A novel method and system for optimizing resource allocation for flexible carbon reduction in power distribution and utilization systems

Abstract

The present invention relates to the technical field of electric power systems, and in particular to a method and system for optimizing the configuration of flexible carbon reduction resources in a new type of power distribution and utilization system, including establishing a plurality of candidate distribution network structure models according to the carbon reduction resource configuration mode, and evaluating the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value; selecting the optimal target new type of power distribution and utilization system grid structure model based on the carbon emission reduction index value, generating a number of power supply mode samples, and classifying the power supply mode samples to obtain a number of typical power supply mode categories; selecting a target power supply mode according to the concentration and discrete points of the typical power supply mode categories; optimizing different carbon reduction resource configuration modes according to the target power supply mode, and generating a new type of power distribution and utilization system flexible carbon reduction resource optimization configuration strategy. The present invention realizes the optimal configuration of flexible carbon reduction resources in the new type of power distribution and utilization system by analyzing the impact of different carbon reduction resource configuration modes on the new type of power distribution and utilization system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a novel method and system for optimizing the configuration of flexible carbon reduction resources in a power distribution and utilization system. Background Art

[0002] With the transformation of the global energy structure, new power distribution and utilization systems are facing unprecedented challenges in achieving low-carbon, efficient and flexible power supply. In new power distribution and utilization systems, the research on the optimal allocation strategy of flexible carbon reduction resources has become an important issue facing the current power industry. However, there are still many problems and shortcomings in dealing with the system optimization configuration under different carbon emission reduction scenarios.

[0003] First, the existing flexible carbon reduction resource configuration mode has poor adaptability in different scenarios. Different carbon reduction targets and environmental conditions require the system to flexibly adjust resource configuration to achieve a balance between power supply reliability, flexibility and carbon reduction potential. However, how to select a suitable configuration mode according to the actual needs of the system to form a new grid form that takes into account all aspects of performance is a key issue; secondly, the diversity of new grid forms increases the difficulty of system optimization configuration. Different new grid forms have different impacts on the system's power supply reliability, flexibility and carbon reduction potential. Existing technologies often find it difficult to fully consider these factors, resulting in difficulty in achieving a balance and optimization among the three in practical applications. For example, some grid forms may perform well in carbon reduction, but are insufficient in power supply reliability and flexibility, and vice versa. This contradiction makes the system optimization configuration more complex and difficult; in addition, the dynamic and uncertain nature of the power grid operating environment also brings challenges to the optimal configuration of flexible carbon reduction resources. Changes in load demand, fluctuations in new energy output and other factors may lead to real-time changes in system performance parameters. This requires that the configuration strategy of flexible carbon reduction resources must be able to respond to these changes in real time to maintain the stable operation and economy of the system.

[0004] In summary, the existing technology still has many shortcomings in the optimal allocation of flexible carbon reduction resources in new power distribution and utilization systems. How to achieve real-time optimal allocation in a dynamically changing environment is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a novel method and system for optimizing the allocation of flexible carbon-reduction resources in a power distribution and utilization system.

[0006] In a first aspect, the present invention provides a novel method for optimizing the configuration of flexible carbon reduction resources in a power distribution and utilization system, the method comprising the following steps:

[0007] Determine the carbon reduction resource allocation method for different carbon reduction scenarios based on the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system;

[0008] Establishing multiple candidate distribution network structure models according to all the carbon reduction resource configuration methods, and evaluating the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value;

[0009] The candidate distribution grid structure models are ranked based on the carbon emission reduction index value, and the optimal target new distribution and utilization system grid structure model is selected;

[0010] Generating a number of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model, and classifying the power supply mode samples to obtain a number of typical power supply mode categories;

[0011] Screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories;

[0012] Different carbon reduction resource configuration methods are optimized according to the target power supply mode, and a new type of power distribution and utilization system flexible carbon reduction resource optimization configuration strategy is generated.

[0013] In a further embodiment, the step of evaluating the carbon emission reduction potential of each candidate distribution grid structure model to obtain a carbon emission reduction index value comprises:

[0014] According to the real-time power supply capacity and power demand of each candidate distribution network structure model, the start and stop status of each unit in the new power distribution system is randomly sampled multiple times to obtain the unit operation status data set;

[0015] According to the unit operation status data set, a random forest algorithm is used to analyze the carbon emission variation trend under the start and stop states of different units, and a relationship curve between the regulation amount and the carbon emission is generated;

[0016] Based on the relationship curve between the adjustment amount and the carbon emission, the optimal unit scheduling optimization sequence output power data is calculated using an optimization algorithm;

[0017] According to the output power data of the optimal unit scheduling optimization sequence, direct carbon emissions are calculated using the carbon emission coefficient of the generator set obtained in advance, and indirect carbon emissions caused by power grid transmission losses are calculated according to the power loss data during power grid transmission;

[0018] The direct carbon emissions and the indirect carbon emissions are analyzed using a time series regression analysis method to obtain a carbon emission reduction index value.

[0019] In a further implementation scheme, the step of ranking the candidate distribution network structure models based on the carbon emission reduction index value and selecting the optimal target new distribution and utilization system grid structure model comprises:

[0020] According to the capacity parameters of each node and the line load parameters of each candidate distribution network structure model, a multi-layer neural network model is used to map and transform the physical connection relationship between the nodes of the distribution network to obtain a node mapping relationship matrix;

[0021] According to the node mapping relationship matrix, the maximum transmission capacity of each section load is calculated using the power system flow calculation method;

[0022] The response sensitivity of node voltage to load change is analyzed according to the node mapping relationship matrix and line load parameters to obtain the voltage sensitivity index;

[0023] According to the ratio between the section impedance and the maximum transmission capacity, the candidate distribution network structure models are sorted by correlation degree to obtain a grid correlation degree sorting result;

[0024] According to the voltage sensitivity index, the voltage stability parameter set is extracted from the grid correlation ranking results;

[0025] Based on the voltage stability parameter set and the carbon emission reduction index value, the optimal target new power distribution system grid structure model is screened out from the candidate distribution network grid structure models using the empirical Bayesian method.

[0026] In a further embodiment, the voltage stability parameter set includes voltage stability, power factor, harmonic content, and three-phase unbalance.

[0027] In a further embodiment, the step of selecting the optimal target new power distribution system grid structure model from the candidate distribution network grid structure models using the empirical Bayesian method based on the voltage stability parameter set and the carbon emission reduction index value comprises:

[0028] According to the voltage stability parameter set, the voltage quality and power loss sequences of each candidate distribution network structure model under different load conditions are classified and counted, and a distribution network stability feature vector is extracted;

[0029] According to the line electrical state parameters, the empirical Bayes method is used to perform probability evaluation on the load center drift amplitude and obtain the load center drift probability distribution.

