A Smart Control Method for Flexible Interconnection of Distribution Radio Areas

Abstract

This invention discloses an intelligent control method for flexible interconnection of distribution substations, relating to the technical field of distribution network control. Step S1 involves data acquisition of the target distribution substation to obtain an initial dataset, and processing based on historical operating data to obtain static topology parameters. Step S2 involves processing historical operating data to obtain a dynamic constraint envelope. Step S3 involves establishing a system differential flat state equation based on the static topology parameters and dynamic constraint envelope, and obtaining a feasible solution hypersurface by intercepting the safe domain based on the flat output space. Step S4 involves performing path search on the feasible solution hypersurface using an improved ant colony optimization algorithm, updating the pheromone increment, and completing iterative calculations. Step S5 involves selecting the globally optimal path based on the iterative calculation results and generating the SOP control command for the target distribution substation. This method overcomes the shortcomings of existing technologies that focus only on local optimal solutions and are prone to response lag when generating control commands for distribution substations.

Description

Technical Field

[0001] This invention relates to the technical field of power distribution network control, and in particular to an intelligent control method for flexible interconnection of power distribution substations. Background Technology

[0002] In recent years, thanks to the rapid development of power electronics, intelligent control, and computer information technology, new loads such as distributed photovoltaics and electric vehicles have begun to be connected to distribution networks on a large scale. The traditional closed-loop design and open-loop operation structure of distribution networks can no longer meet the demands of diverse loads, such as power supply reliability and renewable energy absorption. Flexible interconnection technology for distribution substations possesses power electronic conversion and intelligent control functions, and aligns with the trends of green and intelligent development. Therefore, developing a flexible interconnection control method for distribution substations with real-time regulation capabilities is a promising application direction, addressing issues such as load imbalance between substations and voltage / current exceeding limits at nodes.

[0003] Currently, Chinese invention patent application CN202510254649.4 discloses a method and system for intelligent control of photovoltaic energy storage in a distribution substation. This application includes: collecting data from photovoltaic inverters, energy storage devices, and grid equipment; real-time monitoring of the operating status, current, voltage, active power, reactive power, and power factor of the aforementioned equipment; and constructing a voltage over-limit control strategy for the grid-connected nodes of the photovoltaic inverters in the distribution substation based on data analysis, thereby controlling the photovoltaic inverters. This invention solves the problem of voltage over-limit at the grid-connected nodes of the photovoltaic inverters in the distribution substation, increasing the amount of electricity generated. However, this application only relies on real-time operating data to control the photovoltaic inverters, lacking consideration of the inherent physical characteristics of the substation, such as the node impedance matrix, and historical operating patterns such as power fluctuations. Furthermore, it only adjusts the output power of the photovoltaic inverters to solve local node voltage anomalies, but does not systematically define the overall safe operating boundary of the substation, limiting the adjustment range. Summary of the Invention

[0004] The technical problem solved by this invention is that existing technologies often only analyze and control local problems of single devices, lacking control over the overall operation of the distribution substation, thus limiting their applicability. While relying on real-time operational data for monitoring and control, they do not adequately consider the inherent physical characteristics and historical operating patterns of the distribution substation. Furthermore, existing technologies use relatively simple constraint conditions and fail to set constraints from the perspective of global voltage and current over-limits.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A smart control method for flexible interconnection of distribution radio areas;

[0007] Step S1: Collect data from the target distribution radio area to obtain an initial dataset, and process the historical data to obtain static topology parameters;

[0008] Step S2: Obtain the dynamic constraint envelope based on historical running data;

[0009] Step S3: Establish the system differential flat state equation based on static topological parameters and dynamic constraint envelope, and obtain the feasible solution hypersurface by truncation of the safe domain based on the flat output space;

[0010] Step S4: Path search is performed on the feasible solution hypersurface using the improved ant colony optimization algorithm, the pheromone increment is updated, and iterative calculation is completed.

[0011] Step S5: Based on the iterative calculation results, the globally optimal path is selected, and the SOP control command for the target distribution radio area is generated.

[0012] Preferably, the initial dataset includes network topology data of the target distribution area, system parameters of the flexible interconnection device, real-time operating data, and historical operating data;

[0013] Step S1: Based on the historical data in the initial dataset, static topology parameters are obtained by processing the data. These static topology parameters include the node impedance matrix and the State of Operation (SOP) capacity limit. The processing logic includes:

[0014] Based on the network topology data of the target distribution area, the resistance and reactance data between each power grid node are statistically obtained.

