Intelligent Orchestration and Risk Prevention and Control Method for Power Operation Modes under the Access of a Large Amount of New Energy

Abstract

The present invention discloses an intelligent scheduling and risk prevention and control method for power operation modes under the access of a large amount of new energy, which relates to the technical field of power system operation control and includes: constructing a main and distribution network architecture data model, performing fusion processing on dynamic data of different voltage levels and professional attributes to generate a system operation state model; based on the system operation state model, performing topological structure analysis to establish a network connection relationship model; according to the network connection relationship model, calculating an optimal new energy access scheme to generate an operation mode scheduling result and a risk prevention and control strategy. The present invention effectively overcomes the problems of data islands and inaccurate topological identification in traditional technologies. By constructing a multi-objective optimization model, the present invention can scientifically evaluate the new energy access capacity and generate an optimal operation scheme, significantly improving the operation efficiency and safety and reliability of the power system under the condition of the access of a large amount of new energy.

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 the access of a large amount of new energy. 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 new energy access, the operation mode of the distribution network is undergoing profound changes. The access of large-scale distributed new energy has transformed the distribution network from a traditional unidirectional radial structure to a two-way power flow and loop 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 mainly focuses on aspects such as voltage control and power dispatching, but there are still many technical problems in fields such as dynamic reconstruction of the grid structure and intelligent scheduling of operation modes. Existing coordinated analysis methods for the main and distribution networks are mostly based on static topological structures and are difficult to accurately describe the network dynamic characteristics caused by the fluctuations in new energy output; conventional operation mode scheduling technologies mainly rely on expert experience and lack a systematic optimization decision-making mechanism.

[0003] In the scenario of large-scale new energy access, the operation of the distribution network shows highly dynamic and uncertain characteristics. The random fluctuations in new energy output lead to frequent changes in the network power flow distribution, posing higher requirements for the scheduling of operation modes. The mutual influence between the main network and the distribution network is deepening day by day, and it is difficult to achieve global optimality through the optimization control of a single network level. Existing technologies have obvious deficiencies in dealing with new energy volatility, main and distribution network coordination, etc.: problems such as inaccurate operation state perception, untimely topological structure identification, and low optimization decision-making efficiency seriously restrict the improvement of the operation efficiency of the distribution network. Summary of the Invention

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

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

[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 the access of a large amount of new energy, including: constructing a main and distribution network architecture data model, performing fusion processing on dynamic data of different voltage levels and professional attributes to generate a system operation state model; the system operation state model includes network topology structure information and equipment operation parameters; based on the system operation state model, performing topology structure analysis to establish a network connection relationship model; the network connection relationship model is used to represent the electrical connection between each node of the main and distribution networks; according to the network connection relationship model, calculating an optimized new energy access plan to generate 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 a preferred embodiment of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of a large amount of new energy according to the present invention, the process of generating the system operation state model includes the following steps: collecting voltage level data, grid structure data, and equipment operation data of the main network and the distribution network; performing topology mapping on the voltage level data and the grid structure data to generate network topology structure information; performing time synchronization processing on the equipment operation data to generate equipment operation parameters; combining the network topology structure information and the equipment operation parameters to construct a system operation state model.

[0008] As a preferred embodiment of the method for intelligent scheduling and risk prevention and control of power operation modes under the access of a large amount of new energy according to the present invention, combining the network topology structure information and the equipment operation parameters to construct a system operation state model specifically includes: establishing a network topology structure information mapping table for the main network and the distribution network, the network topology structure information mapping table including primary equipment identification data, primary equipment connection relationship data, and primary equipment status data; establishing an equipment operation data mapping table for the main network and the distribution network, the equipment operation data mapping table including primary equipment electrical quantity data, primary equipment working condition data, and primary equipment status identification data; associating the network topology structure information mapping table and the equipment operation data mapping table according to equipment identification matching to generate a system operation state model.

[0009] Among them, the method of the equipment identification matching association is to establish a mapping relationship through the physical coding in the primary equipment identification data, and make the primary equipment connection relationship data in the network topology structure information mapping table correspond one by one to the electrical quantity data and the working condition data in the equipment operation data mapping table.