[0030] According to the load center drift probability distribution and the distribution network stability characteristic vector, the connectivity, power supply radius and substation layout of each candidate distribution network structure model are quantitatively ranked by multiple objectives to extract the grid optimization adjustment index;

[0031] Obtaining transformer operation characteristic data under the grid optimization adjustment index, and extracting transformer operation state characteristic values ​​from the transformer operation characteristic data using a principal component analysis method;

[0032] Calculating the fault recovery time distribution and the spare capacity margin value according to the transformer operation state characteristic value;

[0033] According to the fault recovery time distribution, the spare capacity margin value and the carbon emission reduction index value, a hierarchical analysis method is used to perform a multi-dimensional evaluation on all candidate distribution network structure models to establish a priority ranking set;

[0034] According to the priority sorting set, the optimal grid structure parameters are selected using a particle swarm optimization algorithm to form a target new power distribution system grid structure model.

[0035] In a further implementation scheme, the step of generating a plurality of power supply mode samples according to the optimal target new power distribution system grid structure model comprises:

[0036] According to the line parameters and load characteristics of each node in the new power distribution system grid structure model with the optimal target, the Monte Carlo method is used to randomly sample and generate multiple groups of load fluctuation sequences.

[0037] According to the optimal target new power distribution system grid structure model, the electrical connection relationship and load density between each feeder section are identified;

[0038] According to the electrical connection relationship and the load density, the electrical breakpoints of the feeder section are identified, and the electrical breakpoints are used as segmentation reference nodes, and the optimal target new power distribution system grid structure model is segmented according to the power supply radius threshold to obtain the segmented power supply area;

[0039] According to the connection relationship between the divided power supply areas and the electrical breakpoints, a power supply mode topology map is obtained;

[0040] Different power supply modes are simulated according to the power supply mode topology map and multiple groups of load fluctuation sequences to generate several power supply mode samples.

[0041] In a further embodiment, the step of classifying the power supply mode samples to obtain several typical power supply mode categories includes:

[0042] According to the power supply radius data, load density data and equipment utilization data, the power supply mode samples are subjected to k-means clustering analysis to obtain several typical power supply mode categories; wherein the typical power supply mode categories include centralized power supply category, distributed power supply category and smart grid power supply category.

[0043] In a further embodiment, the step of screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories includes:

[0044] Calculating the power supply mode characteristics of each typical power supply mode category according to the cluster center of each typical power supply mode category;

[0045] According to the power supply mode characteristics and power supply area data, the power supply mode sample concentration is modeled using mixed Gaussian distribution to obtain Gaussian distribution characteristics;

[0046] According to the Gaussian distribution characteristics, the grid structure model of the optimal target new power distribution system is identified with discrete points, and the power supply category sample dispersion is calculated using information entropy based on the discrete points; the power supply category sample dispersion includes the power supply radius dispersion, load rate dispersion, and transformer capacity utilization coefficient dispersion;

[0047] According to the dispersion of power supply category samples, the hierarchical clustering method is used to screen out the target power supply mode from the typical power supply mode categories.

[0048] In a further implementation scheme, the step of optimizing different carbon reduction resource configuration modes according to the target power supply mode to generate a new type of power distribution system flexible carbon reduction resource optimization configuration strategy includes:

[0049] Simulate the target power supply mode under different carbon reduction resource configuration modes in the power system simulation software to obtain the target power supply space electrical mapping data;

[0050] According to the electrical mapping data of the target power supply space, a genetic algorithm is used to iteratively calculate the fitness of each carbon reduction resource configuration ratio to generate the optimized data for each carbon reduction resource configuration;

[0051] The spatial distribution of carbon reduction resource allocation is optimized according to the optimization data of each carbon reduction resource configuration, and an optimal configuration strategy for flexible carbon reduction resources for a new power distribution and utilization system is generated.

[0052] In a second aspect, the present invention provides a novel power distribution and utilization system flexibility carbon reduction resource configuration optimization system, the system comprising:

[0053] The resource preliminary configuration module is used to determine the carbon reduction resource configuration method for different carbon reduction scenarios based on the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system;

[0054] A carbon emission reduction assessment module is used to establish multiple candidate distribution network structure models according to all the carbon reduction resource configuration methods, and to evaluate the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value;

[0055] An optimal model screening module is used to sort the candidate distribution network structure models based on the carbon emission reduction index value, and screen out the optimal target new distribution and utilization system grid structure model;

[0056] A power supply mode analysis module is used to generate a number of power supply mode samples according to the optimal target new power distribution system grid structure model, and classify the power supply mode samples to obtain a number of typical power supply mode categories;

[0057] A power supply mode screening module, used for screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories;

[0058] The resource configuration optimization module is used to optimize different carbon reduction resource configuration methods according to the target power supply mode, and generate a new type of power distribution system flexible carbon reduction resource optimization configuration strategy.

[0059] The present invention provides a method and system for optimizing the flexible carbon reduction resource configuration of a new power distribution and utilization system. The method determines the carbon reduction resource configuration mode for different carbon reduction scenarios according to the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system; establishes multiple candidate distribution network frame structure models according to all carbon reduction resource configuration modes, and evaluates the carbon reduction potential of each candidate distribution network frame structure model to obtain a carbon reduction index value; sorts the candidate distribution network frame structure models based on the carbon reduction index value, and screens out the optimal target new power distribution and utilization system frame structure model; generates a number of power supply mode samples according to the optimal target new power distribution and utilization system frame structure model, and classifies the power supply mode samples to obtain a number of typical power supply mode categories; screens out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories; optimizes different carbon reduction resource configuration modes according to the target power supply mode, and generates a flexible carbon reduction resource optimization configuration strategy for the new power distribution and utilization system. Compared with the existing technology, this method comprehensively considers power supply reliability, flexibility and carbon emission reduction potential, analyzes the impact of different configuration methods of flexible carbon reduction resources on the new power distribution and utilization system, realizes the optimal configuration of flexible carbon reduction resources in the new power distribution and utilization system, improves the comprehensive performance of the new power distribution and utilization system, and thus ensures the stability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic flow chart of a novel method for optimizing the configuration of flexible carbon reduction resources for power distribution and utilization systems provided by an embodiment of the present invention;

[0061] Figure 2 It is a block diagram of a new type of power distribution and utilization system flexible carbon reduction resource configuration optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limitations of the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute limitations on the scope of patent protection of the present invention, because many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0063] refer to Figure 1 , the embodiment of the present invention provides a novel method for optimizing the configuration of flexible carbon reduction resources in a power distribution and utilization system, such as Figure 1 As shown, the method comprises the following steps:

[0064] S1. Determine the carbon reduction resource allocation method for different carbon reduction scenarios based on the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system.

