Electric power operation mode intelligent arrangement and risk prevention and control method under mass new energy access

Abstract

The invention discloses a power operation mode intelligent arrangement and risk prevention and control method under mass new energy access, and relates to the technical field of power system operation control, and the method comprises the steps: constructing a main distribution network architecture data model, carrying out the fusion processing of dynamic data of different voltage levels and professional attributes, and generating a system operation state model; based on the system operation state model, topological structure analysis is executed, and a network connection relation model is established; and calculating a new energy access optimization scheme according to the network connection relation model, and generating an operation mode arrangement result and a risk prevention and control strategy. According to the method, the problems of data islands and inaccurate topology identification in the prior art are effectively solved. By constructing the multi-objective optimization model, the new energy access capacity can be scientifically evaluated, the optimal operation scheme can be generated, and the operation efficiency, safety and reliability of a power system under the condition of massive new energy access are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation control, and specifically to an intelligent scheduling and risk prevention and control method for power operation modes under massive new energy access. Background Art

[0002] The global energy transformation is accelerating, and the new power system is entering a critical development stage with a high proportion of new energy access. As an important carrier for the access of new energy, the operation mode of the distribution network is undergoing profound changes. The access of large-scale distributed new energy has caused the distribution network to change from the traditional one-way radial structure to a two-way power flow and ring network structure, and the boundary between the main network and the distribution network is becoming increasingly blurred. At present, the research on the coordinated operation of the main and distribution networks is mainly focused on voltage control and power dispatching, but there are still many technical difficulties in the fields of dynamic reconstruction of grid structure and intelligent scheduling of operation mode. The existing main and distribution network collaborative analysis methods are mostly based on static topological structures, which makes it difficult to accurately characterize the dynamic characteristics of the network caused by fluctuations in the output of new energy; conventional operation mode scheduling technology mainly relies on expert experience and lacks a systematic optimization decision-making mechanism.

[0003] In the scenario of large-scale access to new energy, the operation of the distribution network presents highly dynamic and uncertain characteristics. The random fluctuations in the output of new energy lead to frequent changes in the distribution of network currents, which puts higher requirements on the operation mode arrangement. The mutual influence between the main network and the distribution network is becoming increasingly profound, and the optimization control at a single network level is difficult to achieve the global optimum. Existing technologies have obvious deficiencies in dealing with the volatility of new energy and the coordination of the main and distribution networks: inaccurate perception of the operating status, untimely identification of the topological structure, and low optimization decision-making efficiency, which seriously restrict the improvement of the operating efficiency of the distribution network. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for intelligent scheduling and risk prevention of power operation modes under the access of massive new energy sources, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent scheduling and risk prevention and control of power operation modes under massive new energy access, comprising: constructing a main distribution network architecture data model, fusing dynamic data of different voltage levels and professional attributes, and generating a system operation status model; the system operation status model includes network topology information and equipment operation parameters; based on the system operation status model, performing topology analysis and establishing a network connection relationship model; the network connection relationship model is used to characterize the electrical connection between each node of the main distribution network; according to the network connection relationship model, calculating the new energy access optimization plan, generating an operation mode scheduling result and a risk prevention and control strategy; the operation mode scheduling result is used to guide system operation scheduling.

[0007] As an optimal solution for the method for intelligent orchestration and risk prevention and control of power operation modes under the access of massive new energy sources described in the present invention, the process of generating a system operation status model includes the following steps: collecting voltage level data, grid structure data and equipment operation data of the main network and distribution network; performing topological mapping on the voltage level data and the grid structure data to generate network topology information; performing time synchronization processing on the equipment operation data to generate equipment operation parameters; and combining the network topology information and the equipment operation parameters to construct a system operation status model.

[0008] As an optimal solution of the method for intelligent orchestration and risk prevention and control of power operation modes under the access of massive new energy sources described in the present invention, the network topology information and the equipment operation parameters are combined to construct a system operation status model, specifically: a network topology information mapping table for the main network and the distribution network is established, the network topology information mapping table includes primary equipment identification data, primary equipment connection relationship data and primary equipment status data; an equipment operation data mapping table for the main network and the distribution network is established, the equipment operation data mapping table includes primary equipment electrical quantity data, primary equipment operating condition data and primary equipment status identification data; the network topology information mapping table and the equipment operation data mapping table are matched and associated according to the equipment identification to generate a system operation status model.

