Source-grid-load-storage integrated micro-grid intelligent scheduling system

Abstract

The invention discloses a source-grid-load-storage integrated micro-grid intelligent scheduling system, and relates to the technical field of micro-grid management, the system comprises a comprehensive scheduling platform, the comprehensive scheduling platform is in communication connection with an energy data acquisition module, an energy prediction module, an intelligent scheduling module, an energy storage management module and a fault early warning module, and the modules are in electric signal connection. By integrating the sensor network and the data acquisition and processing technology, the running states of various energy devices and energy storage devices in the micro-grid are monitored in real time, the reliability and stability of the system are remarkably improved, potential device faults can be found and early warned in time, fault enlargement is avoided, and the reliability and stability of the system are improved. The power failure time and economic loss caused by faults are reduced, the charging and discharging strategy of the energy storage equipment can be flexibly adjusted according to the real-time load requirement and the renewable energy power generation condition, it is ensured that the microgrid can keep stable electric energy supply under various working conditions, and the power utilization requirement of a user is met.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid management, and in particular to a source-grid-load-storage integrated microgrid intelligent dispatching system. Background Art

[0002] With the acceleration of global energy transformation, the proportion of renewable energy such as wind power, photovoltaics and other new energy sources continues to increase. The absorption and utilization of new energy is one of the important challenges in the current energy field. Digital technology has emerged. Through the application of digital technology, the operation of the power system has become more intelligent and efficient, and the coordinated operation and intelligent scheduling of energy production, transmission, consumption and storage have been realized.

[0003] For example, a microgrid intelligent monitoring and scheduling method with Chinese patent publication number: CN118300195A, the method includes: modeling to obtain power generation models under different energy forms, and collecting energy information of different power generation energy sources at the current moment, using the power generation power model to predict and calculate the power generation corresponding to different power generation energy sources at future moments, modeling the scheduling process of the microgrid multi-energy storage system, and constructing and solving the microgrid optimization scheduling objective function.

[0004] In the prior art, the dispatching process of the microgrid multi-energy storage system is modeled according to the power generation corresponding to different power generation energy sources at future moments, and the microgrid optimization dispatching objective function with the highest energy storage efficiency of the microgrid multi-energy storage system as the goal is constructed and solved to obtain the optimal dispatching strategy at future moments. However, the source-grid-load-storage integrated microgrid contains various types of energy and energy storage equipment, and the complementarity and coordination between the equipment are poor. In addition, due to the intermittent and uncertain nature of new energy power generation such as wind power and photovoltaic power generation, it is difficult to predict its power generation, which will lead to deviations between the microgrid dispatching plan and the actual power generation situation, and thus it is difficult to accurately monitor the abnormal state of the power grid during operation. For this reason, an intelligent dispatching system for a source-grid-load-storage integrated microgrid is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a source-grid-load-storage integrated microgrid intelligent dispatching system to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] The source-grid-load-storage integrated microgrid intelligent dispatching system includes an integrated dispatching platform, which is communicatively connected with an energy data acquisition module, an energy prediction module, an intelligent dispatching module, an energy storage management module, and a fault warning module, wherein electrical signals are connected between the modules;

[0008] The energy data acquisition module is used to collect the original data of the operation data and status information of wind power, photovoltaic energy equipment and energy storage equipment in the microgrid, provide accurate and comprehensive data support for subsequent scheduling decisions, and ensure the accuracy and real-time performance of the scheduling plan;

[0009] The energy prediction module uses the variational mode decomposition algorithm to predict the power generation of new energy sources such as wind power and photovoltaic power, and analyzes the change trend of the load, so as to improve the accuracy of power generation prediction and reduce the scheduling deviation caused by prediction deviation;

[0010] The intelligent scheduling module uses the sparrow search algorithm to analyze and formulate a scheduling plan based on the information of the energy data acquisition module and the energy prediction module, so as to realize the configuration and scheduling of various energy and energy storage equipment in the microgrid, improve energy utilization efficiency and reduce operating costs;

[0011] The energy storage management module is used to classify and manage the charging and discharging process of the energy storage device, control the process of releasing and storing electric energy in the microgrid, improve the response speed and efficiency of the energy storage system, and enhance the flexibility and stability of the microgrid;

[0012] The fault warning module is used to monitor the operating status of various energy devices and energy storage devices in the microgrid in real time, identify abnormal modes during operation, and send out an alarm signal when a fault occurs, thereby improving the system's fault response speed and fault handling capabilities and reducing losses caused by faults.

