A distributed energy intelligent regulation method and system based on power grid operation constraints

Abstract

The application discloses a kind of based on power grid operation constraint distributed energy intelligent regulation and control method and system, it is related to the technical field of intelligent power grid and distributed energy collaborative control, including: obtaining the power grid operation data of distributed energy access area, and generates power grid operation risk evaluation index;According to the change trend prediction result of power grid operation risk evaluation index and power grid operation state, determine dynamic constraint adjustment parameter;Dynamic constraint adjustment parameter is used to adjust the preset power grid operation constraint boundary, and generate dynamic operation constraint condition;Based on dynamic operation constraint condition, the regulation and control parameter of distributed energy is calculated, and the corresponding distributed energy equipment operation is controlled;The application solves the problem that the existing distributed energy regulation and control adopts fixed operation constraint boundary parameter, cannot adapt to the dynamic change of power grid operation state, leading to regulation and control response lag, node voltage out-of-limit risk increases and the problem of insufficient flexibility of distributed energy regulation and control.

Description

Technical Field

[0001] This invention relates to the field of smart grid and distributed energy coordinated control technology, and more specifically, to a method and system for intelligent regulation of distributed energy based on grid operation constraints. Background Technology

[0002] With the development of new energy technologies, the scale of distributed energy sources such as photovoltaic power generation, wind power generation, energy storage devices, and flexible loads connected to distribution networks is constantly increasing. Due to the characteristics of distributed energy sources, such as power output fluctuations and dispersed connection locations, large-scale integration can alter the original power flow distribution of the distribution network, easily causing problems such as node voltage fluctuations and line power flow changes. Therefore, existing technologies typically employ distributed energy regulation methods based on grid operation constraints. These methods collect distribution network operation status data, establish optimization models that include node voltage constraints, line capacity constraints, and equipment operation constraints, and coordinate the control of distributed energy devices such as photovoltaic and energy storage devices based on the optimization results to ensure the safe and stable operation of the power grid.

[0003] In existing distributed energy regulation methods based on grid operation constraints, fixed operational constraint boundary parameters are typically preset, such as setting fixed upper and lower limits for node voltage as the basis for regulation. When a node voltage is detected to exceed a preset threshold, corresponding regulation measures are initiated.

[0004] However, in actual operation, the operating status of the distribution network changes dynamically with factors such as renewable energy output and load variations. Using fixed constraint boundary parameters cannot reflect the real-time changes in node voltage relative to the constraint boundary. For example, when the node voltage gradually approaches the upper voltage limit, existing methods cannot initiate control measures in advance because the preset constraint threshold has not yet been reached. This can easily lead to a delayed control response, causing the node voltage to rise further, or even resulting in voltage exceeding the limit. Furthermore, fixed constraint parameters are difficult to adapt to changes in the grid state under different operating scenarios, reducing the flexibility and accuracy of distributed energy resource control. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for intelligent control of distributed energy based on grid operation constraints. By using a dynamic constraint adjustment method based on grid operation risk assessment and risk change trend prediction, the present invention solves the problems that existing distributed energy control uses fixed operation constraint boundary parameters, which cannot adapt to dynamic changes in grid operation status, resulting in delayed control response, increased risk of node voltage exceeding limits, and insufficient flexibility of distributed energy control.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, this application provides a method for intelligent control of distributed energy based on grid operation constraints. The method includes: acquiring grid operation data of the distributed energy access area and generating grid operation risk assessment indicators; determining dynamic constraint adjustment parameters based on the grid operation risk assessment indicators and the prediction results of the changing trend of grid operation status; adjusting the preset grid operation constraint boundary using the dynamic constraint adjustment parameters to generate dynamic operation constraint conditions; calculating the control parameters of the distributed energy based on the dynamic operation constraint conditions, and controlling the operation of the corresponding distributed energy equipment.

[0007] In one embodiment, acquiring grid operation data of the distributed energy access area includes: The node voltage, distributed energy output power, and load power of each power grid node in the region are acquired according to a preset sampling period to form an operation data sequence. Based on the operation data sequence, the node voltage offset, voltage change rate, distributed energy power fluctuation rate, and load change rate are calculated respectively to form a set of power grid operation status characteristic parameters.

[0008] In one embodiment, generating a power grid operation risk assessment index includes: normalizing the characteristic parameters of the power grid operation status to obtain single-factor risk parameters corresponding to each operation factor; calculating the correlation degree between different risk parameters based on the changing trend of each single-factor risk parameter within a preset time window, and constructing a risk parameter coupling relationship matrix based on the correlation degree; determining the coupled risk value generated by the combined effect of multiple risk parameters based on the risk parameter coupling relationship matrix; and fusing the single-factor risk parameters and the coupled risk value to generate a nonlinear comprehensive power grid operation risk assessment index.

[0009] In one embodiment, dynamic constraint adjustment parameters are determined based on power grid operation risk assessment indicators and prediction results of power grid operation status change trends. This includes: constructing a risk change sequence based on power grid operation risk assessment results over historical continuous periods, and calculating risk change trend parameters based on the risk change sequence; predicting the power grid operation risk change trend over a future preset time period based on the risk change trend parameters to obtain risk trend prediction results; fusing the current power grid operation risk assessment indicators with the risk trend prediction results to generate risk impact characteristics that simultaneously characterize the current risk status and future risk change trends; and determining the dynamic adjustment coefficients corresponding to various power grid operation constraints based on the risk impact characteristics to obtain dynamic constraint adjustment parameters.

[0010] In one embodiment, the risk trend of power grid operation within a preset time period is predicted based on risk change trend parameters to obtain a risk trend prediction result. This includes: identifying the main risk drivers based on the risk parameters corresponding to the current risk assessment indicators; acquiring historical operating status data of the risk drivers and constructing an evolution sequence; determining and correcting the evolutionary correlation between each risk driver based on the evolution sequence and the risk parameter coupling relationship matrix; predicting the future change trend of each risk driver based on the corrected evolutionary correlation and the current change state of the risk drivers; and integrating the prediction results of multiple risk drivers to obtain a comprehensive risk trend prediction result.

[0011] In one embodiment, dynamic adjustment coefficients corresponding to various grid operation constraints are determined based on risk impact characteristics to obtain dynamic constraint adjustment parameters. This includes: determining the corresponding constraint adjustment coefficients based on risk impact characteristics; determining the adjustment direction and amount of the node voltage constraint range, the distributed energy output power change constraint range, and the reactive power regulation range based on the constraint adjustment coefficients, and correcting the preset grid operation constraint parameters to obtain adjusted constraint parameters; and combining the adjusted constraint parameters to generate dynamic constraint adjustment parameters.

[0012] In one embodiment, dynamic operating constraint conditions are generated by adjusting preset grid operation constraint boundaries using dynamic constraint adjustment parameters. This includes: obtaining the corresponding constraint boundary adjustment amount based on the dynamic constraint adjustment parameters; updating the preset node voltage constraint boundary, distributed energy output power change constraint boundary, and reactive power regulation constraint boundary based on the constraint boundary adjustment amount to obtain the adjusted operating constraint boundary; performing constraint consistency verification on the adjusted operating constraint boundary; and generating dynamic operating constraint conditions that meet the requirements for safe operation based on the verification results.