[0009] Among them, the device identification matching and association method is to establish a mapping relationship through the physical code in the primary device identification data, and to correspond the primary device connection relationship data in the network topology information mapping table with the electrical quantity data and operating condition data in the device operation data mapping table one by one.

[0010] As a preferred solution of the method for intelligent orchestration and risk prevention and control of power operation modes under the access of massive new energy sources described in the present invention, the construction process of the network connection relationship model includes the following steps: constructing an initial topology connection matrix based on the primary equipment connection relationship data in the system operation status model; the row and column identifiers of the initial topology connection matrix are the primary equipment physical codes, and the matrix elements are the electrical connection type codes; in the electrical connection type code, the series connection is assigned a value of 1, the parallel connection is assigned a value of 2, the T-connection is assigned a value of 3, and the no connection is assigned a value of 0; a matrix mapping relationship is established based on the starting end device code and the ending end device code in the primary equipment connection relationship data; the initial topology connection matrix is ​​hierarchically traversed based on the depth-first search algorithm to establish a voltage level hierarchical connection relationship table, and the connection topology between devices of each voltage level is recorded through the voltage level hierarchical connection relationship table; connectivity analysis is performed on the voltage level hierarchical connection relationship table to identify key connection nodes and establish a network connection relationship model.

[0011] As an optimal solution for the method for intelligent orchestration and risk prevention and control of power operation modes under the access of massive new energy sources described in the present invention, a depth-first search algorithm with voltage level constraints is adopted, and the traversal rules are as follows: if the current node is a transformer node, the voltage level value at both ends of the transformer is recorded, and a cross-layer connection identifier is established; if the current node is a T-connected node and there are three or more connecting branches, the T-connected node is marked as a key connection point; if a closed-loop path of the same voltage level is searched, a loop node load assessment is performed; if it is detected that the equipment operating status is maintenance or failure, the connection relationship of the node is temporarily cut off and the traversal is performed again; through the above traversal rules, a voltage level hierarchical connection relationship table containing node connection relationships, voltage level associations, and key node identifiers is generated.

[0012] As an optimal solution for the method of intelligent orchestration and risk prevention and control of power operation modes under the access of massive new energy sources described in the present invention, the loop node load evaluation includes: calculating the comprehensive load index of each node in the loop, and the comprehensive load index includes a first load indicator, a second load indicator, a third load indicator, and a fourth load indicator.

[0013] Among them, the first load indicator is the ratio of the node active power to the total active power of the loop; the second load indicator is the ratio of the number of node connection branches to the total number of loop branches; the third load indicator is the absolute value of the relative deviation between the node voltage and the nominal voltage; the fourth load indicator is the ratio of the node cross-over power to the rated capacity of the branch.

[0014] After normalizing the first load index, the second load index, the third load index, and the fourth load index, a comprehensive load index is calculated according to a preset weight; based on the comprehensive load index, a cut-off point is selected in order of priority, specifically, if it belongs to the first priority, the node with the largest comprehensive load index is selected; if it belongs to the second priority, when there are multiple nodes with the same comprehensive load index, the node with the largest second load index is selected; if it belongs to the third priority, when the second load indicators are the same, the node with the largest fourth load indicator is selected; if the loop contains a new energy access node, a new energy access evaluation is performed: the output fluctuation rate of the new energy access node is calculated; the load node group adjacent to the new energy access node is identified; the load characteristic index of the load node group is calculated; and the node with the smallest load characteristic index and meeting the system stability requirements is selected as the cut-off point in the load node group.

[0015] Among them, the output fluctuation rate is calculated by the ratio of the standard deviation of the new energy output to the average value; the load characteristic indicators include load power factor, load average utilization rate, and load response capability; the system stability requirements include voltage stability margin and rationality of power flow distribution.

[0016] As a preferred solution of the method for intelligent scheduling and risk prevention and control of power operation modes under massive renewable energy access described in the present invention, wherein: according to the network connection relationship model, the new energy access optimization plan is calculated, and the operation mode scheduling results and risk prevention and control strategies are generated, including the following steps: based on the distribution of key connection nodes in the network connection relationship model, the power flow distribution characteristics and voltage distribution characteristics of the new energy access point are calculated, and the new energy access capacity assessment results are generated; a multi-objective optimization model including system stability margin indicators and economic operation indicators is constructed, and the optimal output plan and network reconstruction plan of each new energy access point are calculated respectively; according to the combined score of the stability margin indicator and the economic operation indicator, the optimal output plan and the network reconstruction plan are sorted, and the operation mode scheduling results and risk prevention and control strategies are output.

