Source-network-load-storage integrated transaction decision support platform

Abstract

The invention relates to the technical field of energy internet and power transaction, in particular to a source-network-load-storage integrated transaction decision support platform. According to the technical scheme, the source-network-load-storage integrated transaction decision support platform comprises a data acquisition and preprocessing module, an intelligent analysis module, a transaction decision module, an execution control module, a monitoring and early warning module and a communication and interface module, according to the invention, by constructing the highly integrated data acquisition and preprocessing module, heterogeneous data sources from different sources, networks, loads and storages can be seamlessly jointed and fused, and the module executes strict cleaning, conversion and normalization processes on inrush mass original data; according to the method and the system, intelligent analysis and transaction decision are guaranteed to be based on complete, accurate and timely global situation information, the information quality is improved, and a platform can insight into previously hidden associations and opportunities, so that the accuracy and the applicability of a transaction strategy are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of energy internet and power transaction technology, and particularly relates to a source-grid-load-storage integrated transaction decision support platform. BACKGROUND

[0002] With the rapid development of energy internet, source-grid-load-storage integration has become an important direction of the development of power systems, and the traditional power transaction system mainly faces bilateral transactions of the power generation side and the power consumption side, which is difficult to adapt to the new needs of source-grid-load-storage multi-subject collaborative interaction.

[0003] The existing transaction decision support platform generally has the serious defects of data island and information fragmentation, various energy systems, load units and energy storage facilities are usually independently constructed and operated, their data formats, communication protocols and storage methods are different, and there is a lack of unified standards and coordination mechanisms, which leads to great obstacles in collecting information, the decision view formed is incomplete and seriously lagging, the decision maker cannot simultaneously grasp the overall and real system state, and the transaction strategy formulated is bound to be out of touch with the actual operation condition of the system, not only losing the optimization space, but also hiding the operation risk caused by information loss.

[0004] In view of the above problems, the present application can seamlessly interface and integrate heterogeneous data sources from sources, grids, loads and storages by constructing a highly integrated data acquisition and preprocessing module, which performs strict cleaning, conversion and normalization processes on the massive raw data, ensures that the intelligent analysis and transaction decision are based on complete, accurate and timely overall situation information, improves the information quality, and the platform can understand the previously hidden correlations and opportunities, thereby greatly improving the precision and applicability of the transaction strategy. SUMMARY

[0005] In order to overcome the serious defects of data island and information fragmentation of the existing transaction decision support platform, various energy systems, load units and energy storage facilities are usually independently constructed and operated, their data formats, communication protocols and storage methods are different, and there is a lack of unified standards and coordination mechanisms, which leads to great obstacles in collecting information, the decision view formed is incomplete and seriously lagging, the decision maker cannot simultaneously grasp the overall and real system state, and the transaction strategy formulated is bound to be out of touch with the actual operation condition of the system, not only losing the optimization space, but also hiding the operation risk caused by information loss.

[0006] The technical solution of the present application is: a source-grid-load-storage integrated transaction decision support platform, which comprises the following modules: A data acquisition and preprocessing module is used to collect multi-source data from sources, grids, loads and storages in real time, and to clean, normalize and store the data, so as to eliminate data islands. Intelligent analysis module: for analyzing data using machine learning and optimization algorithms to predict energy supply and demand balance, price trends and system risks; Transaction decision module: for generating optimal transaction strategies and decision plans based on intelligent analysis results; Execution control module: for controlling the operation of source network load storage equipment and executing transaction commands according to decision instructions; Monitoring and early warning module: for monitoring system operation status around the clock and discovering abnormalities and issuing warnings in a timely manner; Communication and interface module: for providing internal and external communication and data exchange interfaces.

[0007] As preferred, the data collection and preprocessing module includes: A11: Data collection unit, including sensor network, data collection card and communication module, for collecting real-time data through distributed interface, including energy output, grid state, load demand and energy storage level; A12: Data cleaning unit, including data processing chip, algorithm processor and memory module, for detecting outliers, imputing missing values and filtering noise in raw data; A13: Data storage unit, including solid state disk, database server and data bus, for storing preprocessed data into structured database.

