Distributed power intelligent grid-connected evaluation and optimal dispatching system based on multi-source data fusion

Abstract

The application provides a kind of distributed power intelligent grid-connected evaluation and optimization scheduling system based on multi-source data fusion.The system realizes the efficient collaborative operation of distributed power and power grid through multiple modules.Data acquisition module is used to collect original operation data from power and power grid;Data fusion module cleans, detects anomalies, aligns and fuses time series, and generates a fusion dataset;Grid evaluation module analyzes the impact of distributed power grid on power grid based on fusion data, and outputs evaluation indicators;Multi-stage scheduling module generates initial, dynamic adjustment and emergency response scheduling strategies according to evaluation indicators;The execution feedback module generates scheduling instructions and collects power grid response data to form feedback information;The stage control module determines and switches the scheduling stage according to the feedback information, and adjusts the strategy.The system improves the stability and reliability of the power grid through real-time evaluation and optimization scheduling, and has high intelligent level.

Description

Technical Field

[0001] This invention relates to the field of distributed generation grid connection technology, and in particular to a distributed generation intelligent grid connection evaluation and optimization scheduling system based on multi-source data fusion. This system is applied to the intelligent scheduling and optimization control of power systems, and can improve grid stability and operational efficiency during the distributed generation grid connection process. Background Technology

[0002] With the rapid development of renewable energy and distributed generation, the power grid faces challenges in power quality and security after integrating a large number of distributed generation sources. Efficient coordinated operation between distributed generation and the power grid has become a key issue in ensuring grid stability. Traditional grid dispatching methods rely heavily on fixed models and rules, making it difficult to cope with the uncertainties and complexities brought about by distributed generation.

[0003] Currently, although some power grid dispatching systems based on data acquisition and monitoring exist, most systems still suffer from inaccurate assessments, delayed responses, and a lack of flexible adjustment mechanisms when facing real-time fusion of multi-source data and multi-stage dispatching strategies. Therefore, there is an urgent need for an intelligent grid-connected optimization system capable of assessing the power grid's operating status in real time and dynamically adjusting dispatching strategies based on the assessment results, in order to improve the reliability, stability, and operational efficiency of the power grid.

[0004] This invention addresses the shortcomings of existing technologies by combining multi-source data fusion and intelligent scheduling strategies, and provides an intelligent grid-connected system capable of dynamically optimizing scheduling. Summary of the Invention

[0005] The purpose of this invention is to provide a smart grid-connected assessment and optimized scheduling system for distributed power sources based on multi-source data fusion, addressing the uncertainties and complexities encountered by existing power grid scheduling systems when facing the integration of distributed power sources. By real-time acquisition, processing, and fusion of multi-source data from distributed power sources and the power grid, this invention aims to improve the accuracy of grid connection assessment and the intelligence level of scheduling strategies, thereby achieving efficient, stable, and secure power grid operation. Simultaneously, through a multi-stage scheduling and dynamic optimization mechanism, this invention can adjust scheduling strategies in real time according to the power grid's operating status, enhancing emergency response capabilities, reducing system risks and energy costs, and promoting the efficient utilization of power grid resources.

[0006] The present invention provides a distributed power intelligent grid-connected evaluation and optimization scheduling system based on multi-source data fusion, comprising:

[0007] The data acquisition module is used to collect raw operating data from distributed power sources and grid nodes. The raw operating data includes voltage, current, active power, reactive power, and environmental parameters.

[0008] A data fusion module is used to preprocess and fuse the original running data to generate a fused dataset;

[0009] The grid connection evaluation module is used to analyze the impact of distributed power generation grid connection on the grid operation status based on the fused dataset and output evaluation indicators.

[0010] A multi-stage scheduling module is used to generate a multi-stage scheduling strategy based on the evaluation indicators. The scheduling strategy includes an initialization stage, a dynamic adjustment stage, and an emergency response stage.

[0011] An execution feedback module is used to generate scheduling instructions based on the current scheduling stage and collect power grid response data to form feedback information.

[0012] A stage control module is used to determine whether to switch the current scheduling stage and coordinate the strategy adjustment of the multi-stage scheduling module based on the feedback information and the power grid operating status.

[0013] Furthermore, the data fusion module includes:

[0014] The preprocessing unit is used to perform data cleaning, anomaly detection and normalization on the raw operating data, and output the fused dataset to the grid-connected evaluation module and the multi-stage scheduling module.

[0015] A synchronous fusion unit is used to perform temporal alignment and fusion on the preprocessed data to generate the fused dataset.

[0016] Furthermore, the grid connection assessment module includes:

[0017] A status analysis unit, wherein the status analysis unit is used to identify the operating status of the power grid;

[0018] The indicator extraction unit is used to extract evaluation indicators such as voltage fluctuation, frequency offset and power flow distribution from the fused dataset.

[0019] The risk assessment unit is used to determine the risk level of distributed power generation grid-connected operation based on the grid operation status identified by the status analysis unit and the evaluation indicators.

[0020] The state analysis unit is further configured to perform a short-term estimate of the future operating state of the power grid based on the power grid operating state, using the following estimation model:

[0021]

[0022] in, These are short-term forecasts of the power grid status. The voltage is the current value, and η is the adjustment coefficient. The rate of change of voltage.

[0023] Furthermore, the multi-stage scheduling module includes:

[0024] The initial strategy unit 1041 is used to generate a default scheduling strategy for the initial stage based on the evaluation index when the distributed power source is first connected to the grid.

[0025] A dynamic optimization unit is configured to dynamically modify the current scheduling strategy based on the feedback information.

