A transformer area carrying capacity evaluation method and system based on dynamic coupling empowerment

Abstract

This invention relates to the field of electric vehicle management technology, specifically to a method and system for evaluating the carrying capacity of distribution transformer areas based on dynamic coupling weighting. The method includes: first, determining the subjective weight vector of subjective indicators and the initial objective weight vector of objective indicators under a fixed time series sample size; second, establishing a comprehensive evaluation index system from the dimensions of distribution network safety, power supply indicators, and economic efficiency, and dynamically calculating the weights using a game theory-grey relational dynamic coupling weighting method, combined with type-adaptive robust normalization to optimize index processing; finally, achieving a comprehensive quantitative evaluation of carrying capacity through nonlinear hierarchical scoring and modular fusion of domain data. Simulation results from the IEEE 31-bus system demonstrate that this method can accurately identify risk periods such as peak loads and carrying capacity, providing theoretical support for charging facility planning and grid operation optimization.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle management technology, specifically to a method and system for evaluating the load-bearing capacity of transformer substations based on dynamic coupling weighting. Background Technology

[0002] In recent years, against the backdrop of comprehensively promoting the electrification of transportation, the massive heterogeneous distributed energy resources and electric vehicles have been connected to the power grid, bringing multiple challenges to the power distribution network.

[0003] (1) Residential areas, commercial parks, highway service areas and other areas are prone to charging load accumulation, which leads to excessive load density in local power distribution networks, causing problems such as voltage drop and increased line loss. The charging power of a single electric vehicle is significantly higher than the typical power load level of traditional residential users. After large-scale access, the peak load of the power distribution network will increase significantly, and some old lines and transformers may exceed their rated capacity, causing overload risk.

[0004] (2) The limitations of traditional electricity pricing mechanisms are becoming increasingly apparent. Fixed electricity pricing mechanisms are difficult to effectively incentivize users to participate in demand response and cannot achieve a balanced allocation of charging load in time and space. Faced with the large-scale access of electric vehicles, traditional electricity pricing methods are inadequate, further exacerbating the pressure on power grid operation and lacking the flexibility and responsiveness to adapt to dynamically changing system conditions.

[0005] Based on the above problems, existing capacity assessment methods mostly focus on "post-event analysis" or "state monitoring" of the distribution network's operating status, lacking proactive control mechanisms based on assessment results. That is, while they can identify load peaks and capacity bottlenecks, they cannot directly use these quantitative indicators to guide the cross-regional flow of electric vehicles (spatial scheduling) and in-station power regulation (temporal scheduling), leading to a disconnect between "assessment" and "control." The invention patent with publication number CN120834564A simplifies the urban power grid topology using a transfer unit; based on the simplified urban power grid topology, it establishes an output system model, a high-voltage distribution network flexible reconstruction model, and an electric vehicle cluster flexible scheduling model; based on the output system model, high-voltage distribution network flexible reconstruction model, and electric vehicle cluster flexible scheduling model, it establishes an objective function for a collaborative scheduling strategy; and by solving the objective function, it obtains the optimal collaborative scheduling strategy. This prior art, through a dual-dimensional flexible operation mechanism of coordinated electric vehicle charging and discharging flexibility and HVDN flexible reconfiguration, solves the risk of equipment overload and improves the operational safety of the distribution network. However, it only focuses on the power flow constraints of the physical power system and the operational characteristics of distributed resources. The model construction is too complex, and the solution speed cannot be guaranteed. The invention patent with publication number CN118457330A uses the limited available charging capacity of the transformer substations allocated by the designated charging operators as a constraint to optimize and adjust the charging power timing of the designated charging operators, so as to obtain the orderly charging control result of the electric vehicle distribution network with multi-operator cooperation. However, it mainly considers the situation of the charging operators themselves to build the relevant model, and does not involve the carrying capacity of different distribution substations or the corresponding coping strategies based on different distribution substations, so the solution has certain limitations. Summary of the Invention

[0006] Purpose of the invention: In view of the technical problems existing in the background art, the present invention provides a method for evaluating the carrying capacity of distribution substations based on dynamic coupling weighting, which solves the problem that the large-scale access of electric vehicles will greatly increase the peak load of the distribution network and may cause overload risk. The present invention also discloses a distribution substation carrying capacity evaluation system based on dynamic coupling weighting.

[0007] Technical solution: In a first aspect, the present invention provides a method for evaluating the bearing capacity of transformer substations based on dynamic coupling weighting, the method comprising:

[0008] The load data of electric vehicle charging stations and the grid parameters of the distribution network within the carrying capacity assessment range of each distribution area are collected to determine the operating parameters of the distribution network. Based on the operating parameters of the distribution network, the comprehensive assessment index of the carrying capacity of each distribution area is calculated. The comprehensive assessment index is classified into levels. The classification principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indices obtained by combining the objective indices.

[0009] The subjective weight vector of the subjective indicator and the initial objective weight vector of the objective indicator under a fixed number of time series samples are determined respectively. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight.

[0010] The game coupling weights are corrected based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current transformer area, thereby obtaining the comprehensive carrying capacity score of each transformer area.

[0011] The comprehensive load-bearing capacity scores of each transformer substation are sorted from largest to smallest. The sorted scatter points are then fitted with a polynomial to obtain the fitted curve. The slope of the curve is calculated for each substation as the deviation feature of the load-bearing capacity of that substation. A deviation feature dataset of the load-bearing capacity of all transformer substations is constructed. Based on the substation numbers of the mean and median in the deviation feature dataset, the transformer substations are divided into several state regions.

[0012] Based on the constraints of electric vehicle charging stations set for different state regions, a spatiotemporal hierarchical guidance model for electric vehicles is constructed. After determining the joint objective, the spatiotemporal hierarchical guidance model for electric vehicles is solved to obtain the optimal total power scheduling instruction for each substation in the future time period.

[0013] Furthermore, including:

[0014] The process of determining the subjective weight vector of the subjective indicators and the initial objective weight vector of the objective indicators under a fixed number of time series samples includes:

[0015] Construct a judgment matrix to determine the importance of two subjective indicators, where the rows and columns of the judgment matrix represent the total number of subjective indicators. Determine the geometric mean of each row based on the judgment matrix, and normalize the geometric mean of each row to obtain the subjective weight vector for each subjective indicator, expressed as: , m The total number of subjective indicators;

[0016] The number of time series samples within a fixed time period is determined, and an indicator data matrix is ​​constructed by combining it with the number of objective indicators. The elements of the indicator data matrix are the objective indicators corresponding to the current time and the objective indicator sequence. The rows and columns of the matrix correspond to the number of sequence samples and the total number of objective indicators, respectively. Each indicator in the indicator data matrix is ​​normalized, and the entropy value of each objective indicator is calculated. This yields the objective weight vector of the objective indicators at the current time, expressed as: , n The total number of objective indicators. .

[0017] Furthermore, including:

[0018] The process involves obtaining a weighted objective weight vector based on a constructed time decay factor, defining coupling weights based on the corresponding sets of subjective weight vectors and weighted objective weight vectors at different times, and solving the optimization model based on these coupling weights to obtain game-theoretic coupling weights, including:

[0019] For a time series within a fixed time period T, define the first... t Time decay factor at time λ t , is represented as: ,in, β >0 represents the decay coefficient, used to control the decay rate of the weights over time; combining the time decay factor with the objective weights obtained by the entropy weight method yields the time-weighted objective weight vector: ;

[0020] Let the set of weights participating in the game be... The coupling weight is defined as a linear combination of the two. Among them, the combination coefficient satisfy and ;

[0021] An optimization model is constructed with the objective of minimizing the deviation between the coupling weights and the initial weights: Taking the derivative of the optimization model and setting it to zero, we obtain the system of equations: Solving this system of equations yields the optimal combination coefficients. , Substituting these values ​​into the coupling weights yields the game coupling weights. .

[0022] Furthermore, including:

[0023] The process of correcting the game coupling weights based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current station area includes:

[0024] Define reference sequence Take the optimal value sequence of each indicator in time period T, and compare the sequence as the first... t index value sequence at time point ;

[0025] The grey relational coefficient is calculated and expressed as: ,in, The resolution coefficient is used to calculate the grey relational degree, which is expressed as: ;

[0026] Dynamically adjusted coupling weights are expressed as: ;in, This represents the Hadamard product. This is the gray relational degree vector.

[0027] Furthermore, including:

[0028] The process of constructing a deviation feature dataset of the carrying capacity of all transformer substations, and dividing the substations into several state regions based on the substation numbers of the mean and median values ​​in the deviation feature dataset, includes:

[0029] A comprehensive bearing capacity scoring dataset was established using a polynomial fitting method. With ranking Functional mapping relationship The first derivative of the fitted function is then calculated to obtain the slope curve of the score change. The slope The slope absolute value characterizes the sensitivity to condition deterioration at the current bearing capacity level. The larger the value, the more likely the transformer area's carrying capacity score will plummet with even a slight increase in load or a slight change in conditions within that ranking range, indicating that the system is in a critically unstable state.

