A multi-energy complementary intelligent micro-grid energy management system and a control method thereof

Abstract

The application discloses a kind of multi-purpose complementary intelligent micro-grid energy management system and control method thereof, belong to micro-grid energy scheduling technical field.System includes main grid, AC bus, DC bus, photovoltaic power generation unit, wind power generation unit, distributed biogas power generation unit, energy storage battery group, conversion unit, AC and DC load and global metering node;Main grid realizes AC and DC bus two-way intercommunication by conversion unit, and photovoltaic, wind power, biogas power generation, energy storage, DC charging pile and DC load are uniformly accessed to DC bus, and AC load is hung in AC bus;Energy storage is matched with bidirectional converter to complete DC bus voltage stabilization, MPPT power tracking and bidirectional flow control;Distributed biogas power generation is configured with desulfurization and dehydration gas cabinet, and can realize biogas storage and gas power generation peak shaving.The application builds double-layer load prediction model, climate adaptation wind and light output calibration model, micro-grid bearing capacity prediction and early warning model, according to energy storage state of charge, gas cabinet volume threshold divides multiple operating conditions, realizes wind and light, biogas gaseous energy storage, electrochemical energy storage, four-source collaborative scheduling of large power grid backup.The application solves the problems of intermittent fluctuation of wind and light output in rural scene, biogas gas production waste, poor power supply stability, low energy utilization, can be grid-connected / island dual-mode operation, scheduling is high in degree of refinement, with good economic benefit and power supply reliability, applicable to rural park, breeding base distributed integrated energy scene.

Description

Technical Field

[0001] This invention relates to the fields of microgrid energy dispatch and distributed renewable energy collaborative control technology, and is particularly applicable to energy management of rural off-grid / grid-connected smart microgrids with multi-source complementarity of wind, solar and biogas. Background Technology

[0002] Rural areas have widely deployed photovoltaic power generation, wind power generation, and biogas distributed power generation, but existing microgrid technology has many shortcomings: 1. Wind and solar power generation is affected by weather conditions and the output fluctuates greatly. Relying solely on lithium battery energy storage for peak shaving is costly and has insufficient long-term energy storage capacity, making it unable to adapt to large-scale power fluctuations during the day and night and seasons. Rural biogas continuously produces gas, but without a supporting storage and control mechanism, excess biogas is directly released into the air, wasting energy. During peak electricity demand periods, biogas supply is also insufficient to make up for the load shortfall. 2. Traditional microgrid load forecasting often uses single-layer short-term forecasting algorithms without adjusting for seasonality, holidays, and weather deviations, resulting in large forecasting errors and lagging dispatching strategies; wind and solar power output relies solely on theoretical numerical calculations without calibration using historical climate data, leading to significant discrepancies between actual and estimated output. 3. Existing energy management systems lack an online assessment mechanism for microgrid carrying capacity, making it impossible to predict potential operational risks such as overload and voltage exceeding limits in advance. Fault handling is mostly reactive, resulting in poor system operational safety. 4. Conventional microgrid dispatch only coordinates the power interaction among wind, solar and energy storage, without incorporating biogas gas holder gas storage into the system's peak-shaving system. The energy dispatching conditions are divided in a coarse manner, the path for consuming surplus power and replenishing power in short supply is singular, and the overall comprehensive energy utilization rate is low.

[0003] Therefore, there is an urgent need to design an integrated smart microgrid system that combines wind and solar power, biogas gaseous energy storage, hybrid electrochemical energy storage, and large power grid backup, and to provide supporting strategies for refined load forecasting, output calibration, carrying capacity early warning, and gas-electric linkage dispatching to improve the stability and utilization efficiency of rural comprehensive energy supply. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-energy complementary smart microgrid energy management system and its control method. The system uses biogas holders as gaseous energy storage units to participate in microgrid peak shaving. It is combined with multi-algorithm collaborative management and control, including dual-layer load forecasting, meteorological, wind and solar power output calibration, and system carrying capacity early warning. The system refines the operating conditions based on the energy storage SOC and the gas holder volume threshold, thereby achieving efficient and complementary scheduling of multiple energy sources and taking into account power supply stability, energy utilization, and operation and maintenance safety.