[0017] To further solve the above technical problems, the present invention provides the following technical solutions: an intelligent orchestration and risk prevention and control system for power operation modes under massive new energy access, including: a data fusion module, used to construct a main distribution network architecture data model, fuse dynamic data of different voltage levels and professional attributes, and generate a system operation status model; a topology identification module, used to perform topology structure analysis based on the system operation status model, and establish a network connection relationship model; a solution optimization module, used to calculate the new energy access optimization plan according to the network connection relationship model, and generate an operation mode orchestration result and a risk prevention and control strategy.

[0018] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of massive new energy sources are implemented as described above.

[0019] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of massive new energy sources as described above are implemented.

[0020] Beneficial effects of the present invention: The present invention achieves significant technical effects through the three core links of data fusion, topology identification and scheme optimization: the data fusion module realizes the fusion processing of dynamic data of different voltage levels and different professional attributes by establishing a main distribution network architecture data model, overcomes the data island problem in traditional technology, and improves the accuracy and real-time performance of system operation status perception; the topology identification module performs hierarchical traversal based on the depth-first search algorithm, and establishes a network connection relationship model containing voltage level constraints, which can more accurately identify and characterize the complex electrical connections between nodes of the main distribution network compared with the existing technology; the scheme optimization module constructs a multi-objective optimization model, comprehensively considers system stability and economic operation indicators, and can not only scientifically evaluate the new energy access capacity, but also generate the optimal output plan and network reconstruction plan in a targeted manner, effectively solving the problem that traditional empirical decision-making methods are difficult to cope with the system operation optimization in the scenario of massive new energy access. Overall, the present invention realizes the intelligence of the entire process from data processing to decision optimization, and significantly improves the operating efficiency and safety and reliability of the power system under the conditions of large-scale new energy access. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 This is a schematic diagram of the overall process of the method for intelligent arrangement of power operation modes and risk prevention and control under massive renewable energy access proposed by the present invention; Figure 2 This is a diagram of computer equipment in the method for intelligent scheduling and risk prevention of power operation modes under massive renewable energy access proposed in the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for intelligent scheduling and risk prevention and control of power operation modes under massive new energy access, including the following contents: S1: Build a data model for the main distribution network architecture, integrate dynamic data of different voltage levels and professional attributes, and generate a system operation status model.

[0026] Specifically, the system operation status model includes network topology information and device operation parameters.

[0027] S1.1: Collect voltage level data, grid structure data and equipment operation data of the main grid and distribution grid.

[0028] Specifically, the voltage level data includes the AC 110kV, 220kV, and 500kV voltage level node data of the main grid, and the AC 10kV and 35kV voltage level node data of the distribution network; the node data includes voltage level attribute identification, voltage amplitude data, and phase angle data.

[0029] The equipment operation data includes the active power data, reactive power data, current data, equipment temperature data, and equipment status data of the primary equipment; the equipment status data includes the operation status identification, maintenance status identification, fault status identification, and lockout status identification.

[0030] The grid structure data includes the circuit breaker status data, disconnector status data, switch status data, interconnection switch status data, and primary equipment connection relationship data of the main network and distribution network; the primary equipment includes transformers, busbars, lines, capacitors, and reactors.

[0031] S1.2: Perform topological mapping on the voltage level data and the grid structure data to generate network topological structure information; perform time synchronization processing on the equipment operation data to generate equipment operation parameters.

[0032] S1.3: Combine network topology information with equipment operation parameters to construct a system operation status model.

[0033] S1.3.1: Establish a network topology information mapping table for the main network and the distribution network. The network topology information mapping table includes primary device identification data, primary device connection relationship data, and primary device status data.

[0034] Specifically, the primary equipment identification data includes the equipment physical code, equipment type code, and equipment voltage level code; the primary equipment connection relationship data includes the starting end equipment code, the ending end equipment code, and the electrical connection type code, wherein the electrical connection type code identifies series connection, parallel connection, and T-connection.

