Topology reconstruction network load one-graph dynamic construction method, system and equipment of power system

Abstract

The invention discloses a method, a system and equipment for dynamically constructing a topology reconstruction network load graph of a power system. The method comprises the following steps: acquiring power system data through at least one type of data sources to determine a current topological state of a power system and form a topological state matrix; based on the topological state matrix and the load prediction information, constructing a target function for reconstructing a topological state; and based on a preset optimization algorithm and the power system operation security constraint, solving the objective function to determine a topology reconstruction scheme. When an objective function used for quantifying the comprehensive performance of the topology reconstruction scheme is constructed, load prediction information is added, a network loss index and a voltage quality index forming components of the objective function are determined through the load prediction information, and the load prediction information comprises the change condition of the load in a future time period. Therefore, the influence of the load change on the topology reconstruction scheme is considered, so that the finally determined topology reconstruction scheme can adapt to the load change.

Description

Technical Field

[0001] This invention relates to the field of power topology technology, specifically to a method, system, and equipment for dynamically constructing a single topology reconfiguration network-load diagram for a power system. Background Technology

[0002] In the context of large-scale digitalization of power systems, integrated dynamic management based on a "unified grid-load map" has become a core requirement for intelligent power grid operation monitoring. This "unified grid-load map" is a power system visualization tool primarily used to integrate grid and load distribution information, enabling unified display and scheduling of power source, grid, load, and storage resources. It can overlay grid topology and load distribution, monitor real-time operating status, and optimize resource scheduling.

[0003] To address power system state changes, resolve operational issues, and optimize performance, it is often necessary to restructure the power grid topology. Currently, most power grid topology restructuring methods are based on static optimization algorithms, lacking consideration for the dynamic characteristics of the load. Consequently, the restructured results often fail to adapt to real-time load changes; that is, there is a problem where topology restructuring schemes are ill-suited to adapting to load variations. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and device for dynamically constructing a topology reconfiguration network-load diagram for power systems. By considering load forecast information when constructing the objective function of a quantified topology reconfiguration scheme, the problem of topology reconfiguration schemes being unable to adapt to load changes is solved.

[0005] This invention is achieved through the following technical solution:

[0006] The first aspect of this application provides a method for dynamically constructing a single network-load graph for topology reconfiguration in a power system, including:

[0007] Power system data is obtained through at least one type of data source to determine the current topology of the power system and form a topology state matrix.

[0008] Based on the topology state matrix and load forecast information, an objective function for reconstructing the topology state is constructed; the objective function is used to quantify the overall performance of the topology reconstructing scheme.

[0009] Based on a preset optimization algorithm and power system operation safety constraints, the objective function is solved to determine the topology reconfiguration scheme;

[0010] The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

[0011] In one feasible implementation, the method further includes:

[0012] Based on the relationship between the network structure and load of the power system, the first influence characteristic of the network structure on load accessibility of the topology reconfiguration scheme and the second influence characteristic of the load distribution on the network loss of the topology reconfiguration scheme are determined respectively.

[0013] In one feasible implementation, the determination of the first influencing feature and the second influencing feature specifically includes:

[0014] Based on network connectivity, power quality indicators, and power supply reliability, the first influence characteristic of the network structure of the topology reconfiguration scheme on load reachability is determined.

[0015] Based on load variation, load spatial distribution and phase distribution, as well as load characteristic parameters, the second influence characteristic of load distribution on network loss of topology reconfiguration scheme is determined.

[0016] In one feasible implementation, the method further includes:

[0017] Based on the first and second impact features, the mutual information theory is used to quantify the coupling strength between the network and the load in the topology reconfiguration scheme.

[0018] Based on the coupling strength, a time-varying coupling model is established; the time-varying coupling model provides the network-load coupling state for topology reconfiguration decisions.

[0019] In one feasible implementation, the method further includes:

[0020] Construct a multi-dimensional architecture that includes at least geographic information, network topology, load distribution, operating status, and topology reconstruction structure for node rendering.

[0021] In one feasible implementation, the method further includes:

[0022] Monitor events in the power system;

[0023] When an event change occurs, a topology reconfiguration process is triggered; the event change includes at least one of topology change, load fluctuation, and system failure.

[0024] In one feasible implementation, the step of obtaining power system data through at least one type of data source to determine the current topology of the power system specifically includes:

[0025] The system acquires power system operation status data from the data acquisition and monitoring system, phasor data of the distribution network from the phase measurement unit, data from the end of the distribution network from the smart terminal, and geographic data of the distribution network from the geographic information system.

[0026] When there is uncertainty in multi-source data obtained from multiple data sources, execute:

[0027] The multi-source data is fused in parallel, and the current topology of the power system is determined based on the Bayesian posterior probability algorithm.

