A dynamic zoning management method and system for an electric-carbon collaborative distribution network

Abstract

The present invention discloses an electro-carbon collaborative distribution network dynamic zoning management method and system, which relates to the technical field of distribution network zoning management, and includes the following steps: obtaining the first constraint condition, and performing an initial division on the distribution network to obtain an initial zoning set; performing a state evaluation on the initial zoning set, and correcting the initial zoning by using an improved greedy algorithm according to the evaluation result; expanding the adjacent areas layer by layer outward for the corrected zoning, and performing an electro-carbon relationship analysis on each layer of zoning by using the analytic hierarchy process; judging whether to continue with hierarchical expansion and update the correction range according to the analysis result; dynamically adjusting the zoning boundary according to the correction range, and performing zoning management according to the adjustment result, which solves the problem that the existing distribution network has fixed division rules and cannot adapt to the real-time scheduling requirements when performing zoning division.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network zoning management. More specifically, the present invention relates to a method and system for dynamic zoning management of an electric-carbon collaborative distribution network. Background Art

[0002] As a key part of the power system, the distribution network undertakes the responsibilities of energy distribution and scheduling. With the access of distributed energy, renewable energy, and intelligent devices, the distribution network faces increasingly complex scheduling and load management problems. At the same time, the carbon emission problem has become a challenge that the global power industry urgently needs to solve. Traditional power dispatching methods often ignore carbon emission control, resulting in low energy use efficiency and high emission levels. Therefore, how to achieve the collaborative optimization of power dispatching and carbon emissions in the distribution network has become an important issue in the current research of power systems. The dynamic zoning management of the electric-carbon collaborative distribution network is one of the important technical paths to realize the green transformation of the power system. With the increasing global demand for sustainable development, the traditional power system has gradually exposed the problems of lack of effective control and optimized scheduling of carbon emissions. In this context, the electric-carbon collaborative distribution network emerged, aiming to achieve the collaborative optimization of high-efficiency power supply and low carbon emissions through the real-time monitoring and intelligent scheduling of the distribution network.

[0003] For example, a method and system for dynamic planning of urban distribution network lines announced in the invention patent with the publication number CN113239540B divides the planning period into multiple time periods; obtains the street layout and load power data in a set area within a set time period, and models the street layout situation in the area; processes the loads of each block in the planning area; takes the minimum load moment as the optimization goal, considers the line non-crossing constraint, and establishes a dynamic planning model of the distribution network line for different time periods; processes the non-linear part in the dynamic planning model of the distribution network line and converts it into a mixed-integer linear programming model; solves the converted model to obtain the optimal dynamic planning scheme of the distribution network line. The present invention considers the line non-crossing constraint, can improve the reliability of the distribution line, and at the same time can make the boundaries of each distribution line clear, facilitating zoning management; it solves the need of distribution network managers for non-crossing distribution lines.

[0004] For example, a method for dynamically partitioning a distribution network based on an improved VVS calculation model announced in the invention patent announcement with the announcement number of CN114861459A calculates the voltage / reactive power sensitivity matrix between each node by adding a voltage-reactive power output perturbation amount to the reactive power source. Secondly, the voltage / reactive power sensitivity matrix is fuzzified to obtain a membership degree matrix between each node; and the membership relationship between the reactive power source nodes is analyzed according to the membership degree matrix between each node, and the reactive power source nodes are pre-partitioned. Then, the membership relationship between each load node and the reactive power source node is analyzed according to the membership matrix between each node to determine the partitioning result. Finally, a partitioning membership degree index is constructed to evaluate the partitioning result to ensure reactive power balance within the partition. By adding a voltage-reactive power output perturbation amount to the reactive power source, the present invention calculates the VVS of each reactive power source to the remaining nodes, which can reflect the time-sequential changes of the power source and the load, and better ensure the reasonable distribution of voltage and reactive power within each partition.

