Multi-Operating Mode Switching Control Method for Flexible Interconnected Distribution Network

Abstract

The present invention discloses a multi-operation mode switching control method for a flexible interconnected distribution network, which relates to the field of intelligent control. The method includes: real-time monitoring of the flexible interconnected distribution network by an operation perception unit to obtain multiple power operation data, determining operation switching demand information in combination with operation scenario parameters, introducing switching constraint conditions, determining the target operation mode according to the multiple power operation data and the operation switching demand information, performing operation analysis in combination with the flexible interconnected distribution network, formulating a switching control strategy according to the analysis results, and combining multiple control stages to execute the switching control strategy for intelligent operation scheduling of the flexible interconnected distribution network. It solves the technical problem that the traditional distribution network has a low level of automation and limited regulation means, resulting in low efficiency of grid switching control, and achieves the technical effect of reasonably and accurately performing multi-operation mode switching on the flexible interconnected distribution network, realizing the stable operation and optimal configuration of the distribution network.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and particularly to a multi-operation mode switching control method for a flexible interconnected distribution network. Background Art

[0002] With the rapid development of power electronics technology and the large-scale access of distributed power sources, along with the wide application of renewable energy and the large-scale access of distributed power sources (such as photovoltaic, wind power, etc.), the distribution network faces multiple challenges such as diverse power consumption demands, large-scale power source access, and complex power flow coordinated control. However, the traditional distribution network has low automation level, unreasonable structure, and limited regulation means, resulting in the technical problem of low efficiency in grid switching control, and it is difficult to meet the operation requirements of modern distribution networks. Summary of the Invention

[0003] This application provides a multi-operation mode switching control method for a flexible interconnected distribution network, which solves the technical problems of low automation level and limited regulation means of the traditional distribution network, resulting in low efficiency in grid switching control, and achieves the technical effect of reasonably and accurately performing multi-operation mode switching on the flexible interconnected distribution network, realizing the stable operation and optimal configuration of the distribution network.

[0004] This application provides a multi-operation mode switching control method for a flexible interconnected distribution network, including: real-time monitoring of the flexible interconnected distribution network by an operation perception unit to obtain a plurality of power operation data; determining operation switching demand information based on the plurality of power operation data combined with operation scenario parameters; introducing switching constraint conditions, and determining a target operation mode according to the plurality of power operation data and the operation switching demand information; performing operation analysis on the flexible interconnected distribution network based on the target operation mode, and formulating a switching control strategy according to the analysis result; combining multiple control stages, and executing the switching control strategy to perform intelligent operation scheduling on the flexible interconnected distribution network.

[0005] In a possible implementation, based on the multiple power operation data and the operation scenario parameters, the operation switching requirement information is determined, and the following processing is performed: data mining is performed based on the multiple power operation data and the operation scenario parameters to generate an operation data mining result; the operation data mining result is identified to obtain multiple operation characteristics, and multiple operation change curve graphs are constructed according to the multiple operation characteristics; the multiple operation change curve graphs are traversed and randomly selected to obtain multiple operation change data; an operation training data set and an operation test data set are constructed based on the multiple operation change data; a power grid operation prediction model is constructed using the operation training data set, the power grid operation prediction model is verified using the operation training data set, and the power grid operation prediction model is output according to the verification result; the flexible interconnected distribution network is operationally predicted through the power grid operation prediction model, and the switching requirement information is determined through analysis of the operation prediction result.

[0006] In a possible implementation, for the switching constraint conditions, the following processing is performed: the flexible interconnected distribution network is risk-assessed based on the operation scenario parameters to generate multiple assessment values, where the multiple assessment values include a voltage fluctuation risk assessment value, a frequency deviation risk assessment value, and a power grid overload risk assessment value; based on the voltage fluctuation risk assessment value, the frequency deviation risk assessment value, and the power grid overload risk assessment value, the multiple power operation data are traversed to extract voltage operation fluctuation data, frequency operation fluctuation data, and power grid operation load data; constraint control is performed based on the voltage fluctuation risk assessment value in combination with the voltage operation fluctuation data to generate a voltage range constraint; constraint control is performed based on the frequency deviation risk assessment value in combination with the frequency operation fluctuation data to generate a frequency range constraint; constraint control is performed based on the power grid overload risk assessment value in combination with the power grid operation load data to generate a power limit constraint; the voltage range constraint, the frequency range constraint, and the power limit constraint are integrated to generate the switching constraint conditions.

