An active power distribution network configuration evaluation reconstruction method, system, device and medium

Abstract

The application relates to the technical field of power distribution network operation optimization and intelligent reconstruction, and discloses an active power distribution network configuration evaluation and reconstruction method, system, device and medium. The method comprises the following steps: a multi-dimensional evaluation index system is established, the operation state is distinguished, and the original values of various indexes are calculated; the satisfaction degrees of various indexes are normalized; the basic constant weights of various scenes under different states are calculated by using an analytic hierarchy process; in response to the situation that the satisfaction degree of any index is lower than a preset threshold value, the weights are corrected through an exponential state variable weight penalty function, a comprehensive evaluation target function is established; the function is used as fitness, a genetic algorithm is used to iteratively optimize switch combinations, a scheme that does not satisfy a radial topology is limited, and optimal switch instructions are output. According to the application, the variable weight mechanism is used to adaptively amplify the weights of deteriorated indexes, the evaluation and reconstruction are combined in a closed loop, and accurate identification and global optimization reconstruction under extreme working conditions are realized.

Description

Technical Field

[0001] This invention relates to the field of distribution network operation optimization and intelligent reconfiguration technology, specifically to an active distribution network configuration assessment and reconfiguration method, system, equipment, and medium. Background Technology

[0002] The high proportion of distributed generation access transforms the distribution network from a unidirectional power flow to an active distribution network with bidirectional fluctuations between source and load. Network reconfiguration is a crucial means to address its operational uncertainties. Current methods for configuration assessment and reconfiguration of distribution networks under extreme conditions have several shortcomings: fixed-weight comprehensive assessment methods lack adaptability to operating states. When extreme conditions such as severe node voltage exceedances or line overloads occur, they cannot exponentially amplify the weight of critical indicators, easily misjudging schemes with severe exceedances but low network losses as optimal; existing technologies separate assessment from reconfiguration control, lacking a closed-loop mechanism that directly uses multi-dimensional health assessment as the fitness function of the optimization algorithm; existing indicator systems focus primarily on static cross-sectional electrical quantities, lacking quantitative assessment of the spatiotemporal matching degree between distributed generation output and load, making it difficult to guide the topology towards promoting the local consumption of renewable energy. Summary of the Invention

[0003] In view of the above-mentioned existing problems, the present invention provides an active power distribution network configuration assessment and reconfiguration method, system, equipment and medium.

[0004] Therefore, the technical problem solved by this invention is: how to establish an evaluation model that dynamically adjusts weights according to the operating status of the distribution network, and directly use the evaluation results as the fitness function of the genetic algorithm to form an evaluation-reconstruction closed loop, so as to realize adaptive penalty of critical indicators and multi-objective comprehensive optimal network configuration reconstruction under extreme operating conditions.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an active distribution network configuration assessment and reconfiguration method, comprising, Establish a multi-dimensional evaluation indicator system, distinguish the operating status, and calculate the indicators under different statuses; Based on the aforementioned multi-dimensional evaluation index system, the original values ​​of each index are first processed to obtain the satisfaction level of each index. Based on the satisfaction level of each indicator, the first analysis method is used to calculate the indicator weights of each scenario under different states. If the satisfaction level of any indicator falls below a preset critical threshold, the system is corrected using a first penalty function to obtain dynamic weights and establish a first objective function. Based on the comprehensive evaluation objective function, adaptive optimization and reconstruction are completed through iterative optimization using a genetic algorithm.

[0006] As a preferred embodiment of the active distribution network configuration evaluation and reconfiguration method described in this invention, the first processing of the original values ​​of each indicator to obtain the satisfaction level of each indicator includes, Based on the physical characteristics of each indicator, they are divided into minimum-optimal type and maximum-optimal type. A satisfaction evaluation function is constructed, and the normalized value of each indicator is calculated.

[0007] As a preferred embodiment of the active distribution network configuration evaluation and reconfiguration method described in this invention, the method employs a first analysis method to calculate the index weights of each scenario under different states, including: A hierarchical structure adapted to the evaluation of distribution network configuration schemes is established. By constructing a judgment matrix and performing consistency checks, the basic constant weights of the scheme layer relative to the target layer are calculated.

[0008] As a preferred embodiment of the active distribution network configuration evaluation and reconfiguration method described in this invention, the establishment of the first objective function includes: The basic constant weights are dynamically modified by introducing variable weight theory. For the satisfaction of each indicator, an exponential state variable weight penalty function is constructed to calculate the dynamic weight of each indicator. Combined with the normalized value of each indicator, a comprehensive evaluation objective function is established.