[0028] The second aspect of this application provides a dynamic topology reconfiguration network-load diagram construction system for power systems, comprising:

[0029] The topology state determination unit is used to obtain power system data through at least one type of data source in order to determine the current topology state of the power system and form a topology state matrix.

[0030] The objective function construction unit constructs an objective function for reconstructing the topology state based on the topology state matrix and load forecast information; the objective function is used to quantify the overall performance of the topology reconstruction scheme.

[0031] The topology reconfiguration scheme determination unit solves the objective function based on a preset optimization algorithm and power system operation safety constraints to determine the topology reconfiguration scheme;

[0032] The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

[0033] A third aspect of this application provides an electronic device, comprising: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the above-described method.

[0034] A fourth aspect of this application provides a storage medium, comprising: storing a program or instructions on the storage medium, wherein the program or instructions, when executed by a processor, implement the steps of the above-described method.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0036] This embodiment incorporates load forecasting information when constructing the objective function for quantifying the overall performance of a topology reconfiguration scheme. This load forecasting information is used to determine the network loss and voltage quality indices that constitute the components of the objective function, and it includes information on load changes over future periods. Therefore, this embodiment considers the impact of load changes on the topology reconfiguration scheme, enabling the final determined topology reconfiguration scheme to adapt to load variations. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0038] Figure 1 A flowchart illustrating a power system topology reconfiguration method provided in this application embodiment;

[0039] Figure 2 A schematic diagram illustrating the process of performing topology reconfiguration of an urban distribution network using the power system topology reconfiguration method provided in the embodiments of this application;

[0040] Figure 3 A schematic diagram of the structure of a power system topology reconfiguration system provided in this application embodiment;

[0041] Figure 4 A schematic diagram of the structure of the computing device provided in the application embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explanation only and are not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application.

[0043] As will be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0044] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, product, or apparatus.

[0045] Example 1

[0046] Embodiment 1 of this application provides a method for dynamically constructing a single topology reconfiguration network-load diagram for a power system, which solves the problem that existing topology reconfiguration schemes cannot adapt to load changes.

[0047] The subject executing this method can be any computing device capable of implementing the method, such as a server, mobile phone, personal computer, smart wearable device, smart robot, etc.

[0048] Furthermore, the embodiments of this application do not limit the execution order of different steps. When using the method provided in the embodiments of this application, the execution order of different steps can be adjusted according to actual needs.

[0049] For ease of description, the following uses a dynamic topology reconfiguration network-load diagram construction device for a power system as the execution subject of this method to provide a detailed description of the method provided in this application embodiment.

[0050] like Figure 1 The diagram shown is a flowchart illustrating the specific implementation of a dynamic topology reconfiguration method for a power system using a single load map, as provided in this application embodiment. The method includes the following steps 11 to 13:

[0051] Step 11: Obtain power system data through at least one type of data source to determine the current topology state of the power system and form a topology state matrix.

[0052] The data sources in this embodiment include at least a SCADA (Supervisory Control and Data Acquisition) data acquisition and monitoring system, a PMU (Phasor Measurement Unit), an intelligent terminal, and a GIS (Geographic Information System).

[0053] Among them, the SCADA data acquisition and monitoring system realizes the monitoring, control and data acquisition of the real-time operating status of the power system. The data it collects includes at least the switch status (open / closed status of circuit breakers and disconnectors), analog data (voltage, current, active power, reactive power, frequency, etc.), and equipment alarm information (overload, voltage limit violation, fault signal, etc.) and other operating status data of the power system.

[0054] The PMU (Phase Measurement Unit) is based on synchronous phasor measurement technology and acquires voltage / current phasor information of the power grid with high precision and high frequency. The acquired data includes at least voltage phasors (amplitude + phase), current phasors (amplitude + phase), frequency and rate of change of frequency, and instantaneous values ​​of active / reactive power, etc., of the distribution network phasor data.

[0055] Intelligent terminals are localized data acquisition and control devices deployed at the end of the distribution network (such as transformer substations and user sides) for load and equipment status monitoring. The data they collect includes real-time load (active / reactive power, current, voltage) on the user side, output data of distributed power sources (photovoltaic, wind power), status of low-voltage equipment (such as transformer load rate and switch status in transformer substations), and user electricity consumption characteristics (such as peak-valley electricity consumption patterns and interruptible load capacity) at the end of the distribution network.

[0056] GIS (Geographic Information System) uses geospatial data as its carrier to store and manage the geographic coordinates and spatial relationships of power equipment. The data it collects includes at least the geographic coordinates of the equipment (location of substations, route of lines, coordinates of towers), spatial topological constraints (such as whether the physical path of the line crosses rivers or roads, and the geographic distance between equipment), and geographic information such as the surrounding environment (such as areas with high load density and areas prone to natural disasters).

[0057] For multi-source data collected from multiple data sources, timestamps, coordinate systems, and coding standards are standardized and unified to establish a unified data model and map the original data into comparable feature vectors; missing and abnormal data are labeled for subsequent processing.