[0013] In one embodiment, obtaining the corresponding constraint boundary adjustment amount based on the dynamic constraint adjustment parameters includes: obtaining the initial constraint adjustment parameters corresponding to each node based on the dynamic constraint adjustment parameters; determining the node operation sensitivity parameter to characterize the sensitivity of each node to risk changes based on the historical operation status data and current operation status data of each node; performing differentiated correction on the initial constraint adjustment parameters based on the node operation sensitivity parameters to obtain the adaptive constraint adjustment parameters corresponding to each node; and determining the constraint boundary adjustment amount corresponding to each node based on the adaptive constraint adjustment parameters and the corresponding constraint adjustment direction.

[0014] In one embodiment, calculating the regulation parameters of distributed energy based on dynamic operating constraints and controlling the operation of the corresponding distributed energy devices includes: obtaining the target operating constraint range corresponding to each distributed energy device according to the dynamic operating constraints; obtaining the current operating status data of each distributed energy device and determining the distributed energy regulation demand based on the deviation between the current operating status data and the target operating constraint range; correcting the regulation demand based on the actual regulation capability of each distributed energy device to obtain an effective regulation demand; calculating the target regulation parameters of the distributed energy based on the effective regulation demand and the dynamic operating constraints; performing constraint verification on the target regulation parameters according to the dynamic operating constraints to obtain the final control parameters that satisfy the dynamic operating constraints; and generating device control commands based on the final control parameters and sending them to the corresponding distributed energy devices.

[0015] Secondly, this application provides a distributed energy intelligent control system based on grid operation constraints. The system includes: a grid operation risk assessment module for acquiring grid operation data in the distributed energy access area and generating grid operation risk assessment indicators; a risk trend prediction and constraint adjustment module for determining dynamic constraint adjustment parameters based on the grid operation risk assessment indicators and the predicted trend of grid operation status changes; an adaptive dynamic constraint generation module for adjusting preset grid operation constraint boundaries using the dynamic constraint adjustment parameters to generate dynamic operation constraint conditions; and a distributed energy optimization control module for calculating the control parameters of the distributed energy based on the dynamic operation constraint conditions and controlling the operation of the corresponding distributed energy equipment.

[0016] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: By collecting real-time grid operation data in the distributed energy access area and combining it with grid operation risk assessment indicators and risk change trend prediction results, the grid operation status can be dynamically perceived and predicted in advance. Compared with existing distributed energy control methods based on fixed operation constraints, this method can adaptively adjust the operation constraint boundaries according to changes in grid risk, avoiding the problem of constraint adjustment lag or insufficient adaptability of control strategies due to sudden risk changes. At the same time, by using dynamic operation constraints to calculate distributed energy control parameters, the control process can comprehensively consider the current grid security status and distributed energy regulation capacity. While ensuring node voltage stability and reducing operation risks, it can improve the absorption capacity and operational flexibility of distributed energy, achieving a unity of security, real-time performance, and adaptability in the distributed energy control process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a distributed energy intelligent control method based on grid operation constraints, provided as an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of a distributed energy intelligent control system structure based on grid operation constraints, provided as an embodiment of this application.

[0020] Figure 3 The dynamic change curve of the power grid operation risk assessment index provided in the embodiments of this application is shown.

[0021] Figure 4 A comparison chart of the risk trend prediction results and the actual risk changes provided in the embodiments of this application.

[0022] Figure 5 This is a schematic diagram showing the distribution of node operation sensitivity parameters for different nodes within a distributed energy access area, as provided in an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Reference Figure 1 As shown in the diagram, the present invention provides a schematic flowchart of a distributed energy intelligent control method based on grid operation constraints, which includes the following steps: S1. Collect grid operation data in the distributed energy access area, calculate grid operation status characteristic parameters based on the grid operation data, the grid operation status characteristic parameters include node voltage offset, voltage change rate, distributed energy power fluctuation rate and load change rate, and generate grid operation risk assessment index based on the grid operation status characteristic parameters. In this embodiment, grid operation data of the distributed energy access area is collected, and grid operation status characteristic parameters are calculated based on the grid operation data, including: Acquire real-time operating data of each grid node within the distributed energy access area and form a grid operating data sequence according to a preset sampling period; The real-time operating data includes node voltage data, distributed energy output power data, and load power data for each node. The node voltage data is used to characterize the current voltage operating status of each grid node, the distributed energy output power data is used to characterize the real-time power output status of photovoltaic, wind power, or other distributed energy sources, and the load power data is used to characterize the user-side load consumption change status.

[0025] Based on the power grid operation data sequence, the node operating status corresponding to the current sampling period is determined, and the difference between the voltage data of each node and the rated voltage is calculated to obtain the node voltage offset. The specific formula for calculating the node voltage offset is as follows:

[0026] In the formula, Let be the node voltage offset of the i-th node at time t. Let be the actual node voltage of the i-th node at time t. Let be the rated voltage of the i-th node.

[0027] The node voltage change rate is calculated based on the changes in node voltage offset within adjacent sampling periods. The specific formula for calculating the node voltage change rate is as follows:

[0028] In the formula, Let be the rate of voltage change at the i-th node. This is the time interval between two adjacent sampling periods.

[0029] Calculate the power fluctuation rate of distributed energy sources based on the output power data of distributed energy sources within a continuous sampling period; The specific formula for calculating the power volatility of the distributed energy source is as follows:

[0030] In the formula, For distributed energy power fluctuations, This represents the output power of the distributed energy source at the current moment. The output power of the distributed energy source in the previous sampling period. This is a constant term used to avoid a denominator of zero.

[0031] Calculate the load change rate based on the load power data within a continuous sampling period; The specific formula for calculating the load change rate is as follows:

[0032] In the formula, For the load change rate, This represents the current load power. This represents the load power in the previous sampling period.

[0033] The node voltage offset, voltage change rate, distributed energy power fluctuation rate, and load change rate are combined to form a set of characteristic parameters of the power grid operation status. The set of power grid operation status characteristic parameters is used to characterize the voltage change status, new energy fluctuation status, and load change status of the current distributed energy access area, and serves as input data for the subsequent calculation of power grid operation risk assessment indicators.

[0034] Furthermore, based on the power grid operating status characteristic parameters, power grid operating risk assessment indicators are generated, including: Risk parameters are constructed based on the power grid operation status characteristic parameters to obtain a set of risk parameters used to characterize the degree of influence of different operating factors. Specifically, based on the node voltage offset, voltage change rate, distributed energy power fluctuation rate, and load change rate, the characteristic parameters of each operating state are normalized to obtain the corresponding risk parameters. The set of risk parameters is used to uniformly describe the risk level corresponding to different operating factors.

[0035] Calculate the correlation between different risk parameters based on the set of risk parameters, and construct a risk parameter coupling relationship matrix; Specifically, based on the historical risk parameter change sequence within a preset time window, the correlation between any two risk parameters is calculated: For the i-th and j-th risk parameters, the degree of correlation is determined based on their changing trends:

[0036] In the formula, For the i-th risk parameter With the j-th risk parameter The degree of correlation between them This is a risk parameter correlation calculation function. Its function is to calculate the degree of correlation between two risk parameters based on their changes within a preset time window. The correlation coefficient function can be used.