[0009] As a preferred embodiment of the active distribution network configuration assessment and reconfiguration method described in this invention, the step of establishing a multi-dimensional assessment index system to distinguish operating states includes: Construct an evaluation index system covering power supply capacity, power supply reliability, voltage quality and economy, and divide the operation status of the distribution network into normal state, risk state and recovery state.

[0010] As a preferred embodiment of the active distribution network configuration evaluation and reconfiguration method described in this invention, the calculation of evaluation indicators under different states includes: For different operating states, corresponding indicators to be calculated are determined from each dimension of indicators, and the original values ​​of each indicator to be calculated are calculated.

[0011] As a preferred embodiment of the active distribution network configuration evaluation and reconfiguration method described in this invention, the step of iteratively optimizing and reconfiguring using a genetic algorithm includes: Using the comprehensive evaluation objective function value as the fitness, a genetic algorithm is used to optimize the distribution network switch combination. During the iteration process, restrictions are imposed on schemes that do not meet the radial topology constraints, and finally the optimal switch action command is output.

[0012] This invention unifies the dimensions of multi-dimensional indicators by constructing a satisfaction normalization function, uses the analytic hierarchy process to determine the basic constant weights, and introduces an exponential state-variable weight penalty function to dynamically correct the weights to form a comprehensive evaluation objective function. This can adaptively amplify the weights of deteriorating indicators, thereby achieving accurate quantification and evaluation of extreme operating risks in the power distribution network.

[0013] This invention provides an active distribution network configuration assessment and reconfiguration system, comprising: The indicator system construction module is used to establish an evaluation indicator system covering multi-dimensional operational efficiency, identify the current operating status of the distribution network, and collect the raw data of each evaluation indicator. The satisfaction calculation module is used to perform initial processing on the raw values ​​of each evaluation indicator to obtain the satisfaction value of each evaluation indicator. The weight calculation module is used to calculate the weights of each scenario under different states based on the satisfaction values ​​of each evaluation indicator, using the first analysis method, to obtain the basic constant weights. The dynamic evaluation module is used to monitor the satisfaction values ​​of each indicator. When any satisfaction value falls below the preset safety threshold, a penalty mechanism is activated to dynamically adjust the indicator weights and generate a comprehensive evaluation score based on the adjusted weights and satisfaction values. The optimization and reconfiguration module is used to use the comprehensive evaluation objective function value as the fitness, employ a genetic algorithm to iteratively optimize the distribution network switch combination, impose restrictions on schemes that do not meet the radial topology constraints during the iteration process, and output the optimal switch action command.

[0014] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an active power distribution network configuration evaluation and reconfiguration method.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an active power distribution network configuration assessment and reconfiguration method.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: First, a multi-dimensional indicator system covering power supply capacity, power supply reliability, voltage quality, and economy is established, distinguishing between three operating states: normal, risk, and recovery, and calculating the original values ​​of each indicator. A dimensionless satisfaction function is used, and the basic constant weights are determined using the analytic hierarchy process (AHP). An exponential state-variable weighting penalty function is introduced, exponentially amplifying the weight when the satisfaction level falls below a critical threshold, thus establishing a comprehensive evaluation objective function. This function is used as the fitness of a genetic algorithm, iteratively optimizing through loop coding and radial topological constraints to output the optimal switching command. Existing fixed-weight methods are prone to evaluation distortion under extreme conditions, and evaluation and reconstruction are disconnected. This invention adaptively highlights deterioration indicators through a variable weighting mechanism, avoiding misjudgment of dangerous solutions; the evaluation results are directly embedded into the genetic algorithm to form a closed loop, achieving adaptive reconstruction that balances economy and safety. Through satisfaction calculation and threshold triggering, the ability to identify extreme risks is improved, and it has broad scenario adaptability. Attached Figure Description

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

[0018] Figure 1 This is an implementation diagram of an active distribution network configuration evaluation and reconfiguration method provided in one embodiment of the present invention.

[0019] Figure 2 This is a diagram illustrating the evaluation system of distribution network indicators including distributed power sources, provided as an embodiment of the present invention, for an active distribution network configuration evaluation and reconfiguration method.

[0020] Figure 3 This is a hierarchical structure diagram of the distribution network configuration scheme evaluation of an active distribution network configuration evaluation and reconfiguration method provided in an embodiment of the present invention.

