A risk-aware heterogeneous flexible resource aggregation feasible domain and security control feasible domain representation method, system, terminal and computer readable storage medium

Abstract

A method, system, terminal, and computer-readable storage medium for characterizing the feasible domains of heterogeneous flexible resource aggregation and safety control under risk perception, belonging to the field of active distribution networks, are disclosed. The system includes: a data acquisition and preprocessing module, a topology and spatiotemporal state construction module, a risk scoring construction module, a physical feasible domain construction module, a callable domain construction module, a risk correction and aggregation module, a safety control feasible domain construction module, and a feasible domain evaluation module. This invention can connect the results of distribution network operation risk identification with the characterization of the safety control capabilities of heterogeneous flexible resources, realizing the dynamic quantification of the feasible domains of resource aggregation and safety control under risk conditions, such as energy storage, electric vehicles, and flexible loads. It solves the technical problem that existing methods cannot uniformly consider operational risks, resource heterogeneity, market response, and network security constraints.

Description

Technical Field

[0001] This invention belongs to the field of active distribution networks, and particularly relates to a method, system, terminal, and computer-readable storage medium for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control for risk perception. Background Technology

[0002] With the continuous integration of distributed photovoltaic, decentralized wind power, new energy storage, electric vehicles, and flexible loads into the distribution network, the distribution network is gradually transforming from a traditional unidirectional passive power receiving network into an active distribution system with multiple sources, multiple loads, and multiple control entities. The large number of distributed resources has improved the operational flexibility of the distribution network, but it has also made node voltage, line power flow, transformer load, and power exchange with the upstream grid exhibit stronger spatiotemporal volatility and uncertainty.

[0003] Under conditions of high proportion of distributed generation and flexible load access, the operational risks of the distribution network are no longer limited to local equipment exceeding limits, but may manifest as a coupled superposition of multiple types of risks, such as voltage exceeding limits, line overload, transformer overload, and reverse power flow. Different types of risks have different impacts on the available direction and capacity of flexible resources. For example, voltage exceeding limit risk may limit negative active power regulation or require local active power support; line overload risk may limit regulation in the direction of increasing power flow; transformer overload risk may limit further increase in the net load of the entire network; and reverse power flow risk may limit continued reverse power transmission in scenarios with surplus distributed generation.

[0004] Existing flexible resource scheduling methods for distribution networks often start from the resource's own capacity or single operational constraints, performing aggregated modeling of resources such as energy storage, electric vehicles, and flexible loads, and describing their adjustability through upper and lower power limits. However, the physical adjustability of a resource does not equate to its safe availability under the current network risk state. Ignoring node risk states and network security boundaries may lead to aggregated resource adjustment commands exacerbating voltage overruns, line overloads, transformer overloads, or reverse power flow problems.

[0005] On the other hand, the participation of heterogeneous flexible resources is also related to market compensation mechanisms. Energy storage regulation results in lifespan loss, electric vehicle regulation affects user charging demand, and flexible load regulation affects energy comfort or production continuity. If regulation compensation is insufficient, resources, although physically adjustable, are not economically willing to respond; when compensation prices increase, the willingness to respond increases, and the economically available capacity expands accordingly. Therefore, the feasible domain of resources not only has physical attributes but also economic and security attributes.

[0006] In existing technologies, some methods only calculate the physical feasible region of resources without considering the real-time operational risks of the distribution network; some methods only screen adjustment actions based on power flow constraints without distinguishing between risk types such as voltage overruns, line overloads, transformer overloads, and reverse power flow; and some methods fail to unify market incentives, node risk sensitivity, and network security constraints into the same feasible region representation framework. Therefore, it is necessary to propose a risk-aware heterogeneous flexible resource aggregation feasible region and security control feasible region representation method, which transforms the operational risk identification results into resource availability correction factors, and further superimposes voltage, line, transformer, and reverse power flow constraints to quantify the safe availability range under different risk states. Summary of the Invention

[0007] To address the problem that existing technologies struggle to reflect the operational risks, economic resource response, and cybersecurity constraints of heterogeneous flexible resource aggregation feasible domains, this invention provides a risk-aware method and system for characterizing the aggregation feasible domain and security control feasible domain of heterogeneous flexible resources. This method uses the distribution network operational risk identification results as state input and resources such as energy storage, electric vehicles, and flexible loads as adjustment objects. Through the layered construction of the resource physical feasible domain, economic mobilization domain, risk-corrected aggregation feasible domain, and security control feasible domain, it achieves a quantitative characterization of the resource security mobilization capability under different risk states.

[0008] In a first aspect, the present invention discloses a method for characterizing the feasible domains of heterogeneous flexible resource aggregation and security control based on risk perception, the method comprising the following steps: Step 1: Collect data on the distribution network topology, node operating status, distributed generation output, upstream grid exchange power, and heterogeneous flexible resource operating status. Step 2: Construct a topological adjacency matrix based on the connection relationship of distribution network nodes, and organize the node operating status data into a node-level spatiotemporal state tensor; Step 3: Based on the pre-trained distribution network operation risk discrimination model, perform risk identification on the node-level spatiotemporal state tensor to obtain risk type, risk probability, risk severity, and information on nodes affected by the risk; Step 4: Based on the risk type, risk probability, risk severity, physical security margin, and topological relationship between nodes and risky devices, construct node risk sensitivity and node risk impact coefficient; Step 5: Construct the physical feasible region of heterogeneous flexible resources based on the physical operational constraints of energy storage, electric vehicles, and flexible loads; Step 6: Based on the resource participation adjustment willingness or market incentive response parameters, perform callability correction on the physical feasible domain of the heterogeneous flexible resources to construct the callable domain of the heterogeneous flexible resources; Step 7: Use the node risk impact coefficient to perform boundary correction on the heterogeneous flexible resource callable domain to obtain the risk-corrected aggregated feasible domain; Step 8: Based on voltage constraints, line load factor constraints, transformer load factor constraints, and reverse power flow constraints, perform safety screening or boundary shrinking on the risk-corrected aggregated feasible region to obtain a safe control feasible region; Step 9: Evaluate the secure callability of heterogeneous flexible resources under different operational risk states by using the upper and lower boundaries of the feasible region, the width of the feasible region, and the shrinkage rate of the feasible region.

[0009] More preferably, In step 1, the collected data includes: distribution network topology data, node load data, distributed power generation data, node voltage, line load rate, transformer load rate, upstream grid switching power, and heterogeneous flexible resource operation status data; the heterogeneous flexible resources include energy storage, electric vehicles, and flexible loads.