[0010] As a preferred solution of the intelligent scheduling and risk prevention and control method for power operation mode under the access of a large amount of new energy according to the present invention, wherein: 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 state model, an initial topology connection matrix is constructed; the row and column identifiers of the initial topology connection matrix are the physical codes of primary equipment, and the matrix elements are electrical connection type codes; in the electrical connection type codes, the series connection is assigned a value of 1, the parallel connection is assigned a value of 2, the T-junction 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 start-end equipment code and the end-end equipment code in the primary equipment connection relationship data; the initial topology connection matrix is traversed layer by layer based on the depth-first search algorithm, and a voltage level hierarchical connection relationship table is established, and the connection topology between equipment of each voltage level is recorded through the voltage level hierarchical connection relationship table; a 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 a preferred solution of the intelligent scheduling and risk prevention and control method for power operation mode under the access of a large amount of new energy according to the present invention, wherein: the 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 values at both ends of the transformer are recorded, and a cross-layer connection identifier is established; if the current node is a T-junction node and there are three or more connection branches, the T-junction 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 operation state is maintenance or failure, the connection relationship of this node is temporarily cut off and traversed 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.

[0012] As a preferred solution of the intelligent scheduling and risk prevention and control method for power operation mode under the access of a large amount of new energy according to the present invention, wherein: the loop node load assessment includes: calculating the comprehensive load index of each node in the loop, and the comprehensive load index includes a first load index, a second load index, a third load index, and a fourth load index.

[0013] Among them, the first load index is the ratio of the active power of the node to the total active power of the loop; the second load index is the ratio of the number of connection branches of the node to the total number of branches of the loop; the third load index is the absolute value of the relative deviation of the node voltage from the nominal voltage; the fourth load index is the ratio of the node's crossing 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, calculate the comprehensive load index according to the preset weight; based on the comprehensive load index, select the cut-off points in the 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 new energy access nodes, perform new energy access evaluation: calculate the output power volatility of the new energy access nodes; identify the load node groups adjacent to the new energy access nodes; calculate the load characteristic indexes of the load node groups; select the node with the smallest load characteristic index and meeting the system stability requirements in the load node groups as the cut-off point.

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

[0016] As a preferred solution of the intelligent scheduling and risk prevention and control method for power operation mode under massive new energy access of the present invention, it includes: according to the network connection relationship model, calculate the new energy access optimization plan, and generate the operation mode scheduling result and the risk prevention and control strategy, including the following steps: 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 points, and generate the new energy access capacity evaluation result; construct a multi-objective optimization model including the system stability margin index and the economic operation index, and calculate the optimal output power plan and network reconstruction plan for each new energy access point respectively; sort the optimal output power plan and the network reconstruction plan according to the combined score of the stability margin index and the economic operation index, and output the operation mode scheduling result and the risk prevention and control strategy.

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

[0018] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of the intelligent arrangement and risk prevention and control method for the power operation mode under the access of the above-mentioned massive new energy are realized.

[0019] A computer-readable storage medium stores a computer program thereon. It is characterized in that when the computer program is executed by a processor, the steps of the intelligent arrangement and risk prevention and control method for the power operation mode under the access of the above-mentioned massive new energy are realized.

[0020] Advantages of the present invention: The present invention achieves remarkable technical effects through three core links of data fusion, topology recognition, and scheme optimization: The data fusion module realizes the fusion processing of dynamic data with different voltage levels and different professional attributes by establishing a main and distribution network architecture data model, overcomes the data island problem in traditional technologies, and improves the accuracy and real-time performance of system operation state perception; The topology recognition module performs hierarchical traversal based on the depth-first search algorithm and establishes a network connection relationship model including voltage level constraints, which can more accurately identify and characterize the complex electrical connections between each node of the main and distribution networks compared with the prior art; The scheme optimization module constructs a multi-objective optimization model, comprehensively considers the system stability and economic operation indicators, can not only scientifically evaluate the new energy access capacity, but also specifically generate the optimal output scheme and network reconstruction scheme, effectively solving the problem that the traditional empirical decision-making method is 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 condition 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 drawings required to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0022] Figure 1 It is a schematic diagram of the overall process of the intelligent arrangement and risk prevention and control method for the power operation mode under the access of the massive new energy proposed by the present invention;

[0023] Figure 2 It is a diagram of the computer device in the intelligent arrangement and risk prevention and control method for the power operation mode under the access of the massive new energy proposed by the present invention. Detailed Embodiments

[0024] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0025] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0026] Example 1, referring to 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 the access of a large amount of new energy, including the following contents:

[0027] S1: Construct a main and distribution network architecture data model, perform fusion processing on dynamic data of different voltage levels and professional attributes, and generate a system operation state model.