[0013] A further improvement of the technical solution of the present invention is that in the energy data acquisition module, the process of acquiring the original data of the operating data and the status information is as follows:

[0014] Deploy data acquisition systems and load monitoring systems, and connect various energy devices and energy storage devices in the microgrid to the data acquisition system by connecting them to sensors and controllers of wind power equipment, photovoltaic equipment, and energy storage batteries. Among them, wind power equipment and photovoltaic equipment are energy devices, and energy storage batteries are energy storage devices. The load monitoring system is used to monitor the load status in the microgrid;

[0015] The original data of the operation data and status information of energy equipment and energy storage equipment are obtained in real time through sensors, wherein the operation data of energy equipment includes the operation parameters of power generation, power, voltage and current of wind power and photovoltaic equipment, and the status information of energy storage equipment includes the charge and discharge status of energy storage batteries and the status information of remaining capacity;

[0016] The collected raw data is transmitted to the integrated dispatching platform through the wireless communication network. During the transmission process, the integrity and security of the data must be ensured to avoid data loss or tampering. After the integrated dispatching platform receives the raw data, it is classified and stored according to time sequence and equipment type;

[0017] The collected raw data is preprocessed, where the preprocessing includes data cleaning, screening and formatting steps to remove erroneous data and noise and convert the data into a format suitable for analysis.

[0018] A further improvement of the technical solution of the present invention is that in the energy prediction module, the process of predicting power generation and analyzing load change trends is as follows:

[0019] Collect historical power generation data and load data from wind power, photovoltaic equipment and load monitoring systems, and perform data cleaning and normalization on the collected data;

[0020] The time series data of historical power generation data of wind power and photovoltaic power generation are respectively input into the variational mode decomposition algorithm. Through the iterative optimization process, each time series is decomposed into multiple intrinsic mode functions. Each intrinsic mode function represents a specific frequency component in the power generation time series. The center frequency and bandwidth characteristics of each intrinsic mode function are analyzed to extract the characteristic information in the power generation time series.

[0021] Based on the extracted feature information and the decomposed intrinsic mode function, the historical power generation data and the corresponding intrinsic mode function are used as input, and the energy prediction model is built in combination with the convolutional neural network. The trained energy prediction model is used to predict the power generation of wind power and photovoltaic power.

[0022] Use the variational mode decomposition algorithm to decompose the time series data of the load data to obtain multiple intrinsic mode functions, analyze the change trend of each intrinsic mode function, identify the periodic components and non-periodic components in the load, and analyze the change trend of the load;

[0023] Combining power generation forecast results with load change trend analysis to predict future power generation and load will help the power system better balance supply and demand and make scheduling decisions.

[0024] A further improvement of the technical solution of the present invention is that the calculation expression for predicting the power generation of wind power and photovoltaic power is:

[0025]

[0026]

[0027] Among them, P t For the predicted electricity generation, IMF i (t) is the i-th intrinsic mode function at time t in power generation forecasting, n is the number of intrinsic mode functions in power generation forecasting, α is the adjustment factor used to adjust the influence of the sum, is the benchmark value, representing the historical average power generation, γ is the root parameter, used to adjust the nonlinear effect of the sum, λ is the benchmark value of the exponential function, S is the time variable, representing the prediction time point, K i is the number of items in the ith IMF when predicting power generation, a k is the amplitude of the kth item in power generation forecast, f k，i is the center frequency of the kth item in power generation forecast, θ k，i is the phase of the kth item in power generation prediction;

[0028] The calculation expression for analyzing the load change trend is:

[0029]

[0030]

[0031] Among them, L t is the predicted load at time t, is the benchmark value, representing the average value of historical load data, w j is the weight of the jth intrinsic mode function during load forecasting, indicating the contribution of this mode to load forecasting. j (t) is the value of the jth intrinsic mode function at time t during load forecasting, representing the specific frequency component in the load time series, m is the number of intrinsic mode functions during load forecasting, ∈ t is a random noise term, θ is a model parameter, representing the nonlinear trend of load change, T b is the time variable, K j is the number of items in the jth IMF during load forecasting, a u is the amplitude of the uth item in load forecasting, f u，j is the center frequency of the uth item in load forecasting, φ u，j is the phase of the u-th item in load forecasting.

[0032] A further improvement of the technical solution of the present invention is that in the intelligent scheduling module, the process of configuring and scheduling various energy sources and energy storage devices in the microgrid is:

[0033] Obtain real-time operation data and historical data from the energy data acquisition module, including wind power, photovoltaic power generation, energy storage equipment status (power, power limit), and load demand;

[0034] The energy forecasting module is used to forecast the power generation and load demand of wind power and photovoltaic power based on historical data and current weather and environmental parameters, and the forecast results are integrated into the input of the intelligent scheduling module as the basis for scheduling decisions;

[0035] The microgrid dispatch problem is defined as an optimization problem using the sparrow search algorithm, and iterative calculations are performed to converge the algorithm in order to find the optimal dispatch strategy.

