Micro-grid controller and micro-grid communication method applied by micro-grid controller

Abstract

The invention relates to the technical field of micro-grid communication, in particular to a micro-grid controller and a micro-grid communication method applied by the micro-grid controller, and realizes real-time data acquisition and monitoring of terminal equipment of a micro-grid by integrating a main controller, a micro photovoltaic inverter, a micro energy storage device and a communication module. And the main controller analyzes and processes the collected data, formulates a dynamic load adjustment plan, and optimizes power resource allocation according to the real-time electricity price and supply and demand conditions, and the method introduces an elastic load management mechanism, can automatically adjust the user load according to the real-time condition, and significantly improves the energy utilization efficiency. In addition, by implementing switch control and a feedback mechanism, the system can monitor the flow direction of electric power in real time, and the stability and reliability of power supply are ensured. According to the invention, through intelligent data analysis and decision making, the purpose of improving the overall operation efficiency and the user experience is achieved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid communication technology, and in particular to a microgrid controller and a microgrid communication method using the same. Background Art

[0002] With the rapid development of renewable energy and the transformation of energy structure, microgrids, as a new distributed energy management solution, have gradually attracted attention. Microgrids can not only integrate multiple energy resources, such as photovoltaics, wind energy and energy storage equipment, but also realize flexible management and optimal configuration of electricity locally. This flexibility makes microgrids show great potential in improving energy utilization efficiency, reducing carbon emissions and improving power supply reliability, especially playing an increasingly important role in remote areas and urban load centers.

[0003] However, existing microgrid technologies still face many challenges in practical applications. Traditional microgrid management systems often rely on independent, decentralized sensors and data processing modules, resulting in insufficient timeliness and accuracy in data collection. At the same time, the lack of intelligent energy efficiency management and load control means makes it difficult for the system to dynamically adjust the load according to real-time electricity prices and changes in supply and demand. This not only limits the response speed and decision-making ability of the microgrid, but may also cause energy waste and unstable power supply. Summary of the invention

[0004] The present invention provides a microgrid controller and a microgrid communication method using the same.

[0005] A microgrid controller comprises a main controller, a micro photovoltaic inverter, a micro energy storage device and a communication module, wherein;

[0006] The main controller includes a core processing unit for data collection, analysis, strategy formulation and issuance of control instructions;

[0007] The micro photovoltaic inverter is used to convert the direct current generated by the photovoltaic power generation system into alternating current;

[0008] The micro energy storage device is used to store excess electrical energy;

[0009] The communication module supports WiFi, 4G and Ethernet communication channels.

[0010] A microgrid communication method of a microgrid controller comprises the following steps:

[0011] S1, data collection: through the main controller and communication module of the microgrid controller, real-time power data, equipment status data and environmental data are collected from micro photovoltaic inverters, micro energy storage devices and other terminal devices;

[0012] S2, data processing: After the master station receives the data, it freezes the data through the main controller, records the timestamp and specific content of the data collection, and classifies and summarizes the frozen data, calculates the average value, and performs trend analysis;

[0013] S3, Energy Efficiency Management: The main controller is responsible for analyzing energy consumption data, identifying high-energy-consuming equipment and unreasonable energy-using behaviors. Based on the energy efficiency analysis results, the main controller formulates optimization strategies to improve the overall energy efficiency of the system. At the same time, according to the real-time electricity price and supply and demand conditions, it generates a dynamic load adjustment plan to achieve reasonable allocation and efficient use of power resources;

[0014] S4, elastic load management: the main controller introduces an elastic load management mechanism to dynamically adjust load distribution according to real-time electricity prices, supply and demand conditions, and user load conditions;

[0015] S5, microgrid control and switch operation: The main controller monitors the power flow in the microgrid in real time, formulates control strategies based on data feedback and preset strategies, and adjusts the power flow by adjusting the closing and opening of switches;