[0028] Specifically, the system operation state model includes network topology structure information and equipment operation parameters.

[0029] S1.1: Collect voltage level data, grid structure data, and equipment operation data of the main network and the distribution network.

[0030] Specifically, the voltage level data includes node data of AC 110 kV, 220 kV, and 500 kV voltage levels of the main network, and node data of AC 10 kV and 35 kV voltage levels of the distribution network; among them, the node data includes voltage level attribute identification, voltage amplitude data, and phase angle data.

[0031] The equipment operation data includes active power data, reactive power data, current data, equipment temperature data, and equipment status data of primary equipment; among them, the equipment status data includes operation status identification, maintenance status identification, fault status identification, and locking status identification.

[0032] The grid structure data includes breaker status data, disconnector status data, knife switch status data, tie switch status data, and primary equipment connection relationship data of the main network and the distribution network; among them, the primary equipment includes transformers, buses, lines, capacitors, and reactors.

[0033] S1.2: Perform topological mapping on the voltage level data and the grid structure data to generate network topology structure information; perform time synchronization processing on the equipment operation data to generate equipment operation parameters.

[0034] S1.3: Combine the network topology information and device operation parameters to construct a system operation status model.

[0035] S1.3.1: Establish a mapping table for the network topology information of the main network and the distribution network. The network topology information mapping table includes primary equipment identification data, primary equipment connection relationship data, and primary equipment status data.

[0036] Specifically, the primary equipment identification data includes the physical code of the equipment, the type code of the equipment, and the voltage level code of the equipment; the primary equipment connection relationship data includes the starting-end equipment code, the terminating-end equipment code, and the electrical connection type code, where the electrical connection type code identifies series connection, parallel connection, and T-connection.

[0037] S1.3.2: Establish a mapping table for the device operation data of the main network and the distribution network. The device operation data mapping table includes primary equipment electrical quantity data, primary equipment working condition data, and primary equipment status identification data.

[0038] Specifically, the primary equipment electrical quantity data includes the telemetered value of active power, the telemetered value of reactive power, the effective value of current, the effective value of voltage, and the power factor value; the primary equipment working condition data includes the equipment load rate value, the equipment temperature value, and the equipment vibration frequency value. The primary equipment status data records the closing status, opening status, and grounding status of the switchgear; the primary equipment status identification data records the operation identification, maintenance identification, fault identification, and locking identification, where the operation identification includes the normal operation code, overload operation code, and economic operation code.

[0039] S1.3.3: Match and associate the network topology information mapping table and the device operation data mapping table according to the device identification to generate a system operation status model.

[0040] Specifically, the device identification matching and association method is to establish a mapping relationship through the physical code in the primary equipment identification data, and make the primary equipment connection relationship data in the network topology information mapping table correspond one by one with the electrical quantity data and working condition data in the device operation data mapping table.

[0041] Preferably, in S1.3, the deep integration of primary and distribution network data is achieved by establishing a dual mapping table structure. Among them, the network topology structure information mapping table adopts a three-level identification system of physical coding - type coding - voltage level coding, ensuring the unique identification of devices with different voltage levels; the device operation data mapping table adopts a hierarchical storage structure of electrical quantities - operating conditions - status identifiers, realizing the associated mapping of dynamic data. In particular, the electrical connection type coding introduces an identification mechanism for three connection methods: series, parallel, and T-junction, solving the problem of difficult topology construction caused by diverse connection methods of primary and distribution network devices; the operation identifiers are subdivided into three operation codes: normal, overload, and economy, providing data support for subsequent optimization and control. Finally, through the one-to-one mapping mechanism established by physical coding, not only the problem of associating heterogeneous data between the primary and distribution networks is solved, but also the dynamic coupling of the topology structure and the operating state is realized, providing a complete data basis for system state assessment. This data fusion method based on dual mapping tables improves data retrieval efficiency, reduces storage redundancy, and ensures data consistency compared with the traditional single data table storage method.

