Method and system for evaluating new energy consumption capability of power distribution network based on artificial intelligence

Abstract

The invention provides a power distribution network new energy consumption capability evaluation method and system based on artificial intelligence, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining the data of the new energy of a power distribution network; according to the new energy data of the power distribution network, establishing a power grid model and setting load levels in different time periods to obtain a simulated power grid model; the power flow condition of each node in the power grid is calculated by using a simulation power grid model according to the data of the new energy of the power distribution network, the distribution condition of power flow in the power grid is analyzed according to a flow factor, the stability of the power grid is evaluated according to a power grid fluctuation factor and a preset threshold value, and the stability of the power grid is evaluated according to the power grid fluctuation factor and a stability evaluation result. And evaluating the consumption capability of the power grid. According to the invention, modeling and analysis are carried out on the number of power stations and line data through an artificial intelligence technology, and quantitative evaluation of the new energy consumption capability of the power distribution network is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and particularly to a method and system for evaluating the new energy consumption capacity of a distribution network based on artificial intelligence. Background Art

[0002] With the rapid development of new energy power generation technology, the access ratio of new energy in the distribution network is increasing day by day. However, the volatility, intermittency, and unpredictability of new energy pose challenges to the stable operation of the distribution network and the effective consumption of new energy.

[0003] The increase in the number of power generation stations, especially new energy power generation stations, can bring economies of scale, reduce the unit power generation cost, and improve the overall economic efficiency. The transmission capacity of the line determines the amount of new energy power generation that can be transmitted to the distribution network. If the line transmission capacity is insufficient, it may lead to the inability to effectively transmit new energy power generation to the load center, thereby affecting its consumption capacity.

[0004] By qualitatively analyzing the influence of the number of power generation stations and lines on the new energy consumption capacity of the distribution network, an evaluation of the new energy consumption capacity of the distribution network is formed. Summary of the Invention

[0005] The present invention provides a method and system for evaluating the new energy consumption capacity of a distribution network based on artificial intelligence. By using artificial intelligence technology to model and analyze key data such as the number of power generation stations and lines, a quantitative evaluation of the new energy consumption capacity of the distribution network is realized.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a method for evaluating the new energy consumption capacity of a distribution network based on artificial intelligence, the method includes:

[0008] Obtain data of new energy in the distribution network, where the data of new energy in the distribution network includes substation information, transmission line information, and load data of each region;

[0009] According to the data of new energy in the distribution network, establish a power grid model and set the load levels at different time periods to obtain a simulated power grid model;

[0010] According to the data of new energy in the distribution network, use the simulated power grid model to calculate the power flow situation of each node in the power grid to obtain a flow factor;

[0011] According to the flow factor, analyze the distribution situation of the power flow in the power grid to obtain a power grid fluctuation factor;

[0012] According to the power grid fluctuation factor and a preset threshold, evaluate the stability of the power grid to obtain a stability evaluation result;

[0013] Evaluate the grid's accommodation capacity based on the grid fluctuation factor and the stability assessment results.

[0014] Furthermore, obtain the data of new energy in the distribution grid. The data of new energy in the distribution grid includes substation information, transmission line information, and load data of each region, including:

[0015] Obtain substation information, where the substation information includes the geographical location, voltage level, transformer capacity, and wiring method of each substation;

[0016] Obtain transmission line information, where the transmission line information includes the conductor type, length, transmission capacity, impedance of the transmission line, and the starting and ending substations of the line;

[0017] Obtain load data, where the load data includes historical load data and future load growth trends.

[0018] Furthermore, based on the data of new energy in the distribution grid, establish a grid model and set the load levels for different time periods to obtain a simulated grid model, including:

[0019] Based on the substation information and transmission line information, establish a grid model and set parameters to obtain a preset grid model;

[0020] Compare the preset grid model with the operation data of the actual grid to obtain the verification model results;

[0021] Draw a load curve based on the verification model results and load data collection;

[0022] Set the power of the load points according to the load curve to obtain the load levels for different time periods;

[0023] Simulate the load change situation using the preset grid model according to the load levels for different time periods to obtain a simulated grid model.