[0005] Technical solution

[0006] (1) A multi-energy complementary smart microgrid energy management system A multi-energy complementary smart microgrid energy management system, (1) A multi-energy complementary smart microgrid energy management system includes a main grid MG, an AC bus ACW, a DC bus DCW, several photovoltaic power generation units PV, several wind power generation units WT, a distributed biogas power generation unit DG, an energy storage battery pack ESS, a converter unit, a DC load DCLoad, an AC load ACLoad, a charging pile EV, and several metering nodes.

[0007] The main power grid MG is bidirectionally connected to the AC bus ACW and the DC bus DCW through the converter unit; All photovoltaic power generation units (PV), wind power generation units (WT), distributed biogas power generation units (DG), energy storage battery packs (ESS), charging piles (EV), and various DC loads (DCLoad) are connected in parallel to the DC bus (DCW); all AC loads (ACLoad) are connected to the AC bus (ACW). All power supply equipment, energy storage equipment, and electrical load ports connected to the AC and DC bus are equipped with metering nodes. The metering nodes serve as energy data acquisition terminals, collecting power, voltage, and current data from each circuit in real time and uploading them to the system controller. The energy storage battery pack ESS is electrically connected to a bidirectional converter; the bidirectional converter has three functions: ① to realize the charging and discharging power regulation of the energy storage battery in MPPT maximum power point tracking mode; ② to adjust the DC bus DCW output voltage in real time to maintain the bus voltage regulation operation; ③ to manage the bidirectional power flow between the energy storage and the DC bus. The Energy Storage Battery Pack (ESS) uses any one or more of lithium-ion batteries, lead-acid batteries, and supercapacitors to form a hybrid energy storage system. The distributed biogas power generation unit (DG) includes a biogas pretreatment device, a gas storage tank, and a generator set. The biogas is pretreated by desulfurization, dehydration, and denitrification and then stored in the gas storage tank. The generator set is an internal combustion engine generator set or a gas turbine generator set. The gas storage tank can store excess biogas and release biogas to supply the generator set for power generation during peak electricity demand periods. The system has a built-in main controller, which includes a load forecasting module, a wind and solar power output calibration module, a microgrid carrying capacity forecasting and early warning module, and an energy dispatch control module.

[0008] The system can operate in two modes: grid-connected operation and islanded off-grid operation.

[0009] The metering node is the basic data acquisition terminal for system energy dispatch, and it monitors and meters the energy flow throughout the entire domain.

[0010] (2) A method for energy management and control of multi-energy complementary smart microgrids The control method based on the above-mentioned intelligent microgrid energy management system includes four major execution steps: S1 dual-layer load forecasting, S2 wind and solar power output calibration and forecasting under multiple climate conditions, S3 online prediction and risk warning of microgrid carrying capacity, and S4 closed-loop energy collaborative scheduling under multiple operating conditions.

[0011] S1 Two-tier load forecasting steps based on historical data S101: The system collects historical operating load data, real-time operating data, historical meteorological data, and day-ahead forecast meteorological data of the microgrid; historical load data integrates the electricity consumption records of all electrical equipment, and incorporates multi-dimensional datasets of seasonal changes, holidays, meteorological impacts, user electricity consumption habits, and sudden intervention events to build an equipment electricity consumption analysis database; S102: Build a microgrid hybrid simulation model to make preliminary point predictions of photovoltaic power generation output, wind power generation output, and overall electricity load; construct a day-ahead load deviation correction model based on historical predicted load, measured load, and weather forecast deviation data, and output the day-ahead load correction prediction value. S103: Integrate historical load, real-time operating parameters, meteorological conditions, and human intervention events to build an intraday rolling load forecast model and output intraday hourly load forecast results; S104: Select N sets of system operation characteristic parameters and input them into the hybrid simulation model to complete iterative training and verification; S105: The trained simulation model continuously predicts the future output and load changes of the microgrid, providing data for energy dispatch.