[0010] More preferably, The process of constructing a topological adjacency matrix based on the connection relationships of distribution network nodes can be described as follows: When constructing a topological adjacency matrix based on the node connection relationships of a distribution network, the distribution network is first represented as a graph structure: in: This represents the set of bus nodes participating in power flow calculation. The number of nodes; This indicates the electrical connections between nodes, including power distribution line connections and transformer high- and low-voltage side connections. If nodes... With nodes If there is an operating power line or transformer connecting the two, they are considered topologically adjacent and assigned a value of 1 in the adjacency matrix; otherwise, they are assigned a value of 0. The original topological adjacency matrix can be constructed as follows: Its element definition is as follows: Since the connections between distribution network lines and transformers can generally be considered as undirected electrical connections, the constructed adjacency matrix satisfies: To preserve the node's own running state information, an identity matrix is ​​added to the original adjacency matrix: The adjacency matrix containing self-connections is obtained: Further calculate the node degree matrix: To avoid the impact of differences in connectivity between nodes on feature propagation, the adjacency matrix is ​​symmetrically normalized to obtain a normalized topological adjacency matrix: The normalized topological adjacency matrix Topological feature propagation is used in the process of node risk state perception and heterogeneous flexible resource feasible domain characterization, so that node voltage, line load rate, transformer load rate, upstream grid exchange power and resource operation status can be transmitted between adjacent nodes along the distribution network physical topology, thereby characterizing the spatial coupling relationship between the distribution network operation risk state and the heterogeneous flexible resource regulation capability.

[0011] Furthermore, for any given moment The first in the distribution network The running status and resource status of each node are represented as a node-level comprehensive feature vector: in, Represents a node At any moment The voltage amplitude; Represents nodes Average load rate of connected lines; Represents nodes Maximum load rate of connected lines; Represents nodes Related transformer load factor; Indicates the net load power of the system; Indicates the power exchanged by the upstream power grid; Represents a node The operational status of the accessed heterogeneous flexible resources.

[0012] Combine the comprehensive features of all nodes into a node state matrix: in: Represents the state feature dimension of each node To characterize the temporal evolution of the distribution network's operating status and resource regulation capabilities, a length of [length missing] was selected. The historical time window combines the node state matrices of consecutive moments into a node-level spatiotemporal state tensor: in: Indicated by time The node-level spatiotemporal operational state sample is the endpoint.

[0013] More preferably, Step 4 involves constructing node risk sensitivity and node risk impact coefficients based on the distribution network operation risk identification results, node state deviation degree, and risk severity. This includes the following steps: S41: Obtain the results of distribution network operation risk identification. For any running sample time The results of the risk identification of the power distribution network operation are obtained. The results of the risk identification include risk labels, risk probabilities, risk severity vectors and risk impact node information.

[0014] The risk labels include normal operation status, voltage over-limit risk, line overload risk, transformer overload risk, and reverse power flow risk, which are defined as follows: Among them, voltage overload risk corresponds to node voltage constraint, line overload risk corresponds to line load rate constraint, transformer overload risk corresponds to transformer load rate constraint, and reverse power flow risk corresponds to upstream grid exchange power constraint.

[0015] S42: Obtain the risk probability vector For time The risk identification model outputs the predicted probability for each risk category, resulting in a risk probability vector: in, Indicates time Belongs to the Predicted probability of operational risk states.

[0016] S43: Obtain the risk severity vector Based on the degree of exceeding limits of node voltage, line load rate, transformer load rate, and upstream grid switching power, a risk severity vector is constructed: in, Indicates the severity of voltage exceeding the limit. Indicates the severity of line overload. Indicates the severity of transformer overload. Indicates the severity of reverse current flow.

[0017] S44: Physical security margin of computing nodes For nodes The physical safety margin of each node is calculated based on the node voltage, relevant line load rate, relevant transformer load rate, and upstream grid switching power. The node voltage safety margin is expressed as: in, Represents a node At any moment voltage amplitude, and These represent the lower and upper limits of the node voltage, respectively.

[0018] With nodes The relevant line load safety margin is expressed as follows: With nodes The relevant transformer load safety margin is expressed as follows: in, Represents nodes Related route collection, Represents nodes The relevant transformer set, This indicates the upper limit of the line load rate. This indicates the upper limit of the transformer load rate.

[0019] Based on the aforementioned physical security margin, the node physical risk proximity is constructed as follows: in, Represents a node The overall physical security margin, Represents a node The safety margin benchmark value, It is a very small positive number. The larger the value, the closer the node is to the physical security boundary.

[0020] S45: Risk Sensitivity of Construction Nodes Node risk sensitivity is constructed based on the predicted probability of risk category, risk severity, proximity of nodes to physical risks, and degree of topological correlation. in, Indicates the probability of abnormal operation risk; Indicates the proximity of nodes in terms of physical risk; This indicates the severity of the risk corresponding to the dominant risk type; Represents a node The degree of topological correlation between the risk-affected nodes, risk lines, or risk transformers; These are the weighting coefficients.

[0021] S46: Risk Impact Coefficient of Construction Nodes Construct a node risk impact coefficient based on node risk sensitivity: in, Represents a node Risk score threshold, It is a very small positive number, used to avoid the denominator being zero.

[0022] S47: Form a set of node risk impact coefficients For all nodes in the distribution network, a set of node risk impact coefficients is formed: The set of node risk impact coefficients is used for subsequent risk correction of the callable boundary of heterogeneous flexible resources.

[0023] 5. The method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception according to claim 1, characterized in that: More preferably, S51: Constructing the Physically Feasible Domain for Energy Storage Resources For access nodes The energy storage resources have an active power regulation capacity of [missing information]. The physical feasible region of energy storage is represented as: in, and They represent the time of energy storage. The minimum and maximum active power regulation. The active power regulation boundary of energy storage is determined by the rated power of energy storage, state of charge, and direction of charging and discharging.

[0024] S52: Constructing the Physically Feasible Domain for Electric Vehicle Resources For access nodes The electric vehicle aggregation resources have an active power regulation capacity of The physical feasible region of electric vehicles is represented as: in, and These represent the aggregated resources of electric vehicles at time [time]. The minimum and maximum active power regulation. The regulation boundary of electric vehicles is determined by the grid connection status, charging power, off-grid time, and user charging demand.

[0025] S53: Constructing the Physically Feasible Domain for Flexible Load Resources For access nodes The flexible load resources have an active power regulation capacity of The physical feasible region of the flexible load is represented as: in, and These represent the flexible load at time [time]. The minimum and maximum active power regulation. The flexible load regulation boundary is determined by the reductionable capacity, the shiftable time period, and user comfort constraints.

[0026] S54: Forming a single resource's physically feasible domain For any heterogeneous flexible resource node Its physical feasible region is uniformly represented as: in, Indicates the resource at time The lower adjustment boundary, Indicates the resource at time The upper adjustment boundary.