[0024] Furthermore, based on the data of new energy in the distribution grid, calculate the power flow situation of each node in the grid using the simulated grid model to obtain the flow factor, including:

[0025] Based on the data of new energy in the distribution grid, use Obtain the active power of the grid, where P i is the active power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between node i and j, Δθ ij is the phase shift, and P loss,i is the active power loss of node i;

[0026] Use to obtain the reactive power of the power grid, where Q i is the reactive power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between nodes i and j, Δθ ij is the phase shift, Q loss,i is the reactive power loss of node i;

[0027] According to the active power and reactive power of the power grid, iteratively calculate the power flow of each node;

[0028] According to the power flow of each node, use to obtain the flow factor, where P inj,i is the power injection of the node, P flow,ij is the line power flowing from node i to node j, ne(i) is the set of adjacent nodes of node i, ∈ is a small positive number, V i is the voltage amplitude of the node, R ij and X ij are the resistance and reactance of line j respectively, P loss,i is the active power loss at the node, Q flow,ij is the reactive power flowing from node i to node j.

[0029] Furthermore, according to the flow factor, analyze the distribution of the power flow in the power grid to obtain the power grid fluctuation factor, including:

[0030] According to the flow factor, compare the fluctuation amounts of the active power and reactive power of each node at different time points to obtain the power fluctuation amount of the node;

[0031] According to the flow factor, calculate the change rates of the active power and reactive power of each line to obtain the power flow change rate of the line;

[0032] According to the power fluctuation amount of the node and the power flow change rate of the line, use to calculate and obtain the power grid fluctuation factor, where N is the number of nodes in the power grid, ΔP i and ΔQ i are the active power and reactive power fluctuation amounts of node i, P ij,0 and Q ij,0 are the initial active power and reactive power flows on line j, V i,max and V i,arg are the maximum voltage amplitude and average voltage amplitude of node i, R ij,max and R ij,argThe maximum and average resistance of line j, X ij,max and X ij,arg are the maximum and average reactance of line j. T is the set of all time steps within the observation time window, and ∈ is a positive number.

[0033] Furthermore, based on the grid fluctuation factor and a preset threshold, evaluate the stability of the power grid to obtain a stability evaluation result, including:

[0034] Use the power grid monitoring system to collect data of each node of the power grid in real time and calculate the grid fluctuation factor;

[0035] Compare the calculated grid fluctuation factor with the preset threshold to obtain a comparison result;

[0036] Based on the comparison result, obtain the stability evaluation result. If the fluctuation factor is within the normal range, it is considered that the power grid is in a stable state. If the fluctuation factor exceeds the warning threshold but does not reach the emergency threshold, it is considered that there are potential instability factors in the power grid and close attention is needed. If the fluctuation factor exceeds the emergency threshold, it is considered that the power grid is in an unstable state and immediate measures need to be taken for intervention.

[0037] Furthermore, based on the grid fluctuation factor and the stability evaluation result, evaluate the power grid's accommodation capacity, including:

[0038] Analyze the relationship between the grid fluctuation factor and the power grid's accommodation capacity according to the flow factor and the stability of the power grid to obtain the analysis result of the grid fluctuation factor. Among them, the smaller the grid fluctuation factor, the more stable the power grid and the stronger the accommodation capacity;

[0039] Analyze the impact of stability on the power grid's accommodation capacity according to the stability evaluation result of the power grid to obtain the stability analysis result;

[0040] Based on the analysis result of the grid fluctuation factor and the stability analysis result, comprehensively evaluate the power grid's accommodation capacity to obtain an evaluation result. The evaluation result includes the accommodation capacity level of the power grid under different operating states, the change trend of the accommodation capacity, and the possible accommodation bottlenecks.

[0041] In the second aspect, an evaluation system for the accommodation capacity of new energy in a distribution network based on artificial intelligence includes:

[0042] An acquisition module for acquiring data of new energy in the distribution network. The data of new energy in the distribution network includes substation information, transmission line information, and load data of each region;

[0043] A processing module, configured to establish a power grid model and set load levels for different time periods according to the data of new energy sources in the distribution network, so as to obtain a simulated power grid model. According to the data of new energy sources in the distribution network, use the simulated power grid model to calculate the power flow conditions of each node in the power grid, so as to obtain flow factors. According to the flow factors, analyze the distribution of power flow in the power grid, so as to obtain power grid fluctuation factors. According to the power grid fluctuation factors and a preset threshold, evaluate the stability of the power grid, so as to obtain a stability evaluation result. According to the power grid fluctuation factors and the stability evaluation result, evaluate the accommodation capacity of the power grid.