[0037] A risk parameter coupling matrix C is constructed based on the degree of correlation between various risk parameters. This risk parameter coupling matrix is ​​used to represent the degree of mutual influence between different operational risk factors.

[0038] The coupling risk value between risk parameters is determined based on the risk parameter coupling relationship matrix. Specifically, based on the degree of correlation in the risk parameter coupling relationship matrix, the synergistic relationship between any two risk parameters is calculated to obtain the corresponding coupling risk value; The specific formula for calculating the coupling risk value is as follows:

[0039] In the formula, The coupled risk value is the result of the combined effect of the i-th risk parameter and the j-th risk parameter.

[0040] The coupled risk value characterizes the nonlinear impact on the power grid's operating state when multiple risk factors change simultaneously.

[0041] A nonlinear comprehensive risk assessment index is generated based on the set of risk parameters and the coupled risk values. Specifically, the single-factor risk values ​​in the risk parameter set are fused with the coupled risk values ​​to obtain a comprehensive power grid operation risk evaluation index; The specific calculation formula for the comprehensive power grid operation risk assessment index is as follows:

[0042] In the formula, The indicators for assessing the operational risks of the power grid at the current moment. For single-factor risk weights, This is the risk coupling impact coefficient. This refers to the number of risk parameters.

[0043] The above calculation method enables the power grid operation risk assessment index to simultaneously include the impact of single-factor risks and the impact of multi-factor coupling effects.

[0044] Specifically, such as Figure 3 The figure shows the variation curves of power grid operation risk assessment indicators obtained from simulations based on historical operation data. The horizontal axis represents the continuous sampling time series, and the vertical axis represents the normalized power grid operation risk assessment indicators. Figure 3 It can be seen that during the period when distributed energy output power fluctuations, node voltage deviations and load changes occur together, the power grid operation risk assessment index shows a gradual upward trend. However, after the risk factors are restored, the risk assessment index gradually decreases, indicating that the constructed risk assessment index can comprehensively reflect the impact of changes in multiple operating factors on the power grid risk status.

[0045] The single-factor risk weights are determined based on the correlation between each risk parameter and abnormal power grid operation events in historical power grid operation data. Specifically, the changes in each risk parameter before the occurrence of a historical risk event are statistically analyzed, and the changes in each risk parameter are normalized using the maximum-minimum normalization method according to the proportion of the changes in all risk parameters. The risk coupling influence coefficient is determined based on the degree of actual risk deviation caused by the simultaneous changes of two risk parameters in historical operation data. Specifically, the difference between the actual risk value generated by the combined effect of the two risk factors and the risk value generated by the individual effects of the two risk factors is calculated, and the difference is normalized using the maximum-minimum normalization method.

[0046] The power grid operation risk assessment results are determined based on the comprehensive risk assessment indicators. The comprehensive risk assessment index is compared with the preset risk assessment threshold to determine the risk level corresponding to the current power grid operation status. in: When the comprehensive risk assessment index is less than the first risk threshold, it is determined to be in a low-risk state; When the comprehensive risk assessment index is greater than or equal to the first risk threshold and less than the second risk threshold, it is determined to be a risk warning state; When the comprehensive risk assessment index is greater than or equal to the second risk threshold, it is determined to be a high-risk state; The comprehensive risk assessment indicators and their corresponding risk levels are used as the results of the power grid operation risk assessment and input into the next step to calculate the dynamic operation constraint adjustment parameters.

[0047] The first risk threshold is determined statistically based on the range of changes in the power grid operation risk assessment indicators of the distributed energy access area under historical normal operation conditions, and is used to characterize the risk assessment indicator boundary corresponding to the transition of the power grid from normal operation to risk warning state; the second risk threshold is determined statistically based on the changes in the power grid operation risk assessment indicators of the distributed energy access area under historical abnormal operation conditions or before the occurrence of operation constraint risk events, and is used to characterize the risk assessment indicator boundary corresponding to the transition of the power grid from risk warning state to high-risk state.

[0048] It should be noted that, compared with existing conventional technical solutions for power grid operation risk assessment based on fixed weights or linear weighting, this embodiment extracts features from multi-source operational data such as node voltage, distributed energy output power, and load power in the distributed energy access area. It constructs a set of characteristic parameters for power grid operation status, including node voltage offset, voltage change rate, distributed energy power fluctuation rate, and load change rate. Furthermore, by establishing a risk parameter coupling relationship matrix, it analyzes the correlation between different risk factors and introduces coupled risk values ​​generated by the combined effects of risk parameters. This allows the generated power grid operation risk assessment index to not only reflect risk changes caused by a single operational factor but also characterize the nonlinear impact of multiple factors' synergistic changes on the power grid operation status. Simultaneously, by determining single-factor risk weights and risk coupling influence coefficients based on historical operational data, the risk assessment results can adapt to the operational characteristics of different distributed energy access areas, thereby improving the accuracy and reliability of power grid operation risk identification. This provides a more accurate risk basis for subsequent dynamic operational constraint adjustments and solves the problem in existing technologies where risk assessment results are limited by fixed weights and single-factor analysis, making it difficult to reflect the power grid operation risks under the combined effects of distributed energy fluctuations and load changes in a timely manner.

[0049] S2. Based on the power grid operation risk assessment indicators and the prediction results of power grid operation status change trends, determine the dynamic constraint adjustment parameters.

[0050] In this embodiment, dynamic constraint adjustment parameters are determined based on the power grid operation risk assessment indicators and the prediction results of power grid operation status change trends, including: Obtain the power grid operation risk assessment results for continuous historical periods, and construct a risk change sequence based on the power grid operation risk assessment results; Specifically, the power grid operation risk assessment indicators corresponding to multiple sampling times obtained in step S1 are arranged in chronological order to form a risk change sequence, which is used to characterize the change pattern of power grid operation risk over time.

[0051] Based on the changes in power grid operation risk assessment indicators within adjacent sampling periods, risk change trend parameters are calculated to determine whether the current power grid operation risk is rising, stable, or falling. The specific calculation formula for the risk change trend parameter is as follows:

[0052] In the formula, This is a parameter representing the trend of power grid operation risk at the current time t, used to characterize the direction and rate of change of power grid operation risk assessment indicators over time. For the tth The power grid operation risk assessment indicators corresponding to k+1 sampling times For the tth k sampling times correspond to power grid operation risk assessment indicators, where m is the number of historical sampling periods involved in risk trend calculation, i.e., the number of risk assessment indicators included within the preset time window.

[0053] When the risk change trend parameter is positive, it indicates that the power grid operation risk is increasing. When the risk change trend parameter is close to zero, it indicates that the power grid operation risk remains stable; When the risk change trend parameter is negative, it indicates that the power grid operation risk is decreasing.

[0054] Based on the risk change trend parameters and historical operating status data, the risk change trend of power grid operation within a preset time period is predicted, and the risk trend prediction result is obtained. Based on the current power grid operation risk assessment indicators and the risk trend prediction results, the impact of the current risk status and the impact of the future risk trend are determined respectively. The current risk status impact is used to characterize the current power grid operation risk level, and the future risk trend impact is used to characterize the direction and degree of change of power grid operation risk within a preset time period in the future. The impact of the current risk status and the impact of the future risk trend are fused to obtain the risk impact characteristics:

[0055] In the formula, This represents the risk impact characteristics at the current moment, used to characterize the combined impact of the current risk status and future risk trends on the need to adjust operational constraints. The indicators for assessing the operational risks of the power grid at the current moment. For predicting risk trends within a predetermined timeframe, b represents the weight of the current risk status, and b represents the weight of the future risk trend. This refers to the time frame for risk prediction.