[0036] After the algorithm converges, the result with the highest fitness is selected as the optimal solution, that is, the optimal energy and energy storage equipment configuration and scheduling plan. The optimal solution found by the algorithm is converted into specific operation instructions, including adjusting the output power of the power generation equipment, controlling the charging and discharging of the energy storage equipment, sending the scheduling instructions to the corresponding energy and energy storage equipment through the communication network, and monitoring the execution status.

[0037] A further improvement of the technical solution of the present invention is that in the energy storage management module, the process of controlling the release and storage of electric energy in the microgrid is:

[0038] Collect data from energy storage devices through sensors and data acquisition systems, monitor the status of energy storage devices, including power, charging and discharging status, and health status, and integrate data from energy data acquisition modules and energy prediction modules to collect data related to energy storage, including wind power and photovoltaic power generation, the status of energy storage devices, and load demand information;

[0039] Formulate specific charging and discharging strategies based on grid demand, power generation forecasts, current market prices, equipment efficiency and other factors, analyze the capacity limitations of energy storage equipment, charging and discharging efficiency and the overall operation requirements of the microgrid, convert the formulated charging and discharging strategies into specific operating instructions, and send them to the energy storage equipment for execution;

[0040] Monitor the operating status of the energy storage device and the power supply and demand of the microgrid in real time to ensure the smooth progress of the charging and discharging process, pay attention to the power limit of the energy storage device, and adjust the charging and discharging status of the energy storage device when it is found that the power of the energy storage device is close to the upper or lower limit, respond to the real-time changes of the power grid, and avoid overcharging or over-discharging.

[0041] A further improvement of the technical solution of the present invention is that in the fault warning module, the abnormal mode recognition process is:

[0042] The operating data of all energy devices and energy storage devices in the microgrid are continuously collected through sensors and data acquisition systems, including historical fault data and normal operating data. The collected operating data are preprocessed, data cleaning is performed, and noise and outliers are removed to ensure the accuracy and reliability of the data. Abnormal features associated with abnormal patterns are extracted from the preprocessed data, including abnormal temperature features, abnormal power features, and abnormal load features, to obtain an abnormal feature set, which is then divided into a training set and a test set.

[0043] A fault warning model is established by combining the training set data and the decision tree model. The trained fault warning model is evaluated using the test set data so that the fault warning model can learn the behavior pattern of the equipment and identify abnormal patterns that deviate from the normal pattern.

[0044] Based on the relevant feature data of the abnormal feature set and the fault warning model, the abnormal warning coefficient is obtained through comprehensive analysis, and the risk analysis is performed on the abnormal pattern identified by the fault warning model to determine the severity of the abnormal pattern;

[0045] According to the historical fault data and abnormal warning coefficient, different warning levels are divided for the identified abnormal patterns, namely, the first warning level, the second warning level and the third warning level. The severity of the warning level increases from level one to level three, and the corresponding warning threshold is matched for each warning level;

[0046] Integrated fault warning model, input the real-time collected data into the fault warning model, conduct real-time analysis, compare the difference between the current data and the normal behavior pattern, determine whether there is an abnormal pattern, and respond to the corresponding warning level when the calculated abnormal warning coefficient exceeds the warning threshold;

[0047] Based on the determined warning level, an alarm signal is issued and a risk report is generated and distributed to relevant personnel. The risk report includes the risk level, the time, location, type and handling process of the fault, and provides corresponding fault handling suggestions.

[0048] A further improvement of the technical solution of the present invention is that the calculation expression of the abnormal warning coefficient is:

[0049]

[0050] Among them, AC is the abnormal warning coefficient, T p is the real-time temperature observation value, T nor is the temperature reference value during normal operation, P cu is the current power, P th is the power safety threshold, L cu is the current load, L max is the historical maximum load, N is the number of observations, and the larger the AC value is, the more serious the abnormal pattern is.

[0051] A further improvement of the technical solution of the present invention is that: the multiple warning levels correspond to multiple warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold;

[0052] The multiple warning levels and the multiple warning thresholds satisfy the following relationship:

[0053] Level 1 Warning 0 <AC≤AC L; Indicates low risk or early stage abnormality, which may not cause failure immediately, but requires attention;

[0054] Level 2 warning level AC L <AC≤AC M ; Indicates medium risk, the abnormal pattern is more obvious, and there is a high possibility of developing into a failure;

[0055] Level 3 warning level AC>AC M ; Indicates high risk, severe abnormal mode, and a high probability of impending failure;

[0056] Among them, AC is the abnormal warning coefficient, AC L is the lower threshold corresponding to the second warning level and the upper threshold corresponding to the first warning level, AC M It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the second warning level.

