A power load scheduling system, method and electronic device

Abstract

The present invention discloses a power load scheduling system, method and electronic device. The system includes: a cloud intelligent management platform that uses historical meteorological data to intelligently predict power generation and consumption, issues scheduling strategies, and receives and processes power sensor data and alarm information; a switch device controller that receives the issued scheduling strategies, generates scheduling control commands according to the scheduling strategies, issues the scheduling control commands to the switch device, and reports the power sensor data and alarm information to the cloud intelligent management platform; automatically generates and issues scheduling control commands in the case of losing connection with the cloud intelligent management platform; power sensors on the power generation side and the user side respectively collect voltage and current data on the power generation side and the user side in real time; a switch device that receives the scheduling control commands issued by the switch device controller and controls the switching of the on / off states of each channel switch. The present invention effectively reduces the investment in large-scale energy storage devices, reduces the cost of new energy utilization, and improves the direct utilization rate of new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and particularly to a power load dispatching system, method and electronic device. Background Art

[0002] New energy power generation technologies have advanced rapidly in the past decade or so, especially wind power generation and photovoltaic power generation. The newly installed capacity has reached record highs, and at the same time, the power generation cost has decreased significantly. With technological progress and cost reduction, the investment attractiveness of new energy power generation projects has been increasing continuously. Policy support and the improvement of market mechanisms, such as green power trading and carbon markets, have provided more development opportunities for the industry.

[0003] Traditional power load dispatching methods and application deployment methods do not match well with the inherent characteristics of new energy, resulting in too low utilization rate of new energy and serious phenomena such as "wind curtailment and light curtailment", and the expected investment effect has not been achieved. The source-side output prediction and load-side power consumption prediction often rely on historical data for prediction and cannot be corrected in real time by combining the real-time collected data at that time, resulting in too large dispatching granularity and low new energy consumption rate. In addition, large energy storage devices need to be equipped in traditional wind power generation and photovoltaic power generation systems, increasing the cost of new energy use. This solution adopts a "make the most of it" power supply mode, effectively reducing the energy storage investment in new energy configuration and reducing the cost of new energy utilization. Through the cascading of different input and output levels before and after the switching device, more application scenarios are adapted, further improving the primary consumption rate of new energy. Summary of the Invention

[0004] In view of the problems of inaccurate source-side output prediction and load-side power consumption prediction of existing power, insufficient dispatching fineness, and reduced primary utilization rate of wind power generation and photovoltaic power generation, the present invention proposes a power load dispatching system, method and electronic device. By using historical meteorological data to intelligently predict power generation and power consumption, and being able to select, flexibly connect and adapt the consumption load to the current power output status of the power source according to the output status of the power source under different mechanisms, different characteristics, different time periods and different meteorological conditions, adopting a "make the most of it" power supply mode, effectively reducing the investment in large-scale energy storage devices, reducing the cost of new energy utilization, and improving the direct utilization rate of new energy. At the same time, it has the function of local dispatching strategy. In the case of the disconnection between the switching device controller and the cloud intelligent management platform, the local control component can independently implement the operation management of the switching device to ensure the reliable and stable operation of the entire system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] On the one hand, the present invention proposes a power load dispatching system, including: a cloud intelligent management platform, a switching device controller, a power sensor and a switching device;

[0007] The cloud-based intelligent management platform is used to intelligently predict power generation and power consumption by using historical meteorological data, issue dispatching strategies, receive and process power sensor data and alarm information sent by the switch device controller, and monitor the operating status of the entire microgrid power load dispatching system;

[0008] The switch device controller is used to receive the dispatching strategy issued by the cloud-based intelligent management platform, generate a dispatching control command according to the dispatching strategy, issue the dispatching control command to the switch device, and report the power sensor data and alarm information to the cloud-based intelligent management platform; it is also used to automatically generate and issue a dispatching control command in the case of losing connection with the cloud-based intelligent management platform;

[0009] The power sensors are respectively installed on the power generation side and the power consumption side. The power sensor on the power generation side is used to collect the voltage and current data on the power generation side in real time to estimate the maximum power of power generation; the power sensor on the user side is used to collect the voltage and current data on the user side and use it as the load power at the next moment in a short time.

[0010] The switch device is used to receive the dispatching control command issued by the switch device controller and control the switching of the on / off state of each channel switch.

[0011] Further, the switch device controller is one or more, each switch device controller is connected to multiple power sensors and multiple switch devices, and the dispatching system composed of multiple switch device controllers is cascaded by a single switch device controller.

[0012] Further, the cloud-based intelligent management platform predicts the photovoltaic power generation on the power generation side in the following way:

[0013] Collect data sources, where the data sources include meteorological data, photovoltaic system data, geographical location information, and historical power generation records; the meteorological data includes temperature, humidity, wind speed, and solar radiation intensity within the photovoltaic power generation area; the photovoltaic system data includes component type, installation angle, tilt angle, real-time output power, voltage, and current; the geographical location information includes latitude, longitude, and altitude; the historical power generation record is the photovoltaic power generation data in the past period of time;

[0014] Preprocess the collected data, including: cleaning and standardizing the data to ensure no missing values and outliers, and unifying the data from different sources into a consistent time series format;

[0015] Extract features from the original data that are helpful to improve the prediction accuracy, including time features, weather conditions, geographical factors, historical similar day analysis, and social and economic activities;

[0016] Select artificial intelligence models suitable for time series data analysis, including long short-term memory networks and XGBoost regression models, and train them;

[0017] Model tuning, according to the evaluation results, further adjust the model structure or parameters.

[0018] Furthermore, when the cloud intelligent management platform predicts wind power generation on the power generation side, the data sources collected include meteorological data, wind farm operation data, geographical location information, and historical power generation records; the meteorological data includes temperature, humidity, wind speed, and solar radiation intensity within the range of the photovoltaic power generation area; the wind farm operation data includes the output power, rotation speed, pitch angle, equipment type, and installation location of each wind turbine; the geographical location information includes altitude, terrain features, and the surrounding environment; the historical power generation record is the wind power generation data over a period of time in the past.

