Distributed power supply grid-connected optimization management system and control method thereof

Abstract

The invention discloses a distributed power supply grid-connected optimization management system and a control method thereof, and belongs to the technical field of distributed power supply grid-connected management, and the control method comprises the steps: judging the operation state of distributed power supply equipment according to natural microenvironment data and operation state data in real time, and when the distributed power supply equipment is judged to be abnormal, executing the operation of the distributed power supply equipment; and issuing a control instruction of equipment shutdown to the distributed interface equipment in the region, predicting the output state of each region based on the operation state judgment result of the distributed power supply equipment in the region, and comprehensively considering the power market transaction and power selling market conditions. And the output state of the distributed power supply equipment in each region is coordinated and distributed based on the overall optimization target, so that the whole distributed power supply grid-connected optimization management system achieves optimal scheduling. According to the method, the comprehensive effect of multiple influence factors can be comprehensively considered, the operation state of the equipment can be accurately judged in a complex operation environment, and then the optimal management strategy is formulated according to different conditions.

Description

Technical Field

[0001] This invention belongs to the field of distributed power grid connection management technology, specifically relating to a distributed power grid connection optimization management system and its control method. Background Technology

[0002] With the acceleration of the global energy transition, distributed power sources (such as solar, wind, small-scale hydropower, and biomass energy) are being used more and more widely in power systems. The integration of distributed power sources provides flexibility and reliability to the power grid, but also brings many challenges, especially in the optimal management of power supply equipment. Typical power supply equipment includes generation equipment (such as photovoltaic panels and wind turbines), power electronic converters (inverters), and energy storage equipment (such as lithium batteries and supercapacitors). The performance, capacity, and operating status of these devices are important aspects of the optimized management of distributed power generation grid connection.

[0003] The operation of distributed power supply equipment is influenced by various factors, such as environmental conditions (temperature, light intensity, wind speed, etc.), equipment aging, and load changes, making its management highly complex. Current technologies typically rely on single operational status data (such as current, voltage, and power) to determine the operational status of power supply equipment, neglecting the combined effects of multiple influencing factors. This can easily lead to misjudgments in complex operating environments. Furthermore, existing technologies primarily rely on set status thresholds for anomaly detection. When faced with complex and changing operating environments, such as fluctuations in equipment operational status data, existing systems may not be able to accurately determine whether the fluctuations are due to normal load changes or equipment failure, thus failing to formulate optimal management strategies for different situations. Summary of the Invention

[0004] The purpose of this invention is to address existing problems by providing a distributed power generation grid-connected optimization management system and its control method. This system determines the operating status of distributed power generation equipment in real time based on natural micro-environment data and operational status data. When a distributed power generation device is determined to be abnormal, a shutdown control command is issued to the distributed interface devices within the region. Based on the operational status determination results of the distributed power generation equipment within the region, the power output status of each region is predicted. Furthermore, this invention comprehensively considers the electricity market transaction and electricity sales market conditions, coordinating and allocating the power output status of distributed power generation equipment in each region based on the overall optimization objective, thereby achieving optimal scheduling for the entire distributed power generation grid-connected optimization management system.

[0005] According to one aspect of this specification, a distributed power grid-connected optimization management system is provided, comprising:

[0006] The distributed interface device is used to collect operating status data of distributed power generation equipment and natural microenvironment data related to distributed power generation, upload the collected data to the distributed area management and control equipment in real time, and receive control commands from the distributed area management and control equipment to adjust the operating status of the distributed power generation equipment.

[0007] Distributed area management equipment is used to manage the operating status of all distributed power devices within a designated area. It judges the operating status of distributed power devices in real time based on natural micro-environment data and operating status data, and when a distributed power device is determined to be abnormal, it issues a control command to the abnormal distributed interface device to shut down the device.

[0008] The remote cluster control center is used to communicate with various distributed regional management and control devices, and predict the power output status of each region based on the judgment results of the operating status of distributed power devices in the region.

[0009] Furthermore, the operating status of distributed power devices is determined in real time based on natural microenvironment data and operational status data, including:

[0010] Determine the current operating scenario of the distributed power supply equipment based on real-time natural microenvironment data;

[0011] The probability density of real-time running status data under different distribution models in the current running scenario is calculated by obtaining the probability density distribution function, and then the abnormal situation of the probability density under the corresponding distribution model is further judged.