[0030] Calculate the scoring dataset arithmetic mean With median , is represented as: ;

[0031] To enhance the system's robustness to data fluctuations and sample distribution skewness, an adaptive buffer coefficient is introduced. Its range of values Constructing a dual dynamic threshold: Threshold determination criteria Judgment threshold standard ;

[0032] Based on real-time calculated comprehensive score Based on the relationship with the judgment threshold standard, the load-bearing capacity status of the transformer area is divided into three zones: Preferred zone: when... When the transformer area is deemed to be in a state of sufficient load-bearing capacity, the slope |k| in this area indicates that the system is operating stably and has strong acceptance capacity; Warning zone: when When the transformer area is in a critical transition state, this region corresponds to a section with drastic changes in slope |k|, indicating that the bearing capacity is extremely sensitive to load growth and requires flexible adjustment; Flow restriction zone: when If the transformer area is determined to be in a state of insufficient load-bearing capacity or overload risk, mandatory constraints must be implemented.

[0033] Furthermore, including:

[0034] The constraint conditions for electric vehicle charging stations set according to different state regions include:

[0035] The electric vehicle charging stations in the preferred zone implement a basic time-of-use pricing strategy, and there are no additional restrictions on charging power, aiming to utilize price advantages to absorb new energy or fill off-peak periods; the electric vehicle charging stations in the early warning zone implement an adaptive floating pricing strategy based on scoring and slope coupling, and the upper limit of charging power remains unchanged, aiming to flexibly suppress the load growth rate through price leverage; the electric vehicle charging stations in the current-limited zone implement a punitive peak pricing strategy, and at the same time implement mandatory power reduction constraints.

[0036] Furthermore, including:

[0037] The specific strategy for setting corresponding constraints for electric vehicle charging stations based on different state regions includes:

[0038] Preferred Zone Strategy: When a transformer area is in the preferred zone, it indicates sufficient carrying capacity. Constraints include: Incentive pricing constraints. Power boundary constraints: ;in: The issued guiding electricity price; The basic time-of-use electricity price for the power grid; This refers to the maximum allowable output power of the charging station. The rated power of the charging pile;

[0039] Warning Zone Strategy: When a transformer area is in a warning zone, it indicates a decrease in carrying capacity and a deterioration in condition. Constraints include: Guiding electricity price constraints. Power boundary constraints: 。 ;In the formula: As a scoring penalty factor; This is a sensitivity penalty factor; The scoring threshold is used to delineate the preferred zone from the warning zone. This represents the absolute value of the slope of the score change at the current moment;

[0040] Current limiting zone strategy: When a transformer area is in a current limiting zone, it indicates that the system faces overload risk. Constraints include: guidance electricity price constraints: Power boundary constraints: In the formula: The punitive peak electricity price is set; This refers to the safety margin factor. The scoring threshold is used to demarcate the warning zone and the flow restriction zone.

[0041] Furthermore, including:

[0042] The construction of the spatiotemporal hierarchical guidance model for electric vehicles includes: taking the maximization of the overall carrying capacity score of the transformer substation and the minimization of the comprehensive charging cost for users in the region as the joint objective; under the dynamically changing guidance price and power boundary constraints, solving for the optimal total power scheduling instruction at the transformer substation level in future time periods, and issuing this instruction as a regional regulation target to smart charging piles or aggregation control terminals to guide the subsequent execution of electric vehicle power allocation.

[0043] Furthermore, including:

[0044] The minimized comprehensive charging cost for users within the region includes: each electric vehicle user's decision objective is to complete the charging task while minimizing the economic cost, with the objective function... Represented as: ;

[0045] in, This indicates the remaining time before the electric vehicle is de-griddled, and includes several scheduling periods; This is the index for the current scheduling period; For the first The guiding electricity price for the specified time period, Let be the decision variable to be solved, i.e., the th The actual charging power of electric vehicles during the time period This is the scheduling time interval.

[0046] Furthermore, including:

[0047] The construction of the spatiotemporal hierarchical guidance model for electric vehicles also includes:

[0048] To ensure that the response strategy meets both power grid security requirements and user travel needs, the following constraints need to be defined:

[0049] Power boundary constraints: In the formula: This is the maximum allowed charging power at the current moment. If the charging station is in the preferred or warning zone, then... , This is the rated power; if the transformer area is in the current-limiting zone, then... The power value after forced reduction;

[0050] State of Charge (SOC) and Energy Demand Constraints: ;

[0051] In the formula: This represents the initial state of charge of the electric vehicle battery at the current moment. The target state of charge when the user expects to disconnect from the grid; For charging efficiency; This formula defines the rated capacity of an electric vehicle's battery, ensuring that the total charge added to the initial charge when the user leaves the vehicle meets the user's preset needs.

[0052] On the other hand, the present invention also provides a transformer area bearing capacity evaluation system based on dynamic coupling weighting, the system comprising:

[0053] The indicator grading module is used to collect load data of electric vehicle charging stations and distribution network parameters within the carrying capacity assessment range of each distribution area in the distribution network, determine the operating parameters of the distribution network, calculate the comprehensive assessment index of the carrying capacity of each distribution area based on the operating parameters of the distribution network, and grade the comprehensive assessment index. The grading principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indicators obtained by comprehensively considering the objective indicators.

[0054] The game coupling weight determination module is used to determine the subjective weight vector of subjective indicators and the initial objective weight vector of objective indicators under a fixed number of time series samples. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight.

[0055] The comprehensive carrying capacity scoring module is used to correct the game coupling weights based on the grey relational model, determine the final comprehensive weight of each objective indicator under the current transformer area, and thus obtain the comprehensive carrying capacity score of each transformer area.

[0056] The transformer substation division module is used to sort the comprehensive load-bearing capacity scores of each transformer substation from largest to smallest, perform polynomial fitting on the sorted scatter points to obtain the fitted curve, calculate the slope of the curve change for each substation as the deviation feature of the load-bearing capacity of that substation, construct a deviation feature dataset of the load-bearing capacity of all transformer substations, and divide the transformer substations into several state regions based on the substation numbers of the mean and median in the deviation feature dataset.

[0057] The scheduling instruction determination module is used to set corresponding electric vehicle charging station constraints based on different state areas, construct a spatiotemporal hierarchical guidance model for electric vehicles, and solve the spatiotemporal hierarchical guidance model for electric vehicles after determining the joint objective to obtain the optimal total power scheduling instruction for each area in the future time period.

[0058] Finally, the present invention also provides a computer system including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described above.

[0059] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0060] (1) This application is based on the comprehensive evaluation index of the carrying capacity of the distribution network area, including subjective and objective indicators. Different methods are used to determine the weights of the subjective and objective indicators. The game theory is combined to realize the static coupling of subjective weights and time-weighted objective weights. Then, the gray relational degree is used to realize dynamic correction, and finally the score of the carrying capacity of the distribution network area is obtained. This effectively solves the limitations of the single weighting method and the problem of missing time dimension, and significantly improves the scientificity, rationality and dynamic adaptability of weight allocation in the 24-hour carrying capacity evaluation system of the distribution network area.

[0061] (2) Based on the distribution network area carrying capacity score, this invention proposes an adaptive calculation method for the distribution network area classification threshold: the collected distribution network area carrying capacity comprehensive score is sorted from largest to smallest, the sorted scatter points are fitted with a polynomial to obtain the fitting curve, and then the slope of the curve change is calculated as the deviation feature of the area carrying capacity. A deviation feature dataset of all area carrying capacity is constructed. According to the area number of the mean and median in the deviation feature dataset, the area is divided into 3 subsets, of which the area with high score is the preferred area, the area with medium score is the warning area, and the area with low score is the current-limiting area. For the different levels of area, the electric vehicle charging station constraints of the preferred area, the warning area and the current-limiting area are reset. The electric vehicle charging station in the preferred area implements the basic time-of-use pricing strategy, and the charging power is not subject to additional restrictions. The aim is to use the price advantage to absorb new energy vehicles. Energy or valley filling; in the early warning zone, electric vehicle charging stations implement an adaptive floating electricity price strategy based on scoring and slope coupling, while maintaining the upper limit of charging power at the rated value. This aims to flexibly suppress the rate of load growth through price leverage. In the current-limited zone, electric vehicle charging stations implement a punitive peak electricity price strategy, while simultaneously enforcing mandatory power reduction constraints. This aims to ensure the safe operation of the distribution network through a dual approach of "economic + physical" means. Thus, through spatial hierarchical guidance, the load pressure between different distribution stations is balanced using price leverage. Through in-station collaborative optimization, the risk of a sudden drop in carrying capacity score caused by disorderly charging is fundamentally eliminated, ensuring that the distribution network always operates in a safe and economical "preferred zone." This achieves a win-win situation of reduced user charging costs and improved grid operation safety, providing a more reliable decision-making basis for the future planning and layout of electric vehicle charging infrastructure and the safe and stable operation of the power grid. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1This is a flowchart of a method for assessing the bearing capacity of transformer substations and guiding electric vehicles in a spatiotemporal manner using dynamic coupling and weighting, as proposed in this invention.

