Method and device for dividing operation domain of micro-integrated system, equipment and storage medium

Abstract

The application discloses a micro-integrated system operation domain division method and device, equipment and a storage medium. The method comprises the following steps: collecting the operation state data of each node in a preset time period, and dividing the time sequence operation state data into multiple scenes; for each scene, determining the active power correlation coefficient and the static electrical distance between the nodes based on the time sequence operation state data, determining the comprehensive coupling strength matrix, and determining the optimal division scheme of operation domain division based on the comprehensive coupling strength matrix; summarizing the optimal division scheme of each scene to form a plan set; in the process of executing the target optimal division scheme in the plan set, if the change rate of the current comprehensive coupling strength matrix exceeds a preset threshold, the optimal division scheme corresponding to the current comprehensive coupling strength matrix in the plan set is switched to be executed. Therefore, the internal mechanism of system dynamic evolution can be accurately described, the operation domain can be reasonably and accurately divided, and the system can be adaptively controlled in multiple scenes.

Description

Technical Field

[0001] This application relates to the field of coordinated operation control technology for distribution networks and microgrids, and in particular to a method, apparatus, equipment and storage medium for dividing the operating domain of an integrated distribution and microgrid system. Background Technology

[0002] With the increasing penetration rate of renewable energy, integrated distribution and micro-distribution systems, which include a high proportion of distributed wind power, photovoltaics, and energy storage, have become an important development direction for improving the absorption capacity of new energy and the reliability of power supply. However, the output fluctuation of distributed resources in the system poses a huge challenge to the overall operation and control, and traditional overall operation methods can no longer adapt to the new operational needs.

[0003] The operation mode of integrated distribution and microgrid systems divides the physical distribution network into multiple autonomous operating domains, enabling local absorption of distributed resources and autonomous power supply in isolated fault scenarios, thus becoming a core technology for addressing the aforementioned challenges. However, existing methods for dividing the operating domains of integrated distribution and microgrid systems neglect the inherent mechanisms of dynamic system evolution, leading to unreasonable domain division. Furthermore, current research is largely limited to offline static division, making it difficult for integrated distribution and microgrid systems to adapt to fluctuations in various scenarios. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for dividing the operating domain of an integrated distribution microgrid system. It can accurately depict the internal mechanism of the dynamic evolution of the system, reasonably and accurately divide the operating domain, and adaptively control the fluctuations of the system in multiple scenarios.

[0005] In a first aspect, embodiments of this application provide a method for partitioning the operating domain of an integrated distribution and micro-system, including:

[0006] Collect the operational status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operational status data of multiple typical multi-source load fluctuation scenarios;

[0007] For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series operating status data. The comprehensive coupling strength matrix of the distribution-micro system is determined based on the active power correlation coefficient and the static electrical distance. The optimal partitioning scheme for the operating domain of the distribution-micro system is determined based on the comprehensive coupling strength matrix.

[0008] The optimal partitioning schemes for each typical multi-source load fluctuation scenario are summarized to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the distribution and micro-integrated system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength in the set of contingency plans is switched to be executed.

[0009] Secondly, embodiments of this application provide an operating domain partitioning device for an integrated distribution and micro-system, comprising:

[0010] The first processing module is used to collect the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operating status data of multiple typical multi-source load fluctuation scenarios.

[0011] The second processing module is used to determine the active power correlation coefficient and static electrical distance between each node based on the time-series operating status data for each typical multi-source load fluctuation scenario, and to determine the comprehensive coupling strength matrix of the distribution-micro system based on the active power correlation coefficient and the static electrical distance, and to determine the optimal partitioning scheme for the operating domain of the distribution-micro system based on the comprehensive coupling strength matrix.

[0012] The third processing module is used to summarize the optimal partitioning schemes for each typical multi-source load fluctuation scenario to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the integrated distribution and micro-system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength in the set of contingency plans will be switched to be executed.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method provided in embodiments of this application.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method provided in embodiments of this application.

[0015] The technical solution provided in this application collects the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divides it into time-series operating status data for multiple typical multi-source load fluctuation scenarios. For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series status data. A comprehensive coupling strength matrix is ​​then determined based on this active power correlation coefficient and static electrical distance. This comprehensive coupling strength matrix is ​​used to determine the optimal partitioning scheme of the operating domain in the integrated distribution and micro-distribution system. In other words, by fusing time-series operating status data to determine the comprehensive coupling strength matrix, the system's time-series evolution characteristics are integrated, and a dynamic-static combined approach is proposed. The comprehensive coupling strength matrix determines the optimal partitioning scheme for the operating domain, accurately depicting the intrinsic mechanism of the system's dynamic evolution and enabling reasonable and precise partitioning of the operating domain. During the execution of the optimal partitioning scheme in the contingency plan set, if the rate of change of the current comprehensive coupling strength matrix exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the contingency plan set is switched to be executed. In other words, by integrating the comprehensive coupling strength matrix with dynamic temporal evolution characteristics, the control switching of the operating domain partitioning scheme is determined by the rate of change of the comprehensive coupling strength matrix, breaking the limitations of traditional static partitioning and enabling real-time adaptive control of system fluctuations in multiple scenarios. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for partitioning the operating domain of an integrated micro-distribution system, as provided in this application embodiment;