[0016] S6, data storage and reporting: All processed data, including energy efficiency analysis, trend statistics and average value calculation results, are stored in the master station database, and the communication module uploads the processed data to the master station regularly;

[0017] S7, microgrid dispatching and feedback optimization: The main controller generates a power generation plan and load dispatching scheme according to the actual situation of the grid connection point, and dynamically adjusts the power output by controlling the power generation and discharge strategy of the micro photovoltaic inverter and energy storage device. At the same time, the master station monitors the execution of the dispatching plan and adjusts the power generation plan and load dispatching strategy according to the feedback data. After the dispatching is executed, the master station evaluates the dispatching effect through data feedback and continuously optimizes the system strategy.

[0018] Optionally, the S1 specifically includes:

[0019] S11, device identification and initialization: through the main controller of the microgrid controller, identify the connected micro photovoltaic inverter, micro energy storage device and other terminal devices, and perform initialization settings;

[0020] S12, data collection configuration: set the data collection frequency and data type according to the functions and parameters of each terminal device to ensure the real-time collection of power data, equipment status data and environmental data;

[0021] S13, real-time data collection: through the main controller and the communication module, the data of each terminal device is collected in real time, and the data is converted into electrical signals and temporarily stored in the main controller;

[0022] S14, data integrity check: during the data collection process, perform data integrity check;

[0023] S15, data formatting: formatting the collected power data, equipment status data and environmental data into a unified data structure.

[0024] Optionally, S2 includes:

[0025] S21, data reception and freezing: the main controller receives real-time data from each terminal device and freezes the data;

[0026] S22, data classification and aggregation: classify the frozen data and aggregate the data according to device type and data type;

[0027] S23, average value calculation: calculate the average value of each type of data;

[0028] S24, trend analysis: Use the moving average method to perform trend analysis on the data;

[0029] S25, data storage: storing the processed data, including frozen data, classification summary results, calculated average values ​​and trend analysis results, in a database of the master station.

[0030] Optionally, the S3 includes:

[0031] S31, energy consumption analysis: the main controller analyzes the energy consumption data collected in real time, identifies high-energy-consuming devices and unreasonable energy-using behaviors, and finds out the devices that exceed the normal range by comparing the energy consumption data of different devices;

[0032] S32, optimization strategy formulation: based on the energy consumption analysis results, the main controller formulates an optimization strategy, which includes adjusting the equipment running time, changing the equipment working mode, and optimizing the load distribution;

[0033] S33, dynamic load adjustment plan generation: the main controller generates a dynamic load adjustment plan according to the real-time electricity price and supply and demand conditions;

[0034] S34, energy efficiency calculation: the main controller accumulates and calculates the forward and reverse electric energy of the photovoltaic inverter to evaluate the optimization effect;

[0035] S35, feedback and adjustment: Based on the results of energy efficiency calculation and the implementation effect of the load adjustment plan, the main controller performs feedback analysis.

[0036] Optionally, the S4 includes:

[0037] S41, introduction of elastic load management mechanism: the main controller introduces an elastic load management mechanism to monitor the price changes of the power market and the supply and demand status of the system in real time, and establish a dynamic adjustment model;

[0038] S42, real-time electricity price acquisition: the main controller obtains current electricity price information from the electricity market, including real-time electricity price and historical electricity price data;

[0039] S43, load demand forecasting: the main controller uses historical power consumption data and market electricity price data to forecast load demand;

[0040] S44, dynamic load adjustment: according to the real-time electricity price and load demand forecast results, the main controller dynamically adjusts the user-side load;

[0041] S45, user load feedback mechanism: establish a user load feedback mechanism to allow users to adjust load according to their own needs and system prompts.