[0021] Figure 4 The IEEE 33-node circuit diagram is provided for an active distribution network configuration evaluation and reconfiguration method according to an embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 The first embodiment of the present invention provides an active distribution network configuration evaluation and reconfiguration method, comprising: S1: Establish a multi-dimensional evaluation indicator system, distinguish the operating status, and calculate the indicators under different statuses.

[0024] S2: Based on a multi-dimensional evaluation index system, the original values ​​of each index are processed to obtain the satisfaction level of each index.

[0025] S3: Based on the satisfaction level of each indicator, the first analysis method is used to calculate the indicator weights of each scenario under different states.

[0026] S4: In response to any indicator's satisfaction level falling below a preset critical threshold, the system is corrected using a first penalty function to obtain dynamic weights and establish a first objective function.

[0027] S5: Based on the comprehensive evaluation objective function, adaptive optimization and reconstruction are completed through iterative optimization using a genetic algorithm.

[0028] It should be noted that existing technologies mainly employ a fixed-weight comprehensive evaluation method. Under extreme conditions such as node voltage exceeding limits or line overload, this method cannot adaptively amplify the weights of critical indicators, easily misjudging unsafe solutions with severe limit exceedances but low network losses as optimal. Furthermore, evaluation and reconstruction are disconnected, lacking a closed-loop mechanism that directly embeds multi-dimensional health scores into the optimization algorithm, making it difficult to achieve adaptive reconstruction that balances economy and safety. Therefore, establishing an evaluation model that dynamically adjusts weights according to operational status and using the evaluation results as the fitness of a genetic algorithm to form a synergistic evaluation-reconstruction linkage is also crucial.

[0029] This embodiment first constructs a multi-dimensional indicator system covering power supply capacity, power supply reliability, voltage quality, and economy, and distinguishes between three states: normal, risk, and recovery. The basic constant weights are determined by satisfaction normalization and analytic hierarchy process. Then, an exponential state-variable penalty function is introduced, which exponentially amplifies the weight when the satisfaction level is below a threshold, thus establishing a comprehensive evaluation objective function. Finally, this function is used as the fitness of the genetic algorithm. Through loop coding and radial constraint iterative optimization, the optimal switching command is output, realizing accurate identification of extreme risks and global optimal reconstruction.

[0030] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, an active distribution network configuration evaluation and reconfiguration method is provided.

[0031] In this embodiment of the application, step S1 establishes a multi-dimensional evaluation index system, distinguishes operating states, and calculates each index under different states, specifically including the following steps A1-A2: A1: Establish a multi-dimensional evaluation indicator system to differentiate operational status.

[0032] It should be noted that, as Figure 2 As shown, considering multiple factors such as the power supply capacity, reliability, voltage quality, and economic benefits of the distribution system, a multi-level evaluation index for distribution network configuration schemes is established. The operating states of the distribution network are divided into normal, risk, and recovery states, and indexes are selected through correlation analysis. Considering the importance of various optimization indicators under different operating states in various typical scenarios, the analytic hierarchy process (AHP) is used to determine the weight of each indicator in various typical scenarios. A comprehensive satisfaction evaluation method is used to evaluate each indicator, and a satisfaction evaluation model for network configuration schemes is constructed with the goal of maximizing the weighted sum of satisfaction for each operating state indicator.

[0033] A2: Calculate the evaluation indicators under different conditions.

[0034] It should be noted that, for different operating states, corresponding indicators to be calculated are determined from each dimension of indicators, and the original values ​​of each indicator in the indicators to be calculated are calculated.

[0035] A2.1: Power supply capacity.

[0036] Maximum load rate of the power grid line: This mainly assesses the highest load rate level of the power grid line. If the maximum load rate is heavily loaded or overloaded, it indicates that the remaining power supply margin in the power supply area is small, and there are potential safety hazards. The calculation formula under long-term normal operation is as follows: Under real-time recovery and short-term risk conditions, the calculation formula is as follows: in, This represents the maximum load rate of the line under long-term normal operating conditions. For time period index, The total number of time periods included in the statistics. For time period Flowing through the branch road power, branch road Maximum power, For branch road collection, This represents the maximum load rate of the line under long-term normal operation.

[0037] Average line load factor: This mainly assesses the overall load factor level of the power grid lines. The calculation formula is as follows: in, The average load factor of the line. This represents the number of branch roads.