[0064] Figure 2 This is a schematic diagram of the distribution network area carrying capacity assessment index system proposed in this invention;

[0065] Figure 3 This diagram illustrates the calculation steps of the dynamically coupled weighted transformer area bearing capacity assessment and spatiotemporal hierarchical guidance electric vehicle method proposed in this invention.

[0066] Figure 4 The flowchart of the improved game theory-grey relational dynamic coupling weighting calculation method with added time decay factor proposed in this invention is shown below.

[0067] Figure 5 This is a diagram of the 31-node network topology of a local power distribution network proposed in this invention.

[0068] Figure 6 This is a schematic diagram of the distribution points and adaptive substation classification based on statistical characteristics for evaluating the bearing capacity of transformer areas proposed in this invention.

[0069] Figure 7 This is a slope sensitivity distribution diagram of the bearing capacity scoring fitting curve proposed in this invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] An embodiment of the present invention provides a flowchart of a method for evaluating the bearing capacity of transformer substations and guiding the spatiotemporal classification of electric vehicles using dynamic coupling and weighting. The method includes:

[0072] The load data of electric vehicle charging stations and the grid parameters of the distribution network within the carrying capacity assessment range of each distribution area are collected to determine the operating parameters of the distribution network. Based on the operating parameters of the distribution network, the comprehensive assessment index of the carrying capacity of each distribution area is calculated. The comprehensive assessment index is classified into levels. The classification principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indices obtained by combining the objective indices.

[0073] The subjective weight vector of the subjective indicator and the initial objective weight vector of the objective indicator under a fixed number of time series samples are determined respectively. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight.

[0074] The game coupling weights are corrected based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current transformer area, thereby obtaining the comprehensive carrying capacity score of each transformer area.

[0075] The comprehensive load-bearing capacity scores of each transformer substation are sorted from largest to smallest. The sorted scatter points are then fitted with a polynomial to obtain the fitted curve. The slope of the curve is calculated for each substation as the deviation feature of the load-bearing capacity of that substation. A deviation feature dataset of the load-bearing capacity of all transformer substations is constructed. Based on the substation numbers of the mean and median in the deviation feature dataset, the transformer substations are divided into several state regions.

[0076] Based on the constraints of electric vehicle charging stations set for different state regions, a spatiotemporal hierarchical guidance model for electric vehicles is constructed. After determining the joint objective, the spatiotemporal hierarchical guidance model for electric vehicles is solved to obtain the optimal total power scheduling instruction for each substation in the future time period.

[0077] Specifically, such as Figure 1 As shown, it illustrates a flowchart of a method for evaluating the bearing capacity of transformer substations and guiding the spatiotemporal classification of electric vehicles using dynamic coupling and weighting. This method includes the following steps:

[0078] Step 1: Construct a power flow calculation model for the distribution network based on multi-source data acquisition, and establish a comprehensive evaluation index system for the carrying capacity of distribution substations, covering safety, quality, and economy. Collect load data of electric vehicle charging stations and distribution network parameters within the evaluation scope of the distribution substation carrying capacity from the power grid information management system. Construct a power flow calculation model for the distribution network to be evaluated and calculate the distribution network operating parameters, including: node voltage, line current, transformer operating power, line active power loss, and total power supply of the distribution substations. Based on the distribution network operating parameters calculated from the above power flow, establish a comprehensive evaluation index system for the carrying capacity of the distribution network substations.

[0079] The primary indicators include three dimensions: distribution network safety, power supply quality, and economy. The secondary indicators include: transformer maximum load rate, line maximum load rate, voltage qualification rate, voltage deviation rate, line network loss rate, and transformer substation line loss rate. Figure 2As shown, this embodiment aims to accurately quantify the impact of electric vehicle charging station access on the power distribution network and proposes a scientific method for assessing carrying capacity. Therefore, to accurately simulate load characteristics, the electric vehicle load is subdivided into private cars, electric buses, and battery swapping stations, and a multi-type charging station load model with temporal stochasticity is constructed using Monte Carlo simulation technology. To address the power distribution network capacity limitation problem, a comprehensive index set including key parameters such as transformer and line maximum load rate and system voltage qualification rate is constructed.

[0080] (1) Maximum load rate of transformer

[0081] The ratio of the actual maximum load of the transformer in the distribution network area to the rated capacity of the transformer after the connection of various types of electric vehicle charging stations is used to assess the transformer's load margin and overload risk under the impact of electric vehicle charging loads. In the formula, TOR This represents the transformer's maximum load rate. for t The active power of the transformer under normal load at any given time; for t Active power of electric vehicle charging load at any given time; This refers to the rated power of the transformer.

[0082] (2) Maximum load rate of the line

[0083] The maximum load factor of a power distribution network refers to the ratio of its actual maximum load to the active power capacity corresponding to the rated current carrying capacity of the power supply line after multiple types of electric vehicle charging stations are connected. It is used to assess the overload risk and transmission capacity of the line after the electric vehicle charging load is connected. In the formula, LOR This represents the maximum load rate of the line. for t Active power of the line under normal load at any given time; This refers to the active power corresponding to the line's rated current carrying capacity.

[0084] (3) Voltage qualification rate

[0085] Voltage qualification rate refers to the ratio of the number of nodes whose voltage amplitude is within the allowable range of national standards after multiple types of electric vehicle charging station loads are connected to the distribution network area to the total number of nodes. It is used to evaluate the stability of the distribution network power supply voltage caused by electric vehicle charging loads.

[0086]

[0087] In the formula, VQR Voltage qualification rate; for t The number of nodes whose voltage amplitude is within the standard allowable range at any given time; N This represents the total number of nodes.

[0088] (4) Voltage deviation rate

[0089] Voltage deviation rate refers to the degree of deviation between the voltage amplitude and the rated voltage when the load of an electric vehicle charging station is connected to the distribution network area. It is used to quantify the degree of voltage fluctuation caused by the charging load.

[0090]

[0091] In the formula, VBR Voltage deviation rate; For nodes i The actual voltage amplitude; This is the rated voltage of the power distribution network.

[0092] (5) Network loss rate

[0093] Line network loss rate refers to the ratio of active power loss of the line to the total active power transmitted by the line after an electric vehicle charging station is connected to the distribution network area. It is used to evaluate the economic efficiency of electric vehicle charging load on line power loss.

[0094] In the formula, LLR For line network loss rate; for t The active power loss of the line at any given time.

[0095] (6) Transformer area line loss rate

[0096] Distribution network line loss rate refers to the ratio of total power loss in a distribution network area to total power supply in the area after an electric vehicle charging station load is connected. It is used to evaluate the economic efficiency of power loss due to charging load at the distribution network area level.

[0097] In the formula, TLR The line loss rate of the transformer area; for t The active power loss of the transformer at any given time.

[0098] Step 2: Define a time decay factor to improve the game theory-grey relational dynamic coupling weighting method, and propose a dynamic coupling weighting method for evaluating the load-bearing capacity of transformer substations: define a time decay factor calculation method, and construct an improved game theory-grey relational dynamic coupling weighting model. The process is as follows: Figure 3 As shown, the type-adaptive robust normalization method is used to perform dimensionless processing on the index data. The dimensionless data is then combined with the weighted model to calculate the score of the distribution network area carrying capacity.

[0099] like Figure 4As shown, the subjective weights of the standard layer (first-level indicators) are determined by the analytic hierarchy process (AHP), the objective weights of the indicator layer (second-level indicators) are determined by the entropy weight method, and the recent data are given higher objective weights by the time decay factor. The static coupling of subjective weights and time-weighted objective weights is achieved by combining game theory, and dynamic correction is achieved by using grey relational analysis. This effectively solves the limitations of the single weighting method and the problem of missing time dimension, and significantly improves the scientificity, rationality and dynamic adaptability of weight allocation in the 24-hour carrying capacity assessment system of distribution network areas.