[0042] Optionally, the S5 includes:

[0043] S51, power flow monitoring: the main controller monitors the power flow in the microgrid in real time and collects power data of each terminal device to determine the inflow and outflow status of power;

[0044] S52, control strategy formulation: based on the real-time monitoring data and the preset operation strategy, the main controller formulates a corresponding control strategy, which includes optimizing the power flow by adjusting the working state of each device;

[0045] S53, switch operation: according to the control strategy, the power flow direction is adjusted by adjusting the closing and opening of the switch;

[0046] S54, feedback data monitoring: after executing the control strategy, the main controller continuously monitors the feedback data to evaluate the effect of adjusting the power flow direction;

[0047] S55, event reporting mechanism: when an abnormal situation or failure is detected, the main controller reports the event.

[0048] Optionally, the S6 includes:

[0049] S61, data storage management: the main controller classifies and summarizes the processed data, and stores the energy efficiency analysis results, trend statistics and average value calculation results in the main station database;

[0050] S62, data reporting mechanism: through the communication module, the main controller regularly uploads the processed data to the main station, and the uploaded data includes energy efficiency analysis results, load scheduling records and equipment status information;

[0051] S63, data integrity check: during the data upload process, the main controller performs a data integrity check;

[0052] S64, abnormal data processing: During the data storage and reporting process, the main controller identifies and marks abnormal data.

[0053] Optionally, the S7 includes:

[0054] S71, generating a power generation plan: the main controller generates a power generation plan according to the actual situation of the grid connection point;

[0055] S72, load dispatching plan formulation: according to the generated power generation plan, the main controller formulates a corresponding load dispatching plan;

[0056] S73, dynamic adjustment of control strategy: During the implementation of the scheduling plan, the main controller monitors the operation status and power flow of each device in real time. If it is found that the actual power flow is inconsistent with the expected direction, the control strategy is adjusted for dynamic optimization;

[0057] S74, execution effect evaluation: After the scheduling is executed, the main controller evaluates the execution of the power generation plan. The evaluation indicators include actual power generation, load satisfaction rate and system efficiency;

[0058] S75, feedback adjustment mechanism: according to the results of the execution effect evaluation, the main controller adjusts the subsequent power generation plan and load scheduling plan.

[0059] Beneficial effects of the present invention:

[0060] The present invention, through real-time data collection and in-depth energy consumption analysis, the main controller can accurately identify high-energy consumption equipment and unreasonable energy consumption behavior. Based on the formulation of dynamic load adjustment plans, the system can optimize the allocation of power resources under different electricity prices and supply and demand conditions, thereby significantly improving the overall energy efficiency of the microgrid and reducing operating costs.

[0061] The present invention implements a flexible load management mechanism and real-time power flow monitoring, enabling the system to quickly respond to load changes and power market fluctuations. By formulating and adjusting control strategies, it ensures the optimization of power flow, reduces power supply pressure, and improves the stability and reliability of the microgrid during peak periods and emergencies.

[0062] The present invention combines data storage and reporting mechanisms, and the master station can perform flexible data query and visual analysis to support intelligent decision-making. By evaluating the execution effect of the power generation plan and the feedback adjustment mechanism, the system can continuously optimize the operation strategy, improve the intelligence level of the microgrid, and meet the diverse power needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the present invention or 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 in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A schematic diagram of a microgrid controller process according to an embodiment of the present invention;

[0065] Figure 2 The present invention is a flow chart of a microgrid communication method of a microgrid controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0067] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0068] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0069] like Figure 1 As shown, a microgrid controller includes a main controller, a micro photovoltaic inverter, a micro energy storage device and a communication module, wherein;

[0070] The main controller includes a core processing unit for data collection, analysis, strategy formulation and issuance of control instructions;

[0071] The micro photovoltaic inverter is used to convert the direct current generated by the photovoltaic power generation system into alternating current;

[0072] The micro energy storage device is used to store excess electrical energy for emergency use;

[0073] The communication module supports WiFi, 4G and Ethernet communication channels.