[0027] More preferably, In step 6, based on the resource participation adjustment willingness or market incentive response parameters, the callability of the heterogeneous flexible resource physical feasible domain is modified to construct the heterogeneous flexible resource economic callability domain, which specifically includes the following steps: S61: Obtaining market compensation price and resource reservation price For time Obtain market compensation price and resources reservation price Among them, the market compensation price represents the unit adjustment compensation paid by the dispatching entity for calling up heterogeneous flexible resources, and the resource reservation price represents the minimum compensation price required for the resource entity to participate in the adjustment.

[0028] S62: Constructing Resource Response Coefficients Based on the relationship between market compensation prices and resource retention prices, resource response coefficients are constructed: in, Representing resources Sensitivity coefficient to market compensation price.

[0029] S63: Constructing an economically callable domain By correcting the boundary of the physical feasible region of resources using resource response coefficients, the economically available domain is obtained. Resources are thus obtained Economic callable domains: This economic callable domain is used to characterize the actual adjustable capacity of resources under the current market compensation level.

[0030] More preferably, Steps 7 to 9 involve using the node risk impact coefficient to correct the boundary of the heterogeneous flexible resource economic mobilization domain and introducing physical security constraints to obtain the safe control feasible domain. Specifically, this includes the following steps: S71: Constructing a Risk-Modified Individual Feasible Domain For access nodes Heterogeneous and flexible resources, utilizing node risk impact coefficients Adjust its economic callable boundary: in, Representing resources The risk reduction factor.

[0031] Resources are thus obtained Risk-corrected single-unit feasible domain: S72: Constructing a risk-corrected aggregate feasible region For resource collections The risk-corrected individual feasible regions of each resource node are aggregated to obtain the risk-corrected aggregated feasible region: in: S73: Set safe operation constraints Based on the requirements for safe operation of the distribution network, node voltage constraints, line load rate constraints, transformer load rate constraints, and reverse power flow constraints are set: in, These represent the node voltage, line load rate, transformer load rate, and upstream grid exchange power after resource adjustment, respectively.

[0032] S74: Constructing a feasible domain for security control Based on the aforementioned safety operation constraints, the risk-corrected aggregated feasible region is subjected to safety screening or boundary shrinking to obtain the safety control feasible region: For a one-dimensional active power safety control feasible region, it can be represented as: in, This represents the minimum active power regulation boundary that satisfies safety constraints. This represents the maximum active power regulation boundary that satisfies safety constraints.

[0033] S75: Calculate the feasible region width For the physical aggregation feasible region, the risk-corrected aggregation feasible region, and the security control feasible region, calculate the feasible region width separately: S76: Calculate the feasible region shrinkage rate Using the resource physical aggregation feasible region as a benchmark, calculate the risk-corrected feasible region shrinkage rate and the safety control feasible region shrinkage rate: in, This represents the rate of contraction of the feasible region due to risk correction. This represents the shrinkage rate of the final feasible domain for safety control caused by safety constraints.

[0034] S77: Forming feasible region evaluation results For different operational risk states, the system outputs the upper and lower boundaries of the physical aggregate feasible region, the upper and lower boundaries of the risk-corrected aggregate feasible region, the upper and lower boundaries of the safety control feasible region, the feasible region width, and the feasible region shrinkage rate, forming an evaluation result of the safe and callable capability of heterogeneous flexible resources.

[0035] Secondly, this invention discloses a risk-aware heterogeneous flexible resource aggregation feasible domain and security control feasible domain characterization system, including a data acquisition and preprocessing module, a topology and spatiotemporal state construction module, a risk scoring construction module, a physical feasible domain construction module, a callable domain construction module, a risk correction aggregation module, a security control feasible domain construction module, and a feasible domain evaluation module. The data acquisition and preprocessing module is used to acquire data on the distribution network topology, operating status, and heterogeneous flexible resource operating status, and to perform missing value processing, time alignment, and standardization. The topology and spatiotemporal state construction module is used to construct a normalized topological adjacency matrix based on node connection relationships and organize the node running states at consecutive times into node-level spatiotemporal state tensors. The risk scoring construction module is used to construct node risk sensitivity and node risk impact coefficient based on the results of operational risk identification, risk probability, risk severity, and degree of deviation of node status. The Physically Feasible Domain Construction Module is used to construct the physically feasible domain of heterogeneous flexible resources based on the physical operational constraints of energy storage, electric vehicles, and flexible loads. The callable domain construction module is used to modify the callability of the physical feasible domain based on the resource participation adjustment intention or market incentive response parameters, and to construct heterogeneous flexible resource callable domains. The risk correction and aggregation module is used to correct the boundaries of the callable domain using the node risk impact coefficient, and to aggregate multiple types of heterogeneous flexible resources to obtain the risk correction and aggregation feasible domain. The safe control feasible region construction module is used to perform safety screening or boundary shrinking on the risk-modified aggregated feasible region based on physical safety constraints such as node voltage, line load rate, transformer load rate and upstream power grid exchange power to obtain the safe control feasible region. The feasible region evaluation module is used to calculate the upper and lower boundaries of the feasible region, the feasible region width, and the feasible region shrinkage rate, and to evaluate the secure callability of heterogeneous flexible resources under different operational risk states.

[0036] Thirdly, the present invention provides a terminal, including a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.

[0037] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

[0038] The beneficial effects of this invention are compared with those of the prior art: 1. This invention introduces the results of operational risk identification into the modeling of the feasible domain of heterogeneous flexible resources, so that the resource availability is no longer determined solely by the resource's own capacity, but can be dynamically adjusted according to operational risk states such as voltage overload, line overload, transformer overload, and reverse power flow.

[0039] 2. This invention constructs a layered representation framework of resource physical feasible domain, economic callable domain, risk correction aggregation feasible domain and security control feasible domain, which can simultaneously characterize resource physical capabilities, market response willingness, node risk impact and network security boundaries.

[0040] 3. This invention introduces market compensation price and resource reservation price into the resource availability representation, which can reflect the willingness of resources such as energy storage, electric vehicles and flexible loads to participate under different incentive levels, thereby enhancing the economic rationality of resource regulation modeling.

[0041] 4. This invention introduces voltage constraints, line load rate constraints, transformer load rate constraints, and reverse power flow constraints to form a safety control feasible domain screening mechanism consistent with the risk label of the distribution network operation, thereby improving the safety of resource allocation decisions under risk conditions.