[0012] Taking into account the results of various abnormal situations, it is determined whether the real-time operating status data of the acquired distributed power supply device is abnormal data. When the real-time operating status data is determined to be abnormal data, the current operating status of the distributed power supply device is determined to be abnormal.

[0013] Furthermore, before determining the operating status of distributed power devices in real time based on natural microenvironment data and operating status data, the following steps are included:

[0014] Historical natural microenvironment data are divided according to state level and then randomly combined to construct the operating scenarios of distributed power devices.

[0015] The operating status data of distributed power devices under a set number of operation scenarios is extracted as historical operating status data under normal conditions. Different distribution models are used to describe the extracted status data, and the probability density distribution function of historical operating status data under different distribution models under a set number of operation scenarios is obtained.

[0016] Further, determining anomalies in the probability density under the corresponding distribution model includes:

[0017] Define the state space of probability density data as ={ , }, where, when = When, it indicates that the probability density data is in a normal state; when = When this occurs, it indicates that the probability density data is in an abnormal state;

[0018] Set the first The anomaly detection threshold for this distribution model is ,in, For the types of distribution models, and It is an integer not less than 1;

[0019] Calculate real-time running status data in the first probability density under a distribution model ,when < At that time, the real-time operating status data is determined in the [number]th [period]. Under this distribution model, the data is considered outlier; conversely, when... ≥ At that time, the real-time operating status data is determined in the [number]th [period]. The data is considered normal under this distribution model.

[0020] Further, after determining anomalies in the probability density under the corresponding distribution model, the process includes:

[0021] Obtain the anomaly judgment results under three probability density distribution models, including the first judgment result under the first distribution model, the second judgment result under the second distribution model, and the third judgment result under the third distribution model;

[0022] The confidence levels of the probability density of real-time running status data under three distribution models are obtained, and the confidence levels of the probability density are defined from high to low as the maximum confidence level, the intermediate confidence level, and the minimum confidence level.

[0023] Based on the confidence level of probability density and the results of anomaly judgment, it is further determined whether the real-time operating status data of distributed power devices is abnormal data.

[0024] Furthermore, the criteria for determining whether the real-time operating status data of distributed power supply devices is abnormal include:

[0025] When the probability density of at least two confidence levels is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be normal data.

[0026] When there is only one confidence level, and the probability density of the maximum confidence level or the intermediate confidence level is judged as normal data, then the real-time operating status data of the distributed power supply device is judged as abnormal data.

[0027] When there is only one confidence level and the probability density of the minimum confidence level is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be abnormal data.

[0028] If the probability density of any confidence level indicates abnormal data, then the real-time operating status data of the distributed power supply device is determined to be abnormal data.

[0029] Furthermore, the power output status of each region is predicted, including:

[0030]

[0031] in, This represents the total power output state of the i-th region; The predicted output state of the u-th distributed power supply device in normal operating condition at time t; This represents the number of distributed power supply devices operating normally in the i-th region. The output status or rated power of the vth distributed power supply device with abnormal operating status before shutdown; Let be the number of distributed power supply devices with abnormal operating status in the i-th region.

[0032] According to one aspect of this specification, a distributed power source grid-connected optimization control method is provided, comprising:

[0033] The system collects operating status data of distributed power supply equipment through distributed interface devices and collects natural microenvironment data through intelligent sensors. The operating status data of distributed power supply equipment and natural microenvironment data are then uploaded to the distributed area management and control equipment in real time.

[0034] Based on natural microenvironment data and operating status data of distributed power devices, the operating status of distributed power devices is determined; when the operating status of distributed power devices is determined to be abnormal, the distributed area management and control device sends a control command to the abnormal distributed interface device to shut down the device.

[0035] Based on the judgment results of the operating status of distributed power devices, the power output status of each region is predicted through a remote cluster control center.

[0036] Furthermore, the operating status data of the distributed power supply device includes: electrical parameters, device status parameters, efficiency parameters, and battery-related parameters.

[0037] Furthermore, the natural microenvironment data includes: meteorological data, geographical data, seasonal data, and temporal data.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. This invention uses distributed interface devices, distributed area management devices, and a remote cluster control center to predict the output status of distributed areas, enabling the entire distributed grid-connected optimization management to achieve optimal scheduling.

