A power distribution network operation state regulation method and system for load fluctuation

Abstract

This invention discloses a method and system for regulating the operation status of a distribution network under load fluctuations, relating to the field of distribution network technology. The method includes: acquiring historical electricity consumption data and real-time operation data; performing collaborative analysis to obtain the index degradation degree of multiple user nodes; and weighting these to obtain the load importance coefficient of each user node; introducing random fluctuation parameters to fit the power demand fluctuation curves of multiple user nodes to obtain a demand response feature matrix; constructing a joint optimization model with the demand response feature matrix and load importance coefficient as the joint objective of minimizing the operating cost of the distribution network and maximizing voltage regulation benefits; solving the model to obtain a comprehensive regulation strategy; and performing orderly regulation of controllable equipment based on preset priorities. This invention solves the technical problem of insufficient regulation accuracy of distribution networks under load fluctuations in existing technologies, achieving the technical effect of improving the accuracy of distribution network operation status regulation and enhancing voltage regulation effectiveness.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and specifically to a method and system for regulating the operation status of power distribution networks in response to load fluctuations. Background Technology

[0002] With the expansion of new energy access and the rapid growth of flexible loads such as electric vehicles and air conditioners, the load on the user side of the distribution network exhibits stronger randomness, volatility, and time-varying characteristics, which can easily cause operational problems such as node voltage deviation, changes in power flow distribution, and frequent equipment adjustments. During distribution network operation and control, if the load change characteristics and importance of different user nodes cannot be accurately reflected, it is difficult to formulate control strategies that match the actual operating conditions in a timely manner, thus affecting the accuracy and stability of distribution network operation and control. Summary of the Invention

[0003] This application provides a method and system for regulating the operation status of a distribution network under load fluctuations, which addresses the technical problem of insufficient regulation accuracy of distribution networks under load fluctuations in the prior art.

[0004] In view of the above problems, this application provides a method and system for controlling the operation status of distribution networks in response to load fluctuations.

[0005] The first aspect of this application provides a method for regulating the operation status of a distribution network in response to load fluctuations, the method comprising: Historical electricity consumption data and real-time operation data of multiple user nodes in the distribution network are acquired; the user load importance assessment system is used to collaboratively analyze the historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and the load importance coefficient of each user node is obtained by weighting; random fluctuation parameters are introduced to fit the power demand fluctuation curve of the multiple user nodes to obtain the demand response feature matrix; with the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefit, a joint optimization model is constructed by combining the demand response feature matrix and the load importance coefficient; the joint optimization model is solved to obtain a comprehensive control strategy, and the controllable equipment in the distribution network is controlled in an orderly manner according to the preset priority.

[0006] A second aspect of this application provides a distribution network operation status control system oriented towards load fluctuations, the system comprising: The system comprises the following modules: a data acquisition module for acquiring historical electricity consumption data and real-time operation data of multiple user nodes in the distribution network; a collaborative analysis module for retrieving user load importance assessment system data to perform collaborative analysis on historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and weighting the data to obtain the load importance coefficient of each user node; a fitting module for introducing random fluctuation parameters to fit the power demand fluctuation curve of the multiple user nodes to obtain a demand response feature matrix; a model building module for constructing a joint optimization model with the joint objectives of minimizing the operating cost of the distribution network and maximizing voltage regulation benefits, combining the demand response feature matrix and the load importance coefficient; and a regulation module for solving the joint optimization model to obtain a comprehensive regulation strategy, and performing orderly regulation of controllable equipment in the distribution network based on preset priorities.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application acquires historical electricity consumption data and real-time operation data of multiple user nodes in a distribution network; it retrieves a user load importance assessment system to collaboratively analyze the historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and weights them to obtain the load importance coefficient of each user node; it introduces random fluctuation parameters to fit the power demand fluctuation curves of the multiple user nodes to obtain a demand response feature matrix; it constructs a joint optimization model with the demand response feature matrix and the load importance coefficient as the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefit; it solves the joint optimization model to obtain a comprehensive regulation strategy, and performs orderly regulation of controllable equipment in the distribution network in combination with preset priorities. This invention solves the technical problem of insufficient regulation accuracy of distribution networks under load fluctuations in the prior art. By constructing a joint optimization model and generating a comprehensive regulation strategy, it achieves the technical effect of improving the accuracy of distribution network operation status regulation and enhancing voltage regulation effect. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic flowchart of a distribution network operation status control method for load fluctuations provided in this application embodiment; Figure 2This is a schematic diagram of a distribution network operation status control system for load fluctuations, provided as an embodiment of this application.

[0010] Figure labeling: Data acquisition module 11, collaborative analysis module 12, fitting module 13, model building module 14, regulation module 15. Detailed Implementation

[0011] This application provides a method and system for regulating the operation status of distribution networks under load fluctuations. It addresses the technical problem of insufficient regulation accuracy of distribution networks under load fluctuations in the prior art by constructing a joint optimization model and generating a comprehensive regulation strategy, thereby improving the accuracy of distribution network operation status regulation and enhancing voltage regulation performance.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a method for regulating the operation status of a distribution network in response to load fluctuations, the method comprising: Step S100: Obtain historical electricity consumption data and real-time operation data of multiple user nodes in the distribution network.

[0015] In this embodiment, multiple user nodes participating in operation status control are first determined based on the distribution network topology, and each user node is associated with a corresponding smart meter, electricity consumption information acquisition device, and distribution automation terminal. Then, the electricity consumption, load power, voltage, current, and electricity consumption cycle data of each user node within a set historical interval are retrieved from the electricity consumption information acquisition system to form historical electricity consumption data. Then, the current load power, node voltage, line current, switch status, and controllable equipment operation status of each user node are continuously collected through the smart meter and distribution automation terminal to form real-time operation data.

[0016] Step S200: Retrieve the user load importance assessment system to perform collaborative analysis on historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and weight them to obtain the load importance coefficient of each user node among the multiple user nodes.

[0017] In this embodiment, firstly, the user load importance assessment system is retrieved, and historical electricity consumption data and real-time operation data are used as the basis for collaborative analysis; secondly, based on any indicator in the user load importance assessment system, any user parameter corresponding to any user node among multiple user nodes is obtained; then, the arbitrary user parameter is compared with any preset threshold of any indicator to determine the indicator degradation degree of multiple user nodes; and weighted calculation is performed according to the arbitrary weight coefficient corresponding to the indicator degradation degree to obtain the load importance coefficient of each user node among multiple user nodes.

[0018] Furthermore, the method provided in the application embodiments also includes: The user load importance assessment system includes new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators.