[0042] It should be noted that S1 realizes the state perception of devices with different voltage levels through three-layer data acquisition and two-level mapping conversion. In the data acquisition layer, voltage level data, grid structure data, and device operation data of the main network from 110 kV to 500 kV and the distribution network from 10 kV to 35 kV are collected separately, constructing a complete data source system; in the mapping conversion layer, through two parallel processing paths of topology mapping and time synchronization, the problems of topological association between primary and distribution network devices and inconsistent data time series are solved; in the model construction layer, a dual mapping table structure is adopted to achieve deep data integration, where the network topology structure information mapping table adopts a three-level identification system to ensure the uniqueness of device identification, and the device operation data mapping table adopts a hierarchical storage structure to realize the association of dynamic data. Through the processing link of acquisition - mapping - fusion, this scheme not only solves the problem of fusing heterogeneous data between the primary and distribution networks, but also realizes the unified representation of the states of devices with different voltage levels, laying a data foundation for subsequent situation awareness and collaborative control. Compared with the traditional regional independent processing method, it improves data processing efficiency and enhances the integrity of state monitoring.

[0043] S2: Based on the system operation state model, perform topological structure analysis and establish a network connection relationship model.

[0044] The purpose of step S2 is to solve the technical problem of dynamic identification of topological structures in a distribution network with high penetration of new energy. The existing technologies mainly have the following deficiencies: First, traditional topological analysis methods only consider static connection relationships and cannot adapt to the dynamic characteristics of the network caused by fluctuations in new energy output. Second, conventional cut-off point selection methods rely too much on a single power index and are difficult to meet the requirements for the safe and stable operation of new energy distribution networks. Third, the existing key node identification methods have low calculation efficiency and are difficult to meet the real-time analysis requirements of large-scale distribution networks.

[0045] Specifically, the network connection relationship model is used to characterize the electrical connections between the nodes of the main and distribution networks.

[0046] S2.1: Based on the connection relationship data of primary equipment in the system operation state model, construct an initial topological connection matrix; the row and column identifiers of the initial topological connection matrix are the physical codes of primary equipment, and the matrix elements are electrical connection type codes.

[0047] Specifically, 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-junction 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 start-end equipment code and end-end equipment code in the primary equipment connection relationship data.

[0048] S2.2: Based on the depth-first search algorithm, perform hierarchical traversal on the initial topological connection matrix, establish a voltage level hierarchical connection relationship table, and record the connection topology between equipment of each voltage level through the voltage level hierarchical connection relationship table.

[0049] Specifically, use a depth-first search algorithm with voltage level constraints, and the traversal rules are as follows:

[0050] If the current node is a transformer node, record the voltage level values at both ends of the transformer and establish a cross-layer connection identifier;

[0051] If the current node is a T-junction node and there are three or more connection branches, mark the T-junction node as a key connection point;

[0052] If a closed-loop path at the same voltage level is searched, perform loop node load evaluation:

[0053] 1. Calculate the comprehensive load index of each node in the loop.

[0054] Among them, 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 connection branches to the total number of branches in the loop; the third load index is the absolute value of the relative deviation of the node voltage from the nominal voltage; the fourth load index is the ratio of the node transfer power to the branch rated capacity.

[0055] 2. After normalizing the first load index, the second load index, the third load index, and the fourth load index, calculate the comprehensive load index according to the preset weights.

[0056] 3. Based on the comprehensive load index, select the cut-off points in the 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 indices are the same, select the node with the largest fourth load index.

[0057] 4. If the loop contains new energy access nodes, perform new energy access assessment: First, calculate the output volatility of the new energy access nodes; second, identify the load node groups adjacent to the new energy access nodes; then calculate the load characteristic indices of the load node groups; finally, select the node with the smallest load characteristic index and meeting the system stability requirements in the load node groups as the cut-off point.

[0058] Specifically, the cut-off point refers to: the node whose electrical connection can be disconnected to optimize the network structure on the premise of ensuring power supply reliability and economy. Specifically, it includes:

[0059] 1) Load-side cut-off point: the loop node determined by evaluating the comprehensive load index, and its disconnection will not cause load power loss;

[0060] 2) New energy-side cut-off point: the low-load characteristic node adjacent to the new energy access point and meeting the stability requirements, and its disconnection can reduce the impact of new energy output fluctuations on the system;

[0061] 3) Cut-off point adjacent to the overhaul or fault node: the healthy operating node adjacent to the overhauled or faulty equipment, used for fault isolation and power supply restoration.