[0008] As preferred, the intelligent analysis module includes: A21: Prediction analysis unit, including GPU accelerator, prediction algorithm module and data buffer, for predicting energy generation, load demand and market price fluctuations based on historical data and real-time information; A22: Optimization analysis unit, including optimization processor, mathematical calculation library and configuration memory, for multi-objective optimization of source network load storage resources; A23: Risk assessment unit, including risk model processor, random number generator and report generator, for assessing market risk, operational risk and credit risk in transactions and generating risk reports.

[0009] As preferred, the intelligent analysis module includes the following working steps when working: S11: Extract historical energy production, load records, market transaction prices and real-time source network load storage operation data from the data storage unit and load them into the memory of the analysis module; S12: Standardize the loaded raw data to eliminate dimensional effects and construct feature variables related to the prediction target; S13: According to the specific requirements of the prediction task, the corresponding model is called from the prediction algorithm module, and the LSTM model is used to predict the short-term load and the ARIMA model is used to predict the market price trend; S14: The selected prediction model is trained using the historical data set, and the hyperparameters inside the model are adjusted by the GPU accelerator to minimize the prediction error and improve the model accuracy; S15: Based on the current system state and the prediction results, multiple optimization objectives such as the lowest economic cost and the highest system stability are set, and the optimal source network load storage resource allocation scheme is solved by the optimization processor; S16: The risk model processor performs Monte Carlo simulation to randomly generate a large number of future market scenarios and calculates the maximum loss probability that the trading strategy may face under various scenarios; S17: The prediction results, optimization scheme and risk assessment conclusions are summarized, and the report generator is used to format the output into a structured analysis report and transmit it to the trading decision module through the interface.

[0010] As a preferred, the trading decision module includes: A31: Strategy generation unit, including decision algorithm chip, strategy database and interface controller, used to integrate prediction and optimization results to generate trading strategies, including trading timing, quantity and price; A32: Simulation verification unit, including simulation processor, verification algorithm module and result display, used to simulate trading strategies in a virtual environment to test their effectiveness and robustness under different scenarios; A33: Decision output unit, including output interface, log storage and decision trigger, used to convert the final decision into executable instructions and record the decision process for auditing.

[0011] As a preferred, the trading decision module includes the following working steps when working: S21: Through the interface controller, obtain the prediction report, optimization scheme and risk assessment report from the intelligent analysis module, and parse the key data and constraints in them; S22: In the strategy generation unit, the core goal of this decision is determined, and the hard constraint conditions that the power grid safety or device power cannot be violated are set; S23: The decision algorithm chip calculates and generates one or more alternative trading strategies, including specific trading time and quantity, based on the set goals and constraints combined with historical experience in the strategy database; S24: The simulation processor creates a high-fidelity virtual operating environment based on the current power grid topology and device parameters to simulate the actual execution effect of the trading strategy; S25: Set up multiple possible disturbance scenarios in the simulation environment, including extreme cases of sudden fluctuation of energy prices, unexpected jump of key loads, or sharp drop of distributed photovoltaic output; S26: Run each alternative trading strategy in the virtual environment, and verify the algorithm module to record the execution results of the strategy under different disturbance scenarios, including revenue, network loss, and stability indicators; S27: Compare the simulation evaluation results of all alternative strategies, select the one with the best overall performance, and convert it into standardized control instructions recognizable by the device through the decision output unit.

[0012] As preferred, the execution control module comprises: A41: Instruction analysis unit, including instruction parser, command generator and protocol converter, for analyzing decision instructions, generating specific control commands, and adapting to different device protocols; A42: Device control unit, including control relay, execution driver and feedback sensor, for sending control signals to energy devices to adjust the operating state; A43: Execution monitoring unit, including state monitor, feedback collector and abnormal alarm, for real-time monitoring of command execution, collecting feedback data and detecting abnormalities.

[0013] As preferred, the monitoring and early warning module comprises: A51: State monitoring unit, including monitoring sensor, data collector and state display, for continuously monitoring key parameters of source, network, load and storage; A52: Abnormality detection unit, including abnormality detection algorithm module, comparator and alarm trigger, for detecting abnormal patterns using rules and algorithms; A53: Early warning release unit, including early warning generator, communication interface and notification display, for releasing early warning information to operators through various channels.