[0056] in, The weights of the current risk status and the future risk trend are determined by statistical analysis of the impact of the current risk status and the future risk trend on the adjustment effect of operation constraints in historical power grid operation data, and are normalized according to the contribution ratio of each influencing factor.

[0057] Based on the aforementioned risk impact characteristics, dynamic adjustment coefficients corresponding to various power grid operation constraints are determined, thereby obtaining dynamic constraint adjustment parameters.

[0058] It should be noted that by comprehensively considering the current power grid operation risk assessment indicators and the predicted risk change trends within a preset time period, risk impact characteristics that reflect the current risk status and the future direction of risk evolution are generated. Based on these risk impact characteristics, preset power grid operation constraint parameters are dynamically adjusted, enabling the operation constraint parameters to be adaptively updated as the power grid risk status changes. Compared with existing distributed energy regulation methods that use fixed constraint boundaries, this scheme can avoid the problem of constraint adjustment lag caused by sudden changes in risk. It tightens operation constraints in advance before the power grid risk increases and appropriately releases adjustment space after the risk decreases, thereby improving the response capability of the distributed energy regulation process to changes in the power grid operation status. While ensuring the safe and stable operation of the power grid, it also improves the absorption capacity and operational flexibility of distributed energy.

[0059] Furthermore, based on the aforementioned risk change trend parameters and historical operating status data, the risk change trend of the power grid operation within a preset time period is predicted to obtain risk trend prediction results, including: Based on the set of risk parameters corresponding to the power grid operation risk assessment indicators, determine the risk drivers that affect the changes in power grid operation risk at the current moment; Specifically, risk parameters are sorted according to the power grid operation risk assessment index, and risk parameters whose power grid operation risk assessment index exceeds the preset impact condition are identified as risk driving factors. The risk driving factors include node voltage deviation risk factors, distributed energy power fluctuation risk factors, and load change risk factors. The risk driving factors are used to represent the main operating state factors that cause changes in power grid operation risk at the current moment.

[0060] The preset impact conditions are determined by statistical analysis of the comprehensive impact of each risk parameter in historical power grid operation data, and risk parameters with higher impact are selected as risk driving factor screening thresholds according to preset proportions.

[0061] The operational status data corresponding to the risk drivers within a continuous historical time period are obtained and arranged in the order of sampling time to form an evolution sequence of risk drivers. The risk driver evolution sequence includes: the change value of the risk parameter corresponding to the risk driver, the change direction of the risk driver, and the change rate of the risk driver. The risk driver evolution sequence is used to describe the risk evolution process formed by the change of different risk factors over time and their mutual influence.

[0062] Based on the evolution sequence of the risk drivers and the coupling matrix of the risk parameters, the evolutionary relationships among the risk drivers are determined. Specifically, based on the changes of each risk driver within a preset historical time window, the synchronous change relationship between any two risk drivers is analyzed. When two risk drivers exhibit the same direction of change within multiple consecutive historical time windows, and a change in one risk driver leads to a corresponding change in the other, an evolutionary correlation is determined between the two risk drivers. This evolutionary correlation characterizes the influence relationship between different risk drivers over time. The evolutionary correlation is then modified based on the risk parameter coupling matrix. When the coupling strength between two risk drivers is high, the influence of the corresponding evolutionary correlation is increased; when the coupling strength between two risk drivers is low, the influence of the corresponding evolutionary correlation is decreased.

[0063] For example, the coupling strength is quantified and determined based on the coupling correlation value in the risk parameter coupling matrix. The coupling correlation value is a dimensionless parameter after normalization, and its value ranges from 0 to 1. When correcting the evolutionary correlation between risk driving factors based on the coupling correlation value, the coupling correlation value is used as the correction coefficient of the evolutionary correlation. The corrected evolutionary correlation is obtained by multiplying the initial evolutionary correlation value with the corresponding coupling correlation value. When the coupling correlation value is close to 1, it indicates that there is a strong correlation between the two risk driving factors, and the influence of the initial evolutionary correlation is retained in a larger proportion. When the coupling correlation value is close to 0, it indicates that there is a weak correlation between the two risk driving factors, and the influence of the initial evolutionary correlation is retained in a smaller proportion.

[0064] Based on the evolutionary correlation of risk drivers, predict the changes in risk drivers within a predetermined time period, specifically including: Based on the risk parameter values, direction of change, and rate of change of each risk driver at the current moment, and in conjunction with the evolutionary correlation of the risk driver, the changing trend of each risk driver within a future preset time period is determined. Specifically, when a risk driver is currently in an enhancing state and has an enhancing evolutionary relationship with other risk drivers, it is predicted that the risk driver will continue to enhance in the future within a preset time period; when a risk driver is currently in a weakening state and has no enhancing evolutionary relationship with other risk drivers, it is predicted that the risk driver will decrease in the future within a preset time period.

[0065] Based on the above prediction process, the future risk status changes corresponding to each risk driver are obtained.

[0066] It should be noted that, in an enhanced state, the risk driving factor is defined as the risk parameter corresponding to the risk driving factor continuously increasing within a preset continuous sampling period, and the rate of change of the risk parameter calculated based on adjacent sampling periods being greater than a preset rate of change threshold; in a weakened state, the risk parameter corresponding to the risk driving factor continuously decreasing within a preset continuous sampling period, and the rate of change of the risk parameter being less than a negative preset rate of change threshold; the enhanced evolutionary correlation refers to a modified evolutionary correlation value between two risk driving factors being greater than a preset correlation threshold, and the risk parameters corresponding to the two risk driving factors exhibiting the same trend of change in the same direction during historical evolution, i.e., when the risk parameter corresponding to one risk driving factor increases, the risk parameter corresponding to the other risk driving factor also increases synchronously; when the modified evolutionary correlation value between two risk driving factors is less than or equal to the preset correlation threshold, or when the risk parameters corresponding to the two risk driving factors do not have a continuously synchronous increasing relationship, it is determined that there is no enhanced evolutionary correlation between the two risk driving factors.

[0067] Among them, the preset rate of change threshold can be determined by statistical analysis of the range of changes in the corresponding risk parameters under historical normal operating conditions; the preset correlation threshold can be determined by statistical analysis of the distribution range of evolutionary correlation values ​​between different risk drivers before the occurrence of historical risk events.

[0068] Based on the future risk status changes corresponding to each risk driver and the risk weights corresponding to each risk driver, the prediction results of multiple risk drivers are integrated to obtain the risk trend prediction results within a preset time period. The risk trend prediction results include: the direction of future power grid operation risk changes, the degree of future power grid operation risk changes, and risk trend prediction indicators corresponding to a preset future time period.