[0017] Figure 2 A flowchart illustrating a method for partitioning the operating domain of an integrated micro-distribution system, as provided in this application embodiment;

[0018] Figure 3 This is an improved IEEE 33-node distribution network topology diagram in the embodiments of this application;

[0019] Figure 4 Diagram showing the installation locations of DG and energy storage;

[0020] Figure 5 This is a schematic diagram of the optimal partitioning scheme for the runtime domain;

[0021] Figure 6 A structural block diagram of an operating domain partitioning device for an integrated micro-distribution system provided in this application embodiment;

[0022] Figure 7 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0023] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Figure 1 This is a flowchart illustrating a method for partitioning the operating domain of an integrated distribution and micro-distribution system, as provided in an embodiment of this application. The method can be executed by an operating domain partitioning device for the integrated distribution and micro-distribution system. This device can be implemented using software and / or hardware, and can be configured in electronic devices such as computers. Figure 1 As shown, the method provided in this application embodiment includes the following steps:

[0025] S110: Collects the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divides it into time-series operating status data of multiple typical multi-source load fluctuation scenarios.

[0026] In this embodiment, data can be collected based on the time period of the smart terminal deployed on the distribution network end side. The operational status data of each node, including the active power of each node. reactive power and voltage amplitude These data can be divided into time-series operational status data for multiple typical multi-source load fluctuation scenarios. Specifically, multiple time sliding windows can be divided or time sliding windows can be defined for each time period. The operational status data of each node within each time sliding window is the time-series operational status data for the corresponding typical multi-source load fluctuation scenario.

[0027] In this embodiment, a sample set can also be constructed from the time-series operational status data of M typical multi-source load fluctuation scenarios. The elements in the sample set include time-series operational status data corresponding to typical multi-source load fluctuation scenarios. Typical multi-source load fluctuation scenarios can include superimposed multi-source load fluctuation scenarios with different meteorological and electricity consumption behaviors, such as extreme heat with no wind, morning and evening peak hours, and high winds with low loads.

[0028] S120: For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series operating status data. The comprehensive coupling strength matrix of the distribution-micro system is determined based on the active power correlation coefficient and the static electrical distance. The optimal partitioning scheme for the operating domain of the distribution-micro system is determined based on the comprehensive coupling strength matrix.

[0029] In this embodiment, for each typical multi-source load fluctuation scenario, the time series of active power of each node can be extracted from the time series operating state data, and the co-evolution consistency of its time series state can be calculated using the dynamic Pearson correlation coefficient, that is, the co-evolution consistency of the time series state can be calculated using the active power correlation coefficient:

[0030] (1);

[0031] in, , They are nodes and nodes exist The net injected active power at time t, i.e., the node and nodes exist Active power at any given moment; and For nodes and nodes exist Average active power over the period; For nodes With nodes The correlation coefficient of active power between them; , The closer the value is to 1, the more synchronized the output / load fluctuations of the two are.

[0032] In this embodiment, based on the Jacobian matrix or network topology of the integrated distribution microgrid system, the sensitivity matrix between nodes is calculated using time-series operational status data from typical multi-source load fluctuation scenarios. Among them, defining nodes With nodes static electrical distance between This represents the sensitivity attenuation of voltage amplitude to reactive power.

[0033] (2);

[0034] in, Sensitivity matrix The elements in are nodes. Voltage amplitude for nodes The sensitivity coefficient of injected reactive power, i.e., the node Voltage amplitude for nodes The sensitivity coefficient of reactive power.

[0035] In this embodiment, the comprehensive coupling strength between nodes can be calculated based on the active power correlation coefficient and the static electrical distance, and a comprehensive coupling strength matrix of the integrated distribution microgrid system can be constructed based on the comprehensive coupling strength between nodes. Optionally, determining the comprehensive coupling strength matrix of the integrated distribution microgrid system based on the active power correlation coefficient and the static electrical distance includes:

[0036] The elements in the comprehensive coupling strength matrix are determined based on the following formula:

[0037] (3);

[0038] in, The elements in the comprehensive coupling strength matrix are nodes. With nodes The overall coupling strength between them; , These are the weighting coefficients; For nodes With nodes The static electrical distance between them; For nodes With nodes The correlation coefficient of active power between them; The smoothing parameter, used to control the sensitivity of dynamic response, is set to a value of 0.1 to 0.5.

[0039] in, , Each can be set to 0.5. If the topology of the integrated microsystem can be relatively weak with high line impedance, focusing on physical network constraints, then... A larger value can be chosen, for example, 0.6 to 0.7; if the integrated distribution system connects to a high proportion of new energy sources such as wind and solar power, and the source-load fluctuations are extremely drastic, emphasizing dynamic temporal characteristics, then the consistency of dynamic behavior needs to be strengthened. It can be appropriately large, for example, a value of 0.6 to 0.7.