[0044] In a third aspect, a computing device includes:

[0045] One or more processors;

[0046] A storage system for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the above method.

[0047] In a fourth aspect, a computer-readable storage medium stores a program, which when executed by a processor, implements the above method.

[0048] The above solution of the present invention has at least the following beneficial effects:

[0049] The above solution of the present invention models and analyzes key data such as the number of power generation stations and lines through artificial intelligence technology. This method can achieve a quantitative evaluation of the accommodation capacity of new energy sources in the distribution network, which is more accurate and objective, and can provide more reliable data support for power grid planning and operation; by simulating the power grid model and calculating the power flow conditions of each node in the power grid, it can more accurately reflect the impact of new energy access on the power grid; by calculating the flow factors and power grid fluctuation factors, it can deeply analyze the distribution of power flow in the power grid, and then evaluate the stability of the power grid, which helps to timely discover potential problems in power grid operation and take corresponding measures for improvement to ensure the stable operation of the power grid; according to the power grid fluctuation factors and the stability evaluation result, it can evaluate the accommodation capacity of the power grid, which helps the power grid operator to formulate a reasonable dispatching strategy, optimize the access and accommodation of new energy sources, improve the utilization rate and economy of new energy sources; it can adapt to new energy power generation stations of different scales and types and different line transmission capabilities, which helps to ensure the accuracy and applicability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic flowchart of a method for evaluating the accommodation capacity of new energy sources in a distribution network based on artificial intelligence provided by an embodiment of the present invention.

[0051] Figure 2It is a schematic diagram of an evaluation system for the new energy consumption capacity of a distribution network based on artificial intelligence provided by an embodiment of the present invention. Specific Embodiments

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0053] As Figure 1 shown, an embodiment of the present invention proposes an evaluation method for the new energy consumption capacity of a distribution network based on artificial intelligence, and the method includes:

[0054] 11. Obtain data of new energy in the distribution network, where the data of new energy in the distribution network includes substation information, transmission line information, and load data of each region;

[0055] 12. According to the data of new energy in the distribution network, establish a power grid model and set the load levels at different time periods to obtain a simulated power grid model;

[0056] 13. According to the data of new energy in the distribution network, use the simulated power grid model to calculate the power flow conditions of each node in the power grid to obtain a flow factor;

[0057] 14. According to the flow factor, analyze the distribution of power flow in the power grid to obtain a power grid fluctuation factor;

[0058] 15. According to the power grid fluctuation factor and a preset threshold, evaluate the stability of the power grid to obtain a stability evaluation result;

[0059] 16. According to the power grid fluctuation factor and the stability evaluation result, evaluate the consumption capacity of the power grid.

[0060] In the embodiments of the present invention, data related to new energy is obtained from the distribution network, including but not limited to substation information (such as substation location, capacity, operating status, etc.), transmission line information (such as line length, transmission capacity, impedance, etc.), and load data of each region (such as load magnitude, load change rate, peak load period, etc.); according to the obtained data, an artificial intelligence technology is used to establish a power grid model; the load levels in different time periods are set to simulate the operating status of the power grid under different working conditions, and a simulated power grid model is obtained; the simulated power grid model is used to calculate the power flow conditions of each node in the power grid; according to the calculation results, a flow factor is obtained, which reflects the power transmission situation between each node in the power grid; according to the flow factor, an in-depth analysis of the distribution of power flow in the power grid is carried out; the abnormal points and potential risk points in the power flow distribution are identified to obtain a power grid fluctuation factor; according to the power grid fluctuation factor and a preset threshold, the stability of the power grid is evaluated; the preset threshold can be set according to the actual operating conditions of the power grid and industry standards to reflect the stability status of the power grid under different fluctuation degrees; combining the power grid fluctuation factor and the stability evaluation result, the accommodation capacity of the power grid is evaluated; the impact of new energy access on the accommodation capacity of the power grid is analyzed, and optimization suggestions and improvement measures are proposed; it provides strong support for power grid planning and operation, and helps to promote the sustainable development of new energy and the stable operation of the power grid.

[0061] As Figure 1 shown in, 11, obtain the data of new energy in the distribution network, and the data of new energy in the distribution network includes substation information, transmission line information, and load data of each region, including:

[0062] Obtain substation information, where the substation information includes the geographical location, voltage level, transformer capacity, and wiring method of each substation;

[0063] Obtain transmission line information, where the transmission line information includes the conductor type, length, transmission capacity, impedance of the transmission line, and the starting and ending substations of the line;

[0064] Obtain load data, where the load data includes historical load data and future load growth trends.