[0057] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0058] 1. The present invention provides an integrated source-grid-load-storage microgrid intelligent dispatching system. By integrating sensor networks, data acquisition and processing technologies, the system can monitor the operating status of various energy devices and energy storage devices in the microgrid in real time, significantly improving the reliability and stability of the system. It can timely discover and warn of potential equipment failures, avoid the expansion of failures, reduce power outages and economic losses caused by failures, and flexibly adjust the charging and discharging strategies of energy storage devices according to real-time load demand and renewable energy power generation, ensuring that the microgrid can maintain a stable power supply under various working conditions to meet the power needs of users.

[0059] 2. The present invention provides an integrated source-grid-load-storage microgrid intelligent dispatching system. By integrating the data of the energy data acquisition module and the energy prediction module, it realizes the accurate prediction of the power generation of renewable energy sources such as wind power and photovoltaic power. Through real-time analysis and processing capabilities, it can identify abnormal modes and potential faults in the microgrid in a short time. Once an abnormality is found, an alarm signal is immediately issued, and a risk report is automatically generated to provide detailed fault information and processing suggestions for operation and maintenance personnel, which greatly shortens the fault processing time and reduces the impact of faults on the operation of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 It is a module structure diagram of the present invention;

[0062] Figure 2 A flow chart for predicting power generation and analyzing load change trends for the present invention;

[0063] Figure 3 This is a flow chart for identifying abnormal patterns of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a source-grid-load-storage integrated microgrid intelligent dispatching system, including a comprehensive dispatching platform, the comprehensive dispatching platform is communicatively connected with an energy data acquisition module, an energy prediction module, an intelligent dispatching module, an energy storage management module and a fault warning module, wherein the electrical signals between the modules are connected;

[0066] The energy data acquisition module is used to collect the original data of the operation data and status information of wind power, photovoltaic energy equipment and energy storage equipment in the microgrid, provide accurate and comprehensive data support for subsequent dispatching decisions, ensure the accuracy and real-time nature of the dispatching plan, deploy data acquisition systems and load monitoring systems, and connect various energy equipment and energy storage equipment in the microgrid to the data acquisition system by connecting to the sensors and controllers of wind power equipment, photovoltaic equipment and energy storage batteries. Among them, wind power equipment and photovoltaic equipment are energy equipment, and energy storage batteries are energy storage equipment. The load monitoring system is used to monitor the load status in the microgrid. Wind power equipment is installed with wind speed sensors, temperature sensors, and generator status sensors to collect wind speed, temperature, and speed data. Photovoltaic equipment is installed with light intensity sensors, temperature sensors, and current and voltage sensors to collect light intensity, temperature, current, and voltage data. The energy battery is equipped with a battery status monitoring sensor to collect the battery's charge and discharge status, remaining capacity, and temperature data. The sensor is used to obtain the original data of the operation data and status information of the energy equipment and energy storage equipment in real time. The operation data of the energy equipment includes the operating parameters of the power generation, power, voltage, and current of the wind power and photovoltaic equipment. The status information of the energy storage equipment includes the charge and discharge status of the energy storage battery and the status information of the remaining capacity. The collected original data is transmitted to the integrated dispatching platform through the wireless communication network. During the transmission process, the integrity and security of the data must be ensured to avoid data loss or tampering. After receiving the original data, the integrated dispatching platform classifies and stores it according to the time sequence and equipment type, and pre-processes the collected original data. The pre-processing includes data cleaning, screening, and formatting steps to eliminate erroneous data and noise and convert the data into a format suitable for analysis.

[0067] The energy forecasting module uses the variational mode decomposition algorithm to predict the new energy power generation of wind power and photovoltaics, and analyzes the change trend of load, improves the accuracy of power generation prediction, and reduces the scheduling deviation caused by prediction deviation. It collects historical power generation data and load data from wind power, photovoltaic equipment and load monitoring systems, and performs data cleaning and normalization on the collected data. After data cleaning, outliers and missing values ​​are removed to ensure the integrity and accuracy of the data. The cleaned data is normalized so that data of different dimensions can be compared and analyzed on the same scale. The time series data of historical power generation data of wind power and photovoltaics are respectively input into the variational mode decomposition algorithm. Through the iterative optimization process, each time series is decomposed into multiple intrinsic mode functions. Each intrinsic mode function represents the power generation time series. A specific frequency component, and analyze the central frequency and bandwidth characteristics of each intrinsic mode function, extract the characteristic information in the power generation time series, based on the extracted characteristic information and the decomposed intrinsic mode function, use the historical power generation data and the corresponding intrinsic mode function as input, and build an energy prediction model in combination with a convolutional neural network. Use the trained energy prediction model to predict the power generation of wind power and photovoltaic power, use the variational mode decomposition algorithm to decompose the time series data of the load data, obtain multiple intrinsic mode functions, analyze the change trend of each intrinsic mode function, identify the periodic components and non-periodic components in the load, and analyze the change trend of the load. Combined with the power generation prediction results and the load change trend analysis, predict the future power generation and load, which will help the power system to better balance supply and demand and make scheduling decisions;