[0019] In this embodiment, the user load importance assessment system consists of new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators. The new energy benefit indicators include new energy power generation, new energy consumption, new energy utilization rate, and abandoned power. The voltage quality indicators include node voltage value, positive voltage deviation, negative voltage deviation, and voltage fluctuation value. The electricity load benefit indicators include user electricity consumption, load power, peak-valley load difference, adjustable load capacity, and load response. These indicators correspond to new energy access operation, voltage operation quality, and user load regulation, respectively, providing a basis for subsequently calculating the load importance coefficient of user nodes.

[0020] Furthermore, in the method provided in the application embodiment, the method further includes retrieving the user load importance assessment system to perform collaborative analysis on historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and weighting the results to obtain the load importance coefficient of each user node among the multiple user nodes, and also includes: Extract any indicator from the user load importance assessment system; extract any user node from the multiple user nodes, and obtain any user parameter of the arbitrary user node based on the arbitrary indicator; compare the arbitrary user parameter with any preset threshold of the arbitrary indicator to obtain an arbitrary degree of degradation; obtain an arbitrary weight coefficient of the arbitrary degree of degradation, and calculate the arbitrary coefficient of the arbitrary user node by weighting, and form the load importance coefficient.

[0021] In this embodiment, the indicator list is first read from the user load importance assessment system. The indicator list includes new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators. One of the indicators is selected as an arbitrary indicator according to the indicator number order. The indicator name, data fields, unit of measurement, evaluation direction, and arbitrary preset threshold corresponding to the arbitrary indicator are read. The evaluation direction is the comparison direction in which any user parameter deteriorates relative to any preset threshold.

[0022] Next, from multiple user nodes in the distribution network, one user node is selected as the arbitrary user node according to its user node number. Then, based on the user node number of the arbitrary user node, data items matching the data fields of the arbitrary indicator are searched in historical electricity consumption data and real-time operation data. The found data items are then standardized in units and aligned in time to obtain the arbitrary user parameters of the arbitrary user node under any indicator.

[0023] Then, any user parameter is compared with any preset threshold of any metric. If the evaluation direction of any metric is that the value is higher than any preset threshold, indicating degradation, then the difference between any user parameter and any preset threshold is calculated; if the evaluation direction of any metric is that the value is lower than any preset threshold, indicating degradation, then the difference between any preset threshold and any user parameter is calculated. The difference is then normalized by dividing by any preset threshold, and any normalization result less than zero is set to zero, thus obtaining the degree of degradation of any user node under any metric.

[0024] Next, arbitrary weight coefficients for arbitrary degradation levels are obtained. In this process, the subjective weights of arbitrary indicators are first determined based on the improved analytic hierarchy process (AHP), and the objective weights of arbitrary indicators are determined based on the improved indicator correlation weighting method. Then, the subjective and objective weights are integrated using the principle of minimum discriminative information to obtain a combined weight that balances subjective evaluation and the correlation of objective data. Furthermore, a variable weighting mechanism is introduced to modify the combined weights. When any degradation level is below a preset threshold, a penalty-type variable weighting function is used to increase the combined weight; when any degradation level is above the preset threshold, an incentive-type variable weighting function is used to decrease the combined weight; and when any degradation level is at the preset threshold, the combined weight is used as the arbitrary weight coefficient, thus obtaining the modified arbitrary weight coefficient.

[0025] Finally, the arbitrary degree of degradation is multiplied by the arbitrary weight coefficient to obtain the weighted degradation value of any user node under any index. According to the new energy benefit index, voltage quality index and electricity load benefit index in the user load importance assessment system, the weighted degradation value corresponding to each user node is calculated respectively, and the weighted degradation values ​​are summed to obtain the arbitrary coefficient of any user node. Multiple user nodes are processed in the same way to obtain the arbitrary coefficient corresponding to each user node, and the arbitrary coefficient corresponding to each user node is used as the load importance coefficient of each user node.

[0026] Furthermore, in the method provided in the application embodiments, obtaining the arbitrary weighting coefficient of the arbitrary degradation degree further includes: The subjective weights of the arbitrary indicators are determined based on an improved analytic hierarchy process (AHP); the objective weights of the arbitrary indicators are determined based on an improved indicator correlation weighting method; the subjective weights and objective weights are fused using the principle of minimum discriminative information to obtain a combined weight; a variable weighting mechanism is introduced to modify the combined weight to obtain the modified arbitrary weight coefficient; wherein, the variable weighting mechanism to modify the combined weight includes: when the arbitrary degradation degree is lower than a preset threshold, increasing the combined weight using a penalized variable weighting function according to the variable weighting mechanism to obtain the arbitrary weight coefficient; when the arbitrary degradation degree is higher than a preset threshold, decreasing the combined weight using an incentive variable weighting function according to the variable weighting mechanism to obtain the arbitrary weight coefficient; when the arbitrary degradation degree is at a preset threshold, using the combined weight as the arbitrary weight coefficient according to the variable weighting mechanism.

[0027] In this embodiment, an arbitrary weighting coefficient for arbitrary degradation degree is obtained. First, multiple user nodes are sorted according to their user node numbers. Then, the new energy benefit index, voltage quality index, and electricity load benefit index in the user load importance assessment system are sorted according to their index numbers. An arbitrary user node is one of the sorted user nodes, an arbitrary index is one of the sorted indices, and an arbitrary degradation degree is the degradation degree obtained by any user node under any index.

[0028] First, the subjective weights of arbitrary indicators are determined based on the improved analytic hierarchy process (AHP). In this process, new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators are compared pairwise, and judgment matrices are filled using a 1-9 scale. The elements in the judgment matrix comparing the same indicator to itself are set to 1. After determining the elements of the judgment matrix for one indicator relative to another, the elements in the reverse position are taken as their reciprocals. After completing the judgment matrix, the elements in each column are summed, and each element in each column is divided by the sum of its elements to obtain a normalized judgment matrix. Then, the average of the elements in each row of the normalized judgment matrix is ​​calculated to obtain the initial subjective weights of each indicator. Subsequently, the maximum eigenvalue of the judgment matrix is ​​calculated, and the consistency ratio is calculated based on the maximum eigenvalue, the number of indicators, and the random consistency index. When the consistency ratio is less than 0.1, the initial subjective weights are determined as the subjective weights of arbitrary indicators; when the consistency ratio is greater than or equal to 0.1, the elements in the judgment matrix that do not meet the consistency requirements are readjusted, and the normalization process, row average calculation, and consistency ratio calculation are repeated until the consistency ratio is less than 0.1.

[0029] Subsequently, the objective weights of any indicator are determined based on the improved indicator correlation weighting method. The indicator degradation degrees of multiple user nodes under the new energy benefit indicator, voltage quality indicator, and electricity load benefit indicator are filled into the degradation degree data matrix according to the user node number and indicator number. For the same indicator, the indicator degradation degree of all user nodes under that indicator is read, and the maximum and minimum degradation degrees are determined. The indicator degradation degree of each user node under that indicator is standardized using the standardization function: Standardization result = (Current degradation degree - Minimum degradation degree) ÷ (Maximum degradation degree - Minimum degradation degree). When the maximum degradation degree equals the minimum degradation degree, the standardized result for that indicator is set to 0.