[0040] In this embodiment, optionally, determining the optimal partitioning scheme for the operating domain in the integrated distribution and micro-distribution system based on the comprehensive coupling strength matrix includes: mapping the integrated distribution and micro-distribution system into an undirected weighted graph based on the comprehensive coupling strength matrix; identifying a set of separable flexible boundaries in the integrated distribution and micro-distribution system, and determining a partitioning decision vector based on the set of separable flexible boundaries; wherein the partitioning decision vector is used to limit the binary variable search for branches in the set of separable flexible boundaries; solving a mixed integer linear programming (MILP) model for operating domain partitioning based on the partitioning decision vector to obtain multiple partitioning schemes for the operating domain in the integrated distribution and micro-distribution system; determining a modularity function index based on the undirected weighted graph, and selecting the optimal partitioning scheme from the multiple partitioning schemes based on the modularity function index, the operating domain power supply rate index, and the power interaction cost index.

[0041] Specifically, the comprehensive coupling strength matrix can be mapped to the edge weight matrix in an undirected weighted graph, wherein the undirected weighted graph is:

[0042] (4);

[0043] in, Let be the edge weight matrix; where the elements in the edge weight matrix are... for:

[0044] (5);

[0045] in, The elements in the comprehensive coupling strength matrix are nodes. With nodes The overall coupling strength between them; For a set of nodes, For the set of branches; branches For nodes With nodes Branch roads between them.

[0046] In this embodiment, identifying the set of separable flexible boundaries in the integrated distribution and micro-distribution system includes: determining the set of separable flexible boundaries based on switch configuration information in the integrated distribution and micro-distribution system. Specifically, the switch configuration information may include the configuration of remote-controlled sectionalizing switches, tie switches, etc., in the integrated distribution and micro-distribution system, and defining a branch splitting status indication function. It also extracts the set of separable boundaries, which can be understood as a list of flexible branches / switches that can be intelligently controlled or remotely disconnected.

[0047] The set of cuttable flexible boundaries is as follows:

[0048] (6);

[0049] in, A set of shearable flexible boundaries; branch road The segmentation state indication function;

[0050] Among them, when the branch road When the conditions for physical segmentation or reconstruction are met, ; when a side road When it is a rigid physical connection, .

[0051] Therefore, by identifying the set of separable flexible boundaries in the integrated distribution and microsystem, the search problem of branch switch states on the global topology can be transformed into a combinatorial optimization problem for the set of separable flexible boundaries, thus achieving pre-dimensionality reduction of the search space at the physical level.

[0052] Specifically, a partitioning decision vector can be defined based on the set of separable flexible boundaries, limiting the search to binary variables on the branches within the set of separable flexible boundaries. The partitioning decision vector is:

[0053] (7);

[0054] in, ; To divide the decision vector;

[0055] Therefore, by dividing the decision vector, we can lay the foundation for solving the MILP model while preserving the electrical characteristics of the integrated distribution and micro-system. This can reduce the search space and significantly improve the generation efficiency of the operating domain partitioning scheme.

[0056] Specifically, the step of solving the mixed integer linear programming (MILP) model for partitioning the runtime domain based on the partitioning decision vector includes: constructing an optimization function with the objective of maximizing self-consistency and minimizing interaction cost within the runtime domain; constructing the MILP model based on the optimization function and preset constraints; and solving the MILP model based on the partitioning decision vector.

[0057] Specifically, considering the actual operating scenarios of integrated distribution and micro-distribution systems that include multiple types of distributed power sources such as wind turbines, photovoltaics, and energy storage, a binary decision variable belonging to the operating domain can be introduced to construct an optimization function that aims to maximize self-consistency and minimize interaction costs within the operating domain. The optimization function is as follows:

[0058] (8);

[0059] in, To optimize function values; , These are the weight coefficients for the corresponding items; For nodes Binary decision variables belonging to the operating domain, When node Belongs to the runtime domain hour, Otherwise, it is 0; This is the number of the operating domain; The number of runtime domains; The number of nodes; For nodes Total output of the connected wind turbines or photovoltaic power plants; The charging and discharging power of the energy storage system; For load demand; , for branch road Cross-domain cut edge state variables, The interaction penalty cost for the corresponding cut edge; if the branch Across different operating domains, .

[0060] In the absolute value term of the optimization function in formula (8), the part inside the parentheses represents the actual net injection power of the source, load, and storage at the node, multiplied by the binary decision variable. After summing over all nodes in the operating domain, it represents the first... The total net power deviation (i.e., source-load imbalance) within each operating domain. The optimization objective of this embodiment is to minimize the overall optimization function value F, which means forcing the net power deviation within each operating domain to be as close to 0 as possible. This physically achieves the maximization of local absorption of distributed power output and load demand within the domain, i.e., achieving the highest degree of self-consistency.