[0058] Standardize and unify timestamps, coordinate systems, and coding specifications. Specifically, this can be achieved by: converting all timestamps to a standard format of "UTC time + millisecond precision" (e.g., "2025-10-22T17:21:15.123Z") to ensure the comparability of time-series data, addressing the issue that GIS-collected data uses local coordinate systems while user addresses may use text descriptions, making direct spatial location association impossible; converting all geographic coordinates to the National Geodetic Coordinate System 2000 (CGCS2000), achieving accurate mapping between different coordinate systems through coordinate transformation parameters (e.g., the seven-parameter method); and resolving text descriptions such as user addresses into latitude and longitude coordinates through geocoding technology (e.g., calling the Gaode / Baidu Maps API); and establishing cross-system coding mapping relationships based on unique device attributes (e.g., name + voltage level + location) to address the situation where different coding rules for the same device in different systems lead to confusing device identification.

[0059] Raw data from power systems collected from multiple data sources (such as switch status strings from SCADA and phasor waveforms from PMU) cannot usually be directly used for subsequent algorithm calculations. In this embodiment, the data is structured using a unified data model and mapped to numerical feature vectors.

[0060] One approach to implementing a unified data model design is to divide the raw data into several core entities and enable cross-entity queries through related fields. For example, the raw data can be divided into device entities, status entities, measurement entities, constraint entities, and event entities. The device entities store basic device information (unified code, type, coordinates, rated parameters, etc.), such as "Line #001, Type = 220kV transmission line, Resistance = 0.05Ω / km"; the status entities record the real-time status of the device (switch on / off, voltage and current values, etc.), associated with the device code and timestamp, such as "Device #001, Time = 10:00:00, Status = Closed, Current = 500A"; the measurement entities store high-precision measurement data (PMU phasors, intelligent terminal loads), including attributes such as amplitude, phase, and accuracy, such as "Node #101, Time = 10:00:00.001, Voltage Amplitude = 230kV, Phase = 15°"; the constraint entities store GIS physical constraints (such as "Line #001 cannot connect to Node #202") and operational constraints (such as "Line #001 maximum current carrying capacity = ...). 800A”); Event entity records fault alarms, data anomalies and other events (such as “Time=10:05:00, Event=Line #001 overload, Source=SCADA”).

[0061] The entity data in the above design are mapped to numerical feature vectors of fixed dimensions.

[0062] Multi-source data inevitably contains missing data (e.g., communication interruptions) or anomalies (e.g., sensor malfunctions). These need to be identified and labeled using detection algorithms to avoid affecting subsequent analysis. Missing or anomaly data can be labeled at the corresponding positions in the feature vector.

[0063] In one feasible implementation, step 11 further includes: performing quality assessment on each data in the feature vector, calculating the weight and confidence vector of each data source, and using methods such as window alignment, interpolation, and denoising to form time-series data fragments and quality weight matrices, providing quantifiable weighting basis for subsequent multi-source fusion and topology recognition.

[0064] Data quality assessment can be based on multiple pre-set assessment dimensions, such as accuracy, completeness, and timeliness. By assessing data from each data source in multiple dimensions, the data quality of each data source is quantified, thereby adding source weights and corresponding confidence vectors to the data from each data source. Each element in the confidence vector corresponds to the confidence level of each element in the feature vector.

[0065] Window alignment is used to unify the time granularity of various data sources. When multiple data sources have different sampling frequencies (e.g., PMU 100 times / second, SCADA 1 time / second), the data is aligned to the same time granularity, such as 100ms / step, by using a "sliding time window".

[0066] Missing data interpolation is used to fill data gaps. Interpolation is performed for the missing data in the above-mentioned standards.

[0067] Denoising is used to filter out interference signals. For noise in the measurement data (such as high-frequency fluctuations of PMU phasors and pulse interference of SCADA), a layered filtering method is used to filter out interference signals.

[0068] The output time-series data segments can be continuous data sequences divided by time windows, containing standardized feature vectors from each data source (such as one data point every 100ms, including PMU phasors, SCADA switch status, smart terminal load, etc.). Alignment, interpolation, and denoising have been completed to ensure time continuity and numerical reliability.

[0069] The quality weight matrix is ​​a matrix that corresponds one-to-one with time series data segments. Each element in the quality weight matrix corresponds to the source weight of each data point in the time series data segment.

[0070] In one implementation of this embodiment, step 11 further includes, when there is uncertainty in the multi-source data obtained from multiple data sources, performing: parallel fusion of the multi-source data, and determining the current topology state of the power system based on the Bayesian posterior probability algorithm.

[0071] The topology state is determined by the on / off states of all sectionalizing switches and tie switches in the distribution network, and is represented by a topology state matrix. For example, in a system containing four switches (S1, S2, S3, S4), the topology state matrix is ​​T = [s1, s2, s3, s4], where s i ∈{0,1}, where 0 indicates the switch is open and 2 indicates the switch is closed.