[0069] Furthermore, such as Figure 4 The figure shows a comparison curve between the predicted risk trend and the actual risk changes. The solid line represents the risk trend prediction index obtained based on the evolutionary relationship of risk driving factors, while the dashed line represents the changes in risk assessment indicators collected during actual operation. Figure 4 It can be seen that the risk trend prediction index and the actual risk assessment index maintain a high degree of consistency at different time stages. It can reflect the risk change trend in advance during the rapid risk rise stage and reflect the risk recovery process in sync during the risk reduction stage. This indicates that the trend prediction method based on the evolution relationship of risk driving factors can effectively predict the future direction of power grid operation risk changes and provide a basis for the generation of dynamic constraint adjustment parameters.

[0070] It should be noted that by further decomposing the power grid operation risk assessment indicators, the main risk drivers affecting power grid risk changes are identified. Furthermore, by analyzing the evolutionary relationships between different risk drivers using historical operation data, risk trend prediction no longer relies solely on the temporal changes of a single risk indicator. Instead, it comprehensively considers the risk evolution process under the combined influence of multiple factors such as distributed energy power fluctuations, node voltage shifts, and load changes. Simultaneously, by introducing a risk parameter coupling matrix to correct the evolutionary relationships between risk drivers, the ability to identify risk amplification caused by the synergistic changes of multiple risk factors is improved. This allows for more accurate prediction of the direction and extent of power grid operation risk changes within a predetermined time period, providing a more reliable basis for subsequent dynamic adjustment of power grid operation constraint parameters. This enables distributed energy regulation strategies to respond in advance to potential risk changes, improving power grid operation safety and distributed energy absorption capacity.

[0071] Furthermore, based on the aforementioned risk impact characteristics, dynamic adjustment coefficients corresponding to various power grid operation constraints are determined to obtain dynamic constraint adjustment parameters, including: The corresponding constraint adjustment coefficient is determined based on the value of the risk impact characteristics. Specifically, the risk impact characteristics corresponding to the current moment are obtained, and the values ​​of the risk impact characteristics are normalized by performing maximum and minimum value normalization to obtain normalized risk impact characteristics. Based on the value range of the normalized risk impact characteristics, the normalized risk impact characteristics are divided into multiple risk impact intervals, and the constraint adjustment coefficients corresponding to each risk impact interval are determined based on the historical power grid operation status changes corresponding to each risk impact interval. The constraint adjustment coefficients are used to characterize the degree of adjustment of preset power grid operation constraint parameters by changes in risk status, and a one-to-one correspondence is established between the constraint adjustment coefficients and the risk impact characteristics.

[0072] It should be noted that the risk impact range is divided according to the value range of risk impact characteristics in historical operating data. The occurrence of abnormal power grid operation events corresponding to different risk impact characteristic value ranges is statistically analyzed, and a mapping relationship between risk impact characteristics and constraint adjustment coefficients is established based on the correspondence between changes in risk impact characteristics and changes in power grid operation constraint requirements. The constraint adjustment coefficient is represented by a normalized value and is used to control the adjustment ratio of preset power grid operation constraint parameters.

[0073] The constraint adjustment direction and constraint adjustment amount corresponding to the preset power grid operation constraint parameters are determined based on the constraint adjustment coefficient. The process involves obtaining pre-set adjustment rules for grid operation constraint parameters, and determining the adjustment amounts for node voltage constraint ranges, distributed energy output power variation constraint ranges, and reactive power regulation ranges based on the correspondence between the constraint adjustment coefficients and the preset adjustment rules. Specifically, when the constraint adjustment coefficient is greater than a preset adjustment benchmark value, the current grid operation risk is determined to be in a state requiring increased safety margins. The allowable offset range of node voltage is adjusted to a value less than the original preset node voltage constraint range, the distributed energy output power variation range is adjusted to a value less than the original preset output power variation range, and the reactive power regulation range is adjusted to a value greater than the original preset reactive power regulation range. Conversely, when the constraint adjustment coefficient is less than or equal to the preset adjustment benchmark value, the current grid operation risk is determined to be in a state allowing increased energy utilization. The allowable offset range of node voltage is adjusted to a value greater than or equal to the original preset node voltage constraint range, the distributed energy output power variation range is adjusted to a value greater than or equal to the original preset output power variation range, and the reactive power regulation range is adjusted to a value less than or equal to the original preset reactive power regulation range.

[0074] The preset adjustment benchmark value is determined based on the statistical distribution range of risk impact characteristics under the historical normal operation of the power grid, and the average or median value of the risk impact characteristics during normal operation is used as the benchmark value for judging the constraint adjustment coefficient.

[0075] The preset power grid operation constraint parameters are corrected according to the constraint adjustment coefficient to obtain the adjusted constraint parameters; Specifically, based on the mapping relationship between the constraint adjustment coefficient and the preset power grid operation constraint parameters, the original node voltage constraint range, the distributed energy output power change constraint range, and the reactive power adjustment range are proportionally adjusted to obtain the corresponding adjusted constraint parameters; the adjusted constraint parameters include the adjusted node voltage upper limit, node voltage lower limit, maximum allowable power change of distributed energy, and maximum reactive power adjustment range.

[0076] Generate dynamic constraint adjustment parameters based on the adjusted constraint parameters; The adjusted node voltage constraint parameters, distributed energy output power change constraint parameters, and reactive power adjustment range constraint parameters are combined to form dynamic constraint adjustment parameters. The dynamic constraint adjustment parameters are used to replace the original preset power grid operation constraint parameters and serve as constraint boundaries in the subsequent distributed energy power control and reactive power regulation control processes.

[0077] S3, Based on the dynamic constraint adjustment parameters, adjust the preset power grid operation constraint boundary to generate dynamic operation constraint conditions.

[0078] In this embodiment, the preset power grid operation constraint boundary is adjusted based on the dynamic constraint adjustment parameters to generate dynamic operation constraint conditions, including: The corresponding constraint boundary adjustment amount is obtained based on the dynamic constraint adjustment parameters. Specifically, based on the dynamic constraint adjustment parameters, the node voltage constraint adjustment parameters, the distributed energy output power constraint adjustment parameters, and the reactive power regulation constraint adjustment parameters are obtained respectively. Based on the mapping relationship between each constraint adjustment parameter and the corresponding preset constraint boundary, the adjustment amount corresponding to each preset grid operation constraint boundary is determined. The constraint boundary adjustment amount is used to characterize the degree of change of each operating constraint boundary relative to the original constraint boundary under the current risk state.

[0079] Furthermore, based on the mapping relationship between each constraint adjustment parameter and the corresponding preset constraint boundary, the adjustment amount corresponding to each preset power grid operation constraint boundary is determined, including: The initial constraint adjustment parameters for each node are obtained based on the dynamic constraint adjustment parameters. Specifically, based on the node voltage constraint adjustment parameters, distributed energy output power constraint adjustment parameters, and reactive power regulation constraint adjustment parameters in the dynamic constraint adjustment parameters, the initial node voltage constraint adjustment parameters, initial distributed energy output power constraint adjustment parameters, and initial reactive power regulation constraint parameters corresponding to each node are determined respectively; the initial constraint adjustment parameters are used to represent the initial adjustment requirements of various operating constraint boundaries based on the current grid risk status without considering node operating differences.