[0005] In the above disclosed technical solution, there are at least the following technical problems:

[0006] Existing distribution network partitioning methods are based on electrical characteristics or static load distribution, lacking a dynamic response mechanism for carbon emissions. When performing partition division, the division rules are fixed and cannot meet the requirements of real-time scheduling. In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above defects of the prior art, an embodiment of the present invention provides a method and system for dynamically partitioning and managing an electric-carbon collaborative distribution network, which dynamically adjusts the partition boundary through a hierarchical correction and overlap detection mechanism to solve the problem that the existing distribution network has fixed division rules and cannot meet the requirements of real-time scheduling when performing partition division.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for dynamically partitioning and managing an electric-carbon collaborative distribution network includes the following steps: obtaining a first constraint condition and performing an initial partition on the distribution network to obtain an initial partition set; performing a state evaluation on the initial partition set and correcting the initial partition using an improved greedy algorithm according to the evaluation result; expanding the adjacent areas of the corrected partition layer by layer outward, and analyzing the electric-carbon relationship of each layer of partition using the analytic hierarchy process; according to the analysis result, determining whether to continue with hierarchical expansion and updating the correction range; dynamically adjusting the partition boundary according to the correction range, and performing partition management according to the adjustment result.

[0010] In a preferred embodiment, the obtaining of the first constraint condition is specifically as follows: Obtain the historical operation data of the distribution network and perform data analysis to obtain the load fluctuation range and carbon emission intensity of each node; construct the distribution network topology structure, and calculate the maximum allowable number of partitions supported by the system and the dynamic partition upper limit that meets the carbon emission constraint in combination with the load fluctuation range and carbon emission intensity of each node; determine the minimum number of partitions supported by the system according to the topological connectivity of the distribution network topology structure; construct the first constraint condition according to the maximum allowable number of partitions, the dynamic partition upper limit that meets the carbon emission constraint, and the minimum number of partitions.

[0011] In a preferred embodiment, the initial partitioning of the distribution network to obtain the initial partition set is specifically as follows: Calculate the node electro-carbon coupling strength matrix, and cluster the distribution network nodes in combination with the spectral clustering algorithm to obtain the first partition set; set the minimum partition capacity threshold, and merge the partitions in the first partition set with a capacity lower than the threshold until the number of partitions meets the first constraint condition to obtain the initial partition set.

[0012] In a preferred embodiment, the state evaluation of the initial partition set and the correction of the initial partition using an improved greedy algorithm according to the evaluation result are specifically as follows: Construct a partition state evaluation index system and use the entropy weight method to calculate the weights of each index, and perform weighted summation of the index values of each partition based on the index weights to obtain the state evaluation value of the partition; according to the state evaluation value, perform anomaly identification on the partitions, and extract the adjacent node set of the abnormal partitions; use the greedy algorithm to add adjacent nodes to the abnormal partitions, and count the number of newly added nodes; construct a dynamic second constraint condition according to the change in the ratio of the number of newly added nodes to the number of partition nodes; based on the dynamic second constraint condition, correct the initial partition to obtain the corrected partition.

[0013] In a preferred embodiment, the adjacent areas of the corrected partition are expanded layer by layer outward, and the electro-carbon relationship analysis of each layer of partitions is performed using the analytic hierarchy process, specifically as follows: Take the corrected partition as the parent layer and calculate the coupling strength values of all nodes in the parent layer partition, and select the corresponding nodes as the core nodes based on the coupling strength values; obtain the power grid topology map of the parent layer, and use the Dijkstra algorithm to calculate the shortest path hop count from each node to the core node; take the maximum value of the shortest path hop counts from all nodes to the core node as the initial expansion radius; according to the initial expansion radius, expand the adjacent areas of the parent layer layer by layer outward to obtain several sub-layers; construct the electro-carbon interaction matrix between each sub-layer and the parent layer, and perform eigenvalue solution on the electro-carbon interaction matrix based on the analytic hierarchy process to obtain the layer association eigenvalue.