[0072] The parallel fusion of the multi-source data, based on the Bayesian posterior probability algorithm, determines the current topological state of the power system. This can be achieved by using SCADA operating status data for switch status visibility, PMU phasor data for phasor measurement likelihood, data from the end of the distribution network that can only be interrupted for local measurement evidence, and GIS geographic data for geographic topological constraints.

[0073] The specific process of the Bayesian posterior probability algorithm may include the following steps S1 to S5:

[0074] Step S1: Calculate the prior probability of the SCADA switch state. The prior probability of the switch state reflects the topology T based solely on SCADA. i The possibility that it is true.

[0075] Step S2: Calculate the likelihood probability of PMU phasor measurement. The likelihood probability reflects the probability if the topology is T. i The probability of a PMU measurement occurring is then determined.

[0076] Step S3: Calculate the probability of local measurement evidence from the smart terminal. Data from the smart terminal reflects whether a local line is energized, which is reliable evidence and can directly determine topology T. i Is it true?

[0077] Step S4: Calculate the probability of geographic topological constraints. Since GIS provides physical connection constraints, T can be directly determined. i Whether it conforms to geographical topological constraints, i.e. whether it is true.

[0078] Step S5: The posterior probability can be expressed as E all ={E SC E PMU E IT E GIS} represents the total evidence from four data sources, with the denominator P(E) all ) is a normalization constant for all topological T i They are all the same and can be ignored when comparing.

[0079] Prior probability In the absence of any evidence, assume that all candidate topologies are equally probable (P(T)). i) = 1 / m, where m is the candidate topology number); joint likelihood probability When the evidence from the four data sources is independent, it is the product of the probabilities of each data source, that is, the product of the probability values ​​calculated in steps S1 to S4 above.

[0080] Therefore, the posterior probability of each candidate topology can be calculated to determine the current topological state of the power system, i.e., the topology corresponding to the highest posterior probability.

[0081] Step 12: Based on the topology state matrix and load forecast information, construct an objective function for reconstructing the topology state; the objective function is used to quantify the overall performance of the topology reconstructing scheme.

[0082] The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

[0083] The objective function can be specifically expressed as:

[0084]

[0085] Among them, loss weight =0.35, voltage weight =0.30, reliability weight =0.25, operation complexity weight =0.10.

[0086] The network loss function quantifies the power transmission loss of candidate topology T in the load forecasting L scenario. The smaller the value, the lower the loss and the better the economic efficiency. , where ∑ i=n P is the sum of power losses across all power grid lines. total The total power supply of the system is given by the loss rate (e.g., 0.05 indicates a line loss rate of 5%).

[0087] Voltage quality function, which assesses the degree of voltage deviation under topology T and load L conditions; Where vi is the actual voltage of node i, vnominal is the rated voltage, and the result is the sum of the absolute values ​​of the voltage deviations of each node (e.g., 0.06 means that the sum of the absolute values ​​of the voltage deviations of all nodes is 6%).

[0088] The power supply reliability function is used to evaluate the power supply reliability level of topology T. =1-SAIDI, where SAIDI (System Average Interruption Duration Index) is the "System Average Interruption Duration Index", which measures the average total duration of power outages experienced by each user within a certain period.

[0089] The operation complexity function calculates the number of operations required to switch from the current topology T0 to the target topology T. It is usually quantified by the number of switching operations (e.g., if 3 switching operations are performed, the function value is 3). The smaller the value, the simpler the operation and the lower the cost.

[0090] Step 13: Based on the preset optimization algorithm and power system operation safety constraints, solve the objective function to determine the topology reconfiguration scheme.

[0091] The optimization algorithm can be an improved genetic algorithm, which uses an improved genetic algorithm for global optimization and combines it with local search for fine optimization.

[0092] Genetic algorithms are global optimization algorithms that simulate biological evolution, finding the optimal solution through iterative processes of "encoding-selection-crossover-mutation". This step improves upon traditional genetic algorithms by "increasing convergence speed" and "enhancing global search capabilities", adapting to the discrete nature (on / off states) of topology reconstruction.

[0093] The specific implementation process can be: based on the current topology T in step 11 *， An initial population is generated by randomly flipping 1-2 switch states (simulating small-scale adjustments) to ensure that the initial solution closely resembles the actual operating state and reduce invalid searches;

[0094] In this embodiment, the fitness function of the genetic algorithm is designed to correspond to the multi-objective function determined in step 12, and the mapping relationship of "the smaller the objective function value, the higher the fitness" is adopted.

[0095] Randomly select 3-5 individuals from the population, choose the one with the highest fitness to enter the next generation, and increase the probability of high-quality solutions being inherited; directly copy the top 10% of high-fitness individuals in the population to the next generation to avoid the loss of high-quality solutions due to crossover / mutation.