[0019] Furthermore, the cloud intelligent management platform predicts the user-side load in the following manner:

[0020] Use ensemble empirical mode decomposition to decompose the load sequence into subsequences of different frequencies;

[0021] Use multivariate linear regression and long short-term memory neural network to model and predict the low-frequency and high-frequency subsequences respectively;

[0022] Fuse the predicted values of each subsequence to obtain the future predicted value of a single user load.

[0023] Furthermore, the cloud intelligent management platform generates a scheduling strategy in the following manner:

[0024] First, receive the power sensor data reported by the switch device controller, read the data through Socket communication or directly from the database, obtain the current power generation output situation and user-side load situation through the power sensor data, classify and grade the power supply and consumption situations, and match the required power generation sources for the user side from the power generation output according to the principles of supply-demand matching, safety constraints, economic considerations, and environmental protection principles. The user side is matched in order of priority from high to low, and multiple sets of scheduling control commands are generated according to the matching results. The scheduling control commands represent the opening and closing states of the multi-in-one-out switches on the power generation side and the user side.

[0025] Furthermore, the switch device controller includes a scheduling control module, and the scheduling control module includes a policy reading sub-module, a policy parsing sub-module, a scheduling decision sub-module, a data collection sub-module, a control instruction generation sub-module, and a control instruction sending sub-module;

[0026] The policy reading sub-module is used to obtain the latest scheduling strategy from the cloud intelligent management platform;

[0027] The policy analysis sub-module is used to analyze the scheduling policy issued by the cloud intelligent management platform, including script ID, script scenario, effective time, deadline difference, effective conditions, and scheduling control commands;

[0028] The data collection sub-module is used to collect real-time power sensor data;

[0029] The scheduling decision sub-module is used to ensure that the scheduling control commands generated by the switch device controller are within the scope of the scheduling control commands issued by the cloud intelligent management platform based on the scheduling policy and real-time power sensor data;

[0030] The control instruction generation sub-module is used to generate switch matrix control instructions according to the two-dimensional switch matrix, the agreement between the switch device controller and the switch device register;

[0031] The control instruction sending sub-module is used to send the control instructions to the switch device.

[0032] Furthermore, when there are multiple switch device controllers in the system, the power load scheduling is carried out in the following manner:

[0033] According to the power generation and load prediction results, different switch devices obtain the latest scripts from the cloud intelligent management platform. The system generates scheduling control commands according to the pre-set scheduling policy and sends the generated scheduling control commands to each switch device;

[0034] Each switch device analyzes the script corresponding to the scheduling control command issued by the cloud intelligent management platform, including script ID, script scenario, effective time, deadline difference, effective conditions, control commands, and the issued batch mark number;

[0035] Each switch device collects real-time power generation side and user side power sensor data and uploads it to the cloud intelligent management platform;

[0036] Based on the scheduling policy and real-time power sensor data, the cloud intelligent management platform generates two-dimensional switch matrix scheduling control commands;

[0037] According to the two-dimensional switch matrix, the agreement between the switch device controller and the switch device register, generate switch matrix control instructions, and judge whether the scheduling control commands generated by each switch device controller are executable according to the source-load matching conditions;

[0038] If it is executable, at a fixed moment, according to the effective time and effective conditions of the scheduling policy, send the control instructions corresponding to the scheduling control commands to each switch device to control the simultaneous switching of the multi-in-one-out switches in each switch device;

[0039] If one or more scheduling control commands generated by the switch device controllers are not executable, the corresponding switch device controllers upload the script distribution batch mark numbers corresponding to the non-executable scheduling control commands to the cloud intelligent management platform. The cloud intelligent management platform sends a cancellation command with the distribution batch mark number to other switch device controllers. After receiving the cancellation command issued by the cloud intelligent management platform, each switch device controller cancels the script information of the switch device corresponding to the distribution batch mark number and waits for the next script distribution.

[0040] Another aspect of the present invention provides a power load scheduling method, including:

[0041] The cloud intelligent management platform uses historical meteorological data to perform intelligent prediction on power generation and power consumption, generates ultra-short-term predictions for 4 hours, short-term predictions for 24 to 72 hours, and medium- and long-term prediction curves for 1 to 12 months;

[0042] Collect real-time power sensor data on the power generation side and the user side;

[0043] Classify and grade the power supply and power consumption situations, and match eligible power generation sources for the user side from the power generation side output according to the principles of supply-demand matching, safety constraints, economic considerations, economic principles, and environmental protection principles. The user side matches them in descending order of priority. According to the matching results, the cloud intelligent management platform generates multiple groups of scheduling control commands for the switch device controllers;

[0044] The cloud intelligent management platform issues a scheduling strategy to the switch device controllers. The scheduling strategy includes scheduling control commands. The switch device controllers receive the scheduling strategy issued by the cloud intelligent management platform and generate scheduling control commands based on the scheduling strategy and real-time power sensor data, so that the switch scheduling control commands generated by the switch device controllers are within the scope of the multiple groups of scheduling control commands issued by the cloud intelligent management platform;

[0045] Generate switch matrix control instructions according to the two-dimensional switch matrix and the agreement between the switch device controller and the switch device register;

[0046] According to the scheduling strategy effective time and effective conditions, send the switch matrix control instructions to the switch device to control the opening and closing of the multi-input and single-output switches in the switch device.