[0100] Specifically, a judgment matrix is ​​constructed to determine the degree of importance between two subjective indicators. The rows and columns of this judgment matrix represent the total number of subjective indicators. Based on this judgment matrix, the geometric mean of each row is determined, and the geometric mean of each row is normalized to obtain the subjective weight vector for each subjective indicator, expressed as: , m The total number of subjective indicators;

[0101] The number of time series samples within a fixed time period is determined, and an indicator data matrix is ​​constructed by combining it with the number of objective indicators. The elements of the indicator data matrix are the objective indicators corresponding to the current time and the objective indicator sequence. The rows and columns of the matrix correspond to the number of sequence samples and the total number of objective indicators, respectively. Each indicator in the indicator data matrix is ​​normalized, and the entropy value of each objective indicator is calculated. This yields the objective weight vector of the objective indicators at the current time, expressed as: ,and , n The total number of objective indicators. .

[0102] The process involves obtaining a weighted objective weight vector based on a constructed time decay factor, defining coupling weights based on the corresponding sets of subjective weight vectors and weighted objective weight vectors at different times, and solving the optimization model based on these coupling weights to obtain game-theoretic coupling weights, including:

[0103] For a time series within a fixed time period T, define the first... t Time decay factor at time λ t , is represented as: ,in, β >0 represents the decay coefficient, used to control the decay rate of the weights over time; combining the time decay factor with the objective weights obtained by the entropy weight method yields the time-weighted objective weight vector: ;

[0104] Specifically, a preferred embodiment of this invention is determined using the following scheme:

[0105] 1. Initial weight calculation for the standard layer and the indicator layer

[0106] (1) Determine the subjective weights of the standard layer

[0107] Assume the standard layer includes three criteria: distribution network security, power supply quality, and economy (m=3). A judgment matrix is ​​constructed through pairwise comparisons by experts. ,in, Representation Criteria i Relative Criteria j The degree of importance.

[0108] Calculate the geometric mean of each row of the judgment matrix:

[0109] Normalization yields the standard layer subjective weight vector:

[0110]

[0111] in, The rationality of the judgment matrix is ​​ensured by passing a consistency test (CR<0.1).

[0112] (2) Determine the objective weights of the indicator layer

[0113] In a preferred embodiment, the indicator layer includes six indicators: transformer maximum load rate, line maximum load rate, voltage qualification rate, voltage deviation rate, line loss rate, and transformer substation line loss rate (n=6). The 24-hour time series sample size is s=24, and an indicator data matrix is ​​constructed. ( t For a moment, j (As an indicator).

[0114] Indicator normalization processing: Calculate the first j Entropy value of the item indicator: in,

[0115] Calculate the objective weight vector of the indicator layer:

[0116] in, .

[0117] 2. Improve game theory coupling weighting by adding a time decay factor.

[0118] (1) Definition of time decay factor

[0119] For a 24-hour time series, define the first... t Time decay factor at time λ t This is to reflect the higher contribution of recent data to the weighting: ;

[0120] in,β >0 represents the decay coefficient, used to control the decay rate of the weights over time; normalization ensures... .

[0121] (2) Time-weighted objective weight correction

[0122] Combining the time decay factor with the objective weights obtained by the entropy weight method, we obtain the time-weighted objective weight vector:

[0123] That is, the first t The objective weight of a time point is the product of the original entropy weight and the corresponding time decay factor.

[0124] Construct a game theory model and solve it.

[0125] Let the set of weights participating in the game be... The coupling weight is defined as a linear combination of the two: ;

[0126] Among them, the combination coefficient satisfy .

[0127] An optimization model is constructed with the objective of minimizing the deviation between the coupling weights and the initial weights:

[0128] Taking the derivative of this optimization problem and setting it to zero, we obtain the system of equations:

[0129] Solving this system of equations yields the optimal combination coefficients. Substitution The game coupling weights can then be obtained. .

[0130] 3. Dynamic Correction of Grey Relational Weights

[0131] The game coupling weights are corrected based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current station area, including:

[0132] Define reference sequence Take the optimal value sequence of each indicator in time period T, and compare the sequence as the first... t index value sequence at time point ;

[0133] The grey relational coefficient is calculated and expressed as: ,in, The resolution coefficient is used to calculate the grey relational degree, which is expressed as: ;

[0134] Dynamically adjusted coupling weights are expressed as: ; where represents the Hadamard product, and is the grey relational degree vector.

[0135] Specifically, in this embodiment, to further improve the dynamic adaptability of the weights, the grey relational degree is introduced to correct the game coupling weights:

[0136] Define the reference sequence , take the 24-hour optimal value sequence of each index, and the comparison sequence is the index value sequence at the t th moment .

[0137] Calculate the grey relational coefficient: where is the resolution coefficient, take .

[0138] Calculate the grey relational degree:

[0139] Dynamically correct the coupling weights:

[0140] where represents the Hadamard product, is the grey relational degree vector. By correcting the original adjusted weights and eliminating the influence of repeated weight assignment, the comprehensive weights with both subjective and objective advantages, time dynamic characteristics and data adaptability are finally obtained.

[0141] 4. Type Adaptive Robust Normalization of Index Data

[0142] ① Safety indicators: applicable to the maximum load rate TOR of the transformer and the maximum load rate LOR of the line, and adopt the threshold segmentation normalization logic.

[0143] When the index value x ≤ 80% (safe interval), the normalized score y is calculated according to the formula . After mapping to the [0, 1] interval, it is converted to a percentage system, and the score range is from 100 points to 0 points. The lower the index value, the better the bearing state;

[0144] When 80% < x ≤ 100% (critical interval), the score y is calculated according to the formula . After mapping, the percentage system score range is from 50 points to 0 points, showing a non-linear downward trend;

[0145] When x > 100% (overload interval), the score y is calculated according to the formula . After mapping, the percentage system score range is from 10 points to 0 points. For every 5% excess in the overload degree, the score is halved, strengthening the penalty in the overload state.

[0146] ② Power supply quality indicators: applicable to voltage qualification rate (VQR) and voltage deviation rate (VBR), using interval mapping-deviation correction logic.

[0147] For VQR (a higher value is better), the normalized score y is calculated using the formula... The calculations show that 95% of the values ​​represent the set ideal voltage qualification level.

[0148] When VQR exceeds 95%, it is counted as 1, and the percentage score after mapping is 100.

[0149] For VBR (lower values ​​are better), the score y is calculated using the formula... The calculation is performed, where 0.05 is the maximum allowable voltage deviation. When VBR exceeds 0.05, it is counted as 0, and the percentage score after mapping is 0. At the same time, for the voltage data of the end nodes 28-33, the normalized score obtained by calculation needs to be corrected. The corrected score = original score × 1.3, which amplifies the impact of the voltage deviation of the end nodes on the overall bearing capacity.

[0150] ③ Economic indicators: Applicable to line network loss rate (LLR) and transformer area line loss rate (TLR), using a benchmark-trend calibration logic.

[0151] First, set the domain benchmark values, where the LLR benchmark value is 2.4% and the TLR benchmark value is 0.2%;

[0152] For LLR, the normalized score y is calculated according to the formula. Calculate, map, and convert to a percentage score;

[0153] For TLR, the score y is calculated according to the formula. The calculation and mapping process converts the score into a percentage system, ensuring that the score remains stable when the indicator value fluctuates around the benchmark value, and the larger the deviation from the benchmark value, the lower the score.

[0154] Step 3: Based on the distribution network area carrying capacity score, an adaptive calculation method for the distribution network area classification threshold is proposed: The collected distribution network area carrying capacity comprehensive scores are sorted from largest to smallest. The sorted scatter points are subjected to polynomial fitting to obtain the fitting curve. Then, the slope of the curve change is calculated as the deviation feature of the area carrying capacity. A deviation feature dataset of all area carrying capacity is constructed. According to the area number of the mean and median in the deviation feature dataset, the area is divided into 3 subsets, of which the area with the high score is the preferred area, the area with the medium score is the warning area, and the area with the low score is the current limiting area.

[0155] (1) Data acquisition and curve fitting: Collect the comprehensive load-bearing capacity score data of the target area at each time point within the evaluation period (24 hours) to form a set. . Set Sort the scores from highest to lowest to obtain an ordered score sequence. ,in, For the ranking number ( A polynomial fitting method (such as a fifth-order polynomial) is used to establish the scoring. With ranking Functional mapping relationship The first derivative of the fitted function is then calculated to obtain the slope curve of the score change. (Slope sensitivity distribution as shown) Figure 7 (As shown). The slope It has a clear physical meaning: it characterizes the sensitivity to deterioration at the current bearing capacity level. The absolute value of the slope. The larger the value, the more likely the transformer area's carrying capacity score will plummet with even a slight increase in load or a slight change in conditions within that ranking range, indicating that the system is in a critically unstable state.

[0156] (2) Adaptive region segmentation threshold calculation steps: Calculate the score set arithmetic mean With median ;

[0157]

[0158] To enhance the system's robustness to data fluctuations and sample distribution skewness, an adaptive buffer coefficient is introduced. (range of values) Construct a dual dynamic threshold:

[0159] Judgment threshold standard 1

[0160] Judgment threshold standard 2

[0161] (3) State region division: based on the comprehensive score calculated in real time. Based on the relationship with the aforementioned dynamic thresholds, the load-bearing capacity status of the transformer area is divided into three regions:

[0162] Preferred area: When At this point, the transformer area is determined to be in a state of sufficient load-bearing capacity. The slope |k| corresponding to this area is usually small, indicating that the system is operating stably and has strong acceptance capacity.