[0035] S1.3.2: Establish a mapping table of equipment operation data between the main network and the distribution network. The equipment operation data mapping table includes primary equipment electrical quantity data, primary equipment operating condition data, and primary equipment status identification data.

[0036] Specifically, the electrical quantity data of primary equipment includes active power telemetry value, reactive power telemetry value, current effective value, voltage effective value, and power factor value; the operating condition data of primary equipment includes equipment load rate value, equipment temperature value, and equipment vibration frequency value. The status data of primary equipment records the closing state, opening state, and grounding state of the switchgear; the status identification data of primary equipment records the operation identification, maintenance identification, fault identification, and locking identification, among which the operation identification includes normal operation code, overload operation code, and economic operation code.

[0037] S1.3.3: The network topology information mapping table and the equipment operation data mapping table are matched and associated according to the equipment identification to generate a system operation status model.

[0038] Specifically, the device identification matching association method is to establish a mapping relationship through the physical code in the primary device identification data, and to correspond the primary device connection relationship data in the network topology information mapping table with the electrical quantity data and operating condition data in the device operation data mapping table one by one.

[0039] Preferably, in S1.3, the deep integration of the main distribution network data is achieved by establishing a dual mapping table structure. Among them, the network topology information mapping table adopts a three-level identification system of physical coding-type coding-voltage level coding to ensure the unique identification of equipment at different voltage levels; the equipment operation data mapping table adopts a hierarchical storage structure of electrical quantity-operating condition-state identification to achieve the association mapping of dynamic data. In particular, the electrical connection type coding introduces the identification mechanism of three connection modes: series, parallel, and T-connection, which solves the problem of difficult topology construction caused by the diversified connection modes of the main distribution network equipment; the operation identification is subdivided into three operation codes: normal, overload, and economic, providing data support for subsequent optimization and regulation. Finally, the one-to-one mapping mechanism established by physical coding not only solves the association problem of heterogeneous data in the main distribution network, but also realizes the dynamic coupling of topological structure and operation status, providing a complete data basis for system status evaluation. Compared with the traditional single data table storage method, this data fusion method based on dual mapping tables improves data retrieval efficiency, reduces storage redundancy, and ensures data consistency.

[0040] It should be noted that S1 realizes the state perception of equipment at different voltage levels through three-layer data collection and two-level mapping conversion. At the data collection layer, the voltage level data, grid structure data and equipment operation data of the main network 110kV-500kV and the distribution network 10kV-35kV are classified and collected to build a complete data source system; at the mapping conversion layer, the topological association problem and data timing inconsistency problem between the main and distribution network equipment are solved through two parallel processing paths of topological mapping and time synchronization; at the model construction layer, a dual mapping table structure is used to achieve deep data fusion, in which the network topology information mapping table adopts a three-level identification system to ensure the uniqueness of equipment identification, and the equipment operation data mapping table adopts a hierarchical storage structure to achieve dynamic data association. Through the processing link of collection-mapping-fusion, this solution not only solves the fusion problem of heterogeneous data in the main and distribution networks, but also realizes the unified representation of the status of equipment at different voltage levels, laying a data foundation for subsequent situational awareness and collaborative control. Compared with the traditional regional independent processing method, it improves data processing efficiency and enhances the integrity of status monitoring.

[0041] S2: Based on the system operation status model, perform topology analysis and establish a network connection relationship model.

[0042] Step S2 aims to solve the technical problem of dynamic identification of topological structure in distribution networks with high penetration of new energy. The existing technologies mainly have the following shortcomings: First, the traditional topological analysis method only considers static connection relationships and cannot adapt to the dynamic characteristics of the network caused by fluctuations in the output of new energy; second, the conventional cut-off point selection method relies too much on a single power indicator and is difficult to meet the requirements of safe and stable operation of new energy distribution networks; third, the existing key node identification method has low computational efficiency and is difficult to meet the requirements of real-time analysis of large-scale distribution networks.

[0043] Specifically, the network connection relationship model is used to characterize the electrical connection between nodes in the main distribution network.

[0044] S2.1: Based on the primary equipment connection relationship data in the system operation status model, an initial topology connection matrix is ​​constructed; the row and column identifiers of the initial topology connection matrix are the physical codes of the primary equipment, and the matrix elements are the electrical connection type codes.