[0062] Among them, the output volatility is calculated by the ratio of the standard deviation to the average value of the new energy output; the load characteristic indices include load power factor, load average utilization rate, and load response ability; the system stability requirements include voltage stability margin and rationality of power flow distribution.

[0063] If the operating state of the device is detected as overhaul or fault, temporarily cut off the connection relationship of this node and traverse again.

[0064] Through the above traversal rules, generate a voltage-level hierarchical connection relationship table including node connection relationships, voltage-level associations, and key node identifiers.

[0065] 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; and the key node identifier is used to distinguish ordinary nodes and key connection nodes.

[0066] S2.3: Perform connectivity analysis on the voltage level hierarchical connection relationship table, identify key connection nodes, and establish a network connection relationship model.

[0067] Specifically, calculate the shortest connection path between nodes based on the depth-first search algorithm, and identify substation nodes, T-junction nodes, and loop network nodes as key connection nodes; use the adjacency list structure to store the topological connection relationship between each key connection node to form a network connection relationship model.

[0068] Furthermore, key connection nodes:

[0069] 1) Substation nodes: Hub nodes with voltage conversion and power distribution functions that connect different voltage level networks;

[0070] 2) T-junction nodes: Branch nodes with three or more connection branches in the same voltage level network;

[0071] 3) Loop network nodes: Connection nodes that form a closed-loop structure in the same voltage level network.

[0072] Preferably, in step S2, a depth-first search algorithm based on voltage level constraints is first used for hierarchical traversal. Combining the hierarchical mapping mechanism of the initial topological connection matrix, the cross-layer connection relationships between devices of different voltage levels can be accurately identified, laying a foundation for subsequent analysis. Secondly, after normalizing indicators such as the proportion of node active power, the proportion of branch connection quantity, voltage deviation, and the proportion of crossing power, a comprehensive evaluation is carried out. By setting a three-level priority selection mechanism, the key cut-off points in the network can be effectively identified. Especially when dealing with the scenario of new energy access, by introducing a joint constraint mechanism of output volatility and load characteristics indicators, the impact of cut-off point selection on system stability can be accurately evaluated. The present invention can effectively reduce the network reconstruction frequency caused by new energy fluctuations and improve the system operation stability by introducing an evaluation system based on a multi-dimensional comprehensive load index and combining the joint constraint mechanism of new energy output volatility and load characteristics indicators. During the topological analysis process, an adjacency list structure is used to store connection relationships. Compared with the traditional matrix storage method, it can effectively reduce data redundancy and significantly improve the calculation efficiency of depth-first search. The application of the present invention in a distribution network with high new energy penetration shows that the present invention can adapt to the network dynamic characteristics, realize the 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 specific new energy distribution network application scenarios, and relevant parameters still need to be reasonably adjusted according to specific situations in actual applications.

[0073] S3: According to the network connection relationship model, calculate the new energy access optimization plan, and generate the operation mode arrangement result and risk prevention and control strategy.

[0074] Specifically, the operation mode arrangement result is used to guide the system operation and dispatching.

[0075] 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 new energy access points, and generate the new energy access capacity evaluation result.

[0076] 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 amount to the power change amount.

[0077] S3.2: Construct a multi-objective optimization model including system stability margin indicators and economic operation indicators, and calculate the optimal output plan and network reconstruction plan for each new energy access point respectively.

[0078] Specifically, the system stability margin indicators include voltage stability margin, power transfer margin, and transient stability margin; the economic operation indicators include line loss rate, equipment utilization rate, and load transfer rate.

[0079] Among them, the voltage stability margin is calculated by the difference between the critical point load level and the current load level, the line loss rate is calculated by the ratio of the active power loss of the line to the input active power, and the equipment utilization rate is calculated by the ratio of the actual load to the rated capacity.

[0080] S3.3: Sort the optimal power output plan and the network reconfiguration plan according to the combined scoring of the stability margin indicators and the economic operation indicators, and output the operation mode arrangement result and the risk prevention and control strategy.