[0163] Warning zone: When When this occurs, the transformer area is determined to be in a critical transition state. This region typically corresponds to a section where the slope |k| changes drastically (i.e., the derivative extreme value region), indicating that the bearing capacity is extremely sensitive to load growth and requires flexible adjustment.

[0164] Traffic restriction zone: when If the transformer area is determined to be in a state of insufficient load-bearing capacity or overload risk, mandatory constraints must be implemented.

[0165] Step 4: Based on the results of the distribution zone classification, reset the constraints of electric vehicle charging stations in the preferred zone, warning zone, and current-limited zone: Electric vehicle charging stations in the preferred zone implement the basic time-of-use pricing strategy, and there are no additional restrictions on charging power (i.e., full power output is allowed), aiming to utilize price advantages to absorb new energy or fill off-peak periods; Electric vehicle charging stations in the warning zone implement an adaptive floating price strategy based on scoring and slope coupling, and the upper limit of charging power remains unchanged at the rated value, aiming to flexibly suppress the load growth rate through price leverage; Electric vehicle charging stations in the current-limited zone implement a punitive peak price strategy, and at the same time implement mandatory power reduction constraints (the upper limit of power is forcibly reduced according to the degree of overload), aiming to ensure the safe operation of the distribution network through a combination of "economic + physical" means;

[0166] (1) Preferred Zone Strategy: When a transformer station is in a preferred zone, it indicates that the carrying capacity is sufficient. The system applies the base electricity price and does not impose additional charging power restrictions in order to maximize the user charging experience.

[0167] Guiding electricity price constraints:

[0168] Power boundary constraints:

[0169] In the formula: The issued guiding electricity price; The basic time-of-use electricity price for the power grid; This refers to the maximum allowable output power of the charging station. This refers to the rated power of the charging station.

[0170] (2) Warning Zone Strategy: When a transformer area is in a warning zone, it indicates that its carrying capacity is decreasing and its condition is deteriorating rapidly. An adaptive electricity pricing mechanism based on "scoring-slope" coupling is activated to flexibly reduce the load using price elasticity.

[0171] Guiding electricity price constraints:

[0172] Power boundary constraints:

[0173] In the formula: As a scoring penalty factor; This is a sensitivity penalty factor; The scoring threshold is used to delineate the preferred zone from the warning zone. This represents the absolute value of the slope of the score change at the current moment. The formula means that the lower the score (the further it deviates from the optimal zone) or the steeper the slope (the faster it deteriorates), the greater the increase in electricity price.

[0174] (3) Current limiting zone strategy: When a transformer is in a current limiting zone, it indicates that the system is facing the risk of overload. At this time, relying solely on price guidance will result in a delayed response, and physical forced power reduction measures need to be added.

[0175] Guiding electricity price constraints: Power boundary constraints:

[0176] In the formula: The punitive peak electricity price is set; Safety margin coefficient (range of values) ); This is the scoring threshold that separates the warning zone from the current-limiting zone. This formula causes the power limit to decrease linearly as the score decreases, thus forcibly restricting high-power charging behavior.

[0177] Step 5: Based on the reset hierarchical transformer area constraints, construct a spatiotemporal hierarchical guidance model for electric vehicles and solve it using a heuristic algorithm: with the joint objective of maximizing the overall carrying capacity score of the transformer area and minimizing the comprehensive charging cost for users in the area, under the aforementioned dynamically changing guidance electricity price and power boundary constraints, solve for the optimal total power scheduling instruction for the transformer area level in future time periods, and issue this instruction as a regional regulation target to smart charging piles or aggregation control terminals to guide the subsequent execution of electric vehicle power allocation.

[0178] (1) Construct the user response cost objective function

[0179] Each electric vehicle user's decision-making objective is to complete the charging task while minimizing economic costs. Objective function Represented as: ;

[0180] In the formula: This indicates the remaining time before the electric vehicle leaves the network (the user-set pick-up time), and includes several scheduling periods; : Index of the current scheduling period; The first one calculated and issued in step 4 Dynamic time-based guidance price (unit: yuan / kWh). This price includes the base price for the preferred zone, the penalty for price increases in the early warning zone, or the peak price for the flow-limited zone; The decision variable to be solved, i.e., the first... Actual charging power of electric vehicles during the time period (unit: kW); : Scheduling time interval (unit: h).

[0181] (2) Define physical and demand constraints

[0182] To ensure that the response strategy meets both power grid security requirements and user travel needs, the following constraints need to be defined:

[0183] Power boundary constraints:

[0184] In the formula: This is the maximum allowable charging power at the current moment, determined in step 4. If the transformer area is in the preferred / warning zone, then... (Rated power); If the transformer area is in the current-limiting zone, then This is the power value after being forcibly reduced, here it is 50% of the rated power. At this point, the user must comply with this physical limit and cannot break the limit by paying a higher price.

[0185] State of Charge (SOC) and Energy Demand Constraints:

[0186] In the formula: : The initial state of charge (%) of the electric vehicle battery at the current moment; The user's desired state of charge (e.g., 90% or 100%) when disconnected from the grid. Charging efficiency (range) (e.g., 0.95). : Rated battery capacity of the electric vehicle (unit: kWh). This formula ensures that the total charge added to the initial charge when the user leaves will meet the user's preset needs.

[0187] (3) Execution of the spatiotemporal hierarchical guidance results for electric vehicles

[0188] 1. The terminal equipment receives the guiding electricity price sequence issued by the distribution area in real time. and the physical power limit at the current moment This serves as the boundary condition for the execution strategy.

[0189] 2. During periods of ample capacity (preferred zone), base or preferential electricity prices are applied to fully utilize rated power limits and low-cost advantages to meet users' charging speed requirements; during periods of tight capacity (early warning zone), adaptive floating electricity prices are applied to intelligently reduce charging rates and automatically avoid high costs while ensuring users' off-grid SOC requirements; during periods of overload risk (current limiting zone), physical power reduction commands and peak electricity prices are strictly enforced to transfer the main load to the preferred zone for release through time and space shifting.

[0190] The method and system for assessing the carrying capacity of distribution substations and guiding electric vehicles in a dynamic coupled weighted manner, as described in this embodiment, focuses on the impact of the spatiotemporal distribution characteristics of various types of electric vehicle charging loads on the carrying capacity status of distribution substations, providing an important basis for formulating graded guidance strategies and charging-discharging collaborative optimization schemes.

[0191] This embodiment takes into account the rapid growth in the number of electric vehicles and the large-scale access of charging facilities. Furthermore, the charging behavior is highly influenced by users' travel habits and road conditions, resulting in significant randomness and clustering of loads. This poses a serious threat and challenge to the safe and stable operation of the power distribution network.

[0192] To address this, this invention proposes transformer / line maximum load rate, voltage qualification / deviation rate, and line / distributor area loss rate to comprehensively reflect the multidimensional impact of electric vehicle access on distribution network safety, power quality, and economy. Using a game theory-grey relational dynamic coupling weighting method that considers time decay factors, the subjective and objective coupling weights and dynamic correction coefficients of each indicator are calculated, ultimately yielding a comprehensive score for each time point. This invention's weighting method simultaneously introduces time decay factors and grey relational degrees, combining objective data characteristics with subjective expert experience. This avoids the one-sidedness of single weighting methods and captures the evolution of load over time. Combined with type-adaptive robust normalization processing, this method is more suitable for handling highly volatile, multidimensional distribution network carrying capacity assessment systems.

[0193] The following example uses a local power distribution network system to verify the above method, demonstrating its effectiveness and rationality. The calculation steps for the example are as follows: Figure 3 As shown.

[0194] like Figure 5 The diagram shows the topology of a 31-node system in a certain area. This system mainly consists of 31 distribution bus nodes of 12.66kV, one main power supply access point (balance node, i.e., node 1), and 30 overhead and cable transmission lines. The total power supply capacity of the entire network is set at 10MVA, the base voltage is 12.66kV, the total active load of the conventional foundation is 4850.5kW, and the total reactive load is 2300.2kVar. This embodiment fully considers the electricity consumption characteristics of different functional areas. At node 31 (residential area) at the end of the original system, a group of slow charging piles for private cars (7kW rated power per pile) is connected to meet the long-term charging needs at night. At node 18 (commercial and transportation hub area), a fast charging station for electric buses (60kW rated power per pile) is connected to meet the high-frequency and fast charging needs during the day. At node 22, a bidirectional battery swapping station with V2G function (120kW rated power) is connected to participate in the peak shaving and valley filling interaction of the power grid. At the same time, the regular load of the entire network is set to fluctuate according to the load curve of a typical summer working day, and the random charging load of the above-mentioned multiple types of electric vehicles is superimposed.