[0065] Specifically, this embodiment preliminarily determines the types and scales of flexible carbon reduction resources required for the new power distribution and utilization system according to different carbon emission reduction scenarios. The carbon reduction resource types may include demand response, distributed power generation (such as solar energy and wind power), flexible scheduling (such as energy storage systems), smart grid technology, etc., and then sets different combinations of carbon reduction resource configuration methods for each scenario according to the types and scales of flexible carbon reduction resources required for the new power distribution and utilization system. These configuration methods will serve as the basis for constructing candidate grid structure models. For example, in a technology joint scenario, demand response and distributed power generation can be used in combination; in a resource joint scenario, distributed power generation and flexible scheduling can be given priority.

[0066] S2. Establish multiple candidate distribution network structure models based on all the carbon reduction resource configuration methods, and evaluate the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value.

[0067] In this embodiment, the step of evaluating the carbon emission reduction potential of each candidate distribution grid structure model to obtain a carbon emission reduction index value includes:

[0068] According to the real-time power supply capacity and power demand of each candidate distribution network structure model, the start and stop status of each unit in the new power distribution system is randomly sampled multiple times to obtain the unit operation status data set;

[0069] According to the unit operation status data set, a random forest algorithm is used to analyze the carbon emission variation trend under the start and stop states of different units, and a relationship curve between the regulation amount and the carbon emission is generated;

[0070] Based on the relationship curve between the adjustment amount and the carbon emission, the optimal unit scheduling optimization sequence output power data is calculated using an optimization algorithm;

[0071] According to the output power data of the optimal unit scheduling optimization sequence, direct carbon emissions are calculated using the carbon emission coefficient of the generator set obtained in advance, and indirect carbon emissions caused by power grid transmission losses are calculated according to the power loss data during power grid transmission;

[0072] The direct carbon emissions and the indirect carbon emissions are analyzed using a time series regression analysis method to obtain a carbon emission reduction index value.

[0073] Specifically, this embodiment determines the access points, transmission paths, control methods, etc. of various resources according to each combination of carbon reduction resource configuration methods, and constructs the corresponding power distribution system grid structure. In order to accurately reflect the performance of each candidate distribution network structure model in actual operation, for each candidate distribution network structure model, this embodiment monitors its power supply capacity and power demand in real time, and obtains a unit operation status data set by performing multiple random sampling of the start and stop status of each unit in the new power distribution system. For example, this embodiment can set a sampling cycle (for example, once every 15 minutes), randomly select a number of units (for example, 10% of the units) in each cycle, record their start and stop status, repeat this process, collect data within a certain time range, and form a unit operation status data set. Then, this embodiment uses a random forest algorithm to analyze the unit operation status data set, identify the carbon emission change trend under different unit start and stop states, and generate a relationship curve between the adjustment amount (such as the number of unit start and stop times, power changes, etc.) and the carbon emissions. The curve intuitively shows the direct impact of unit scheduling on carbon emissions. Then, this embodiment Based on the relationship curve between the adjustment amount and the carbon emissions, combined with actual needs such as power supply demand and carbon emission limit, an optimization algorithm such as a genetic algorithm or a particle swarm algorithm is used to calculate the output power data of the computer group scheduling sequence. The goal of the optimization algorithm is to reduce carbon emissions as much as possible under the premise of meeting the power supply demand. Through iterative calculation, a set of optimal unit scheduling sequence output power data can be obtained. According to the optimal unit scheduling optimization sequence output power data, the direct carbon emissions are calculated using the pre-acquired carbon emission coefficient of the generator set, wherein the carbon emission coefficient of the generator set is a fixed parameter, which describes the carbon emissions generated by the generator set under unit output power. In this embodiment, the direct carbon emissions can be obtained by multiplying the output power data of the optimal unit scheduling sequence. Finally, the indirect carbon emissions generated by the power grid transmission loss are calculated according to the power loss data in the power grid transmission process. The direct carbon emissions and the indirect carbon emissions are analyzed by the time series regression analysis method to obtain a carbon emission reduction index value, which reflects the carbon emission reduction effect achieved by optimizing the unit scheduling under different candidate distribution network structure models.

[0074] In another embodiment, in order to comprehensively evaluate the carbon emission reduction potential of the distribution network structure model, this embodiment can also take into account the role of energy storage devices, by collecting key parameters of the energy storage devices such as the charging and discharging efficiency, number of cycles, and capacity attenuation of the energy storage devices, and establishing an energy storage efficiency loss model between the charging and discharging efficiency and life loss of the energy storage devices based on the key parameters of the energy storage devices, so as to better understand the performance changes of the energy storage devices under different scheduling strategies and their impact on carbon emissions. After obtaining the current grid operation status parameters and grid power compensation requirements, a polynomial fitting method is used to obtain the optimal energy storage scheduling instructions from the energy storage efficiency loss model. Specifically, the polynomial fitting method can obtain a polynomial function by fitting the data in the energy storage efficiency loss model. The function describes the efficiency and life loss of the energy storage device under different energy storage scheduling instructions, and then uses optimization algorithms such as the gradient descent method. Solve the optimal solution of the polynomial function to obtain the optimal energy storage scheduling instruction, which aims to achieve efficient utilization of the energy storage device while minimizing its contribution to the carbon emissions of the power grid. This embodiment combines the optimal energy storage scheduling instruction with the output power data of the optimal unit scheduling sequence, and uses the pre-acquired carbon emission coefficient of the generator set to calculate the direct carbon emissions of each unit to reflect the direct impact of the power grid scheduling strategy on carbon emissions. In addition, this embodiment takes into account the indirect carbon emissions caused by the power loss during the power grid transmission process, and uses the time series regression analysis method to conduct a comprehensive analysis of direct carbon emissions and indirect carbon emissions. It can more accurately evaluate the carbon emission reduction potential of the candidate distribution network structure model, and obtain the carbon emission reduction index value of each candidate distribution network structure model. The index value reflects the carbon emission reduction effect achieved by optimizing unit scheduling and energy storage scheduling under different candidate distribution network structure models.

[0075] S3. Rank the candidate distribution network structure models based on the carbon emission reduction index value, and select the optimal target new distribution and utilization system grid structure model.