[0038] Inter-line load transfer: This primarily assesses the network's ability to return to normal operation after a fault is repaired. The calculation formula for long-term normal operation is as follows: in, This refers to the load transfer amount between lines under long-term normal operating conditions. For the set of load nodes in the distribution network, Number the load nodes. For the first Load nodes during a typical period The average active load, For load transfer state variables; when When, it means Time-of-use load nodes The load is switched from its original power supply path to another line or power supply path under the corresponding state. When, it means Time-of-use load nodes No power transfer between lines occurred.

[0039] Under real-time recovery and short-term risk conditions, the calculation formula is as follows: in, This is a coefficient used to determine whether the load has been transferred. When, it indicates the load node. The load is transferred to another power source when When, it indicates the load node. The load was not transferred to another power source. For load nodes The magnitude of the load.

[0040] A2.2: Power supply reliability: System reliability: This mainly assesses the system's reliability level, and the calculation formula is as follows: in, For system reliability, After dividing the time period, the first Time-of-use load nodes Energy loss due to power outages For load nodes The weighting coefficients, For time period Load Node The average load, For time period Load Node The average power outage time.

[0041] Power outages of critical loads: This mainly assesses the recovery status of critical loads after fault recovery. The calculation formula is as follows: in, For load nodes The magnitude of the important load power, For state change parameters, take... This indicates that power has not been restored. This indicates that power has been restored. It is a set of nodes containing significant loads.

[0042] Non-critical load power outage: This mainly assesses the general load restoration situation after fault recovery. The calculation formula is as follows: in, For power outages affecting non-critical loads, For state change parameters, take... This indicates that power has not been restored. This indicates that power has been restored. This is a set of nodes that do not contain significant loads.

[0043] Feeder capacity margin: Feeder capacity margin refers to the degree of recovery reserve in the event of a secondary fault. It mainly assesses the current grid's ability to withstand secondary faults. The calculation formula is as follows: in, For the feeder The spare capacity of the connected interconnecting switch, For feeder The load value, The number of feeders that participate in power restoration and are connected by a tie switch.

[0044] A2.3: Voltage Quality: Voltage Deviation Level: This mainly assesses the voltage deviation at various load points in the power grid. Under long-term normal operation, the calculation formula is as follows: Under real-time recovery and short-term risk conditions, the calculation formula is as follows: in, This represents the level of voltage deviation under long-term normal operating conditions. for Time-of-use load nodes average voltage, For load nodes Maximum allowable voltage, For load nodes The minimum allowable voltage, This refers to the level of voltage deviation under real-time recovery and short-term risk conditions. For load nodes The voltage.

[0045] A2.4: Economic efficiency: Line loss: This mainly assesses the cost incurred due to line losses. Under long-term normal operation, the calculation formula is as follows: Under real-time recovery and short-term risk conditions, the calculation formula is as follows: in, This refers to the losses incurred during long-term normal operation of the line. for Electricity prices during specific time periods; for Active power loss in time-of-use networks, The duration of a single time period. This refers to the losses incurred by the line under real-time recovery and short-term risk conditions. The current electricity price; This represents the current active power loss of the network.

[0046] Switching operation costs: This mainly assesses the costs incurred by all switching actions within the system. Under long-term normal operation, the calculation formula is as follows: Under real-time recovery and short-term risk conditions, the calculation formula is as follows: in, The operating cost of the switch under long-term normal operation. The number of switches in the system. Cost per single switch operation for Time period The state of each switch for Time period The state of each switch The cost of operating the switch under real-time recovery and short-term risk conditions. To reconstruct the previous The state of each switch For the reconstructed first The state of each switch.

[0047] Furthermore, as shown in Table 1, the degree of concern and urgency of various optimization objectives such as system security, power supply capacity, power supply reliability, and economy are not entirely consistent in different operating states. Therefore, the indicators selected in each operating state are also different.

[0048] Table 1. Attribute Table of Various Indicators under Different Operating States

[0049] Based on step S1, this embodiment establishes an evaluation system that covers multiple dimensions and distinguishes between normal, risk, and recovery states. This system can comprehensively quantify the performance of the distribution network under different operating scenarios, providing a data foundation for subsequent dynamic weight-change evaluation and solving the problems of the existing indicator system being too singular in dimensions and lacking state differentiation.

[0050] In this embodiment of the invention, step S2 involves performing a first processing on the original values ​​of each indicator based on a multi-dimensional evaluation index system to obtain the satisfaction level of each indicator. Specifically, this includes the following steps: Based on the physical characteristics of each indicator, they are divided into minimum-optimal type and maximum-optimal type. A satisfaction evaluation function is constructed, and the normalized value of each indicator is calculated.