[0074] like Figure 2 As shown, a microgrid communication method of a microgrid controller comprises the following steps:

[0075] S1, data collection: through the main controller and communication module of the microgrid controller, power data, equipment status data and environmental data are collected in real time from micro photovoltaic inverters, micro energy storage devices and other terminal devices (such as smart meters, sensor networks). These data are converted from physical quantities sensed by sensors into electrical signals and temporarily stored in the main controller to ensure the timeliness and integrity of the data;

[0076] S2, data processing: After the master station receives the data, it freezes the data through the main controller, records the timestamp and specific content of the data collection, and classifies and aggregates the frozen data, calculates the average value, and performs trend analysis to provide data support for subsequent energy efficiency management and system optimization;

[0077] S3, Energy Efficiency Management: The main controller is responsible for analyzing energy consumption data, identifying high-energy-consuming equipment and unreasonable energy-using behaviors. Based on the energy efficiency analysis results, the main controller formulates optimization strategies to improve the overall energy efficiency of the system. At the same time, according to the real-time electricity price and supply and demand conditions, it generates a dynamic load adjustment plan to achieve reasonable allocation and efficient use of power resources;

[0078] S4, elastic load management: the main controller introduces an elastic load management mechanism to dynamically adjust load distribution according to real-time electricity prices, supply and demand conditions, and user load conditions. User loads are automatically adjusted according to system requirements to ensure maximum system efficiency while reducing power supply pressure during peak load periods;

[0079] S5, microgrid control and switch operation: The main controller monitors the power flow in the microgrid in real time, formulates control strategies based on data feedback and preset strategies, and adjusts the power flow by adjusting the closing and opening of switches. Through the feedback data of the controller, it ensures the optimization of power flow and load balance;

[0080] S6, data storage and reporting: All processed data, including energy efficiency analysis, trend statistics and average value calculation results, are stored in the master station database. The communication module uploads the processed data to the master station regularly for further analysis and decision support;

[0081] S7, microgrid dispatching and feedback optimization: The main controller generates power generation plans and load dispatching schemes according to the actual situation of the grid connection point, and dynamically adjusts the power output by controlling the power generation and discharge strategy of the micro photovoltaic inverter and energy storage device. At the same time, the master station monitors the execution of the dispatching plan and adjusts the power generation plan and load dispatching strategy according to the feedback data to ensure the efficient coordinated operation of the microgrid and the national grid. After the dispatch is executed, the master station evaluates the dispatching effect through data feedback and continuously optimizes the system strategy.

[0082] Optionally, the S1 specifically includes:

[0083] S11, device identification and initialization: through the main controller of the microgrid controller, identify the connected micro photovoltaic inverter, micro energy storage device and other terminal devices, and perform initialization settings to ensure that each device can operate normally;

[0084] S12, data collection configuration: set the data collection frequency and data type according to the functions and parameters of each terminal device to ensure the real-time collection of power data, equipment status data and environmental data;

[0085] S13, real-time data collection: through the main controller and the communication module, the data of each terminal device is collected in real time, and the data is converted into electrical signals and temporarily stored in the main controller;

[0086] S14, data integrity check: During the data collection process, data integrity check is performed to ensure that the data has not been tampered with or lost during transmission;

[0087] S15, data formatting: formatting the collected power data, equipment status data and environmental data into a unified data structure for subsequent processing and analysis.

[0088] Optionally, S2 includes:

[0089] S21, data reception and freezing: the main controller receives real-time data from each terminal device and freezes the data. The freezing process records the timestamp and specific content of data collection to ensure the timeliness and accuracy of the data;

[0090] S22, data classification and aggregation: Classify the frozen data and aggregate the data according to device type (such as micro photovoltaic inverters, energy storage devices, smart meters, etc.) and data type (such as power data, equipment status data, environmental data) for subsequent effective analysis;

[0091] S23, average value calculation: calculate the average value of each type of data;

[0092] S24, trend analysis: Use the moving average method to perform trend analysis on the data;

[0093] S25, data storage: the processed data, including frozen data, classification summary results, calculated average values ​​and trend analysis results, are stored in the database of the master station to ensure long-term storage of data and facilitate subsequent query and analysis;

[0094] The main controller can effectively freeze, classify, summarize and analyze the data in the microgrid, providing a solid data foundation for energy efficiency management and system optimization, and ensuring the accuracy and timeliness of system decisions.