[0195] This embodiment simulates the power grid operation of a 31-node system in a certain location at a specific moment. Since the electric vehicle data in this region changes dynamically, this embodiment uses Monte Carlo simulation to generate data on electric vehicle charging stations. Combined with the distribution network parameters within the assessment range of the transformer area's carrying capacity collected from the power grid information management system, a power flow calculation model for the distribution network to be evaluated is constructed using the power flow calculation formula, and the distribution network operation parameters at that moment are calculated. The key parameter data obtained are shown in Table 1.

[0196] Table 1. Simulation results of key operating parameters of the 31-node distribution network area (typical moments)

[0197]

[0198]

[0199] Based on the basic operational data of the distribution network nodes, such as actual voltage, active and reactive loads, as shown in Table 1, and according to the comprehensive evaluation index system for transformer area carrying capacity constructed in step 1 of this invention, the multi-dimensional carrying capacity index values ​​of each transformer area node at the current moment are quantified by substituting the values ​​into the calculation formulas for TOR and VBR. The specific calculation results of each index are shown in Table 2.

[0200]

[0201]

[0202] To conduct a scientific and comprehensive evaluation of the multidimensional indicators in Table 2, it is necessary to determine the weight of each indicator in the evaluation system. Based on the improved dynamic coupling weighting method described in step 2 of this invention, the subjective weights of the standard layer and the objective weights of the indicator layer are first determined. A judgment matrix is ​​constructed through expert scoring, and a consistency check is performed. The calculation process and results are shown below.

[0203] 1. Initial weight calculation for the standard layer and the indicator layer

[0204]

[0205] Normalized calculation yields the subjective weights:

[0206]

[0207] 2. Improve game theory coupling weighting by adding a time decay factor.

[0208] (1) Taking into account the characteristics of peak charging periods (18:00-22:00 is the core period, with a total of 5 time points t=18,19,20,21,22), the attenuation coefficient is taken. β =0.4

[0209]

[0210] (2) Calculation of time-weighted objective weights:

[0211]

[0212] (3) Constructing and solving the game theory model

[0213] An optimization model is constructed with the objective of minimizing the deviation between the coupling weights and the initial weights:

[0214] Taking the derivative of this optimization problem and setting it to zero, we obtain the system of equations:

[0215]

[0216] Solving this system of equations yields the optimal combination coefficients. .

[0217] The final game-theoretic coupling weight result is as follows:

[0218]

[0219] 3. Dynamic Correction of Grey Relational Weights

[0220] Taking area 22 as an example, the grey relational degree during peak charging times was calculated. r 22 =0.386. (This is achieved through the Hadamard product.) After adjusting the weights, the final weights are shown in Table 8:

[0221] After obtaining the final comprehensive weights shown in Table 8 through the above steps, and combining the indicator data of each transformer substation after type-adaptive robust normalization processing, a nonlinear hierarchical weighted summation model is used for calculation. The final comprehensive carrying capacity score of each transformer substation is shown in Table 9. This process comprehensively considers the objective data characteristics and subjective importance of the indicators.

[0222]

[0223] Based on the comprehensive load-bearing capacity score of each transformer substation calculated above, this embodiment further employs an adaptive grading method based on slope sensitivity to classify the operating status of the substations. Firstly, as... Figure 6 As shown, the scores of the 31 substations in Table 9 are sorted from highest to lowest, and a polynomial curve is fitted. Then, the first derivative of the fitted curve is calculated to obtain the slope characteristics of the score changes, as shown below. Figure 7 As shown.

[0224] Through calculation and analysis, the critical point where the slope of the scoring curve abruptly changes was identified, thereby determining the dynamic threshold for state classification. In this example, the optimal judgment threshold was calculated. Points, rate limiting threshold Based on this, the 31 transformer substations were divided into three levels: nodes with a score above 68.0 were classified into the "preferred zone," where the substation's carrying capacity was sufficient, the slope was relatively small, and the system was operating stably; nodes with a score between 63.8 and 68.0 were classified into the "warning zone," where the carrying capacity was in a critical transition state, the slope changed drastically, and flexible guidance was required; nodes with a score below 63.8 were classified into the "flow restriction zone," such as nodes 18 (bus stop), 22 (battery swapping station), and 31 (residential terminal), indicating that the system faced overload risk and mandatory power constraints were required. The specific substation carrying capacity status classification results are shown in Table 10.

[0225] Table 10 Adaptive Classification Results of Operation Status of 31-Node Distribution Network Areas Based on Bearing Capacity Scoring

[0226]

[0227] Based on the classification results of the operating status of the transformer substations determined in Table 10, the system automatically matches and executes differentiated spatiotemporal classification guidance and control strategies. Specifically, for substations classified as 'preferred zones' (such as No. 15 and No. 1), the system maintains the basic time-of-use electricity price and sets the upper limit of the charging pile power to the rated value to ensure users' rapid energy replenishment needs; for substations classified as 'warning zones' (such as No. 2 and No. 11), the system triggers a dynamic electricity price increase mechanism to guide users to actively reduce charging power or charge during off-peak hours through price elasticity; and for substations classified as 'current-limiting zones' (such as No. 18 and No. 31), given that their load-bearing capacity score is below the safety threshold, the system immediately initiates a forced power reduction program, strictly limiting the maximum output power of the charging piles according to the degree of overload, regardless of users' willingness to pay.

[0228] After establishing the aforementioned dynamic power boundary and guiding electricity price constraints, this embodiment uses maximizing the transformer area carrying capacity score and minimizing user charging costs as the joint objective to perform optimization and solve the problem, calculating the value of each node at the current high load time. ) and the timing of future load relief ( The optimal charging power command is determined by the optimal scheduling. The specific optimization results are shown in Table 11.

[0229] Table 11 Results of Spatiotemporal Hierarchical Guidance Power Optimization Scheduling for Electric Vehicles under Different Bearing Capacity Zones

[0230]

[0231]

[0232] As shown in Table 11, the system implements differentiated guidance and control strategies based on the classification results.

[0233] Preferred nodes (e.g., nodes 1, 3, and 15): have sufficient carrying capacity and are permitted by the system. The power allocation mode. The user at the current moment ( It can then charge at full speed at the rated power of 7.0kW, meeting most charging needs. The moment is satisfied ( (Power is reduced to 0 at all times), maximizing user convenience. Warning zone nodes (e.g., 2, 12, 20): Capacity is at a critical state. Although electricity prices have increased slightly, considering users' urgent needs such as needing to use their vehicles, the system still allows... At this time, the smart terminal will The power output was moderately reduced to 4.6kW-5.5kW (approximately 70% of the rated power), which not only met the main energy replenishment needs of users but also reduced the peak pressure on the power grid through fine-tuning. The remaining small amount of power (approximately 1.5kW-2.4kW) was shifted to [other locations]. Complete immediately. Nodes in the flow control zone (nodes 18, 22, and 31): The system faces overload risk; forced execution required. The "load shifting" mode. Bus stop No. 18: Power was forcibly reduced to 18kW (only 30% of the rated value), and much of the demand was postponed until after grid conditions eased. Timing (Restore 60kW); End node No. 31: At any given time, only 1.4kW is allowed to maintain basic connectivity, with the vast majority of the charging load forcibly transferred to [other sources]. It releases power at all times, thus effectively avoiding the risk of voltage collapse.

[0234] contrast At any given time, the optimized power of all nodes recovers to the rated or user-demanded level. This example strongly demonstrates that the method proposed in this invention can find the optimal balance between ensuring grid security and reducing user costs based on spatiotemporal carrying capacity conditions.

[0235] To further verify the effectiveness of this system in reducing user charging costs, based on the early warning zone guidance price calculation formula determined in step 4 and the objective function for minimizing user charging costs constructed in step 5, a typical early warning zone node (No. 2 transformer station) in Table 11 was selected for specific example analysis.

[0236] 1. Parameter setting and electricity price calculation

[0237] Set a basic time-of-use electricity price The price is set at 1.0 yuan / kWh. Based on the early warning zone electricity price strategy described in step 4, the threshold for determining the preferred zone is selected. Price elasticity coefficient (Ignore the slope factor to simplify single-point calculations).

[0238] For transformer substation No. 2, which is located in the warning zone, its comprehensive load-bearing capacity score is 65.77. Substitute these scores into the formula in step 4 to calculate the current load-bearing capacity. Indicative electricity price at any time:

[0239]

[0240] and The electricity distribution area has been restored to the preferred zone, and the electricity price has returned to the base value. .