[0061] in, , The value of this parameter mainly depends on the current focus of coordinated control in the integrated distribution and microgrid system: if the system is currently operating during a period of high renewable energy demand and difficulty in grid integration (with a greater emphasis on local balancing), or if the distribution network topology is fragile and requires high reliability, then priority should be given to ensuring self-consistency within the operating domain. A larger value can be taken, for example, 0.6~0.7; where, and The sum is 1.

[0062] in, Typically, static engineering values ​​can be assigned directly based on the physical transmission capacity of each tie line and segment line in the integrated distribution and micro-distribution system, the economic cost calculated from daily network losses, or the communication weight of the control channel. With fixed line impedance and switch configuration, it manifests as an element in a constant matrix; for example, in the IEEE 33-node example of this embodiment, it is uniformly set to a standard scalar constant of 1.0.

[0063] In this embodiment, the preset constraints may include physical connectivity constraints, distributed power source existence constraints, and line branch power flow constraints, etc. Among them, in order to eliminate electrical islands, "virtual power flow topology connectivity constraints" are introduced:

[0064] (9);

[0065] (10);

[0066] in, branch road The above refers to the runtime domain Virtual traffic; branch road For the runtime domain Virtual traffic; For nodes The set of adjacent nodes; , representing a node Is it a runtime domain? The root node (only one root node is allowed per run domain). For sufficiently large positive integers (Big-M method).

[0067] At the same time, the uniqueness constraint of node affiliation must be satisfied: (11).

[0068] The power flow constraints for lined branches employ a linearized DistFlow method. By ignoring higher-order network loss terms in the branch power flow equations of the integrated distribution and microsystem system, the originally nonlinear and complex AC power flow problem is transformed into an efficiently solvable mixed-integer linear programming (MILP) problem. The power flow constraints for lined branches are considered conventional constraints for the power grid.

[0069] In this embodiment, the above-mentioned partitioning decision vector can be used. As input for heuristic feasible solutions, the above MILP model is solved using solvers such as CPLEX, yielding multiple partitioning schemes that satisfy preset constraints. Each partitioning scheme can be a refined partitioning matrix. The matrix It is A matrix of dimension, where, To determine the number of nodes in the integrated micro-system; This represents the number of run domains. The rows of this matrix represent "nodes," and the columns represent "run domains." The elements in the matrix... If it equals 1, it represents a node. Belongs to the runtime domain If it equals 0, then it does not belong to the runtime domain. Among them, solvers such as CPLEX can use branch-and-cut enumeration and solution pooling techniques to systematically cut different interconnection lines during the solution of MILP models, generate all feasible partitioning schemes that satisfy "each operating domain contains distributed power sources", complete the accurate boundary delineation of the operating domain of the integrated distribution and micro-power system, and finally obtain multiple partitioning schemes.

[0070] In this embodiment, to quantify the merits of various partitioning schemes, a modularity function index is defined. Operating domain power supply rate indicators Power interaction cost A multi-dimensional evaluation system. For any feasible division scheme, its comprehensive evaluation indicators... The calculation formula is as follows:

[0071] (12);

[0072] in, , and These are the weighting coefficients for the corresponding items, which can be set according to the actual situation.

[0073] The modularity function, proposed by Girvan and Newman, aims to solve the region partitioning problem in complex networks. Higher modularity indicates lower similarity among external nodes and stronger connections within the cluster. To adapt to the topological characteristics of the integrated distribution and micro-management system, this embodiment improves the traditional active modularity function based on the constructed undirected weighted graph, replacing it with a modularity function that considers comprehensive coupling strength. The modularity function index can be determined based on the following formula:

[0074] (13);

[0075] in, The modularity function index; , Representing the nodes respectively and nodes The sum of the weights of all connected edges; This represents the sum of the weights of all edges in the micro-allocation system.

[0076] and They are nodes and nodes Binary decision variables belonging to the operating domain; This is the number of the operating domain; The number of runtime domains; The number of nodes.

[0077] In this embodiment, the integration of distributed renewable energy sources enables the integrated distribution and microgrid system to operate more flexibly. When a major fault occurs in the main grid, leading to a widespread power outage or microgrid disconnection, the distributed power sources and energy storage systems within the operating domain can provide necessary power support for the system in islanded mode, thereby enhancing the independence and autonomy of the operating domain. When dividing the system, it is essential to ensure that each operating domain has sufficient reliable power supply capacity. Therefore, an operating domain power supply rate index is defined. as follows:

[0078] (14);

[0079] In this embodiment, under different operating domain partitioning schemes, cross-domain power interaction will generate corresponding network losses and transaction costs. This indicator is designed to minimize the operating costs caused by frequent power exchanges between operating domains, thereby improving the overall economy and operating efficiency of the integrated distribution and micro-distribution system. The power interaction cost is calculated as follows:

[0080] (15);

[0081] in, branch road The actual interactive active power flowing through the system. Considering that the lower the power interaction cost, the better the economic efficiency of the partitioning result, in this embodiment, the power interaction cost is negative, and then min-max normalization is performed.