[0080] The node operation sensitivity parameters are determined based on the historical and current operation status data of each node. Specifically, the node voltage offset, node voltage change rate, distributed energy access capacity ratio, line power flow change, and reactive power regulation reserve capacity for each node are obtained. Based on the correspondence between the above operating status parameters and changes in historical risk assessment indicators, the degree of impact of changes in the operating status of each node on changes in grid risk is determined. The degree of impact is normalized to obtain the node operating sensitivity parameters for each node. The node operating sensitivity parameters are used to characterize the sensitivity of different nodes to changes in risk caused by changes in operating status.

[0081] The specific formula for calculating the node's operational sensitivity parameter is as follows:

[0082] In the formula, This is the node operation sensitivity parameter corresponding to the i-th power grid node, used to represent the degree of impact of changes in the node's operating state on changes in power grid risk. Its value ranges from 0 to 1; a larger value indicates that the node is more sensitive to changes in risk. The number of runtime state characteristic parameters involved in the calculation of node runtime sensitivity. Let jjj be the running state feature parameter corresponding to the i-th node. This is the normalized parameter value of the j-th running state feature parameter of the i-th node, used to eliminate the dimensional differences between different types of parameters. The sensitivity weight is the weight corresponding to the j-th operating state characteristic parameter. It is used to represent the proportional coefficient of the degree of influence of the operating state parameter on the node risk change. The sensitivity weight is obtained by statistically analyzing the degree of change of risk assessment indicators before and after the change of each operating state characteristic parameter in historical power grid operation data, calculating the proportion of the influence of each operating state characteristic parameter on the risk change, and normalizing the weight according to the proportion of the influence of each parameter.

[0083] The initial constraint adjustment parameters are corrected based on the node operation sensitivity parameters to obtain the node adaptive constraint adjustment parameters; Specifically, the adjustment ratio is determined based on the comparison between the node operation sensitivity parameter and the preset sensitivity benchmark value. When the node operation sensitivity parameter is greater than the preset sensitivity benchmark value, the adjustment ratio corresponding to the initial constraint adjustment parameter is increased according to the ratio between the node operation sensitivity parameter and the preset sensitivity benchmark value. When the node operation sensitivity parameter is less than or equal to the preset sensitivity benchmark value, the adjustment ratio corresponding to the initial constraint adjustment parameter is maintained or decreased. The node voltage adaptive constraint adjustment parameters, distributed energy output power adaptive constraint adjustment parameters, and reactive power regulation adaptive constraint adjustment parameters corresponding to each node are obtained through the above method.

[0084] The preset sensitivity benchmark value is determined based on the statistical distribution range of the operating sensitivity parameters of each node under historical normal operating conditions. Specifically, it involves obtaining the node operating sensitivity parameters corresponding to multiple historical normal operating times and determining them based on their average or median values, which is used to distinguish the response degree of different nodes to changes in operating risks.

[0085] The direction of constraint adjustment is determined based on the current power grid operation risk assessment indicators and risk trend prediction results. Specifically, the direction of constraint boundary adjustment is determined based on the future risk change direction in the risk trend prediction results. When the predicted grid operation risk is on the rise within a preset time period, the node voltage constraint boundary is adjusted to reduce the allowable offset range, the distributed energy output power constraint boundary is adjusted to reduce the power change range, and the reactive power regulation constraint boundary is adjusted to expand the reactive power regulation range. When the predicted grid operation risk is on the fall within a preset time period, the corresponding constraint boundary is adjusted to restore the preset operation range.

[0086] Based on the node adaptive constraint adjustment parameters, constraint adjustment direction and corresponding preset constraint boundary, determine the constraint boundary adjustment amount for each node; Specifically, based on the changes in constraint boundaries under different risk states in historical power grid operation data, the correspondence between the allowable offset range of node voltage, the range of distributed energy output power variation, and the reactive power regulation range and constraint adjustment parameters is statistically analyzed, and a mapping relationship between constraint adjustment parameters and constraint boundary adjustment ratios is established. According to the mapping relationship, the adaptive constraint adjustment parameters of node voltage are converted into adjustment amounts corresponding to the upper and lower limits of node voltage, the adaptive constraint adjustment parameters of distributed energy output power are converted into adjustment amounts corresponding to the maximum output power variation range, and the adaptive constraint adjustment parameters of reactive power regulation are converted into adjustment amounts corresponding to the upper and lower limits of reactive power.

[0087] The preset power grid operation constraint boundary is updated according to the constraint boundary adjustment amount to obtain the adjusted operation constraint boundary; Specifically, the preset upper and lower limits of node voltage are adjusted according to the node voltage constraint adjustment amount to obtain a new allowable range of node voltage; the preset maximum output power variation range of distributed energy is adjusted according to the distributed energy output power constraint adjustment amount to obtain a new range of distributed energy power variation; the preset reactive power regulation range is adjusted according to the reactive power regulation constraint adjustment amount to obtain a new range of reactive power regulation; the adjusted operating constraint boundaries are used to limit the allowable power variation range and the operating state range of grid nodes during the operation of distributed energy.

[0088] Based on the adjusted operational constraint boundaries, a constraint consistency check is performed to determine the effective constraint boundaries that meet the power grid operation safety requirements. Specifically, the adjusted allowable range of node voltage, the range of distributed energy power variation, and the range of reactive power adjustment are compared with the preset safe operating range. When the adjusted operating constraint boundary is within the preset safe operating range, the adjusted operating constraint boundary is determined as a valid constraint boundary. When the adjusted operating constraint boundary exceeds the preset safe operating range, the adjusted operating constraint boundary is corrected according to the boundary value of the corresponding safe operating range to meet the requirements of safe grid operation.

[0089] Dynamic operational constraints are generated based on the effective constraint boundaries. Specifically, the node voltage constraint range, the distributed energy output power variation range, and the reactive power adjustment range in the effective constraint boundary are combined to form dynamic operating constraints. These dynamic operating constraints are used to replace the preset grid operating constraints and serve as constraints in the subsequent distributed energy power optimization and control process.

[0090] like Figure 5 The diagram shows the distribution of node operational sensitivity parameters for different nodes within a distributed energy access area. The sensitivity parameters for different nodes are calculated based on node voltage offset, line power flow variation, the proportion of distributed energy access capacity, and reactive power reserve capacity. Figure 5 It is known that different nodes have different responses to risk changes due to their different operating status and the scale of new energy access. Therefore, using the node operating sensitivity parameter to correct the constraint adjustment ratio can avoid the problem of insufficient or excessive constraint adjustment for some nodes caused by using a uniform adjustment ratio.

[0091] It should be noted that by introducing node operation sensitivity parameters, the mapping relationship between dynamic constraint adjustment parameters and preset grid operation constraint boundaries is adaptively corrected, avoiding the problem in existing technologies where fixed adjustment ratios lead to different nodes using the same constraint adjustment strategy under the same risk conditions. By combining multi-dimensional operating state characteristics such as node voltage offset, voltage change rate, distributed energy access capacity ratio, line power flow changes, and reactive power regulation reserve capacity, the responsiveness of different nodes to grid risk changes is quantified, enabling the constraint adjustment amount to be generated differently according to the actual operating characteristics of the nodes, thus improving the matching degree between dynamic constraint boundaries and actual grid operating states. Simultaneously, the constraint adjustment direction is determined based on risk trend prediction results, and consistency verification is performed in conjunction with the safe operating range. This ensures that the generated dynamic operating constraints can both tighten operating constraints in a timely manner when risks increase, improving grid operation safety, and appropriately release adjustment space when risks decrease, improving the distributed energy absorption capacity. Therefore, this technical solution realizes the transformation from fixed constraint adjustment to adaptive constraint adjustment based on operating state perception, improving the accuracy, flexibility, and security of constraint boundary generation in the distributed energy regulation process.