[0014] In a preferred embodiment, based on the analysis result, it is determined whether to continue with hierarchical expansion and update the correction range, specifically: obtaining the sub-layers to be corrected according to the layer correlation eigenvalue, and adding the sub-layers to be corrected to a preset correction range set; detecting the overlapping area between the partition of the sub-layers to be corrected in the correction range set and the parent layer partition; merging the overlapping partitions with the parent layer partition according to the detection result, and updating the correction range set.

[0015] In a preferred embodiment, the partition boundary is dynamically adjusted according to the correction range, and partition management is performed according to the adjustment result, specifically: dynamically adjusting the partition boundary according to the correction range, and generating a partition scheduling instruction from the dynamic partition result, where the instruction includes the carbon emission quota, load adjustment priority, and spare capacity allocation strategy of the power generation units in each partition; during the scheduling process, the partition operation data is collected in real time, and if carbon emission or load over-limit is detected, a partition re-partitioning process is triggered and the scheduling instruction is updated.

[0016] The technical effects and advantages of a method and system for dynamic partition management of an electric-carbon collaborative distribution network according to the present invention:

[0017] 1. By obtaining historical operation data to construct the first constraint condition, the present invention comprehensively considers the node load volatility and carbon emission intensity, and determines the number of partitions under the minimum, maximum, and carbon constraints through topological structure analysis, effectively improving the feasibility and rationality of the partitions; the initial partition is divided by using the electric-carbon coupling strength matrix and the improved spectral clustering algorithm, which not only ensures the internal correlation of nodes in terms of energy consumption and carbon emission, but also avoids the formation of too small isolated areas through the minimum capacity threshold control, improving the partition stability and resource allocation efficiency.

[0018] 2. In the partition correction stage, the present invention introduces the entropy weight method to construct a multi-index state evaluation system, comprehensively quantifies the partition state, effectively identifies abnormal areas and performs targeted correction through the improved greedy algorithm, ensuring the optimal compromise between the operation stability and carbon efficiency of the partition structure, and enhancing the dynamic adaptability of the system; further, the analytic hierarchy process is used to analyze the hierarchical electric-carbon relationship of the corrected partitions, the expansion boundary is accurately delimited through the Dijkstra algorithm, an electric-carbon interaction matrix is constructed and eigenvalues are extracted, systematically revealing the electric-carbon collaborative characteristics between layers, providing a quantitative basis for whether to expand and boundary adjustment in the future, and realizing the rational decision-making and dynamic optimization of the expansion process; in addition, the partition boundary is dynamically adjusted through the hierarchical correction and overlapping detection mechanism, avoiding unreasonable overlap or breakage between partitions, and improving the coherence and scheduling accuracy of the partition result. In terms of scheduling application, the method generates a scheduling instruction including key parameters such as carbon emission quota, load priority, and spare strategy from the partition result, and has the ability of real-time monitoring and adaptive re-partitioning, realizing the intelligent, low-carbon, and highly resilient operation of the distribution system scheduling. Description of the Drawings

[0019] Figure 1 This is a schematic flow chart of a method for dynamic zoning management of an electric-carbon collaborative distribution network according to the present invention.

[0020] Figure 2 This is a schematic structural diagram of a system for dynamic zoning management of an electric-carbon collaborative distribution network according to the present invention. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1 Figure 1 A method and system for dynamic zoning management of an electric-carbon collaborative distribution network according to the present invention are provided, including the following steps:

[0023] S1. Obtain the first constraint condition and perform an initial division of the distribution network to obtain an initial partition set;

[0024] In this example, obtaining the first constraint condition specifically includes:

[0025] Obtain the historical operation data of the distribution network and perform data analysis to obtain the load fluctuation range and carbon emission intensity of each node;

[0026] Construct the topological structure of the distribution network, and calculate the maximum allowable number of partitions supported by the system and the upper limit of dynamic partitions that meet the carbon emission constraints in combination with the load fluctuation range and carbon emission intensity of each node;

[0027] Determine the minimum number of partitions supported by the system according to the topological connectivity of the topological structure of the distribution network;

[0028] Construct the first constraint condition according to the maximum allowable number of partitions, the upper limit of dynamic partitions that meet the carbon emission constraints, and the minimum number of partitions.