[0051] It should be noted that, considering the inconsistent dimensions of the various evaluation indicators constructed in step S1, direct comprehensive calculation is not possible. Based on the multi-dimensional evaluation indicator system, the original values ​​of each indicator undergo a first processing step, namely, dimensionless data processing.

[0052] The raw values ​​of each indicator were normalized using a satisfaction assessment method. Based on their physical characteristics, indicators are classified into maximum-optimal type and minimum-optimal type. For minimum-optimal type indicators... (Such as maximum line load rate, voltage deviation level, line loss cost, etc.), its satisfaction evaluation function is constructed as follows: For the maximum optimal type index (Such as load transfer between lines, feeder capacity margin, etc.), its satisfaction evaluation function is constructed as follows: in, For the first The calculation results of each index in a specific network configuration scheme This represents the theoretical maximum value of the indicator. This is the theoretical minimum value of the indicator.

[0053] Through the above calculations, we obtain the range of each indicator. Normalized value of satisfaction within The larger the value, the better the performance of the indicator.

[0054] Based on step S2, this embodiment distinguishes the optimization direction of the indicators and constructs a satisfaction function, thus mapping the original numerical values ​​with different dimensions to a unified system. The interval solves the problem that multiple indicators cannot be directly integrated for calculation, making indicators with different physical characteristics comparable.

[0055] In this embodiment of the application, step S3, based on the satisfaction level of each indicator, uses the first analysis method to calculate the indicator weights of each scenario under different states, specifically including the following steps B1-B3: After obtaining the dimensionless indicators, the Analytic Hierarchy Process (AHP) is used as the first analytical method to determine the basic weights of each indicator.

[0056] B1: Establish a hierarchical structure to adapt to the evaluation of distribution network configuration schemes.

[0057] It should be noted that, as Figure 3 As shown, based on the selected evaluation indicators, various elements in the distribution network are classified and their interrelationships are explored in depth, thus constructing a hierarchical structure diagram suitable for the evaluation of distribution network configuration schemes.

[0058] Using the comprehensive evaluation G of distribution network configuration schemes as the target layer aims to provide a basis for judging the merits of network configuration schemes. At the criterion layer, the evaluation functions and analysis objectives of the distribution network are categorized from a macro perspective, including power supply capacity evaluation (A), power supply reliability evaluation (B), voltage quality evaluation (C), and economic evaluation (D). The bottom layer, composed of specific parameter indicators, forms the foundation for the evaluation of distribution network operation modes. Its comprehensiveness, scientific rigor, and targeted approach determine the final effectiveness of the distribution network configuration scheme evaluation.

[0059] B2: Construct the judgment matrix and perform consistency checks.

[0060] It should be noted that the survey was completed by experts for a specific assessment subject. Table 2 Expert Survey Form

[0061] in, Reflects the indicators relative to indicators The importance of each indicator is determined by its relative importance. .

[0062] Furthermore, a judgment matrix was established based on the survey questionnaire. : in, This represents the total number of evaluation indicators.

[0063] Find the judgment matrix Maximum eigenvalue and its corresponding eigenvectors : For eigenvectors Perform normalization to obtain the first... Criteria corresponding to each indicator Weights relative to the target layer : in, For the number of criteria, For feature vectors The Middle One portion, The formula uses the index for summing the criterion layer components.

[0064] Similarly, for each criterion, construct the next-level judgment matrix, and calculate the result of the first-level judgment matrix in the scheme layer. The indicator relative to its corresponding number Local weights of each criterion .

[0065] Consistency check ( (No check is needed when =1, 2) Random Consistency Ratio for: In the formula: As a consistency indicator, This is the average random consistency index.

[0066] Consistency Indicators It can be determined by the following formula: when If the judgment matrix meets the consistency requirement, then it is considered to meet the consistency requirement; otherwise, the values ​​of the elements in the judgment matrix need to be adjusted and the weights need to be redefined.

[0067] B3: The basic constant weights of the calculation scheme layer relative to the target layer.

[0068] It should be noted that after solving the judgment matrices at each layer to obtain the local weights, the global weights of each specific indicator at the scheme layer relative to the target layer are calculated. These global weights are defined as the basic constant weights of the indicators. ( The calculation formula is as follows: Calculated The basic constant weights satisfy the normalization condition. .