[0076] In this embodiment, the step of ranking the candidate distribution network structure models based on the carbon emission reduction index value and selecting the optimal target new distribution and utilization system grid structure model includes:

[0077] According to the capacity parameters of each node and the line load parameters of each candidate distribution network structure model, a multi-layer neural network model is used to map and transform the physical connection relationship between the nodes of the distribution network to obtain a node mapping relationship matrix;

[0078] According to the node mapping relationship matrix, the maximum transmission capacity of each section load is calculated using the power system flow calculation method;

[0079] The response sensitivity of node voltage to load change is analyzed according to the node mapping relationship matrix and line load parameters to obtain the voltage sensitivity index;

[0080] According to the ratio between the section impedance and the maximum transmission capacity, the candidate distribution network structure models are sorted by correlation degree to obtain a grid correlation degree sorting result;

[0081] According to the voltage sensitivity index, a voltage stability parameter set is extracted from the grid correlation ranking results; wherein the voltage stability parameter set includes voltage stability, power factor, harmonic content and three-phase unbalance;

[0082] Based on the voltage stability parameter set and the carbon emission reduction index value, the optimal target new power distribution system grid structure model is screened out from the candidate distribution network grid structure models using the empirical Bayesian method.

[0083] Specifically, this embodiment collects the capacity parameters of each node (such as generator capacity, transformer capacity, etc.) and line load parameters (such as line current, voltage, etc.) of each candidate distribution network structure model, and constructs a multi-layer neural network model containing multiple fully connected layers. The input of the multi-layer neural network model is the node capacity and line load parameters, and the output of the multi-layer neural network model is a node mapping relationship matrix. This embodiment can train the model through a large amount of sample data so that it can accurately map the connection relationship between nodes, and apply the trained model to the candidate distribution network structure model to obtain a node mapping relationship matrix, which reflects the relative position and connection strength between each node of the distribution network. Then, according to the node mapping relationship matrix, combined with the node capacity parameters and line load parameters, the power system power flow calculation method such as the Newton-Raphson method or the 1 DC power flow method is used to perform power flow calculation to obtain the maximum transmission capacity of each section load. The maximum transmission capacity represents the maximum power load that can be transmitted by each section under the current network structure.

[0084] This embodiment analyzes the response sensitivity of voltage to load changes by simulating the system state under different load levels based on the node mapping relationship matrix and the line load parameters, and calculates the voltage sensitivity index, which reflects the stability of the system voltage under load changes. The higher the voltage sensitivity index, the more sensitive the node voltage is to load changes. This embodiment evaluates the flexibility by evaluating the system's response ability to changes, such as load changes, power generation changes, etc. Then, this embodiment calculates the impedance of each section according to the line parameters (such as resistance and reactance) of the distribution network, and calculates the ratio between the section impedance and the maximum transmission capacity as the grid correlation index. The ratio reflects the degree of correlation between the system transmission capacity and the impedance. The smaller the ratio, the stronger the transmission capacity of the section and the better the grid structure. In this way, the candidate distribution network structure models are sorted by correlation according to the ratio to obtain the grid correlation sorting result. This step is intended to identify the compactness and transmission capacity of the grid structure. The higher the ranking of the model, the higher the correlation between its transmission capacity and impedance. The grid correlation ranking result is obtained by using the voltage sensitivity index to identify the voltage stability of the power grid when the load changes. The voltage stability parameter set is extracted from the grid correlation ranking result, including voltage stability, power factor, harmonic content and three-phase imbalance. These parameters jointly reflect the voltage stability of the system. An empirical Bayesian model is constructed, and the voltage stability parameter set and the carbon emission reduction index value are input into the empirical Bayesian model for data processing and calculation. The empirical Bayesian method combines prior knowledge and observation data to score the candidate distribution network structure model, so as to sort the candidate distribution network structure model according to the score, and select the optimal target new distribution and utilization system grid structure model with the highest score. Through the above steps, the present embodiment can comprehensively consider the physical connection relationship, load transmission capacity, voltage stability and other aspects of the distribution network, and comprehensively evaluate and rank multiple candidate distribution network structure models. The optimal target new distribution and utilization system grid structure model finally selected has the best performance in terms of carbon emission reduction index value and can meet the safety and economy requirements of the power system.

[0085] In this embodiment, the step of selecting the optimal target new power distribution system grid structure model from the candidate distribution network grid structure models by using the empirical Bayesian method based on the voltage stability parameter set and the carbon emission reduction index value includes:

[0086] According to the voltage stability parameter set, the voltage quality and power loss sequences of each candidate distribution network structure model under different load conditions are classified and counted, and a distribution network stability feature vector is extracted;

[0087] According to the line electrical state parameters, the empirical Bayes method is used to perform probability evaluation on the load center drift amplitude and obtain the load center drift probability distribution.

[0088] According to the load center drift probability distribution and the distribution network stability characteristic vector, the connectivity, power supply radius and substation layout of each candidate distribution network structure model are quantitatively ranked by multiple objectives to extract the grid optimization adjustment index;

[0089] Obtaining transformer operation characteristic data under the grid optimization adjustment index, and extracting transformer operation state characteristic values ​​from the transformer operation characteristic data using a principal component analysis method;

[0090] Calculating the fault recovery time distribution and the spare capacity margin value according to the transformer operation state characteristic value;

[0091] According to the fault recovery time distribution, the spare capacity margin value and the carbon emission reduction index value, a hierarchical analysis method is used to perform a multi-dimensional evaluation on all candidate distribution network structure models to establish a priority ranking set;

[0092] According to the priority sorting set, the optimal grid structure parameters are selected using a particle swarm optimization algorithm to form a target new power distribution system grid structure model.

[0093] Specifically, this embodiment classifies and counts the voltage quality and power supply loss sequences of each candidate distribution network structure model under different load conditions according to the voltage stability parameter set, and extracts the distribution network stability characteristic vector. Then, this embodiment collects the line electrical state parameters of each candidate grid, and the line electrical state parameters may include data such as line load rate and voltage deviation. Combined with prior knowledge and currently observed line electrical state parameters, the empirical Bayesian method is used to perform probability evaluation on the load center drift amplitude. The probability distribution of the load center drift can be obtained through Bayesian updating. According to the load center drift probability distribution and the distribution network stability characteristic vector, the Pareto optimization method can be used to perform multi-objective quantitative sorting of the connectivity, power supply radius, and substation layout of each candidate distribution network structure model. Through sorting, indicators that have an important impact on grid optimization can be extracted, such as the connectivity improvement potential, power supply radius optimization space, etc. as grid optimization adjustment indicators.