[0026] An emergency strategy unit is used to quickly generate a control strategy for the emergency response phase when a high-risk operating state is detected.

[0027] The dynamic optimization unit adjusts and dynamically corrects the scheduling strategy based on the following objective function:

[0028] dt

[0029] Where J is the overall optimization objective of the scheduling strategy, aiming to balance power error and the smoothness of control variable changes. and These are the weighting coefficients related to risk level and scheduling stability, respectively. Let P(t) be the target power value, P(t) be the current power, and u(t) be the scheduling control quantity.

[0030] Furthermore, the multi-stage scheduling module further includes a policy switching mechanism, which is used to set the switching conditions and priorities between each scheduling stage, and to perform version management and coordination control of the scheduling policies of different stages.

[0031] Furthermore, the execution feedback module includes:

[0032] A control instruction generation unit, which is used to generate specific control instructions based on the current scheduling strategy;

[0033] The feedback information acquisition unit is used to acquire power grid response data after the control command is executed, and to provide the feedback information to the stage control module and the multi-stage scheduling module.

[0034] Furthermore, the stage control module includes:

[0035] A stage determination unit is used to determine the applicable scheduling stage based on the feedback information and the power grid operating status.

[0036] The coordination and control unit is used to switch the scheduling strategy between different stages and to achieve linkage control with the multi-stage scheduling module and the execution feedback module.

[0037] It further includes a communication and security module to enable data transmission between modules, and ensures data transmission security through encryption and authentication mechanisms, and has a redundancy mechanism to ensure stable system operation.

[0038] The system further includes a human-computer interaction module, which comprises:

[0039] A visualization interface is provided to display information on the evaluation indicators, the scheduling strategy, and the power grid operating status.

[0040] A control input unit is used to receive control commands or strategy adjustment requests input by the user and transmit the requests to the stage control module or the multi-stage scheduling module.

[0041] This invention provides a distributed power generation intelligent grid connection assessment and optimized scheduling system based on multi-source data fusion. Through the collaborative work of multiple modules, this system effectively improves the accuracy of assessing the impact of distributed power generation on the power grid and optimizes scheduling strategies. The system includes a data acquisition module, a data fusion module, a grid connection assessment module, a multi-stage scheduling module, an execution feedback module, and a stage control module. The data acquisition module collects raw operating data from distributed power generation and grid nodes. The data fusion module preprocesses and fuses the data to generate a fused dataset. The grid connection assessment module analyzes the grid operating status based on the fused dataset and outputs assessment indicators. The multi-stage scheduling module generates grid connection scheduling strategies based on the assessment indicators. The execution feedback module generates control commands based on scheduling instructions and collects feedback data. The stage control module determines the scheduling stage and coordinates adjustments. Through fusion technology, dynamic adjustment, and emergency response mechanisms, this system achieves real-time assessment and optimized scheduling of the power grid, improving the stability, flexibility, and intelligence level of grid operation. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the framework of the distributed power intelligent grid connection evaluation and optimization scheduling system based on multi-source data fusion provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the fusion and grid connection evaluation process provided by the present invention.

[0044] Figure 3 This is a schematic diagram of the multi-stage scheduling and feedback mechanism provided by the present invention.

[0045] Figure 4A schematic diagram of stage control and human-computer interaction provided for the invention.

[0046] Figure label:

[0047] The system comprises a distributed power intelligent grid connection evaluation and optimization scheduling system based on multi-source data fusion, including: a data acquisition module 101, a data fusion module 102, a grid connection evaluation module 103, a multi-stage scheduling module 104, an execution feedback module 105, a stage control module 106, a communication and security module 107, and a human-computer interaction module 108.

[0048] The system includes a preprocessing unit 1021, a synchronization and fusion unit 1022, a state estimation unit 1023, a state analysis unit 1031, an index extraction unit 1032, a risk judgment unit 1033, an initial strategy unit 1041, a dynamic optimization unit 1042, an emergency strategy unit 1043, a control command generation unit 1051, a feedback information acquisition unit 1052, a strategy switching mechanism 1044, a stage determination unit 1061, a coordination and control unit 1062, a visualization interface 1081, and a control input unit 1082.

[0049] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "multiple" means two or more.

[0052] To more clearly illustrate the technical solution of the present invention, the present invention will be described in detail below with reference to specific embodiments, but it should not be construed as a limitation on the scope of protection of the present invention.

[0053] In this embodiment, as Figure 1 As shown, a distributed power intelligent grid-connected assessment and optimization scheduling system 100 based on multi-source data fusion is provided, which can realize real-time assessment and optimization scheduling of the power grid.

[0054] Specifically, the distributed power intelligent grid-connected evaluation and optimization scheduling system 100 based on multi-source data fusion includes a data acquisition module 101, a data fusion module 102, a grid-connected evaluation module 103, a multi-stage scheduling module 104, an execution feedback module 105, and a stage control module 106. The data acquisition module 101 collects raw operating data such as voltage, current, active power, reactive power, and environmental parameters in real time through sensors installed at distributed power sources and grid nodes. The raw data is transmitted to the data fusion module 102 for processing via a wireless communication network. In this embodiment, the sensors are equipped with high-precision measuring devices and are connected to a cloud data processing platform through a smart gateway to ensure the real-time performance and accuracy of the data.