[0030] Next, the objective weight of any indicator is calculated based on the standardized results. First, the standard deviation of any indicator across multiple user nodes is calculated, then the correlation coefficient between the arbitrary indicator and other indicators is calculated. For any indicator and any other indicator, the correlation coefficient is first calculated based on the standardized results corresponding to multiple user nodes, then the absolute value of the correlation coefficient is taken, and the correlation difference value is calculated according to the formula: correlation difference value = 1 - absolute value of correlation coefficient. The correlation difference values ​​corresponding to any indicator are summed, and then multiplied by the standard deviation of the arbitrary indicator to obtain the information content of the arbitrary indicator. The information content calculation function is: Information content = Standard deviation × Sum of correlation difference values. The information content of all indicators is summed, and the objective weight of the arbitrary indicator is calculated according to the formula: Objective weight = Information content of arbitrary indicator ÷ Sum of information content of all indicators.

[0031] Then, the subjective and objective weights are fused using the principle of least discriminative information to obtain the combined weight. Specifically, for any indicator, its subjective and objective weights are read, their product is calculated, and the square root of this product is taken. The same calculation is performed on all indicators, resulting in the sum of the square root results for all indicators. Subsequently, the combined weight of any indicator is calculated according to the formula: Combined Weight = Square root of the product of subjective and objective weights of any indicator ÷ Sum of the square roots of the products of subjective and objective weights of all indicators. Through this calculation process, the combined weight corresponding to any indicator is obtained.

[0032] When introducing a variable weighting mechanism to adjust the combined weights, an arbitrary degree of degradation is compared with a preset threshold, and pre-set penalty and incentive adjustment coefficients are read, both of which are greater than 0. When the arbitrary degree of degradation is lower than the preset threshold, a penalty adjustment coefficient is calculated using a penalty-type variable weighting function: Penalty Adjustment Coefficient = 1 + Penalty Adjustment Coefficient × (Preset Threshold - Arbitrary Degradation) ÷ Preset Threshold. The combined weights are then multiplied by the penalty adjustment coefficient to increase the combined weights, resulting in the variable weighting adjustment. When the arbitrary degree of degradation is higher than the preset threshold, an incentive adjustment coefficient is calculated using an incentive-type variable weighting function: Incentive Adjustment Coefficient = 1 ÷ [1 + Incentive Adjustment Coefficient × (Arbitrary Degradation - Preset Threshold) ÷ Preset Threshold]. The combined weights are then multiplied by the incentive adjustment coefficient to decrease the combined weights, resulting in the variable weighting adjustment. When the arbitrary degree of degradation is at the preset threshold, the combined weights are not amplified or reduced, and the combined weights are used as the variable weighting adjustment result.

[0033] Finally, the weighted adjustment results for the same arbitrary user node under each indicator are normalized. This process involves first summing the weighted adjustment results for the arbitrary user node under the new energy benefit indicator, voltage quality indicator, and electricity load benefit indicator to obtain a total adjustment result. Then, the arbitrary weight coefficient corresponding to any degree of degradation is calculated according to the formula: Arbitrary weight coefficient = Weighted adjustment result under any indicator ÷ Total weighted adjustment results for the same arbitrary user node under all indicators. This process is repeated for each user node and each indicator to obtain the arbitrary weight coefficients corresponding to the degree of degradation for each indicator across multiple user nodes.

[0034] Step S300: Introduce random fluctuation parameters to fit the power demand fluctuation curves of the multiple user nodes to obtain the demand response feature matrix.

[0035] In this embodiment, when fitting the electricity demand fluctuation curve of multiple user nodes using random fluctuation parameters, firstly, arbitrary historical electricity consumption data of any user node is obtained, and arbitrary electricity load fluctuation characteristics are extracted from the arbitrary historical electricity consumption data; secondly, random fluctuation parameters are introduced to characterize the uncertain fluctuations in the arbitrary electricity load fluctuation characteristics, and curve fitting is performed on the correspondence between electricity price changes and load changes based on the random fluctuation parameters to obtain the arbitrary electricity demand fluctuation curve of any user node; then, the user demand response value corresponding to the target time period in the arbitrary electricity demand fluctuation curve is extracted; finally, according to the correspondence between multiple user nodes and the target time period, the user demand response value of each user node in the target time period is used as the diagonal element of the matrix to construct the demand response feature matrix.

[0036] Furthermore, in the method provided in the application embodiment, the method introduces random fluctuation parameters to fit the power demand fluctuation curves of the multiple user nodes to obtain a demand response feature matrix, and further includes: Obtain any historical electricity consumption data of any user node, and introduce the random fluctuation parameter to characterize the arbitrary electricity load fluctuation characteristics in the arbitrary historical electricity consumption data, and fit an arbitrary electricity demand fluctuation curve, which represents the nonlinear response relationship between electricity price changes and load changes; extract the user demand response value of the target period in the arbitrary electricity demand fluctuation curve, and use the user demand response value of the target period as the diagonal element of the matrix to construct the demand response feature matrix.

[0037] In this embodiment, firstly, arbitrary historical electricity consumption data of any user node is obtained. Based on the distribution network topology and user node number, an arbitrary user node is determined from multiple user nodes. Then, the load power, electricity consumption, time-of-use price, and collection time of any user node within a set historical interval are read from the electricity information collection system, smart meter, or distribution automation system, and sorted according to the collection time. Subsequently, the arbitrary historical electricity consumption data is processed, missing data is filled in using the average of adjacent collection times, and abnormal data changes are replaced with adjacent valid data to obtain continuous arbitrary historical electricity consumption data corresponding to the collection time.

[0038] Next, a random fluctuation parameter is introduced to characterize the load fluctuation characteristics of any historical electricity consumption data. The load change rate and electricity price change rate between adjacent data collection times are calculated. The load change rate is calculated as: load change rate = difference between the load power at the current data collection time and the load power at the previous data collection time ÷ load power at the previous data collection time; the electricity price change rate is calculated as: time-of-use price at the current data collection time = difference between the time-of-use price at the previous data collection time ÷ time-of-use price at the previous data collection time. Then, the historical average load change rate for the same data collection period is calculated, and the difference between the actual load change rate and the historical average load change rate is used as the random fluctuation parameter, ensuring that the load change rate, electricity price change rate, and random fluctuation parameter are all dimensionless data. Subsequently, the electricity price change... Using the load change rate as the input and the load change rate as the output, a quadratic nonlinear fitting relationship is established. The quadratic nonlinear fitting relationship is: Load change rate = First fitting coefficient × Square of electricity price change rate + Second fitting coefficient × Electricity price change rate + Third fitting coefficient + Random fluctuation parameter, where the first, second, and third fitting coefficients are dimensionless fitting coefficients. The least squares method is used to minimize the sum of squared errors between the actual load change rate and the fitted load change rate at each data collection time, and the first, second, and third fitting coefficients are obtained. Thus, an arbitrary electricity demand fluctuation curve is fitted, which represents the nonlinear response relationship between electricity price changes and load changes.