[0012] S2 Wind and Solar Power Generation Calibration Procedures under Different Climate Conditions S201: Collect historical weather data for the region over many years and actual wind / solar power output data for the corresponding time periods to construct a meteorological-power output correlation database; S202: Obtain weather forecast data for future periods, retrieve historical meteorological samples for the same period, and perform proportional compensation calibration on the forecast wind speed and light intensity based on historical wind speed and actual measured values ​​of light intensity. S203: Divide the database samples into training set, test set, and validation set, input them into the microgrid collaborative power model to complete training and verification, and proceed to the next step after passing the verification. S204: Import the calibrated weather forecast data into the power model and output preliminary wind power and photovoltaic power output forecasts; S205: Real-time acquisition of on-site ambient light and wind speed data, dynamically correcting the predicted wind and solar power output values; S206: Calculate the theoretical maximum power of photovoltaic and wind power systems, and ensure stable operation of the systems based on the corrected actual predicted output.

[0013] S3 Microgrid Capacity Prediction and Risk Warning Steps S301: Obtain historical operating parameters (load, voltage, current, historical fault records) and real-time operating parameters of the microgrid, as well as the rated constraint parameters of each device, through the SCADA data acquisition system; S302: Call the trained hybrid simulation model and, in combination with various system operating constraints, predict the current upper limit of the microgrid's carrying capacity online; S303: Determine whether the system power supply and demand are unbalanced. If the supply and demand are unbalanced, start grid-connected power regulation. S304: Iteratively revise the load-bearing capacity prediction results based on grid-connected operation data, and compare the real-time operating load with the load-bearing capacity limit; S305: When the actual operating load exceeds the set threshold of the bearing capacity, the system immediately triggers an audible and visual alarm, pushes risk warning information, and automatically executes emergency response measures such as load reduction, power allocation, and grid connection to avoid overload and abnormal voltage faults.

[0014] S4 Multi-condition Closed-Loop Coordinated Energy Dispatch Steps The system uses metering nodes across the entire region as the data core, dynamically switches scheduling strategies based on load forecasting results, and classifies seven typical operating conditions: Condition 1: Total wind and solar power generation > total load demand, and the state of charge (SOC) of the energy storage battery < the maximum SOC threshold; Dispatch strategy: Wind and solar power output is prioritized to supply local AC and DC loads, and surplus power is used to charge energy storage batteries through bidirectional converters, which operate in charging mode. Operating Condition 2: Total wind and solar power generation < total load demand, and energy storage battery SOC > minimum SOC threshold; Scheduling strategy: The wind and solar units operate in MPPT mode to output maximum power, and the power deficit is made up by the discharge of the energy storage battery. The bidirectional converter operates in boost mode and works in discharge mode. Operating Condition 3: Total wind and solar power generation > total load demand, and the SOC of the energy storage battery ≥ the maximum SOC threshold; Dispatch strategy: Energy storage stops charging, surplus power is prioritized for local AC loads, and the remaining power is fed back to the main grid for sale. Condition 4: Total wind and solar power generation is greater than or equal to the total load demand, and the gas storage tank volume is less than the maximum gas tank volume threshold. Dispatch strategy: Distributed biogas generator sets operate at low load and no load, and all biogas continuously produced by the biogas digester is stored in the gas storage tank, which is in the gas filling and biogas storage mode. Condition 5: Total wind and solar power generation is less than total load demand, and the gas storage tank volume is greater than the minimum gas tank volume threshold. Dispatch strategy: Increase the operating load of biogas generator sets to meet electricity demand. When the biogas produced by the biogas digester is insufficient, the biogas stored in the gas holder is used to supplement combustion and power generation. The gas holder is in the gas release and energy release mode. Condition 6: Total wind and solar power generation > total load demand, and gas storage tank volume ≥ maximum gas tank volume threshold; Dispatch strategy: The biogas generator set fully utilizes the biogas produced by the biogas digester to generate electricity, which is then supplied to local loads; any surplus electricity is successively consumed through energy storage charging, AC load consumption, and grid connection to the main power grid. Operating Condition 7: Total wind and solar power generation < total load demand, gas storage tank volume ≤ minimum volume threshold, energy storage SOC ≤ minimum threshold; Dispatch strategy: If wind, solar, biogas, and energy storage cannot make up for the power deficit, the main grid will transmit power in reverse to supplement the load and ensure uninterrupted power supply to the microgrid.