[0014] As preferred, the monitoring and early warning module includes the following working steps when working: S31: Collect key operating data such as voltage, current, power, and device temperature at a fixed sampling frequency through monitoring sensors distributed in source, network, load, and storage; S32: Filter the collected raw data to eliminate obvious abnormal jump points, and calculate the current total load, total power generation, and network loss state variables based on these data; S33: Compare the calculated state variables with the preset safe operation threshold in real time to assess whether the overall system and each component is in a safe, warning, or dangerous interval; S34: The anomaly detection algorithm module runs rule-based logical judgment and machine learning-based outlier detection in parallel to identify device failures, communication interruptions, or data anomalies; S35: For the detected anomaly, determine its severity level as slight, general, or serious according to its deviation from the normal value and duration, and analyze the possible chain reaction it may cause; S36: The early warning generator automatically generates a standardized early warning information text containing the anomaly time, location, description, and treatment suggestions according to the anomaly level and type; S37: The communication interface simultaneously publishes the formatted early warning information to designated operation and management personnel through the SMS gateway, email system, and platform-embedded notification display.

[0015] As a preferred, the communication and interface module comprises: A61: Internal communication unit, including internal bus, message queue and synchronization controller, for coordinating data transmission between modules; A62: External interface unit, including API gateway, protocol adapter and security encryptor, for secure data exchange with external systems; A63: User interface unit, including graphics processor, touch screen and input device, for providing a visual operation interface.

[0016] Advantages of the present application: The existing transaction decision support platform generally has the serious defects of data island and information fragmentation. Various energy systems, load units and energy storage facilities are usually independently constructed and operated, with different data formats, communication protocols and storage methods, lacking unified standards and coordination mechanisms, which leads to great obstacles in information collection, and the decision view formed is incomplete and seriously lagging, decision makers cannot grasp the overall and real system state, and the transaction strategy formulated is bound to be out of touch with the actual system operation condition, not only losing optimization space, but also hiding the operation risk caused by information loss. The present scheme can seamlessly connect and integrate heterogeneous data sources from source, grid, load and storage by building a highly integrated data collection and preprocessing module, which performs strict cleaning, conversion and normalization processes on the massive raw data, ensuring that the intelligent analysis and transaction decision are based on complete, accurate and timely global situation information. The improvement of information quality enables the platform to uncover hidden correlations and opportunities, greatly improving the precision and applicability of transaction strategies. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A source-grid-load-storage integrated transaction decision support platform framework process diagram is shown. Figure 2A workflow diagram of an intelligent analysis module of a source-grid-load-storage integrated transaction decision support platform is shown. Figure 3 A workflow diagram of a transaction decision module of a source-grid-load-storage integrated transaction decision support platform is shown. Figure 4 A workflow diagram of a monitoring and early warning module of a source-grid-load-storage integrated transaction decision support platform is shown. DETAILED DESCRIPTION

[0018] The application will be further described below in conjunction with the accompanying drawings and examples.

[0019] Please refer to Figures 1-4 The application provides an embodiment: a source-grid-load-storage integrated transaction decision support platform, which comprises the following modules: A data acquisition and preprocessing module: used for real-time acquisition of multi-source data from source, grid, load and storage parties, and for cleaning, normalization and storage, eliminating data islands; An intelligent analysis module: used for analyzing data using machine learning and optimization algorithms to predict energy supply and demand balance, price trends and system risks; A transaction decision module: used for generating optimal transaction strategies and decision schemes according to intelligent analysis results; An execution control module: used for controlling the operation of source-grid-load-storage equipment according to decision instructions and executing transaction commands; A monitoring and early warning module: used for monitoring system operation status around the clock, discovering abnormalities in time and issuing early warnings; A communication and interface module: used for providing internal and external communication and data exchange interfaces of the platform.