[0095] Optionally, the S3 includes:

[0096] S31, energy consumption analysis: the main controller analyzes the energy consumption data collected in real time, identifies high-energy-consuming devices and unreasonable energy-using behaviors, and finds out the devices that exceed the normal range by comparing the energy consumption data of different devices;

[0097] S32, optimization strategy formulation: based on the energy consumption analysis results, the main controller formulates an optimization strategy, which includes adjusting the equipment running time, changing the equipment working mode, and optimizing the load distribution to reduce the use frequency of high energy consumption equipment;

[0098] S33, dynamic load adjustment plan generation: the main controller generates a dynamic load adjustment plan based on the real-time electricity price and supply and demand conditions to ensure the reasonable allocation of power resources. The plan will consider reducing the load of certain equipment during peak electricity prices to reduce electricity costs;

[0099] S34, energy efficiency calculation: The main controller accumulates the forward and reverse electric energy of the photovoltaic inverter to evaluate the optimization effect, which is expressed as:

[0100]

[0101] Where E represents the percentage of system energy efficiency, P f Represents the positive electrical energy, that is, the amount of electricity generated by the photovoltaic system, P r Represents the reverse electrical energy, that is, the amount of electricity delivered to the photovoltaic system;

[0102] S35, feedback and adjustment: Based on the results of energy efficiency calculation and the implementation effect of the load adjustment plan, the main controller conducts feedback analysis. If the energy efficiency does not reach the expected target, the optimization strategy is further adjusted to improve the energy efficiency of the overall system;

[0103] The main controller can effectively analyze energy consumption data, formulate optimization strategies and implement dynamic load adjustment, thereby significantly improving the overall energy efficiency of the microgrid, reducing operating costs and achieving reasonable allocation of resources.

[0104] Optionally, the S4 includes:

[0105] S41, introduction of elastic load management mechanism: The main controller introduces an elastic load management mechanism to monitor the price changes of the power market and the supply and demand status of the system in real time, and establish a dynamic adjustment model to ensure that the load achieves the best balance between economy and reliability;

[0106] S42, real-time electricity price acquisition: the main controller obtains current electricity price information from the electricity market, including real-time electricity price and historical electricity price data, so as to provide a basis for load adjustment;

[0107] S43, load demand forecast: The main controller uses historical power consumption data and market electricity price data to forecast load demand, and uses linear regression analysis to forecast load demand, which is expressed as:

[0108] D t =αD t-1 +βP t +γT t +∈;

[0109] Among them, D t represents the load demand at time point t, D t-1 represents the load demand at time point t-1, P t represents the electricity price at time point t, T t represents the temperature at time point t, α, β and γ are the parameters of the linear regression model, representing the impact of various factors on load demand, ∈ is the random error term, representing the prediction error of the model;

[0110] S44, dynamic load adjustment: based on the real-time electricity price and load demand forecast results, the main controller dynamically adjusts the user-side load, and optimizes resource usage by reducing the load of non-essential equipment during peak electricity price periods;

[0111] S45, User load feedback mechanism: Establish a user load feedback mechanism to allow users to adjust loads according to their own needs and system prompts to ensure that the system optimizes power usage while meeting user needs;

[0112] The main controller can effectively and dynamically adjust the load, utilize the load demand prediction model, give full play to the flexible load management mechanism, achieve the best balance between economy and reliability, reduce the pressure of peak load on the system, and improve overall energy efficiency and user satisfaction.