[0096] Improved crossover operator: Divide chromosomes into segments according to the lines (e.g., the switch of feeder 1 is one segment, and the switch of feeder 2 is another segment), and crossover only occurs within the same segment to reduce damage to the overall topology; immediately after crossover, check whether the topology satisfies the radial constraint (see constraint verification step), and if it does not, discard the offspring to avoid invalid iteration.

[0097] Improved mutation operator: This embodiment adopts load sensitivity-based directional mutation, which can specifically calculate the load sensitivity of each switch (e.g., closing a certain tie switch can significantly reduce the load rate of heavily loaded lines) and mark it as a "high-value switch"; during mutation, high-value switches are preferentially flipped (probability increased to 30%), while the mutation probability of ordinary switches is 5%, which improves the efficiency of finding high-quality solutions.

[0098] Set the maximum number of iterations, end the iteration, and output the global optimal solution.

[0099] Refined optimization through local search: The optimal solution obtained through global optimization may be a local optimum, requiring further optimization within its neighborhood through local search. Specifically, this can be done by defining a neighborhood centered on the optimal topology obtained through global optimization; for each candidate topology within the neighborhood, calculating the two terms with the highest weights in the objective function (network loss and voltage quality), and prioritizing the topology that can reduce these two metrics, thereby determining the local optimum.

[0100] In one feasible implementation, the topology reconfiguration scheme needs to meet the constraints of power grid operation. In this embodiment, by introducing constraints such as radiation, capacity, and voltage for linkage verification, a traceable and rollback-capable adaptive reconfiguration process is achieved, avoiding the output of infeasible schemes.

[0101] The verification of radial constraints can be performed by constructing an adjacency matrix based on the topological connectivity and using a depth-first search (DFS) to determine if a closed loop exists; if so, it is marked as infeasible. Capacity constraint verification can be performed based on candidate topologies and load forecasts (L), to calculate the actual load rate of each device and compare it with the rated value; the load rate of all lines and transformers must not exceed the rated value (e.g., line load rate ≤ 80%, transformer load rate ≤ 90%). Voltage constraint verification can be performed by calculating the voltage at each node through power flow calculations, calculating the deviation rate, and marking it as infeasible if it exceeds the range.

[0102] Traceable design can record the optimal solution, fitness change curve, and operation history of key switches for each generation of the population, generating a "reconstruction process log" that includes information such as timestamps, topology status, objective function values, and constraint verification results.

[0103] The rollback mechanism can be triggered if a sudden failure occurs during the execution of the refactoring scheme (such as the switch cabinet refusing to operate): quickly restore to the topology T0 before refactoring based on historical logs; select the suboptimal solution from the alternative schemes and re-execute to ensure uninterrupted power supply.

[0104] In one feasible implementation, the method of this embodiment further includes determining, based on the association between the network structure and load of the power system, a first influence characteristic of the network structure of the topology reconfiguration scheme on load accessibility, and a second influence characteristic of the load distribution on the network loss of the topology reconfiguration scheme.

[0105] Specifically, this includes: analyzing the impact mechanism of network structure on load distribution, calculating the impact of network structure on load accessibility, and determining the primary influencing characteristic. ;

[0106] in, Represents network connectivity / structural characteristics (such as impedance matrix, node degree, path redundancy, topological radius); Indicates power quality indicators (voltage deviation, harmonics, frequency deviation, three-phase imbalance, etc.); Indicates power supply reliability metrics (such as SAIDI, Mean Time Between Failures (MTBF), etc.).

[0107] Analyze the impact of load changes on network power flow, calculate the impact of load distribution on network losses, and determine the second influencing characteristic: .

[0108] Indicates load variation / fluctuation (time-series increment, variance, peak-to-valley ratio, ramp rate); This indicates the spatial / phase distribution of the load (the load percentage of each node / feeder / phase). These represent load characteristic parameters (power factor, transferability, interruptibility, response delay, temperature / time sensitivity coefficient).

[0109] In this implementation, the mutual influence of grid-load interaction is analyzed, the specific laws of grid-load interaction are determined, optimization direction is provided for the reconstruction scheme determined above, and feature input is provided for subsequent coupling quantization.

[0110] In one feasible implementation, the method of this embodiment further includes: based on the first influence feature and the second influence feature, using mutual information theory to quantify the coupling strength between the network and the load in the topology reconfiguration scheme; based on the coupling strength, establishing a time-varying coupling model; the time-varying coupling model provides the network-load coupling state for topology reconfiguration decision-making.