[0020] As a preferred, the data acquisition and preprocessing module comprises: A11: a data acquisition unit, comprising a sensor network, a data acquisition card and a communication module, used for acquiring real-time data through a distributed interface, including energy output, grid state, load demand and energy storage level; A12: a data cleaning unit, comprising a data processing chip, an algorithm processor and a memory module, used for detecting outliers, interpolating missing values and filtering noise for original data; A13: a data storage unit, comprising a solid state disk, a database server and a data bus, used for storing preprocessed data into a structured database.

[0021] As a preferred, the intelligent analysis module comprises: A21: a prediction analysis unit including a GPU accelerator, a prediction algorithm module and a data buffer, for predicting energy generation, load demand and market price fluctuations based on historical data and real-time information; A22: an optimization analysis unit including an optimization processor, a mathematical calculation library and a configuration memory, for multi-objective optimization of source, grid, load and storage resources; A23: a risk assessment unit including a risk model processor, a random number generator and a report generator, for assessing market risk, operational risk and credit risk in transactions, and generating risk reports.

[0022] As a preferred, the intelligent analysis module includes the following working steps when working: S11: extract historical energy production, load records, market transaction prices and real-time source, grid, load and storage operation data from the data storage unit, and load them into the memory of the analysis module; S12: standardize the loaded raw data to eliminate dimensional effects, and construct feature variables related to the prediction target from them; S13: according to the specific requirements of the prediction task, call the corresponding model from the prediction algorithm module, use the LSTM model to predict the short-term load, and use the ARIMA model to predict the market price trend; S14: train the selected prediction model using the historical data set, adjust the hyperparameters inside the model through the GPU accelerator to minimize the prediction error and improve the model accuracy; S15: based on the current system state and the prediction results, set multiple optimization targets such as the lowest economic cost and the highest system stability, and solve the optimal source, grid, load and storage resource allocation scheme through the optimization processor; S16: use the risk model processor to perform Monte Carlo simulation, randomly generate a large number of future market scenarios, and calculate the maximum loss probability that the trading strategy may face under various scenarios; S17: summarize the prediction results, optimization scheme and risk assessment conclusions, format the output into a structured analysis report by the report generator, and transmit it to the trading decision module through the interface.

[0023] As a preferred, the trading decision module includes: A31: a strategy generation unit including a decision algorithm chip, a strategy database and an interface controller, for generating trading strategies including transaction timing, quantity and price based on the integrated prediction and optimization results; A32: a simulation verification unit including a simulation processor, a verification algorithm module and a result display, for simulating trading strategies in a virtual environment to test their effectiveness and robustness under different scenarios; A33: Decision output unit, including output interface, log storage and decision trigger, for converting final decision into executable instructions and recording decision process for audit.

[0024] As preferred, the transaction decision module includes the following working steps when working: S21: Obtain the prediction report, optimization scheme and risk assessment report from the intelligent analysis module through the interface controller, and parse the key data and constraints therein; S22: In the strategy generation unit, the core target of this decision is determined, and the hard constraint conditions that the power grid safety or device power cannot be violated are set; S23: The decision algorithm chip calculates and generates one or more alternative transaction strategies based on the set target and constraints, including specific transaction time and quantity, combined with historical experience in the strategy database; S24: The simulation processor creates a high-fidelity virtual operating environment based on the current power grid topology and device parameters, for simulating the actual execution effect of the transaction strategy; S25: Set multiple possible disturbance scenarios in the simulation environment, including sudden fluctuations in energy prices, unexpected jumps in key loads, or extreme cases of sudden drops in distributed photovoltaic output; S26: Run each alternative transaction strategy in the virtual environment, verify the algorithm module record the execution results of the strategy under different disturbance scenarios, including revenue, network loss and stability indicators; S27: Compare the simulation evaluation results of all alternative strategies, select a strategy with the best overall performance, and convert it into a standardized control instruction recognizable by the device through the decision output unit.

[0025] As preferred, the execution control module includes: A41: Instruction analysis unit, including instruction parser, command generator and protocol converter, for parsing decision instructions, generating specific control commands, and adapting to different device protocols; A42: Device control unit, including control relay, execution driver and feedback sensor, for sending control signals to energy devices to adjust operating status; A43: Execution monitoring unit, including state monitor, feedback collector and abnormal alarm, for real-time monitoring of command execution, collecting feedback data and detecting abnormalities.