[0007] In a possible implementation, when the switching constraint conditions are introduced and the target operation mode is determined according to the multiple power operation data and the operation switching requirement information, the following processing is performed: it is determined whether the multiple operation change data are at a preset change critical value; if the multiple operation change data are at the preset change critical value, the switching time point is determined according to the operation switching requirement information; a to-be-switched instruction is generated according to the switching time point, and the to-be-switched instruction is executed and the multiple operation modes are matched in combination with the switching constraint conditions to determine the target operation mode; if the multiple operation change data are not at the preset change critical value, the operation prediction result is verified, and when the verification fails, the to-be-switched instruction is generated to determine the target operation mode.

[0008] In a possible implementation manner, a to-be-switched instruction is generated according to the switching time point, and the to-be-switched instruction is executed to match multiple operating modes in combination with the switching constraint conditions to determine the target operating mode, and the following processing is performed: the operating switching requirement information and the real-time power grid operating parameters are combined to determine the switching time point; the operating switching analysis is performed according to the switching time point and the historical operating switching record data to determine multiple operating mode switching targets; weight distribution is performed on the multiple operating mode switching targets according to the operating influence factor, and multiple switching priorities are obtained according to the weight distribution result; based on the multiple switching priorities, the multiple operating modes are traversed and matched according to the switching constraint conditions to determine the target operating mode.

[0009] In a possible implementation manner, in combination with multiple control stages, the switching control strategy is executed to perform intelligent operation scheduling on the flexible interconnected distribution network, and the following processing is performed: the multiple control stages are retrieved, and the multiple control stages include a linear control stage and a nonlinear control stage; based on the linear control stage, the switching control strategy is executed to generate a first operation scheduling data set; based on the nonlinear control stage, the switching control strategy is executed to generate a second operation scheduling data set; based on the first operation scheduling data set and the second operation scheduling data set, operation feedback is performed on the flexible interconnected distribution network, and a scheduling adjustment instruction is generated according to the feedback result; based on the scheduling adjustment instruction, dynamic deviation correction is performed on the first operation scheduling data set and the second operation scheduling data set, and intelligent operation scheduling is performed on the flexible interconnected distribution network according to the deviation correction result.

[0010] In a possible implementation manner, dynamic deviation correction is performed on the first operation scheduling data set and the second operation scheduling data set based on the scheduling adjustment instruction, and the following processing is performed: when the control stage of the flexible interconnected distribution network is the linear control stage, based on the scheduling adjustment instruction, the first operation scheduling data set is corrected by using a scheduling optimization step size to obtain a first scheduling deviation correction parameter; the first scheduling deviation correction parameter is used for performing operation switching scheduling prediction to obtain a first scheduling parameter, and a first scheduling fitness is calculated; it is judged whether the first scheduling fitness of the first scheduling parameter is greater than a preset scheduling threshold. If so, the scheduling optimization compensation is reduced. If not, the scheduling optimization compensation is increased, the first scheduling parameter is adjusted to obtain a first scheduling deviation correction parameter, and the first scheduling deviation correction parameter is added to the deviation correction result.

[0011] In a possible implementation manner, for the scheduling optimization compensation, the following processing is performed: a scheduling parameter adjustment space is constructed with the preset scheduling threshold as the center and the scheduling optimization step size as the span; the scheduling optimization compensation is configured according to the scheduling parameter adjustment space.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] The multi-operation mode switching control method for a flexible interconnected distribution network provided in this application solves the technical problem that the traditional distribution network has a low level of automation and limited regulation means, resulting in low efficiency of grid switching control, and achieves the technical effect of reasonably and accurately switching multiple operation modes of the flexible interconnected distribution network, realizing the stable operation and optimal configuration of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of this application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the multi-operation mode switching control method for a flexible interconnected distribution network provided in the embodiments of this application;

[0016] Figure 2 It is a schematic flowchart of determining the target operation mode of the multi-operation mode switching control method for a flexible interconnected distribution network provided in the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the present application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0020] The embodiment of this application provides a multi-operation mode switching control method for a flexible interconnected distribution network, as Figure 1 shown, the method includes:

[0021] Step A100, real-time monitoring of the flexible interconnected distribution network through an operation perception unit to obtain a plurality of power operation data;