[0042] 5. This invention uses indicators such as feasible domain boundary, feasible domain width and feasible domain shrinkage rate to quantify the scope of resource security under different risk states, which makes it easier for distribution network dispatchers to intuitively identify the impact of different risks on the flexible resource control space. Attached Figure Description

[0043] Figure 1 This is a flowchart of the risk perception method for characterizing the safe and feasible domain of heterogeneous flexible resources according to the present invention. Figure 2 This is a diagram showing the system-level risk scoring and alarm results in an embodiment of the present invention; Figure 3 This is a heatmap of node risk sensitivity in an embodiment of the present invention; Figure 4 This is a one-dimensional active power safety feasible domain time-series variation diagram in an embodiment of the present invention; Figure 5 This is a comparison chart of feasible domain shrinkage rates under different risk states in embodiments of the present invention; Figure 6 This is a heatmap of node risk factors in an embodiment of the present invention; Detailed Implementation To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. The embodiments described in this application are only some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0044] like Figure 1 As shown in the figure, this embodiment discloses a method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception, including the following steps: Step 1: Collect data on the distribution network topology, node operating status, distributed generation output, upstream grid exchange power, and heterogeneous flexible resource operating status. Step 2: Construct a topological adjacency matrix based on the connection relationship of distribution network nodes, and organize the node operating status data into a node-level spatiotemporal state tensor; Step 3: Based on the pre-trained distribution network operation risk discrimination model, perform risk identification on the node-level spatiotemporal state tensor to obtain risk type, risk probability, risk severity, and information on nodes affected by the risk; Step 4: Based on the risk type, risk probability, risk severity, physical security margin, and topological relationship between nodes and risky devices, construct node risk sensitivity and node risk impact coefficient; Step 5: Construct the physical feasible region of heterogeneous flexible resources based on the physical operational constraints of energy storage, electric vehicles, and flexible loads; Step 6: Based on the resource participation adjustment willingness or market incentive response parameters, perform callability correction on the physical feasible domain of the heterogeneous flexible resources to construct the callable domain of the heterogeneous flexible resources; Step 7: Use the node risk impact coefficient to perform boundary correction on the heterogeneous flexible resource callable domain to obtain the risk-corrected aggregated feasible domain; Step 8: Based on voltage constraints, line load factor constraints, transformer load factor constraints, and reverse power flow constraints, perform safety screening or boundary shrinking on the risk-corrected aggregated feasible region to obtain a safe control feasible region; Step 9: Evaluate the secure callability of heterogeneous flexible resources under different operational risk states by using the upper and lower boundaries of the feasible region, the width of the feasible region, and the shrinkage rate of the feasible region.

[0045] Example 1: like Figure 1 As shown in the figure, this embodiment discloses a method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception, including the following steps: In step 1, the collected data includes: distribution network topology data, node load data, distributed power generation data, node voltage, line load rate, transformer load rate, upstream grid switching power, and heterogeneous flexible resource operation status data; the heterogeneous flexible resources include energy storage, electric vehicles, and flexible loads.

[0046] Steps 2 and 3: Constructing the topological adjacency matrix based on the node connection relationships of the distribution network can be described as follows: When constructing a topological adjacency matrix based on the node connection relationships of a distribution network, the distribution network is first represented as a graph structure: in: This represents the set of bus nodes participating in power flow calculation. The number of nodes; This indicates the electrical connections between nodes, including power distribution line connections and transformer high- and low-voltage side connections. If nodes... With nodes If there is an operating power line or transformer connecting the two, they are considered topologically adjacent and assigned a value of 1 in the adjacency matrix; otherwise, they are assigned a value of 0. The original topological adjacency matrix can be constructed as follows: Its element definition is as follows: Since the connections between distribution network lines and transformers can generally be considered as undirected electrical connections, the constructed adjacency matrix satisfies: To preserve the node's own running state information, an identity matrix is ​​added to the original adjacency matrix: The adjacency matrix containing self-connections is obtained: Further calculate the node degree matrix: To avoid the impact of differences in connectivity between nodes on feature propagation, the adjacency matrix is ​​symmetrically normalized to obtain a normalized topological adjacency matrix: The normalized topological adjacency matrix Topological feature propagation is used in the process of node risk state perception and heterogeneous flexible resource feasible domain characterization, so that node voltage, line load rate, transformer load rate, upstream grid exchange power and resource operation status can be transmitted between adjacent nodes along the distribution network physical topology, thereby characterizing the spatial coupling relationship between the distribution network operation risk state and the heterogeneous flexible resource regulation capability.

[0047] Furthermore, for any given moment The first in the distribution network The running status and resource status of each node are represented as a node-level comprehensive feature vector: in, Represents a node At any moment The voltage amplitude; Represents nodes Average load rate of connected lines; Represents nodes Maximum load rate of connected lines; Represents nodes Related transformer load factor; Indicates the net load power of the system; Indicates the power exchanged by the upstream power grid; Represents a node The operational status of the accessed heterogeneous flexible resources.

[0048] Combine the comprehensive features of all nodes into a node state matrix: in: Represents the state feature dimension of each node To characterize the temporal evolution of the distribution network's operating status and resource regulation capabilities, a length of [length missing] was selected. The historical time window combines the node state matrices of consecutive moments into a node-level spatiotemporal state tensor: in: Indicated by time The node-level spatiotemporal operational state sample is the endpoint.

[0049] Step 4 involves constructing node risk sensitivity and node risk impact coefficients based on the distribution network operation risk identification results, node state deviation degree, and risk severity. This includes the following steps: Step 4.1: Obtain the results of the distribution network operation risk identification. For any running sample time The results of the risk identification of the power distribution network operation are obtained. The results of the risk identification include risk labels, risk probabilities, risk severity vectors and risk impact node information.

[0050] The risk labels include normal operation status, voltage over-limit risk, line overload risk, transformer overload risk, and reverse power flow risk, which are defined as follows: Among them, voltage overload risk corresponds to node voltage constraint, line overload risk corresponds to line load rate constraint, transformer overload risk corresponds to transformer load rate constraint, and reverse power flow risk corresponds to upstream grid exchange power constraint.

[0051] Step 4.2: Obtain the risk probability vector For time The risk identification model outputs the predicted probability for each risk category, resulting in a risk probability vector: in, Indicates time Belongs to the Predicted probability of operational risk states.

[0052] Step 4.3: Obtain the risk severity vector Based on the degree of exceeding limits of node voltage, line load rate, transformer load rate, and upstream grid switching power, a risk severity vector is constructed: in, Indicates the severity of voltage exceeding the limit. Indicates the severity of line overload. Indicates the severity of transformer overload. Indicates the severity of reverse current flow.

[0053] Step 4.4: Calculate the physical security margin of the nodes For nodes The physical safety margin of each node is calculated based on the node voltage, relevant line load rate, relevant transformer load rate, and upstream grid switching power. The node voltage safety margin is expressed as: in, Represents a node At any moment voltage amplitude, and These represent the lower and upper limits of the node voltage, respectively.