[0015] The control method further includes: energy scheduling is based on real-time power data collected by metering nodes, and the scheduling strategy is dynamically updated in combination with load forecast results; The gas storage tank is linked with the biogas generator set to enable biogas gaseous energy storage to participate in the long-term peak shaving and valley filling of the microgrid.

[0016] Compared with the prior art, the advantages of this invention are as follows:

[0017] 1. This invention is the first to integrate biogas storage tanks as gaseous long-term energy storage units into a microgrid dispatching system. During periods of surplus wind and solar power, biogas is stored, and during peak electricity demand, biogas is released to generate electricity. This makes up for the shortcomings of long-term peak shaving in electrochemical energy storage, significantly reduces the purchase cost of energy storage equipment, and eliminates the waste of biogas by releasing it into the air. 2. The system adopts a two-layer load forecasting method combining day-ahead and intraday forecasting, superimposed with a meteorological, wind, and solar power output calibration algorithm, which effectively reduces the error in renewable energy output and load forecasting, and makes the dispatching strategy more forward-looking; the system is equipped with an online carrying capacity early warning mechanism, which can identify system overload risks in advance and significantly improve operational safety. 3. It refines the classification of 7 types of boundary operating conditions, coordinates the power interaction of five levels of photovoltaic, wind power, biogas, hybrid energy storage and large power grid, improves the energy consumption path, and increases the comprehensive energy utilization rate by 15%~25%; it supports grid-connected and islanded dual-mode operation, and is suitable for scenarios in rural and remote areas without grid power. 4. The system features a full range of port deployment nodes, enabling continuous monitoring and traceability of energy flow. This facilitates operation and maintenance accounting and energy consumption management. The system is highly automated and can operate stably without human intervention for extended periods. Attached Figure Description

[0018] Figure 1 : Overall topology diagram of the system of this invention; Detailed Implementation

[0019] This embodiment selects a rural integrated farming and breeding park in northern my country as the application scenario. The park covers 120 acres and encompasses vegetable greenhouse cultivation, livestock and poultry breeding, primary processing of agricultural products, and electricity consumption for villagers' daily lives. Its daily electricity load fluctuates significantly, and it possesses abundant photovoltaic, wind power, and biogas resources, making it suitable for the implementation of the multi-energy complementary microgrid system of this invention. The system adopts a dual-mode design of grid-connected / islanded operation, which can flexibly switch according to the main grid's operating status, balancing power supply reliability and energy utilization efficiency.

[0020] I. System Hardware Deployment Details 1. Distributed Power Deployment: A 60kW photovoltaic array using monocrystalline silicon photovoltaic modules is deployed in the park, installed on the roofs of greenhouses and office areas, with the tilt angle adapted to the local latitude to maximize sunlight utilization; a 40kW small wind turbine is installed in an open area of ​​the park, with a hub height of 15 meters, adapted to the local annual average wind speed of 3.5m / s; the distributed biogas power generation unit uses a 25kW internal combustion engine generator set, equipped with an 80m³ flexible gas storage tank. The biogas digester uses a mixed fermentation process of livestock and poultry manure and crop straw, producing approximately 120m³ of biogas per day. After desulfurization, dehydration, and denitrification pretreatment, the biogas is transported to the gas storage tank through pipelines. The pretreatment device can effectively remove impurities in the biogas, avoiding damage to the generator set.

[0021] 2. Energy Storage and Converter Unit: The energy storage battery pack adopts a hybrid energy storage system of 80kWh lithium iron phosphate battery + 20kWh supercapacitor, which takes into account both long-term energy storage and short-term power regulation needs. The lithium iron phosphate battery is responsible for smoothing day and night load fluctuations, while the supercapacitor is used for rapid response to short-term sudden changes in wind and solar power output. It is equipped with a bidirectional DC / DC converter with a rated power of 50kW, supports MPPT mode charging and discharging, and can adjust the DC bus voltage in real time to stabilize it at 400V±5%. The converter unit adopts a bidirectional inverter with a rated capacity of 100kVA to realize bidirectional power conversion between AC and DC buses, ensuring smooth power interaction between the main grid and the microgrid.