[0022] To ensure the stable operation of the distribution network, optimize resource allocation, and improve energy utilization efficiency, it is first necessary to perform real-time monitoring of the flexible interconnected distribution network through an operation perception unit. The operation perception unit can be an important part of the flexible interconnected distribution network and can have the functions of real-time data collection, processing, and transmission. The operation perception unit can be deployed on key nodes and devices of the distribution network, such as substations, lines, load points, etc., so as to comprehensively and accurately reflect the operation state of the distribution network. During the real-time monitoring process, the perception unit will collect various power operation data, including but not limited to voltage and current data, load data, fault and abnormal data, distributed energy data. The voltage and current data can be used by the operation perception unit to monitor the voltage and current levels in the distribution network in real time, including voltage amplitude, phase, frequency, and the magnitude and direction of current, etc. The load data can be obtained by collecting the power consumption data of each load point, including active power, reactive power, power factor, etc. The fault and abnormal data can be used by the operation perception unit to monitor the faults and abnormal conditions in the distribution network in real time, such as line short circuits, equipment failures, overloads, etc. The distributed energy data can be used by the operation perception unit to monitor the output power, working status, etc. of these distributed energy sources. Through the real-time monitoring and analysis of these power operation data, a comprehensive control and optimization of the flexible interconnected distribution network can be achieved. On this basis, multiple power operation data are integrated to provide important support for the optimized operation and energy management of the distribution network.

[0023] Execute step A200 to determine operation switching requirement information based on the multiple power operation data in combination with operation scenario parameters; in a possible implementation, step A200 further includes step A210 of performing data mining on the multiple power operation data in combination with the operation scenario parameters to generate an operation data mining result; execute step A220 to identify the operation data mining result to obtain multiple operation features, and construct multiple operation change curve graphs according to the multiple operation features; execute step A230 to randomly select through the multiple operation change curve graphs to obtain multiple operation change data; execute step A240 to construct an operation training data set and an operation test data set based on the multiple operation change data; execute step A250 to construct a power grid operation prediction model using the operation training data set, verify the power grid operation prediction model using the operation training data set, and output the power grid operation prediction model according to the verification result; execute step A260 to perform operation prediction on the flexible interconnected distribution network through the power grid operation prediction model, and analyze according to the operation prediction result to determine the switching requirement information.

[0024] Performing data mining by combining power operation data and operation scenario parameters refers to using data mining algorithms such as clustering analysis, association rule mining, and classification algorithms to extract hidden patterns and relationships in the data, thereby generating an operation data mining result. The generated operation data mining result may include typical features, trend prediction, anomaly detection, etc. under different operation conditions. Subsequently, feature recognition is performed on the data mining result to extract multiple operation features closely related to power grid operation. The multiple operation features may include voltage fluctuation, current change, power factor, load change rate, etc. On this basis, multiple operation change curve graphs are constructed. The multiple operation change curve graphs can be used to represent the change trends of operation features at different time scales, and the time scale can be hours, days, weeks, months, etc.

[0025] Further, traverse all the operation change curve graphs, and randomly or based on a specific strategy, select multiple operation change data points. The multiple operation change data points are used to characterize the power grid operation conditions under different operation conditions and scenarios. Then divide the selected multiple operation change data into a training data set and a test data set. The training data set is used to construct and train a power grid operation prediction model, while the test data set is used to verify the performance of the model. Subsequently, use the training data set to construct a power grid operation prediction model. The power grid operation prediction model can be trained using the training data set based on machine learning algorithms such as neural networks, support vector machines, and random forests, and improve the prediction accuracy of the model by adjusting the parameters of the power grid operation prediction model and optimizing the algorithm. Then use the test data set to verify the trained power grid operation prediction model. By comparing the prediction results of the power grid operation prediction model with the actual operation data, evaluate the accuracy and reliability of the power grid operation prediction model. At the same time, make necessary adjustments and optimizations to the power grid operation prediction model according to the verification results until the predetermined performance indicators are met. When the power grid operation prediction model passes the verification and meets the performance requirements, output and deploy it to the management system of the flexible interconnected distribution network, and use the deployed power grid operation prediction model to perform real-time or regular operation predictions on the flexible interconnected distribution network. According to the prediction results, analyze the performance, stability, and reliability of the power grid under different operation scenarios. Finally, based on the operation prediction and analysis, determine the switching demand information of the flexible interconnected distribution network under different scenarios. The switching demand information may include operations such as switching to different energy supply sources, adjusting the power grid topology structure, and optimizing the load distribution, so as to achieve intelligent operation prediction and management of the flexible interconnected distribution network and improve the operation efficiency and reliability of the power grid.