[0047] The present invention also provides an electronic device, including any one of the above power load scheduling systems.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention uses meteorological historical data to perform intelligent prediction on power generation and power consumption. It can, according to the output status of power sources under different mechanisms, different characteristics, different time periods, and different meteorological conditions, intelligently select and flexibly connect the consumption load that adapts to the current power source output status, adopt a "make the most of it" power supply mode, effectively reduce the investment in large-scale energy storage devices, and reduce the cost of new energy utilization. At the same time, the present invention has the functions of cloud and local scheduling strategies. In the case of the disconnection between the switch device controller and the cloud intelligent management platform, the local control component can independently implement the operation management of the switch device to ensure the reliable and stable operation of the entire system. Through the cascading of the input and output of the front and rear stages of the switch device, the allocation of power supply is made more flexible, more application scenarios are adapted, and the primary consumption rate of new energy is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the hardware operation environment equipment involved in a power load scheduling system according to an embodiment of the present invention;

[0051] Figure 2 It is a flowchart of a power load scheduling method according to an embodiment of the present invention;

[0052] Figure 3 It is a flowchart for predicting the output of the photovoltaic power generation side provided by an embodiment of the present invention;

[0053] Figure 4 It is a flowchart for predicting the output of the wind power generation side provided by an embodiment of the present invention;

[0054] Figure 5 It is a schematic diagram of the multi-input and one-output control of the switch device provided by an embodiment of the present invention;

[0055] Figure 6 It is a flowchart of data collection of the sensor data collection module provided by an embodiment of the present invention;

[0056] Figure 7 It is a flowchart of the switch device controller receiving the scheduling strategy issued by the cloud intelligent management platform provided by an embodiment of the present invention;

[0057] Figure 8 It is a schematic diagram of the cascading method of a multi-switch device controller provided by an embodiment of the present invention;

[0058] Figure 9 It is a schematic diagram of another cascading method of a multi-switch device controller provided by an embodiment of the present invention;

[0059] Figure 10 It is the normal execution process of the scheduling system composed of multi-switch device controllers provided by an embodiment of the present invention;

[0060] Figure 11This is the execution process for the abnormality of the scheduling system composed of multi-switch device controllers provided by the embodiments of the present invention. Specific embodiments

[0061] The following further explains the present invention in conjunction with the accompanying drawings and specific embodiments:

[0062] As Figure 1 shown, the present invention provides a power load scheduling system, including: a cloud intelligent management platform, a switch device controller, a power sensor, a switch device, etc.

[0063] The main functions of the cloud intelligent management platform: Use historical meteorological data to perform intelligent prediction on power generation and consumption, issue scheduling strategies, receive and process power sensor data, alarm information, etc. sent by the switch device controller, and monitor the operating status of the entire microgrid power load scheduling system.

[0064] The main functions of the switch device controller: Receive the scheduling strategy issued by the cloud intelligent management platform, generate a scheduling control command according to the scheduling strategy, issue the scheduling control command to the switch device (such as a time-division switch cabinet), and report the power sensor data and alarm information to the cloud intelligent management platform. In the case of the disconnection between the switch device controller and the cloud intelligent management platform, the switch device controller also has the function of automatically generating and issuing a scheduling command.

[0065] Power sensors: Installed on the power generation side and the power consumption side respectively. The power sensor on the power generation side is used to collect the voltage and current data on the power generation side in real time to estimate the maximum power of power generation. The power sensor on the user side is used to collect the voltage and current data on the user side in real time and use it as the load power at the next moment within a short time.

[0066] Switch device: The switch device receives the scheduling control command issued by the switch device controller and controls the switching of the on / off states of each channel switch.

[0067] In a simple power scheduling scenario, one cloud intelligent management platform can mount one (single) switch device controller, and multiple power sensors and multiple switch devices are mounted under one switch device controller. In some complex power scheduling scenarios, one cloud intelligent management platform can mount multiple switch device controllers, and multiple power sensors and multiple switch devices are mounted under each switch device controller. The number of sensors and switches mounted by the switch device controller is related to the usage scenario. The scheduling system composed of multiple switch device controllers is cascaded by a single switch device controller.

[0068] As Figure 2 shown, the present invention also provides a power load scheduling method, including the following steps:

[0069] S101: The cloud-based intelligent management platform uses historical meteorological data to perform intelligent prediction on power generation and power consumption, generating ultra-short-term predictions for 4 hours, short-term predictions for 24 - 72 hours, and medium- and long-term prediction curves for 1 - 12 months.

[0070] S102: Collect real-time power sensor data on the power generation side and the user side.

[0071] S103: Classify and grade the power supply and power consumption situations. According to the principles of supply-demand matching, safety constraints, economic considerations, and environmental protection, match suitable power generation sources from the power generation side output for the user side, and the user side matches them in descending order of priority. Based on the matching results, the cloud-based intelligent management platform generates multiple sets of scheduling control commands for the controller of the switching device (specifically, such as the CPD transfer switch).

[0072] S104: The cloud-based intelligent management platform issues a scheduling strategy to the switching device controller. The switching device controller receives the control commands issued by the cloud-based intelligent management platform, and compares the control commands generated by the switching device controller with the control commands issued by the cloud-based intelligent management platform. The switching commands generated by the switching device controller fall within the scope of the multiple sets of commands issued by the cloud-based intelligent management platform.

[0073] S105: Generate switching matrix control instructions according to the two-dimensional switching matrix, the agreement between the switching device controller and the switching device register.

[0074] S106: According to the effective time and effective conditions of the scheduling strategy, send the scheduling instructions to the switching device to control the opening and closing of the multi-input and single-output switches in the switching device.

[0075] The following will introduce how a scheduling system composed of a single switching device controller is implemented:

[0076] 1 The cloud-based intelligent management platform uses artificial intelligence to perform intelligent prediction on power generation and power consumption:

[0077] The cloud-based intelligent management platform uses historical meteorological data to perform intelligent prediction on power generation and power consumption. It is a method to optimize energy production and consumption by integrating advanced data analysis technologies and meteorological sciences. Through the analysis of historical data and influencing factors, the user-side demand prediction model and the power generation side output prediction model can accurately predict the user demand load and the power generation side output load within the power load scheduling area.

[0078] 1.1 Predict the power generation side output:

[0079] The power generation side mainly uses new energy sources such as photovoltaic and wind power for power supply. Since the influencing factors affecting the output of photovoltaic and wind power are different, there will be differences in the selection of data sources. The following describes the prediction methods for photovoltaic and wind power generation.