[0022] 3. Load and Metering Node Deployment: The DC bus carries DC loads such as irrigation pumps, DC charging piles (2 units, 7kW each), and LED lighting, with a total power of approximately 30kW; the AC bus carries AC loads such as agricultural product processing equipment, household electricity, and office equipment, with a total power of approximately 80kW; smart metering nodes are deployed at each of the photovoltaic, wind power, biogas power generation, energy storage, AC / DC loads, and charging pile ports, totaling 16 nodes. The metering nodes use high-precision energy metering chips to collect data such as voltage, current, power, and electricity consumption in real time, and upload the data to the system main controller via RS485 bus to achieve full-process monitoring of energy flow.

[0023] 4. Main Control and Monitoring Unit: The main control unit adopts an industrial-grade PLC, which is embedded with four major modules: load prediction, wind and solar power output calibration, load capacity early warning, and energy dispatch. It is equipped with a human-machine interactive touch screen, which can display information such as system operating status, energy flow, and equipment parameters in real time. At the same time, it is equipped with a remote monitoring terminal, which allows maintenance personnel to remotely view system data and receive early warning information through a mobile APP or computer, realizing unattended operation and maintenance.

[0024] II. System Operation Flow and Operating Condition Details 1. Dual-layer load forecasting operation: The system automatically starts the load forecasting program every morning at dawn, collecting historical load data from the past 360 days of the park, local meteorological data, holiday information, and recent electricity consumption habits to construct an electricity consumption dataset; it completes the day-ahead load forecast through a hybrid simulation model, and corrects for deviations in the daily weather forecast, outputting the day-ahead load forecast curve; the forecast results are updated every 15 minutes during the day, correcting load fluctuation deviations in real time, with the forecast error controlled within 8%, providing accurate data support for subsequent energy dispatch. For example, during holidays, the system can automatically identify the upward trend in electricity load and adjust the energy storage charging and discharging strategy and biogas power generation operation mode in advance.

[0025] 2. Wind and Solar Output Calibration Operation: The system has a built-in local meteorological-output correlation database covering the past 10 years. It acquires 24-hour weather forecast data daily, retrieves historical meteorological samples from the same period, and compensates for and calibrates the predicted light intensity and wind speed. It also collects real-time measured data of on-site light and wind speed to dynamically correct the predicted wind and solar output, ensuring that the actual operating power of the wind and solar units deviates from the predicted value by no more than 10%. For example, in cloudy weather, the system can lower the predicted photovoltaic output value based on the real-time light intensity to avoid overcharging and discharging of energy storage due to prediction errors.

[0026] 3. Capacity Warning Operation: The SCADA system collects real-time data on microgrid voltage, current, load, and equipment operating parameters. Every 5 minutes, it uses a hybrid simulation model to estimate the system's capacity limit. When the real-time load approaches 90% of the capacity limit, the system triggers a Level 1 warning and sends a notification. When the load exceeds the capacity limit, a Level 2 audible and visual alarm is triggered, automatically implementing load reduction operations (prioritizing the disconnection of non-essential AC loads) and simultaneously initiating grid-connected supplementary power to prevent system overload failures. During the trial operation, the system successfully issued warnings of three potential overload risks, all of which were handled through emergency response, ensuring stable power supply.

[0027] 4. Multi-condition energy dispatch operation: The system automatically switches between 7 operating conditions based on energy storage SOC, gas holder volume, wind and solar power output, and load demand to achieve refined dispatch. Condition 1 (Sunny daytime): The total output of photovoltaic and wind power is about 75kW, which is greater than the total load of the park of 50kW. At this time, the energy storage SOC is 40% (below the maximum threshold of 80%). The wind and solar power output is given priority to supply the local load. The surplus 25kW of power is used to charge the energy storage through the bidirectional converter. The bidirectional converter works in charging mode. After about 2 hours, the energy storage SOC reaches 80%, and then the system switches to Condition 3.

[0028] Operating Condition 3 (Sunny Afternoon): The energy storage SOC reaches 80%, and the surplus wind and solar power is given priority to local AC loads. The remaining 15kW of power is transmitted back to the main grid to achieve surplus grid connection, and about 200 yuan of power generation revenue can be obtained per day.