[0029] Among them, the formula of the first constraint condition is as follows:

[0030]

[0031] Among them, is the minimum number of partitions, is the actual number of partitions, is the maximum number of partitions, is the upper limit of dynamic partitions based on carbon emission constraints.

[0032] It should be noted that by obtaining the historical operation data of the distribution network, deeply analyzing the load fluctuation range and carbon emission intensity of each node, and comprehensively characterizing the characteristics of the node in terms of energy regulation and carbon emission. Subsequently, on the basis of constructing a complete distribution network topology structure, combined with the above node characteristics, evaluate the maximum allowable number of partitions that the system can bear, and further calculate the dynamic partition upper limit under the condition of ensuring carbon emission compliance, reflecting the regulatory effect of carbon constraints on the partitioning ability. At the same time, according to the connectivity of the topology structure, identify the minimum number of partitions required for the stable operation of the system, ensuring the integrity and robustness of the divided distribution network in terms of physical structure and power supply capacity. Finally, integrate the maximum allowable number of partitions, the partition upper limit under carbon constraints, and the minimum number of partitions to construct the first constraint condition for dynamic partitioning, providing a basic framework for the dynamic adjustment and range control of subsequent partitioning strategies.

[0033] In this example, the distribution network is initially divided to obtain an initial partition set, specifically:

[0034] Calculate the node electrical-carbon coupling intensity matrix, and combine the spectral clustering algorithm to cluster the nodes of the distribution network to obtain the first partition set;

[0035] Set the minimum partition capacity threshold, and merge the partitions in the first partition set with a capacity lower than the threshold until the number of partitions meets the first constraint condition to obtain the initial partition set.

[0036] It should be noted that by calculating the node electrical-carbon coupling intensity matrix, the power load characteristics and carbon emission indicators are quantitatively integrated to construct a numerical model reflecting the collaborative relationship between each node. The electrical-carbon coupling intensity not only covers electrical parameters (such as power flow, node voltage, etc.), but also considers indicators such as carbon emission factors, carbon intensity, and carbon consumption per unit load, ensuring the low-carbon orientation and energy efficiency consistency in the partitioning process. Subsequently, an improved spectral clustering algorithm is used to process the coupling matrix to achieve intelligent clustering of nodes. The spectral clustering algorithm is based on graph theory methods and can accurately identify the internal connections between nodes.

[0037] Furthermore, to avoid the generation of marginal partitions with too small capacity and weak scheduling significance during the clustering process, this method sets a minimum partition capacity threshold and adjusts the capacity based on the first partition set. This merging mechanism ensures that each partition has basic load support capabilities and operational independence, effectively preventing the problems of increased regulation complexity and carbon emission allocation imbalance caused by "over-segmentation".

[0038] S2. Evaluate the state of the initial partition set, and correct the initial partition using an improved greedy algorithm according to the evaluation results;

[0039] In this example, the state of the initial partition set is evaluated, and the initial partition is corrected using an improved greedy algorithm according to the evaluation results. Specifically:

[0040] Construct an index system for evaluating the partition state and use the entropy weight method to calculate the weights of each index. Based on the index weights, the index values of each partition are weighted and summed to obtain the state evaluation value of the partition.

[0041] According to the state evaluation value, abnormal partitions are identified, and the adjacent node sets of the abnormal partitions are extracted.

[0042] The greedy algorithm is used to add adjacent nodes to the abnormal partition, and the number of newly added nodes is counted.

[0043] A dynamic second constraint condition is constructed based on the ratio change of the number of newly added nodes to the number of partition nodes.

[0044] Based on the dynamic second constraint condition, the initial partition is corrected to obtain the corrected partition.