[0015] Establish the node impedance matrix Z based on the resistance and reactance data between each power grid node;

[0016] The expression for calculating the nodal impedance matrix Z is as follows:

[0017] ;

[0018] Where Z represents the node impedance matrix. This represents the per-unit resistance value between node m and node n. This represents the per-unit reactance between node m and node n, where m and n represent the grid node numbers, and j represents an imaginary number.

[0019] The power grid nodes include critical nodes and secondary nodes;

[0020] The key nodes include the low-voltage side busbar node of the transformer and the first end node of the main feeder.

[0021] The secondary nodes include branch box nodes and user table box nodes;

[0022] The real-time operating data includes the remaining capacity of the SOP, the real-time voltage of each grid node, and the real-time current of each grid node.

[0023] The SOP capacity limit is obtained based on the system parameters of the flexible interconnect device.

[0024] Preferably, in step S2, the dynamic constraint envelope is obtained by processing historical running data;

[0025] The dynamic constraint envelope includes the node active power injection interval, net injected active power, and net injected reactive power.

[0026] The historical operating data includes the historical voltage, historical current, active power, and reactive power of each power grid node.

[0027] The historical data was obtained by synchronous sampling at 15-minute intervals.

[0028] The discrete sampling points in the historical active power data are sorted, and the largest per-unit value of the historical active power data is selected. As the upper limit of the interval, select the minimum per-unit value. As the lower bound of the interval, the active power injection interval of the node is obtained. ;

[0029] The expected value of active power load is obtained by calculating the arithmetic mean of all discrete sampling points in the historical active power data. ;

[0030] The reactive power historical data is fitted using a kernel density estimation algorithm to obtain the load power fluctuation probability density function. The expected reactive power value is then calculated by integrating the load power fluctuation probability density function. ;

[0031] Based on the power balance relationship of the target distribution area and the Kirchhoff-Clearinger (KCL) law, power balance calculations are performed on the expected values ​​of active and reactive loads to obtain the net injected active power and net injected reactive power. The calculation expressions are as follows:

[0032] ;

[0033] ;

[0034] in, This represents the per-unit value of net injected active power. This represents the per-unit value of active power injected into the upper-level power grid. This represents the per-unit value of the expected power of the active load. This represents the per-unit value of net injected reactive power. This represents the per-unit value of reactive power injected into the upstream power grid. This represents the per-unit value of the expected power of reactive load.

[0035] Preferably, step S301 involves establishing a system differential flat state equation based on static topological parameters and dynamic constraint envelopes, mapping it to obtain a flat output space, and the processing logic includes:

[0036] Define the voltage and current of the critical node as the flat output y, and the expression for the flat output y is:

[0037] ;

[0038] in, This indicates the per-unit value of the critical node voltage. This represents the per-unit value of the critical node current, where T represents the matrix transpose, and k and l represent the critical node numbers.

[0039] Active power injection into the interval of nodes As a deterministic boundary, a flat output space is obtained by mapping the system's differential flat state equations to a flat space based on the Lie derivative algorithm. The computational expression for this flat output space is as follows:

[0040] ;

[0041] in, The derivative term represents the voltage trend. The derivative term represents the current trend. Represents the voltage state evolution function. Represents the current state evolution function. This indicates the per-unit limit of SOP capacity.

[0042] Preferably, step S302, obtaining a feasible solution hypersurface by truncation of the safe domain based on the flat output space, includes the following processing logic:

[0043] Inverse operation is performed on the impedance matrix Z. A safety-scaled diagonal matrix is ​​established based on the rated voltage and rated current of the target distribution area. The safety constraint matrix P is obtained by multiplying the impedance matrix and the safety-scaled diagonal matrix. Its calculation expression is as follows:

[0044] ;

[0045] Where P represents the security constraint matrix, This indicates the per-unit value of the rated voltage. This indicates the per-unit value of the rated current. The matrix representing the inverse of the impedance matrix;

[0046] Using the safety constraint matrix P as the weight matrix of the Lyapunov function, the feasible solution hypersurface is obtained by truncating the flat output space into an ellipsoidal safety domain. Its calculation expression is as follows:

[0047] ;

[0048] in, This represents a feasible solution hypersurface.