[0055] The data fusion module 102 consists of a preprocessing unit 1021 and a synchronization fusion unit 1022. The preprocessing unit 1021 first cleans, detects anomalies, and normalizes the collected raw operational data to remove noise and abnormal data, ensuring data quality. The processed data is then transmitted to the synchronization fusion unit 1022, which performs time-series alignment and fusion to generate a high-precision fused dataset. This dataset contains synchronous operational data from different power sources and grid nodes, serving as input to the grid connection evaluation module 103 and the multi-stage scheduling module 104.

[0056] The grid connection assessment module 103 analyzes the grid operation status based on the fused dataset. Specifically, the status analysis unit 1031 identifies the current grid operation status by calculating assessment indicators such as voltage fluctuation, frequency offset, and power flow distribution. The risk assessment unit 1033 determines the operational risk level of distributed generation grid connection to the grid based on the assessment results and generates a corresponding risk assessment report. Based on this, the risk level provided by the assessment module serves as input to the multi-stage scheduling module 104 for generating scheduling strategies.

[0057] The multi-stage scheduling module 104 generates a scheduling strategy based on the evaluation indicators output by the grid connection evaluation module 103. The scheduling strategy is divided into an initialization phase, a dynamic adjustment phase, and an emergency response phase. In the initialization phase, the initial strategy unit 1041 formulates a default scheduling strategy based on the evaluation results; in the dynamic adjustment phase, the dynamic optimization unit 1042 corrects the scheduling strategy based on real-time feedback information to ensure load balance and power stability of the power grid; in the emergency response phase, the emergency strategy unit 1043 quickly generates an emergency control strategy to prevent power grid failures when abnormalities or high risks occur in the power grid operation.

[0058] The execution feedback module 105 generates control commands based on the current scheduling strategy and sends the commands to the power grid equipment for execution. The execution results are collected by the feedback information acquisition unit 1052 and provided to the stage control module 106. The stage control module 106 determines whether to switch the current scheduling stage based on the feedback information and coordinates the adjustment of the scheduling strategy. In this way, the present invention can monitor the operating status of the power grid in real time and adjust the scheduling strategy according to different operating stages, thereby improving the operational stability and flexibility of the power grid.

[0059] In some embodiments, such as Figure 2 As shown, the data fusion module 102 includes a preprocessing unit 1021 and a synchronous fusion unit 1022. The preprocessing unit 1021 performs preliminary processing on the raw operating data from the data acquisition module 101. The raw data includes voltage, current, active power, reactive power, and environmental parameters, which may be affected by noise, outliers, etc. Therefore, data cleaning is performed first to remove invalid data and outlier data points, ensuring the accuracy and validity of the data. Next, the preprocessing unit 1021 performs anomaly detection, identifying abnormal fluctuations and outliers in the data, and correcting or deleting them using appropriate algorithms. Then, through normalization processing, data from different sources are converted into a unified standard to facilitate subsequent processing and comparison. After these processes, the preprocessing unit 1021 outputs the cleaned and normalized data to the synchronous fusion unit 1022.

[0060] The synchronization fusion unit 1022 receives the processed data from the preprocessing unit 1021 and performs time-series alignment and fusion. Because there are time differences in the acquisition of operational data from distributed power sources and grid nodes, the timestamps of the data may be inconsistent, and directly using this data can lead to errors in the analysis results. Therefore, the synchronization fusion unit 1022 employs a time-series alignment algorithm, using interpolation or weighted averaging to align data from different time points, ensuring consistency across all data in the time dimension. This process not only considers the differences in data timestamps but also ensures that the physical meaning of the data remains consistent, thereby improving the accuracy and reliability of the fused data. Through these methods, the fused dataset accurately reflects the operational status of distributed power sources and grid nodes, providing a high-quality data foundation for the subsequent grid connection evaluation module 103 and multi-stage scheduling module 104.

[0061] In this embodiment, the preprocessing unit 1021 and the synchronous fusion unit 1022, through distributed computing and parallel processing technologies, can efficiently process large-scale raw data, ensuring real-time requirements without sacrificing data accuracy. The resulting fused dataset provides the system with unified, multi-source data support, making the grid connection assessment and scheduling process more accurate and intelligent.

[0062] In some embodiments, such as Figure 2 As shown, the synchronization fusion unit 1022 of the data fusion module 102, in addition to performing timing alignment and data fusion, also includes a state estimation unit 1023 for estimating the state of the fused dataset. After receiving data from the preprocessing unit 1021, the synchronization fusion unit 1022 first performs timing alignment, using an interpolation algorithm to ensure temporal consistency of data from different sources. Then, the fused datasets are fused using a weighted average or other algorithms to generate a unified, high-precision power grid operation dataset.

[0063] The state estimation unit 1023 performs state estimation on the fused dataset to improve the accuracy of the evaluation results of the grid-connected evaluation module 103. In this embodiment, the state estimation unit 1023 uses Kalman filtering technology to predict and correct the data. Kalman filtering is a recursive filtering algorithm based on a dynamic system model, which can achieve high-precision state estimation when the system is subjected to random disturbances and noise.

[0064] Specifically, the state estimation unit 1023 estimates the current state of the power grid by establishing a mathematical model adapted to the grid's operating characteristics based on multi-dimensional data such as voltage, current, and power. The Kalman filter prediction model predicts the future state based on known historical data, while the correction step updates the model based on newly received data to reduce estimation errors. In this way, the state estimation unit 1023 can provide predictions of future states during real-time grid operation, thus providing more accurate input to the grid connection assessment module 103.