[0026] Execute step A300, introduce switching constraint conditions, and determine the target operating mode according to the multiple power operation data and the operation switching requirement information; in a possible implementation, step A300 further includes step A310, perform a risk assessment on the flexible interconnected distribution network based on the operation scenario parameters, generate multiple evaluation values, the multiple evaluation values include a voltage fluctuation risk evaluation value, a frequency deviation risk evaluation value, and a grid overload risk evaluation value; execute step A320, traverse the multiple power operation data based on the voltage fluctuation risk evaluation value, the frequency deviation risk evaluation value, and the grid overload risk evaluation value to extract voltage operation fluctuation data, frequency operation fluctuation data, and grid operation load data; execute step A330, perform constraint control based on the voltage fluctuation risk evaluation value combined with the voltage operation fluctuation data to generate a voltage range constraint; execute step A340, perform constraint control based on the frequency deviation risk evaluation value combined with the frequency operation fluctuation data to generate a frequency range constraint; execute step A350, perform constraint control based on the grid overload risk evaluation value combined with the grid operation load data to generate a power limit constraint; execute step A360, integrate the voltage range constraint, the frequency range constraint, and the power limit constraint to generate the switching constraint conditions.

[0027] First, based on the operating scenario parameters, the risk assessment of the flexible interconnected distribution network means that the assessment should cover aspects such as voltage fluctuations, frequency deviations, and grid overloads based on parameters such as the weather conditions, load forecasts, and equipment status of the flexible interconnected distribution network, and generate corresponding risk assessment values, namely, voltage fluctuation risk assessment values, frequency deviation risk assessment values, and grid overload risk assessment values. At the same time, according to the voltage fluctuation risk assessment values, frequency deviation risk assessment values, and grid overload risk assessment values, traverse multiple power operation data, extract specific data related to risk assessment in the multiple power operation data, obtain voltage operation fluctuation data, frequency operation fluctuation data, and grid operation load data, and then, based on the voltage fluctuation risk assessment value, combined with the voltage operation fluctuation data, determine a reasonable voltage fluctuation range, and generate a voltage range constraint on the basis of the determined voltage fluctuation range. The voltage range constraint is used to ensure that the voltage level in the grid fluctuates within the restricted range. Secondly, according to the frequency deviation risk assessment value, combined with the frequency operation fluctuation data, determine the frequency fluctuation range of the grid, and generate a frequency range constraint on the basis of the determined frequency fluctuation range of the grid. The frequency range constraint can be used to maintain the stable operation of the grid. Finally, based on the grid overload risk assessment value, combined with the grid operation load data, formulate a power limit for the grid, and generate a power limit constraint on the basis of the determined power limit of the grid. The power limit constraint can be used to prevent the grid from being overloaded and ensure the safety of equipment and the stable operation of the grid. Further, integrate the generated voltage range constraint, frequency range constraint, and power limit constraint to determine the switching constraint conditions. The switching constraint conditions can be used as the rules that the flexible interconnected distribution network must abide by during the switching process to ensure the stable, safe, and efficient operation of the grid.

[0028] In a possible implementation, step A300 further includes step A370 of determining whether the multiple operation change data are at a preset change critical value; performing step A380, if the multiple operation change data are at the preset change critical value, determining a switching time point according to the operation switching requirement information; performing step A390, generating a to-be-switched instruction according to the switching time point, and performing the to-be-switched instruction to match multiple operation modes in combination with the switching constraint conditions to determine the target operation mode; in the management and operation of a flexible interconnected distribution network, determining whether multiple operation change data reach a preset change critical value. The multiple operation change data may include real-time change values of parameters such as voltage, current, frequency, power factor, etc. The preset change critical value may be set according to the operation experience and safety standards of the power grid. The preset change critical value is used to determine whether the power grid is about to enter an unstable or dangerous state. If any one or more of the multiple operation change data reach or exceed the preset change critical value, it indicates that the power grid may be about to enter an unstable state and needs to switch the operation mode. Then, according to the previously determined operation switching requirement information, i.e., the safe operation requirements of the power grid, economic benefit optimization, etc., the best switching time point is determined. After determining the switching time point, a to-be-switched instruction is generated. The to-be-switched instruction can be used to guide the power grid control system to switch the operation mode, and when executing the to-be-switched instruction, it is necessary to combine the switching constraint conditions, i.e., voltage range constraint, frequency range constraint, power limit constraint, etc., to match multiple possible operation modes, and finally select an operation mode that meets both the switching requirements and the switching constraint conditions as the target operation mode for output.

[0029] In a possible implementation, as Figure 2 shown, step A390 further includes step A391 of determining the switching time point by combining the operation switching requirement information with real-time power grid operation parameters; performing step A392, performing operation switching analysis according to the switching time point in combination with historical operation switching record data to determine multiple operation mode switching targets; performing step A393, assigning weights to the multiple operation mode switching targets according to the operation influence factor, and obtaining multiple switching priorities according to the weight assignment result; performing step A394, traversing the multiple operation modes according to the multiple switching priorities in combination with the switching constraint conditions to match and determine the target operation mode.