[0049] Preferably, step S401, generating an initial pheromone distribution based on the feasible solution hypersurface, includes the following processing logic:

[0050] The feasible solution hypersurface is uniformly meshed. Based on the preset voltage dimension step size and current dimension step size, the feasible solution space is equally divided to obtain the surface space grid points. The surface space grid points are numbered and ants are arranged to obtain the initial set of ant positions.

[0051] The initial pheromone concentration is calculated by taking the reciprocal of the per-unit value of the SOP capacity limit.

[0052] Preferably, in step S402, the real-time voltage and real-time current of each key node are substituted into the flat output space to calculate the real-time current trend derivative term, and the discretization process is discretized to obtain the real-time node current gradient.

[0053] The real-time node voltage deviation is obtained by subtracting the rated voltage and real-time voltage of each key node.

[0054] The heuristic factors are calculated for the real-time node current gradient and the real-time node voltage deviation, respectively, and their calculation expressions are as follows:

[0055] ;

[0056] in, Represents the heuristic factor. This represents the per-unit value of the real-time node voltage deviation. This represents the per-unit value of the real-time node current gradient, and s and t represent the grid point numbers in the surface space.

[0057] Preferably, step S403 involves performing path search on the feasible solution hypersurface to obtain the elite ant path and calculating the pheromone increment. The processing logic includes:

[0058] The path transition probability distribution is obtained by calculating pheromone concentration and heuristic factor based on the ant optimization algorithm;

[0059] A grid point sequence is generated based on the path transition probability distribution to obtain the complete ant path set;

[0060] Calculate the path loss value for each path in the complete ant path set. The calculation expression is as follows:

[0061] ;

[0062] Let represent the path loss value corresponding to the a-th ant, u represent the step number of the grid point sequence corresponding to the a-th ant, and U represent the total number of steps of the grid point sequence corresponding to the a-th ant.

[0063] Sort all path loss values ​​in ascending order, select the minimum value to obtain the optimal loss value, and select the grid point sequence corresponding to the optimal loss value from the set of complete ant paths as the elite ant path;

[0064] The pheromone increment is calculated by introducing the Fibonacci ratio pheromone update rule to the elite ant path. The calculation expression is as follows:

[0065] ;

[0066] in, Indicates the pheromone increment. H represents the current optimal loss value, H represents the total amount of pheromone released, and k represents the number of iterations. This indicates the preset maximum number of iterations. This represents the Fibonacci ratio.

[0067] Preferably, in step S404, the pheromone increment is added to the initial pheromone to obtain the first updated pheromone, and the first updated pheromone is substituted back to step S403 for iterative calculation and determination of the iteration termination condition.

[0068] The iteration stops when the termination condition is met, and the iterative calculation results are obtained.

[0069] The iterative calculation results include the optimal loss value and elite ant path corresponding to the last iteration calculation;

[0070] The iteration termination conditions include reaching a preset maximum number of iterations or the optimal solution being continuously stable.

[0071] The optimal solution is continuously stable if the difference between the optimal loss value in the last 5 iterations and the historical optimal loss value is less than 0.01.

[0072] Preferably, in step S5, based on the iterative calculation results, the elite ant path corresponding to the last iteration calculation is selected to obtain the globally optimal path;

[0073] By selecting the endpoint coordinates of the grid point sequence corresponding to the globally optimal path, the globally optimal flat output is obtained;

[0074] The globally optimal flat output includes the real-time optimal voltage setting and the real-time optimal current setting;

[0075] The optimal voltage setpoint is mapped to the active power that the SOP should output, and the real-time optimal current setpoint is mapped to obtain the reactive power that the SOP should output.

[0076] The SOP control command for the target distribution area is obtained based on the active power and reactive power that the SOP should output.