[0065] For example, based on current voltage and current data, the state estimation unit 1023 can estimate the voltage change trend of the power grid over a future period, thereby assisting the assessment module in risk judgment during the grid connection process. Through accurate state estimation, indicators such as voltage fluctuations and frequency deviations during the grid connection process can be analyzed more accurately, greatly improving the assessment accuracy and real-time response capability of the grid connection assessment module 103.

[0066] Therefore, by combining the state estimation unit 1023, the reliability and accuracy of the entire data fusion process are greatly enhanced, providing more accurate operating status data for the grid connection evaluation module 103, and also providing stable input for the subsequent multi-stage scheduling module 104.

[0067] In some embodiments, such as Figure 2As shown, the grid connection assessment module 103 mainly includes a state analysis unit 1031, an index extraction unit 1032, and a risk judgment unit 1033. First, the state analysis unit 1031 obtains raw data such as voltage, current, active power, reactive power, and environmental parameters of the current power grid from the fused dataset output by the data fusion module 102, and analyzes the operating status of the power grid in real time. By detecting the voltage change rate, the state analysis unit 1031 can identify the trend of power grid voltage fluctuations and predict the stability of the power grid in conjunction with the load conditions. For example, if the voltage change of the power grid is large, it may affect the stability of the power grid, especially when multiple distributed power sources are connected to the grid, large voltage fluctuations may lead to system instability. Therefore, the function of the state analysis unit 1031 is to identify potential risk points through the analysis of this data.

[0068] Subsequently, the indicator extraction unit 1032 extracts several important evaluation indicators from the fused dataset, including key power grid operation indicators such as voltage fluctuation, frequency offset, and power flow distribution. These indicators effectively reflect how the power grid's operating status is affected after distributed generation is connected to the grid, and whether any risks exist. Specifically, the indicator extraction unit 1032 calculates the frequency changes and load distribution of the power grid by statistically analyzing data such as voltage, current, and power at each node of the power grid, thereby deriving risk indicators for power grid operation. For example, during high-load operation, the grid voltage may drop, and the power flow distribution may change; changes in these indicators can accurately assess the power grid's load-bearing capacity.

[0069] In the risk assessment unit 1033, by comprehensively analyzing the extracted indicators and considering the actual operating status of the power grid, the system assesses the current risk level of the power grid according to set standards. If the power grid is in a high-risk state, the system will issue an alarm, prompting maintenance personnel to take intervention measures. The risk assessment unit 1033 not only considers the current operating status of the power grid but also compares it with historical data to determine whether there are potential system failure risks. For example, when voltage fluctuations in the power grid exceed a certain threshold, the system will mark it as a high-risk state and adjust the dispatch strategy to prevent power grid accidents.

[0070] Furthermore, to improve the accuracy of risk assessment, the state analysis unit 1031 also employs a short-term power grid state prediction model. This is achieved through the formula:

[0071] in the formula This is the current voltage value. Where η is the voltage change rate, and η is the adjustment coefficient. For the future The formula predicts future voltage conditions, helping to assess grid stability and prevent sudden voltage anomalies from impacting the grid.

[0072] Furthermore, assuming the current voltage is 220V, the voltage change rate is -0.5 V / s, and the adjustment coefficient η=1, the predicted voltage change after 1 second is:

[0073] This provides timely decision-making basis for adjusting the system's strategies.

[0074] In the above formula, η is an adjustment coefficient used to adjust the voltage change rate. Adjustments are made to ensure more accurate predicted voltage values, which typically depend on the specific conditions of the power grid, including its response speed and stability. By adjusting η, the dynamic behavior of the power grid can be regulated, making short-term predictions more closely reflect actual grid changes.

[0075] Understandably, the value of η may differ in different power grid environments. For example, for a fast-responding power grid, η may be set smaller; while for a more stable power grid, η may be set larger. In practical applications, the value of η can be determined through historical data or simulation models, and is usually optimized in power grid modeling and dispatching systems to ensure that the predicted voltage values ​​more accurately reflect the actual situation.

[0076] The risk assessment unit 1033 will comprehensively assess the current risk level of the power grid based on the data provided by the state analysis unit 1031 and the indicator extraction unit 1032, as well as short-term state predictions. If the power grid is in a high-risk state, the system will activate the emergency plan and carry out corresponding emergency responses.

[0077] In this embodiment, by successfully implementing real-time monitoring, analysis, and prediction of the power grid's operating status in the grid connection assessment module 103, potential risks can be identified in advance and warnings can be issued, ensuring the safe and stable operation of the power grid. This technical feature combines dynamic assessment and short-term prediction of the power grid status, providing a more accurate and reliable assessment tool for distributed power generation grid connection.

[0078] In some embodiments, such as Figure 3 As shown, the multi-stage scheduling module 104 specifically implements the intelligent scheduling strategy after distributed power sources are connected to the grid. This scheduling module mainly consists of an initial strategy unit 1041, a dynamic optimization unit 1042, and an emergency strategy unit 1043, and optimizes and adjusts the scheduling strategy through an objective function and a strategy switching mechanism 1044.

[0079] First, when the distributed generation is initially connected to the grid, the initial strategy unit 1041 generates a default scheduling strategy for the initial stage based on the evaluation indicators (such as voltage fluctuation, frequency offset, power flow distribution, etc.) provided by the grid connection evaluation module 103. This strategy mainly includes steady-state operation control in the initial stage of power generation connection to ensure that the grid does not experience sudden voltage or frequency fluctuations after the distributed generation is connected to the grid. At this time, the scheduling strategy focuses on the stable operation of the grid to avoid power supply and demand imbalances caused by load changes or grid interference.

