of each sub-microgrid, the stability evaluation result and the recommended power deviation range; Determining a maximum power deviation acceptable to each of the sub-microgrids according to the preset microgrid power model and the stability report; Determining the maximum power deviation as a data deviation tolerance; The step of controlling the power output of each sub-microgrid according to the power dispatch parameters and the operation constraints includes: Determine the charging and discharging strategy of the energy storage equipment and the grid access strategy of each sub-microgrid according to the power dispatch parameters and the operating constraints; According to the charging and discharging strategy and the grid access strategy, continuously adjusting the energy storage device status and grid connection status of each sub-microgrid to control the power output of each sub-microgrid; The step of determining the charging and discharging strategy of the energy storage device of each sub-microgrid and the grid access strategy according to the power dispatching parameters and the operating constraints includes: Calculate the optimal charging and discharging power of each sub-microgrid and the optimal connection power of the grid according to the power dispatch parameters and the operation constraints; Obtain the current operating status of the energy storage devices of each sub-microgrid, wherein the operating status includes remaining capacity, charging and discharging efficiency, and health status; Determine the charging and discharging strategy of each sub-microgrid according to the optimal charging and discharging power, the optimal connection power of the power grid and the operating state; A grid access strategy is determined according to the power consumption data and the optimal connection power of the grid.

2. The microgrid control method according to claim 1, characterized in that: The control method further comprises: Obtain multiple historical energy storage data and historical electricity consumption data; Analyze the relationship between historical energy storage data and historical electricity consumption data to determine the charging and discharging characteristics of the energy storage device and the operating characteristics of the sub-microgrid under different load conditions; Establishing a target power model according to the charge-discharge characteristics and the operation characteristics; The target power model is trained based on a neural convolution algorithm, and a plurality of current energy storage data and a plurality of current power consumption data of loads are imported into the target power model to generate the preset microgrid power model.

3. The microgrid control method according to claim 1, characterized in that: The step of continuously adjusting the energy storage device status and grid connection status of each sub-microgrid according to the charging and discharging strategy and the grid access strategy to control the power output of each sub-microgrid includes: According to the charging and discharging strategy and the grid access strategy, continuously obtain the current status of the energy storage devices of each sub-microgrid and the real-time status of the grid connection; Determining a state deviation according to a current state of the energy storage device, a real-time state of the grid connection, and a preset optimal sub-microgrid state; Analyze the difference between the current state of the energy storage device of each sub-microgrid and the preset optimal state to generate a first analysis result, wherein the current state of the energy storage device includes a charge and discharge rate, a remaining capacity, and a health status; Analyzing a difference between a real-time state of a grid connection and a preset optimal state to generate a corresponding second analysis result, wherein the real-time state of the grid connection includes voltage stability, frequency stability, and power transmission efficiency; Generate energy storage device state deviation data and grid connection state deviation data of each sub-microgrid according to the first analysis result and the second analysis result; According to the energy storage device state deviation data and the grid connection state deviation data, the energy storage device state and the grid connection state of each sub-microgrid are adjusted so that the power output of each sub-microgrid meets the power dispatching parameters and the operating constraints.

4. A control device, characterized in that: The control device comprises: a memory, a processor, and a microgrid control program stored in the memory and executable on the processor, wherein the microgrid control program is configured to implement the microgrid control method according to any one of claims 1 to 3.

5. A microgrid, characterized in that: Comprising a control device as claimed in claim 4.

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