[0054] With nodes The relevant line load safety margin is expressed as follows: With nodes The relevant transformer load safety margin is expressed as follows: in, Represents nodes Related route collection, Represents nodes The relevant transformer set, This indicates the upper limit of the line load rate. This indicates the upper limit of the transformer load rate.

[0055] Based on the aforementioned physical security margin, the node physical risk proximity is constructed as follows: in, Represents a node The overall physical security margin, Represents a node The safety margin benchmark value, It is a very small positive number. The larger the value, the closer the node is to the physical security boundary.

[0056] Step 4.5: Construct node risk sensitivity Node risk sensitivity is constructed based on the predicted probability of risk category, risk severity, proximity of nodes to physical risks, and degree of topological correlation. in, Indicates the probability of abnormal operation risk; Indicates the proximity of nodes in terms of physical risk; This indicates the severity of the risk corresponding to the dominant risk type; Represents a node The degree of topological correlation between the risk-affected nodes, risk lines, or risk transformers; These are the weighting coefficients.

[0057] Step 4.6: Construct the node risk impact coefficient Construct a node risk impact coefficient based on node risk sensitivity: in, Represents a node Risk score threshold, It is a very small positive number, used to avoid the denominator being zero.

[0058] Step 4.7: Form a set of node risk impact coefficients For all nodes in the distribution network, a set of node risk impact coefficients is formed: The set of node risk impact coefficients is used for subsequent risk correction of the callable boundary of heterogeneous flexible resources.

[0059] In step 5, a heterogeneous flexible resource physical feasible region is constructed based on the physical operational constraints of energy storage, electric vehicles, and flexible loads. This specifically includes the following steps: Step 5.1: Construct the physical feasible region of energy storage resources For access nodes The energy storage resources have an active power regulation capacity of [missing information]. The physical feasible region of energy storage is represented as: in, and They represent the time of energy storage. The minimum and maximum active power regulation. The active power regulation boundary of energy storage is determined by the rated power of energy storage, state of charge, and direction of charging and discharging.

[0060] Step 5.2: Construct the physical feasible region of electric vehicle resources For access nodes The electric vehicle aggregation resources have an active power regulation capacity of The physical feasible region of electric vehicles is represented as: in, and These represent the aggregated resources of electric vehicles at time [time]. The minimum and maximum active power regulation. The regulation boundary of electric vehicles is determined by the grid connection status, charging power, off-grid time, and user charging demand.

[0061] Step 5.3: Construct the physical feasible region of flexible load resources For access nodes The flexible load resources have an active power regulation capacity of The physical feasible region of the flexible load is represented as: in, and These represent the flexible load at time [time]. The minimum and maximum active power regulation. The flexible load regulation boundary is determined by the reductionable capacity, the shiftable time period, and user comfort constraints.

[0062] Step 5.4: Forming the physical feasible region of a single resource For any heterogeneous flexible resource node Its physical feasible region is uniformly represented as: in, Indicates the resource at time The lower adjustment boundary, Indicates the resource at time The upper adjustment boundary.

[0063] In step 6, based on the resource participation adjustment willingness or market incentive response parameters, the callability of the heterogeneous flexible resource physical feasible domain is modified to construct the heterogeneous flexible resource economic callability domain, which specifically includes the following steps: Step 6.1: Obtain the market compensation price and resource reservation price For time Obtain market compensation price and resources reservation price .

[0064] Among them, the market compensation price represents the unit adjustment compensation paid by the dispatching entity for calling up heterogeneous flexible resources, and the resource reservation price represents the minimum compensation price required for the resource entity to participate in the adjustment.

[0065] Step 6.2: Construct resource response coefficients Based on the relationship between market compensation prices and resource retention prices, resource response coefficients are constructed: in, Representing resources Sensitivity coefficient to market compensation price.

[0066] Step 6.3: Construct the economic callable domain By correcting the boundary of the physical feasible region of resources using resource response coefficients, the economically available domain is obtained. Resources are thus obtained Economic callable domains: This economic callable domain is used to characterize the actual adjustable capacity of resources under the current market compensation level.

[0067] Steps 7 to 9 involve using the node risk impact coefficient to correct the boundary of the heterogeneous flexible resource economic mobilization domain and introducing physical security constraints to obtain the safe control feasible domain. Specifically, this includes the following steps: Step 7.1: Construct the risk-corrected individual feasible domain For access nodes Heterogeneous and flexible resources, utilizing node risk impact coefficients Adjust its economic callable boundary: in, Representing resources The risk reduction factor.

[0068] Resources are thus obtained Risk-corrected single-unit feasible domain: Step 7.2: Construct the risk-corrected aggregate feasible region For resource collections The risk-corrected individual feasible regions of each resource node are aggregated to obtain the risk-corrected aggregated feasible region: in: Step 7.3: Set safe operation constraints Based on the requirements for safe operation of the distribution network, node voltage constraints, line load rate constraints, transformer load rate constraints, and reverse power flow constraints are set: in, These represent the node voltage, line load rate, transformer load rate, and upstream grid exchange power after resource adjustment, respectively.

[0069] Step 7.4: Construct a feasible domain for security control Based on the aforementioned safety operation constraints, the risk-corrected aggregated feasible region is subjected to safety screening or boundary shrinking to obtain the safety control feasible region: For a one-dimensional active power safety control feasible region, it can be represented as: in, This represents the minimum active power regulation boundary that satisfies safety constraints. This represents the maximum active power regulation boundary that satisfies safety constraints.

[0070] Step 7.5: Calculate the feasible region width For the physical aggregation feasible region, the risk-corrected aggregation feasible region, and the security control feasible region, calculate the feasible region width separately: Step 7.6: Calculate the feasible region shrinkage rate Using the resource physical aggregation feasible region as a benchmark, calculate the risk-corrected feasible region shrinkage rate and the safety control feasible region shrinkage rate: in, This represents the rate of contraction of the feasible region due to risk correction. This represents the shrinkage rate of the final feasible domain for safety control caused by safety constraints.

[0071] Step 7.7: Generate feasible region evaluation results For different operational risk states, the system outputs the upper and lower boundaries of the physical aggregate feasible region, the upper and lower boundaries of the risk-corrected aggregate feasible region, the upper and lower boundaries of the safety control feasible region, the feasible region width, and the feasible region shrinkage rate, forming an evaluation result of the safe and callable capability of heterogeneous flexible resources.

[0072] Example 2: This embodiment discloses a risk-aware heterogeneous flexible resource aggregation feasible domain and security control feasible domain characterization system, which includes the following modules.