[0026] As preferred, the monitoring and early warning module includes: A51: State monitoring unit, including monitoring sensor, data collector and state display, for continuously monitoring key parameters of source, grid, load and storage; A52: Abnormality detection unit, including abnormality detection algorithm module, comparator and alarm trigger, for detecting abnormal patterns by rules and algorithms; A53: Early warning release unit, including early warning generator, communication interface and notification display, for releasing early warning information to operators through various channels.

[0027] As preferred, the monitoring and early warning module includes the following working steps when working: S31: Collecting key operation data such as voltage, current, power, and equipment temperature at a fixed sampling frequency through monitoring sensors distributed in the source, network, load, and storage links; S32: Filtering the collected raw data to eliminate obvious abnormal jump points, and calculating the current total load, total power generation, and network loss state quantities based on these data; S33: Comparing the calculated state quantities with the preset safe operation threshold in real time to assess whether the overall and each component of the system is in a safe, early warning, or dangerous interval; S34: Abnormality detection algorithm module runs parallelly rule-based logical judgment and machine learning-based outlier detection to identify device failure, communication interruption, or data anomaly; S35: For the detected abnormality, determining its severity level as slight, general, or serious according to its deviation from the normal value and duration, and analyzing the possible chain reaction it may cause; S36: Early warning generator automatically generates standardized early warning information text containing abnormal time, location, description, and handling suggestions according to the abnormality level and type; S37: Communication interface releases the formatted early warning information to designated operation and management personnel through SMS gateway, email system, and platform-embedded notification display simultaneously.

[0028] As preferred, the communication and interface module includes: A61: Internal communication unit, including internal bus, message queue, and synchronization controller, for coordinating data transmission between modules; A62: External interface unit, including API gateway, protocol adapter, and security encryptor, for secure data exchange with external systems; A63: User interface unit, including graphics processor, touch screen, and input device, for providing visual operation interface.

[0029] Embodiment 1 Implementation background: A certain area has built a comprehensive energy system including wind power, photovoltaic, traditional thermal power, industrial and commercial load, electric vehicle charging station and energy storage station. The area is facing challenges such as large renewable energy fluctuation, significant peak-valley difference and complex trading decision-making, and traditional energy management system is difficult to achieve optimal operation.

[0030] Implementation steps: Install wind speed sensors and power transmitters in wind farms, install irradiance sensors and inverter monitoring devices in photovoltaic power stations, install voltage and current sensors at power distribution network nodes, install smart meters in industrial and commercial parks, and install battery management systems in energy storage stations. Real-time collection of power generation, load demand, energy storage SOC and other data is realized through data acquisition units. Data cleaning unit identifies and repairs abnormal data, such as eliminating zero value data caused by communication interruption. The processed data is stored in a distributed database, providing a complete data basis for subsequent analysis.

[0031] The prediction analysis unit predicts the wind power and photovoltaic output curve in the next 24 hours based on historical data and weather forecasts, predicts the load change trend of industrial and commercial and electric vehicle charging in the region by analyzing load characteristics, and optimizes the analysis unit to consider the purchase cost, network loss and equipment operation constraints to develop the optimal scheduling scheme. The risk assessment unit analyzes the market price fluctuation risk and evaluates the impact of extreme weather on trading to generate an analysis report containing prediction results and optimization suggestions, which is transmitted to the trading decision module.

[0032] The strategy generation unit develops a comprehensive trading strategy including day-ahead trading and real-time adjustment based on the analysis report, the simulation verification unit tests the feasibility of the strategy in the digital twin environment, simulates scenarios such as sudden drop in photovoltaic output, selects the optimal decision scheme that balances economic benefits and operational safety through multiple rounds of simulation comparison, and the decision output unit generates specific control instructions including energy storage charging and discharging plan and interruptible load regulation scheme.

[0033] The instruction analysis unit converts the decision instructions into device executable commands, the device control unit sends power instructions to the energy storage converter to adjust the charging and discharging power, sends regulation signals to adjustable loads to realize demand side response, and the execution monitoring unit tracks the instruction execution in real time to ensure that the control effect meets the expectations.