[0080] Specifically, assuming that when distributed generation is connected to the grid, the initial grid load is low, and the grid voltage may rise slightly. In this case, the initial strategy will adjust the output power of the distributed generation to ensure the voltage remains within a safe range. This strategy avoids the risk of excessively high or low voltage by monitoring and adjusting the power output in real time.

[0081] Secondly, the dynamic optimization unit 1042 dynamically modifies the initial scheduling strategy based on feedback information, optimizing the scheduling strategy in real time to adapt to changes in grid operation. This unit uses the following objective function to adjust the scheduling strategy:

[0082] dt

[0083] Where J is the overall optimization objective of the scheduling strategy, aiming to balance power error and the smoothness of control variable changes. and These are weighting coefficients related to risk level and scheduling stability, respectively; their roles differ. This coefficient is related to the risk level and the stability of the power grid. In practical applications, when the power grid faces high risks (such as severe load fluctuations, power grid frequency deviations, etc.), The value of will increase, causing scheduling strategies to focus more on correcting power errors. For example, when grid load fluctuates significantly, An increase in will prompt the system to adjust its power more aggressively in order to reduce power error; This is related to the stationarity of the scheduling strategy, which controls the scheduling amount. The rate of change is adjusted to avoid over-adjustment that could cause grid oscillations. For example, when the grid is operating relatively smoothly, When the value is small, the system adjusts more gently. However, when the power grid condition changes drastically, The value of can be increased to limit the rate of change of scheduling quantities and maintain system stability. The larger the value, the heavier the penalty for excessively rapid changes in scheduling. The target power value represents the ideal power output that the power grid needs to achieve. During implementation, the system determines the target power value based on changes in the grid load and the output capacity of distributed power sources. The current power represents the actual power output of the power grid at present. The system calculates the current power based on real-time monitoring data and compares it with the target power. A comparison is made to determine the error. u(t) is the dispatch control variable, representing the instruction to adjust the output of distributed power sources. The magnitude of this control variable directly affects the power output of the power grid. Its function is to control the magnitude of the dispatch quantity u(t), avoiding excessive control quantities during power grid dispatching that could lead to unnecessary fluctuations and system instability. This objective function, by considering power error and the smoothness of the dispatch control quantity, helps optimize the dispatching strategy and reduce fluctuations and shocks in power grid operation.

[0084] Specifically, the objective function aims to balance power error and dispatch stability by adjusting the dispatch strategy. In practical applications, the system calculates the error between the target power and the current power based on the real-time state of the power grid and load changes, further correcting the dispatch amount. If the power error is large... Larger, in the objective function This will prompt the scheduling strategy to strengthen the correction of power errors and increase the adjustment range of the scheduling control quantity; if the rate of change of the scheduling quantity is too fast... Larger Its function is to limit the rate of change of dispatch control quantities to avoid drastic power grid fluctuations.

[0085] Through this objective function, the system can find the optimal balance between power error and control stability, ensuring that the power grid can operate stably when distributed power sources are connected to the grid.

[0086] Specifically, when the grid load suddenly increases and the voltage drops slightly, the dynamic optimization unit 1042 adjusts the output power of the distributed power source according to the current power error and rate of change, thereby restoring the grid to a stable state. This real-time adjustment prevents voltage instability or over-regulation in the grid.

[0087] Furthermore, during grid operation, when the system detects a high-risk state in the grid (such as excessive voltage or frequency deviation), the emergency strategy unit 1043 will quickly generate a control strategy for the emergency response phase. This strategy includes measures such as immediately reducing the output power of distributed generation sources, quickly activating backup power sources, and adjusting load distribution to address sudden risks to the grid. During the emergency response phase, the system prioritizes ensuring grid safety and preventing the accident from escalating. For example, assuming the system detects a sharp rise in grid voltage due to fluctuations in distributed generation sources, exceeding the set safety range, the system will immediately reduce the output power of distributed generation sources through the emergency strategy unit 1043 and activate backup power sources to regulate the grid voltage. This strategy can restore grid stability in the shortest possible time, ensuring the safe operation of the grid.

[0088] Furthermore, the strategy switching mechanism 1044 is used to set the switching conditions and priorities between different scheduling stages, and to perform version management and coordination control of the scheduling strategies in different stages. During power grid operation, the strategy will switch from the initial stage to the dynamic adjustment stage according to the actual situation, or quickly switch to the emergency response stage in the event of an emergency. By setting the priorities of each stage, the strategy switching mechanism 1044 ensures that the most appropriate scheduling strategy can be switched to in a timely manner when a sudden situation occurs in the power grid.

[0089] Specifically, in the initial stage of power grid operation, basic adjustments may only require relying on the initial strategy. However, when the power grid load changes significantly, the system will automatically switch to a dynamic adjustment phase, making real-time corrections by optimizing the objective function. If a serious fault or abnormal fluctuation occurs in the power grid, the system will trigger an emergency strategy and enter the emergency response phase.

[0090] Through the implementation of the multi-stage scheduling module 104, the system can flexibly adjust the scheduling strategy according to the real-time operating status of the power grid, ensuring the long-term stable operation of the power grid after distributed power generation is connected to the grid. This scheduling strategy not only improves the security of the power grid, but also effectively addresses the challenges of different operating stages, ensuring the stability and reliability of the power grid.