[0080] First, artificial intelligence predicts the photovoltaic power generation on the power generation side, as Figure 3 shown, including:

[0081] S201: Collect data sources, including meteorological data, photovoltaic system data, geographical location information, and historical power generation records. Meteorological data includes key parameters such as temperature, humidity, wind speed, and solar radiation intensity within the photovoltaic power generation area. Photovoltaic system data includes static information such as component type, installation angle, tilt angle, etc., and dynamic data such as real-time output power, voltage, and current. Geographical location information is mainly factors affecting lighting conditions, such as latitude, longitude, altitude, etc. Historical power generation records are photovoltaic power generation data over a past period.

[0082] S202: Preprocess the data, clean and standardize the data to ensure no missing values and outliers. Unify data from different sources into a consistent time series format. If necessary, perform operations such as feature scaling and normalization on the data to better meet the requirements of machine learning algorithms.

[0083] S203: Extract features, extract features from the original data that help improve prediction accuracy, including time features, weather conditions, geographical factors, historical similar day analysis, social and economic activities, etc.

[0084] S204: Model selection and training, select an artificial intelligence model suitable for time series data analysis, such as a long short-term memory network (LSTM), XGBoost regression model, etc. During the training process, use methods such as cross-validation to evaluate the model performance and continuously adjust the hyperparameters to optimize the results.

[0085] S205: Model tuning, further adjust the model structure or parameters according to the evaluation results.

[0086] S206: Implementation and deployment, deploy the trained and verified model to the cloud or a local server so that it can receive real-time input and make predictions. To ensure the timeliness and accuracy of the prediction results, a complete monitoring system also needs to be established to update the model regularly to adapt to environmental changes and technological progress.

[0087] Second, artificial intelligence predicts the wind power generation on the power generation side, as Figure 4 shown, including:

[0088] S301: Collect data sources, including meteorological data, wind farm operation data, geographical location information, and historical power generation records. The meteorological data includes key parameters such as temperature, humidity, wind speed, and solar radiation intensity within the photovoltaic power generation area. The wind farm operation data includes dynamic information such as the output power, rotation speed, and pitch angle of each wind turbine, as well as static information such as equipment type and installation location. The geographical location information mainly refers to the factors affecting wind conditions, including altitude, terrain features, and surrounding environment. The historical power generation records are the wind power generation data over a past period of time.

[0089] S302: Preprocess the data by cleaning and standardizing it to ensure there are no missing values and outliers. Unify the data from different sources into a consistent time series format. If necessary, perform operations such as feature scaling and normalization to better meet the requirements of machine learning algorithms.

[0090] S303: Extract features, including time features, weather conditions, geographical factors, historical similar day analysis, social and economic activities, etc., from the original data, which are helpful for improving prediction accuracy.

[0091] S304: Model selection and training. Select artificial intelligence models suitable for time series data analysis, such as long short-term memory networks (LSTM), CNN_LSTM, XGBoost regression models, etc. During the training process, use methods such as cross-validation to evaluate the model performance and continuously adjust the hyperparameters to optimize the results.

[0092] S305: Model tuning. Further adjust the model structure or parameters according to the evaluation results.

[0093] S306: Implementation and deployment. Deploy the trained and validated model to the cloud or local server so that it can receive real-time input and make predictions. To ensure the timeliness and accuracy of the prediction results, a complete monitoring system also needs to be established to update the model regularly to adapt to environmental changes and technological progress.

[0094] 1.2 Predict the user-side load:

[0095] Accurate prediction of user cluster load can promote demand - side management, assist the dispatching system to achieve peak shaving and valley filling, and improve the utilization rate of power grid assets. In order to more accurately predict the user - side load curve, it is classified according to different load curve shapes, and the load is roughly divided into peak - type load, fluctuating load, and stable load. Among them, the peak - type load refers to the load form that increases sharply in a short time and then drops rapidly. The fluctuating load shows regular up - and - down fluctuations over time, but the overall trend is stable. The stable load refers to the load that remains within a relatively fixed range for a long time without significant fluctuations. The research on load prediction technology for different load curves is an important prerequisite for realizing lean demand - side management, efficient home energy management, and accurate electricity market pricing. The prediction method generally uses a data - driven model to give a deterministic point prediction value or a prediction curve for a certain future moment or time period. A short - term single - user load prediction method based on a hybrid of multivariate linear regression and long - short - term memory neural network is adopted. First, the load sequence is decomposed into subsequences with different frequencies by using ensemble empirical mode decomposition. Secondly, multivariate linear regression and long - short - term memory neural network are respectively used to model and predict the low - frequency and high - frequency subsequences. Finally, the predicted values of each subsequence are fused to obtain the future predicted value of the single - user load.

[0096] The cloud - based intelligent management platform issues a dispatching strategy:

[0097] According to the different prediction time ranges, it can mainly be divided into three types at present: ultra - short - term prediction with a prediction range less than 4h, which is mainly used for controlling and managing the power generation system, power quality assessment, etc.; short - term prediction 24 - 72h in advance, which is mainly used for controlling the operation of the power system, economic dispatching, unit input, etc.; medium - and long - term prediction is the prediction with the longest time scale, generally with a prediction range from 1 month to 1 year, which is mainly used for the maintenance and planning of the power generation system. The prediction accuracy is also different for different time ranges. As the time range becomes longer, the amount of data required and the complexity of the prediction will increase, and the prediction accuracy will decrease accordingly.

[0098] The cloud - based intelligent management platform mainly generates dispatching strategies based on the ultra - short - term prediction of 4h and the short - term prediction of 24 - 72h. Next, how the cloud - based intelligent management platform generates dispatching strategies will be introduced.

[0099] First, the cloud - based intelligent management platform receives the power sensor data reported by the switch device controller, reads the data through Socket communication or directly from the database. Through the power sensor data, the current power generation side output and user - side load conditions can be obtained.

[0100] The power supply on the power generation side comes from wind power, photovoltaic power, hydropower, biomass energy, geothermal energy, storage batteries, and the power grid.

[0101] The user-side load comes from residential life, commercial and office use, industrial production, public facilities, new energy charging piles, etc.