[0069] Following the steps above, the weights of network configuration scheme indicators for each typical scenario under normal, risk, and recovery states were calculated using the analytic hierarchy process (AHP). The results are shown in Table 3.

[0070] Table 3. Calculation results of indicator weights in different typical scenarios

[0071] Based on step S3, this embodiment calculates the indicator weights for different operating states such as normal, risk, and recovery, which solves the problem that fixed weights cannot adapt to changes in scenarios and makes the judgment of the importance of indicators in each scenario more in line with actual working conditions.

[0072] In this embodiment of the application, in step S4, in response to any indicator's satisfaction level falling below a preset critical threshold, a first penalty function is used for correction to obtain dynamic weights, and a first objective function is established. Specifically, this includes the following steps C1-C2: C1: Introduce variable weight theory to dynamically modify the basic constant weights, and construct an exponential state variable weight penalty function as the first penalty function for the satisfaction of each indicator.

[0073] It should be noted that, in order to overcome the defect of the fixed weight method in evaluating distortion when the local operating conditions of the distribution network deteriorate, this embodiment introduces variable weight theory to adjust the basic constant weight. Dynamic adjustments are made for the satisfaction value of each evaluation indicator. The critical threshold for its safe operation is set as follows: Construct an exponential state-change penalty function. : when season ; when At that time, the punishment mechanism will be activated, causing... ; in, An adjustment coefficient for controlling the severity of punishment.

[0074] C2: Calculate the dynamic weights of each indicator and combine them with the normalized values ​​of each indicator to establish a comprehensive evaluation objective function as the first objective function.

[0075] It should be noted that the actual dynamic weights of each indicator under the current network state are calculated using the variable weight formula. : in, This is the vector of original index values ​​corresponding to the current network configuration scheme. To evaluate the total number of indicators, For the first The basic constant weight of each indicator For the first The state-weighted penalty function value corresponding to each indicator.

[0076] Combining the dynamic weights of each evaluation indicator Normalized value of satisfaction Establish a comprehensive evaluation objective function for network configuration schemes. : Evaluation function This represents the overall operational health of the current network configuration scheme; the higher the value, the better the overall operational status.

[0077] Based on step S4, this embodiment sets a critical threshold and constructs an exponential penalty function. When the satisfaction level of an indicator is lower than the threshold, its weight is dynamically amplified, which solves the problem of evaluation distortion caused by the inability of fixed weights to reflect the urgency of indicators under extreme conditions.

[0078] In this embodiment of the application, step S5, based on the comprehensive evaluation objective function, uses a genetic algorithm for iterative optimization to complete the adaptive optimization and reconstruction, specifically including the following steps: Using the comprehensive evaluation objective function value as the fitness, a genetic algorithm is used to optimize the distribution network switch combination. During the iteration process, restrictions are imposed on schemes that do not meet the radial topology constraints, and finally the optimal switch action command is output.

[0079] It should be noted that, guided by the comprehensive evaluation function constructed in step S4, an improved genetic algorithm is used to perform global adaptive optimization in the solution space of the distribution network topology.

[0080] The specific implementation process is as follows: First, chromosome encoding and population initialization are performed, and the distribution network topology is mapped using the "basic loop breakpoint encoding" strategy. Then, the distribution network is identified during loop-closed operation. There are 1 basic independent loop, and the chromosome length of the genetic algorithm is set to be 1. Each gene locus on a chromosome corresponds to a basic loop, and the gene value represents the switch number of the branch in that loop that is in an open state. The system generates these values ​​randomly. An initial switch combination scheme that satisfies the radial operation constraints of the distribution network constitutes a scale of The initial population.

[0081] The population then enters the iterative evolution phase. For each individual in the population (i.e., a specific topology switch combination scheme), a forward-backward power flow calculation is performed to obtain the voltage of each node and the power distribution of each branch in the power grid under that topology. The calculated operating parameters are then substituted into the dynamic evaluation model described in step two to calculate the comprehensive evaluation function value of that individual. This invention directly defines the comprehensive evaluation function value F as the fitness value of the individual.

[0082] After calculating the fitness of individuals, constraint verification and penalties are performed. If the decoded network topology of an individual exhibits islanded or ring-like operation, or causes the power flow calculation to fail to converge, a maximum penalty term is applied to the fitness value of that individual, causing it to be eliminated in subsequent evolutions. Based on the fitness values ​​of each effective individual, selection operations based on roulette wheel selection, single-point crossover operations, and random mutation operations are performed sequentially to generate the next generation of offspring. New individuals generated after crossover and mutation operations must undergo topology connectivity checks to ensure that the generated network configuration always satisfies the topology constraints of radial distribution network operation.