[0094] Then, this embodiment obtains transformer operation characteristic data under the grid optimization adjustment index, which includes the main transformer load rate and short-circuit capacity change data, etc., and uses the principal component analysis method to reduce the dimension of the data, and extracts the key characteristic values ​​that can reflect the transformer operation status. At the same time, according to the transformer operation status characteristic values, combined with the transformer fault history data and the current operation status, the fault recovery time distribution is calculated by simulating the transformer response time under different fault conditions and the use of spare capacity. At the same time, according to the rated capacity and actual load of the transformer, the spare capacity margin value is calculated to evaluate whether the system's spare capacity is sufficient. The fault recovery time distribution, spare capacity margin value and carbon emission reduction index value are used as evaluation dimensions, and the hierarchical analysis method (AHP) is used to evaluate all candidate distribution network frames. The structural model is evaluated in multiple dimensions to obtain the comprehensive score of each candidate grid, and a priority ranking set is established according to the score. According to the priority ranking set, the particle swarm optimization algorithm (PSO) is used to select the optimal grid structure parameters. In this embodiment, the particles in the PSO algorithm can be regarded as a combination of candidate grid structure parameters, and the comprehensive score in the priority ranking set is used as the fitness function. Through the iterative search process of the PSO algorithm, the optimal combination of grid structure parameters is found to form the target new power distribution system grid structure model. In this embodiment, the candidate grid structure model is comprehensively evaluated from three aspects: power supply reliability, flexibility and carbon emission reduction potential, so as to optimize resource allocation, improve the power supply reliability and flexibility of the system, enhance the carbon emission reduction potential, and obtain a more flexible and efficient resource allocation method.

[0095] S4. Generate a number of power supply mode samples according to the optimal target new power distribution system grid structure model, and classify the power supply mode samples to obtain a number of typical power supply mode categories.

[0096] In this embodiment, the step of generating a plurality of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model includes:

[0097] According to the line parameters and load characteristics of each node in the new power distribution system grid structure model with the optimal target, the Monte Carlo method is used to randomly sample and generate multiple groups of load fluctuation sequences.

[0098] According to the optimal target new power distribution system grid structure model, the electrical connection relationship and load density between each feeder section are identified;

[0099] According to the electrical connection relationship and the load density, the electrical breakpoints of the feeder section are identified, and the electrical breakpoints are used as segmentation reference nodes, and the optimal target new power distribution system grid structure model is segmented according to the power supply radius threshold to obtain the segmented power supply area;

[0100] According to the connection relationship between the divided power supply areas and the electrical breakpoints, a power supply mode topology map is obtained;

[0101] Different power supply modes are simulated according to the power supply mode topology map and multiple groups of load fluctuation sequences to generate several power supply mode samples.

[0102] Specifically, after obtaining the parameters such as the line parameters and load characteristics of each node in the grid structure model of the distribution network system, this embodiment uses these parameters as the input variables of the Monte Carlo simulation, and uses a random number generator to generate a large number of random samples according to the probability distribution of the node line parameters and the load characteristics. These samples can be random numbers with uniform distribution or normal distribution. These random samples are substituted into the optimal target new power distribution system grid structure model to simulate the operation status of the power grid under different parameters, and record key performance indicators such as voltage and power loss. According to the simulation results, statistical methods (such as calculating expected values, variances, etc.) are used to analyze the performance of the power grid under different fluctuation sequences, generate multiple groups of load fluctuation sequences, and then identify the electrical connection relationship between the feeder sections by analyzing the topological structure of the optimal target new power distribution system grid structure model. At the same time, combined with the actual load data of the power grid, the performance of each section is analyzed. Load density. Areas with high load density require more electrical breakpoints to improve power supply reliability. Then, this embodiment uses a depth-first search algorithm to identify electrical breakpoints in the feeder section based on the electrical connection relationship and load density. Since these electrical breakpoints represent potential segmentation points in the power grid, these electrical breakpoints will be used as segmentation reference points to determine the boundaries of the power supply area, which will be used for subsequent grid structure optimization. This embodiment can set a threshold for the power supply radius based on the standards of power grid planning and actual power supply needs. Areas exceeding the threshold will be segmented out. According to the power supply radius threshold, the optimal target new power distribution system grid structure model is divided into multiple power supply areas, each with its own power supply center and boundary. After the segmentation is completed, the topological map of the power supply mode is extracted from the segmentation results. According to the power supply mode topological map, combined with the fluctuation sequence generated by the Monte Carlo method, different power supply modes are simulated to generate multiple power supply mode samples.

[0103] In this embodiment, the step of classifying the power supply mode samples to obtain several typical power supply mode categories includes: performing k-means clustering analysis on the power supply mode samples according to power supply radius data, load density data and equipment utilization data to obtain several typical power supply mode categories; wherein the typical power supply mode categories include centralized power supply categories, distributed power supply categories and smart grid power supply categories.

[0104] S5. Filter out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories.

[0105] In this embodiment, the step of screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories includes:

[0106] Calculating the power supply mode characteristics of each typical power supply mode category according to the cluster center of each typical power supply mode category;

[0107] According to the power supply mode characteristics and power supply area data, the power supply mode sample concentration is modeled using mixed Gaussian distribution to obtain Gaussian distribution characteristics;

[0108] According to the Gaussian distribution characteristics, the grid structure model of the optimal target new power distribution system is identified with discrete points, and the power supply category sample dispersion is calculated using information entropy based on the discrete points; the power supply category sample dispersion includes the power supply radius dispersion, load rate dispersion, and transformer capacity utilization coefficient dispersion;

[0109] According to the dispersion of power supply category samples, the hierarchical clustering method is used to screen out the target power supply mode from the typical power supply mode categories.

[0110] Specifically, for each cluster center (i.e., a typical power supply mode category), this embodiment calculates its power supply mode characteristics, such as the average power supply radius, the average load rate, the average transformer capacity, etc., and standardizes the power supply mode characteristics and the power supply area data for subsequent analysis. According to the power supply mode characteristics and the power supply area data, a mixed Gaussian distribution model is used to model the samples of each typical power supply mode category, and Gaussian distribution characteristics such as mean and variance are extracted from the mixed Gaussian distribution model. These characteristics can reflect the concentration of samples of each power supply category. Based on the Gaussian distribution characteristics, the optimal target new power distribution system grid structure model is identified with discrete points. The discrete points can be far away from the cluster center and deviate from the main distribution trend. sample points, and then use the concept of information entropy to calculate the dispersion of power supply category samples. The larger the information entropy, the higher the dispersion of the samples. Specifically, this embodiment can calculate the dispersion of power supply radius, the dispersion of load rate, the dispersion of transformer capacity utilization coefficient, etc., respectively. According to the dispersion of power supply category samples, hierarchical clustering is performed on typical power supply mode categories. Hierarchical clustering is a distance-based clustering method that can gradually merge or split clusters to form a hierarchical clustering structure. In the results of hierarchical clustering, those categories with tight clustering and low dispersion are selected as target power supply modes. These categories usually have relatively stable power supply characteristics and can more accurately reflect the power supply needs and characteristics of each region, and can be used as target power supply modes.