[0092] S4. Calculate the distributed energy regulation parameters based on the dynamic operating constraints, and control the operation of the corresponding distributed energy equipment according to the distributed energy regulation parameters.

[0093] In this embodiment, the distributed energy regulation parameters are calculated based on the dynamic operating constraints, and the operation of the corresponding distributed energy equipment is controlled according to the distributed energy regulation parameters, including: The target operating constraint range for each distributed energy device is obtained based on the dynamic operating constraint conditions. Specifically, based on the node voltage constraint range, distributed energy output power variation range, and reactive power adjustment range in the dynamic operation constraints, the target active power range, target reactive power range, and allowable access node voltage range corresponding to the i-th distributed energy device are determined respectively; the target operation constraint range is used to limit the operating boundary that the distributed energy device is allowed to reach during the current state adjustment process.

[0094] Obtain the current operating status data of each distributed energy device, and determine the distributed energy regulation demand based on the deviation relationship between the current operating status data and the target operating constraint range; Among them, the current active power output, current reactive power output and corresponding access node voltage of the i-th distributed energy device are obtained, and the deviation between the current operating state and the dynamic operating constraint target range, i.e. the difference, are calculated to obtain the active power regulation demand, reactive power regulation demand and voltage support demand. Based on the active power regulation demand and reactive power regulation demand, and combined with the actual regulation capacity of the distributed energy equipment, the regulation demand is corrected to obtain an effective regulation demand that includes effective active power regulation demand and effective reactive power regulation demand; wherein, the maximum active power regulation capacity, maximum reactive power regulation capacity and maximum power change rate corresponding to each distributed energy equipment are obtained, and restrictions are imposed based on the relationship between the regulation demand and the adjustable range of the equipment.

[0095] The formula for calculating the effective active power regulation demand is as follows:

[0096] In the formula, The effective active power regulation demand of the i-th distributed energy device after correction. Let i be the maximum active power regulation capacity currently allowed for the i-th distributed energy device. This is a sign function used to maintain the original control direction.

[0097] The formula for calculating the effective reactive power control demand is as follows:

[0098] In the formula, This represents the effective reactive power regulation demand for the i-th distributed energy device after correction. Let i be the maximum reactive power regulation capacity currently allowed for the i-th distributed energy device. This is a sign function used to maintain the original control direction.

[0099] Based on the effective control demand and dynamic operating constraints, calculate the target control parameters for distributed energy resources. Specifically, based on the corrected active power regulation demand, reactive power regulation demand, and voltage support demand, the corresponding active power control parameters, reactive power control parameters, and voltage support control parameters are obtained respectively.

[0100] The calculation formula for the target regulation parameters of distributed energy is as follows:

[0101] In the formula, Let be the target control parameter corresponding to the i-th distributed energy device. To meet the voltage support requirements, , , These are the control weight coefficients, used to represent the proportion of influence of active power regulation, reactive power regulation, and voltage support in the comprehensive control process. Their values ​​are obtained by statistically analyzing the contribution proportions of active power regulation, reactive power regulation, and voltage support control to the improvement of node voltage deviation in historical operating data, and normalizing them according to the contribution proportions of each control method to obtain the corresponding weight coefficients.

[0102] The target control parameters are constrained and verified according to the dynamic operation constraints to obtain the final control parameters. Specifically, the adjusted distributed energy output power, reactive power, and node voltage state corresponding to the target control parameters are compared with the target operating range in the dynamic operating constraints. When the adjustment result is within the corresponding constraint range, the target control parameter is determined as the final control parameter. When the adjustment result exceeds the corresponding constraint range, the target control parameter is restricted and corrected according to the dynamic operating constraint boundary so that the corrected target control parameter meets the current dynamic operating constraints.

[0103] The restriction correction includes: obtaining the maximum and minimum allowable values ​​in the corresponding dynamic operation constraint boundaries, and correcting the target control parameters using a boundary limiting method; specifically, when the adjusted active power exceeds the maximum active power upper limit in the dynamic operation constraint conditions, the active power control parameter is corrected to the corresponding maximum active power upper limit value; when the adjusted active power is lower than the minimum active power lower limit, the active power control parameter is corrected to the corresponding minimum active power lower limit value; for reactive power control parameters and node voltage support parameters, the same method is used to limit the control results exceeding the dynamic operation constraint boundaries to the corresponding allowable range, thereby obtaining the final control parameters that satisfy the dynamic operation constraint conditions.

[0104] Based on the final control parameters, device control commands are generated and sent to the corresponding distributed energy devices; Specifically, based on the final control parameters, active power control commands, reactive power control commands, and voltage support control commands are generated for the inverter, enabling the distributed energy equipment to adjust its output power and reactive power support capability, so that the operating state of the distributed energy meets the dynamic operating constraints.

[0105] Collect power grid operation data after control is executed, and update the final control parameters based on the feedback operation status; Specifically, the grid operation risk assessment indicators are recalculated based on the controlled node voltage, distributed energy output power, and load change status. When the risk assessment indicators fail to meet the preset safe operation target, the regulation demand calculation process is re-executed to achieve closed-loop optimization of distributed energy regulation parameters.

[0106] The preset safe operation target is determined by statistical analysis of the power grid operation risk assessment indicators, node voltage deviation range and equipment operation constraint range under the historical normal operation status of the distributed energy access area. The range of risk assessment indicators and operation status indicators that meet the requirements of safe and stable operation of the power grid are determined as the preset safe operation target.

[0107] It should be noted that by incorporating dynamic operating constraints into the calculation process of distributed energy control parameters, the distributed energy control strategy can be dynamically adjusted according to the real-time risk status and operational boundary changes of the power grid. This avoids the problem in existing technologies where fixed control targets lead to control results that cannot adapt to changes in the power grid status. By determining the control demand based on the deviation between the current operating status and the dynamic constraint range, and then correcting the control demand in conjunction with the maximum regulation capacity of the distributed energy itself, the generated control parameters can both meet the power grid risk suppression requirements and avoid exceeding the actual regulation capacity of the equipment, thus improving the executability of the control strategy. At the same time, by comprehensively calculating the active power regulation, reactive power regulation, and voltage support requirements, and using dynamic constraint boundaries for verification and correction, the synergistic utilization of the multi-dimensional regulation capabilities of distributed energy is realized. Furthermore, by readjusting the control parameters through post-control operating status feedback, closed-loop optimization of the control process is achieved, enabling distributed energy to continuously adapt to changes in the power grid operating status and improving the safety, stability, and renewable energy absorption capacity of the power grid under distributed energy access conditions.