[0065] In the embodiments of the present invention, the description of the effect: The evaluation method for the accommodation capacity of new energy in the distribution network based on detailed data acquisition

[0066] In the embodiments of the present invention, understanding the geographical locations of each substation helps to analyze the geographical distribution and transmission paths of the power grid, providing a basis for optimizing the power grid layout and reducing transmission losses; the voltage level is an important parameter for evaluating the power transmission capacity and safety of the power grid, helping to determine the access level and transmission efficiency of the substation; the transformer capacity determines the power transmission capacity and regulation range of the substation, having an important impact on the stable operation of the power grid and the access of new energy; the wiring method determines the flexibility and reliability of the substation, having an important impact on the fault recovery ability and dispatching strategy of the power grid; the conductor type determines the transmission capacity and loss of the line, having an important impact on the transmission efficiency of new energy and the economy of the power grid; the line length is a key factor affecting transmission losses and transmission time, having an important impact on the stability and economy of the power grid; the transmission capacity is an important parameter for evaluating the power transmission capacity of the line and the access capacity of new energy, helping to determine the transmission range and dispatching strategy of the line; the impedance is an important factor affecting the transmission efficiency and stability of the line, having an important impact on the power flow distribution and voltage quality of the power grid; understanding the starting and ending substations of the line helps to analyze the topological structure and transmission paths of the power grid, providing a basis for optimizing power grid dispatching and reducing transmission losses; historical load data reflects the load changes of the power grid under different time periods and operating conditions, helping to analyze the load characteristics of the power grid and the access requirements of new energy; predicting the future load growth trend helps to evaluate the future load demand of the power grid and the access potential of new energy, providing a basis for power grid planning and the development of new energy.

[0067] As Figure 1 shown in 12, according to the data of new energy in the distribution network, establish a power grid model and set the load levels for different time periods to obtain a simulated power grid model, including:

[0068] Establish a power grid model and set parameters according to the substation information and transmission line information to obtain a preset power grid model;

[0069] Compare with the operation data of the actual power grid according to the preset power grid model to obtain the verification model result;

[0070] Draw a load curve according to the verification model result and load data collection;

[0071] Set the power of the load points according to the load curve to obtain the load levels for different time periods;

[0072] Simulate the load changes using the preset power grid model according to the load levels for different time periods to obtain a simulated power grid model.

[0073] In the embodiments of the present invention, based on the substation information and the transmission line information, a detailed power grid model is established by using the detailed information such as the geographical location, voltage level, transformer capacity, wiring mode, etc. of the substation, and the parameters such as the conductor type, length, transmission capacity, impedance, etc. of the transmission line, which reflects the physical structure and transmission characteristics of the power grid; in order to ensure the accuracy of the model, the operation results of the preset power grid model are compared with the operation data of the actual power grid, including comparing the simulated values and actual values of key parameters such as voltage, current, power, etc., to verify the reliability of the model; after the model is verified, the historical load data and the future load growth trend prediction are used to draw a load curve, which reflects the load change of the power grid under different time periods and working conditions; through the load curve, the load level in different time periods can be determined, and the load level reflects the actual load demand of the power grid in different time periods and is an important input parameter for simulating the power grid model; using the preset power grid model and combining the load levels set in different time periods, the operation state of the power grid under different working conditions is simulated, and a simulated power grid model that can reflect the actual operation state of the power grid is obtained; the obtained simulated power grid model not only has high accuracy and reliability, but also can flexibly simulate the load changes in different time periods, providing a solid foundation for subsequent power grid stability assessment, power flow distribution analysis and consumption capacity assessment.

[0074] As Figure 1 shown in 13, according to the data of new energy in the distribution network, the power flow of each node in the power grid is calculated by using the simulated power grid model to obtain the flow factor, including:

[0075] According to the data of new energy in the distribution network, use to obtain the active power of the power grid, where P i is the active power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between node i and j, Δθ ij is the phase shift, and P loss,i is the active power loss of node i;

[0076] Use to obtain the reactive power of the power grid, where Q i is the reactive power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between node i and j, Δθ ij is the phase shift, and Q loss,i is the reactive power loss of node i;

[0077] Iteratively calculate the power flow of each node according to the active power and reactive power of the power grid;

[0078] According to the power flow of each node, use to obtain the flow factor, where P inj,i is the power injection of the node, P flow,ij is the line power flowing from node i to node j, ne(i) is the set of adjacent nodes of node i, ∈ is a small positive number, V i is the voltage magnitude of the node, R ij and X ij are the resistance and reactance of line j respectively, P loss,i is the active power loss at the node, Q flow,ij is the reactive power flowing from node i to node j.