[0111] S6. Optimize different carbon reduction resource configuration methods according to the target power supply mode, and generate a new type of power distribution and utilization system flexible carbon reduction resource optimization configuration strategy.

[0112] In this embodiment, the step of optimizing different carbon reduction resource configuration modes according to the target power supply mode to generate a new type of power distribution system flexible carbon reduction resource optimization configuration strategy includes:

[0113] Simulate the target power supply mode under different carbon reduction resource configuration modes in the power system simulation software to obtain the target power supply space electrical mapping data;

[0114] According to the electrical mapping data of the target power supply space, a genetic algorithm is used to iteratively calculate the fitness of each carbon reduction resource configuration ratio to generate the optimized data for each carbon reduction resource configuration;

[0115] The spatial distribution of carbon reduction resource allocation is optimized according to the optimization data of each carbon reduction resource configuration, and an optimal configuration strategy for flexible carbon reduction resources for a new power distribution and utilization system is generated.

[0116] Specifically, after determining the target power supply mode of the new power distribution and utilization system, for example, the target power supply mode is a high-efficiency power supply mode, etc., in combination with different carbon reduction resource configuration methods, such as distributed renewable energy (such as solar energy, wind energy), energy storage systems, electric vehicle charging piles, etc., a power system simulation model including different carbon reduction resource configuration methods is established using power system simulation software (such as MATLAB / Simulink, ETAP, etc.), and the target power supply modes under different carbon reduction resource configuration methods are simulated in the power system simulation model respectively, and the power supply reliability, flexibility and carbon emission reduction potential performance parameters are monitored, and the effects of different carbon reduction resource configuration methods on the power supply reliability, flexibility and carbon emission reduction potential are evaluated. The influence of performance parameters is determined, and the configuration simulation results are obtained. According to the configuration simulation results, all carbon reduction resource configuration methods are ranked according to their advantages and disadvantages to obtain the optimal carbon reduction resource configuration method. In the power system simulation model, the configuration simulation results corresponding to the optimal carbon reduction resource configuration method are converted into the target power supply space electrical mapping data under the target power supply mode. For example, the target power supply space electrical mapping data may include data such as the voltage level, current distribution, power flow state of each node, and the carbon emission of the overall system. Then, a genetic algorithm is used to iteratively calculate the fitness of various carbon reduction resource configuration ratios to generate each carbon reduction resource configuration optimization data. The specific implementation process is as follows:

[0117] The configuration ratio of various carbon reduction resources is encoded as individuals (chromosomes) in the genetic algorithm. Each individual represents a resource configuration plan. A group of individuals is randomly generated as the initial population. The fitness function is defined according to the electrical mapping data of the target power supply space. The fitness function is used to evaluate the quality of each individual. The fitness function can comprehensively consider indicators such as voltage stability, current load balancing and carbon emissions. According to the fitness value, excellent individuals are selected as parents to reproduce the next generation. Crossover and mutation operations are performed on the parent individuals to generate new offspring individuals. The selection, crossover and mutation operations are repeated to iteratively optimize the resource configuration ratio until the predetermined number of iterations is reached or an individual that meets the requirements is found. The optimal individual is selected from the final population, and the optimal configuration ratio of various carbon reduction resources is decoded from the optimal individual, such as the ratio of solar energy and wind energy, the capacity of the energy storage system and the distribution of electric vehicle charging piles.

[0118] After obtaining the resource allocation optimization data, the spatial constraints of carbon reduction resources are analyzed in combination with the geographic information system (GIS) data, and the spatial distribution of different carbon reduction resource allocation methods is optimized using the spatial optimization algorithm. During the optimization process, the resource allocation ratio is kept unchanged, and only the spatial position of the resources is adjusted. This embodiment can consider spatial constraints such as power transmission restrictions, equipment capacity restrictions, and safety distance requirements. By adjusting the resource distribution position, a new type of power distribution system flexibility carbon reduction resource optimization allocation strategy is generated. The new type of power distribution system flexibility carbon reduction resource optimization allocation strategy may include strategies such as the optimized allocation ratio and spatial distribution plan of various resources.

[0119] An embodiment of the present invention provides a method for optimizing the flexible carbon reduction resource configuration of a new power distribution and utilization system. The method determines the carbon reduction resource configuration methods for different carbon reduction scenarios according to the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system; establishes multiple candidate distribution network structure models according to all carbon reduction resource configuration methods, and evaluates the carbon reduction potential of each candidate distribution network structure model to obtain a carbon reduction index value; sorts the candidate distribution network structure models based on the carbon reduction index value to screen out the optimal target new power distribution and utilization system grid structure model; generates a number of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model, and classifies the power supply mode samples to obtain a number of typical power supply mode categories; screens out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories; optimizes different carbon reduction resource configuration methods according to the target power supply mode to generate a flexible carbon reduction resource optimization configuration strategy for the new power distribution and utilization system. Compared with the existing technology, this method comprehensively considers power supply reliability, flexibility and carbon emission reduction potential, analyzes the impact of different configuration methods of flexible carbon reduction resources on the new power distribution and utilization system, realizes the optimal configuration of flexible carbon reduction resources in the new power distribution and utilization system, improves the comprehensive performance of the new power distribution and utilization system, and thus ensures the stability of system operation.

[0120] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0121] In one embodiment, Figure 2 As shown, an embodiment of the present invention provides a novel power distribution system flexibility carbon reduction resource configuration optimization system, the system comprising:

[0122] The resource preliminary configuration module 101 is used to determine the carbon reduction resource configuration mode for different carbon reduction scenarios according to different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system;

[0123] A carbon emission reduction assessment module 102 is used to establish a plurality of candidate distribution network structure models according to all the carbon reduction resource configuration modes, and to assess the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value;

[0124] The optimal model screening module 103 is used to sort the candidate distribution network structure models based on the carbon emission reduction index value, and screen out the optimal target new distribution and utilization system grid structure model;

[0125] The power supply mode analysis module 104 is used to generate a plurality of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model, and classify the power supply mode samples to obtain a plurality of typical power supply mode categories;

[0126] A power supply mode screening module 105, configured to screen out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories;

[0127] The resource configuration optimization module 106 is used to optimize different carbon reduction resource configuration methods according to the target power supply mode, and generate a new type of power distribution system flexible carbon reduction resource optimization configuration strategy.