[0034] The state monitoring unit continuously monitors key system indicators including frequency deviation and line load rate, the abnormal detection unit detects abnormal photovoltaic output drop and immediately starts the analysis program, the early warning publishing unit sends early warning information to operation and maintenance personnel through monitoring screens and mobile terminals, provides abnormal processing suggestions and guides on-site personnel to adjust in time.

[0035] The internal communication unit ensures real-time data synchronization among modules, the external interface unit interfaces with the power transaction center system, automatically submits transaction declaration, and the user interface unit provides a visual monitoring interface for dispatchers and supports manual intervention.

[0036] The embodiments of the present application are described in detail above with reference to the drawings, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.

Claims

1. A source-network-load-storage integrated transaction decision support platform, characterized in that: Comprise the following modules: Data acquisition and preprocessing module: responsible for real-time acquisition of multi-source data from source, network, load, storage, and cleaning, normalization and storage, eliminating data islands; Intelligent analysis module: for using machine learning and optimization algorithms to analyze data, predict energy supply and demand balance, price trends and system risks; Transaction decision module: for generating optimal transaction strategies and decision schemes based on intelligent analysis results; Execution control module: for controlling the operation of source network load storage equipment according to decision instructions and executing transaction commands; Monitoring and early warning module: for monitoring system operation status around the clock, discovering abnormalities in time and issuing early warnings; Communication and interface module: for providing internal and external communication and data exchange interfaces. 2.The source-network-load-storage integrated transaction decision support platform according to claim 1, characterized in that: Data acquisition and preprocessing module includes: A11: data acquisition unit, including sensor network, data acquisition card and communication module, for collecting real-time data through distributed interface, including energy output, power grid state, load demand and energy storage level; A12: data cleaning unit, including data processing chip, algorithm processor and memory module, for detecting outliers, filling missing values and filtering noise in raw data; A13: data storage unit, including solid state disk, database server and data bus, for storing preprocessed data into structured database. 3.The source-network-load-storage integrated transaction decision support platform according to claim 1, characterized in that: Intelligent analysis module includes: A21: prediction analysis unit, including GPU accelerator, prediction algorithm module and data buffer, for predicting energy generation, load demand and market price fluctuations based on historical data and real-time information; A22: optimization analysis unit, including optimization processor, mathematical calculation library and configuration memory, for multi-objective optimization of source network load storage resources; A23: risk assessment unit, including risk model processor, random number generator and report generator, for assessing market risk, operational risk and credit risk in transactions and generating risk reports.

4. The source network load storage integrated transaction decision support platform according to claim 3, characterized in that: The intelligent analysis module includes the following working steps when working: S11: extract historical energy production, load records, market transaction prices and real-time source network load storage operation data from the data storage unit, and load them into the memory of the analysis module; S12: standardize the loaded raw data to eliminate dimension effects, and construct feature variables related to prediction targets from them; S13: according to the specific requirements of the prediction task, call the corresponding model from the prediction algorithm module, use the LSTM model to predict short-term load, and use the ARIMA model to predict market price trends; S14: train the selected prediction model using historical data sets, adjust the hyperparameters inside the model through the GPU accelerator to minimize prediction error and improve model accuracy; S15: based on the current system state and prediction results, set multiple optimization targets such as minimum economic cost and maximum system stability, and solve the optimal source network load storage resource allocation scheme through the optimization processor; S16: use the risk model processor to perform Monte Carlo simulation, randomly generate a large number of future market scenarios, and calculate the maximum loss probability that the transaction strategy may face under various scenarios; S17: The prediction results, optimization schemes, and risk assessment conclusions are summarized, formatted into a structured analysis report by the report generator, and transmitted to the transaction decision module through the interface.