[0045] Specifically, in the electrical connection type coding, the series connection is assigned a value of 1, the parallel connection is assigned a value of 2, the T-connection node is assigned a value of 3, and no connection is assigned a value of 0; a matrix mapping relationship is established based on the starting end device code and the ending end device code in the primary device connection relationship data.

[0046] S2.2: Based on the depth-first search algorithm, the initial topology connection matrix is ​​hierarchically traversed to establish a voltage level hierarchical connection relationship table, and the connection topology between devices of each voltage level is recorded through the voltage level hierarchical connection relationship table.

[0047] Specifically, a depth-first search algorithm with voltage level constraints is used, and the traversal rules are as follows: If the current node is a transformer node, the voltage level value at both ends of the transformer is recorded and a cross-layer connection mark is established; If the current node is a T-junction node and there are three or more connecting branches, the T-junction node is marked as a key connection point; If a closed loop path to the same voltage level is searched, the loop node load evaluation is performed: 1. Calculate the comprehensive load index of each node in the loop.

[0048] The comprehensive load index includes the first load index, the second load index, the third load index, and the fourth load index. The first load index is the ratio of the node active power to the total active power of the loop; the second load index is the ratio of the number of node connected branches to the total number of loop branches; the third load index is the absolute value of the relative deviation between the node voltage and the nominal voltage; the fourth load index is the ratio of the node through power to the rated capacity of the branch.

[0049] 2. After normalizing the first load index, the second load index, the third load index, and the fourth load index, a comprehensive load index is calculated according to a preset weight.

[0050] 3. Based on the comprehensive load index, select the cuttable points in order of priority. Specifically, if it belongs to the first priority, select the node with the largest comprehensive load index; if it belongs to the second priority, when there are multiple nodes with the same comprehensive load index, select the node with the largest second load index; if it belongs to the third priority, when the second load indexes are the same, select the node with the largest fourth load index.

[0051] 4. If the loop contains a new energy access node, perform a new energy access assessment: first calculate the output fluctuation rate of the new energy access node; secondly identify the load node group adjacent to the new energy access node; then calculate the load characteristic index of the load node group; finally, select the node with the smallest load characteristic index and that meets the system stability requirements in the load node group as the cut-off point.

[0052] Specifically, the disconnectable point refers to a node whose electrical connection can be disconnected to achieve network structure optimization under the premise of ensuring power supply reliability and economy. Specifically include: 1) Load-side disconnection point: a loop node determined based on comprehensive load index evaluation, which will not cause load power outage after disconnection; 2) Cut-off points on the new energy side: low-load characteristic nodes that are adjacent to the new energy access points and meet the stability requirements. After being cut off, the impact of new energy output fluctuations on the system can be reduced; 3) Cut-off points adjacent to maintenance fault nodes: Healthy operation nodes adjacent to maintenance or faulty equipment, used to achieve fault isolation and power supply restoration.

[0053] Among them, the output fluctuation rate is calculated by the ratio of the standard deviation of the new energy output to the average value; the load characteristic indicators include load power factor, load average utilization rate, and load response capability; the system stability requirements include voltage stability margin and rationality of power flow distribution.

[0054] If it is detected that the equipment is in maintenance or fault status, the connection relationship of the node is temporarily cut off and the traversal is performed again.

[0055] Through the above traversal rules, a voltage level hierarchical connection relationship table including node connection relationships, voltage level associations, and key node identifiers is generated.

[0056] It should be noted that the node connection relationship includes the electrical connection type, connection path and voltage level information between each node; the voltage level association indicates the cross-layer connection relationship between devices of different voltage levels; the key node identifier is used to distinguish between ordinary nodes and key connection nodes.

[0057] S2.3: Perform connectivity analysis on the voltage level hierarchical connection relationship table, identify key connection nodes, and establish a network connection relationship model.

[0058] Specifically, the shortest connection path between nodes is calculated based on the depth-first search algorithm, and substation nodes, T-junction nodes, and ring network nodes are identified as key connection nodes. The adjacency list structure is used to store the topological connection relationship between each key connection node to form a network connection relationship model.

[0059] Further, key connection nodes: 1) Substation node: a hub node that has voltage conversion and power distribution functions and connects networks of different voltage levels; 2) T-node: a branch node with three or more connected branches in the same voltage level network; 3) Ring network node: a connection node that forms a closed loop structure in a network of the same voltage level.