[0045] It should be noted that an index system for evaluating the partition state is constructed, and the weights of each index are calculated by the entropy weight method to quantify the operating state of the partition. The entropy weight method can effectively eliminate the interference of subjective factors. By objectively assigning weights to the importance of each index, the state evaluation of each partition is made more accurate. Then, based on the calculated index weights, the index values of each partition are weighted and summed to obtain the comprehensive state evaluation value of the partition. This evaluation value provides a basis for subsequent identification of abnormal partitions. By analyzing the evaluation values of the partitions, abnormal partitions with poor operating states can be identified.

[0046] For abnormal partitions, further analysis is carried out by extracting adjacent node sets. On this basis, an improved greedy algorithm is used to add adjacent nodes to the abnormal partition. The greedy algorithm makes the partition correction process more efficient by gradually selecting adjacent nodes that have the greatest improvement effect on the partition state. After adding adjacent nodes, the system counts the number of newly added nodes and constructs a dynamic second constraint condition based on the ratio change of the number of newly added nodes to the number of original partition nodes to ensure that the partition adjustment is carried out within certain limits. The dynamic constraint condition can be flexibly adjusted according to the actual node change situation, making the partition correction process not only meet the performance requirements but also avoid overcorrection or unbalanced partition situations.

[0047] Finally, by applying the dynamic second constraint condition, the initial partition is corrected to obtain the corrected partition set. This process makes the partition more in line with the actual operation of the power grid, improving the overall scheduling efficiency and carbon emission control ability of the system.

[0048] The advantages of this method are as follows: First, the state evaluation system based on the entropy weight method can accurately reflect the operating state of each partition, avoiding human bias; Second, the introduction of the greedy algorithm makes the partition adjustment process efficient and adaptable; Finally, the dynamic constraint conditions ensure the flexibility and rationality of the partition adjustment, thus realizing the precise management and optimal scheduling of the electric-carbon coordinated distribution network, effectively improving the intelligent and green operation level of the distribution network.

[0049] S3. Expand the adjacent areas layer by layer outward from the corrected partition, and use the analytic hierarchy process to analyze the electric-carbon relationship of each layer of partitions.

[0050] In this example, expand the adjacent areas layer by layer outward from the corrected partition, and use the analytic hierarchy process to analyze the electric-carbon relationship of each layer of partitions. Specifically:

[0051] Take the corrected partition as the parent layer and calculate the coupling strength values of all nodes within the parent layer partition. Select the corresponding nodes as the core nodes based on the coupling strength values.

[0052] Obtain the power grid topology map of the parent layer, and use the Dijkstra algorithm to calculate the shortest path hop count from each node to the core node.

[0053] Take the maximum value of the shortest path hop counts from all nodes to the core node as the initial expansion radius.

[0054] According to the initial expansion radius, expand the parent layer outward layer by layer to obtain several sub-layers.

[0055] Construct the electric-carbon interaction matrix between each sub-layer and the parent layer, and solve the eigenvalues of the electric-carbon interaction matrix based on the analytic hierarchy process to obtain the layer correlation eigenvalues.

[0056] Among them, the specific calculation formula for calculating the coupling strength values of all nodes within the parent layer partition is as follows:

[0057]

[0058] Among them, is the coupling strength value between node i and node, is the interaction power, is the preset power threshold, is the carbon emission deviation rate, is the preset carbon emission threshold.

[0059] It should be noted that in the corrected partition, first, the coupling strength values of all nodes within the parent-level partition are calculated. The coupling strength represents the degree of association between electricity and carbon emissions. Nodes with larger coupling strength values are usually important nodes affecting the operation of the system. By selecting one or more nodes with the maximum coupling strength as the core nodes, the key areas of the system can be determined, and then targeted expansion and analysis can be carried out;

[0060] Furthermore, obtain the power grid topology map of the parent layer, and use the Dijkstra algorithm to calculate the shortest path hop count from each node to the core node. The Dijkstra algorithm is a classic graph theory algorithm that can effectively calculate the shortest path between nodes in a graph. Its role in this process is to help identify the distance between nodes and the core node, thereby determining the radius of the expansion area;