[0102] Classify and grade the power supply and consumption situations:

[0103] The power supply that is preferentially used is defined as Class I power supply, such as renewable energy sources like wind, photovoltaic, hydropower, biomass energy, geothermal energy, etc. The power supply from the battery is defined as Class II power supply, and the power supply from the power grid is defined as Class III power supply, which is used as a "backup" when the power supply requirements cannot be met by Class I and Class II power supplies.

[0104] The power demands that are crucial for national security and social stability are defined as Class I loads; the power demands that are important but do not have a direct impact on national security and social stability are defined as Class II loads; the general power demands, such as most residential houses, small and medium-sized commercial enterprises, etc., are defined as Class III loads; those power demands that can be temporarily reduced or transferred under specific conditions are defined as Class IV loads.

[0105] The power generation side and the user side are controlled by a multi-input and single-output switch, such as Figure 5 shown.

[0106] Use an array of 0s and 1s to represent the corresponding open and closed states. As Figure 5 shown, the power supplies for all Load 1, Load 2, Load 3, and energy storage come from the power grid. For example, the switch command: 0001_0001_0001_0001 represents the state of the switch devices at the corresponding positions. The first byte 0001 represents the source situation of the power supply for Load 1. 0001 means that the power supply for Load 1 is connected to the power grid, and the power supplies from other sources are in the off state. Figure 5 The serial numbers 1, 2, 3, and 4 in

[0107] respectively represent four groups of switch devices.

[0108] 3 Receive and process the sensor data sent by the switch device controller:

[0109] The switch device controller is connected to the switch device and the high-efficiency flexible load cloud intelligent management platform. It has the function of collecting sensor data, the function of receiving upper-layer instructions and independently generating control strategies, the function of periodically reporting real-time monitoring data and control strategies, the function of data storage and data query, and the function of an interactive interface for data display.

[0110] Sensor data acquisition module:

[0111] On the power consumption side, the sensor data acquisition module collects the operating condition information of each switching device and the power supply / consumption quality information such as the voltage and current of the source-load side sensors. At the same time, it collects the environmental information such as the working temperature, humidity, and smoke in the switching device, as well as the data of various inverters such as AC / DC, DC / AC, and DC / DC.

[0112] On the power generation side, the sensor data acquisition module mainly collects power generation equipment information, energy storage inverter information, energy storage element data, battery management system information, switching device information, multi-functional power meter information of the power distribution cabinet, energy storage outdoor cabinet information, etc.

[0113] The specific data processing process is as Figure 6 shown, including:

[0114] S401: Establish a connection:

[0115] The switching device controller sends a connection request to the SOCKET server of the switching device through Ethernet.

[0116] The switching device accepts the connection request and establishes a TCP connection.

[0117] S402: Data reading:

[0118] Read power generation equipment information:

[0119] The switching device controller sends a read request to request information such as the power generation status and power. The switching device responds to the read request and returns the data. The switching device controller parses and stores the data.

[0120] Read energy storage inverter information: Similarly, the switching device controller sends a read request to request information such as the charge / discharge status and power. The switching device responds to the read request and returns the data. The switching device controller parses and stores the data.

[0121] Read other equipment information:

[0122] For other equipment such as energy storage elements, battery management systems, switching devices, multi-functional power meters of the power distribution cabinet, and energy storage outdoor cabinets, the switching device controller also sends corresponding read requests and processes the returned data.

[0123] S403: Data processing:

[0124] Data verification: Ensure the integrity and accuracy of the data.

[0125] Data conversion: Convert the original data into a unified data format.

[0126] Anomaly Detection: Use threshold checking or other methods to identify anomalous data points.

[0127] Data Aggregation: Calculate necessary statistics such as total power, average voltage, etc.

[0128] Alarm Handling: Identify and record all alarm and warning signals.

[0129] S404: Data Forwarding:

[0130] Construct Data Packet: Encapulate the processed data into data packets.

[0131] Send Data: Use the protocol to send the data packets to the switch device controller.

[0132] S405: Data Storage:

[0133] Write the read data to the memory storage for use by the scheduling control module.

[0134] S406: Close Connection:

[0135] Before disconnecting the switch device controller from the cloud intelligent management platform, close the TCP connection.

[0136] 3 The switch device controller receives the scheduling policy issued by the cloud intelligent management platform:

[0137] The switch device controller communicates with the cloud intelligent management platform through SOCKET. The switch device controller receives the control commands issued by the cloud intelligent management platform. The switch device controller can receive the scheduling policies issued by the cloud intelligent management platform. However, in some simple scenarios, the cloud intelligent management platform is not essential, and the switch device controller also has the function of generating and issuing scheduling commands. In the absence of the cloud intelligent management platform, the switch device controller issues scheduling commands and compares them with the internal scripts set at the factory. If the switch commands generated by the switch device controller fall within the scope of the built-in scripts, the switch device controller will issue this switch command to the switch device. In the case of the cloud intelligent management platform, the switch device controller receives the control commands issued by the cloud intelligent management platform and compares the control commands generated by the switch device controller with the control commands issued by the cloud intelligent management platform. If the switch commands generated by the switch device controller fall within the scope of multiple groups of commands issued by the cloud intelligent management platform, the switch device controller will issue this switch command to the switch device. The switch device receives the switch command and performs the corresponding operations. This solves the problem that the local control component can independently manage the operation of the switch device in case of disconnection between the switch device controller and the cloud intelligent management platform.

[0138] The switch device controller includes a scheduling control module, which includes a policy reading sub-module, a policy parsing sub-module, a scheduling decision sub-module, a data collection sub-module, a control instruction generation sub-module, a control instruction sending sub-module, etc. The following are the functions of specific modules.

[0139] Policy reading sub-module: Obtain the latest scheduling policy from the cloud intelligent management platform, including script ID, script scenario, effective time, deadline difference, effective conditions, and scheduling control commands. There are priorities in the scheduling control commands, and there may be multiple groups of scheduling control commands at the same time.

[0140] Policy parsing sub-module: Parse the scheduling policy issued by the cloud intelligent management platform.