[0241] 2. Cost Comparison Analysis

[0242] Based on the objective function described in step 5 ( Compare the cost differences between disordered charging and the optimized charging under this system (setting scheduling periods). ):

[0243] Disorderly charging mode: If a user does not respond to the guidance signal and continues to charge at the rated power of 7.0kW during the high-load period t1, the cost incurred during that period will be:

[0244]

[0245] Optimized guidance mode: Under the guidance of this system, as shown in Table 11, the smart terminal automatically solves the objective function in step 5, and... The charging power is adjusted to 4.9kW, and the remaining 2.1kW of demand is shifted to areas with lower electricity prices. At this moment, the total cost is:

[0246]

[0247] 3. Analytical Conclusions

[0248] Calculations show that, through the spatiotemporal hierarchical guidance of this system, the cost of a single charging session is reduced from 9.31 yuan to 8.62 yuan, a decrease of approximately 7.4%. For users in current-limited areas, the cost reduction effect is even more significant because the punitive peak electricity price set in step 4 is avoided. Based on comprehensive network simulation data, the average charging cost for users in the region is reduced by approximately 18.4%, fully verifying that this invention can effectively balance the economic interests of users with the safety of the power grid's carrying capacity, achieving Pareto optimization of "power grid safety - user economy".

[0249] In summary, this invention achieves accurate assessment of the load-bearing capacity of the transformer substation and spatiotemporal hierarchical guidance for electric vehicles through the following progressive steps:

[0250] First, the evaluation index weights at each time point are dynamically calculated: First, the subjective and objective weights are obtained separately, and a time decay factor is introduced to correct the objective weights to reflect the timeliness of the data; Second, an improved game theory is applied to find the optimal linear combination coefficient of the subjective and objective weights to obtain the static coupling weights; Finally, the grey relational degree is used to calculate the closeness between the index data and the optimal reference sequence, and the coupling weights are dynamically corrected.

[0251] Secondly, a comprehensive score is calculated based on the corrected weights to obtain the slope sensitivity of the score change. An adaptive dual threshold is constructed based on the mean and median of the scores to divide the station area into a preferred area, a warning area, and a flow restriction area.

[0252] Finally, a differentiated guidance mechanism is formulated based on the real-time status and slope of the distribution area: the basic strategy is implemented in the preferred area, the adaptive electricity price based on the coupling of score and slope is implemented in the early warning area to flexibly suppress the load, and the forced power reduction constraint is implemented in the current limiting area; and on this basis, the optimal power curve that minimizes user costs and maximizes grid security is solved.

[0253] This invention also provides a transformer area bearing capacity evaluation system based on dynamic coupling weighting, the system comprising:

[0254] The indicator grading module is used to collect load data of electric vehicle charging stations and distribution network parameters within the carrying capacity assessment range of each distribution area in the distribution network, determine the operating parameters of the distribution network, calculate the comprehensive assessment index of the carrying capacity of each distribution area based on the operating parameters of the distribution network, and grade the comprehensive assessment index. The grading principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indicators obtained by comprehensively considering the objective indicators.

[0255] The game coupling weight determination module is used to determine the subjective weight vector of subjective indicators and the initial objective weight vector of objective indicators under a fixed number of time series samples. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight.

[0256] The comprehensive carrying capacity scoring module is used to correct the game coupling weights based on the grey relational model, determine the final comprehensive weight of each objective indicator under the current transformer area, and thus obtain the comprehensive carrying capacity score of each transformer area.

[0257] The transformer substation division module is used to sort the comprehensive load-bearing capacity scores of each transformer substation from largest to smallest, perform polynomial fitting on the sorted scatter points to obtain the fitted curve, calculate the slope of the curve change for each substation as the deviation feature of the load-bearing capacity of that substation, construct a deviation feature dataset of the load-bearing capacity of all transformer substations, and divide the transformer substations into several state regions based on the substation numbers of the mean and median in the deviation feature dataset.

[0258] The scheduling instruction determination module is used to set corresponding electric vehicle charging station constraints based on different state areas, construct a spatiotemporal hierarchical guidance model for electric vehicles, and solve the spatiotemporal hierarchical guidance model for electric vehicles after determining the joint objective to obtain the optimal total power scheduling instruction for each area in the future time period.

[0259] The other technical features of the transformer area bearing capacity evaluation system based on dynamic coupling weighting described in this invention are similar to those of the corresponding transformer area bearing capacity evaluation method based on dynamic coupling weighting, and will not be repeated here.

[0260] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0261] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0262] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0263] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0264] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0265] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0266] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0267] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0268] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0269] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A dynamic coupling empowerment-based evaluation method for the carrying capacity of a transformer area, characterized by, The method includes: The load data of electric vehicle charging stations and the grid parameters of the distribution network within the carrying capacity assessment range of each distribution area are collected to determine the operating parameters of the distribution network. Based on the operating parameters of the distribution network, the comprehensive assessment index of the carrying capacity of each distribution area is calculated. The comprehensive assessment index is classified into levels. The classification principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indices obtained by combining the objective indices. The subjective weight vector of the subjective indicator and the initial objective weight vector of the objective indicator under a fixed number of time series samples are determined respectively. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight. The game coupling weights are corrected based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current transformer area, thereby obtaining the comprehensive carrying capacity score of each transformer area. The comprehensive load-bearing capacity scores of each transformer substation are sorted from largest to smallest. The sorted scatter points are then fitted with a polynomial to obtain the fitted curve. The slope of the curve is calculated for each substation as the deviation feature of the load-bearing capacity of that substation. A deviation feature dataset of the load-bearing capacity of all transformer substations is constructed. Based on the substation numbers of the mean and median in the deviation feature dataset, the transformer substations are divided into several state regions. Based on the constraints of electric vehicle charging stations set for different state regions, a spatiotemporal hierarchical guidance model for electric vehicles is constructed. After determining the joint objective, the spatiotemporal hierarchical guidance model is solved to obtain the optimal total power scheduling instruction for each substation in the future time period. The step of constructing a deviation feature dataset of the carrying capacity of all substations, and dividing the substations into several state regions based on the substation numbers of the mean and median in the deviation feature dataset, includes: A comprehensive bearing capacity scoring dataset was established using a polynomial fitting method. With ranking Functional mapping relationship The first derivative of the fitted function is then calculated to obtain the slope curve of the score change. The slope The slope absolute value characterizes the sensitivity to condition deterioration at the current bearing capacity level. The larger the value, the more likely the transformer area's carrying capacity score will plummet with even a slight increase in load or a slight change in conditions within that ranking range, indicating that the system is in a critically unstable state. Calculate the scoring dataset arithmetic mean With median To enhance the system's robustness to data fluctuations and sample distribution skewness, an adaptive buffer coefficient is introduced. Its range of values Construct a dual dynamic threshold: a threshold determination standard : Judgment threshold standard : Based on the comprehensive score calculated in real time Based on the relationship with the judgment threshold standard, the load-bearing capacity status of the transformer area is divided into three zones: Preferred zone: when... When the transformer area is deemed to be in a state of sufficient load-bearing capacity, the slope |k| in this area indicates that the system is operating stably and has strong acceptance capacity; Warning zone: when When the transformer area is in a critical transition state, this region corresponds to a section with drastic changes in slope |k|, indicating that the bearing capacity is extremely sensitive to load growth and requires flexible adjustment; Flow restriction zone: when If the transformer area is determined to be in a state of insufficient load-bearing capacity or overload risk, mandatory constraints must be implemented.

2. The method for evaluating the bearing capacity of transformer substations based on dynamic coupling weighting according to claim 1, characterized in that, The process of determining the subjective weight vector of the subjective indicators and the initial objective weight vector of the objective indicators under a fixed number of time series samples includes: Construct a judgment matrix to determine the importance of two subjective indicators, where the rows and columns of the judgment matrix represent the total number of subjective indicators. Determine the geometric mean of each row based on the judgment matrix, and normalize the geometric mean of each row to obtain the subjective weight vector for each subjective indicator, expressed as: , m The total number of subjective indicators; The number of time series samples within a fixed time period is determined, and an indicator data matrix is ​​constructed by combining it with the number of objective indicators. The elements of the indicator data matrix are the objective indicators corresponding to the current time and the objective indicator sequence. The rows and columns of the matrix correspond to the number of sequence samples and the total number of objective indicators, respectively. Each indicator in the indicator data matrix is ​​normalized, and the entropy value of each objective indicator is calculated. This yields the objective weight vector of the objective indicators at the current time, expressed as: , n The total number of objective indicators. .