[0061] Take the maximum value among the shortest path hop counts from all nodes to the core node as the initial expansion radius. This radius determines the range when expanding outward from the parent layer, ensuring that the expansion area can cover nodes with a strong coupling relationship with the core node, while avoiding excessive expansion leading to unnecessary complexity of the system. Based on the initial expansion radius, layer by layer, expand the adjacent areas of the parent layer outward to obtain several sub-layers. Each sub-layer has a certain electricity-carbon interaction relationship with the parent layer, and the expansion area can provide necessary data support for subsequent power dispatching and carbon emission management. In addition, between each sub-layer and the parent layer, construct an electricity-carbon interaction matrix. This matrix reflects the interaction between electricity and carbon emissions and serves as an important basis for analyzing the relationship between partitions. By solving the eigenvalues of the electricity-carbon interaction matrix based on the Analytic Hierarchy Process (AHP), hierarchical correlation eigenvalues can be obtained, which reflect the relationship strength between each layer of partitions and provide a reference basis for subsequent decision-making.

[0062] S4. According to the analysis results, judge whether to perform hierarchical expansion correction and update the correction range;

[0063] In this example, according to the analysis results, judge whether to continue hierarchical expansion and update the correction range. Specifically:

[0064] Obtain the sub-layers to be corrected based on the layer correlation eigenvalues, and add the sub-layers to be corrected to the preset correction range set;

[0065] Detect the overlapping areas between the partitions of the sub-layers to be corrected in the correction range set and the parent layer partitions;

[0066] Merge the overlapping partitions with the parent layer partitions according to the detection results, and update the correction range set.

[0067] It should be noted that by analyzing the layer correlation eigenvalues of each sub-layer, it is determined whether each sub-layer needs further correction. The layer correlation eigenvalues are calculated by the Analytic Hierarchy Process (AHP), which reflects the strength of the electro-carbon interaction relationship between each layer partition and the parent layer. If the correlation eigenvalue of a certain sub-layer is low, it indicates that the electro-carbon synergy effect between this sub-layer and the parent layer is weak, and further correction and expansion may not be required. On the contrary, if the eigenvalue is high, it means that there is a strong electro-carbon coupling relationship between this sub-layer and the parent layer, and further correction is needed to ensure the coordinated operation of the system. Therefore, based on these analysis results, it can be determined which sub-layers need to be corrected, and the partitions to be corrected are added to the preset correction range set.

[0068] Next, the system will detect the overlapping areas between the partitions to be corrected in the preset correction range set and the original partitions. The purpose of this step is to check the overlapping parts of the partitions to be corrected and the original partitions to avoid unnecessary repeated corrections and redundant expansions. Through the overlapping area detection, it can be ensured that each correction operation is based on actual needs, thus avoiding affecting the stability and optimization effect of the system.

[0069] Once the overlapping area detection is completed, the system will merge the overlapping partitions with the parent layer partitions and update the correction range set accordingly. By merging the overlapping partitions, the division of the electro-carbon collaborative management area can be further optimized, making the relationship between the power and carbon emissions of each partition closer and improving the collaborative efficiency of the system. At the same time, the updated correction range set provides a clear direction for the next hierarchical expansion, ensuring that the system can continue to be optimized in dynamic changes.

[0070] S5. Dynamically adjust the partition boundaries according to the correction range and perform partition management according to the adjustment results.

[0071] In this example, dynamically adjust the partition boundaries according to the correction range and perform partition management according to the adjustment results, specifically as follows:

[0072] Dynamically adjust the partition boundaries according to the correction range and generate partition scheduling instructions for the dynamic partition results. The instructions include the carbon emission quotas of the power generation units in each partition, the load adjustment priorities, and the reserve capacity allocation strategies;

[0073] During the scheduling process, real-time collect the partition operation data. If carbon emission or load overlimit is detected, trigger the partition re-division process and update the scheduling instructions.