[0039] Subsequently, user demand response values ​​for the target time period are extracted from any electricity demand fluctuation curve. First, the start and end times of the target time period are determined, and the time-of-use electricity price within the target time period is read. Then, the electricity price change rate for the target time period is calculated and input into any electricity demand fluctuation curve to obtain the load change rate corresponding to the target time period. When the target time period includes multiple data collection times, the average load change rate corresponding to each data collection time is calculated to obtain the user demand response value for the target time period. The user demand response values ​​for multiple user nodes in the target time period are calculated sequentially in the same way, and each user demand response value is filled into the diagonal position of the matrix according to the user node number, while the off-diagonal positions are set to 0. Thus, the user demand response values ​​for the target time period are used as the diagonal elements of the matrix to construct the demand response feature matrix.

[0040] Step S400: With the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefits, a joint optimization model is constructed by combining the demand response feature matrix and the load importance coefficient.

[0041] In this embodiment, when constructing a joint optimization model with the joint objectives of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefits, the following steps are taken: First, minimizing the operating cost of the distribution network is taken as a sub-objective, and the flexible load response capability of each user node is determined by combining the demand response feature matrix, thereby constructing a flexible load optimization regulation function. Then, the arbitrary voltage quality improvement score of any user node is calculated, and the arbitrary voltage quality improvement score is multiplied by the arbitrary coefficient corresponding to any user node to obtain an arbitrary voltage regulation benefit value. Next, the arbitrary voltage regulation benefit values ​​corresponding to multiple user nodes are summed to obtain a voltage regulation benefit function. Finally, the flexible load optimization regulation function and the voltage regulation benefit function are weighted and fused, so that the operating cost constraint and the voltage regulation benefit constraint jointly participate in the optimization calculation, forming a joint optimization model that combines the demand response feature matrix and the load importance coefficient.

[0042] Furthermore, the method provided in the application embodiment, with the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefits, and constructing a joint optimization model by combining the demand response feature matrix and the load importance coefficient, further includes: With minimizing the operating cost of the distribution network as the sub-objective, a flexible load optimization control function is constructed in conjunction with the demand response feature matrix; the arbitrary voltage quality improvement score of any user node is calculated; the arbitrary voltage quality improvement score is multiplied by the arbitrary coefficient corresponding to the arbitrary user node to obtain an arbitrary voltage control benefit value; the arbitrary voltage control benefit values ​​are summed to obtain a voltage control benefit function; the flexible load optimization control function and the voltage control benefit function are weighted and fused to form the joint optimization model.

[0043] In this embodiment, with the minimization of distribution network operating costs as a sub-objective, when constructing a flexible load optimization control function based on the demand response feature matrix, the following steps are taken: First, multiple user nodes participating in the control within the target time period are determined, and the baseline load power, time-of-use price, adjustable load capacity, load control compensation unit price, distributed generation output, and line loss power of each user node are read. Then, the diagonal elements corresponding to each user node are extracted from the demand response feature matrix, and these diagonal elements are used as user demand response values. The flexible load adjustment amount is calculated for each user node within the target time period according to the formula: Flexible load adjustment amount = Baseline load power × User demand response value × Price change rate. Finally, the flexible load adjustment amount is superimposed on the baseline load power to obtain the controlled node load power. The controlled node load power is determined within the preset load power range, provided that the flexible load adjustment amount does not exceed the adjustable load capacity. Under the constraints within the specified range, the electricity purchase cost, network loss cost, and flexible load compensation cost are calculated separately. The electricity purchase cost is calculated as: (Time-of-use price for each target time period × Distribution network power purchase × Time period length). The distribution network power purchase is obtained by adding the sum of the load power of each node after regulation and the line loss power, then subtracting the output of distributed power sources. The network loss cost is calculated as: (Network loss price for each target time period × Line loss power × Time period length). The line loss power is calculated based on the product of the square of the line current and the line resistance. The flexible load compensation cost is calculated as: (Load regulation compensation unit price for each user node in each target time period × Absolute value of flexible load regulation × Time period length). Finally, the flexible load optimization regulation function is expressed as: (Electricity purchase cost + Network loss cost + Flexible load compensation cost). The minimum value of the flexible load optimization regulation function is taken as the sub-objective for minimizing the distribution network operating cost.

[0044] Next, the arbitrary voltage quality improvement score for any user node is calculated. In this process, firstly, for any user node, the first positive voltage deviation and the second positive voltage deviation before and after voltage regulation are obtained, and the degree of improvement of the first positive deviation is calculated based on the changes in the first and second positive voltage deviations. Secondly, the first negative voltage deviation and the second negative voltage deviation before and after voltage regulation are obtained, and the degree of improvement of the first negative deviation is calculated based on the changes in the first and second negative voltage deviations. Then, the first voltage fluctuation value and the second voltage fluctuation value before and after voltage regulation are obtained, and the degree of first fluctuation suppression is calculated based on the changes in the first and second voltage fluctuation values. Finally, the degree of improvement of the first positive deviation, the degree of improvement of the first negative deviation, and the degree of first fluctuation suppression are weighted and summed to obtain the arbitrary voltage quality improvement score for any user node.

[0045] Then, the arbitrary voltage quality improvement score is multiplied by an arbitrary coefficient corresponding to any user node to obtain an arbitrary voltage regulation benefit value. Specifically, after obtaining the arbitrary voltage quality improvement score for any user node, the arbitrary coefficient corresponding to that user node is read. The arbitrary coefficient is the coefficient obtained from the aforementioned weighted calculation used to characterize the load importance of that user node. Then, the arbitrary voltage quality improvement score is multiplied by the arbitrary coefficient to make the voltage quality improvement result correspond to the load importance of the user node, thus obtaining the arbitrary voltage regulation benefit value for any user node.

[0046] Then, the arbitrary voltage regulation benefit values ​​are summed, and the arbitrary voltage regulation benefit values ​​corresponding to multiple user nodes are obtained sequentially according to the user node number. The arbitrary voltage regulation benefit values ​​are then accumulated to obtain a voltage regulation benefit function covering multiple user nodes. The voltage regulation benefit function is used to represent the overall voltage regulation benefit formed by the improvement of voltage quality of each user node in the distribution network during the target regulation process.