[0077] The beneficial effects of this invention are as follows: This application considers both the static physical characteristics and dynamic operating laws of distribution transformer substations during regulation, extracting static topology parameters and calculating dynamic constraint envelopes. While preserving historical characteristics of load fluctuations, it avoids distortion through physical constraints. This application establishes a differential flat model to reduce the complexity of the power grid system state and obtains feasible solution hypersurfaces from high-dimensional ellipsoids, clarifying the global safety domain of the substation. This application fully considers the complex characteristics of distribution transformer substation data and specifically improves the ant colony optimization algorithm, overcoming the problems of getting trapped in local optima and slow convergence speed. It effectively solves the defects of generating only local optima and response lag when generating control commands for distribution transformer substations. This application achieves intelligent control of the entire substation, while balancing data processing accuracy and computational efficiency in the flexible interconnection regulation of distribution transformer substations, which is beneficial to improving the safety, economy, and robustness of system control. Attached Figure Description

[0078] Figure 1 A basic flowchart illustrating an intelligent control method for flexible interconnection of distribution radio areas, provided as an embodiment of the present invention;

[0079] Figure 2 This is a schematic diagram of the dynamic constraint envelope generation process provided in one embodiment of the present invention;

[0080] Figure 3 This is a schematic diagram of an improved ant colony optimization iterative calculation process provided in one embodiment of the present invention. Detailed Implementation

[0081] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0082] Reference Figure 1-3 As one embodiment of the present invention, an intelligent control method for flexible interconnection of distribution radio areas is provided, comprising:

[0083] Step S1: Collect data from the target distribution radio area to obtain an initial dataset, and process the historical data to obtain static topology parameters;

[0084] Step S2: Obtain the dynamic constraint envelope based on historical running data;

[0085] Step S3: Establish the system differential flat state equation based on static topological parameters and dynamic constraint envelope, and obtain the feasible solution hypersurface by truncation of the safe domain based on the flat output space;

[0086] Step S4: Path search is performed on the feasible solution hypersurface using the improved ant colony optimization algorithm, the pheromone increment is updated, and iterative calculation is completed.

[0087] Step S5: Based on the iterative calculation results, the globally optimal path is selected, and the SOP control command for the target distribution radio area is generated.

[0088] In this embodiment, the initial dataset includes network topology data of the target distribution area, system parameters of the flexible interconnection device, real-time operating data, and historical operating data;

[0089] Step S1: Based on the historical data in the initial dataset, static topology parameters are obtained by processing the data. These static topology parameters include the node impedance matrix and the State of Operation (SOP) capacity limit. The processing logic includes:

[0090] Based on the network topology data of the target distribution area, the resistance and reactance data between each power grid node are statistically obtained.

[0091] Establish the node impedance matrix Z based on the resistance and reactance data between each power grid node;

[0092] The expression for calculating the nodal impedance matrix Z is as follows:

[0093] ;

[0094] Where Z represents the node impedance matrix. This represents the per-unit resistance value between node m and node n. This represents the per-unit reactance between node m and node n, where m and n represent the grid node numbers, and j represents an imaginary number.

[0095] The power grid nodes include critical nodes and secondary nodes;

[0096] The key nodes include the low-voltage side busbar node of the transformer and the first end node of the main feeder.

[0097] The secondary nodes include branch box nodes and user table box nodes;

[0098] The real-time operating data includes the remaining capacity of the SOP, the real-time voltage of each grid node, and the real-time current of each grid node.

[0099] The SOP capacity limit is obtained based on the system parameters of the flexible interconnect device.

[0100] In this application, a per-unit value system with a reference voltage of 0.4kV and a reference power of 10MVA is adopted, and all power, current, and impedance parameters involved have been dimensionless according to this reference value. This helps to ensure the accuracy and stability of the matrix calculations in this application.

[0101] In this embodiment, step S2 involves processing historical running data to obtain a dynamic constraint envelope;

[0102] The dynamic constraint envelope includes the node active power injection interval, net injected active power, and net injected reactive power.

[0103] The historical operating data includes the historical voltage, historical current, active power, and reactive power of each power grid node.

[0104] The historical data was obtained by synchronous sampling at 15-minute intervals.

[0105] The discrete sampling points in the historical active power data are sorted, and the largest per-unit value of the historical active power data is selected. As the upper limit of the interval, select the minimum per-unit value. As the lower bound of the interval, the active power injection interval of the node is obtained. ;

[0106] The expected value of active power load is obtained by calculating the arithmetic mean of all discrete sampling points in the historical active power data. ;

[0107] The reactive power historical data is fitted using a kernel density estimation algorithm to obtain the load power fluctuation probability density function. The expected reactive power value is then calculated by integrating the load power fluctuation probability density function. ;

[0108] Based on the power balance relationship of the target distribution area and the Kirchhoff-Clearinger (KCL) law, power balance calculations are performed on the expected values ​​of active and reactive loads to obtain the net injected active power and net injected reactive power. The calculation expressions are as follows:

[0109] ;

[0110] ;

[0111] in, This represents the per-unit value of net injected active power. This represents the per-unit value of active power injected into the upper-level power grid. This represents the per-unit value of the expected power of the active load. This represents the per-unit value of net injected reactive power. This represents the per-unit value of reactive power injected into the upstream power grid. This represents the per-unit value of the expected power of reactive load;

[0112] The processing logic for fitting historical reactive power data using the kernel density estimation algorithm includes:

[0113] A continuous probability density curve is obtained by weighting and superimposing discrete sample points in the historical reactive power data using a Gaussian kernel function. The kernel window width parameter of the Gaussian kernel function is optimized based on the Silverman criterion to balance fitting accuracy and smoothness. At the same time, the power cannot be negative as a physical constraint condition for reactive power. The probability density distortion at the distribution edge of the continuous probability density curve is corrected by the boundary reflection method to obtain the load reactive power probability density function.

[0114] In this embodiment, step S301 involves establishing a system differential flat state equation based on static topological parameters and dynamic constraint envelopes, mapping to obtain a flat output space, and the processing logic includes:

[0115] Define the voltage and current of the critical node as the flat output y, and the expression for the flat output y is:

[0116] ;

[0117] in, This indicates the per-unit value of the critical node voltage. This represents the per-unit value of the critical node current, where T represents the matrix transpose, and k and l represent the critical node numbers.

[0118] Active power injection into the interval of nodes As a deterministic boundary, a flat output space is obtained by mapping the system's differential flat state equations to a flat space based on the Lie derivative algorithm. The computational expression for this flat output space is as follows:

[0119] ;

[0120] in, The derivative term represents the voltage trend. The derivative term represents the current trend. Represents the voltage state evolution function. Represents the current state evolution function. This indicates the per-unit limit of SOP capacity.

[0121] Among them, the definition of the flat output y is not affected by the subsequent state equation update and is the basic coordinate system of the safe region.

[0122] In this embodiment, step S302, obtaining a feasible solution hypersurface by performing a safe domain interception based on the flat output space, includes the following processing logic:

[0123] Inverse operation is performed on the impedance matrix Z. A safety-scaled diagonal matrix is ​​established based on the rated voltage and rated current of the target distribution area. The safety constraint matrix P is obtained by multiplying the impedance matrix and the safety-scaled diagonal matrix. Its calculation expression is as follows:

[0124] ;

[0125] Where P represents the security constraint matrix, This indicates the per-unit value of the rated voltage. This indicates the per-unit value of the rated current. The matrix representing the inverse of the impedance matrix;

[0126] Using the safety constraint matrix P as the weight matrix of the Lyapunov function, the feasible solution hypersurface is obtained by truncating the flat output space into an ellipsoidal safety domain. Its calculation expression is as follows:

[0127] ;

[0128] in, This represents a feasible solution hypersurface.

[0129] In the safety constraint matrix P, the rated voltage coefficient of 0.05 represents the allowable voltage deviation of ±5%, and the rated current coefficient of 1.1 represents the line thermal stability limit.

[0130] The security constraint matrix P satisfies the positive definiteness verification, and its smallest eigenvalue is greater than zero.

[0131] The geometric form of the feasible solution hypersurface is a high-dimensional ellipsoid, which describes all SOP operating points that satisfy the N-1 safety criterion in the power grid field. The N-1 safety criterion means that the system can still maintain safe operation after a single line failure.

[0132] In this process, steps S2 and S3 are calculated based on the target distribution substation, its inherent parameters, and historical data, extracting the operating characteristics of the target distribution substation. The voltage and current involved in steps S2 and S3 are obtained from historical data sampling. Step S4 onwards introduces real-time data for calculation to achieve intelligent control of the flexible interconnection of distribution substations. The voltage and current in steps S4 and thereafter are real-time measurement data.

[0133] In this embodiment, step S401, generating an initial pheromone distribution based on the feasible solution hypersurface, includes the following processing logic:

[0134] The feasible solution hypersurface is uniformly meshed. Based on the preset voltage dimension step size and current dimension step size, the feasible solution space is equally divided to obtain the surface space grid points. The surface space grid points are numbered and ants are arranged to obtain the initial set of ant positions.

[0135] The initial pheromone concentration is calculated by taking the reciprocal of the per-unit value of the SOP capacity limit.