[0083] The aforementioned population evolution process iterates continuously until the genetic generation reaches the preset maximum number of iterations, or the optimal fitness in the population remains stable and no longer increases over several generations, at which point the algorithm terminates. At this point, the chromosome corresponding to the optimal individual with the highest fitness value in the population represents the best network configuration scheme obtained through optimization. This is then decoded into specific switching action commands, thereby completing the adaptive optimization and reconfiguration of the distribution network.

[0084] Based on step S5, this embodiment directly uses the dynamic evaluation function as the fitness of the genetic algorithm, and applies penalties to schemes that do not satisfy the radial topology during iteration, thus solving the problem of separation between evaluation and topology control, and outputting directly executable switching instructions.

[0085] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides an active distribution network configuration assessment and reconfiguration system, including: The indicator system construction module is used to establish an evaluation indicator system covering multi-dimensional operational efficiency, identify the current operating status of the distribution network, and collect the raw data of each evaluation indicator. The satisfaction calculation module is used to perform initial processing on the raw values ​​of each evaluation indicator to obtain the satisfaction value of each evaluation indicator. The weight calculation module is used to calculate the weights of each scenario under different states based on the satisfaction values ​​of each evaluation indicator, using the first analysis method, to obtain the basic constant weights. The dynamic evaluation module is used to monitor the satisfaction values ​​of each indicator. When any satisfaction value falls below the preset safety threshold, a penalty mechanism is activated to dynamically adjust the indicator weights and generate a comprehensive evaluation score based on the adjusted weights and satisfaction values. The optimization and reconfiguration module is used to use the comprehensive evaluation objective function value as the fitness, employ a genetic algorithm to iteratively optimize the distribution network switch combination, impose restrictions on schemes that do not meet the radial topology constraints during the iteration process, and output the optimal switch action command.

[0086] This embodiment also provides an electronic device applicable to an active power distribution network configuration assessment and reconfiguration scenario, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an active power distribution network configuration assessment and reconfiguration method as proposed in the above embodiment.

[0087] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements an active distribution network configuration evaluation and reconfiguration method as proposed in the above embodiments.

[0088] The storage medium proposed in this embodiment and the method for implementing an active distribution network configuration evaluation and reconfiguration proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0089] Example 4, the fourth embodiment of the present invention, provides a simulation verification of an active distribution network configuration evaluation and reconfiguration method, including: The practicality of the model algorithm on a single tie line is tested using an improved IEEE 33-bus distribution system. The wiring of this distribution network is as follows: Figure 4 As shown, the voltage level reaches 12.66kV, and it is equipped with one power source, 33 nodes, and 37 branches. The connecting branches include 7-20, 8-14, 11-21, 17-32, and 24-28, with the remaining sections being sectional switches. Two doubly-fed asynchronous wind turbines are connected at nodes 7 and 23, assuming they are located within the same wind farm with consistent external environment and wind speed patterns. The wind turbines have a rated power of 400 kW and a power factor of 0.9. Two micro gas turbines, each with a rated power of 100 kW and a power factor of 0.85, are connected at nodes 13 and 29. Assuming normal system operation and ensuring power supply to every node, the system operates in a typical high-line-loss environment.

[0090] Analysis of calculation results: For the operation scenario of this distribution network, three distribution network configuration optimization schemes are proposed for evaluation and selection. The optimization results are shown in Table 4 below: Table 4 Distribution Network Configuration Scheme

[0091] The values ​​of each index for different configuration schemes were calculated separately, and the satisfaction levels of the schemes were evaluated and compared, as shown in Tables 5 and 6 below: Table 5 Comparison of Satisfaction Evaluation of Distribution Network Configuration Schemes

[0092] Table 6 Comparison of main optimization objectives before and after configuration optimization

[0093] The satisfaction evaluation results of the distribution network configuration under the evaluated scenario are analyzed. In this embodiment, the distribution network is facing the typical problem of high line loss rate, so economic indicators become the focus of evaluation. After the distribution network configuration evaluation, Scheme 3 is considered the optimal choice. Under the implementation of this configuration scheme, the economic indicators have been significantly improved, the total operating cost in each time period has been reduced by 64% compared with that before optimization, and the voltage deviation index has also been significantly improved. By evaluating the satisfaction of various network configuration schemes, we can determine the optimal configuration scheme, which helps to optimize the core indicators in distribution network operation and provides useful suggestions for the optimization of the network structure.