[0047] Finally, the flexible load optimization control function and the voltage regulation benefit function are weighted and fused. In this process, firstly, the stage of load fluctuation in the distribution network is identified based on the real-time operating status of the distribution network. In the initial stage of load fluctuation, the first weight coefficient of the flexible load optimization control function is set greater than the second weight coefficient of the voltage regulation benefit function, giving the sub-objective of minimizing distribution network operating costs a higher weight in the joint optimization model. After the distribution network enters a steady state, the first weight coefficient is gradually reduced while the second weight coefficient is increased, increasing the proportion of the voltage regulation benefit function in the joint optimization model. Simultaneously, the adjustment rate of the first and second weight coefficients is determined based on the real-time detected load fluctuation rate, and the adjustment rate is negatively correlated with the load fluctuation rate. This completes the dynamic weighted fusion of the flexible load optimization control function and the voltage regulation benefit function, forming the joint optimization model.

[0048] Furthermore, in the method provided in the application embodiments, calculating the arbitrary voltage quality improvement score of the arbitrary user node further includes: For any user node, obtain the first positive voltage deviation and the second positive voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first positive deviation; for any user node, obtain the first negative voltage deviation and the second negative voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first negative deviation; for any user node, obtain the first voltage fluctuation value and the second voltage fluctuation value before and after voltage regulation, and calculate the degree of first fluctuation suppression; and perform a weighted summation of the degree of improvement of the first positive deviation, the degree of improvement of the first negative deviation, and the degree of first fluctuation suppression to obtain the arbitrary voltage quality improvement score.

[0049] In this embodiment, when calculating the voltage quality improvement score for any user node, the user node is first determined based on its user node number, and the node voltage sampling values ​​before and after voltage regulation are read within the same statistical time period. The same statistical time period is divided according to a preset sampling interval, such as 15 minutes or 1 hour, ensuring that the number of sampling points before and after voltage regulation is consistent. Subsequently, the rated voltage, the preset upper voltage limit, and the preset lower voltage limit are read, and the node voltage sampling values ​​before and after voltage regulation are compared with the rated voltage, the preset upper voltage limit, and the preset lower voltage limit, respectively.

[0050] For any user node, the first positive voltage deviation and the second positive voltage deviation before and after voltage regulation are obtained. Specifically, it is determined whether the voltage sample value of each node before voltage regulation is higher than the preset voltage upper limit. If it is higher than the preset voltage upper limit, the difference between the voltage sample value of that node and the preset voltage upper limit is calculated, and the average of the differences within the same statistical period is taken as the first positive voltage deviation; if it is not higher than the preset voltage upper limit, the corresponding difference is set to 0. In the same way, it is determined whether the voltage sample value of each node after voltage regulation is higher than the preset voltage upper limit, and the average of the differences within the same statistical period is taken as the second positive voltage deviation. Then, the degree of improvement of the first positive deviation is calculated, where the degree of improvement of the first positive deviation = the difference between the first positive voltage deviation and the second positive voltage deviation ÷ the first positive voltage deviation; when the first positive voltage deviation is 0, the degree of improvement of the first positive deviation is set to 0; when the calculation result is less than 0, the degree of improvement of the first positive deviation is set to 0.

[0051] For any user node, the first and second negative voltage deviations before and after voltage regulation are obtained. Specifically, it is determined whether the voltage sample value of each node before voltage regulation is lower than a preset lower voltage limit. If it is lower than the preset lower voltage limit, the difference between the preset lower voltage limit and the voltage sample value of that node is calculated, and the average of the differences within the same statistical period is taken as the first negative voltage deviation; if it is not lower than the preset lower voltage limit, the corresponding difference is set to 0. In the same way, it is determined whether the voltage sample value of each node after voltage regulation is lower than the preset lower voltage limit, and the average of the differences within the same statistical period is taken as the second negative voltage deviation. Then, the degree of improvement of the first negative deviation is calculated as follows: degree of improvement of the first negative deviation = difference between the first and second negative voltage deviations ÷ first negative voltage deviation; when the first negative voltage deviation is 0, the degree of improvement of the first negative deviation is set to 0; when the calculation result is less than 0, the degree of improvement of the first negative deviation is set to 0.

[0052] Subsequently, for any user node, the first voltage fluctuation value and the second voltage fluctuation value before and after voltage regulation are obtained. During this process, within the same statistical time period, the maximum and minimum values ​​of the node's voltage sampling values ​​before voltage regulation are read, and the difference between the maximum and minimum values ​​is taken as the first voltage fluctuation value. Then, the maximum and minimum values ​​of the node's voltage sampling values ​​after voltage regulation are read, and the difference between the maximum and minimum values ​​is taken as the second voltage fluctuation value. The degree of first fluctuation suppression is then calculated as follows: First fluctuation suppression degree = Difference between the first and second voltage fluctuation values ​​÷ First voltage fluctuation value. When the first voltage fluctuation value is 0, the degree of first fluctuation suppression is set to 0; when the calculation result is less than 0, the degree of first fluctuation suppression is set to 0.

[0053] Finally, the improvement levels of the first positive deviation, the first negative deviation, and the first fluctuation suppression are weighted and summed. Pre-set weights for positive deviation, negative deviation, and fluctuation suppression, ensuring their sum is 1. Then, calculate the arbitrary voltage quality improvement score as follows: Positive deviation weight × First positive deviation improvement level + Negative deviation weight × First negative deviation improvement level + Fluctuation suppression weight × First fluctuation suppression level. Process multiple user nodes sequentially using the above procedure to obtain the arbitrary voltage quality improvement score for each user node.

[0054] Furthermore, in the method provided in the application embodiments, the method of weightedly fusing the flexible load optimization control function and the voltage control benefit function to form the joint optimization model further includes: In the initial stage of load fluctuation in the distribution network, the first weighting coefficient of the flexible load optimization control function is set to be greater than the second weighting coefficient of the voltage regulation benefit function; after the distribution network enters a steady state, the first weighting coefficient is gradually reduced and the second weighting coefficient is increased; the adjustment rate of the first weighting coefficient and the second weighting coefficient is negatively correlated with the real-time detected load fluctuation rate.

[0055] In this embodiment, during the initial stage of distribution network load fluctuation, the real-time total load of the distribution network is first obtained according to a preset sampling period, and the real-time detected load fluctuation rate is calculated. The load fluctuation rate is calculated as the absolute value of the difference between the total load at the current sampling time and the total load at the previous sampling time ÷ the total load at the previous sampling time. When the load fluctuation rate is greater than the preset load fluctuation threshold, it is determined that the distribution network is in the initial stage of load fluctuation. The first weight coefficient of the flexible load optimization control function is set to be greater than the second weight coefficient of the voltage regulation benefit function, and the sum of the first weight coefficient and the second weight coefficient is set to 1. For example, the first weight coefficient is set to 0.7 and the second weight coefficient is set to 0.3. Then, the weighted fusion is performed according to the joint optimization objective value = first weight coefficient × flexible load optimization control function - second weight coefficient × voltage regulation benefit function, so that the minimization of distribution network operating cost and the maximization of voltage regulation benefit are jointly entered into the joint optimization model.