[0060] Preferably, step S2 first uses a depth-first search algorithm based on voltage level constraints to perform hierarchical traversal, and combined with the hierarchical mapping mechanism of the initial topological connection matrix, the cross-layer connection relationship between devices of each voltage level can be accurately identified, laying the foundation for subsequent analysis. Secondly, the indicators such as the proportion of active power of the node, the proportion of the number of branch connections, the voltage deviation and the proportion of crossing power are normalized and comprehensively evaluated. By setting a three-level priority selection mechanism, the key cut-off points in the network can be effectively identified. In particular, when dealing with new energy access scenarios, by introducing a joint constraint mechanism of output fluctuation rate and load characteristic indicators, the impact of cut-off point selection on system stability can be accurately evaluated. The present invention introduces an evaluation system based on a multi-dimensional comprehensive load index, combined with a joint constraint mechanism of new energy output fluctuation rate and load characteristic indicators, which can effectively reduce the frequency of network reconstruction caused by new energy fluctuations and improve system operation stability. In the topological analysis process, the adjacency list structure is used to store the connection relationship, which can effectively reduce data redundancy and significantly improve the computational efficiency of depth-first search compared to the traditional matrix storage method. The application of the present invention in a new energy high penetration distribution network shows that the present invention can adapt to the dynamic characteristics of the network, realize rapid identification and accurate analysis of the topological structure, and provide effective support for the safe and reliable operation of the distribution network. It should be noted that the performance improvement of the present invention is mainly reflected in the specific application scenario of the new energy distribution network. In actual application, the relevant parameters still need to be reasonably adjusted according to the specific situation.

[0061] S3: Based on the network connection relationship model, calculate the new energy access optimization plan, generate the operation mode arrangement results and risk prevention and control strategies.

[0062] Specifically, the operation mode arrangement results are used to guide system operation scheduling.

[0063] S3.1: Based on the distribution of key connection nodes in the network connection relationship model, calculate the power flow distribution characteristics and voltage distribution characteristics of the new energy access point to generate the new energy access capacity assessment results.

[0064] Specifically, the power flow distribution characteristics are characterized by the node power transmission path, line load rate, and node injection power; the voltage distribution characteristics are characterized by the node voltage amplitude, phase angle difference, and voltage sensitivity. Among them, the calculation formula for node injection power is: active power = node voltage × node current × power factor, and the power factor is the ratio of the active power to the apparent power of the corresponding node. The line load rate is calculated by the ratio of the actual transmission power to the rated capacity, and the voltage sensitivity is calculated by the ratio of the node voltage change to the power change.

[0065] S3.2: Construct a multi-objective optimization model that includes system stability margin indicators and economic operation indicators, and calculate the optimal output plan and network reconstruction plan for each new energy access point.

[0066] Specifically, the system stability margin indicators include voltage stability margin, power transmission margin and transient stability margin; the economic operation indicators include network loss rate, equipment utilization rate and load transfer rate.

[0067] Among them, the voltage stability margin is calculated by the difference between the critical point load level and the current load level, the network loss rate is calculated by the ratio of line active loss to input active power, and the equipment utilization rate is calculated by the ratio of actual load to rated capacity.

[0068] S3.3: According to the combined scores of stability margin index and economic operation index, the optimal output plan and network reconstruction plan are ranked, and the operation mode arrangement results and risk prevention and control strategies are output.

[0069] Specifically, the operation mode arrangement results include new energy output adjustment instructions and network topology reconstruction instructions; risk prevention and control strategies include key node monitoring instructions and emergency response instructions. Among them, the combined score is calculated by the weighted sum of the stability margin index score and the economic operation index score, and the weighted coefficient is determined based on the hierarchical analysis method; the stability margin index score is calculated by the ratio of the system stability margin to the benchmark value, and the economic operation index score is calculated by normalizing the network loss rate, equipment utilization rate, and load transfer rate.