[0094] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, 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 a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0095] 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.

Claims

1. A method for assessing and reconfiguring active distribution network configurations, characterized in that: include, Establish a multi-dimensional evaluation indicator system, distinguish the operating status, and calculate the indicators under different statuses; Based on the aforementioned multi-dimensional evaluation index system, the original values ​​of each index are first processed to obtain the satisfaction level of each index. Based on the satisfaction level of each indicator, the first analysis method is used to calculate the indicator weights of each scenario under different states. If the satisfaction level of any indicator falls below a preset critical threshold, the system is corrected using a first penalty function to obtain dynamic weights and establish a first objective function. Based on the comprehensive evaluation objective function, adaptive optimization and reconstruction are completed through iterative optimization using a genetic algorithm.

2. The active distribution network configuration assessment and reconfiguration method as described in claim 1, characterized in that: The first step involves processing the raw values ​​of each indicator to obtain the satisfaction level for each indicator, including: Based on the physical characteristics of each indicator, they are divided into minimum-optimal type and maximum-optimal type. A satisfaction evaluation function is constructed, and the normalized value of each indicator is calculated.

3. The active distribution network configuration assessment and reconfiguration method as described in claim 2, characterized in that: The first analysis method is used to calculate the index weights of each scenario under different states, including: A hierarchical structure adapted to the evaluation of distribution network configuration schemes is established. By constructing a judgment matrix and performing consistency checks, the basic constant weights of the scheme layer relative to the target layer are calculated.

4. The active distribution network configuration evaluation and reconfiguration method as described in claim 3, characterized in that: The establishment of the first objective function includes, The basic constant weights are dynamically modified by introducing variable weight theory. For the satisfaction of each indicator, an exponential state variable weight penalty function is constructed to calculate the dynamic weight of each indicator. Combined with the normalized value of each indicator, a comprehensive evaluation objective function is established.

5. The active distribution network configuration assessment and reconfiguration method as described in claim 4, characterized in that: The establishment of a multi-dimensional evaluation indicator system to differentiate operational status includes... Construct an evaluation index system covering power supply capacity, power supply reliability, voltage quality and economy, and divide the operation status of the distribution network into normal state, risk state and recovery state.

6. The active distribution network configuration evaluation and reconfiguration method as described in claim 5, characterized in that: The calculation of various evaluation indicators under different states, include, For different operating states, corresponding indicators to be calculated are determined from each dimension of indicators, and the original values ​​of each indicator to be calculated are calculated.

7. The active distribution network configuration evaluation and reconfiguration method as described in claim 6, characterized in that: The adaptive optimization and reconstruction achieved through iterative optimization using a genetic algorithm includes: Using the comprehensive evaluation objective function value as the fitness, a genetic algorithm is used to optimize the distribution network switch combination. During the iteration process, restrictions are imposed on schemes that do not meet the radial topology constraints, and finally the optimal switch action command is output.

8. An active distribution network configuration assessment and reconfiguration system, employing the active distribution network configuration assessment and reconfiguration method as described in any one of claims 1 to 7, characterized in that, include: The indicator system construction module is used to establish an evaluation indicator system covering multi-dimensional operational efficiency, identify the current operating status of the distribution network, and collect the raw data of each evaluation indicator. The satisfaction calculation module is used to perform initial processing on the raw values ​​of each evaluation indicator to obtain the satisfaction value of each evaluation indicator. The weight calculation module is used to calculate the weights of each scenario under different states based on the satisfaction values ​​of each evaluation indicator, using the first analysis method, to obtain the basic constant weights. The dynamic evaluation module is used to monitor the satisfaction values ​​of each indicator. When any satisfaction value falls below the preset safety threshold, a penalty mechanism is activated to dynamically adjust the indicator weights and generate a comprehensive evaluation score based on the adjusted weights and satisfaction values. The optimization and reconfiguration module is used to use the comprehensive evaluation objective function value as the fitness, employ a genetic algorithm to iteratively optimize the distribution network switch combination, impose restrictions on schemes that do not meet the radial topology constraints during the iteration process, and output the optimal switch action command.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the active power distribution network configuration evaluation and reconfiguration method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the active distribution network configuration evaluation and reconfiguration method according to any one of claims 1 to 7.

Images