[0068] Furthermore, the calculation expression for predicting the power generation of wind power and photovoltaic power is:

[0069]

[0070]

[0071] Among them, P t For the predicted electricity generation, IMF i (t) is the i-th intrinsic mode function at time t in power generation forecasting, n is the number of intrinsic mode functions in power generation forecasting, α is the adjustment factor used to adjust the influence of the sum, is the benchmark value, representing the historical average power generation, γ is the root parameter, used to adjust the nonlinear effect of the sum, generally greater than 0, indicating the depth of the root, used to simulate the nonlinear trend of power generation growth, λ is the benchmark value of the exponential function, S is the time variable, representing the prediction time point, increasing with time, indicating the trend of the prediction value over time, K i is the number of items in the ith IMF when predicting power generation, a k is the amplitude of the kth item in power generation forecast, fk，i is the center frequency of the kth item in power generation forecast, θ k，i is the phase of the kth item in power generation prediction;

[0072] The calculation expression for analyzing load change trend is:

[0073]

[0074]

[0075] Among them, L t is the predicted load at time t, is the benchmark value, representing the average value of historical load data, w j is the weight of the jth intrinsic mode function during load forecasting, indicating the contribution of this mode to load forecasting. j (t) is the value of the jth intrinsic mode function at time t during load forecasting, representing the specific frequency component in the load time series, m is the number of intrinsic mode functions during load forecasting, ∈ t is a random noise term, which indicates the prediction error at time t or the load change that the model fails to capture, and will remain relatively stable over time. θ is a model parameter, which indicates the nonlinear trend of load change. b is the time variable, K j is the number of items in the jth IMF during load forecasting, a u is the amplitude of the uth item in load forecasting, f u，j is the center frequency of the uth item in load forecasting, φ u，j is the phase of the uth item in load forecasting;

[0076] The intelligent scheduling module uses the sparrow search algorithm to analyze and formulate a scheduling plan based on the information of the energy data acquisition module and the energy prediction module, realizes the configuration and scheduling of various energy and energy storage equipment in the microgrid, improves energy utilization efficiency, and reduces operating costs. It obtains real-time operation data and historical data from the energy data acquisition module, including wind power and photovoltaic power generation, the status of energy storage equipment (power, power limit), and load demand. The energy prediction module is used to predict the wind power and photovoltaic power generation and load demand based on historical data and current weather and environmental parameters, and the prediction results are integrated into the input of the intelligent scheduling module as the basis for scheduling decisions. The sparrow search algorithm is used to define the scheduling problem of the microgrid as an optimization problem. The goal is to minimize the operating cost while meeting the load demand and system constraints (power balance, equipment capacity limit). A certain number of "sparrows" (i.e., solution sets) are randomly generated. Each sparrow represents a possible configuration and scheduling plan for energy and energy storage equipment. According to The current market price, equipment efficiency, and forecast data are used to calculate the fitness (i.e., operating cost) of each sparrow (solution). Some sparrows act as discoverers and conduct random searches near their current positions to explore new solution spaces. The remaining sparrows act as followers and move according to the quality of the discoverers, tending to better solutions. A sentinel mechanism is introduced to prevent the algorithm from falling into local optimality. The diversity of solutions is increased by randomly moving or resetting some sparrows, and the algorithm is judged to be converged according to the preset number of iterations. If it has not converged, the search and iteration steps are returned, and iterative calculations are performed to make the algorithm converge in order to find the optimal scheduling strategy. After the algorithm converges, the result with the highest fitness is selected from all sparrows as the optimal solution, which is the optimal energy and energy storage equipment configuration and scheduling plan. The optimal solution found by the algorithm is converted into specific operation instructions, including adjusting the output power of the power generation equipment and controlling the charging and discharging of the energy storage equipment. The scheduling instructions are sent to the corresponding energy and energy storage equipment through the communication network, and the execution is monitored.