[0091] In some embodiments, such as Figure 3 As shown, the multi-stage scheduling module 104 further includes a strategy switching mechanism 1044. The core objective of the strategy switching mechanism 1044 is to rationally determine when to switch scheduling stages based on the real-time state of the power grid, and to ensure coordination and transition between different stages. In practical applications, the strategy switching mechanism 1044 monitors the state of the power grid (such as key indicators like voltage, current, and power) in real time and assesses the risk level output by the module. Specifically, when the operating state of the power grid changes, the system determines whether a switching scheduling strategy is necessary based on the following conditions:

[0092] 1. From the initial stage to the dynamic adjustment stage: When the power grid enters the normal operation stage and the power error is within the allowable range, the system switches from the initial strategy to the dynamic optimization stage to optimize power dispatch.

[0093] 2. From dynamic adjustment phase to emergency response phase: When the power grid status is detected to deviate significantly from the predetermined target, such as high-frequency fluctuations or excessive power fluctuations, the system automatically switches to the emergency response phase to take rapid adjustment measures to avoid power grid instability.

[0094] 3. Phase rollback: If the power grid's operating status is restored during the emergency response phase, the system will assess the grid's stability and decide whether to roll back to the dynamic adjustment phase or the initial phase.

[0095] To ensure a rapid switch to a high-priority scheduling strategy in emergency situations, the strategy switching mechanism 1044 sets a priority for each stage. When the system identifies multiple switching paths, it makes a decision based on preset priority rules, prioritizing either the emergency response stage or the dynamic adjustment stage to address uncertainties in power grid operation. For example, when the power grid enters a high-risk state, the system will first execute the emergency response stage, even if it is currently in the dynamic adjustment stage; similarly, when the power grid stabilizes, the system will prioritize reverting to the dynamic adjustment stage rather than remaining in the emergency response stage.

[0096] Furthermore, in the multi-stage scheduling module 104, each scheduling stage may have multiple versions of its strategy, and the strategy switching mechanism 1044 is responsible for version management and updates. When a switch to a new scheduling strategy is required, the strategy switching mechanism 1044 selects the most suitable version based on the actual needs of the power grid and ensures seamless connection between scheduling strategies in different stages through coordinated control, avoiding control conflicts caused by strategy switching. For example, in the initial strategy stage, the system generates the first version of the scheduling strategy based on the current power grid state and evaluation results; in the dynamic adjustment stage, the system updates the scheduling strategy version based on new evaluation results and power grid state to ensure more accurate adjustment; in the emergency response stage, the scheduling strategy is quickly adjusted and enters the emergency version.

[0097] Through this strategy switching mechanism 1044, the system can flexibly switch dispatching strategies according to the actual situation of the power grid, ensuring that the power grid remains stable under different operating conditions. For example, during a large-scale load fluctuation, the power grid may rapidly transition from a normal operating state to a high-risk state. The strategy switching mechanism 1044 can quickly switch the system to the emergency response phase and implement emergency control. Once the power grid stabilizes, the system will automatically switch back to the dynamic adjustment phase to further optimize dispatching. The strategy switching mechanism 1044 not only improves the adaptability and stability of the power grid but also ensures the efficiency and safety of distributed power generation grid-connected dispatching in complex operating environments.

[0098] In some embodiments, such as Figure 3 As shown, the main task of the execution feedback module 105 is to generate control commands based on the current scheduling strategy, collect power grid response data, form feedback information, and further optimize the scheduling process. In specific implementation, the execution feedback module 105 includes two main functional units: a control command generation unit 1051 and a feedback information acquisition unit 1052.

[0099] In the control command generation unit 1051, the system generates corresponding dispatch commands based on the current dispatch stage and dispatch strategy, combined with the real-time grid status. For example, during the dynamic adjustment stage, when the grid load fluctuates significantly, the control command generation unit 1051 adjusts power allocation or voltage settings based on real-time voltage and power data, issuing specific dispatch commands to distributed power sources. These commands guide grid equipment to make adaptive adjustments to achieve power balance or voltage stability. During the emergency response stage, the system generates more stringent control commands, such as forcibly starting or stopping some power sources or adjusting the power factor, to quickly respond to abnormal grid conditions.

[0100] The feedback information acquisition unit 1052 is responsible for collecting grid response data in real time and feeding this data back to other modules, especially the stage control module 106 and the multi-stage scheduling module 104. This feedback information includes, but is not limited to, changes in key parameters of the grid such as voltage, current, and power factor. For example, when a scheduling command requires a distributed power source to increase its power output, the feedback information acquisition unit 1052 will monitor the actual response of the power source, such as changes in output power, and transmit the data to the stage control module 106 in real time for subsequent judgment on whether a switching of the scheduling stage is necessary.

[0101] Furthermore, the collection and transmission of feedback information not only verifies the effectiveness of the current dispatching strategy but also provides data support for subsequent strategy optimization. For example, if the feedback information indicates that the power grid is not operating stably as expected, the system will reassess the current dispatching strategy based on the feedback data during the dynamic adjustment phase and make corrections if necessary. This feedback closed-loop mechanism ensures that the power grid can respond to changes in a timely manner at each dispatching phase, avoiding system instability caused by lag.

[0102] In addition, the feedback information will help the stage control module 106 determine whether a switch to a new scheduling stage is necessary. When the grid response meets the expected goals of the current stage, the feedback information will verify the effectiveness of the current scheduling strategy and support the system in maintaining the current scheduling stage. If the grid status deviates significantly from expectations, the feedback information will trigger the stage control module 106 to issue a signal, indicating a switch to the next scheduling stage or entry into emergency response mode.

[0103] By executing the feedback module 105, the system can realize real-time monitoring and adjustment of the power grid operation status, ensuring that the power grid maintains an efficient and stable operation status during multi-stage dispatching, and adapting to dispatching needs under different load and risk conditions.