[0040] 2. This invention comprehensively considers the combined effects of multiple influencing factors, which is beneficial for accurately determining the operating status of equipment in complex operating environments, and thus formulating the best management strategy for different situations. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the system structure according to an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] like Figure 1As shown, this embodiment of the invention provides a distributed power grid-connected optimization management system, including: a distributed interface device, used to collect operating status data of distributed power devices and natural microenvironment data related to distributed power generation, and select appropriate communication methods according to regional communication conditions to upload the collected data to the distributed power regional management and control center in real time, while also receiving control commands from the distributed regional management and control device to adjust the operating status of the distributed power devices; a distributed regional management and control device, used to manage the operating status of all distributed power devices in the assigned area, judge the operating status of the distributed power devices in real time based on natural microenvironment data and operating status data, and when a distributed power device is determined to be abnormal, issue a shutdown control command to the abnormal distributed interface device; and a remote cluster control center, used to communicate with each distributed regional management and control device, and predict the output status of each area based on the judgment results of the operating status of the distributed power devices in the area.

[0046] Specifically, the distributed interface device collects the operating status data of the distributed power generation equipment and the natural microenvironment data related to the distributed power generation, and selects appropriate communication methods according to the regional communication conditions to upload the collected data to the distributed power regional management and control center in real time.

[0047] It should be noted that, on the one hand, the distributed interface device connects to the photovoltaic PT of the photovoltaic modules in the area to collect the operating status data of the distributed power generation equipment. On the other hand, it connects to smart meters to collect natural micro-environmental data related to distributed power generation, such as environmental data such as temperature and humidity. For different types of natural micro-environmental data, different smart sensing devices can be selected for data collection.

[0048] Specifically, for photovoltaic power generation, the data used to reflect the operating status of distributed power generation equipment includes:

[0049] 1. Electrical Parameters. Voltage: The voltage value at the equipment's output terminal, usually measured in volts (V). For distributed power generation equipment, voltage stability and level are important operating indicators. Current: The current value output by the equipment, measured in amperes (A). The magnitude and stability of the current directly affect the equipment's power output. Power: Active Power: The actual power output by the equipment, measured in watts (W) or kilowatts (kW). It represents the equipment's effective power contribution to the power grid. Reactive Power: The reactive power output by the equipment, measured in vars (var) or kilovars (kvar). Reactive power is crucial for maintaining voltage stability in the power grid. Power Factor: The ratio of active power to apparent power, reflecting the quality of the equipment's output power. A power factor close to 1 indicates high equipment operating efficiency.

[0050] 2. Equipment Status Parameters. Operating Time: The cumulative operating time of the equipment since its last startup, in hours (h). Prolonged operation may lead to equipment aging. Temperature: The temperature of critical internal components, such as the inverter and motor. Excessive temperature may cause overheating failure. Start / Stop Count: The number of times the equipment has been started and stopped. Frequent starts and stops may adversely affect the equipment's lifespan. Fault Codes: The types and codes of faults detected by the equipment, used for quick fault location and handling.

[0051] 3. Efficiency Parameters. Conversion Efficiency: The efficiency with which the equipment converts input energy (solar energy) into electrical energy, usually expressed as a percentage (%). Output Efficiency: The ratio of the equipment's output power to its rated power, reflecting the actual operating efficiency of the equipment.

[0052] 4. Battery-related parameters (for energy storage devices). Battery State of Charge (SOC): The remaining battery capacity, usually expressed as a percentage (%). Battery State of Health (SOH): The battery's health condition, reflecting its lifespan and performance. Charge / Discharge Current: The battery's charge / discharge current, measured in amperes (A). Charge / Discharge Voltage: The battery's charge / discharge voltage, measured in volts (V).

[0053] Specifically, natural microenvironment data related to distributed power generation reflects the external environmental conditions for equipment operation and has a direct impact on equipment performance and output status, including:

[0054] 1. Meteorological Data. Light Intensity: Light intensity is a key factor for solar photovoltaic equipment, typically measured in watts per square meter (W / m²). Temperature: Ambient temperature affects the operating efficiency and lifespan of the equipment, measured in degrees Celsius (°C) or degrees Fahrenheit (℉). Humidity: Air humidity can affect the heat dissipation and insulation performance of the equipment, expressed as a percentage (%). Atmospheric Pressure: Atmospheric pressure has a certain impact on the performance of wind power generation equipment, measured in Pascals (Pa) or hectopascals (hPa).