[0077] The energy storage management module is used to classify and manage the charging and discharging process of the energy storage equipment, control the process of releasing and storing electric energy in the microgrid, improve the response speed and efficiency of the energy storage system, enhance the flexibility and stability of the microgrid, collect data of the energy storage equipment through sensors and data acquisition systems, monitor the status of the energy storage equipment, including power, charging and discharging status, and health status, and integrate data from the energy data acquisition module and the energy prediction module to collect data related to energy storage, including wind power and photovoltaic power generation, the status of energy storage equipment, and load demand information. According to grid demand, power generation forecasts, current market prices, equipment efficiency and other factors, formulate specific charging and discharging strategies, analyze the capacity limitations, charging and discharging efficiency and overall operation requirements of the energy storage equipment, convert the formulated charging and discharging strategies into specific operation instructions, and send them to the energy storage equipment for execution. Real-time monitoring of the operating status of the energy storage equipment and the power supply and demand of the microgrid ensures the smooth progress of the charging and discharging process, pay attention to the power limit of the energy storage equipment, and adjust the charging and discharging status of the energy storage equipment when it is found that the power of the energy storage equipment is close to the upper or lower limit, respond to the real-time changes of the grid, and avoid overcharging or over-discharging.

[0078] The fault warning module is used to monitor the operating status of various energy devices and energy storage devices in the microgrid in real time, identify abnormal modes during operation, and send out alarm signals when a fault occurs, thereby improving the system's fault response speed and fault handling capabilities and reducing losses caused by faults.

[0079] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the fault warning module, the abnormal mode recognition process is:

[0080] Through sensors and data acquisition systems, the operating data of all energy devices and energy storage devices in the microgrid are continuously collected, including historical fault data and normal operating data. The collected operating data are preprocessed, data cleaning is performed, and noise and outliers are removed to ensure the accuracy and reliability of the data. Abnormal features associated with abnormal modes are extracted from the preprocessed data, including abnormal temperature features, abnormal power features and abnormal load features, to obtain an abnormal feature set, which is divided into a training set and a test set. A fault warning model is established by combining the training set data and the decision tree model. The trained fault warning model is evaluated using the test set data, so that the fault warning model learns the behavior pattern of the equipment and identifies abnormal modes that deviate from the normal mode. Based on the relevant feature data of the abnormal feature set and the fault warning model, a comprehensive analysis is performed to obtain the abnormal warning coefficient, and the fault warning coefficient is evaluated. The abnormal patterns identified by the warning model are analyzed for risk, and the severity of the abnormal patterns is determined. Different warning levels are divided for the identified abnormal patterns according to historical fault data and abnormal warning coefficients, namely, the first warning level, the second warning level, and the third warning level. The warning levels increase in severity from level one to level three, and the corresponding warning threshold is matched for each warning level. The fault warning model is integrated, and the real-time collected data is input into the fault warning model for real-time analysis. The difference between the current data and the normal behavior pattern is compared to determine whether there is an abnormal pattern. When the calculated abnormal warning coefficient exceeds the warning threshold, the corresponding warning level is responded to. Based on the determined warning level, an alarm signal is issued, and a risk report is generated and distributed to relevant personnel. The risk report includes the risk level, the time, location, type, and handling process of the fault, and provides corresponding fault handling suggestions;

[0081] Furthermore, the calculation expression of the abnormal warning coefficient is:

[0082]

[0083] Among them, AC is the abnormal warning coefficient, T p is the real-time temperature observation value, T nor is the temperature reference value during normal operation, P cu is the current power, P th is the power safety threshold, L cu is the current load, L max is the historical maximum load, N is the number of observations, T p -T nor Indicates the temperature deviation, that is, the difference between the real-time observed value and the normal value. It is an exponential function used to evaluate the abnormality of power relative to the safety threshold. When the power exceeds the threshold, this part will approach 0. is the load ratio, which indicates the ratio of the current load to the historical maximum load. It is used to evaluate the abnormality of the load. The larger the AC value is, the more serious the abnormal mode is. p -T nor Indicates that the observed value is lower than the normal value, and a positive value indicates that the observed value is higher than the normal value. The larger the abnormal warning coefficient, the more likely the system is in a high-risk state;

[0084] Furthermore, the multiple warning levels correspond to multiple warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold;

[0085] Multiple warning levels and multiple warning thresholds satisfy the following relationship:

[0086] Level 1 Warning 0 <AC≤AC L ; Indicates low risk or early stage abnormality, which may not cause failure immediately, but requires attention;

[0087] Level 2 warning level AC L <AC≤AC M ; Indicates medium risk, the abnormal pattern is more obvious, and there is a high possibility of developing into a failure;

[0088] Level 3 warning level AC>AC M ; Indicates high risk, severe abnormal mode, and a high probability of impending failure;