[0079] In the embodiment of the present invention, using the data of new energy in the distribution network and the simulated power grid model, the power flow of each node in the power grid is accurately calculated, and the flow factor is obtained; according to the data of new energy in the distribution network, the active power of each node in the power grid is calculated, and the calculation formula of active power is used, which takes into account the comprehensive influence of voltage magnitude, phase angle difference, phase shift and power loss; the reactive power of each node in the power grid is calculated, and the influence of multiple factors such as voltage magnitude, phase angle difference between adjacent nodes, phase shift and reactive power loss of the node is comprehensively considered; after obtaining the active power and reactive power of each node in the power grid, an iterative method is used to calculate the power flow of each node, which involves complex power flow calculation of the power grid and requires repeated iteration to obtain accurate power flow results; after obtaining the power flow of each node through iterative calculation, the flow factor is calculated. The flow factor is an index reflecting the relationship between power injection and outflow of the node, and comprehensively considers multiple factors such as power injection of the node, power flow of adjacent nodes, resistance and reactance of the line, voltage magnitude of the node and power loss; the flow factor reflecting the power flow of each node in the power grid is obtained. The flow factor not only provides an important basis for subsequent power grid stability assessment, power flow distribution analysis and consumption capacity assessment, but also helps the power grid operator better understand the operating state of the power grid and formulate more scientific dispatching and operation and maintenance strategies.

[0080] As Figure 1 shown in 14, analyze the distribution of power flow in the power grid according to the flow factor to obtain the power grid fluctuation factor, including:

[0081] According to the flow factor, compare the fluctuation amounts of the active power and reactive power of each node at different time points to obtain the power fluctuation amount of the node;

[0082] Calculate the change rates of active power and reactive power for each line according to the flow factor to obtain the power flow change rate of the line;

[0083] According to the power fluctuation amount of the node and the power flow change rate of the line, use Calculate the power grid fluctuation factor, where N is the number of nodes in the power grid, ΔP i and ΔQ i are the active power and reactive power fluctuation amounts of node i, P ij,0 and Q ij,0 are the initial active power and reactive power flows on line j, V i,max and V i,arg are the maximum voltage amplitude and average voltage amplitude of node i, R ij,max and R ij,arg are the maximum resistance value and average value of line j, X ij,max and X ij,arg are the maximum reactance value and average value of line j, T is the set of all time steps within the observation time window, and ∈ is a positive number.

[0084] In the embodiment of the present invention, the distribution of the power flow in the power grid is deeply analyzed according to the flow factor, and the power grid fluctuation factor is calculated accordingly; by using this key index of the flow factor, the fluctuation amounts of active power and reactive power of each node at different time points are compared, which can quantify the fluctuation of node power and provide data support for subsequent analysis; not only the power fluctuation of the node is concerned, but also the change rates of active power and reactive power of each line are deeply analyzed, which helps to understand the dynamic change of the power flow in the power grid and provides an important basis for calculating the power grid fluctuation factor in the future; after obtaining the power fluctuation amount of the node and the power flow change rate of the line, a formula containing multiple factors is used to calculate the power grid fluctuation factor, and the factors include the number of nodes in the power grid, the active power and reactive power fluctuation amounts of the node, the initial active power and reactive power flows on the line, the maximum voltage amplitude and average voltage amplitude of the node, the maximum resistance value and average value of the line, and the maximum reactance value and average value of the line, etc. The comprehensive consideration enables the power grid fluctuation factor to comprehensively reflect the fluctuation of the power grid; the set of all time steps within the observation time window is considered to ensure that the calculated power grid fluctuation factor can accurately reflect the fluctuation of the power grid over a period of time; the power grid fluctuation factor that can comprehensively reflect the fluctuation of the power grid is obtained, which not only provides an important basis for subsequent power grid stability assessment, power flow optimization, and consumption capacity analysis, etc., but also helps the power grid operator to better understand the operation state of the power grid and formulate more scientific dispatching and operation and maintenance strategies.