[0113] Optionally, the S5 includes:

[0114] S51, power flow monitoring: the main controller monitors the power flow in the microgrid in real time and collects power data of each terminal device to determine the inflow and outflow status of power. The monitoring data includes the input power and output power of each device;

[0115] S52, control strategy formulation: Based on the real-time monitoring data and the preset operation strategy, the main controller formulates a corresponding control strategy, which includes optimizing the power flow by adjusting the working state of each device. The control strategy is described as follows:

[0116] P net =P in -P out ;

[0117] Among them, P net Represents the net power in the network, P in represents the power flowing into the microgrid, P out represents the power flowing out of the microgrid;

[0118] If P net >0, it means that power flows into the microgrid. If P net When <0, it means that the power flows out of the microgrid;

[0119] S53, switch operation: According to the control strategy, by adjusting the closing and opening of the switch, the power flow direction is adjusted to ensure the load balance and system stability. The controller performs the switch operation according to the real-time flow direction monitoring result to achieve the optimal distribution of power;

[0120] S54, feedback data monitoring: after executing the control strategy, the main controller continuously monitors the feedback data and evaluates the adjustment effect of the power flow. According to the feedback data, if it is found that the power flow does not reach the expected target, the control strategy is readjusted;

[0121] S55, event reporting mechanism: When an abnormal situation or failure is detected, the main controller reports the event, including alarm, upgrade, reset and other information, so that timely processing can be carried out.

[0122] Optionally, the S6 includes:

[0123] S61, data storage management: the main controller classifies and summarizes the processed data, and stores the energy efficiency analysis results, trend statistics and average value calculation results in the main station database to ensure long-term storage of data and facilitate subsequent inquiries;

[0124] S62, data reporting mechanism: through the communication module, the main controller regularly uploads the processed data to the main station. The uploaded data includes energy efficiency analysis results, load scheduling records and equipment status information. The time interval of the reporting mechanism can be configured according to system requirements;

[0125] S63, data integrity check: During the data upload process, the main controller performs data integrity check to ensure that the uploaded data has not been tampered with or lost. The integrity check uses a hash algorithm, such as SHA-256, to generate a hash value for the uploaded data and compare it at the receiving end;

[0126] S64, abnormal data processing: During the data storage and reporting process, the main controller identifies and marks abnormal data. For example, when the power data of a certain device exceeds the set threshold, the system will record the abnormality and perform corresponding processing. The judgment criteria for abnormal data are:

[0127] D threshold =μ+kσ;

[0128] Among them, D threshold is the abnormal data threshold, μ is the mean value of the sample data, σ is the standard deviation of the sample data, and k is a set constant, usually 2 or 3, to control the sensitivity of anomaly detection.

[0129] Optionally, the S7 includes:

[0130] S71, generating a power generation plan: the main controller generates a power generation plan according to the actual situation of the grid connection point. The power generation plan takes into account the current load demand, renewable energy generation capacity and grid operation status. The generated plan is expressed as:

[0131] P g =D t +P s -P r ;

[0132] Among them, P g represents the amount of electricity that needs to be generated, D t represents the current load demand, P s Represents renewable energy generation capacity, P r Indicates the amount of electricity supplied from an energy storage device or other source;

[0133] S72, load dispatching plan formulation: according to the generated power generation plan, the main controller formulates a corresponding load dispatching plan to ensure that the microgrid maximizes the utilization rate of renewable energy while meeting the load demand;

[0134] S73, dynamic adjustment of control strategy: During the implementation of the scheduling plan, the main controller monitors the operation status of each device and the power flow in real time. If it is found that the actual power flow is inconsistent with the expected direction, the control strategy is adjusted to perform dynamic optimization to ensure the balance between supply and demand;

[0135] S74, execution effect evaluation: After the scheduling is executed, the main controller evaluates the execution of the power generation plan. The evaluation indicators include actual power generation, load satisfaction rate and system efficiency, which are expressed as:

[0136]

[0137] Among them, E efficiencyIndicates the percentage of system efficiency, P actual Indicates the actual power generation, P planned Indicates the planned power generation;

[0138] S75, feedback adjustment mechanism: Based on the results of the execution effect evaluation, the main controller adjusts the subsequent power generation plan and load scheduling plan to optimize the allocation and utilization efficiency of power resources.