[0056] After the distribution network enters a steady state, the first weighting coefficient is gradually reduced while the second weighting coefficient is increased. Specifically, when the load fluctuation rate detected in real time is less than or equal to the preset load fluctuation threshold for multiple consecutive sampling periods, the distribution network is determined to have entered a steady state, and the first and second weighting coefficients are updated according to a preset adjustment period. During each update, the weight adjustment step size is subtracted from the current first weighting coefficient, and the same weight adjustment step size is added to the current second weighting coefficient, so that the first weighting coefficient gradually decreases and the second weighting coefficient gradually increases, while keeping the sum of the first and second weighting coefficients at 1. When the first weighting coefficient decreases to the preset first steady-state weight and the second weighting coefficient increases to the preset second steady-state weight, the weight update stops, and the current first and second weighting coefficients are used to form a joint optimization model under steady state.

[0057] The adjustment rates of the first and second weighting coefficients are negatively correlated with the real-time detected load fluctuation rate. Specifically, the weight adjustment step size is calculated based on the real-time detected load fluctuation rate: Weight adjustment step size = Preset maximum adjustment step size ÷ (1 + Preset adjustment coefficient × Real-time detected load fluctuation rate), where the preset maximum adjustment step size is the upper limit of a single weight adjustment, and the preset adjustment coefficient is a preset parameter greater than 0. When the real-time detected load fluctuation rate increases, the weight adjustment step size decreases, and the adjustment rates of the first and second weighting coefficients decrease; when the real-time detected load fluctuation rate decreases, the weight adjustment step size increases, and the adjustment rates of the first and second weighting coefficients increase. This completes the dynamic weighted fusion of the flexible load optimization control function and the voltage control benefit function, forming a joint optimization model.

[0058] Step S500: Solve the joint optimization model to obtain a comprehensive control strategy, and combine it with preset priorities to perform orderly control of controllable equipment in the distribution network.

[0059] Furthermore, in the method provided in the application embodiments, the preset priority includes: The first priority is to prioritize the reactive power output of distributed power sources and the tap position of on-load tap-changing transformers for user nodes with the highest load importance coefficient; the second priority is to provide voltage support for user nodes with medium load importance coefficients by switching parallel capacitor banks; the third priority is to adjust the operating power of air conditioning equipment or electric vehicle charging piles for user nodes with low load importance coefficients by sending flexible load control commands.

[0060] In this embodiment of the application, when solving the joint optimization model, the adjustable range of reactive power output of the distributed power source, the tap position range of the on-load tap-changing transformer, the on-state or off-state of the parallel capacitor bank, the operating power range of the air conditioning equipment, and the operating power range of the electric vehicle charging pile are read first, and multiple sets of optional control schemes are generated within the above ranges; each set of optional control schemes includes the reactive power output value of the distributed power source, the tap position of the on-load tap-changing transformer, the on-state of the parallel capacitor bank, the operating power of the air conditioning equipment, and the operating power of the electric vehicle charging pile. For any set of optional control schemes, firstly, the node load power after control is calculated based on the operating power of the air conditioning equipment and the operating power of the electric vehicle charging piles. Then, power flow calculation is performed by combining the reactive power output of distributed power sources, the tap position of the on-load tap-changing transformer, and the switching status of the parallel capacitor bank to obtain the node voltage, line current, and line loss power after control. Subsequently, the node load power, line loss power, time-of-use electricity price, and load control compensation unit price after control are substituted into the flexible load optimization control function to obtain the distribution network operating cost. Then, the node voltage before and after control, the first voltage positive deviation, the second voltage positive deviation, the first voltage negative deviation, the second voltage negative deviation, the first voltage fluctuation value, the second voltage fluctuation value, and the load importance coefficient are substituted into the voltage control benefit function to obtain the voltage control benefit. Finally, the joint optimization target value corresponding to the optional control scheme is calculated according to the formula: Joint optimization target value = First weight coefficient × Flexible load optimization control function - Second weight coefficient × Voltage control benefit function. If the optional control scheme causes the node voltage to exceed the preset upper and lower voltage limits, the line current to exceed the upper limit of the line current, the reactive power output of the distributed power source to exceed the adjustable range of reactive power output, the tap position of the on-load tap changer to exceed the range of tap positions, or the operating power of the air conditioning equipment or the operating power of the electric vehicle charging pile to exceed the operating power range, then the optional control scheme will be eliminated, or a preset penalty value will be added to the joint optimization target value corresponding to the optional control scheme. The above calculation will be repeated for multiple optional control schemes, and the optional control scheme that has not been eliminated and has the smallest joint optimization target value will be selected as the comprehensive control strategy. The comprehensive control strategy includes the reactive power output of the distributed power source, the tap position of the on-load tap changer, the switching status of the parallel capacitor bank, the operating power of the air conditioning equipment, and the operating power of the electric vehicle charging pile.

[0061] The system performs orderly control over controllable equipment in the distribution network based on preset priorities. First, multiple user nodes are sorted by load importance coefficient from highest to lowest, with a first threshold and a second threshold set, where the first threshold is greater than the second threshold. User nodes with a load importance coefficient equal to the maximum value are assigned to the first priority; those with a coefficient less than the first threshold but greater than or equal to the second threshold are assigned to the second priority; and those with a coefficient less than the second threshold are assigned to the third priority. For user nodes in the first priority category, the system prioritizes adjusting the reactive power output of distributed generation sources and the tap position of on-load tap-changing transformers according to the comprehensive control strategy. This involves sending reactive power output adjustment commands to the corresponding distributed generation sources and tap position adjustment commands to the on-load tap-changing transformers. For user nodes in the second priority category, the system provides voltage support by switching parallel capacitor banks according to the comprehensive control strategy. This involves sending commands to connect or disconnect parallel capacitor banks. For user nodes in the third priority category, the system adjusts the operating power of their air conditioning equipment or electric vehicle charging piles by sending flexible load control commands according to the comprehensive control strategy. This involves sending operating power adjustment commands to the corresponding air conditioning equipment or charging power adjustment commands to the corresponding electric vehicle charging piles.

[0062] After completing the regulation corresponding to the first, second, and third priorities, the system collects data on node voltage, line current, reactive power output of distributed generation sources, tap position of on-load tap-changing transformers, switching status of parallel capacitor banks, operating power of air conditioning equipment, and operating power of electric vehicle charging piles in the distribution network. The collected results are then compared with the corresponding values ​​in the integrated regulation strategy. If the collected results do not match the corresponding values ​​in the integrated regulation strategy, the corresponding regulation commands are issued in the order of first, second, and third priorities. When the collected results match the corresponding values ​​in the integrated regulation strategy, the orderly regulation of controllable equipment in the distribution network is completed.