5. The source network load storage integrated transaction decision support platform according to claim 1, characterized in that: The transaction decision module includes: A31: A strategy generation unit, including a decision algorithm chip, a strategy database, and an interface controller, is used to integrate the prediction and optimization results to generate a transaction strategy, including the transaction timing, quantity, and price; A32: A simulation verification unit, including a simulation processor, a verification algorithm module, and a result display, is used to simulate the transaction strategy in a virtual environment to test its effectiveness and robustness in different scenarios; A33: A decision output unit, including an output interface, a log storage, and a decision trigger, is used to convert the final decision into executable instructions and record the decision process for auditing. 6.The source-network-load-storage integrated transaction decision support platform according to claim 5, characterized in that: The transaction decision module includes the following working steps when working: S21: The prediction report, optimization scheme, and risk assessment report from the intelligent analysis module are obtained through the interface controller, and the key data and constraint conditions therein are parsed; S22: In the strategy generation unit, the core target of this decision is determined, and the hard constraint conditions that the power grid safety or device power cannot be violated are set; S23: The decision algorithm chip calculates and generates one or more alternative transaction strategies, including specific transaction time and quantity, based on the set target and constraints, combined with historical experience in the strategy database; S24: The simulation processor creates a high-fidelity virtual operating environment based on the current power grid topology and device parameters to simulate the actual execution effect of the transaction strategy; S25: Multiple possible disturbance scenarios are set in the simulation environment, including sudden fluctuations in energy prices, unexpected jumps in key loads, or extreme cases of sudden drops in distributed photovoltaic output; S26: Each alternative transaction strategy is run in the virtual environment, and the verification algorithm module records the execution results of the strategy under different disturbance scenarios, including yield, network loss, and stability indicators; S27: The simulation evaluation results of all alternative strategies are compared, and the strategy with the best overall performance is selected and converted into a standardized control instruction recognizable by the device through the decision output unit.

7. The source network load storage integrated transaction decision support platform according to claim 1, characterized in that: The execution control module includes: A41: An instruction analysis unit, including an instruction parser, a command generator, and a protocol converter, is used to analyze the decision instructions, generate specific control commands, and adapt to different device protocols; A42: A device control unit, including control relays, execution drivers, and feedback sensors, is used to send control signals to energy devices to adjust the operating state; A43: An execution monitoring unit, including a state monitor, a feedback collector, and an abnormality alarm, is used to monitor the command execution in real time, collect feedback data, and detect abnormalities. 8.The source-network-load-storage integrated transaction decision support platform according to claim 1, characterized in that: The monitoring and early warning module includes: A51: A state monitoring unit, including monitoring sensors, data collectors, and state displays, is used to continuously monitor key parameters of sources, grids, loads, and storages; A52: An abnormality detection unit, including an abnormality detection algorithm module, a comparator, and an alarm trigger, is used to detect abnormal patterns using rules and algorithms; A53: Early warning release unit, including early warning generator, communication interface and notification display, for releasing early warning information to operators through various channels. 9.The source-network-load-storage integrated transaction decision support platform of claim 8, wherein: The monitoring and early warning module includes the following working steps when working: S31: Collect key operating data such as voltage, current, power and equipment temperature at a fixed sampling frequency through monitoring sensors distributed in the source, network, load and storage links; S32: Filter the collected raw data, eliminate obvious abnormal jump points, and calculate the current total load, total power generation and network loss state variables based on these data; S33: Compare the calculated state variables with the preset safe operation threshold in real time to evaluate whether the overall system and each component are in a safe, early warning or dangerous interval; S34: The anomaly detection algorithm module runs rule-based logical judgment and machine learning-based outlier detection in parallel to identify device failures, communication interruptions or data anomalies; S35: For detected anomalies, determine their severity level as minor, general or serious according to their deviation from normal values and duration, and analyze the possible chain reactions they may cause; S36: The early warning generator automatically generates standardized early warning information text containing anomaly time, location, description and treatment suggestions according to the anomaly level and type; S37: The communication interface releases the formatted early warning information to designated operation and management personnel through SMS gateway, email system and platform-embedded notification display at the same time. 10.The source-network-load-storage integrated transaction decision support platform according to claim 1, characterized in that: The communication and interface module includes: A61: Internal communication unit, including internal bus, message queue and synchronization controller, for coordinating data transmission between modules; A62: External interface unit, including API gateway, protocol adapter and security encryptor, for secure data exchange with external systems; A63: User interface unit, including graphics processor, touch screen and input device, for providing a visual operation interface.

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