[0055] 2. Geographic Data. Geographic Location: The installation location of the equipment, including latitude and longitude. Geographic location affects the availability of sunlight and wind resources. Shading: For solar photovoltaic equipment, shading from surrounding buildings or trees will affect sunlight intensity.

[0056] 3. Seasonal and Time Data. Season: Sunlight, wind speed, and temperature vary significantly across different seasons, affecting equipment output. Time (hours): Sunlight intensity and wind speed also differ throughout the day.

[0057] Specifically, in this embodiment of the invention, the active power of the photovoltaic module is used as the operating status data of the distributed power generation equipment. Taking all the natural microenvironmental data related to distributed power generation mentioned above as examples, the operating status of the distributed power generation equipment is determined in real time based on the natural microenvironmental data and the operating status data. When the distributed power generation equipment is determined to be abnormal, a control command to shut down the equipment is issued to the distributed interface equipment in the area. The specific process includes the following steps:

[0058] 1. Obtain historical operating status data of distributed power generation equipment and corresponding natural microenvironment data related to distributed power generation, and classify the status levels of different types of natural microenvironment data. The classification of the status levels of different types of natural microenvironment data needs to be based on the data type. For example, for numerical data, including light intensity, temperature, humidity, air pressure, etc., a threshold method can be used to set different classification thresholds for status level classification. For example, when the temperature is below 20℃, it is the first status level; when the temperature is between 20℃ and 25℃, it is the second status level; when the temperature is above 25℃, it is the third status level. The classification principle for other numerical data is the same. For categorical data, such as geographical location, shading, season, time, etc., a coding method can be used for status level classification. For example, shading can be classified into two categories: completely unshaded and partially shaded, coded as 1 and 2 respectively. The classification principle for other categorical data is the same.

[0059] 2. Randomly combine the state levels of each natural microenvironment data to construct the operating scenarios of the distributed power supply device. Obtain the same number of operating scenarios as the random combination results of the state levels, and define them as the first operating scenario, the second operating scenario, ..., the nth operating scenario, respectively.

[0060] 3. Extract historical operating status data of the distributed power device when its operating status is normal under the i-th operating scenario. Use different statistical models to describe the extracted status data and obtain the probability density distribution function of the status data under different distribution models in the i-th operating scenario. In one embodiment, a normal distribution model can be used for data description, wherein the mathematical expression of the normal distribution model is:

[0061] (1)

[0062] In the formula, The probability density function follows a normal distribution; For the first One data value; This represents the data mean. The variance of the data; Pi;

[0063] In one embodiment, a Poisson distribution model can also be used for data description, wherein the mathematical expression of the Poisson distribution model is:

[0064] (2)

[0065] In the formula, The probability density function follows a Poisson distribution; Represented as data value Number of times it appears; It is expressed as the average value of the data; for Factorial operation; is the base of the natural logarithm;

[0066] In one embodiment, a uniform distribution model can also be used for data description, wherein the mathematical expression of the uniform distribution model is:

[0067] (3)

[0068] In the formula, It is a probability density function that follows a uniform distribution; For the first One data value; This represents the lower bound of the data distribution. This represents the upper limit of the data distribution.

[0069] 4. Obtain real-time operating status data of distributed power generation equipment and corresponding real-time natural microenvironment data related to distributed power generation, and determine the current operating scenario of distributed power generation equipment based on the collected real-time natural microenvironment data.

[0070] 5. Calculate the probability density of real-time running status data under different distribution models in the current running scenario using the obtained probability density distribution function, and further determine any anomalies in the obtained probability density under the corresponding distribution model; the specific process includes the following steps:

[0071] (1) Define the state space of probability density data as: ={ , }, where, when = When, it indicates that the probability density data is in a normal state; when = When this occurs, it indicates that the probability density data is in an abnormal state;

[0072] (2) Set the number respectively The anomaly detection threshold for this distribution model is ,in, For the types of distribution models, and It is an integer not less than 1;

[0073] (3) Calculate the real-time operating status data in the first... probability density under a distribution model ,when < At that time, the real-time operating status data is determined in the [number]th [period]. Under this distribution model, the data is considered outlier; conversely, when... ≥ At that time, the real-time operating status data is determined in the [number]th [period]. The data is considered normal under this distribution model.