[0141] Data collection sub-module: Collect real-time power sensor data.

[0142] Scheduling decision sub-module: Based on the scheduling policy and real-time power sensor data, it is required that the scheduling control commands generated by the switch device controller be within the scope of the scheduling control commands issued by the cloud intelligent management platform, so as to ensure that the switch device controller operates stably within the grid safety range.

[0143] Control instruction generation sub-module: Generate switch matrix control instructions according to the two-dimensional switch matrix and the agreement between the switch device controller and the switch device register.

[0144] Control instruction sending sub-module: Send the control instructions to the switch device.

[0145] Input the scheduling policy of the cloud intelligent management platform, source-side power sensor data, load-side power sensor data, switch device switch array, and environmental information into the scheduling control module. After processing, output the switch matrix (to the switch device) information. The specific execution process is as Figure 7 shown, including:

[0146] S501: Obtain the latest scheduling control commands from the cloud intelligent management platform. There are priorities in the scheduling control commands, and there may be multiple groups of scheduling control commands at the same time.

[0147] S502: Parse the scheduling policy issued by the cloud intelligent management platform.

[0148] S503: Collect real-time sensor data on the power generation side and the user side.

[0149] S504: Based on the scheduling policy and real-time sensor data, it is required that the scheduling control commands generated by the switch device controller be within the scope of the scheduling control commands issued by the cloud intelligent management platform.

[0150] S505: Generate a switch matrix control instruction according to the two-dimensional switch matrix, switch device controller, and switch device register convention.

[0151] S506: Send the switch matrix control instruction corresponding to the scheduling control command to the switch device according to the scheduling policy effective time and effective condition, and control the state switching of the multi-input and single-output switch in the switch device.

[0152] The following will introduce how to implement a scheduling system composed of complex multi-switch device controllers:

[0153] The scheduling system composed of multi-switch device controllers is cascaded by single-switch device controllers. Therefore, functions such as intelligent prediction of power generation and consumption, issuing scheduling policies, receiving and processing sensor data sent from switch device controllers are similar. The difference is that the scheduling policy of a single-switch device controller is issued by the cloud intelligent management platform and is only sent to one switch device controller. The scheduling policy of the multi-switch device controllers is that the cloud intelligent management platform issues scheduling policies to multiple switch device controllers simultaneously. Each switch device controller generates corresponding switch control commands according to the corresponding scheduling policy, thereby controlling the open and closed states of the switches.

[0154] Figure 8 It is a cascading method of multi-switch device controllers. Switch device 1, switch device 2, switch device 3, and switch device 4 form the front-stage switch group. Among them, switch device 1, switch device 2, switch device 3, and switch device 4 are all composed of a first switch, a second switch, a third switch, and a fourth switch. The first switch of the front-stage switch group is connected to photovoltaic power source 1, the second switch is connected to wind power source 1, the third switch is connected to energy storage power source 1, and the fourth switch is connected to grid power source 1. Switch device 5, switch device 6, switch device 7, and switch device 8 form the rear-stage switch group. Among them, switch device 5, switch device 6, switch device 7, and switch device 8 are all composed of a first switch, a second switch, a third switch, and a fourth switch. The first switch of the rear-stage switch group is connected to photovoltaic power source 2, the second switch is connected to wind power source 2, the third switch is connected to energy storage power source 2, and the fourth switch is connected to the output of the fourth switch of the front-stage switch group. The output of switch device 4 of the front-stage switch group is used as the input of the fourth switch of the rear-stage switch group, thus forming the connection between the front and rear stages of the switch device.

[0155] Flexibly allocate different types of power sources (photovoltaic, wind, diesel, grid) to different loads (load 1 to load 7) through multiple switch devices. By adjusting the position of the switch devices, dynamic connection between power sources and loads can be achieved, thereby optimizing energy management and distribution.

[0156] Figure 9It is another cascading method for the multi-switch device controller. Switch device 1, switch device 2, switch device 3, and switch device 4 form the front-stage switch group. Among them, switch device 1, switch device 2, switch device 3, and switch device 4 are all composed of a first switch, a second switch, a third switch, and a fourth switch. The first switch of the front-stage switch group is connected to photovoltaic power source 1, the second switch is connected to wind power source 1, the third switch is connected to the output of switch device 8 of the rear-stage switch group, and the fourth switch is connected to grid power source 1. Switch device 5, switch device 6, switch device 7, and switch device 8 form the rear-stage switch group. Among them, switch device 5, switch device 6, switch device 7, and switch device 8 are all composed of a first switch, a second switch, a third switch, and a fourth switch. The first switch of the rear-stage switch group is connected to photovoltaic power source 2, the second switch is connected to wind power source 2, the third switch is connected to energy storage power source 2, and the fourth switch is connected to the output of the fourth switch of the front-stage switch group. The output of switch device 4 of the front-stage switch group serves as the input of the fourth switch of the rear-stage switch group. Different from the previous case, at the same time, the output of switch device 8 of the rear-stage switch group serves as the input of the third switch of the front-stage switch group, thus making the connection between the front and rear stages of the switch device more complex. Through this similar topological connection, flexible allocation of power supply is achieved.

[0157] It should be noted that Figure 8 、 Figure 9 The serial number "1" in photovoltaic power source 1, wind power source 1, energy storage power source 1, and grid power source 1 refers to the front stage, and the serial number "2" in photovoltaic power source 2, wind power source 2, and energy storage power source 2 refers to the rear stage, rather than representing the quantity or serial number.

[0158] The difference between the scheduling system composed of multi-switch device controllers and the scheduling system composed of single-switch device controllers mainly lies in that multiple switch device controllers are mounted under a cloud intelligent management platform, and the execution commands of each switch device controller are uniformly issued by the cloud intelligent management platform. Each switch device controller performs time synchronization through a time server. The system time error between each switch device controller does not exceed 10 ms to solve the problem of instantaneous shortage of power supply caused by inconsistent switching times of each switch device controller.