[0030] First, comprehensively analyze the current operating state of the power grid based on real-time power grid operating parameters such as voltage, current, frequency, power factor, etc., and operating switching demand information such as safety, economy, and stability. According to the analysis results, determine one or more potential switching time points. The switching time points should ensure that the power grid can smoothly transition during the switching process, avoid causing excessive impact on the power grid or affecting the power supply quality. Then, after determining the switching time points, analyze in combination with historical operating switching record data, and the historical data can provide experience and lessons from past switches, which helps to predict and evaluate the possibility and effect of future switches. Further, by analyzing the historical data, multiple possible operating mode switching targets can be determined, that is, multiple operating mode switching targets. The multiple operating mode switching targets can meet the current operating requirements of the power grid and comply with the constraints of operating switching.

[0031] Furthermore, assign weights to multiple operating mode switching targets according to the operating impact factors generated such as safety, economy, stability, and reliability. Different impact factors can have different importance and priorities during the switching process. The greater the impact on the flexible interconnected distribution network, the greater the impact factor, and the greater the weight and priority. The weight assignment can adopt methods such as expert scoring method, analytic hierarchy process, and fuzzy comprehensive evaluation method. Quantify the relative importance and priority of different operating mode switching targets through weight assignment. Then, according to the multiple switching priorities obtained from the weight assignment results, sort and screen multiple operating modes, and preferentially select the operating modes with high switching priorities and meeting the switching constraint conditions. At the same time, traverse multiple operating modes and match them in combination with switching constraint conditions, that is, voltage range constraint, frequency range constraint, power limit constraint, etc. Ensure that the selected target operating mode not only meets the switching priority requirements but also complies with the safe and stable operation requirements of the power grid. On this basis, determine the target operating mode, and the obtained target operating mode can be used as the new state of the power grid's next operation to meet the current operating requirements and future operation predictions of the power grid.

[0032] Execute step A3100. If the multiple operating change data are not within the preset change critical value, verify according to the operating prediction result. When the verification fails, generate the to-be-switched instruction and determine the target operating mode.

[0033] If multiple operation change data do not reach the preset change critical value, it indicates that the power grid is currently in a stable operation state and does not require an immediate switch of the operation mode. However, to prevent possible future operation problems, the power grid operation prediction model constructed above can be used to verify the prediction results. If the prediction results show that the power grid will reach or exceed the preset change critical value at a certain future time point, then a pending switch instruction also needs to be generated. Similarly, the pending switch instruction is executed to match multiple operation modes in combination with the switch constraint conditions, that is, in combination with the switch constraint conditions, such as voltage range constraint, frequency range constraint, power limit constraint, etc., to match multiple possible operation modes, and finally select an operation mode that not only meets the switch requirements but also conforms to the switch constraint conditions as the target operation mode for output.

[0034] And whether it is because the operation change data reaches the critical value or the prediction result verification fails, once the target operation mode and the switch time point are determined, the switch operation needs to be executed. And when executing the switch operation, it is necessary to ensure the safe and stable operation of the power grid, avoid excessive impact during the switch process or affecting the power supply quality of the power grid. At the same time, after the switch is completed, it is necessary to continuously monitor the operation state of the power grid to ensure that the new operation mode can meet the safety and economic benefit requirements of the power grid. It is also possible to continuously optimize and adjust the preset change critical value, switch constraint conditions, and operation prediction model according to the actual operation situation and experience feedback to improve the operation efficiency and stability of the power grid.

[0035] Execute step A400 to conduct an operation analysis based on the target operation mode in combination with the flexible interconnected distribution network, and formulate a switch control strategy according to the analysis results; first, deeply analyze the characteristics of the target operation mode, which can include voltage level, frequency stability, power distribution, energy supply mode, etc., to predict the operation state and possible problems that may be encountered after the power grid switches to the new mode. Then, evaluate the current state of the flexible interconnected distribution network, which can include the working state of each device, the topological structure of the power grid, energy reserves and dispatching capabilities, etc. Then use power system simulation software or models to simulate the operation state of the power grid in the target operation mode. Through simulation, the change trends of key parameters such as voltage, frequency, and power flow of the power grid can be predicted. At the same time, combined with historical data and real-time data, the operation state of the power grid is predicted to evaluate the stability and reliability of the power grid during the switch process. And based on the operation simulation and prediction, identify the risks and problems that may occur during the switch process of the power grid. The risks can include voltage fluctuations, frequency deviations, equipment overload, etc. Then evaluate the identified risks to determine their possibility and impact degree. The evaluation results will be an important basis for formulating the switch control strategy.