[0073] The data acquisition and preprocessing module is used to acquire data on the distribution network topology, operating status, and heterogeneous flexible resource operating status, and to perform missing value processing, time alignment, and standardization. The risk status input module is used to receive the risk identification results of normal operation, voltage over-limit, line overload, transformer overload and reverse power flow, and read the risk probability, risk severity and risk sensitive node information; The node risk sensitivity module is used to calculate the node risk impact coefficient based on the degree of deviation of node status, risk category, risk severity, and topological correlation. The resource physical feasible domain construction module is used to calculate the upper and lower boundaries of the adjustable power of individual resources based on the physical constraints of energy storage, electric vehicles and flexible loads; The economic callable domain construction module is used to calculate the economic callable boundary of resources based on the market compensation price, resource reservation price, and price response function. The risk correction and aggregation module is used to correct the economically available boundary of resources based on the node risk impact coefficient and form a regional or network-wide aggregated feasible domain. The safety control feasible domain screening module is used to screen the safe active power adjustment range based on voltage, line load rate, transformer load rate and reverse power flow constraints; The feasible region evaluation module is used to output the basic aggregated feasible region, risk-corrected feasible region, safety control feasible region and their shrinkage rate, and generate a risk score heatmap, feasible region time series diagram and shrinkage rate comparison diagram under risk conditions.

[0074] Example 3: This embodiment provides a terminal, including a processor and a storage medium. The storage medium stores a computer program, and the processor invokes the computer program to execute the risk-aware heterogeneous flexible resource aggregation feasible domain and security control feasible domain characterization method described in this invention.

[0075] Example 4: This embodiment provides a computer-readable storage medium on which a computer program is stored. When executed by a processor, the program implements the steps of the methods described in any of the above embodiments.

[0076] Risk status settings and resource parameter descriptions in the embodiments In this embodiment, to ensure a one-to-one correspondence between the feasible domains of resource security control and operational risk states, five operational state conditions are set. E1 represents the normal operating state, which characterizes the baseline feasible domain when the system does not trigger voltage over-limit, line overload, transformer overload, or reverse power flow risks. E2 represents the voltage over-limit risk, which characterizes the change in resource regulation capability when the voltage of a local node is below the lower limit or above the upper limit. E3 represents the line overload risk, which characterizes the limitations on resource regulation direction and capacity when the line load rate exceeds the overload threshold. E4 represents the transformer overload risk, which characterizes the limitations on the network-wide aggregated resource regulation capability when the transformer load rate exceeds the safety threshold. E5 represents the reverse power flow risk, which characterizes the resource security call boundary when the surplus of distributed power sources causes the upper-level grid's switching power to fall below the reverse power flow discrimination threshold.

[0077] Table 1 shows the calculation results of the risk area location index in the embodiments of the present invention; Table 2 shows the calculation results of the safe and feasible domain shrinkage rate under different risk states in the embodiments of the present invention.

[0078] In this embodiment, energy storage resources are deployed at multiple representative nodes to provide bidirectional rapid active power regulation capabilities; electric vehicle aggregation resources are deployed at nodes with concentrated loads to participate in regulation through charging power reduction or recovery; flexible load resources are deployed at interruptible or transferable load nodes to provide load reduction and recovery capabilities. All types of resources are directly aggregated according to their physical power boundaries in the basic aggregation feasible region, their boundaries are contracted according to the node risk impact coefficient in the risk-corrected feasible region, and further filtered by network security constraints in the safety control feasible region.

[0079] To avoid confusion between resource boundaries under normal operating conditions and those under risk conditions, this embodiment outputs the basic lower boundary, basic lower boundary, risk-corrected upper boundary, risk-corrected lower boundary, network security upper boundary, and network security lower boundary, respectively. Schedulers can use these boundaries to determine the positive and negative active power regulation capabilities of the resources available for use at any given time.

[0080] Under normal operating conditions, the availability of resources is mainly affected by the remaining capacity of the lines and the operational safety margin. Under voltage over-limit risk conditions, resource adjustment should prioritize meeting voltage recovery requirements. Under line overload risk conditions, adjustment directions that would further increase the power flow of overloaded lines should be restricted. Under transformer overload risk conditions, resource allocation that would further increase the main transformer load should be restricted. Under reverse power flow risk conditions, adjustment directions that would increase reverse power transmission should be restricted.

[0081] Interpretation of the results of the embodiments For E1 under normal operating conditions, the risk correction shrinkage rate is very small, indicating that the node risk sensitivity has a weak impact on resource availability. However, the final safe and feasible domain still shrinks to some extent, indicating that even under normal operating conditions, network security constraints still limit some adjustment directions. This result is consistent with the actual operation of the distribution network, that is, the physical capacity of resources cannot be directly equated with the safe and available capacity.

[0082] For the E2 voltage exceedance risk, the final safe feasible region shrinkage rate is significantly higher than the risk correction shrinkage rate, indicating that voltage safety constraints dominate the resource regulation capability. This result shows that under voltage risk conditions, relying solely on the available capacity of resources for regulation is insufficient; it is essential to determine whether the node voltage returns to the safe range after regulation.

[0083] For the E3 line overload risk, the line constraint contraction rate increases significantly, indicating that branch capacity has become a key factor limiting resource allocation. If resource adjustment would further increase the power flow of the overloaded line, then this adjustment direction will be eliminated from the feasible domain of safety control.

[0084] Regarding the E4 transformer overload risk, the transformer constraint contraction rate is high, indicating that the capacity margin of the main transformer or equivalent transformer has a strong constraint effect on the overall network resource aggregation and regulation capability. Under this risk condition, even if some nodes still have physical resource regulation capabilities, they may not be able to fully utilize them due to transformer safety constraints.

[0085] Regarding the E5 reverse power flow risk, the reverse power flow constraint contraction rate is close to 1, indicating that the backflow power caused by the surplus of distributed generation will severely limit the direction of resource regulation. If the reverse power flow constraint is not considered, the regulation action may further increase the backflow power to the upper-level grid, thereby exacerbating the operational risk.

Claims

1. A risk-aware heterogeneous flexible resource aggregation feasible region and security control feasible region characterization method, characterized in that, The method includes the following steps: Step 1: Collect data on the distribution network topology, node operating status, distributed generation output, upstream grid exchange power, and heterogeneous flexible resource operating status. Step 2: Construct a topological adjacency matrix based on the connection relationship of distribution network nodes, and organize the node operating status data into a node-level spatiotemporal state tensor; Step 3: Based on the pre-trained distribution network operation risk discrimination model, perform risk identification on the node-level spatiotemporal state tensor to obtain risk type, risk probability, risk severity, and information on nodes affected by the risk; Step 4: Based on the risk type, risk probability, risk severity, physical security margin, and topological relationship between nodes and risky devices, construct node risk sensitivity and node risk impact coefficient; Step 5: Construct the physical feasible region of heterogeneous flexible resources based on the physical operational constraints of energy storage, electric vehicles, and flexible loads; Step 6: Based on the resource participation adjustment willingness or market incentive response parameters, perform callability correction on the physical feasible domain of the heterogeneous flexible resources to construct the callable domain of the heterogeneous flexible resources; Step 7: Use the node risk impact coefficient to perform boundary correction on the heterogeneous flexible resource callable domain to obtain the risk-corrected aggregated feasible domain; Step 8: Based on voltage constraints, line load factor constraints, transformer load factor constraints, and reverse power flow constraints, perform safety screening or boundary shrinking on the risk-corrected aggregated feasible region to obtain a safe control feasible region; Step 9: Evaluate the secure callability of heterogeneous flexible resources under different operational risk states by using the upper and lower boundaries of the feasible region, the width of the feasible region, and the shrinkage rate of the feasible region.