[0108] Reference Figure 2 As shown in the diagram, this invention provides a schematic diagram of a distributed energy intelligent control system based on grid operation constraints. The system includes a grid operation risk assessment module, a risk trend prediction and constraint adjustment module, an adaptive dynamic constraint generation module, and a distributed energy optimization control module. These modules are interconnected. The power grid operation risk assessment module is used to acquire power grid operation data in the distributed energy access area and generate power grid operation risk assessment indicators. The risk trend prediction and constraint adjustment module is used to determine dynamic constraint adjustment parameters based on the power grid operation risk assessment indicators and the prediction results of the power grid operation status change trends. The adaptive dynamic constraint generation module is used to adjust the preset power grid operation constraint boundaries using dynamic constraint adjustment parameters to generate dynamic operation constraint conditions. The distributed energy optimization and control module is used to calculate the regulation parameters of distributed energy based on dynamic operating constraints and control the operation of the corresponding distributed energy equipment.

[0109] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0110] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0112] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of distributed energy resources based on grid operation constraints, characterized in that, include: Acquire grid operation data in the distributed energy access area and generate grid operation risk assessment indicators; Based on the power grid operation risk assessment indicators and the prediction results of the power grid operation status change trend, the dynamic constraint adjustment parameters are determined; The preset power grid operation constraint boundaries are adjusted using dynamic constraint adjustment parameters to generate dynamic operation constraint conditions; The control parameters of distributed energy are calculated based on dynamic operating constraints, and the operation of the corresponding distributed energy equipment is controlled.

2. The method according to claim 1, characterized in that, The acquisition of power grid operation data in the distributed energy access area includes: The node voltage, distributed energy output power, and load power of each power grid node in the region are obtained according to the preset sampling period to form an operation data sequence; Based on the operational data sequence, the node voltage offset, voltage change rate, distributed energy power fluctuation rate, and load change rate are calculated respectively, forming a set of characteristic parameters of the power grid operation status.

3. The method according to claim 2, characterized in that, The generated power grid operation risk assessment indicators include: Normalize the characteristic parameters of the power grid operation status to obtain the single-factor risk parameters corresponding to each operation factor; Based on the changing trends of each single-factor risk parameter within a preset time window, the degree of correlation between different risk parameters is calculated, and a risk parameter coupling relationship matrix is ​​constructed based on the degree of correlation. Based on the risk parameter coupling relationship matrix, determine the coupling risk value generated by the combined effect of multiple risk parameters; By integrating single-factor risk parameters with coupled risk values, a nonlinear comprehensive power grid operation risk assessment index is generated.

4. The method according to claim 1, characterized in that, The determination of dynamic constraint adjustment parameters based on power grid operation risk assessment indicators and prediction results of power grid operation status change trends includes: A risk change sequence is constructed based on the power grid operation risk assessment results over a continuous historical period, and risk change trend parameters are calculated based on the risk change sequence. Based on the risk change trend parameters, the risk change trend of power grid operation within a preset time period is predicted, and the risk trend prediction results are obtained. By integrating the current power grid operation risk assessment indicators with the risk trend prediction results, a risk impact feature that simultaneously characterizes the current risk status and future risk change trends is generated. Based on the characteristics of risk impact, dynamic adjustment coefficients corresponding to various power grid operation constraints are determined, and dynamic constraint adjustment parameters are obtained.

5. The method according to claim 4, characterized in that, The process of predicting the trend of power grid operation risk changes within a preset time period based on risk change trend parameters, and obtaining risk trend prediction results, includes: Identify the main risk drivers based on the risk parameters corresponding to the current risk assessment indicators; Obtain historical operational data of risk drivers and construct an evolutionary sequence; Based on the evolutionary sequence and the risk parameter coupling matrix, the evolutionary correlation between each risk driver is determined and corrected. Based on the revised evolutionary relationships and the current changes in risk drivers, predict the future trends of each risk driver. By integrating the prediction results of multiple risk drivers, a comprehensive risk trend prediction result is obtained.

6. The method according to claim 4, characterized in that, The process of determining dynamic adjustment coefficients corresponding to various power grid operation constraints based on risk impact characteristics, and obtaining dynamic constraint adjustment parameters, includes: Determine the corresponding constraint adjustment coefficients based on the characteristics of the risk impact; Based on the constraint adjustment coefficients, the adjustment direction and amount of the node voltage constraint range, the distributed energy output power change constraint range, and the reactive power regulation range are determined, and the preset grid operation constraint parameters are corrected to obtain the adjusted constraint parameters. The adjusted constraint parameters are combined to generate dynamic constraint adjustment parameters.

7. The method according to claim 1, characterized in that, The step of adjusting the preset power grid operation constraint boundaries using dynamic constraint adjustment parameters to generate dynamic operation constraint conditions includes: Obtain the corresponding constraint boundary adjustment amount based on the dynamic constraint adjustment parameters; The preset node voltage constraint boundary, distributed energy output power change constraint boundary, and reactive power regulation constraint boundary are updated according to the constraint boundary adjustment amount to obtain the adjusted operation constraint boundary. The adjusted operational constraint boundaries are subjected to constraint consistency verification, and dynamic operational constraint conditions that meet the requirements for safe operation are generated based on the verification results.

8. The method according to claim 7, characterized in that, The step of obtaining the corresponding constraint boundary adjustment amount based on the dynamic constraint adjustment parameters includes: The initial constraint adjustment parameters for each node are obtained based on the dynamic constraint adjustment parameters. Based on the historical and current operational status data of each node, determine the node operational sensitivity parameters used to characterize the sensitivity of each node to changes in risk; The initial constraint adjustment parameters are differentiated and corrected based on the node operation sensitivity parameters to obtain the adaptive constraint adjustment parameters corresponding to each node. Based on the adaptive constraint adjustment parameters and the corresponding constraint adjustment direction, determine the constraint boundary adjustment amount for each node.

9. The method according to claim 1, characterized in that, The calculation of control parameters for distributed energy based on dynamic operating constraints, and the control of the corresponding distributed energy equipment operation, includes: The target operating constraint range for each distributed energy device is obtained based on the dynamic operating constraint conditions. Obtain the current operating status data of each distributed energy device, and determine the distributed energy regulation demand based on the deviation between the current operating status data and the target operating constraint range; The regulation demand is adjusted based on the actual regulation capacity of each distributed energy device to obtain the effective regulation demand. Calculate the target control parameters for distributed energy resources based on the effective control demand and the dynamic operating constraints. The target control parameters are constrained and verified according to the dynamic operating constraints to obtain the final control parameters that satisfy the dynamic operating constraints. Based on the final control parameters, device control commands are generated and sent to the corresponding distributed energy devices.

10. A system using a distributed energy intelligent control method based on grid operation constraints as described in any one of claims 1-9, characterized in that, include: The power grid operation risk assessment module is used to acquire power grid operation data in the distributed energy access area and generate power grid operation risk assessment indicators. The risk trend prediction and constraint adjustment module is used to determine dynamic constraint adjustment parameters based on the power grid operation risk assessment indicators and the prediction results of the power grid operation status change trends. The adaptive dynamic constraint generation module is used to adjust the preset power grid operation constraint boundaries using dynamic constraint adjustment parameters to generate dynamic operation constraint conditions. The distributed energy optimization and control module is used to calculate the regulation parameters of distributed energy based on dynamic operating constraints and control the operation of the corresponding distributed energy equipment.

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