[0029] Operating Condition 4 (Sunny daytime, gas holder not full): The biogas generator set operates at low load (5kW), and all the biogas produced by the biogas digester is stored in the gas holder, increasing the gas holder volume from 30m³ to 65m³, thus realizing biogas energy storage.

[0030] Condition 5 (Cloudy and windless weather): The total output of photovoltaic and wind power is only 10kW, which is lower than the load demand of 50kW. At this time, the gas holder volume is 65m³ (20m³ higher than the minimum threshold). The biogas generator set is upgraded to 25kW and operates at full load. The power deficit is made up by energy storage discharge. The bidirectional converter works in discharge mode, and the gas holder volume gradually decreases to ensure uninterrupted power supply to the park.

[0031] Condition 7 (3 consecutive days of rain): The gas holder volume drops to 15m³, and the energy storage SOC drops to 20% (below the minimum threshold of 30%). Wind, solar, biogas, and energy storage are all unable to make up for the load shortfall. The system automatically switches to grid-connected mode, and the main power grid supplies 20kW of power to replenish the load. Multi-energy complementary dispatch will resume after the weather improves.

[0032] This embodiment is only a preferred example. In actual applications, the capacity of distributed power sources, energy storage ratio and gas holder volume can be adjusted according to the resource conditions and load requirements of different scenarios to adapt to various scenarios such as rural settlements, breeding bases, and small industrial parks. All of these can achieve the technical effects of multi-energy complementarity, efficient scheduling and safe and stable operation.

Claims

1. A multi-energy complementary smart microgrid energy management system, characterized in that: It includes the main grid (MG), AC bus (ACW), DC bus (DCW), at least one set of photovoltaic power generation units (PV), at least one set of wind power generation units (WT), distributed biogas power generation units (DG), energy storage battery packs (ESS), converter units, DC loads (DCLoad), AC loads (ACLoad), charging piles (EV), and several metering nodes. The main power grid (MG) achieves bidirectional electrical connection between the AC bus (ACW) and the DC bus (DCW) through the converter unit; The DC bus (DCW) is connected in parallel to photovoltaic power generation units (PV), wind power generation units (WT), distributed biogas power generation units (DG), energy storage battery packs (ESS), charging piles (EV), and DC loads (DCLoad); the AC bus (ACW) is connected to all AC loads (ACLoad). Each power supply device, energy storage device, and electrical load port connected to the AC bus or DC bus is equipped with a metering node. The metering node is used to collect power, voltage, and current operating data in real time and upload them to the system main controller. The energy storage battery pack (ESS) is electrically connected to a bidirectional converter; the bidirectional converter is used to realize MPPT maximum power point charging and discharging, DC bus voltage regulation and control, and bidirectional power flow management between the energy storage and the DC bus; the energy storage battery pack (ESS) is a hybrid energy storage system composed of any one or more combinations of lithium-ion batteries, lead-acid batteries, and supercapacitors. The distributed biogas power generation unit (DG) includes a biogas pretreatment device, a gas storage tank, and a generator set. The biogas is pretreated by desulfurization, dehydration, and denitrification and then stored in the gas storage tank. The generator set is an internal combustion engine generator set or a gas turbine generator set. The gas storage tank is used for storing surplus biogas and releasing biogas to generate electricity during peak electricity demand. The main controller is embedded with a load prediction module, a wind and solar power output calibration module, a microgrid carrying capacity prediction and early warning module, and an energy dispatch control module.

2. The multi-energy complementary smart microgrid energy management system according to claim 1, characterized in that: The system can operate in two modes: grid-connected operation and islanded off-grid operation.

3. The multi-energy complementary smart microgrid energy management system according to claim 1, characterized in that: The metering node is the basic data acquisition terminal for system energy dispatch, and it monitors and meters the energy flow throughout the entire domain.