[0074] It should be noted that in the distribution network dispatching system, dynamically adjusting the partition boundary and generating corresponding dispatching instructions are the keys to achieving flexible and efficient energy management. By adjusting the partition boundary according to the correction range, the operation partition of the power grid can be continuously optimized according to the actual operation conditions to ensure that the distribution network can continuously provide stable and efficient power supply under various changing load demands and carbon emission targets. Specifically, the system first dynamically adjusts the partition boundary of the distribution network according to the correction range to obtain a new partition structure. Based on this new structure, the system generates corresponding partition dispatching instructions. Each dispatching instruction contains key information such as the carbon emission quota of the power generation units in each partition, the load adjustment priority, and the spare capacity allocation strategy. These instructions guide the power dispatching process of each partition to ensure the coordination and balance of load and carbon emission control.

[0075] During the dispatching process, the system will monitor the operation status of each partition in real time, especially the carbon emission and load data. If during the operation, the carbon emission or load of a certain partition exceeds the preset limit, the system will immediately trigger the partition redivision process and update the dispatching instructions. During the redivision process, the system will readjust the partitions to ensure that each partition can reasonably allocate the power load on the premise of meeting the carbon emission limit value. Through this dynamic adjustment and real-time response mechanism, the system can continuously optimize the operation status of the power grid, avoid excessive carbon emissions or uneven load problems, and ensure the stability and sustainability of the system.

[0076] Embodiment 2 Figure 2 A power-carbon collaborative distribution network dynamic partition management system of the present invention is given, including a data acquisition module, a partition correction module, a partition expansion module, a correction update module, and a partition adjustment module:

[0077] The data acquisition module is used to acquire the first constraint condition and perform an initial division on the distribution network to obtain an initial partition set;

[0078] The partition correction module is used to evaluate the status of the initial partition set and correct the initial partition using an improved greedy algorithm according to the evaluation result;

[0079] The partition expansion module is used to expand the adjacent areas layer by layer outward from the corrected partition and perform an analysis of the power-carbon relationship on each layer of partitions using the analytic hierarchy process;

[0080] The correction update module is used to judge whether to continue with the hierarchical expansion and update the correction range according to the analysis result;

[0081] The partition adjustment module is used to dynamically adjust the partition boundary according to the correction range and perform partition management according to the adjustment result.

[0082] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0083] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0084] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians 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.

[0085] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0086] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0087] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic zoning management method for an electric-carbon collaborative distribution network, characterized in that It includes the following steps: Obtain the first constraint condition, and perform an initial division of the distribution network to obtain an initial partition set; The obtaining of the first constraint condition is specifically: Obtain the historical operation data of the distribution network and perform data analysis to obtain the load fluctuation range and carbon emission intensity of each node; Construct the topological structure of the distribution network, and calculate the maximum allowable number of partitions supported by the system and the dynamic partition upper limit that meets the carbon emission constraint in combination with the load fluctuation range and carbon emission intensity of each node; Determine the minimum number of partitions supported by the system according to the topological connectivity of the distribution network topology; Construct the first constraint condition according to the maximum allowable number of partitions, the dynamic partition upper limit that meets the carbon emission constraint, and the minimum number of partitions; Perform a state evaluation on the initial partition set, and correct the initial partition using an improved greedy algorithm according to the evaluation result; The performing of a state evaluation on the initial partition set and correcting the initial partition using an improved greedy algorithm according to the evaluation result is specifically: Construct an index system for evaluating the partition state and use the entropy weight method to calculate the weights of each index. Based on the index weights, perform a weighted sum of the index values of each partition to obtain the state evaluation value of the partition; According to the state evaluation value, perform anomaly identification on the partitions, and extract the adjacent node set of the abnormal partitions; Use the greedy algorithm to add adjacent nodes to the abnormal partitions, and count the number of newly added nodes; Construct a dynamic second constraint condition according to the change in the ratio of the number of newly added nodes to the number of partition nodes; Based on the dynamic second constraint condition, correct the initial partition to obtain the corrected partition; Expand the adjacent areas layer by layer outward from the corrected partition, and use the analytic hierarchy process to analyze the electrical-carbon relationship of each layer of partitions; According to the analysis result, judge whether to continue the hierarchical expansion and update the correction range; Dynamically adjust the partition boundary according to the correction range, and perform partition management according to the adjustment result.