[0128] For the specific definition of a new type of power distribution system flexible carbon reduction resource allocation optimization system, please refer to the above-mentioned definition of a new type of power distribution system flexible carbon reduction resource allocation optimization method, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0129] An embodiment of the present invention provides a new type of power distribution and utilization system flexible carbon reduction resource configuration optimization system, the system determines the carbon reduction resource configuration mode for different carbon reduction scenarios according to the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system through a preliminary resource configuration module; the carbon reduction evaluation module establishes multiple candidate distribution network frame structure models according to all carbon reduction resource configuration modes, and evaluates the carbon reduction potential of each candidate distribution network frame structure model to obtain a carbon reduction index value; the optimal model screening module ranks the candidate distribution network frame structure models based on the carbon reduction index value, and screens out the optimal target new power distribution and utilization system frame structure model; the power supply mode analysis module generates a number of power supply mode samples according to the optimal target new power distribution and utilization system frame structure model, and classifies the power supply mode samples to obtain a number of typical power supply mode categories; the power supply mode screening module screens out the target power supply mode according to the concentration and discrete points of the typical power supply mode category; the resource configuration optimization module optimizes different carbon reduction resource configuration modes according to the target power supply mode, and generates a new type of power distribution and utilization system flexible carbon reduction resource optimization configuration strategy. Compared with existing technologies, this system comprehensively considers power supply reliability, flexibility and carbon emission reduction potential, analyzes the impact of different configuration methods of flexible carbon reduction resources on the new power distribution and utilization system, realizes the optimal configuration of flexible carbon reduction resources in the new power distribution and utilization system, improves the comprehensive performance of the new power distribution and utilization system, and thus ensures the stability of system operation.

[0130] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the allocation of flexible carbon reduction resources in a power distribution system, characterized in that: The following steps are involved: Determine the carbon reduction resource allocation method for different carbon reduction scenarios based on the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system; Establishing multiple candidate distribution network structure models according to all the carbon reduction resource configuration methods, and evaluating the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value; The candidate distribution grid structure models are ranked based on the carbon emission reduction index value, and the optimal target new distribution and utilization system grid structure model is selected; Generating a number of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model, and classifying the power supply mode samples to obtain a number of typical power supply mode categories; Screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories; According to the target power supply mode, different carbon reduction resource configuration methods are optimized to generate a new type of power distribution system flexible carbon reduction resource optimization configuration strategy; The step of ranking the candidate distribution network structure models based on the carbon emission reduction index value and selecting the optimal target new distribution and utilization system grid structure model comprises: According to the capacity parameters of each node and the line load parameters of each candidate distribution network structure model, a multi-layer neural network model is used to map and transform the physical connection relationship between the nodes of the distribution network to obtain a node mapping relationship matrix; According to the node mapping relationship matrix, the maximum transmission capacity of each section load is calculated using the power system flow calculation method; The response sensitivity of node voltage to load change is analyzed according to the node mapping relationship matrix and line load parameters to obtain the voltage sensitivity index; According to the ratio between the section impedance and the maximum transmission capacity, the candidate distribution network structure models are sorted by correlation degree to obtain a grid correlation degree sorting result; According to the voltage sensitivity index, the voltage stability parameter set is extracted from the grid correlation ranking results; Based on the voltage stability parameter set and the carbon emission reduction index value, an optimal target new power distribution system grid structure model is selected from the candidate distribution network grid structure models using an empirical Bayesian method; The step of selecting the optimal target new power distribution system grid structure model from the candidate power distribution grid structure models by using the empirical Bayesian method based on the voltage stability parameter set and the carbon emission reduction index value comprises: According to the voltage stability parameter set, the voltage quality and power loss sequences of each candidate distribution network structure model under different load conditions are classified and counted, and a distribution network stability feature vector is extracted; According to the line electrical state parameters, the empirical Bayes method is used to perform probability evaluation on the load center drift amplitude and obtain the load center drift probability distribution. According to the load center drift probability distribution and the distribution network stability characteristic vector, the connectivity, power supply radius and substation layout of each candidate distribution network structure model are quantitatively ranked by multiple objectives to extract the grid optimization adjustment index; Obtaining transformer operation characteristic data under the grid optimization adjustment index, and extracting transformer operation state characteristic values ​​from the transformer operation characteristic data using a principal component analysis method; Calculating the fault recovery time distribution and the spare capacity margin value according to the transformer operation state characteristic value; According to the fault recovery time distribution, the spare capacity margin value and the carbon emission reduction index value, a hierarchical analysis method is used to perform a multi-dimensional evaluation on all candidate distribution network structure models to establish a priority ranking set; According to the priority sorting set, the optimal grid structure parameters are selected using a particle swarm optimization algorithm to form a target new power distribution system grid structure model.

2. A method for optimizing the allocation of flexible carbon reduction resources for power distribution and utilization systems as claimed in claim 1, characterized in that: The step of evaluating the carbon emission reduction potential of each candidate distribution grid structure model to obtain a carbon emission reduction index value comprises: According to the real-time power supply capacity and power demand of each candidate distribution network structure model, the start and stop status of each unit in the new power distribution system is randomly sampled multiple times to obtain the unit operation status data set; According to the unit operation status data set, a random forest algorithm is used to analyze the carbon emission change trend under different unit start-up and shutdown states, and a relationship curve between the adjustment amount and the carbon emission is generated; the adjustment amount includes the number of unit start-up and shutdown times and power changes; Based on the relationship curve between the adjustment amount and the carbon emission, the optimal unit scheduling optimization sequence output power data is calculated using an optimization algorithm; According to the output power data of the optimal unit scheduling optimization sequence, direct carbon emissions are calculated using the carbon emission coefficient of the generator set obtained in advance, and indirect carbon emissions caused by power grid transmission losses are calculated according to the power loss data during power grid transmission; The direct carbon emissions and the indirect carbon emissions are analyzed using a time series regression analysis method to obtain a carbon emission reduction index value.

3. The method for optimizing the allocation of flexible carbon reduction resources in a power distribution and utilization system according to claim 1, characterized in that: The voltage stability parameter set includes voltage stability, power factor, harmonic content and three-phase unbalance.