[0081] Specifically, the operation mode arrangement result includes new energy power output adjustment instructions and network topology reconfiguration instructions; the risk prevention and control strategy includes key node monitoring instructions and emergency response instructions. Among them, the combined scoring is calculated by the weighted sum of the stability margin indicator score and the economic operation indicator score, and the weighting coefficient is determined based on the analytic hierarchy process; the stability margin indicator score is calculated by the ratio of the system stability margin to the reference value, and the economic operation indicator score is calculated by the normalization processing of the line loss rate, equipment utilization rate, and load transfer rate.

[0082] Preferably, step S3 of the present invention solves the following technical problems through three links: new energy access capacity assessment, multi-objective optimization modeling, and combined scoring and sorting: the decision-making of power grid operation mode adjustment under the condition of large-scale new energy grid connection is complex, the optimization objectives are multi-dimensional and there are conflicts, and it is difficult to balance system stability and economy. This solution unifies the power flow distribution characteristics and voltage distribution characteristics into the access capacity assessment link for the first time, and uses the power factor to calculate the node injection power, which improves the accuracy of new energy access assessment; by establishing a multi-objective optimization model including stability margin and economy, the coordinated unity of system safe and stable operation and economic benefits is realized; the combined scoring method based on normalization processing is used to sort the optimization schemes, overcoming the evaluation deviation caused by the inconsistent dimensions of various indicators in the traditional method. In practical applications, this solution can evaluate the new energy access capacity more accurately, balance system stability and economy more reasonably compared with the existing technology, provide more reliable decision-making support for power grid dispatching operation, and effectively reduce the system operation risks brought by large-scale new energy access.

[0083] In summary, the present invention achieves remarkable technical effects through three core links: data fusion, topology recognition, and scheme optimization. The data fusion module realizes the fusion processing of dynamic data with different voltage levels and professional attributes by establishing a main and distribution network architecture data model, overcomes the data island problem in traditional technologies, and improves the accuracy and real-time performance of system operation state perception. The topology recognition module performs hierarchical traversal based on the depth-first search algorithm and establishes a network connection relationship model including voltage level constraints, which can more accurately identify and characterize the complex electrical connections between various nodes of the main and distribution networks compared with the prior art. The scheme optimization module constructs a multi-objective optimization model and comprehensively considers the system stability margin and economic operation indicators. It can not only scientifically evaluate the new energy access capacity but also specifically generate the optimal output scheme and network reconstruction scheme, effectively solving the problem that the traditional empirical decision-making method is difficult to cope with the system operation optimization under the scenario of massive new energy access. Overall, the present invention realizes the full-process intelligence from data processing to decision optimization, significantly improving the operation efficiency, safety, and reliability of the power system under the condition of large-scale new energy access.

[0084] Embodiment 2 is an embodiment of the present invention, which provides an intelligent scheduling and risk prevention and control system for power operation modes under massive new energy access, including:

[0085] A data fusion module, configured to construct a main and distribution network architecture data model, perform fusion processing on dynamic data with different voltage levels and professional attributes, and generate a system operation state model;

[0086] A topology recognition module, configured to perform topology structure analysis based on the system operation state model and establish a network connection relationship model;

[0087] A scheme optimization module, configured to calculate an optimized new energy access scheme according to the network connection relationship model and generate an operation mode scheduling result and a risk prevention and control strategy.

[0088] Embodiment 3, refer to Figure 2, which is an embodiment of the present invention. What is different from the previous embodiment is that when 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs, Read-Only Memories), random access memories (RAMs, Random Access Memories), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0089] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0090] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0091] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above 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, any one of the following techniques well known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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, a topology structure analysis is performed to establish a network connection relationship model; the network connection relationship model is used to characterize the electrical connection between the nodes of the main distribution network; the construction process of the network connection relationship model includes the following steps: Based on the primary device connection relationship data in the system operation status model, construct an initial topology connection matrix; 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; 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; 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; 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.

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 row and column identifiers of the initial topological connection matrix are primary equipment physical codes, and the matrix elements are electrical connection type codes; in the electrical connection type codes, 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 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.

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: 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: 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.

7. 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 6, 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.

8. 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 new energy sources as described in any one of claims 1 to 6 are implemented.

9. 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 renewable energy sources as described in any one of claims 1 to 6 are implemented.

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