[0139] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0140] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A microgrid controller, characterized in that: It includes a main controller, a micro photovoltaic inverter, a micro energy storage device and a communication module, wherein; The main controller includes a core processing unit for data collection, analysis, strategy formulation and issuance of control instructions; The micro photovoltaic inverter is used to convert the direct current generated by the photovoltaic power generation system into alternating current; The micro energy storage device is used to store excess electrical energy; The communication module supports WiFi, 4G and Ethernet communication channels.

2. A microgrid communication method of a microgrid controller according to claim 1, characterized in that: The following steps are involved: S1, data collection: through the main controller and communication module of the microgrid controller, real-time power data, equipment status data and environmental data are collected from micro photovoltaic inverters, micro energy storage devices and other terminal devices; S2, data processing: After the master station receives the data, it freezes the data through the main controller, records the timestamp and specific content of the data collection, and classifies and summarizes the frozen data, calculates the average value, and performs trend analysis; S3, Energy Efficiency Management: The main controller is responsible for analyzing energy consumption data, identifying high-energy-consuming equipment and unreasonable energy-using behaviors. Based on the energy efficiency analysis results, the main controller formulates optimization strategies to improve the overall energy efficiency of the system. At the same time, according to the real-time electricity price and supply and demand conditions, it generates a dynamic load adjustment plan to achieve reasonable allocation and efficient use of power resources; S4, elastic load management: the main controller introduces an elastic load management mechanism to dynamically adjust load distribution according to real-time electricity prices, supply and demand conditions, and user load conditions; S5, microgrid control and switch operation: The main controller monitors the power flow in the microgrid in real time, formulates control strategies based on data feedback and preset strategies, and adjusts the power flow by adjusting the closing and opening of switches; S6, data storage and reporting: All processed data, including energy efficiency analysis, trend statistics and average value calculation results, are stored in the master station database, and the communication module uploads the processed data to the master station regularly; S7, microgrid dispatching and feedback optimization: The main controller generates a power generation plan and load dispatching scheme according to the actual situation of the grid connection point, and dynamically adjusts the power output by controlling the power generation and discharge strategy of the micro photovoltaic inverter and energy storage device. At the same time, the master station monitors the execution of the dispatching plan and adjusts the power generation plan and load dispatching strategy according to the feedback data. After the dispatching is executed, the master station evaluates the dispatching effect through data feedback and continuously optimizes the system strategy.

3. A microgrid communication method of a microgrid controller according to claim 2, characterized in that: The S1 specifically includes: S11, device identification and initialization: Identify the connected micro photovoltaic inverter, micro energy storage device and other terminal devices through the main controller of the microgrid controller, and perform initialization settings; S12, data collection configuration: set the data collection frequency and data type according to the functions and parameters of each terminal device to ensure the real-time collection of power data, equipment status data and environmental data; S13, real-time data collection: through the main controller and the communication module, the data of each terminal device is collected in real time, and the data is converted into electrical signals and temporarily stored in the main controller; S14, data integrity check: during the data collection process, implement data integrity check; S15, data formatting: formatting the collected power data, equipment status data and environmental data into a unified data structure.

4. The microgrid communication method of a microgrid controller according to claim 3, characterized in that: The S2 includes: S21, data reception and freezing: the main controller receives real-time data from each terminal device and freezes the data; S22, data classification and aggregation: classify the frozen data and aggregate the data according to device type and data type; S23, average value calculation: calculate the average value of each type of data; S24, trend analysis: use the moving average method to perform trend analysis on the data; S25, data storage: storing the processed data, including frozen data, classification summary results, calculated average values ​​and trend analysis results, in a database of the master station.