[0159] As Figure 10 shown, the normal execution process of the scheduling system composed of multi-switch device controllers is as follows:

[0160] S601: According to the prediction results of the scheduling strategy, different switch devices obtain the latest scripts from the cloud intelligent management platform, and the scheduling control commands have priorities. The system generates scheduling control commands according to the pre-set scheduling strategy and sends these commands to each switch device.

[0161] S602: Each switching device analyzes the scheduling script issued by the cloud intelligent management platform, including the script ID, script scenario, effective time, cut-off time difference, effective conditions, control commands, and issued batch mark number.

[0162] S603: The controller of each switching device collects real-time sensor data on the power generation side and the user side and reports it to the cloud intelligent management platform.

[0163] S604: Based on the scheduling strategy and real-time sensor data, the cloud intelligent management platform generates specific two-dimensional switching matrix scheduling control commands.

[0164] S605: According to the agreement between the two-dimensional switching matrix, the switching device controller and the switching device register, generate switching matrix control instructions. According to the source-load matching condition, judge whether the scheduling control commands generated by each switching device controller can be executed.

[0165] S606: According to the effective time and effective conditions of the scheduling strategy, at a fixed moment, send the switching matrix control instructions to each switching device.

[0166] As Figure 11 shown, the abnormal execution process of the scheduling system composed of multiple switching device controllers is as follows:

[0167] S701: According to the prediction results of the scheduling strategy, different switching devices obtain the latest scripts and scheduling control commands with priorities from the cloud intelligent management platform. The system generates scheduling control commands according to the preset scheduling strategy and sends these commands to each switching device.

[0168] S702: Each switching device analyzes the scheduling script issued by the cloud intelligent management platform, including the script ID, script scenario, effective time, cut-off time difference, effective conditions, control commands, and issued batch mark number.

[0169] S703: The controller of each switching device will collect real-time sensor data from the power generation side (such as photovoltaic, wind power, etc.) and the user side (such as load status) and upload this data to the cloud intelligent management platform.

[0170] S704: Based on the scheduling strategy and real-time sensor data, the cloud intelligent management platform generates specific two-dimensional switching matrix scheduling control commands.

[0171] S705: According to the agreement between the two-dimensional switching matrix, the switching device controller and the switching device register, generate switching matrix control instructions. According to the two-dimensional switching matrix and the agreement of the switching device controller and register, generate specific control instructions for controlling the operation of the switching device. Judge that the scheduling control commands generated by one or more switching device controllers cannot be executed by the source-load matching method.

[0172] S706: Calculate the non-executable switch device controller, and upload the revocation command with the non-executable script distribution batch mark number to the cloud intelligent management platform. The cloud intelligent management platform uniformly distributes the script for revoking the distribution batch mark number to each switch device controller.

[0173] S707: After each switch device controller receives the revocation command issued by the cloud intelligent management platform, revoke the script information of the distribution batch mark number of this switch device, and wait for the next script distribution.

[0174] Based on the above embodiments, the present invention further provides an electronic device, including any one of the above power load scheduling systems.

[0175] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A power load dispatching system, characterized in that: include: Cloud-based intelligent management platform, switch device controller, power sensor and switch device; The cloud-based intelligent management platform is used to use historical meteorological data to make intelligent predictions on power generation and consumption, issue dispatch strategies, receive and process power sensor data and alarm information sent from the switch device controller, and monitor the operating status of the entire microgrid power load dispatch system; The switch device controller is used to receive the dispatching strategy issued by the cloud-based intelligent management platform, generate a dispatching control command according to the dispatching strategy, issue the dispatching control command to the switch device, and report the power sensor data and alarm information to the cloud-based intelligent management platform; it is also used to automatically generate and issue a dispatching control command in the event of a loss of connection with the cloud-based intelligent management platform; The power sensors are installed on the power generation side and the power consumption side respectively. The power sensor on the power generation side is used to collect the voltage and current data on the power generation side in real time to estimate the maximum power generated; the power sensor on the user side is used to collect the voltage and current data on the user side in real time, which is used as the load power at the next moment in a short time. The switch device is used to receive the dispatch control command issued by the switch device controller to control the switching of the switch in and out of each channel; The switch device controller includes a dispatch control module, which includes a strategy reading submodule, a strategy parsing submodule, a dispatch decision submodule, a data collection submodule, a control instruction generating submodule and a control instruction sending submodule; The strategy reading submodule is used to obtain the latest scheduling strategy from the cloud-based intelligent management platform; The policy parsing submodule is used to parse the scheduling policy issued by the cloud-based intelligent management platform, including the script ID, script scenario, effective time, deadline time difference, effective conditions and scheduling control commands; The data collection submodule is used to collect real-time power sensor data; The dispatch decision submodule is used to make the dispatch control command generated by the switch device controller fall within the scope of the dispatch control command issued by the cloud-based intelligent management platform based on the dispatch strategy and real-time power sensor data; The control instruction generation submodule is used to generate switch matrix control instructions according to the two-dimensional switch matrix, the switch device controller and the switch device register convention; The control instruction sending submodule is used to send the control instruction to the switch device.

2. The power load dispatching system according to claim 1, characterized in that: The switch device controller is single or multiple, each switch device controller is connected to multiple power sensors and multiple switch devices, and the dispatching system composed of multiple switch device controllers is formed by cascading single switch device controllers.

3. The power load dispatching system according to claim 1, characterized in that: The cloud-based intelligent management platform predicts photovoltaic power generation on the power generation side in the following manner: Collect data sources, which include meteorological data, photovoltaic system data, geographic location information and historical power generation records; the meteorological data includes temperature, humidity, wind speed and solar radiation intensity within the photovoltaic power generation area; the photovoltaic system data includes component type, installation angle, tilt angle, real-time output power, voltage and current; the geographic location information includes latitude, longitude and altitude; the historical power generation record is the photovoltaic power generation data over a period of time in the past; Preprocess the collected data, including: cleaning and standardizing the data to ensure there are no missing values ​​and outliers, and unifying data from different sources into a consistent time series format; Extract features from raw data that help improve forecast accuracy, including temporal features, weather conditions, geographic factors, historical similar day analysis, and socioeconomic activities; Select AI models suitable for time series data analysis, including long short-term memory networks and XGBoost regression models, and train them; Model tuning: further adjust the model structure or parameters based on the evaluation results.