[0082] In this embodiment, for a typical multi-source load fluctuation scenario, for various partitioning schemes of the operating domain, a comprehensive evaluation index for each partitioning scheme is calculated. After normalizing the indexes of each dimension, they are integrated, and the scheme with the highest comprehensive evaluation index is selected. The optimal partitioning scheme is selected based on the largest possible value. Alternatively, the rationality and effectiveness of the partitioning results in eliminating load limits and improving local power consumption can be verified using an improved IEEE 33-node integrated distribution and micro-distribution system example. Furthermore, by selecting the comprehensive optimal partitioning scheme, a complete operating domain partitioning scheme is formed for different load levels and renewable energy output scenarios, providing underlying support for distribution network coordinated control.

[0083] S130: Summarize the optimal partitioning schemes for each typical multi-source load fluctuation scenario to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the distribution-micro system exceeds a preset threshold, switch to execute the optimal partitioning scheme in the set of contingency plans corresponding to the current comprehensive coupling strength.

[0084] In this embodiment, during the execution of the optimal partitioning scheme in the pre-set plan set as the target optimal partitioning scheme, i.e., the online operation phase, the corresponding typical multi-source load fluctuation scenario can be determined by sensing the operating status data within the sliding time window. The target optimal partitioning scheme corresponding to this scenario is selected from the pre-set plan set and executed. During execution, the operating domain is dynamically updated and adaptively evolved. Specifically, if the rate of change of the current integrated coupling strength matrix of the distribution-micro system exceeds a preset threshold or the power of a local boundary point exceeds the limit, the optimal partitioning scheme corresponding to the current integrated coupling strength matrix in the pre-set plan set is switched to be executed. If there is no partitioning scheme corresponding to the current coupling strength matrix in the pre-set plan set, the process can return to S110 to regenerate the partitioning scheme of the operating domain, thereby realizing the adaptive closed loop of the coordinated control of the distribution-micro system.

[0085] In this embodiment, if the rate of change of the current integrated coupling strength matrix of the distribution and micro-distribution system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current integrated coupling strength matrix in the pre-set plan set is switched to be executed. This includes: collecting real-time operating status data of the distribution and micro-distribution system within a sliding time window; determining the current integrated coupling strength matrix of the distribution and micro-distribution system based on the real-time operating status data; and if the rate of change of the current integrated coupling strength matrix relative to the integrated coupling strength matrix corresponding to the target optimal partitioning scheme exceeds a preset threshold, the optimal partitioning scheme corresponding to the current integrated coupling strength matrix in the pre-set plan set is switched to be executed. The rate of change of the integrated coupling strength matrix is ​​mainly achieved by calculating the relative deviation of the Frobenius norm of the matrix. Each optimal partitioning scheme in the pre-set plan set has a corresponding integrated coupling strength matrix. The optimal partitioning scheme corresponding to the current integrated coupling strength matrix can be selected from the pre-set plan set, and the distribution and micro-distribution system can be controlled to switch to execute this optimal partitioning scheme.

[0086] In related technologies, the actual operating environment of integrated distribution and micro-systems is complex and variable. Existing methods lack a comprehensive evaluation system that includes multi-dimensional quantitative indicators, making it difficult to accurately select the optimal partitioning scheme from a large number of operating domain partitioning schemes. The embodiments of this application calculate the modularity function index by integrating the comprehensive coupling strength matrix with dynamic evolution characteristics. By using the modularity function index, the operating domain power supply rate index, and the power interaction cost index, the optimal partitioning scheme can be selected from a variety of partitioning schemes, which can accurately select the optimal partitioning scheme for the operating domain. In related technologies, the partitioning of the operating domain of integrated distribution and microgrid systems faces challenges such as single indicators, neglect of electrical coupling characteristics, and lack of system evaluation. In this embodiment, the linearized branch power flow constraints are combined with the comprehensive evaluation of multi-dimensional indicators. The operating domain is partitioned by considering the comprehensive coupling strength matrix, which realizes the effective integration of linearized DC power flow optimization and the MILP model used for operating domain partitioning. The partitioning scheme is evaluated by modularity function indicators, operating domain power supply rate indicators, and power interaction cost indicators. This can overcome the defects of traditional partitioning methods such as single indicators, neglect of electrical coupling characteristics, and lack of system evaluation. While ensuring that the partitioning results meet the grid security constraints, it also takes into account the economy and reliability of operation, realizes the adaptable operation of integrated distribution and microgrid systems in multiple scenarios, and effectively improves the renewable energy absorption capacity and the operational stability of the system.