[0036] Further, based on the operation analysis results and risk assessment results, a detailed switching control strategy is formulated. The switching control strategy may include the switching time point and sequence, that is, determining the optimal switching time point and the switching sequence of equipment and lines, control parameter settings, that is, setting the control ranges and thresholds of key parameters such as voltage, frequency, and power, emergency measures, that is, formulating emergency measures for dealing with emergencies such as equipment failures and energy supply interruptions, dispatching instructions, that is, generating specific dispatching instructions to guide the grid control system to execute the switching operation, and finally formulating the switching control strategy to improve the operation efficiency and stability of the power grid.

[0037] Next, step A500 is executed. Combining multiple control stages, the switching control strategy is executed to perform intelligent operation scheduling on the flexible interconnected distribution network.

[0038] In a possible implementation manner, step A500 further includes step A510 of retrieving the multiple control stages, where the multiple control stages include a linear control stage and a non-linear control stage; step A520 of executing the switching control strategy based on the linear control stage to generate a first operation scheduling data set; step A530 of executing the switching control strategy based on the non-linear control stage to generate a second operation scheduling data set; step A540 of performing operation feedback on the flexible interconnected distribution network based on the first operation scheduling data set and the second operation scheduling data set, and generating a scheduling adjustment instruction according to the feedback result; and step A550 of dynamically correcting the first operation scheduling data set and the second operation scheduling data set based on the scheduling adjustment instruction, and performing intelligent operation scheduling on the flexible interconnected distribution network according to the correction result.

[0039] First, determine multiple control stages in the operation of the flexible interconnected distribution network. The multiple control stages may include a linear control stage and a non - linear control stage. The linear control stage is applicable to the situation where the changes in grid operation parameters are small and the system stability is high; while the non - linear control stage is used to handle the situation where the parameter changes are large and the system dynamic characteristics are complex. And in the linear control stage, according to the pre - formulated switching control strategy, corresponding control operations are executed. The control operations may include adjusting key parameters such as voltage, frequency, power, or optimizing the operation status of equipment and the topological structure of the power grid. After executing the strategy of the linear control stage, a first operation scheduling data set is generated. The first operation scheduling data set is used to record information such as the operation status, parameter changes, and equipment scheduling of the power grid under the linear control stage. In the non - linear control stage, control operations are also executed according to the switching control strategy. Since the non - linear control stage involves more complex system dynamic characteristics, more advanced control algorithms and strategies are required. After executing the strategy of the non - linear control stage, a second operation scheduling data set is generated. The second operation scheduling data set is used to record information such as the operation status, parameter changes, and equipment scheduling of the power grid under the non - linear control stage.

[0040] Furthermore, based on the first operation scheduling data set and the second operation scheduling data set, perform an operation feedback analysis on the flexible interconnected distribution network. The feedback analysis may include evaluating indicators such as the operation stability, economy, and security of the power grid, as well as identifying possible problems and potential risks. And according to the feedback results, generate a scheduling adjustment instruction. The scheduling adjustment instruction may include operations such as adjusting control parameters, optimizing equipment scheduling, and changing the topological structure of the power grid, aiming to improve the operation efficiency and stability of the power grid.

[0041] Finally, based on the scheduling adjustment instruction, perform dynamic deviation correction on the first operation scheduling data set and the second operation scheduling data set, which means correcting and adjusting the switching control strategy and the original scheduling plan according to real - time data and feedback results to adapt to the actual operation situation and requirements of the power grid. After dynamic deviation correction, perform intelligent operation scheduling on the flexible interconnected distribution network according to the deviation correction results. The intelligent scheduling uses advanced information technology and control algorithms to achieve real - time monitoring, prediction, and optimization of the power grid operation status to ensure the safe, efficient, and stable operation of the power grid.

[0042] In a possible implementation, step A550 further includes step A551. When the control phase of the flexible interconnected distribution network is the linear control phase, based on the scheduling adjustment instruction, the first operating scheduling data set is corrected using a scheduling optimization step size to obtain a first scheduling correction parameter; step A552 is executed, and the first scheduling correction parameter is used for operation switching scheduling prediction to obtain a first scheduling parameter, and a first scheduling fitness is calculated; step A553 is executed to determine whether the first scheduling fitness of the first scheduling parameter is greater than a preset scheduling threshold. If so, the scheduling optimization compensation is reduced. If not, the scheduling optimization compensation is increased, the first scheduling parameter is adjusted to obtain a first scheduling correction parameter, and the first scheduling correction parameter is added to the correction result. In a possible implementation, step A553 further includes step A5531, centered on the preset scheduling threshold and with the scheduling optimization step size as the span, a scheduling parameter adjustment space is constructed; step A5532 is executed, and the scheduling optimization compensation is configured according to the scheduling parameter adjustment space.