5. A microgrid communication method of a microgrid controller according to claim 4, characterized in that: The S3 includes: S31, energy consumption analysis: the main controller analyzes the energy consumption data collected in real time, identifies high-energy-consuming devices and unreasonable energy-using behaviors, and finds out the devices that exceed the normal range by comparing the energy consumption data of different devices; S32, optimization strategy formulation: based on the energy consumption analysis results, the main controller formulates an optimization strategy, which includes adjusting the equipment running time, changing the equipment working mode, and optimizing the load distribution; S33, dynamic load adjustment plan generation: the main controller generates a dynamic load adjustment plan according to the real-time electricity price and supply and demand conditions; S34, energy efficiency calculation: the main controller accumulates and calculates the forward and reverse electric energy of the photovoltaic inverter to evaluate the optimization effect; S35, feedback and adjustment: Based on the results of energy efficiency calculation and the implementation effect of the load adjustment plan, the main controller performs feedback analysis.

6. A microgrid communication method of a microgrid controller according to claim 5, characterized in that: The S4 includes: S41, introduction of elastic load management mechanism: the main controller introduces an elastic load management mechanism to monitor the price changes of the power market and the supply and demand status of the system in real time, and establish a dynamic adjustment model; S42, real-time electricity price acquisition: the main controller obtains current electricity price information from the electricity market, including real-time electricity price and historical electricity price data; S43, load demand forecasting: the main controller uses historical power consumption data and market electricity price data to forecast load demand; S44, dynamic load adjustment: according to the real-time electricity price and load demand forecast results, the main controller dynamically adjusts the user-side load; S45, user load feedback mechanism: establish a user load feedback mechanism to allow users to adjust load according to their own needs and system prompts.

7. A microgrid communication method of a microgrid controller according to claim 6, characterized in that: The S5 includes: S51, power flow monitoring: the main controller monitors the power flow in the microgrid in real time and collects power data of each terminal device to determine the inflow and outflow status of power; S52, control strategy formulation: based on the real-time monitoring data and the preset operation strategy, the main controller formulates a corresponding control strategy, which includes optimizing the power flow by adjusting the working state of each device; S53, switch operation: according to the control strategy, the power flow direction is adjusted by adjusting the closing and opening of the switch; S54, feedback data monitoring: after executing the control strategy, the main controller continuously monitors the feedback data to evaluate the effect of adjusting the power flow direction; S55, event reporting mechanism: when an abnormal situation or failure is detected, the main controller reports the event.

8. A microgrid communication method of a microgrid controller according to claim 7, characterized in that: The S6 includes: S61, data storage management: the main controller classifies and summarizes the processed data, and stores the energy efficiency analysis results, trend statistics and average value calculation results in the main station database; S62, data reporting mechanism: through the communication module, the main controller regularly uploads the processed data to the main station, and the uploaded data includes energy efficiency analysis results, load scheduling records and equipment status information; S63, data integrity check: during the data upload process, the main controller performs a data integrity check; S64, abnormal data processing: During the data storage and reporting process, the main controller identifies and marks abnormal data.

9. A microgrid communication method of a microgrid controller according to claim 8, characterized in that: The S7 includes: S71, generating a power generation plan: the main controller generates a power generation plan according to the actual situation of the grid connection point; S72, load dispatching plan formulation: according to the generated power generation plan, the main controller formulates a corresponding load dispatching plan; S73, dynamic adjustment of control strategy: During the implementation of the scheduling plan, the main controller monitors the operation status and power flow of each device in real time. If it is found that the actual power flow is inconsistent with the expected direction, the control strategy is adjusted for dynamic optimization; S74, execution effect evaluation: After the scheduling is executed, the main controller evaluates the execution of the power generation plan. The evaluation indicators include actual power generation, load satisfaction rate and system efficiency; S75, feedback adjustment mechanism: according to the results of the execution effect evaluation, the main controller adjusts the subsequent power generation plan and load scheduling plan.

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