[0063] In summary, the embodiments of this application have at least the following technical effects: This application acquires historical electricity consumption data and real-time operation data of multiple user nodes in a distribution network; it retrieves a user load importance assessment system to collaboratively analyze the historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and weights them to obtain the load importance coefficient of each user node; it introduces random fluctuation parameters to fit the power demand fluctuation curves of the multiple user nodes to obtain a demand response feature matrix; it constructs a joint optimization model with the demand response feature matrix and the load importance coefficient as the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefit; it solves the joint optimization model to obtain a comprehensive regulation strategy, and performs orderly regulation of controllable equipment in the distribution network in combination with preset priorities. This invention solves the technical problem of insufficient regulation accuracy of distribution networks under load fluctuations in the prior art. By constructing a joint optimization model and generating a comprehensive regulation strategy, it achieves the technical effect of improving the accuracy of distribution network operation status regulation and enhancing voltage regulation effect.

[0064] Example 2 is based on the same inventive concept as the distribution network operation status control method for load fluctuations in the aforementioned examples, such as... Figure 2 As shown, this application provides a distribution network operation status control system oriented towards load fluctuations. The system and method embodiments in this application are based on the same inventive concept. The system includes: The data acquisition module 11 is used to acquire historical electricity consumption data and real-time operation data of multiple user nodes in the distribution network; the collaborative analysis module 12 is used to retrieve the user load importance assessment system to perform collaborative analysis on the historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and to obtain the load importance coefficient of each user node among the multiple user nodes by weighting; the fitting module 13 is used to introduce random fluctuation parameters to fit the power demand fluctuation curve of the multiple user nodes to obtain the demand response feature matrix; the model construction module 14 is used to construct a joint optimization model with the joint objective of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefit, combined with the demand response feature matrix and the load importance coefficient; the regulation module 15 is used to solve the joint optimization model to obtain a comprehensive regulation strategy, and to perform orderly regulation of controllable equipment in the distribution network in combination with preset priorities.

[0065] Furthermore, the system is also used to implement the following functions: The user load importance assessment system includes new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators.

[0066] Furthermore, the system is also used to implement the following functions: Extract any indicator from the user load importance assessment system; extract any user node from the multiple user nodes, and obtain any user parameter of the arbitrary user node based on the arbitrary indicator; compare the arbitrary user parameter with any preset threshold of the arbitrary indicator to obtain an arbitrary degree of degradation; obtain an arbitrary weight coefficient of the arbitrary degree of degradation, and calculate the arbitrary coefficient of the arbitrary user node by weighting, and form the load importance coefficient.

[0067] Furthermore, the system is also used to implement the following functions: The subjective weights of the arbitrary indicators are determined based on an improved analytic hierarchy process (AHP); the objective weights of the arbitrary indicators are determined based on an improved indicator correlation weighting method; the subjective weights and objective weights are fused using the principle of minimum discriminative information to obtain a combined weight; a variable weighting mechanism is introduced to modify the combined weight to obtain the modified arbitrary weight coefficient; wherein, the variable weighting mechanism to modify the combined weight includes: when the arbitrary degradation degree is lower than a preset threshold, increasing the combined weight using a penalized variable weighting function according to the variable weighting mechanism to obtain the arbitrary weight coefficient; when the arbitrary degradation degree is higher than a preset threshold, decreasing the combined weight using an incentive variable weighting function according to the variable weighting mechanism to obtain the arbitrary weight coefficient; when the arbitrary degradation degree is at a preset threshold, using the combined weight as the arbitrary weight coefficient according to the variable weighting mechanism.

[0068] Furthermore, the system is also used to implement the following functions: Obtain any historical electricity consumption data of any user node, and introduce the random fluctuation parameter to characterize the arbitrary electricity load fluctuation characteristics in the arbitrary historical electricity consumption data, and fit an arbitrary electricity demand fluctuation curve, which represents the nonlinear response relationship between electricity price changes and load changes; extract the user demand response value of the target period in the arbitrary electricity demand fluctuation curve, and use the user demand response value of the target period as the diagonal element of the matrix to construct the demand response feature matrix.

[0069] Furthermore, the system is also used to implement the following functions: With minimizing the operating cost of the distribution network as the sub-objective, a flexible load optimization control function is constructed in conjunction with the demand response feature matrix; the arbitrary voltage quality improvement score of any user node is calculated; the arbitrary voltage quality improvement score is multiplied by the arbitrary coefficient corresponding to the arbitrary user node to obtain an arbitrary voltage control benefit value; the arbitrary voltage control benefit values ​​are summed to obtain a voltage control benefit function; the flexible load optimization control function and the voltage control benefit function are weighted and fused to form the joint optimization model.

[0070] Furthermore, the system is also used to implement the following functions: For any user node, obtain the first positive voltage deviation and the second positive voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first positive deviation; for any user node, obtain the first negative voltage deviation and the second negative voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first negative deviation; for any user node, obtain the first voltage fluctuation value and the second voltage fluctuation value before and after voltage regulation, and calculate the degree of first fluctuation suppression; and perform a weighted summation of the degree of improvement of the first positive deviation, the degree of improvement of the first negative deviation, and the degree of first fluctuation suppression to obtain the arbitrary voltage quality improvement score.

[0071] Furthermore, the system is also used to implement the following functions: In the initial stage of load fluctuation in the distribution network, the first weighting coefficient of the flexible load optimization control function is set to be greater than the second weighting coefficient of the voltage regulation benefit function; after the distribution network enters a steady state, the first weighting coefficient is gradually reduced and the second weighting coefficient is increased; the adjustment rate of the first weighting coefficient and the second weighting coefficient is negatively correlated with the real-time detected load fluctuation rate.

[0072] Furthermore, the system is also used to implement the following functions: The first priority is to prioritize the reactive power output of distributed power sources and the tap position of on-load tap-changing transformers for user nodes with the highest load importance coefficient; the second priority is to provide voltage support for user nodes with medium load importance coefficients by switching parallel capacitor banks; the third priority is to adjust the operating power of air conditioning equipment or electric vehicle charging piles for user nodes with low load importance coefficients by sending flexible load control commands.