[0085] Such as Figure 1As shown in Figure 15, according to the grid fluctuation factor and a preset threshold value, the stability of the power grid is evaluated to obtain a stability evaluation result, including:

[0086] Use the power grid monitoring system to collect data of each node of the power grid in real time, and calculate the grid fluctuation factor;

[0087] Compare the calculated grid fluctuation factor with the preset threshold value to obtain a comparison result;

[0088] According to the comparison result, obtain the stability evaluation result. If the fluctuation factor is within the normal range, it is considered that the power grid is in a stable state. If the fluctuation factor exceeds the warning threshold but does not reach the emergency threshold, it is considered that there are potential unstable factors in the power grid and close attention is required. If the fluctuation factor exceeds the emergency threshold, it is considered that the power grid is in an unstable state and immediate measures need to be taken for intervention.

[0089] In the embodiment of the present invention, the effect description: Evaluate the stability of the power grid based on the grid fluctuation factor

[0090] In the embodiment of the present invention, using the data collected in real time by the power grid monitoring system, the grid fluctuation factor is calculated, and the stability of the power grid is comprehensively evaluated according to the preset threshold value; the power grid monitoring system is deployed to collect data of each node of the power grid in real time, including key parameters such as voltage, current, and power; based on the collected data, the grid fluctuation factor is calculated, which can comprehensively reflect the fluctuation situation of the power grid within a period of time and provide an important basis for subsequent stability evaluation; in order to ensure the accuracy of the evaluation, according to the actual situation and operation experience of the power grid, reasonable grid fluctuation factor threshold values are set, including the normal range, warning threshold, and emergency threshold; the calculated grid fluctuation factor is compared with the preset threshold value to obtain a comparison result; according to the comparison result, the stability of the power grid is evaluated. If the fluctuation factor is within the normal range, it is considered that the power grid is in a stable state and the operation condition is good; if the fluctuation factor exceeds the warning threshold but does not reach the emergency threshold, it is considered that there are potential unstable factors in the power grid and the operation state of the power grid needs to be closely monitored, and the monitoring and early warning are strengthened; if the fluctuation factor exceeds the emergency threshold, it is considered that the power grid is in an unstable state and there are serious safety risks. At this time, immediate measures need to be taken for intervention, such as adjusting the power grid structure, optimizing the dispatching strategy, increasing the standby power supply, etc., to ensure the safe and stable operation of the power grid; it can not only evaluate the stability of the power grid in real time, but also take corresponding countermeasures in a timely manner according to the evaluation result. It helps to improve the operation efficiency and safety of the power grid and reduce the risk of faults and accidents.

[0091] As Figure 1 shown in Figure 16, according to the grid fluctuation factor and the stability evaluation result, evaluate the power grid's accommodation capacity, including:

[0092] Analyze the relationship between the grid fluctuation factor and the grid accommodation capacity according to the flow factor and the stability of the power grid, so as to obtain the analysis result of the grid fluctuation factor. Among them, the smaller the grid fluctuation factor, the more stable the power grid and the stronger the accommodation capacity.

[0093] Analyze the impact of stability on the grid accommodation capacity according to the stability evaluation result of the power grid, so as to obtain the stability analysis result.

[0094] Comprehensively evaluate the grid accommodation capacity according to the grid fluctuation factor analysis result and the stability analysis result, so as to obtain the evaluation result. The evaluation result includes the accommodation capacity level of the power grid under different operating states, the change trend of the accommodation capacity, and the possible accommodation bottlenecks.

[0095] In the embodiment of the present invention, the effect description: Evaluate the grid accommodation capacity based on the grid fluctuation factor and the stability evaluation result

[0096] In the embodiment of the present invention, first, according to the flow factor and the stability of the power grid, the internal relationship between the grid fluctuation factor and the grid accommodation capacity is analyzed, and it is recognized that the smaller the grid fluctuation factor, the more stable the power grid and the stronger its ability to accommodate new energy; the analysis result of the grid fluctuation factor is obtained, revealing the negative correlation between the grid fluctuation and the accommodation capacity, providing an important basis for subsequent evaluation; further, according to the stability evaluation result of the power grid, the impact of stability on the grid accommodation capacity is analyzed, and by comparing the grid accommodation capacity under different stability states, the stability analysis result is obtained, clarifying the positive promotion effect of the power grid stability on the accommodation capacity, that is, the more stable the power grid, the stronger its ability to accommodate new energy; on the basis of obtaining the grid fluctuation factor analysis result and the stability analysis result, the grid accommodation capacity is comprehensively evaluated, comprehensively considering the accommodation capacity level of the power grid under different operating states, the change trend of the accommodation capacity, and the possible accommodation bottlenecks; the evaluation result of the grid accommodation capacity is obtained, which not only reflects the current accommodation capacity status of the power grid, but also predicts the future change trend of the accommodation capacity and points out the possible accommodation bottlenecks.