[0087] In related technologies, the division of the operating domain is mostly limited to static division, lacking a dynamic self-adaptive matching mechanism for sudden changes in the scene or topology reconstruction. The embodiments of this application do not rely on the static topology structure, but extract the active power correlation coefficient of the dynamic fluctuation of the source and load and the static electrical distance from the time-series operating status data, and fuse them to generate a comprehensive coupling strength matrix, which accurately describes the dynamic evolution mechanism under multiple scenarios. By determining the rate of change of the current comprehensive coupling strength matrix through real-time sensing data, the switching of the division scheme is controlled, breaking the limitations of static division, solving the problem of difficulty in real-time adaptive control of the system's multi-scenario fluctuations, improving the practicality and flexibility of the division results, and significantly improving the local consumption capacity and collaborative operation flexibility of the distribution and micro-distribution integrated system in complex multi-scenario scenarios while strictly ensuring safety constraints.

[0088] The technical solution provided in this application collects the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divides it into time-series operating status data for multiple typical multi-source load fluctuation scenarios. For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series status data. A comprehensive coupling strength matrix is ​​then determined based on this active power correlation coefficient and static electrical distance. This comprehensive coupling strength matrix is ​​used to determine the optimal partitioning scheme of the operating domain in the integrated distribution and micro-distribution system. In other words, by fusing time-series operating status data to determine the comprehensive coupling strength matrix, the system's time-series evolution characteristics are integrated, and a dynamic-static combined approach is proposed. The comprehensive coupling strength matrix determines the optimal partitioning scheme for the operating domain, accurately depicting the intrinsic mechanism of the system's dynamic evolution and enabling reasonable and precise partitioning of the operating domain. During the execution of the optimal partitioning scheme in the contingency plan set, if the rate of change of the current comprehensive coupling strength matrix exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the contingency plan set is switched to be executed. That is, by integrating the comprehensive coupling strength matrix with dynamic temporal evolution characteristics, the switching of the operating domain partitioning scheme is controlled by the rate of change of the comprehensive coupling strength matrix, breaking the limitations of traditional static partitioning and enabling real-time adaptive control of system fluctuations in multiple scenarios.

[0089] For example, Figure 2 This is a flowchart of a method for dividing the operating domain of an integrated power distribution network system according to an embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0090] S210: Collect the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operating status data of multiple typical multi-source load fluctuation scenarios.

[0091] S220: For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series operating status data, and the comprehensive coupling strength matrix of the distribution-micro system is determined based on the active power correlation coefficient and the static electrical distance.

[0092] S230: The integrated micro-distribution system is mapped as an undirected weighted graph based on the comprehensive coupling strength matrix.

[0093] S240: Identify the set of separable flexible boundaries in the integrated distribution system, and determine the partitioning decision vector based on the set of separable flexible boundaries; wherein the partitioning decision vector is used to limit the binary variable search of the branches in the set of separable flexible boundaries.

[0094] S250: Based on the partitioning decision vector, solve the mixed integer linear programming (MILP) model for partitioning the operating domain to obtain multiple partitioning schemes for the operating domain in the integrated distribution and micro-management system.

[0095] S260: Determine the modularity function index based on the undirected weighted graph, and select the optimal partitioning scheme from the multiple partitioning schemes based on the modularity function index, the operating domain power supply rate index, and the power interaction cost index.

[0096] S270: Summarize the optimal partitioning schemes for each typical multi-source load fluctuation scenario to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the integrated distribution and micro-system exceeds a preset threshold, switch to execute the optimal partitioning scheme in the set of contingency plans corresponding to the current comprehensive coupling strength.

[0097] The descriptions of S210-S270 can be found in the above embodiments.

[0098] To verify the effectiveness of the technical solutions provided in the embodiments of this application, an exemplary case analysis is performed on an improved IEEE 33-node integrated system, the system topology of which is shown below. Figure 3 As shown in the diagram. The system has a total of 33 nodes, a total base capacity of 1 MVA, a voltage level of 12.66 kV, and a maximum total load of 3715 kW. It includes two wind turbines, four photovoltaic systems, and three energy storage systems, with their installation locations as shown in the diagram. Figure 4 As shown in the image. The black nodes represent distributed generation (DG) installation nodes.

[0099] The wind turbine has a cut-in wind speed of 4 m / s, a rated wind speed of 12 m / s, a cut-out wind speed of 25 m / s, and a rated power of 500 kW. The total area of ​​the photovoltaic system is 10,000 m², and the photoelectric conversion efficiency is 0.14. Simulation analysis was performed using MATLAB R2024b.

[0100] In this scenario, after distributed generation (DG) is connected, due to its inherent power output fluctuations, traditional operating domain partitioning methods often ignore electrical coupling characteristics when the DG output does not match the load. This leads to problems such as power imbalance within operating domains, frequent power interactions between operating domains, and voltage exceeding limits at some nodes. Since the preset 10kV line voltage deviation is not allowed to exceed ±7%, some nodes in this scenario experience voltage exceeding limits. Furthermore, the cost of power interactions between operating domains is high, and the capacity for renewable energy absorption is insufficient. To address these issues, the technical solution provided in this application's embodiments is introduced to optimize the operating domain partitioning for this scenario. The DG parameter information extracted in this application's embodiments is shown in Table 1, and the energy storage installation parameters are shown in Table 2.