[0089] Among them, AC is the abnormal warning coefficient, AC L is the lower threshold corresponding to the second warning level and the upper threshold corresponding to the first warning level, AC M It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the second warning level.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The source-grid-load-storage integrated microgrid intelligent dispatching system includes a comprehensive dispatching platform, which is characterized by: The integrated dispatching platform is communicatively connected with an energy data acquisition module, an energy prediction module, an intelligent dispatching module, an energy storage management module and a fault warning module, wherein electrical signals are connected between the modules; The energy data acquisition module is used to collect the original data of the operation data and status information of wind power, photovoltaic energy equipment and energy storage equipment in the microgrid; The energy prediction module uses the variational mode decomposition algorithm to predict the power generation of new energy sources such as wind power and photovoltaic power, and analyze the change trend of the load; The intelligent scheduling module uses the sparrow search algorithm to analyze and formulate a scheduling plan based on the information of the energy data acquisition module and the energy prediction module, so as to realize the configuration and scheduling of various energy and energy storage devices in the microgrid; The energy storage management module is used to classify and manage the charging and discharging process of the energy storage device, and control the process of releasing and storing electric energy in the microgrid; The fault warning module is used to monitor the operating status of various energy devices and energy storage devices in the microgrid in real time, identify abnormal modes during operation, and send out an alarm signal when a fault occurs.

2. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 1 is characterized by: In the energy data acquisition module, the process of acquiring the original data of the operating data and status information is as follows: Deploy data acquisition systems and load monitoring systems, and connect various energy devices and energy storage devices in the microgrid to the data acquisition system by connecting them to sensors and controllers of wind power equipment, photovoltaic equipment, and energy storage batteries. Among them, wind power equipment and photovoltaic equipment are energy devices, and energy storage batteries are energy storage devices. The load monitoring system is used to monitor the load status in the microgrid; The original data of the operation data and status information of energy equipment and energy storage equipment are obtained in real time through sensors, wherein the operation data of energy equipment includes the operation parameters of power generation, power, voltage and current of wind power and photovoltaic equipment, and the status information of energy storage equipment includes the charge and discharge status of energy storage batteries and the status information of remaining capacity; The collected raw data is transmitted to the integrated dispatching platform through the wireless communication network. After receiving the raw data, the integrated dispatching platform classifies and stores them according to time sequence and equipment type; The collected raw data is preprocessed, where the preprocessing includes data cleaning, screening and formatting steps.

3. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 2 is characterized by: In the energy prediction module, the process of predicting power generation and analyzing load change trends is as follows: Collect historical power generation data and load data from wind power, photovoltaic equipment and load monitoring systems, and perform data cleaning and normalization on the collected data; The time series data of historical power generation data of wind power and photovoltaic power generation are respectively input into the variational mode decomposition algorithm. Each time series is decomposed into multiple intrinsic mode functions through an iterative optimization process. The center frequency and bandwidth characteristics of each intrinsic mode function are analyzed to extract the characteristic information in the power generation time series. Based on the extracted feature information and the decomposed intrinsic mode function, the historical power generation data and the corresponding intrinsic mode function are used as input, and the energy prediction model is built in combination with the convolutional neural network. The trained energy prediction model is used to predict the power generation of wind power and photovoltaic power. Use the variational mode decomposition algorithm to decompose the time series data of the load data to obtain multiple intrinsic mode functions, analyze the change trend of each intrinsic mode function, identify the periodic components and non-periodic components in the load, and analyze the change trend of the load; Combining the power generation forecast results and load change trend analysis, the future power generation and load are predicted.

4. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 3 is characterized by: The calculation expression for predicting the power generation of wind power and photovoltaic power is: Among them, P t For the predicted electricity generation, IMF i (t) is the i-th intrinsic mode function at time t in power generation forecasting, n is the number of intrinsic mode functions in power generation forecasting, ɑ is the adjustment factor, is the benchmark value, representing the historical average power generation, γ is the root parameter, λ is the benchmark value of the exponential function, S is the time variable, representing the forecast time point, K i is the number of items in the ith IMF when predicting power generation, a k is the amplitude of the kth item in power generation forecast, f k，i is the center frequency of the kth item in power generation forecast, ɑ k，i is the phase of the kth item in power generation prediction; The calculation expression for analyzing the load change trend is: Among them, L t is the predicted load at time t, is the benchmark value, representing the average value of historical load data, w j is the weight of the jth intrinsic mode function in load forecasting, IMF j (t) is the value of the jth intrinsic mode function at time t during load forecasting, m is the number of intrinsic mode functions during load forecasting, ε t is a random noise term, θ is a model parameter, representing the nonlinear trend of load change, T b is the time variable, K j is the number of items in the jth IMF during load forecasting, a u is the amplitude of the uth item in load forecasting, f u，j is the center frequency of the uth item in load forecasting, φ u，j is the phase of the u-th item in load forecasting.

5. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 4 is characterized in that: In the intelligent dispatching module, the process of configuring and dispatching various energy sources and energy storage devices in the microgrid is as follows: Obtain real-time operation data and historical data from the energy data acquisition module, including wind power, photovoltaic power generation, energy storage equipment status, and load demand; The energy forecasting module is used to forecast the power generation and load demand of wind power and photovoltaic power based on historical data and current weather and environmental parameters, and the forecast results are integrated into the input of the intelligent scheduling module as the basis for scheduling decisions; The microgrid dispatch problem is defined as an optimization problem using the sparrow search algorithm, and iterative calculations are performed to converge the algorithm in order to find the optimal dispatch strategy. After the algorithm converges, the result with the highest fitness is selected as the optimal solution, that is, the optimal energy and energy storage equipment configuration and scheduling plan. The optimal solution found by the algorithm is converted into specific operation instructions, including adjusting the output power of the power generation equipment, controlling the charging and discharging of the energy storage equipment, sending the scheduling instructions to the corresponding energy and energy storage equipment through the communication network, and monitoring the execution status.

6. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 5 is characterized by: In the energy storage management module, the process of controlling the release and storage of electric energy in the microgrid is as follows: Collect data from energy storage devices through sensors and data acquisition systems, monitor the status of energy storage devices, including power, charging and discharging status, and health status, and integrate data from energy data acquisition modules and energy prediction modules to collect data related to energy storage, including wind power and photovoltaic power generation, the status of energy storage devices, and load demand information; Formulate specific charging and discharging strategies based on grid demand, power generation forecasts, current market prices, and equipment efficiency factors, analyze the capacity limitations of energy storage equipment, charging and discharging efficiency, and the overall operation requirements of the microgrid, and convert the formulated charging and discharging strategies into specific operating instructions, which are sent to the energy storage equipment for execution; Monitor the operating status of energy storage equipment and the power supply and demand of the microgrid in real time, pay attention to the power limit of energy storage equipment, adjust the charging and discharging status of energy storage equipment, and respond to real-time changes in the power grid.

7. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 6 is characterized by: In the fault warning module, the abnormal mode recognition process is as follows: The operating data of all energy devices and energy storage devices in the microgrid are continuously collected through sensors and data acquisition systems, which are historical fault data and normal operating data, and the collected operating data are preprocessed. Abnormal features associated with abnormal modes are extracted from the preprocessed data, which are abnormal temperature features, abnormal power features and abnormal load features, to obtain an abnormal feature set, which is divided into a training set and a test set; A fault warning model is established by combining the training set data and the decision tree model. The trained fault warning model is evaluated using the test set data so that the fault warning model can learn the behavior pattern of the equipment and identify abnormal patterns that deviate from the normal pattern. Based on the relevant feature data of the abnormal feature set and the fault warning model, the abnormal warning coefficient is obtained through comprehensive analysis, and the risk analysis is performed on the abnormal pattern identified by the fault warning model to determine the severity of the abnormal pattern; According to the historical fault data and abnormal warning coefficient, different warning levels are divided for the identified abnormal patterns, namely, the first warning level, the second warning level and the third warning level. The severity of the warning level increases from level one to level three, and the corresponding warning threshold is matched for each warning level; Integrated fault warning model, input the real-time collected data into the fault warning model, conduct real-time analysis, compare the difference between the current data and the normal behavior pattern, determine whether there is an abnormal pattern, and respond to the corresponding warning level when the calculated abnormal warning coefficient exceeds the warning threshold; Based on the determined warning level, an alarm signal is issued, a risk report is generated and distributed to relevant personnel, and corresponding fault handling suggestions are provided.

8. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 7 is characterized by: The calculation expression of the abnormal warning coefficient is: Among them, AC is the abnormal warning coefficient, T p is the real-time temperature observation value, T nor is the temperature reference value during normal operation, P cu is the current power, P th is the power safety threshold, L cu is the current load, L max is the historical maximum load, N is the number of observations, and the larger the AC value is, the more serious the abnormal pattern is.

9. The source-grid-load-storage integrated microgrid intelligent dispatching system according to claim 8 is characterized by: A plurality of the warning levels correspond to a plurality of the warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple warning thresholds satisfy the following relationship: Level 1 warning 0 <AC≤AC L ; Level 2 warning level AC L <AC≤AC M ; Level 3 warning level AC>AC M ; Among them, AC is the abnormal warning coefficient, AC L is the lower threshold corresponding to the second warning level and the upper threshold corresponding to the first warning level, AC M It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the second warning level.

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