[0097] As Figure 2 shown, the embodiment of the present invention also provides an evaluation system 20 for the new energy accommodation capacity of a distribution network based on artificial intelligence, including:

[0098] An acquisition module 21, configured to acquire data of new energy in the distribution network, where the data of new energy in the distribution network includes substation information, transmission line information, and load data of each region.

[0099] A processing module 22, configured to establish a power grid model and set load levels for different time periods based on the data of new energy sources in the distribution network, so as to obtain a simulated power grid model; calculate the power flow conditions of each node in the power grid using the simulated power grid model based on the data of new energy sources in the distribution network, so as to obtain flow factors; analyze the distribution of power flow in the power grid based on the flow factors, so as to obtain power grid fluctuation factors; evaluate the stability of the power grid based on the power grid fluctuation factors and a preset threshold value, so as to obtain a stability evaluation result; and evaluate the accommodation capacity of the power grid based on the power grid fluctuation factors and the stability evaluation result.

[0100] It should be noted that this system corresponds to the above method, and all implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0101] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0102] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is caused to execute the method as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0103] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0104] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0105] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the system or unit can be in electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0108] 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 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 can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0109] In addition, it should be noted that in the system and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed chronologically in the described order, but it is not necessary to execute them necessarily in chronological order. Some steps can be executed in parallel or independently of each other. For those of ordinary skill in the art, it is possible to understand that all or any steps or components of the method and system of the present invention can be implemented in any computing system (including processors, storage media, etc.) or a network of computing systems in the form of hardware, firmware, software, or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0110] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing system. The computing system can be a well-known general-purpose system. Therefore, the object of the present invention can also be achieved only by providing a program product containing program code for implementing the method or system. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be noted that in the system and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed chronologically in the described order, but it is not necessary to execute them necessarily in chronological order. Some steps can be executed in parallel or independently of each other.

[0111] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the new energy consumption capacity of a distribution network based on artificial intelligence, characterized in that: The method comprises: Acquire data on new energy sources in the distribution network, wherein the data on new energy sources in the distribution network includes substation information, transmission line information, and load data of each area; According to the data of new energy sources in the distribution network, a power grid model is established and the load levels in different time periods are set to obtain a simulated power grid model; According to the data of new energy sources in the distribution network, the power flow of each node in the power grid is calculated using a simulated power grid model to obtain the flow factor; According to the flow factor, the distribution of power flow in the power grid is analyzed to obtain the power grid fluctuation factor; According to the grid fluctuation factor and the preset threshold, the stability of the grid is evaluated to obtain the stability evaluation result; Based on the grid fluctuation factors and stability assessment results, the grid's absorption capacity is evaluated.

2. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 1 is characterized in that: The data of new energy sources in the distribution network are obtained, wherein the data of new energy sources in the distribution network include substation information, transmission line information, and load data of each area, including: Obtaining substation information, including the geographical location, voltage level, transformer capacity, and wiring method of each substation; Acquire transmission line information, wherein the transmission line information includes the conductor model, length, transmission capacity, impedance, and starting and ending substations of the transmission line; Obtain load data, wherein the load data includes historical load data and future load growth trends.

3. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 2 is characterized in that: According to the data of new energy sources in the distribution network, a power grid model is established and the load levels in different time periods are set to obtain a simulated power grid model, including: According to the substation information and the transmission line information, a power grid model is established and parameters are set to obtain a preset power grid model; According to the preset power grid model, the actual power grid operation data is compared to obtain the verification model results; Draw the load curve based on the verification model results and load data collection; According to the load curve, set the power of the load point to obtain the load level in different time periods; According to the load levels in different time periods, the preset power grid model is used to simulate the load changes to obtain a simulated power grid model.

4. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 3 is characterized in that: Based on the data of new energy sources in the distribution network, the power flow of each node in the power grid is calculated using a simulated power grid model to obtain flow factors, including: According to the data of new energy in the distribution network, the use Get the active power of the grid, where P i is the active power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between nodes i and j, Δθ ij is the phase shift, P loss,i is the active power loss of node i; use Get the reactive power of the grid, where Q i is the reactive power of node i, V i and V j are the voltage amplitudes of i and j respectively, θ ij is the phase angle difference between nodes i and j, Δθ ij is the phase shift, Q loss,i is the reactive power loss of node i; According to the active power and reactive power of the power grid, iteratively calculate the power flow of each node; According to the power flow of each node, use The flow factor is obtained, where P inj,i is the power injected into the node, P flow,ij is the line power flowing from node i to node j, ne(i) is the set of neighboring nodes of node i, ∈ is a small positive number, V i is the voltage amplitude at the node, R ij and X ij are the resistance and reactance of line j, P loss,i is the active power loss at the node, Q flow,ij is the reactive power flowing from node i to node j.

5. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 4 is characterized in that: According to the flow factor, the distribution of power flow in the power grid is analyzed to obtain the power grid fluctuation factor, including: According to the flow factor, the fluctuation of active power and reactive power of each node at different time points is compared to obtain the power fluctuation of the node; According to the flow factor, the change rate of active power and reactive power of each line is calculated to obtain the power flow change rate of the line; According to the power fluctuation of the node and the power flow change rate of the line, use The grid fluctuation factor is calculated, where N is the number of nodes in the grid, ΔP i and ΔQ i is the active power and reactive power fluctuation of node i, P ij,0 and Q ij,0 is the initial active power and reactive power flow on line j, V i,max and V i,arg is the maximum voltage amplitude and average voltage amplitude of node i, R ij,max and R ij,arg is the maximum and average resistance of line j, X ij,max and X ij,arg is the maximum and average reactance of line j, T is the set of all time steps in the observation time window, and ∈ is a positive number.

6. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 5 is characterized in that: According to the grid fluctuation factor and the preset threshold, the grid stability is evaluated to obtain the stability evaluation results, including: Use the power grid monitoring system to collect data from each node of the power grid in real time and calculate the power grid fluctuation factor; Comparing the calculated power grid fluctuation factor with a preset threshold value to obtain a comparison result; Based on the comparison results, the stability assessment results are obtained. If the fluctuation factor is within the normal range, the power grid is considered to be in a stable state. If the fluctuation factor exceeds the warning threshold but does not reach the emergency threshold, it is considered that there are potential unstable factors in the power grid and close attention is required. If the fluctuation factor exceeds the emergency threshold, the power grid is considered to be in an unstable state and immediate intervention measures are required.

7. The method for evaluating the new energy consumption capacity of distribution network based on artificial intelligence according to claim 6 is characterized in that: According to the grid fluctuation factors and stability assessment results, the grid's absorption capacity is evaluated, including: According to the flow factor and the stability of the power grid, the relationship between the power grid fluctuation factor and the power grid absorption capacity is analyzed to obtain the power grid fluctuation factor analysis results. The smaller the power grid fluctuation factor, the more stable the power grid is and the stronger the absorption capacity is. According to the stability assessment results of the power grid, analyze the impact of stability on the power grid's absorption capacity to obtain stability analysis results; According to the results of the grid fluctuation factor analysis and stability analysis, a comprehensive assessment is conducted on the grid's absorption capacity to obtain the assessment results. The assessment results include the grid's absorption capacity level under different operating conditions, the change trend of the absorption capacity, and possible absorption bottlenecks.

8. An artificial intelligence-based distribution network new energy consumption capacity assessment system, characterized in that: include: An acquisition module is used to acquire data on new energy sources in the distribution network, wherein the data on new energy sources in the distribution network includes substation information, transmission line information, and load data of each area; The processing module is used to establish a power grid model and set load levels for different time periods according to the data of the new energy in the distribution network to obtain a simulated power grid model; according to the data of the new energy in the distribution network, the simulated power grid model is used to calculate the power flow of each node in the power grid to obtain a flow factor; according to the flow factor, the distribution of the power flow in the power grid is analyzed to obtain a power grid fluctuation factor; according to the power grid fluctuation factor and a preset threshold, the stability of the power grid is evaluated to obtain a stability evaluation result; according to the power grid fluctuation factor and the stability evaluation result, the absorption capacity of the power grid is evaluated.

9. A computing device, characterized in that include: one or more processors; A storage system for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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