[0074] Specifically, the process comprehensively considers the results of various anomaly assessments to determine whether the acquired real-time operating status data of the distributed power supply device is abnormal. If the real-time operating status data is determined to be abnormal, the current operating status of the distributed power supply device is deemed abnormal. The specific process includes the following steps:

[0075] 1. Obtain the anomaly judgment results of probability density under any three distribution models, including the first judgment result under the first distribution model, the second judgment result under the second distribution model, and the third judgment result under the third distribution model; obtain the confidence level of the probability density of real-time running status data under the above three distribution models, and define the confidence level of probability density from high to low as the maximum confidence level, the intermediate confidence level, and the minimum confidence level;

[0076] 2. Based on the confidence level of the probability density and the results of the anomaly assessment, further determine whether the real-time operating status data of the distributed power supply equipment is abnormal data. The assessment principle is as follows:

[0077] When the probability density of at least two confidence levels is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be normal data.

[0078] When there is only one confidence level, and the probability density of the maximum confidence level or the intermediate confidence level is judged as normal data, then the real-time operating status data of the distributed power supply device is judged as abnormal data.

[0079] When there is only one confidence level and the probability density of the minimum confidence level is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be abnormal data.

[0080] If the probability density of any confidence level indicates abnormal data, then the real-time operating status data of the distributed power supply device is determined to be abnormal data.

[0081] Specifically, based on the results of the judgment of the operating status of distributed power equipment in the region, the output status of each region is predicted, so that the entire distributed power grid-connected optimization management system can achieve optimal scheduling.

[0082] The power output state of the region is determined using the following method:

[0083] (4)

[0084] in, This represents the total power output state of the i-th region; The predicted output state of the u-th distributed power supply device in normal operating condition at time t; This represents the number of distributed power supply devices operating normally in the i-th region. The output status or rated power of the vth distributed power supply device with abnormal operating status before shutdown; Let be the number of distributed power supply devices with abnormal operating status in the i-th region.

[0085] like Figure 2 As shown, this embodiment of the invention provides a distributed power grid-connected optimization control method, including: collecting operating status data of distributed power devices through distributed interface devices, collecting natural microenvironment data through intelligent sensors, and uploading the operating status data of distributed power devices and natural microenvironment data to a distributed area management and control device in real time; judging the operating status of distributed power devices based on the natural microenvironment data and the operating status data of distributed power devices; when the operating status of distributed power devices is judged to be abnormal, the distributed area management and control device issues a control command to the abnormal distributed interface device to shut down the device; and predicting the output status of each area through a remote cluster control center based on the judgment result of the operating status of distributed power devices. This embodiment of the invention predicts the output status of distributed areas through distributed interface devices, distributed area management and control devices, and a remote cluster control center, enabling the entire distributed grid-connected optimization management to achieve optimal scheduling. In addition, it comprehensively considers the combined effects of multiple influencing factors, which is beneficial for accurately judging the operating status of equipment in complex operating environments, and thus formulating the best management strategy for different situations.

[0086] Finally, it should be noted that the above specific embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above specific embodiments based on the technical essence of the present invention should be considered within the protection scope of the present invention.

Claims

1. A distributed power grid-connected optimization management system, characterized in that, include: The distributed interface device is used to collect operating status data of distributed power generation equipment and natural microenvironment data related to distributed power generation, upload the collected data to the distributed area management and control equipment in real time, and receive control commands from the distributed area management and control equipment to adjust the operating status of the distributed power generation equipment. Distributed area management equipment is used to manage the operating status of all distributed power devices within a designated area. It judges the operating status of distributed power devices in real time based on natural micro-environment data and operating status data, and when a distributed power device is determined to be abnormal, it issues a control command to the abnormal distributed interface device to shut down the device. The remote cluster control center is used to communicate with various distributed regional management and control devices, and predict the power output status of each region based on the judgment results of the operating status of distributed power devices in the region.

2. The distributed power grid-connected optimization management system according to claim 1, characterized in that, Real-time assessment of the operating status of distributed power devices based on natural microenvironment data and operational status data, including: Determine the current operating scenario of the distributed power supply equipment based on real-time natural microenvironment data; The probability density of real-time running status data under different distribution models in the current running scenario is calculated by obtaining the probability density distribution function, and then the abnormal situation of the probability density under the corresponding distribution model is further judged. Taking into account the results of various abnormal situations, it is determined whether the real-time operating status data of the acquired distributed power supply device is abnormal data. When the real-time operating status data is determined to be abnormal data, the current operating status of the distributed power supply device is determined to be abnormal.