[0111] Among them, coupling strength Among them, coupling strength The network state is characterized by the degree of correlation between the network and the load; a larger value indicates a stronger coupling. 'n' represents the network state, such as comprehensive characteristics like topology, line load rate, and voltage level. For example, "ring network topology + low load" is a network state. 'l' represents the load state, such as comprehensive characteristics like total load, fluctuation characteristics, and type proportion. For example, "high load + 60% industrial load" is a load state. 'P(n)' represents the marginal probability of network state n occurring (the frequency of this network state in historical data). 'P(l)' represents the marginal probability of load state l occurring (the frequency of this load state in historical data). 'P(n,l)' represents the joint probability of network state n and load state l occurring simultaneously (the frequency of their co-occurrence).

[0112] Establish a time-varying coupled model To achieve integrated optimization of grid-load coordination, among which, The dynamic coupling strength at time t represents the real-time correlation between the network and the load at different times; α(t) and β(t) are time-varying weighting coefficients. This is a static coupling model, where ΔM(t) is the dynamic coupling increment.

[0113] In the above implementation method, the tightness of the network-load correlation is quantified by mutual information, and the dynamic changes of the coupling relationship are captured by time-varying model. Finally, the coordinated linkage of network topology optimization and load regulation is realized, so that the power grid operation shifts from passive adaptation to active matching of network-load characteristics, thereby improving the overall operating efficiency and reliability.

[0114] In one feasible implementation, this embodiment also includes constructing a multi-dimensional architecture that includes at least geographic information, network topology, load distribution, operating status, and topology reconstruction structure for node rendering.

[0115] Specifically, it can be: to build a multi-layered visualization architecture, including a geographic information layer (basic geographic background), a network topology layer (dynamic topology), a load distribution layer (real-time load heat map), an operation status layer (equipment operation status), and an analysis results layer (reconstruction and optimization results), integrating dimensions such as geographic information, network topology, load distribution, operation status, and analysis results, and supporting real-time rendering of 1000+ nodes.

[0116] Furthermore, WebGL technology is used to achieve high-performance dynamic rendering and interactive analysis, supporting real-time visualization of large-scale networks, providing multi-scale scaling and detail display, achieving smooth animation effects and interactive response, supporting operation modes such as topology editing, load analysis, and reconstruction simulation, and maintaining a frame rate of over 60 FPS.

[0117] In one feasible implementation, this embodiment further includes monitoring events in the power system; triggering a topology reconfiguration process when an event change occurs; the event change includes at least one of topology change, load fluctuation, and system fault.

[0118] Specifically, it can be a registered event listening rule that triggers local re-identification, re-reconstruction, and global consistency maintenance mechanisms for events such as topology changes, load fluctuations, and system failures. According to time budget and priority queue, it ensures convergence and dynamic update of the network load map within a limited time limit, and the response time is shortened to the millisecond level.

[0119] The feasibility of the above embodiments is verified through a specific example below.

[0120] Case Study: Adaptive Reconfiguration and Integrated Mapping of Urban Power Distribution Network During Evening Peak Hour, Including EV Charging Stations and Distributed Photovoltaics.

[0121] Scenario Description: A 10kV distribution network in a certain urban area includes 3 charging stations (total capacity 6MW), 4 rooftop photovoltaic access points (5MWp installed capacity), and 2 important public load substations. During the evening peak (18:30–20:30), concentrated EV charging causes branch overload and voltage sag. Power fluctuations in photovoltaic output during the evening glow period, compounded by power disturbances caused by cloud shadows, result in issues such as missing data and inconsistent time scales. Data sources include: SCADA (Switching / Measuring), PMU (Phasor Module Unit), smart terminals (transformer load / terminal status), and GIS (Spatial and Connectivity Relationships).

[0122] like Figure 2 As shown, the specific implementation process includes steps 21 to 26:

[0123] Step 21: Multi-source data fusion acquisition and quality assessment. UTC+8 timescale alignment, WGS84 coordinate unification, and IEC 61850 encoding standardization are performed on 2400 records from SCADA, 1200 records from PMU, 1800 records from smart terminals, and 600 records from GIS. Example source weights and confidence vectors are [0.38, 0.32, 0.20, 0.10] and [0.93, 0.90, 0.86, 0.91], generating a quality weight matrix for subsequent weighting.

[0124] Step 22: Topology state Bayesian fusion identification and anomaly localization, fusion of four-source evidence to calculate posterior P(T|D), output topology state matrix T* and confidence level C*; anomaly (0.58) confidence level of A branch switch SA is detected, located to the end segment point of charging station #2 feeder, triggering verification marking and local power flow rapid verification.

[0125] Step 23: Adaptive topology reconstruction based on load perception, constructing a multi-objective function using T* and short-term load forecast L (including charging flow curve and photovoltaic output forecast interval). The weights are set to [0.35, 0.30, 0.25, 0.10]. An improved genetic algorithm and local search are used to solve the problem within a 60-second budget. Feasibility checks and rollback strategies are implemented for constraints such as radiation, capacity, voltage, and operation to obtain the operation sequence of "transferring part of the load of charging station #2 to the adjacent feeder, closing the loop at SB, and disconnecting SC".