[0043] To preliminarily adjust the operating parameters of the power grid to make it closer to the ideal state, the control phase of the flexible interconnected distribution network is determined. When the flexible interconnected distribution network is in the linear control phase, first, according to the scheduling adjustment instruction, an initial scheduling optimization step size is used to correct the first operating scheduling data set, and a first scheduling correction parameter is obtained after the correction. The first scheduling correction parameter is used to characterize the possible operating state of the power grid after adjustment. Then, using the first scheduling correction parameter for operation switching scheduling prediction means predicting the operating state of the power grid after correction through simulation or calculation, including the change trends of key parameters such as voltage, frequency, and power flow. After prediction, a first scheduling parameter is obtained. The first scheduling parameter is used to reflect the actual operating state of the power grid after correction. At the same time, the first scheduling parameter is used to calculate the scheduling fitness according to specific scheduling objectives and evaluation criteria, and a first scheduling fitness corresponding to the first scheduling parameter is obtained. The scheduling fitness can be used to measure whether the operating state of the power grid under the current scheduling parameter meets the preset scheduling objectives, and the preset scheduling objectives can be obtained based on settings such as economy, stability, and security.

[0044] Further, it is determined whether the first scheduling fitness of the first scheduling parameter is greater than a preset scheduling threshold. The preset scheduling threshold can be a standard value set according to the actual situation of the power grid and the scheduling objective. If the first scheduling fitness is greater than the preset scheduling threshold, it indicates that the current scheduling parameter is already good enough and does not require major adjustments. At this time, the scheduling optimization compensation can be reduced, that is, the step size of the next correction can be decreased to perform more refined adjustments. If the first scheduling fitness is less than or equal to the preset scheduling threshold, it indicates that the current scheduling parameter still needs to be further optimized. At this time, the scheduling optimization compensation should be increased, that is, the step size of the next correction should be increased to accelerate the adjustment speed. On this basis, the first scheduling parameter is adjusted to obtain a new first scheduling correction parameter.

[0045] The above scheduling optimization compensation is first to construct a scheduling parameter adjustment space centered on the preset scheduling threshold with the scheduling optimization step size as the span. The scheduling parameter adjustment space contains all possible scheduling parameter value ranges and the corresponding scheduling fitness evaluation results. Constructing the adjustment space helps to more intuitively understand the optimization direction and potential of the scheduling parameters, providing strong support for subsequent scheduling decisions. Then, according to the specific situation of the scheduling parameter adjustment space and the real-time operating state of the power grid, the scheduling optimization compensation is reasonably configured. The value of the optimization compensation should ensure both the stable operation of the power grid and the optimization of the scheduling objective. Finally, the configured scheduling optimization compensation will be used as the input parameter for the next scheduling correction to guide the further adjustment and optimization of the power grid. The first scheduling correction parameter obtained after the above steps is added to the correction result. The process of obtaining the second scheduling correction parameter is the same as that of the first scheduling correction parameter, and there is a one-to-one correspondence between the second scheduling correction parameter and the second operating scheduling data set, which will not be elaborated here. The first scheduling correction parameter and the first scheduling correction parameter will be used as important bases for power grid scheduling decisions to guide the real-time operation and scheduling optimization of the power grid, realizing precise scheduling correction and parameter optimization in the linear control stage of the flexible interconnected distribution network, and ensuring the stable operation of the power grid and the optimal realization of the scheduling objective.

[0046] The embodiments of the present application solve the technical problems of low automation level and limited control means in traditional distribution networks, resulting in low grid switching control efficiency, and achieve the technical effect of rational and precise multi-operation mode switching of the flexible interconnected distribution network, realizing the stable operation and optimal configuration of the distribution network.