2. The method for characterizing the feasible domain and security control feasible domain of heterogeneous flexible resource aggregation based on risk perception according to claim 1, characterized in that: In step 1, the collected data includes: distribution network topology data, node load data, distributed power generation data, node voltage, line load rate, transformer load rate, upstream grid switching power, and heterogeneous flexible resource operation status data; the heterogeneous flexible resources include energy storage, electric vehicles, and flexible loads.

3. The method for characterizing the feasible domain and security control feasible domain of heterogeneous flexible resource aggregation based on risk perception according to claim 1, characterized in that: The method for constructing a topological adjacency matrix based on the connection relationship of distribution network nodes is as follows: When constructing a topological adjacency matrix based on the node connection relationships of a distribution network, the distribution network is first represented as a graph structure: in: This represents the set of bus nodes participating in power flow calculation. The number of nodes; This indicates the electrical connection relationships between nodes, including power distribution line connections and transformer high and low voltage side connections. If a node With nodes If there is an operating line or transformer connection between them, they are considered topologically adjacent and assigned a value of 1 in the adjacency matrix; otherwise, they are assigned a value of 0. This is how the original topological adjacency matrix is ​​constructed. Its element definition is as follows: The connections between power distribution lines and transformers are considered as undirected electrical connections; therefore, the constructed adjacency matrix satisfies: Add the identity matrix to the original adjacency matrix: The adjacency matrix containing self-connections is obtained: Calculate the node degree matrix: The adjacency matrix is ​​symmetrically normalized to obtain the normalized topological adjacency matrix: Normalized topological adjacency matrix Topological feature propagation is used in the process of node risk state perception and heterogeneous flexible resource feasible domain characterization, so that node voltage, line load rate, transformer load rate, upstream grid exchange power and resource operation status can be transmitted between adjacent nodes along the distribution network physical topology, thereby characterizing the spatial coupling relationship between distribution network operation risk state and heterogeneous flexible resource regulation capability. For any time The first in the distribution network The running status and resource status of each node are represented as a node-level comprehensive feature vector: in, Represents a node At any moment The voltage amplitude; Represents nodes Average load rate of connected lines; Represents nodes Maximum load rate of connected lines; Represents nodes Related transformer load factor; Indicates the net load power of the system; Indicates the power exchanged by the upstream power grid; Represents a node The operational status of the accessed heterogeneous and flexible resources; Combine the comprehensive features of all nodes into a node state matrix: in: This represents the state feature dimension of each node; To characterize the temporal evolution of the distribution network's operating status and resource regulation capabilities, a length of [length missing] was selected. The historical time window combines the node state matrices of consecutive moments into a node-level spatiotemporal state tensor: in: Indicated by time The node-level spatiotemporal operational state sample is the endpoint.

4. The method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception according to claim 1, characterized in that: Step 4 involves constructing node risk sensitivity and node risk impact coefficients based on the distribution network operation risk identification results, node state deviation degree, and risk severity. This includes the following steps: S41: Obtain the results of distribution network operation risk identification. For any running sample time The results of the distribution network operation risk identification are obtained, including risk labels, risk probabilities, risk severity vectors, and risk impact node information. The risk labels include normal operation status, voltage over-limit risk, line overload risk, transformer overload risk, and reverse power flow risk, which are defined as follows: Among them, voltage overload risk corresponds to node voltage constraint, line overload risk corresponds to line load rate constraint, transformer overload risk corresponds to transformer load rate constraint, and reverse power flow risk corresponds to upstream grid exchange power constraint. S42: Obtain the risk probability vector For time The risk identification model outputs the predicted probability for each risk category, resulting in a risk probability vector: in, Indicates time Belongs to the The predicted probability of a class's operational risk state; S43: Obtain the risk severity vector Based on the degree of exceeding limits of node voltage, line load rate, transformer load rate, and upstream grid switching power, a risk severity vector is constructed: in, Indicates the severity of voltage exceeding the limit. Indicates the severity of line overload. Indicates the severity of transformer overload. Indicates the severity of reverse current flow; S44: Physical security margin of computing nodes For nodes Based on the node voltage, relevant line load rate, relevant transformer load rate, and upstream grid exchange power, the physical safety margin of the node is calculated. The node voltage safety margin is expressed as: in, Represents a node At any moment voltage amplitude, and These represent the lower and upper limits of the node voltage, respectively. With nodes The relevant line load safety margin is expressed as follows: With nodes The relevant transformer load safety margin is expressed as follows: in, Represents nodes Related route collection, Represents nodes The relevant transformer set, This indicates the upper limit of the line load rate. Indicates the upper limit of transformer load rate; Based on the aforementioned physical security margin, construct the node physical risk proximity: in, Represents a node The overall physical security margin, Represents a node The safety margin benchmark value, It is a very small positive number. The larger the value, the closer the node is to the physical safety boundary; S45: Risk Sensitivity of Construction Nodes Node risk sensitivity is constructed based on the predicted probability of risk category, risk severity, proximity of nodes to physical risks, and degree of topological correlation. in, Indicates the probability of abnormal operation risk; Indicates the proximity of nodes in terms of physical risk; This indicates the severity of the risk corresponding to the dominant risk type; Represents a node The degree of topological correlation between the risk-affected nodes, risk lines, or risk transformers; These are the weighting coefficients; S46: Risk Impact Coefficient of Construction Nodes Construct a node risk impact coefficient based on node risk sensitivity: in, Represents a node Risk score threshold, It is a very small positive number, used to avoid the denominator being zero. S47: Form a set of node risk impact coefficients For all nodes in the distribution network, a set of node risk impact coefficients is formed: The set of node risk impact coefficients is used for subsequent risk correction of the callable boundary of heterogeneous flexible resources.