[0101] Table 1

[0102] Installed capacity / kW 400 450 350 450 200 500

[0103] Table 2

[0104] 16 200 0.90 0.30 0.8 27 200 0.90 0.30 0.8 25 300 0.85 0.30 0.8

[0105] To verify the rationality of the quantitative indicators provided in the embodiments of this application for evaluating the runtime domain partitioning scheme, based on the various runtime domain partitioning results obtained by the enumeration method, the comprehensive evaluation indicators for runtime domain partitioning provided in the embodiments of this application are used to conduct optimization and quantitative evaluation, and finally the optimal runtime domain partitioning scheme is selected as follows: Figure 5 As shown, Figure 5 As shown, the integrated distribution system is divided into four operating domains, with each domain containing approximately eight nodes, and the distribution scale is relatively reasonable. Simultaneously, each operating domain is equipped with approximately two distributed power sources, which effectively maintains the power supply and demand balance within the operating domain and enables local consumption of the distributed power output.

[0106] The various operational domain partitioning schemes are compared based on comprehensive evaluation indicators, as shown in Table 3. Furthermore, to verify whether the comprehensive evaluation indicators provided in this embodiment can effectively evaluate the operational domain partitioning effect from multiple dimensions, the comprehensive evaluation indicators of each partitioning scheme obtained by dividing different branches using the enumeration method are statistically analyzed. The comparison results of the comprehensive evaluation indicators of different partitioning schemes are shown in Table 4. In this embodiment, the comprehensive evaluation indicator for operational domain partitioning obtained is 0.802, which performs best in the multi-dimensional comprehensive evaluation.

[0107] Table 3

[0108] Comprehensive evaluation indicators 0.772 0.821 0.787 0.828

[0109] Table 4

[0110] 1 3、9、15、30 0.597 0.770 0.796 0.721 2 2、8、26 0.740 0.630 0.821 0.730 3 8、18、22、25 0.693 0.782 0.740 0.738 4 3、9、25 0.798 0.780 0.827 0.802

[0111] The technical solution provided in this application relies on linearized branch power flow constraints and a multi-dimensional comprehensive evaluation system. It generates multiple partitioning schemes using a branch-cutting enumeration method, selects the optimal scheme, and verifies the effectiveness of the optimization results in a virtual model. Once verified, the optimized control decisions can be fed back to the physical system, driving collaborative optimization and control, effectively improving the overall optimization efficiency of the system. This provides a novel solution to the challenge of partitioning the operating domain of new systems with high-proportion distributed energy penetration.

[0112] Figure 6 This is a structural block diagram of an operating domain partitioning device for an integrated micro-distribution system provided in an embodiment of this application, such as... Figure 6 As shown, the device includes:

[0113] The first processing module 610 is used to collect the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operating status data of multiple typical multi-source load fluctuation scenarios.

[0114] The second processing module 620 is used to determine the active power correlation coefficient and static electrical distance between each node based on the time-series operating status data for each typical multi-source load fluctuation scenario, and to determine the comprehensive coupling strength matrix of the distribution-micro system based on the active power correlation coefficient and the static electrical distance, and to determine the optimal partitioning scheme for the operating domain of the distribution-micro system based on the comprehensive coupling strength matrix.

[0115] The third processing module 630 is used to summarize the optimal partitioning schemes for each typical multi-source load fluctuation scenario to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the distribution and micro-integrated system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the set of contingency plans will be switched to be executed.

[0116] like Figure 7 As shown in the figure, this application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0117] Memory 113 is used to store computer programs;

[0118] In one embodiment of this application, the processor 111, when executing a program stored in the memory 113, implements the method provided in any of the foregoing method embodiments.

[0119] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.

[0120] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0122] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A method for partitioning the operating domain of a micro-integrated distribution system, characterized in that, include: Collect the operational status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operational status data of multiple typical multi-source load fluctuation scenarios; For each typical multi-source load fluctuation scenario, the active power correlation coefficient and static electrical distance between each node are determined based on the time-series operating status data. The comprehensive coupling strength matrix of the distribution-micro system is determined based on the active power correlation coefficient and the static electrical distance. The optimal partitioning scheme for the operating domain of the distribution-micro system is determined based on the comprehensive coupling strength matrix. The optimal partitioning schemes for each typical multi-source load fluctuation scenario are summarized to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the distribution and micro-integrated system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the set of contingency plans is switched to be executed.