[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for regulating the operation status of a distribution network in response to load fluctuations, characterized in that, include: Acquire historical electricity consumption data and real-time operation data of multiple user nodes in the power distribution network; The user load importance assessment system is retrieved to perform collaborative analysis on historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and the load importance coefficient of each user node among the multiple user nodes is obtained by weighting. By introducing random fluctuation parameters, the power demand fluctuation curves of the multiple user nodes are fitted to obtain the demand response feature matrix; With the joint objectives of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefits, a joint optimization model is constructed by combining the demand response characteristic matrix and the load importance coefficient. The joint optimization model is solved to obtain a comprehensive control strategy, and the controllable equipment in the distribution network is controlled in an orderly manner in combination with preset priorities.

2. The distribution network operation status control method for load fluctuation as described in claim 1, characterized in that, The user load importance assessment system includes new energy benefit indicators, voltage quality indicators, and electricity load benefit indicators.

3. The distribution network operation status control method for load fluctuation as described in claim 2, characterized in that, The user load importance assessment system is used to collaboratively analyze historical electricity consumption data and real-time operational data to obtain the index degradation degree of the multiple user nodes, and then weighted to obtain the load importance coefficient of each user node among the multiple user nodes, including: Extract any indicator from the user load importance assessment system; Extract any user node from the plurality of user nodes, and obtain any user parameters of the arbitrary user node based on the arbitrary indicator; The arbitrary degree of degradation is obtained by comparing the arbitrary user parameter with any preset threshold of the arbitrary indicator; Obtain arbitrary weight coefficients for any degree of degradation, and calculate arbitrary coefficients for any user node using weighted averages, and then form the load importance coefficient.

4. The distribution network operation status control method for load fluctuation as described in claim 3, characterized in that, Obtaining the arbitrary weighting coefficient for the arbitrary degree of degradation includes: The subjective weights of the arbitrary indicators are determined based on the improved analytic hierarchy process. The objective weight of any indicator is determined based on the improved indicator correlation weighting method; The subjective weights and objective weights are fused using the principle of minimum discriminative information to obtain combined weights; A weighting mechanism is introduced to adjust the combined weights, resulting in the adjusted weight coefficients of the arbitrary weights. The method of introducing a weighting adjustment mechanism to modify the combined weights includes: When the arbitrary degradation degree is lower than the preset threshold, the combined weight is increased by a penalty-type variable weight function according to the variable weight mechanism to obtain the arbitrary weight coefficient; When the arbitrary degradation degree is higher than the preset threshold, the combined weight is reduced by an incentive-type variable weight function according to the variable weight mechanism to obtain the arbitrary weight coefficient. When the arbitrary degradation degree is at a preset threshold, the combined weight is used as the arbitrary weight coefficient according to the weighting mechanism.

5. The distribution network operation status control method for load fluctuation as described in claim 3, characterized in that, By introducing random fluctuation parameters to fit the electricity demand fluctuation curves of the multiple user nodes, a demand response feature matrix is ​​obtained, including: The system acquires arbitrary historical electricity consumption data of any user node and introduces the random fluctuation parameter to characterize the arbitrary electricity load fluctuation characteristics in the arbitrary historical electricity consumption data. It then fits an arbitrary electricity demand fluctuation curve, which characterizes the nonlinear response relationship between electricity price changes and load changes. Extract the user demand response value for the target time period from the arbitrary electricity demand fluctuation curve, and use the user demand response value for the target time period as the diagonal element of the matrix to construct the demand response feature matrix.

6. The distribution network operation status control method for load fluctuation as described in claim 5, characterized in that, With the joint objectives of minimizing the operating cost of the distribution network and maximizing the benefits of voltage regulation, a joint optimization model is constructed by combining the demand response characteristic matrix and the load importance coefficient, including: With minimizing the operating cost of the distribution network as a sub-objective, a flexible load optimization control function is constructed by combining the aforementioned demand response characteristic matrix. Calculate the voltage quality improvement score for any user node; Multiply the arbitrary voltage quality improvement score by the arbitrary coefficient corresponding to the arbitrary user node to obtain the arbitrary voltage regulation benefit value; Summing the arbitrary voltage regulation benefit values ​​yields the voltage regulation benefit function; The flexible load optimization control function and the voltage regulation benefit function are weighted and fused to form the joint optimization model.

7. The distribution network operation status control method for load fluctuation as described in claim 6, characterized in that, Calculating the voltage quality improvement score for any user node includes: For any user node, obtain the first positive voltage deviation and the second positive voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first positive deviation; For any user node, obtain the first negative voltage deviation and the second negative voltage deviation before and after voltage regulation, and calculate the degree of improvement of the first negative deviation; For any user node, obtain the first voltage fluctuation value and the second voltage fluctuation value before and after voltage regulation, and calculate the first fluctuation suppression degree. The improvement scores of the first positive deviation, the improvement scores of the first negative deviation, and the improvement scores of the first fluctuation suppression are weighted and summed to obtain the arbitrary voltage quality improvement score.

8. The distribution network operation status control method for load fluctuation as described in claim 6, characterized in that, The flexible load optimization control function and the voltage regulation benefit function are weighted and fused to form the joint optimization model, which includes: In the initial stage of load fluctuation in the distribution network, the first weighting coefficient of the flexible load optimization control function is set to be greater than the second weighting coefficient of the voltage regulation benefit function; After the distribution network enters a steady state, the first weighting coefficient is gradually reduced and the second weighting coefficient is increased. The adjustment rates of the first and second weighting coefficients are negatively correlated with the real-time detected load fluctuation rate.

9. The distribution network operation status control method for load fluctuation as described in claim 1, characterized in that, The preset priority includes: First priority: For the user node with the highest load importance coefficient, prioritize the allocation of reactive power output of distributed power sources and tap positions of on-load tap-changing transformers. The second priority is to provide voltage support for user nodes with medium load importance coefficients by switching parallel capacitor banks. The third priority is to adjust the operating power of the air conditioning equipment or electric vehicle charging piles of user nodes with low load importance coefficients by sending flexible load control commands.

10. A distribution network operation status control system oriented towards load fluctuations, characterized in that, The system is used to execute a distribution network operation status control method oriented towards load fluctuations as described in any one of claims 1-9, and the system includes: The data acquisition module is used to acquire historical electricity consumption data and real-time operation data of multiple user nodes in the power distribution network; The collaborative analysis module is used to retrieve the user load importance assessment system to perform collaborative analysis on historical electricity consumption data and real-time operation data to obtain the index degradation degree of the multiple user nodes, and to obtain the load importance coefficient of each user node among the multiple user nodes by weighting. The fitting module is used to introduce random fluctuation parameters to fit the power demand fluctuation curves of the multiple user nodes and obtain the demand response feature matrix. The model building module is used to construct a joint optimization model by combining the demand response feature matrix and the load importance coefficient, with the joint objectives of minimizing the operating cost of the distribution network and maximizing the voltage regulation benefits. The control module is used to solve the joint optimization model to obtain a comprehensive control strategy, and to perform orderly control of controllable equipment in the distribution network in combination with preset priorities.

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