[0104] In some embodiments, such as Figure 4As shown, the main task of the stage control module 106 is to determine the applicable scheduling stage based on feedback information and the power grid operating status, and to coordinate the scheduling strategies between different stages to ensure the efficient operation of the system. The stage control module 106 includes two key units: a stage determination unit 1061 and a coordination control unit 1062.

[0105] In the stage determination unit 1061, the system first evaluates the real-time operating status of the power grid, analyzes parameters such as load, frequency, and voltage, and combines this with feedback information from the execution feedback module 105 to determine whether the current conditions meet the criteria for a specific scheduling stage. For example, when the power grid load is within a stable range and voltage fluctuations are small, the system may determine that it is in the "dynamic adjustment stage"; while when the power grid experiences abnormal fluctuations or faults, the system will determine, based on feedback information, that it has entered the "emergency response stage." The stage determination unit 1061 monitors the changes in these key parameters in real time and determines which scheduling stage the power grid should currently be in based on preset judgment rules.

[0106] Furthermore, in the coordination control unit 1062, the system coordinates and switches the strategy of the current scheduling stage based on the judgment result of the stage determination unit 1061 to ensure that the system operates in an optimal manner. For example, when the system determines that the power grid has entered the emergency response stage, the coordination control unit 1062 will immediately adjust the scheduling strategy, execute high-priority control commands, and quickly respond to the abnormal state of the power grid. If the power grid returns to stability, the coordination control unit 1062 will adjust the scheduling strategy to the dynamic optimization or initialization stage to ensure that the power grid maintains a stable operating state in the long term.

[0107] The coordination control unit 1062 not only needs to adjust according to the conditions of phase switching, but also needs to ensure that the strategies of different scheduling phases can be smoothly transitioned to avoid grid fluctuations caused by phase switching. For example, when the grid switches from the dynamic adjustment phase to the emergency response phase, the system needs to adjust the control parameters of power and voltage, and may start the backup power supply; when returning to the dynamic adjustment phase, the coordination control unit 1062 will gradually restore the original scheduling state and optimize power allocation to reduce the impact on the grid.

[0108] The phase control module 106 ensures that the strategy for each scheduling phase is the optimal choice for the current power grid operating state through continuous feedback monitoring and phase determination. In this way, the system can flexibly respond to the needs of the power grid under different load and risk conditions, ensuring the stability and security of power grid operation.

[0109] In some embodiments, the system further includes a communication and security module 107, which is responsible for ensuring efficient and secure data transmission between the various modules of the system. The main function of this module is to safeguard data security through encryption and authentication mechanisms, and to implement redundancy mechanisms to ensure system stability under various operating conditions.

[0110] First, regarding data transmission, the communication and security module 107 uses secure communication protocols, such as TLS (Transport Layer Security) or VPN (Virtual Private Network), to ensure that data transmitted between modules within the system is not stolen or tampered with by unauthorized third parties. During data transmission, all sensitive data (such as control commands, power grid status, feedback information, etc.) is encrypted to ensure the security of information transmission.

[0111] To enhance data transmission reliability and prevent single points of failure, the communication and security module 107 is also equipped with a redundancy mechanism. When a communication channel fails, the redundancy mechanism can quickly switch to a backup channel to ensure continuous data transmission. For example, if the primary network connection fails, the system can automatically switch to the backup link to avoid system downtime or data loss, ensuring the stability of the power grid operation.

[0112] Regarding identity authentication, the communication and security module 107 integrates a multi-factor authentication mechanism. Data interaction between modules requires authentication to ensure that only authorized modules can access or modify data. The system uses digital certificates, two-factor authentication, and other methods to ensure that each module involved in data transmission and processing is verified, preventing malicious attackers from impersonating others to perform unauthorized operations.

[0113] Furthermore, the communication and security module 107 can monitor the status and security of data transmission in real time. Upon detecting any anomalies, it will immediately take appropriate measures, such as interrupting data transmission, activating backup channels, or notifying the administrator for handling. Through these security measures, the communication and security module 107 can provide efficient and reliable support for the system, ensuring that data remains protected in complex power grid operating environments and preventing power grid malfunctions due to data leakage or tampering.

[0114] Overall, the communication and security module 107 provides the necessary security for the entire system, ensuring the integrity, confidentiality and reliability of data during transmission, thereby achieving the efficient and stable operation of the present invention.

[0115] In some embodiments, such as Figure 4As shown, this system also includes a human-computer interaction module 108, which in this invention mainly provides users with an intuitive and convenient interface and operating methods, enabling users to monitor the power grid operation status in real time, view dispatching strategies, and input and adjust control commands to the system. This module provides users with comprehensive interactive functions through a visual interface 1081 and a control input unit 1082.

[0116] Firstly, regarding the visualization interface 1081, the system graphically displays the power grid's operating status, evaluation indicators, and dispatch strategies. Users can view the real-time trends of data such as voltage, frequency, and power on the interface, and key evaluation indicators (such as voltage fluctuations and frequency deviations) are displayed through intuitive charts and graphs. Furthermore, the system uses maps or distributed power source location markers to help users intuitively understand the operating status and position of each power grid node, thereby enabling more accurate decision-making. The visualization interface 1081 dynamically adjusts the displayed content according to different dispatch stages, allowing users to obtain the current specific operating status and dispatch strategies of the power grid at any time.