3. The distributed power grid-connected optimization management system according to claim 2, characterized in that, Before determining the operating status of distributed power devices in real time based on natural microenvironment data and operating status data, the following steps are included: Historical natural microenvironment data are divided according to state level and then randomly combined to construct the operating scenarios of distributed power devices. The operating status data of distributed power devices under a set number of operation scenarios is extracted as historical operating status data under normal conditions. Different distribution models are used to describe the extracted status data, and the probability density distribution function of historical operating status data under different distribution models under a set number of operation scenarios is obtained.

4. The distributed power grid-connected optimization management system according to claim 2, characterized in that, Determining anomalies in the probability density under the corresponding distribution model includes: Define the state space of the probability density as ={ , }, where, when = When, it indicates that the probability density state is normal; when = When the probability density is abnormal, it indicates that the state is abnormal. Set the first The anomaly detection threshold for this distribution model is ,in, For the types of distribution models, and The value is 3; Calculate real-time running status data in the first probability density under a distribution model ,when < At that time, the real-time operating status data is determined in the [number]th [period]. Under this distribution model, it is considered outlier data; conversely, when... ≥ At that time, the real-time operating status data is determined in the [number]th [period]. The data is considered normal under this distribution model.

5. A distributed power grid-connected optimization management system according to claim 4, characterized in that, After determining the anomalies of the probability density under the corresponding distribution model, the process includes: Obtain the anomaly judgment results under three probability density distribution models, including the first judgment result under the first distribution model, the second judgment result under the second distribution model, and the third judgment result under the third distribution model; The confidence levels of the probability density of real-time running status data under three distribution models are obtained, and the confidence levels of the probability density are defined from high to low as the maximum confidence level, the intermediate confidence level, and the minimum confidence level. Based on the confidence level of probability density and the results of anomaly judgment, it is further determined whether the real-time operating status data of distributed power devices is abnormal data.

6. The distributed power grid-connected optimization management system according to claim 5, characterized in that, Further principles for determining whether the real-time operating status data of distributed power devices is abnormal include: When the probability density of at least two confidence levels is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be normal data. When there is only one confidence level, and the probability density of the maximum confidence level or the intermediate confidence level is judged as normal data, then the real-time operating status data of the distributed power supply device is judged as abnormal data. When there is only one confidence level and the probability density of the minimum confidence level is determined to be normal data, the real-time operating status data of the distributed power supply device is determined to be abnormal data. If the probability density of any confidence level indicates abnormal data, then the real-time operating status data of the distributed power supply device is determined to be abnormal data.

7. A distributed power grid-connected optimization management system according to claim 1, characterized in that, Predict the power output status of each region, including: , in, This represents the total power output state of the i-th region; The predicted output state of the u-th distributed power supply device in normal operating condition at time t; This represents the number of distributed power supply devices operating normally in the i-th region. The output status or rated power of the vth distributed power supply device with abnormal operating status before shutdown; Let be the number of distributed power supply devices with abnormal operating status in the i-th region.

8. A distributed power source grid-connected optimization control method, characterized in that, include: The system collects operating status data of distributed power supply equipment through distributed interface devices and collects natural microenvironment data through intelligent sensors. The operating status data of distributed power supply equipment and natural microenvironment data are then uploaded to the distributed area management and control equipment in real time. Based on natural microenvironment data and operating status data of distributed power devices, the operating status of distributed power devices is determined; when the operating status of distributed power devices is determined to be abnormal, the distributed area management and control device sends a control command to the abnormal distributed interface device to shut down the device. Based on the judgment results of the operating status of distributed power devices, the power output status of each region is predicted through a remote cluster control center.

9. The distributed power generation grid-connected optimization control method according to claim 8, characterized in that, The operating status data of the distributed power supply equipment includes: electrical parameters, equipment status parameters, efficiency parameters, and battery-related parameters.

10. A distributed power source grid-connected optimization control method according to claim 8, characterized in that, The natural microenvironment data includes: meteorological data, geographical data, seasonal data, and temporal data.

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