[0047] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recited in the present application can be executed in an order different from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-operation mode switching control method for a flexible interconnected distribution network, characterized in that: The method comprises: The flexible interconnected distribution network is monitored in real time through the operation sensing unit to obtain multiple power operation data; Determine operation switching requirement information based on the plurality of power operation data combined with operation scenario parameters; Introducing a switching constraint condition, and determining a target operation mode according to the plurality of power operation data and the operation switching requirement information; Based on the target operation mode and the flexible interconnected distribution network, an operation analysis is performed, and a switching control strategy is formulated according to the analysis results; In combination with multiple control stages, the switching control strategy is executed to perform intelligent operation scheduling of the flexible interconnected distribution network; Wherein, determining the operation switching requirement information based on the plurality of power operation data in combination with the operation scenario parameters includes: Performing data mining based on the plurality of power operation data in combination with the operation scenario parameters to generate operation data mining results; Identify the operation data mining results to obtain multiple operation characteristics, and construct multiple operation change curve graphs according to the multiple operation characteristics; Traversing the plurality of operation change curve graphs and performing random selection to obtain a plurality of operation change data; Constructing an operation training data set and an operation test data set based on the multiple operation change data; constructing a power grid operation prediction model using the operation training data set, verifying the power grid operation prediction model using the operation training data set, and outputting the power grid operation prediction model according to the verification result; Performing operation prediction of the flexible interconnected distribution network by using the power grid operation prediction model, analyzing the operation prediction results, and determining the switching demand information; The switching constraint conditions include: Performing a risk assessment on the flexible interconnected distribution network based on the operation scenario parameters, generating a plurality of assessment values, wherein the plurality of assessment values ​​include a voltage fluctuation risk assessment value, a frequency offset risk assessment value, and a grid overload risk assessment value; Based on the voltage fluctuation risk assessment value, the frequency offset risk assessment value, and the power grid overload risk assessment value, the plurality of power operation data are traversed to extract voltage operation fluctuation data, frequency operation fluctuation data, and power grid operation load data; Perform constraint control based on the voltage fluctuation risk assessment value in combination with the voltage operation fluctuation data to generate a voltage range constraint; Perform constraint control based on the frequency offset risk assessment value combined with the frequency operation fluctuation data to generate a frequency range constraint; Based on the power grid overload risk assessment value and the power grid operation load data, constraint control is performed to generate a power limit constraint; Integrate the voltage range constraint, the frequency range constraint, and the power limit constraint to generate the switching constraint condition; The switching constraint condition is introduced, and the target operation mode is determined according to the plurality of power operation data and the operation switching requirement information, including: Determining whether the plurality of operation change data exceeds a preset change critical value; If the plurality of operation change data exceeds the preset change critical value, determining the switching time point according to the operation switching requirement information; Generate a to-be-switched instruction according to the switching time point, execute the to-be-switched instruction and match multiple operation modes in combination with the switching constraint condition to determine the target operation mode; If the plurality of operation change data do not exceed the preset change critical value, verification is performed according to the operation prediction result, and when the verification fails, the to-be-switched instruction is generated to determine the target operation mode; Generating a to-be-switched instruction according to the switching time point, executing the to-be-switched instruction and matching a plurality of operating modes in combination with the switching constraint condition, and determining the target operating mode, including: The operation switching requirement information is combined with real-time grid operation parameters to determine the switching time point; Performing operation switching analysis based on the switching time point in combination with historical operation switching record data to determine multiple operation mode switching targets; Weighting the multiple operation mode switching targets according to the operation influencing factors, and obtaining multiple switching priorities according to the weighting results; Traversing the multiple operating modes for matching according to the switching constraint conditions based on the multiple switching priorities, and determining the target operating mode; In which, in combination with multiple control stages, the switching control strategy is executed to perform intelligent operation and scheduling of the flexible interconnected distribution network, including: Retrieving the multiple control stages, wherein the multiple control stages include a linear control stage and a nonlinear control stage; Executing the switching control strategy based on the linear control stage to generate a first operation scheduling data set; Executing the switching control strategy based on the nonlinear control stage to generate a second operation scheduling data set; Based on the first operation scheduling data set and the second operation scheduling data set, operation feedback is performed on the flexible interconnected distribution network, and a scheduling adjustment instruction is generated according to the feedback result; Dynamically correct the first operation scheduling data set and the second operation scheduling data set based on the scheduling adjustment instruction, and perform intelligent operation scheduling on the flexible interconnected distribution network according to the correction result; The dynamically correcting the first operation scheduling data set and the second operation scheduling data set based on the scheduling adjustment instruction includes: When the control stage of the flexible interconnected distribution network is the linear control stage, based on the dispatch adjustment instruction, the first operation dispatch data set is corrected by using a dispatch optimization step size to obtain a first dispatch correction parameter; Using the first scheduling correction parameter to perform operation switching scheduling prediction, obtain the first scheduling parameter, and calculate the first scheduling fitness; Determine whether the first scheduling fitness of the first scheduling parameter is greater than a preset scheduling threshold, if so, reduce the scheduling optimization step size, if not, increase the scheduling optimization step size, adjust the first scheduling parameter, obtain a first scheduling correction parameter, and add the first scheduling correction parameter to the correction result; The scheduling optimization step size includes: Taking the preset scheduling threshold as the center and the scheduling optimization step as the span, constructing a scheduling parameter adjustment space; The scheduling optimization step size is configured according to the scheduling parameter adjustment space.

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