[0136] In this embodiment, step S402 involves substituting the real-time voltage and real-time current of each key node into the flat output space, calculating the real-time current trend derivative term, and then discretizing the discretization process to obtain the real-time node current gradient.

[0137] The real-time node voltage deviation is obtained by subtracting the rated voltage and real-time voltage of each key node.

[0138] The heuristic factors are calculated for the real-time node current gradient and the real-time node voltage deviation, respectively, and their calculation expressions are as follows:

[0139] ;

[0140] in, Represents the heuristic factor. This represents the per-unit value of the real-time node voltage deviation. This represents the per-unit value of the real-time node current gradient, and s and t represent the grid point numbers in the surface space.

[0141] In this embodiment, step S403 involves performing path search on the feasible solution hypersurface to obtain the elite ant path and calculating the pheromone increment. The processing logic includes:

[0142] The path transition probability distribution is obtained by calculating pheromone concentration and heuristic factor based on the ant optimization algorithm;

[0143] A grid point sequence is generated based on the path transition probability distribution to obtain the complete ant path set;

[0144] Calculate the path loss value for each path in the complete ant path set. The calculation expression is as follows:

[0145] ;

[0146] Let represent the path loss value corresponding to the a-th ant, u represent the step number of the grid point sequence corresponding to the a-th ant, and U represent the total number of steps of the grid point sequence corresponding to the a-th ant.

[0147] Sort all path loss values ​​in ascending order, select the minimum value to obtain the optimal loss value, and select the grid point sequence corresponding to the optimal loss value from the set of complete ant paths as the elite ant path;

[0148] The pheromone increment is calculated by introducing the Fibonacci ratio pheromone update rule to the elite ant path. The calculation expression is as follows:

[0149] ;

[0150] in, Indicates the pheromone increment. H represents the current optimal loss value, H represents the total amount of pheromone released, and k represents the number of iterations. This indicates the preset maximum number of iterations. This represents the Fibonacci ratio.

[0151] To adapt to the high-fluctuation source load scenario in the distribution radio area while also considering computational efficiency, the maximum number of iterations is preset to 120, and the Fibonacci ratio is [not specified]. The value is set to 0.618. The total pheromone release H is set to 0.9 in residential areas and 2.0 in industrial areas.

[0152] In this embodiment, in step S404, the pheromone increment is added to the initial pheromone to obtain the first updated pheromone. The first updated pheromone is then substituted back to step S403 for iterative calculation and determination of the iteration termination condition.

[0153] The iteration stops when the termination condition is met, and the iterative calculation results are obtained.

[0154] The iterative calculation results include the optimal loss value and elite ant path corresponding to the last iteration calculation;

[0155] The iteration termination conditions include reaching a preset maximum number of iterations or the optimal solution being continuously stable.

[0156] The optimal solution is continuously stable if the difference between the optimal loss value in the last 5 iterations and the historical optimal loss value is less than 0.01.

[0157] Among these measures, the initial pheromone concentration is set based on the reciprocal of the SOP capacity limit, which prioritizes guiding ants to explore high-capacity areas and helps avoid ineffective searches. The heuristic factor is calculated by combining real-time voltage deviation and current gradient, which enhances the sensitivity to deviations of the current operating state from the target. The pheromone increment is only updated for the elite ant path, that is, the path corresponding to the optimal loss value, to accelerate the accumulation of pheromone on the advantageous path. The Fibonacci proportional coefficient is introduced for iterative calculation, which improves the accuracy of the optimal solution calculation.

[0158] In this embodiment, step S5 involves selecting the elite ant path corresponding to the last iteration calculation based on the iterative calculation results to obtain the globally optimal path.

[0159] By selecting the endpoint coordinates of the grid point sequence corresponding to the globally optimal path, the globally optimal flat output is obtained;

[0160] The globally optimal flat output includes the real-time optimal voltage setting and the real-time optimal current setting;

[0161] The optimal voltage setpoint is mapped to the active power that the SOP should output, and the real-time optimal current setpoint is mapped to obtain the reactive power that the SOP should output.

[0162] The mapping of the optimal voltage setpoint to SOP active power and the mapping of the real-time optimal current setpoint to SOP reactive power are achieved based on the voltage-active power correspondence parameter table and the current-reactive power correspondence parameter table pre-calibrated by the SOP of the flexible interconnection device in the target distribution substation.