5. The method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception according to claim 1, characterized in that: In step 5, a heterogeneous flexible resource physical feasible region is constructed based on the physical operational constraints of energy storage, electric vehicles, and flexible loads. This specifically includes the following steps: S51: Constructing the Physically Feasible Domain for Energy Storage Resources For access nodes The energy storage resources have an active power regulation capacity of [missing information]. The physical feasible region of energy storage is represented as: in, and They represent the time of energy storage. The minimum and maximum active power regulation power of the energy storage is determined by the rated power of the energy storage, the state of charge, and the direction of charging and discharging. S52: Constructing the Physically Feasible Domain for Electric Vehicle Resources For access nodes The electric vehicle aggregation resources have an active power regulation capacity of The physical feasible region of electric vehicles is represented as: in, and These represent the aggregated resources of electric vehicles at time [time]. The minimum and maximum active power regulation power, the regulation boundary of electric vehicles is determined by the access status, charging power, off-grid time and user charging demand. S53: Constructing the Physically Feasible Domain for Flexible Load Resources For access nodes The flexible load resources have an active power regulation capacity of The physical feasible region of the flexible load is represented as: in, and These represent the flexible load at time [time]. The minimum and maximum active power regulation, and the flexible load regulation boundary are jointly determined by the capacity that can be reduced, the time period that can be transferred, and user comfort constraints; S54: Forming a single resource's physically feasible domain For any heterogeneous flexible resource node Its physical feasible region is uniformly represented as: in, Indicates the resource at time The lower adjustment boundary, Indicates the resource at time The upper adjustment boundary.

6. The method for characterizing the feasible domain and security control feasible domain of heterogeneous flexible resource aggregation based on risk perception according to claim 1, characterized in that: In step 6, based on the resource participation adjustment willingness or market incentive response parameters, the callability of the heterogeneous flexible resource physical feasible domain is modified to construct the heterogeneous flexible resource economic callability domain, which specifically includes the following steps: S61: Obtaining market compensation price and resource reservation price For time Obtain market compensation price and resources reservation price ; Among them, the market compensation price represents the unit adjustment compensation paid by the dispatching entity for calling up heterogeneous flexible resources, and the resource reservation price represents the minimum compensation price required for the resource entity to participate in the adjustment; S62: Constructing Resource Response Coefficients Based on the relationship between market compensation prices and resource retention prices, resource response coefficients are constructed: in, Representing resources Sensitivity coefficient to market compensation price; S63: Constructing an economically callable domain By correcting the boundary of the physical feasible region of resources using resource response coefficients, the economically available domain is obtained. Resources are thus obtained Economic callable domains: The economically callable domain is used to characterize the actual adjustable capacity of resources under the current market compensation level.

7. The method for characterizing the feasible domain of heterogeneous flexible resource aggregation and the feasible domain of security control based on risk perception according to claim 1, characterized in that: Steps 7 to 9 involve using the node risk impact coefficient to correct the boundary of the heterogeneous flexible resource economic mobilization domain and introducing physical security constraints to obtain the safe control feasible domain. Specifically, this includes the following steps: S71: Constructing a Risk-Modified Individual Feasible Domain For access nodes Heterogeneous and flexible resources, utilizing node risk impact coefficients Adjust its economic callable boundary: in, Representing resources Risk reduction factor; Resources are thus obtained Risk-corrected single-unit feasible domain: S72: Constructing a risk-corrected aggregate feasible region For resource collections The risk-corrected individual feasible regions of each resource node are aggregated to obtain the risk-corrected aggregated feasible region: in: S73: Set safe operation constraints Based on the requirements for safe operation of the distribution network, node voltage constraints, line load rate constraints, transformer load rate constraints, and reverse power flow constraints are set: in, These represent the node voltage, line load rate, transformer load rate, and upstream grid switching power after resource adjustment, respectively. S74: Constructing a feasible domain for security control Based on the aforementioned safety operation constraints, the risk-corrected aggregated feasible region is subjected to safety screening or boundary shrinking to obtain the safety control feasible region: The feasible region for one-dimensional active power safety control is represented as: in, This represents the minimum active power regulation boundary that satisfies safety constraints. This represents the maximum active power regulation boundary that satisfies safety constraints. S75: Calculate the feasible region width For the physical aggregation feasible region, the risk-corrected aggregation feasible region, and the security control feasible region, calculate the feasible region width separately: S76: Calculate the feasible region shrinkage rate Using the resource physical aggregation feasible region as a benchmark, calculate the risk-corrected feasible region shrinkage rate and the safety control feasible region shrinkage rate: in, This represents the rate of contraction of the feasible region due to risk correction. This represents the final shrinkage rate of the feasible region for safety control due to safety constraints. S77: Forming feasible region evaluation results For different operational risk states, the system outputs the upper and lower boundaries of the physical aggregate feasible region, the upper and lower boundaries of the risk-corrected aggregate feasible region, the upper and lower boundaries of the safety control feasible region, the feasible region width, and the feasible region shrinkage rate, forming an evaluation result of the safe and callable capability of heterogeneous flexible resources.

8. A risk-aware heterogeneous flexible resource aggregation feasible domain and security control feasible domain characterization system based on the method of any one of claims 1-7, comprising a data acquisition and preprocessing module, a topology and spatiotemporal state construction module, a risk scoring construction module, a physical feasible domain construction module, a callable domain construction module, a risk correction and aggregation module, a security control feasible domain construction module, and a feasible domain evaluation module, characterized in that: The data acquisition and preprocessing module is used to acquire data on the distribution network topology, operating status, and heterogeneous flexible resource operating status, and to perform missing value processing, time alignment, and standardization. The topology and spatiotemporal state construction module is used to construct a normalized topological adjacency matrix based on node connection relationships and organize the node running states at consecutive times into node-level spatiotemporal state tensors. The risk scoring construction module is used to construct node risk sensitivity and node risk impact coefficient based on the results of operational risk identification, risk probability, risk severity, and degree of deviation of node status. The Physically Feasible Domain Construction Module is used to construct the physically feasible domain of heterogeneous flexible resources based on the physical operational constraints of energy storage, electric vehicles, and flexible loads. The callable domain construction module is used to modify the callability of the physical feasible domain based on the resource participation adjustment intention or market incentive response parameters, and to construct heterogeneous flexible resource callable domains. The risk correction and aggregation module is used to correct the boundaries of the callable domain using the node risk impact coefficient, and to aggregate multiple types of heterogeneous flexible resources to obtain the risk correction and aggregation feasible domain. The safe control feasible region construction module is used to perform safety screening or boundary shrinking on the risk-modified aggregated feasible region based on physical safety constraints such as node voltage, line load rate, transformer load rate and upstream power grid exchange power to obtain the safe control feasible region. The feasible region evaluation module is used to calculate the upper and lower boundaries of the feasible region, the width of the feasible region, and the feasible region shrinkage rate, and to evaluate the safe callability of heterogeneous flexible resources under different operational risk states.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

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