4. The method for optimizing the allocation of flexible carbon reduction resources in a power distribution and utilization system according to claim 1, characterized in that: The step of generating a plurality of power supply mode samples according to the optimal target new power distribution and utilization system grid structure model comprises: According to the line parameters and load characteristics of each node in the new power distribution system grid structure model with the optimal target, the Monte Carlo method is used to randomly sample and generate multiple groups of load fluctuation sequences. According to the optimal target new power distribution system grid structure model, the electrical connection relationship and load density between each feeder section are identified; According to the electrical connection relationship and the load density, the electrical breakpoints of the feeder section are identified, and the electrical breakpoints are used as segmentation reference nodes, and the optimal target new power distribution system grid structure model is segmented according to the power supply radius threshold to obtain the segmented power supply area; According to the connection relationship between the divided power supply areas and the electrical breakpoints, a power supply mode topology map is obtained; Different power supply modes are simulated according to the power supply mode topology map and multiple groups of load fluctuation sequences to generate several power supply mode samples.

5. The method for optimizing the allocation of flexible carbon reduction resources in a power distribution and utilization system according to claim 1, characterized in that: The step of classifying the power supply mode samples to obtain a number of typical power supply mode categories includes: According to the power supply radius data, load density data and equipment utilization data, the power supply mode samples are subjected to k-means clustering analysis to obtain several typical power supply mode categories; wherein the typical power supply mode categories include centralized power supply category, distributed power supply category and smart grid power supply category.

6. A method for optimizing the allocation of flexible carbon reduction resources for power distribution and utilization systems as claimed in claim 1, characterized in that: The step of screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories comprises: Calculating the power supply mode characteristics of each typical power supply mode category according to the cluster center of each typical power supply mode category; According to the power supply mode characteristics and power supply area data, the power supply mode sample concentration is modeled using mixed Gaussian distribution to obtain Gaussian distribution characteristics; According to the Gaussian distribution characteristics, the grid structure model of the optimal target new power distribution system is identified with discrete points, and the power supply category sample dispersion is calculated using information entropy based on the discrete points; the power supply category sample dispersion includes the power supply radius dispersion, load rate dispersion, and transformer capacity utilization coefficient dispersion; According to the dispersion of power supply category samples, the hierarchical clustering method is used to screen out the target power supply mode from the typical power supply mode categories.

7. The method for optimizing the allocation of flexible carbon reduction resources in a power distribution and utilization system according to claim 1, characterized in that: The step of optimizing different carbon reduction resource configuration modes according to the target power supply mode to generate a new type of power distribution and utilization system flexible carbon reduction resource optimization configuration strategy includes: Simulate the target power supply mode under different carbon reduction resource configuration modes in the power system simulation software to obtain the target power supply space electrical mapping data; According to the electrical mapping data of the target power supply space, a genetic algorithm is used to iteratively calculate the fitness of each carbon reduction resource configuration ratio to generate the optimized data for each carbon reduction resource configuration; The spatial distribution of carbon reduction resource allocation is optimized according to the optimization data of each carbon reduction resource configuration, and an optimal configuration strategy for flexible carbon reduction resources for a new power distribution and utilization system is generated.

8. A power distribution system flexibility carbon reduction resource allocation optimization system, characterized in that: The system comprises: The resource preliminary configuration module is used to determine the carbon reduction resource configuration method for different carbon reduction scenarios based on the different carbon reduction scenario requirements and carbon reduction resource types of the new power distribution and utilization system; A carbon emission reduction assessment module is used to establish multiple candidate distribution network structure models according to all the carbon reduction resource configuration methods, and to evaluate the carbon emission reduction potential of each candidate distribution network structure model to obtain a carbon emission reduction index value; An optimal model screening module is used to sort the candidate distribution network structure models based on the carbon emission reduction index value, and screen out the optimal target new distribution and utilization system grid structure model; A power supply mode analysis module is used to generate a number of power supply mode samples according to the optimal target new power distribution system grid structure model, and classify the power supply mode samples to obtain a number of typical power supply mode categories; A power supply mode screening module, used for screening out a target power supply mode according to the concentration and discrete points of the typical power supply mode categories; A resource configuration optimization module is used to optimize different carbon reduction resource configuration methods according to the target power supply mode, and generate a new type of power distribution system flexibility carbon reduction resource optimization configuration strategy; Wherein, the optimal model screening module is specifically used for: According to the capacity parameters of each node and the line load parameters of each candidate distribution network structure model, a multi-layer neural network model is used to map and transform the physical connection relationship between the nodes of the distribution network to obtain a node mapping relationship matrix; According to the node mapping relationship matrix, the maximum transmission capacity of each section load is calculated using the power system flow calculation method; The response sensitivity of node voltage to load change is analyzed according to the node mapping relationship matrix and line load parameters to obtain the voltage sensitivity index; According to the ratio between the section impedance and the maximum transmission capacity, the candidate distribution network structure models are sorted by correlation degree to obtain a grid correlation degree sorting result; According to the voltage sensitivity index, the voltage stability parameter set is extracted from the grid correlation ranking results; Based on the voltage stability parameter set and the carbon emission reduction index value, an optimal target new power distribution system grid structure model is selected from the candidate distribution network grid structure models using an empirical Bayesian method; The method of selecting the optimal target new power distribution system grid structure model from the candidate distribution network grid structure models based on the voltage stability parameter set and the carbon emission reduction index value by using the empirical Bayesian method specifically includes: According to the voltage stability parameter set, the voltage quality and power loss sequences of each candidate distribution network structure model under different load conditions are classified and counted, and a distribution network stability feature vector is extracted; According to the line electrical state parameters, the empirical Bayes method is used to perform probability evaluation on the load center drift amplitude and obtain the load center drift probability distribution. According to the load center drift probability distribution and the distribution network stability characteristic vector, the connectivity, power supply radius and substation layout of each candidate distribution network structure model are quantitatively ranked by multiple objectives to extract the grid optimization adjustment index; Obtaining transformer operation characteristic data under the grid optimization adjustment index, and extracting transformer operation state characteristic values ​​from the transformer operation characteristic data using a principal component analysis method; Calculating the fault recovery time distribution and the spare capacity margin value according to the transformer operation state characteristic value; According to the fault recovery time distribution, the spare capacity margin value and the carbon emission reduction index value, a hierarchical analysis method is used to perform a multi-dimensional evaluation on all candidate distribution network structure models to establish a priority ranking set; According to the priority sorting set, the optimal grid structure parameters are selected using a particle swarm optimization algorithm to form a target new power distribution system grid structure model.

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