[0163] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent control method for flexible interconnection of distribution radio areas, characterized in that, include: Step S1: Collect data from the target distribution radio area to obtain an initial dataset, and process the historical data to obtain static topology parameters; Step S2: Obtain the dynamic constraint envelope based on historical running data; Step S3: Establish the system differential flat state equation based on static topological parameters and dynamic constraint envelope, and obtain the feasible solution hypersurface by truncation of the safe domain based on the flat output space; Step S4: Path search is performed on the feasible solution hypersurface using the improved ant colony optimization algorithm, the pheromone increment is updated, and iterative calculation is completed. Step S5: Based on the iterative calculation results, select the globally optimal path and generate the SOP control command for the target distribution radio area; Step S301: Establish the system differential flat state equation based on static topological parameters and dynamic constraint envelope, and map it to obtain a flat output space. The processing logic includes: Define the voltage and current of the critical node as the flat output y, and the expression for the flat output y is: ; in, This indicates the per-unit value of the critical node voltage. This represents the per-unit value of the critical node current, where T represents the matrix transpose, and k and l represent the critical node numbers; Inject active power into the interval using nodes [ , As a deterministic boundary, the flat output space is obtained by mapping the system's differential flat state equations to a flat space based on the Lie derivative algorithm. Its calculation expression is as follows: ; in, The derivative term represents the voltage trend. The derivative term represents the current trend. Represents the voltage state evolution function. Represents the current state evolution function. This indicates the per-unit limit of SOP capacity; Step S302, obtaining a feasible solution hypersurface by performing a safe domain truncation based on the flat output space, the processing logic of which includes: Inverse operation is performed on the impedance matrix Z. A safety-scaled diagonal matrix is ​​established based on the rated voltage and rated current of the target distribution area. The safety constraint matrix P is obtained by multiplying the impedance matrix and the safety-scaled diagonal matrix. Its calculation expression is as follows: ； Where P represents the security constraint matrix, This indicates the per-unit value of the rated voltage. This indicates the per-unit value of the rated current. The matrix representing the inverse of the impedance matrix; Using the safety constraint matrix P as the weight matrix of the Lyapunov function, the feasible solution hypersurface is obtained by truncating the flat output space into an ellipsoidal safety domain. Its calculation expression is as follows: ; in, Indicates a feasible solution hypersurface; Step S401: Generate an initial pheromone distribution based on the feasible solution hypersurface. The processing logic includes: The feasible solution hypersurface is uniformly meshed. Based on the preset voltage dimension step size and current dimension step size, the feasible solution space is equally divided to obtain the surface space grid points. The surface space grid points are numbered and ants are arranged to obtain the initial set of ant positions. The initial pheromone concentration is calculated by taking the reciprocal of the SOP capacity limit per unit value; Step S402: Substitute the real-time voltage and real-time current of each key node into the flat output space, calculate the real-time current trend derivative term, and discretize the discretization process to obtain the real-time node current gradient. The real-time node voltage deviation is obtained by subtracting the rated voltage and real-time voltage of each key node. The heuristic factors are calculated for the real-time node current gradient and the real-time node voltage deviation, respectively, and their calculation expressions are as follows: ; in, Represents the heuristic factor. This represents the per-unit value of the real-time node voltage deviation. This represents the per-unit value of the real-time node current gradient, and s and t represent the grid point numbers in the surface space.

2. The intelligent control method for flexible interconnection of distribution radio areas as described in claim 1, characterized in that: The initial dataset includes network topology data of the target distribution area, system parameters of the flexible interconnection device, real-time operating data, and historical operating data. Step S1: Based on the historical data in the initial dataset, static topology parameters are obtained by processing the data. These static topology parameters include the node impedance matrix and the State of Operation (SOP) capacity limit. The processing logic includes: Based on the network topology data of the target distribution area, the resistance and reactance data between each power grid node are statistically obtained. Establish the node impedance matrix Z based on the resistance and reactance data between each power grid node; The expression for calculating the nodal impedance matrix Z is as follows: ; Where Z represents the node impedance matrix. This represents the per-unit resistance value between node m and node n. This represents the per-unit reactance between node m and node n, where m and n represent the grid node numbers, and j represents an imaginary number. The power grid nodes include critical nodes and secondary nodes; The key nodes include the low-voltage side busbar node of the transformer and the first end node of the main feeder. The secondary nodes include branch box nodes and user table box nodes; The real-time operating data includes the remaining capacity of the SOP, the real-time voltage of each grid node, and the real-time current of each grid node. The SOP capacity limit is obtained based on the system parameters of the flexible interconnect device.