[0117] Secondly, regarding the control input unit 1082, users can input control commands or adjust existing dispatch strategies through this unit. When the system detects grid anomalies or load fluctuations, users can quickly adjust the dispatch strategy based on feedback from the visual interface 1081. For example, users can adjust the parameters of the dispatch strategy using buttons or sliders on the interface, or immediately respond to the grid's operational needs by selecting a preset emergency strategy. The system will update the dispatch strategy in real time based on the control commands input by the user and execute corresponding operations through the execution feedback module 105.

[0118] Regarding strategy adjustment requests, the control input unit 1082 supports users in making personalized modifications and adjustments to existing scheduling strategies. Users can choose to modify the scheduling stage, adjust power allocation, or optimize the load balance of the power grid. All input commands are promptly transmitted to the stage control module 106 or the multi-stage scheduling module 104, and the corresponding adjustment mechanism is activated. To ensure stable system operation, the control input unit 1082 also performs real-time verification before execution to ensure that the input adjustment commands comply with the system's operating rules and complete the task without affecting power grid safety.

[0119] The human-computer interaction module 108 provided in this embodiment provides users with a flexible and highly operable interface. Through intuitive data display and convenient control input methods, users can make timely and effective decisions during grid connection and dispatch, thereby improving the system's response speed and operational efficiency.

[0120] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A distributed power intelligent grid-connected evaluation and optimization scheduling system based on multi-source data fusion, characterized in that, include: The data acquisition module is used to collect raw operating data from distributed power sources and grid nodes. The raw operating data includes voltage, current, active power, reactive power, and environmental parameters. A data fusion module is used to preprocess and fuse the original running data to generate a fused dataset; The grid connection evaluation module is used to analyze the impact of distributed power generation grid connection on the grid operation status based on the fused dataset and output evaluation indicators. A multi-stage scheduling module is used to generate a multi-stage scheduling strategy based on the evaluation indicators. The scheduling strategy includes an initialization stage, a dynamic adjustment stage, and an emergency response stage. The multi-stage scheduling module also includes: An initial strategy unit is used to generate a default scheduling strategy for the initial stage based on the evaluation indicators when the distributed power source is first connected to the grid. A dynamic optimization unit is used to dynamically modify the current scheduling strategy based on feedback information; An emergency strategy unit is used to quickly generate a control strategy for the emergency response phase when a high-risk operating state is detected. The dynamic optimization unit adjusts and dynamically corrects the scheduling strategy based on the following objective function: dt Where J is the overall optimization objective of the scheduling strategy, aiming to balance power error and the smoothness of control variable changes. and These are the weighting coefficients related to risk level and scheduling stability, respectively. For the target power value, Let u(t) be the current power, and u(t) be the scheduling control quantity. An execution feedback module is used to generate scheduling instructions based on the current scheduling stage and collect power grid response data to form feedback information. A stage control module is used to determine whether to switch the current scheduling stage and coordinate the strategy adjustment of the multi-stage scheduling module based on the feedback information and the power grid operating status.

2. The system according to claim 1, characterized in that, The data fusion module includes: The preprocessing unit is used to perform data cleaning, anomaly detection and normalization on the raw operating data, and output the fused dataset to the grid-connected evaluation module and the multi-stage scheduling module. A synchronous fusion unit is used to perform temporal alignment and fusion on the preprocessed data to generate the fused dataset.

3. The system according to claim 2, characterized in that, The synchronization and fusion unit further includes a state estimation unit, which is used to estimate the state of the fusion dataset to improve the accuracy of the evaluation results of the grid connection evaluation module.

4. The system according to claim 1, characterized in that, The grid connection assessment module includes: A status analysis unit, wherein the status analysis unit is used to identify the operating status of the power grid; An indicator extraction unit is used to extract evaluation indicators for voltage fluctuation, frequency offset, and power flow distribution from the fused dataset. The risk assessment unit is used to determine the risk level of distributed power generation grid-connected operation based on the grid operation status identified by the status analysis unit and the evaluation indicators. The state analysis unit is further configured to perform a short-term estimate of the future operating state of the power grid based on the power grid operating state, using the following estimation model: in, These are short-term forecasts of the power grid status. The voltage is the current value, and η is the adjustment coefficient. The rate of change of voltage.

5. The system according to claim 1, characterized in that, The multi-stage scheduling module further includes a policy switching mechanism, which is used to set the switching conditions and priorities between each scheduling stage, and to perform version management and coordination control of the scheduling policies of different stages.

6. The system according to claim 1, characterized in that, The execution feedback module includes: A control instruction generation unit, which is used to generate specific control instructions based on the current scheduling strategy; The feedback information acquisition unit is used to acquire power grid response data after the control command is executed, and to provide the feedback information to the stage control module and the multi-stage scheduling module.

7. The system according to claim 1, characterized in that, The stage control module includes: A stage determination unit is used to determine the applicable scheduling stage based on the feedback information and the power grid operating status. The coordination and control unit is used to switch the scheduling strategy between different stages and to achieve linkage control with the multi-stage scheduling module and the execution feedback module.

8. The system according to claim 1, characterized in that, It further includes a communication and security module to enable data transmission between modules, and ensures data transmission security through encryption and authentication mechanisms, and has a redundancy mechanism to ensure stable system operation.

9. The system according to claim 1, characterized in that, The system further includes a human-computer interaction module, which comprises: A visualization interface is provided to display information on the evaluation indicators, the scheduling strategy, and the power grid operating status. A control input unit is used to receive control commands or strategy adjustment requests input by the user and transmit the requests to the stage control module or the multi-stage scheduling module.

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