[0126] Step 24: Network-load coupling modeling and strong / weak link identification: calculation and The system identifies the strongly coupled link formed by "charging station #2 - bus M3 - photovoltaic access point PV-3"; it uses mutual information I(Network,Load) to measure the coupling strength and constructs a time-varying model. It is used to evaluate the combined impact of operation on power quality and losses.

[0127] Step 25: Network Load Integrated Visualization: On the WebGL five-layer architecture, the geographic base map, topology connectivity, heat map of transformer area / site load, key node phasors and optimization suggestion overlay layer are displayed in an integrated manner; interactive simulation and playback of SA / SB / SC are supported, and the frame rate remains approximately 60 FPS for scenarios with more than 1000 nodes.

[0128] Step 26: Event-driven incremental update and consistency maintenance: Establish a listener for events such as "charging station group control power limiting command", "photovoltaic output drop alarm", and "user-side energy storage switching"; when the photovoltaic PV-3 output drops by 15% and lasts for more than 2 minutes, trigger local re-identification and incremental reconstruction, merge the new operation "power limiting curve downshift + local reactive power support" and perform global consistency verification and version solidification.

[0129] This embodiment incorporates load forecasting information when constructing the objective function for quantifying the overall performance of a topology reconfiguration scheme. This load forecasting information is used to determine the network loss and voltage quality indices that constitute the components of the objective function, and it includes information on load changes over future periods. Therefore, this embodiment considers the impact of load changes on the topology reconfiguration scheme, enabling the final determined topology reconfiguration scheme to adapt to load variations.

[0130] Example 2

[0131] To address the problem that existing topology reconfiguration schemes cannot adapt to load changes, and based on the same inventive concept as Embodiment 1, this application also provides a dynamic topology reconfiguration network-load diagram construction system for power systems.

[0132] The specific structural diagram of the system is as follows: Figure 3 As shown, it includes the following functional units 31 to 33:

[0133] The topology state determination unit 31 is used to obtain power system data through at least one type of data source to determine the current topology state of the power system and form a topology state matrix.

[0134] Specifically, it is used to obtain power system operation status data from data acquisition and monitoring systems, obtain distribution network phasor data from phase measurement units, collect data from the end of distribution networks from smart terminals, and obtain distribution network geographic data from geographic information systems.

[0135] When there is uncertainty in multi-source data obtained from multiple data sources, execute:

[0136] The multi-source data is fused in parallel, and the current topology of the power system is determined based on the Bayesian posterior probability algorithm.

[0137] Objective function construction unit 32 constructs an objective function for reconstructing the topology state based on the topology state matrix and load prediction information; the objective function is used to quantify the comprehensive performance of the topology reconstruction scheme.

[0138] The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

[0139] The topology reconfiguration scheme determination unit 33 solves the objective function based on a preset optimization algorithm and power system operation safety constraints to determine the topology reconfiguration scheme.

[0140] In one implementation, the reconfiguration system of this embodiment further includes a network-load correlation analysis unit, used to determine, based on the correlation between the power system's network structure and loads, a first influence characteristic of the network structure of the topology reconfiguration scheme on load accessibility, and a second influence characteristic of the load distribution on the network loss of the topology reconfiguration scheme. Specifically, it is used to: determine the first influence characteristic of the network structure of the topology reconfiguration scheme on load accessibility based on network connectivity, power quality indicators, and power supply reliability; and determine the second influence characteristic of the load distribution on the network loss of the topology reconfiguration scheme based on load variation, load spatial distribution and phase distribution, and load characteristic parameters.

[0141] The reconfiguration system in this embodiment also includes a coupling analysis unit, which is used to quantify the coupling strength between the network and the load of the topology reconfiguration scheme based on the first influence feature and the second influence feature and using mutual information theory; and to establish a time-varying coupling model based on the coupling strength; the time-varying coupling model provides the network-load coupling state for topology reconfiguration decision-making.

[0142] The reconstruction system in this embodiment also includes a rendering building unit, which is used to build a multi-dimensional architecture that includes at least geographic information, network topology, load distribution, operating status and topology reconstruction structure, for node rendering.

[0143] The reconfiguration system in this embodiment also includes an incremental processing unit for monitoring events in the power system; when an event change occurs, a topology reconfiguration process is triggered; the event change includes at least one of topology change, load fluctuation, and system fault.

[0144] This embodiment incorporates load forecasting information when constructing the objective function for quantifying the overall performance of a topology reconfiguration scheme. This load forecasting information is used to determine the network loss and voltage quality indices that constitute the components of the objective function, and it includes information on load changes over future periods. Therefore, this embodiment considers the impact of load changes on the topology reconfiguration scheme, enabling the final determined topology reconfiguration scheme to adapt to load variations.