3. The intelligent control method for flexible interconnection of distribution radio areas as described in claim 1, characterized in that: Step S2: Process historical data to obtain the dynamic constraint envelope; The dynamic constraint envelope includes the node active power injection interval, net injected active power, and net injected reactive power. The historical operating data includes the historical voltage, historical current, active power, and reactive power of each power grid node. The historical data was obtained by synchronous sampling at 15-minute intervals. The discrete sampling points in the historical active power data are sorted, and the largest per-unit value of the historical active power data is selected. As the upper limit of the interval, select the minimum per-unit value. As the lower bound of the interval, the active power injection interval of the node is obtained. , ]; The expected value of active power load is obtained by calculating the arithmetic mean of all discrete sampling points in the historical active power data. ; The reactive power historical data is fitted using a kernel density estimation algorithm to obtain the load power fluctuation probability density function. The expected reactive power value is then calculated by integrating the load power fluctuation probability density function. ; Based on the power balance relationship of the target distribution area and the Kirchhoff-Clearinger (KCL) law, power balance calculations are performed on the expected values ​​of active and reactive loads to obtain the net injected active power and net injected reactive power. The calculation expressions are as follows: ; ; in, This represents the per-unit value of net injected active power. This represents the per-unit value of active power injected into the upper-level power grid. This represents the per-unit value of the expected power of the active load. This represents the per-unit value of net injected reactive power. This represents the per-unit value of reactive power injected into the upstream power grid. This represents the per-unit value of the expected power of reactive load.

4. The intelligent control method for flexible interconnection of distribution radio areas as described in claim 1, characterized in that: Step S403 involves performing path search on the feasible solution hypersurface to obtain the elite ant path and calculating the pheromone increment. The processing logic includes: The path transition probability distribution is obtained by calculating pheromone concentration and heuristic factor based on the ant optimization algorithm; A grid point sequence is generated based on the path transition probability distribution to obtain the complete ant path set; Calculate the path loss value for each path in the complete ant path set. The calculation expression is as follows: ； Let represent the path loss value corresponding to the a-th ant, u represent the step number of the grid point sequence corresponding to the a-th ant, and U represent the total number of steps of the grid point sequence corresponding to the a-th ant. Sort all path loss values ​​in ascending order, select the minimum value to obtain the optimal loss value, and select the grid point sequence corresponding to the optimal loss value from the set of complete ant paths as the elite ant path; The pheromone increment is calculated by introducing the Fibonacci ratio pheromone update rule to the elite ant path. The calculation expression is as follows: ; in, Indicates the pheromone increment. H represents the current optimal loss value, H represents the total amount of pheromone released, and k represents the number of iterations. This indicates the preset maximum number of iterations. This represents the Fibonacci ratio.

5. The intelligent control method for flexible interconnection of distribution radio areas as described in claim 1, characterized in that: Step S404: Add the pheromone increment to the initial pheromone to obtain the first updated pheromone. Substitute the first updated pheromone back to step S403, iterate, and determine the iteration termination condition. The iteration stops when the termination condition is met, and the iterative calculation results are obtained. The iterative calculation results include the optimal loss value and elite ant path corresponding to the last iteration calculation; The iteration termination conditions include reaching a preset maximum number of iterations or the optimal solution being continuously stable. The optimal solution is continuously stable if the difference between the optimal loss value in the last 5 iterations and the historical optimal loss value is less than 0.

01.

6. The intelligent control method for flexible interconnection of distribution radio areas as described in claim 1, characterized in that: Step S5: Based on the iterative calculation results, select the elite ant path corresponding to the last iteration calculation to obtain the globally optimal path; By selecting the endpoint coordinates of the grid point sequence corresponding to the globally optimal path, the globally optimal flat output is obtained; The globally optimal flat output includes the real-time optimal voltage setting and the real-time optimal current setting; The optimal voltage setpoint is mapped to the active power that the SOP should output, and the real-time optimal current setpoint is mapped to obtain the reactive power that the SOP should output. The SOP control command for the target distribution area is obtained based on the active power and reactive power that the SOP should output.

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