[0070] Preferably, the S3 step of the present invention solves the following technical problems through the three links of new energy access capacity assessment, multi-objective optimization modeling and combined scoring and sorting: the decision-making of adjusting the operation mode of the power grid under the condition of large-scale new energy grid connection is complex, the optimization objectives are multi-dimensional and conflicting, and the system stability and economy are difficult to balance. This scheme unifies the flow distribution characteristics and voltage distribution characteristics into the access capacity assessment link for the first time, and adopts the power factor calculation node to inject power, which improves the accuracy of new energy access assessment; by establishing a multi-objective optimization model including stability margin and economy, the coordination and unification of safe and stable operation of the system and economic benefits are achieved; the optimization scheme is sorted based on the combined scoring method of normalization processing, which overcomes the evaluation bias caused by the inconsistent dimensions of various indicators in the traditional method. In practical applications, compared with the existing technology, this scheme can more accurately assess the new energy access capacity, more reasonably balance the system stability and economy, provide more reliable decision support for power grid dispatching and operation, and effectively reduce the system operation risk brought by large-scale new energy access.

[0071] In summary, the present invention achieves significant technical effects through the three core links of data fusion, topology identification and scheme optimization: the data fusion module realizes the fusion processing of dynamic data of different voltage levels and different professional attributes by establishing the main distribution network architecture data model, overcomes the data island problem in traditional technology, and improves the accuracy and real-time performance of system operation status perception; the topology identification module performs hierarchical traversal based on the depth-first search algorithm, establishes a network connection relationship model containing voltage level constraints, and can more accurately identify and characterize the complex electrical connections between nodes of the main distribution network compared with the existing technology; the scheme optimization module constructs a multi-objective optimization model, comprehensively considers the system stability margin and economic operation indicators, and can not only scientifically evaluate the new energy access capacity, but also generate the optimal output plan and network reconstruction plan in a targeted manner, effectively solving the problem that traditional empirical decision-making methods are difficult to cope with the system operation optimization in the scenario of massive new energy access. Overall, the present invention realizes the full process intelligence from data processing to decision optimization, and significantly improves the operation efficiency and safety reliability of the power system under the conditions of large-scale new energy access.

[0072] Embodiment 2 is an embodiment of the present invention, which provides an intelligent scheduling and risk prevention and control system for power operation mode under massive new energy access, including: Data fusion module, used to build the main distribution network architecture data model, fuse the dynamic data of different voltage levels and professional attributes, and generate the system operation status model; A topology identification module is used to perform topology structure analysis and establish a network connection relationship model based on the system operation status model; The solution optimization module is used to calculate the new energy access optimization plan based on the network connection relationship model, and generate the operation mode arrangement results and risk prevention and control strategies.

[0073] Example 3, reference Figure 2 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0074] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0075] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0076] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Intelligent arrangement and risk prevention and control method of power operation mode under massive new energy access, characterized by: include: Build a data model for the main distribution network architecture, integrate dynamic data of different voltage levels and professional attributes, and generate a system operation status model; The system operation status model includes network topology information and equipment operation parameters; Based on the system operation status model, perform topology analysis and establish a network connection relationship model; The network connection relationship model is used to characterize the electrical connection between nodes of the main distribution network; According to the network connection relationship model, calculate the new energy access optimization plan, generate the operation mode arrangement result and risk prevention and control strategy; The operation mode arrangement result is used to guide the system operation scheduling.

2. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 1, characterized in that: The process of generating a system operation status model includes the following steps: Collect voltage level data, grid structure data and equipment operation data of the main grid and distribution grid; Performing topological mapping on the voltage level data and the grid structure data to generate network topological structure information; performing time synchronization processing on the equipment operation data to generate equipment operation parameters; The network topology information and the device operation parameters are combined to construct a system operation status model.

3. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 2, characterized in that: The network topology information and the device operation parameters are combined to construct a system operation status model, specifically: Establishing a network topology information mapping table of the main network and the distribution network, wherein the network topology information mapping table includes primary device identification data, primary device connection relationship data, and primary device status data; Establishing a device operation data mapping table for the main network and the distribution network, wherein the device operation data mapping table includes primary device electrical quantity data, primary device operating condition data, and primary device status identification data; Matching and associating the network topology information mapping table with the device operation data mapping table according to device identification to generate a system operation status model; Among them, the device identification matching and association method is to establish a mapping relationship through the physical code in the primary device identification data, and to correspond the primary device connection relationship data in the network topology information mapping table with the electrical quantity data and operating condition data in the device operation data mapping table one by one.

4. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 3, characterized in that: The construction process of the network connection relationship model includes the following steps: Based on the primary equipment connection relationship data in the system operation status model, an initial topology connection matrix is ​​constructed; the row and column identifiers of the initial topology connection matrix are primary equipment physical codes, and the matrix elements are electrical connection type codes; in the electrical connection type codes, a series connection is assigned a value of 1, a parallel connection is assigned a value of 2, a T-connection is assigned a value of 3, and no connection is assigned a value of 0; a matrix mapping relationship is established based on the starting end equipment code and the terminating end equipment code in the primary equipment connection relationship data; Based on the depth-first search algorithm, the initial topology connection matrix is ​​hierarchically traversed to establish a voltage level hierarchical connection relationship table, and the connection topology between devices of each voltage level is recorded through the voltage level hierarchical connection relationship table; Perform connectivity analysis on the voltage level hierarchical connection relationship table, identify key connection nodes, and establish a network connection relationship model.

5. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 4, characterized in that: A depth-first search algorithm with voltage level constraints is used, and the traversal rules are as follows: If the current node is a transformer node, the voltage level value at both ends of the transformer is recorded and a cross-layer connection mark is established; If the current node is a T-junction node and there are three or more connecting branches, the T-junction node is marked as a key connection point; If a closed-loop path to the same voltage level is searched, loop node load evaluation is performed; If the device is detected to be in maintenance or fault status, the connection relationship of the node is temporarily cut off and the traversal is performed again; Through the above traversal rules, a voltage level hierarchical connection relationship table including node connection relationships, voltage level associations, and key node identifiers is generated.

6. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 5, characterized in that: The loop node load evaluation includes: Calculating a comprehensive load index of each node in the loop, the comprehensive load index comprising a first load index, a second load index, a third load index, and a fourth load index; Among them, the first load index is the ratio of the node active power to the total active power of the loop; the second load index is the ratio of the number of node connection branches to the total number of loop branches; the third load index is the absolute value of the relative deviation between the node voltage and the nominal voltage; the fourth load index is the ratio of the node through power to the branch rated capacity; After normalizing the first load indicator, the second load indicator, the third load indicator, and the fourth load indicator, a comprehensive load index is calculated according to a preset weight; Based on the comprehensive load index, select the cut-off point in order of priority, specifically, if it belongs to the first priority, select the node with the largest comprehensive load index; if it belongs to the second priority, when there are multiple nodes with the same comprehensive load index, select the node with the largest second load index; if it belongs to the third priority, when the second load indexes are the same, select the node with the largest fourth load index; If the loop contains a new energy access node, a new energy access assessment is performed: the output fluctuation rate of the new energy access node is calculated; a load node group adjacent to the new energy access node is identified; a load characteristic index of the load node group is calculated; and a node with the smallest load characteristic index and meeting the system stability requirements is selected from the load node group as a cut-off point; Among them, the output fluctuation rate is calculated by the ratio of the standard deviation of the new energy output to the average value; the load characteristic indicators include load power factor, load average utilization rate, and load response capability; the system stability requirements include voltage stability margin and rationality of power flow distribution.

7. The method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as claimed in claim 6, characterized in that: According to the network connection relationship model, the new energy access optimization plan is calculated to generate the operation mode arrangement result and risk prevention and control strategy, including the following steps: Based on the distribution of key connection nodes in the network connection relationship model, the power flow distribution characteristics and voltage distribution characteristics of the new energy access point are calculated to generate a new energy access capacity assessment result; Construct a multi-objective optimization model that includes system stability margin indicators and economic operation indicators, and calculate the optimal output plan and network reconstruction plan for each new energy access point; According to the combined scores of the stability margin index and the economic operation index, the optimal output plan and the network reconstruction plan are ranked, and the operation mode arrangement results and risk prevention and control strategies are output.

8. A system for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access, based on the method for intelligent arrangement and risk prevention and control of power operation modes under massive renewable energy access as described in any one of claims 1 to 7, characterized in that: include, Data fusion module, used to build the main distribution network architecture data model, fuse the dynamic data of different voltage levels and professional attributes, and generate the system operation status model; A topology identification module, used to perform topology structure analysis and establish a network connection relationship model based on the system operation status model; The solution optimization module is used to calculate the new energy access optimization solution according to the network connection relationship model, and generate the operation mode arrangement result and risk prevention and control strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of massive renewable energy sources as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of massive new energy sources as described in any one of claims 1 to 7 are implemented.

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