3. The method for evaluating the bearing capacity of transformer substations based on dynamic coupling weighting according to claim 2, characterized in that, The process involves obtaining a weighted objective weight vector based on a constructed time decay factor, defining coupling weights based on the corresponding sets of subjective weight vectors and weighted objective weight vectors at different times, and solving the optimization model based on these coupling weights to obtain game-theoretic coupling weights, including: For a time series within a fixed time period T, define the first... t Time decay factor at time λ t , represented as: ,in, β >0 represents the decay coefficient, used to control the decay rate of the weights over time; combining the time decay factor with the objective weights obtained by the entropy weight method yields the time-weighted objective weight vector: ; Let the set of weights participating in the game be... The coupling weight is defined as a linear combination of the two. Among them, the combination coefficient satisfy and ; An optimization model is constructed with the objective of minimizing the deviation between the coupling weights and the initial weights: Taking the derivative of the optimization model and setting it to zero, we obtain the system of equations: Solving this system of equations yields the optimal combination coefficients. , Substituting these values ​​into the coupling weights yields the game coupling weights. .

4. The dynamic coupling empowerment-based evaluation method of the carrying capacity of a transformer area according to claim 3, characterized in that, The process of correcting the game coupling weights based on the grey relational model to determine the final comprehensive weight of each objective indicator under the current station area includes: Define reference sequence Take the optimal value sequence of each indicator in time period T, and compare the sequence as the first... t index value sequence at time point ; The grey relational coefficient is calculated and expressed as: ,in, The resolution coefficient is used to calculate the grey relational degree, which is expressed as: ; Dynamically adjusted coupling weights are expressed as: ;in, This represents the Hadamard product. This is the gray relational degree vector.

5. The method for evaluating the bearing capacity of transformer substations based on dynamic coupling weighting according to claim 4, characterized in that, The constraints for electric vehicle charging stations based on different state areas include: electric vehicle charging stations in the preferred zone implement a basic time-of-use pricing strategy, aiming to utilize price advantages to absorb new energy or fill off-peak periods; electric vehicle charging stations in the early warning zone implement an adaptive upward pricing strategy based on scoring and slope coupling, with the upper limit of charging power remaining unchanged at the rated value, aiming to flexibly suppress the load growth rate through price leverage; and electric vehicle charging stations in the current-limited zone implement a punitive peak pricing strategy, while simultaneously implementing mandatory power reduction constraints.

6. The dynamic coupling empowerment-based evaluation method of the carrying capacity of a transformer area according to claim 5, characterized in that, The specific strategy for setting corresponding constraints for electric vehicle charging stations based on different state regions includes: Preferred Zone Strategy: When a transformer area is in the preferred zone, it indicates sufficient carrying capacity. Constraints include: Incentive pricing constraints. Power boundary constraints: ;in: The issued guiding electricity price; The basic time-of-use electricity price for the power grid; This refers to the maximum allowable output power of the charging station. The rated power of the charging pile; Warning Zone Strategy: When a transformer area is in a warning zone, it indicates a decrease in carrying capacity and a deterioration in condition. Constraints include: Guiding electricity price constraints. Power boundary constraints: In the formula: As a scoring penalty factor; This is a sensitivity penalty factor; The scoring threshold is used to delineate the preferred zone from the warning zone. This represents the absolute value of the slope of the score change at the current moment; Current limiting zone strategy: When a transformer area is in a current limiting zone, it indicates that the system faces overload risk. Constraints include: guidance electricity price constraints: Power boundary constraints: In the formula: The punitive peak electricity price is set; This refers to the safety margin factor. The scoring threshold is used to demarcate the warning zone and the flow restriction zone.

7. The method for evaluating the bearing capacity of transformer substations based on dynamic coupling weighting according to claim 6, characterized in that, The construction of the spatiotemporal hierarchical guidance model for electric vehicles includes: taking the maximization of the overall carrying capacity score of the transformer substation and the minimization of the comprehensive charging cost for users in the region as the joint objective; under the dynamically changing guidance price and power boundary constraints, solving for the optimal total power scheduling instruction at the transformer substation level in future time periods, and issuing this instruction as a regional regulation target to smart charging piles or aggregation control terminals to guide the subsequent execution of electric vehicle power allocation.

8. The dynamic coupling empowerment-based evaluation method of the carrying capacity of a transformer area according to claim 7, characterized in that, The minimized comprehensive charging cost for users within the region includes: each electric vehicle user's decision objective is to complete the charging task while minimizing the economic cost, with the objective function... Represented as: ; in, This indicates the remaining time before the electric vehicle is de-griddled, and includes several scheduling periods; This is the index for the current scheduling period; For the first The guiding electricity price for the specified time period, Let be the decision variable to be solved, i.e., the th The actual charging power of electric vehicles during the time period This is the scheduling time interval.

9. The dynamic coupling empowerment-based evaluation method of the carrying capacity of a transformer area according to claim 8, characterized in that, The construction of the spatiotemporal hierarchical guidance model for electric vehicles also includes: To ensure that the response strategy meets both power grid security requirements and user travel needs, the following constraints need to be defined: Power boundary constraints: In the formula: This is the maximum allowed charging power at the current moment. If the charging station is in the preferred or warning zone, then... , This is the rated power; if the transformer area is in the current-limiting zone, then... The power value after forced reduction; State of Charge (SOC) and Energy Demand Constraints: In the formula: This represents the initial state of charge of the electric vehicle battery at the current moment. The target state of charge when the user expects to disconnect from the grid; For charging efficiency; This formula defines the rated capacity of an electric vehicle's battery, ensuring that the total charge added to the initial charge when the user leaves the vehicle meets the user's preset needs.

10. A dynamic coupling empowerment-based evaluation system for the carrying capacity of a transformer area, characterized by, The system includes: The indicator grading module is used to collect load data of electric vehicle charging stations and distribution network parameters within the carrying capacity assessment range of each distribution area in the distribution network, determine the operating parameters of the distribution network, calculate the comprehensive assessment index of the carrying capacity of each distribution area based on the operating parameters of the distribution network, and grade the comprehensive assessment index. The grading principle is that the lowest level of assessment index is an objective index, and the other levels of indicators are subjective indicators obtained by comprehensively considering the objective indicators. The game coupling weight determination module is used to determine the subjective weight vector of subjective indicators and the initial objective weight vector of objective indicators under a fixed number of time series samples. The weighted objective weight vector is obtained according to the constructed time decay factor. The coupling weight is defined based on the corresponding set of subjective weight vectors and weighted objective weight vectors at different times. The game coupling weight is obtained by solving the optimization model based on the coupling weight. The comprehensive carrying capacity scoring module is used to correct the game coupling weights based on the grey relational model, determine the final comprehensive weight of each objective indicator under the current transformer area, and thus obtain the comprehensive carrying capacity score of each transformer area. The transformer substation division module is used to sort the comprehensive load-bearing capacity scores of each transformer substation from largest to smallest, perform polynomial fitting on the sorted scatter points to obtain the fitted curve, calculate the slope of the curve change for each substation as the deviation feature of the load-bearing capacity of that substation, construct a deviation feature dataset of the load-bearing capacity of all transformer substations, and divide the transformer substations into several state regions based on the substation numbers of the mean and median in the deviation feature dataset. The scheduling instruction determination module is used to set corresponding constraints for electric vehicle charging stations based on different state regions, construct a spatiotemporal hierarchical guidance model for electric vehicles, and solve the spatiotemporal hierarchical guidance model after determining the joint objective to obtain the optimal total power scheduling instruction for each substation in the future time period; the substation division module constructs a deviation feature dataset of the carrying capacity of all substations, and divides the substations into several state regions based on the substation numbers of the mean and median in the deviation feature dataset, including: A comprehensive bearing capacity scoring dataset was established using a polynomial fitting method. With ranking Functional mapping relationship The first derivative of the fitted function is then calculated to obtain the slope curve of the score change. The slope The slope absolute value characterizes the sensitivity to condition deterioration at the current bearing capacity level. The larger the value, the more likely the transformer area's carrying capacity score will plummet with even a slight increase in load or a slight change in conditions within that ranking range, indicating that the system is in a critically unstable state. Calculate the scoring dataset arithmetic mean With median To enhance the system's robustness to data fluctuations and sample distribution skewness, an adaptive buffer coefficient is introduced. Its range of values Constructing a dual dynamic threshold: Threshold determination criteria : Judgment threshold standard : Based on the comprehensive score calculated in real time Based on the relationship with the judgment threshold standard, the load-bearing capacity status of the transformer area is divided into three zones: Preferred zone: when... When the transformer area is deemed to be in a state of sufficient load-bearing capacity, the slope |k| in this area indicates that the system is operating stably and has strong acceptance capacity; Warning zone: when When the transformer area is in a critical transition state, this region corresponds to a section with drastic changes in slope |k|, indicating that the bearing capacity is extremely sensitive to load growth and requires flexible adjustment; Flow restriction zone: when If the transformer area is determined to be in a state of insufficient load-bearing capacity or overload risk, mandatory constraints must be implemented.

11. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method as claimed in any one of claims 1-9.

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