[0145] Based on the same inventive concept as the foregoing embodiments of this application, this application also provides a computing device.

[0146] like Figure 4 As shown, the computing device includes a memory 41 and a processor 42. The memory 41 can be configured to store various other data to support operation on the electronic device. Examples of such data include instructions for any application or method used to operate on the electronic device. The memory 41 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 read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0147] The processor 42, coupled to the memory 41, is used to execute a computer program stored in the memory 41 for performing a method for aggregating feasible domains of a virtual power plant as described in the foregoing embodiments.

[0148] When processor 42 executes the computer program to perform a power system topology reconfiguration method, load forecasting information is incorporated into the objective function used to quantify the overall performance of the topology reconfiguration scheme. This load forecasting information is used to determine the network loss index and voltage quality index that constitute the components of the objective function, and it includes information on load changes over future periods. Therefore, this embodiment considers the impact of load changes on the topology reconfiguration scheme, enabling the final determined topology reconfiguration scheme to adapt to load changes.

[0149] When the processor 42 executes the computer program in the memory 41, in addition to the functions described above, it can also perform other functions, as detailed in the descriptions of the preceding embodiments.

[0150] Furthermore, such as Figure 4 As shown, the computing device also includes other components such as a display 44, a communication component 43, a power supply component 45, and an audio component 46. Figure 4 The diagram only shows some components and does not mean that the computing device includes only these components. Figure 4 The components shown.

[0151] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a computer, can implement the methods provided in the above embodiments.

[0152] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0153] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0154] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamically constructing a single-map topology reconfiguration network of a power system, characterized in that, include: Power system data is obtained through at least one type of data source to determine the current topology of the power system and form a topology state matrix. Based on the topology state matrix and load forecast information, an objective function for reconstructing the topology state is constructed; the objective function is used to quantify the overall performance of the topology reconstructing scheme. Based on a preset optimization algorithm and power system operation safety constraints, the objective function is solved to determine the topology reconfiguration scheme; The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

2. The method according to claim 1, characterized in that, The method further includes: Based on the relationship between the network structure and load of the power system, the first influence characteristic of the network structure on load accessibility of the topology reconfiguration scheme and the second influence characteristic of the load distribution on the network loss of the topology reconfiguration scheme are determined respectively.

3. The method according to claim 2, characterized in that, The determination of the first and second influencing features specifically includes: Based on network connectivity, power quality indicators, and power supply reliability, the first influence characteristic of the network structure of the topology reconfiguration scheme on load reachability is determined; Based on load variation, load spatial distribution and phase distribution, as well as load characteristic parameters, the second influence characteristic of load distribution on network loss of topology reconfiguration scheme is determined.

4. The method according to claim 2, characterized in that, The method further includes: Based on the first and second impact features, the mutual information theory is used to quantify the coupling strength between the network and the load in the topology reconfiguration scheme. Based on the coupling strength, a time-varying coupling model is established; the time-varying coupling model provides the network-load coupling state for topology reconfiguration decisions.

5. The method according to claim 1, characterized in that, The method further includes: Construct a multi-dimensional architecture that includes at least geographic information, network topology, load distribution, operating status, and topology reconstruction structure for node rendering.

6. The method according to claim 1, characterized in that, The method further includes: Monitor events in the power system; When an event change occurs, a topology reconfiguration process is triggered; the event change includes at least one of topology change, load fluctuation, and system failure.

7. The method according to claim 1, characterized in that, The process of obtaining power system data through at least one type of data source to determine the current topology of the power system specifically includes: The system acquires power system operation status data from the data acquisition and monitoring system, phasor data of the distribution network from the phase measurement unit, data from the end of the distribution network from the smart terminal, and geographic data of the distribution network from the geographic information system. When there is uncertainty in multi-source data obtained from multiple data sources, execute: The multi-source data is fused in parallel, and the current topology of the power system is determined based on the Bayesian posterior probability algorithm.

8. A dynamic topology reconfiguration system for power systems, characterized in that: include: The topology state determination unit is used to obtain power system data through at least one type of data source in order to determine the current topology state of the power system and form a topology state matrix. The objective function construction unit constructs an objective function for reconstructing the topology state based on the topology state matrix and load forecast information; the objective function is used to quantify the overall performance of the topology reconstruction scheme. The topology reconfiguration scheme determination unit solves the objective function based on a preset optimization algorithm and power system operation safety constraints to determine the topology reconfiguration scheme; The objective function is determined by a weighted average of the network loss determined by load forecasting information and the reconstructed topology state, the voltage quality determined by load forecasting information and the reconstructed topology state, the power supply reliability determined by the reconstructed topology state, and the operational complexity determined by the current topology state and the reconstructed topology state.

9. An electronic device, characterized in that, include: It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1-7.

10. A storage medium, characterized in that, include: The storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-7.

Images