4. The power load dispatching system according to claim 3, characterized in that: When the cloud-based intelligent management platform predicts wind power generation on the power generation side, the data sources collected include meteorological data, wind farm operation data, geographic location information and historical power generation records; the meteorological data include temperature, humidity, wind speed and solar radiation intensity within the photovoltaic power generation area; the wind farm operation data include the output power, speed, pitch angle, equipment type and installation location of each wind turbine; the geographic location information includes altitude, terrain features and surrounding environment; the historical power generation records are wind power generation data over a period of time in the past.

5. The power load dispatching system according to claim 1, characterized in that: The cloud-based intelligent management platform predicts the user-side load in the following manner: The load sequence is decomposed into subsequences of different frequencies using integrated empirical mode decomposition; Multivariate linear regression and long short-term memory neural network are used to model and predict low-frequency and high-frequency subsequences respectively; The predicted values ​​of each subsequence are combined to obtain the future predicted value of a single user's load.

6. The power load dispatching system according to claim 1, characterized in that: The cloud-based intelligent management platform generates a scheduling strategy in the following manner: First, the power sensor data reported by the switch device controller is received, and the data is read directly from the database through Socket communication or directly from the database. The current power output of the power generation side and the load of the user side are obtained through the power sensor data, and the power supply and power consumption situations are classified and graded. According to the supply and demand matching principles, the principles of safety constraints and economic considerations, the economic principle, and the environmental protection principle, the user side is matched with the power generation source that meets the requirements from the power generation side output. The user side is matched in order from high to low priority, and multiple groups of dispatching control commands are generated according to the matching results. The dispatching control command represents the opening and closing status of the multi-input and one-output switches on the power generation side and the user side.

7. The power load dispatching system according to claim 1, characterized in that: When there are multiple switchgear controllers in the system, power load dispatch is performed in the following manner: According to the power generation and load forecast results, different switch devices obtain the latest scripts from the cloud-based intelligent management platform. The system generates dispatch control commands according to the pre-set dispatch strategy and sends the generated dispatch control commands to each switch device; Each switch device parses the script corresponding to the dispatch control command issued by the cloud-based intelligent management platform, including the script ID, script scenario, effective time, deadline time difference, effective condition, control command and issued batch mark number; Each switch device collects real-time power sensor data from the power generation side and the user side, and uploads it to the cloud-based smart management platform; Based on the scheduling strategy and real-time power sensor data, the cloud-based intelligent management platform generates two-dimensional switch matrix scheduling control commands; Generate switch matrix control instructions according to the two-dimensional switch matrix, switch device controller and switch device register agreement, and judge whether the dispatch control commands generated by each switch device controller are executable according to the source-load matching condition; If executable, according to the effective time and effective conditions of the scheduling strategy, at a fixed time, the control instruction corresponding to the scheduling control command is sent to each switch device to control the simultaneous switching of multiple input and one output switches in each switch device; If one or more dispatching control commands generated by the switch device controllers are unexecutable, the corresponding switch device controller will upload the script issuance batch mark number corresponding to the unexecutable dispatching control command to the cloud-based intelligent management platform, and the cloud-based intelligent management platform will send a revocation command with the issuance batch mark number to other switch device controllers. After receiving the revocation command issued by the cloud-based intelligent management platform, each switch device controller will revoke the script information of the switch device corresponding to the issuance batch mark number and wait for the next script to be issued.

8. A method for dispatching electric load, characterized in that: include: The cloud-based smart management platform uses historical meteorological data to make intelligent predictions on power generation and consumption, generating ultra-short-term predictions of 4 hours, short-term predictions of 24 to 72 hours, and medium- and long-term prediction curves from January to December. Collect real-time power sensor data from the power generation side and the user side; The power supply and power consumption situations are classified and graded. According to the principles of supply and demand matching, safety constraints and economic considerations, economy and environmental protection, the user side is matched with the power generation source that meets the requirements from the power generation side output. The user side is matched in order from high to low priority. According to the matching results, the cloud-based intelligent management platform generates multiple groups of dispatching control commands for the switch device controller; The cloud-based intelligent management platform issues a dispatching strategy to the switch device controller, the dispatching strategy includes a dispatching control command, the switch device controller receives the dispatching strategy issued by the cloud-based intelligent management platform, and generates a dispatching control command based on the dispatching strategy and real-time power sensor data, so that the switch dispatching control command generated by the switch device controller falls within the range of multiple groups of dispatching control commands issued by the cloud-based intelligent management platform; Generate switch matrix control instructions according to the two-dimensional switch matrix, switch device controller and switch device register convention; According to the effective time and effective conditions of the scheduling strategy, the switch matrix control instruction is sent to the switch device to control the opening and closing of the multi-input and one-output switch in the switch device; The switch device controller includes a dispatch control module, which includes a strategy reading submodule, a strategy parsing submodule, a dispatch decision submodule, a data collection submodule, a control instruction generating submodule and a control instruction sending submodule; The strategy reading submodule is used to obtain the latest scheduling strategy from the cloud-based intelligent management platform; The policy parsing submodule is used to parse the scheduling policy issued by the cloud-based intelligent management platform, including the script ID, script scenario, effective time, deadline time difference, effective conditions and scheduling control commands; The data collection submodule is used to collect real-time power sensor data; The dispatch decision submodule is used to make the dispatch control command generated by the switch device controller fall within the scope of the dispatch control command issued by the cloud-based intelligent management platform based on the dispatch strategy and real-time power sensor data; The control instruction generation submodule is used to generate switch matrix control instructions according to the two-dimensional switch matrix, the switch device controller and the switch device register convention; The control instruction sending submodule is used to send the control instruction to the switch device.

9. An electronic device, characterized in that: It comprises an electric power load dispatching system as described in any one of claims 1 to 7.

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