4. A control method for a multi-energy complementary smart microgrid energy management system according to any one of claims 1 to 3, characterized in that, Includes the following steps: S1: Two-layer load forecasting steps; S2: Steps for calibrating and predicting wind and solar power generation under multiple climate conditions; S3: Steps for online prediction and risk warning of microgrid carrying capacity; S4: Multi-condition closed-loop energy collaborative scheduling steps; in: The S1 two-layer load prediction step specifically includes: S101: Collect historical load data, real-time operation data, historical meteorological data, and current weather forecast data of microgrids, and integrate seasonal, holiday, meteorological, electricity consumption habits, and sudden intervention events to construct an electricity consumption dataset; S102: Build a microgrid hybrid simulation model to predict wind and solar power output and system load at preliminary points, construct a day-ahead load deviation correction model, and output day-ahead corrected load prediction values; S103: Build an intraday rolling load forecasting model and output the intraday hourly load forecasting results; S104: Select multiple sets of operational characteristic parameters to iteratively train and verify the hybrid simulation model; S105: The trained simulation model is used to continuously predict the future operating status of the microgrid; The specific steps for calibrating and predicting wind and solar power generation under S2 multi-climate conditions include: S201: Collect historical meteorological data and actual wind and solar power output data for the corresponding time periods to construct a meteorological-power output correlation database; S202: Retrieve historical meteorological samples from the same period and compensate and calibrate the wind speed and light intensity in the weather forecast; S203: Divide the database samples to complete the training and verification of the collaborative power model; S204: Import calibration meteorological data into the model to output preliminary wind and solar power forecasts; S205: Dynamically correct wind and solar power output values ​​based on on-site measured meteorological data; S206: Calculate the theoretical maximum power of the wind-solar system, and ensure the system operates stably with the corrected output. The S3 bearing capacity online prediction and risk warning steps specifically include: S301: Collects historical fault data, voltage and current data, load data, and real-time operating parameters of the system; S302: Use a hybrid simulation model to predict the current upper limit of the microgrid's carrying capacity online; S303: Determine if there is an imbalance between power supply and demand; if there is an imbalance, initiate grid-connected power regulation. S304: Iteratively correct the bearing capacity prediction results and compare the real-time load with the bearing capacity threshold; S305: When the load exceeds the bearing capacity threshold, the system triggers an alarm and performs emergency power allocation; The S4 multi-condition closed-loop energy collaborative scheduling steps divide the operating conditions into 7 categories and execute corresponding strategies: Operating Condition 1: Total wind and solar power generation > total load demand, energy storage SOC < maximum SOC threshold: wind and solar power supply the load, surplus electricity charges the energy storage, and the bidirectional converter operates in charging mode. Operating Condition 2: Total wind and solar power generation < total load demand, energy storage SOC > minimum SOC threshold: wind and solar MPPT at full capacity, power deficit is made up by energy storage discharge, bidirectional converter boost discharge operation; Operating Condition 3: Total wind and solar power generation > total load demand, energy storage SOC ≥ maximum SOC threshold: Energy storage stops charging, surplus power is supplied to local AC loads and the remaining power is connected to the main grid; Operating Condition 4: Total wind and solar power generation ≥ total load demand, gas holder volume < maximum volume threshold: Biogas unit operates at low load, biogas is stored in gas holder, gas holder is filled with gas to store biogas; Operating Condition 5: Total wind and solar power generation < total load demand power, gas holder volume > minimum volume threshold: Increase biogas unit load for power generation, and when self-produced biogas is insufficient, gas storage in the gas holder is used for replenishment, and gas holder is released to release energy. Operating Condition 6: Total wind and solar power generation > total load demand, gas holder volume ≥ maximum volume threshold: biogas is fully consumed for power generation from self-produced biogas, and surplus electricity is processed in stages through energy storage charging, local load consumption, and grid connection. Condition 7: Total wind and solar power generation < total load demand, gas holder volume ≤ minimum volume threshold and energy storage SOC ≤ minimum SOC threshold: the load shortfall is made up by power supplied from the main grid.

5. The control method for a multi-energy complementary smart microgrid energy management system according to claim 4, characterized in that: The energy scheduling is based on real-time power data collected by metering nodes, and the scheduling strategy is dynamically updated in conjunction with load forecasting results.

6. The control method for a multi-energy complementary smart microgrid energy management system according to claim 1, characterized in that: The gas storage tank is linked with the biogas generator set to enable biogas gaseous energy storage to participate in the long-term peak shaving and valley filling of the microgrid.

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