2. The method according to claim 1, characterized in that, The process of determining the optimal partitioning scheme for the operating domain in the integrated distribution and micro-system based on the comprehensive coupling strength matrix includes: The integrated micro-assembly system is mapped as an undirected weighted graph based on the comprehensive coupling strength matrix. Identify the set of separable flexible boundaries in the integrated distribution and micro-system, and determine a partitioning decision vector based on the set of separable flexible boundaries; wherein, the partitioning decision vector is used to limit the binary variable search of the branches in the set of separable flexible boundaries; Based on the partitioning decision vector, a mixed integer linear programming (MILP) model for partitioning the operating domain is solved to obtain multiple partitioning schemes for the operating domain in the integrated distribution and micro-management system. Based on the undirected weighted graph, a modularity function index is determined, and based on the modularity function index, the operating domain power supply rate index, and the power interaction cost index, the optimal partitioning scheme is selected from the multiple partitioning schemes.

3. The method according to claim 1, characterized in that, The determination of the comprehensive coupling strength matrix of the integrated distribution and micro-distribution system based on the active power correlation coefficient and the static electrical distance includes: The elements in the comprehensive coupling strength matrix are determined based on the following formula: ； in, The elements in the comprehensive coupling strength matrix are nodes. With nodes The overall coupling strength between them; , These are the weighting coefficients; For nodes With nodes The static electrical distance between them; For nodes With nodes The correlation coefficient of active power between them; The smoothing parameter, used to control the sensitivity of dynamic response, is set to a value of 0.1 to 0.

5.

4. The method according to claim 2, characterized in that, The undirected weighted graph is: ； in, Let be the edge weight matrix; where the elements in the edge weight matrix are... for: ； in, The elements in the comprehensive coupling strength matrix are nodes. With nodes The overall coupling strength between them; For a set of nodes, For the set of branches; branches For nodes With nodes Branch roads between; The identification of the set of separable flexible boundaries in the integrated micro-distribution system includes: The set of cuttable flexible boundaries is determined based on the switch configuration information in the integrated distribution and micro-system. The set of cuttable flexible boundaries is as follows: ； in, A set of shearable flexible boundaries; branch road The segmentation state indication function; Among them, when the branch road When the conditions for physical segmentation or reconstruction are met, ; when a side road When it is a rigid physical connection, .

5. The method according to claim 4, characterized in that, The partitioning decision vector is: ； in, ; Divide the decision into vectors; The step of solving the mixed-integer linear programming (MILP) model for partitioning the running domain based on the partitioning decision vector includes: Construct an optimization function with the goal of maximizing self-consistency and minimizing interaction cost within the operating domain; The MILP model is constructed based on the optimization function and preset constraints, and the MILP model is solved based on the partitioning decision vector. The optimization function is: ； in, To optimize function values; , These are the weight coefficients for the corresponding items; For nodes Binary decision variables belonging to the operating domain, When node Belongs to the runtime domain hour, Otherwise, it is 0; This is the number of the operating domain; The number of runtime domains; The number of nodes; in, For nodes Total output of the connected wind turbines or photovoltaic power plants; The charging and discharging power of the energy storage system; For load demand; in, , for branch road Cross-domain cut edge state variables, The interaction penalty cost for the corresponding cut edge; if the branch Across different operating domains, .

6. The method according to claim 1, characterized in that, If the rate of change of the current integrated coupling strength matrix of the micro-distribution system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current integrated coupling strength matrix in the pre-set scheme set will be switched to be executed, including: Collect real-time operating status data of the integrated micro-distribution system within a sliding time window; The current integrated coupling strength matrix of the micro-distribution system is determined based on the real-time operating status data. If the rate of change of the current comprehensive coupling strength matrix relative to the comprehensive coupling strength matrix corresponding to the target optimal partitioning scheme exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the scheme set will be switched to be executed.

7. The method according to claim 4, characterized in that, The modularity function index is determined based on the following formula: ； in, The modularity function index; , Representing the nodes respectively and nodes The sum of the weights of all connected edges; This represents the sum of the weights of all edges in the micro-allocation system. and They are nodes and nodes Binary decision variables belonging to the operating domain; This is the number of the operating domain; The number of runtime domains; The number of nodes.

8. A device for dividing the operating domain of a micro-integrated system, characterized in that, include: The first processing module is used to collect the operating status data of each node in the integrated distribution and micro-distribution system within a preset time period, and divide it into time-series operating status data of multiple typical multi-source load fluctuation scenarios. The second processing module is used to determine the active power correlation coefficient and static electrical distance between each node based on the time-series operating status data for each typical multi-source load fluctuation scenario, and to determine the comprehensive coupling strength matrix of the distribution-micro system based on the active power correlation coefficient and the static electrical distance, and to determine the optimal partitioning scheme for the operating domain of the distribution-micro system based on the comprehensive coupling strength matrix. The third processing module is used to summarize the optimal partitioning schemes for each typical multi-source load fluctuation scenario to form a set of contingency plans. During the execution of the optimal partitioning scheme in the set of contingency plans as the target optimal partitioning scheme, if the rate of change of the current comprehensive coupling strength matrix of the distribution and micro-integrated system exceeds a preset threshold, the optimal partitioning scheme corresponding to the current comprehensive coupling strength matrix in the set of contingency plans will be switched to be executed.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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