2. The dynamic zoning management method for the electric-carbon collaborative distribution network according to claim 1, wherein, The performing of an initial division of the distribution network to obtain an initial partition set is specifically: Calculate the node electrical-carbon coupling strength matrix, and cluster the distribution network nodes using the spectral clustering algorithm to obtain the first partition set; Set the minimum partition capacity threshold, and merge the partitions with capacities lower than the threshold in the first partition set until the number of partitions meets the first constraint condition to obtain the initial partition set.

3. The dynamic zoning management method of the electric-carbon collaborative distribution network according to claim 2, wherein, The expanding of the adjacent areas layer by layer outward from the corrected partition and using the analytic hierarchy process to analyze the electrical-carbon relationship of each layer of partitions is specifically: Take the corrected partition as the parent layer and calculate the coupling strength values of all nodes within the parent layer partition. Based on the coupling strength values, select the corresponding nodes as core nodes; Obtain the power grid topology map of the parent layer, and use the Dijkstra algorithm to calculate the shortest path hop count from each node to the core node; Take the maximum value of the shortest path hop counts from all nodes to the core node as the initial expansion radius; According to the initial expansion radius, expand the adjacent areas layer by layer outward from the parent layer to obtain several sub-layers; Construct the electrical-carbon interaction matrix between each sub-layer and the parent layer, and solve the eigenvalues of the electrical-carbon interaction matrix based on the analytic hierarchy process to obtain the layer correlation eigenvalues.

4. The dynamic zoning management method of the electric-carbon collaborative distribution network according to claim 3, wherein The judging whether to continue the hierarchical expansion and update the correction range according to the analysis result is specifically: Obtain the sub-layer to be corrected according to the layer correlation eigenvalue, and add the sub-layer to be corrected to the preset correction range set; Detect the overlapping area between the partition of the sub-layer to be corrected in the correction range set and the parent layer partition; Merge the overlapping partition and the parent layer partition according to the detection result, and update the correction range set.

5. The dynamic zoning management method of the electric-carbon collaborative distribution network according to claim 4, wherein Dynamically adjust the partition boundary according to the correction range, and perform partition management according to the adjustment result, specifically: Collect the partition operation data in real time, dynamically adjust the partition boundary based on the correction range, and generate a partition scheduling instruction for the dynamic partition result. The instruction includes the carbon emission quota, load adjustment priority, and spare capacity allocation strategy of the power generation units in each partition.

6. The dynamic zoning management method of the electric-carbon collaborative distribution network according to claim 5, characterized in that Calculate the coupling strength value of all nodes in the parent layer partition, and the specific calculation formula is as follows: Among them, is the coupling strength value between node i and node j, is the interaction power, is the preset power threshold, is the carbon emission deviation rate, is the preset carbon emission threshold.

7. A dynamic zoning management system for an electric-carbon collaborative distribution network, characterized in that, Applied to a dynamic partition management method for an electric-carbon collaborative distribution network described in any one of claims 1-6, characterized in that it includes a data acquisition module, a partition correction module, a partition expansion module, a correction update module, and a partition adjustment module: The data acquisition module is used to obtain the first constraint condition and perform an initial division of the distribution network to obtain an initial partition set; The partition correction module is used to evaluate the state of the initial partition set and correct the initial partition using an improved greedy algorithm according to the evaluation result; The partition expansion module is used to expand the adjacent areas layer by layer outward for the corrected partition, and analyze the electric-carbon relationship of each layer of partition using the analytic hierarchy process; The correction update module is used to judge whether to continue the hierarchical expansion and update the correction range according to the analysis result; The partition adjustment module is used to dynamically adjust the partition boundary according to the correction range, and perform partition management according to the adjustment result.

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