Distribution network carrying capacity evaluation method, device, computer equipment and storage medium

Abstract

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the carrying capacity of a power distribution network. The method includes: acquiring historical grid operating data for photovoltaic (PV) power generation devices, wind power generation devices, and grid loads in a substation in a first region, as well as historical operating data for electric vehicle (EV) charging loads in the first region; calculating the output coefficient and grid load rate of the PV power generation devices and wind power generation devices, respectively, and calculating the comprehensive demand coefficient of the EV charging loads; sequentially calculating the comprehensive carrying capacity value formed by the PV power generation devices and wind power generation devices in a single substation, a second region, and the first region, and sequentially calculating the comprehensive carrying capacity value of the EV charging loads in a single substation, the second region, and the first region. Using this method, the combined effects of wind power, PV, and EVs can be comprehensively considered.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for evaluating the carrying capacity of a distribution network. Background Art

[0002] With the popularization of renewable energy and the expansion of electric vehicles (EVs), the instability of wind and solar power generation and the disorderly charging of EVs have changed the load characteristics, which have different degrees of negative impact on the safety and economy of distribution networks. The carrying capacity (HC) of the distribution network is defined as the maximum number of distributed generators (DG) and EVs that can be integrated into the grid while maintaining stable system operation and not exceeding various electrical limits. Evaluating HC provides a feasible technical approach to promote the coordinated development of DG and distribution networks.

[0003] In traditional technologies, the distribution network carrying capacity assessment technology is usually for the power supply and demand balance and power transmission within a single system. There is no specific assessment strategy and indicators for multi-layer and multi-dimensional power grids. In addition, the current research on distributed generation high energy efficiency usually focuses on the separate assessment of a single type of photovoltaic power generation (DPV), distributed wind power generation (DWG) or electric vehicles, while ignoring the comprehensive impact of multiple new energy sources and electric vehicles on overall high energy efficiency. In addition, the existing high energy efficiency assessment technology relies on mathematical modeling and optimization technology, which lacks practicality and applicability in actual distribution network planning.

[0004] Therefore, there is an urgent need for a distribution network carrying capacity assessment method, device, computer equipment, computer-readable storage medium and computer program product that can comprehensively consider the combined impact of wind power, photovoltaics and electric vehicles, and provide a multi-level and multi-dimensional distribution network carrying capacity assessment method to improve the accuracy and practicality of the assessment. Summary of the invention

[0005] Based on this, it is necessary to provide a distribution network carrying capacity assessment method, device, computer equipment, computer-readable storage medium and computer program product that can comprehensively consider the combined impact of wind power, photovoltaics and electric vehicles, and provide a multi-level and multi-dimensional distribution network carrying capacity assessment method to improve the accuracy and practicality of the assessment.

[0006] In a first aspect, the present application provides a method for evaluating the carrying capacity of a distribution network, comprising:

[0007] Obtaining historical grid operation data of photovoltaic power generation devices, wind power generation devices, and grid loads in the substation in the first area, and historical equipment operation data of electric vehicle charging loads in the first area;

[0008] According to the historical working data of the power grid, the output coefficients of the photovoltaic power generation device and the wind power generation device, as well as the power grid load rate are calculated respectively; and according to the historical working data of the equipment, the comprehensive demand coefficient of the electric vehicle charging load is calculated;

[0009] The comprehensive carrying capacity of the photovoltaic power generation device and the wind power generation device of a single substation is calculated based on the output coefficient of the photovoltaic power generation device and the wind power generation device, as well as the load rate of the power grid; and the comprehensive carrying capacity of the electric vehicle charging load of a single substation is calculated based on the comprehensive demand coefficient of the electric vehicle charging load;

[0010] Determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area;

[0011] The comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area is determined based on the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the subordinate second areas within the first area; and the comprehensive carrying capacity value of the electric vehicle charging load in the first area is determined based on the comprehensive carrying capacity value of the electric vehicle charging load in all the subordinate second areas within the first area.

[0012] In one embodiment, the equipment historical working data includes equipment charging load and equipment capacity; the comprehensive demand coefficient of the electric vehicle charging load is calculated based on the equipment historical working data, including:

[0013] Determine a first sum of the equipment charging loads at each moment in a preset time period on the interval prediction curve of the comprehensive demand coefficient, and a second sum of the equipment capacities in the area;

[0014] The ratio between the first sum and the second sum is used as the comprehensive demand coefficient of the electric vehicle charging load.

[0015] In one embodiment, the process of obtaining the interval prediction curve of the comprehensive demand coefficient includes:

[0016] The non-parametric Bootstrap resampling method is used to resample the historical working data of the power grid and the historical working data of the equipment respectively;

[0017] By performing M iterations of replacement sampling and repeating the iteration process N times, N power grid Bootstrap sample sets and N device Bootstrap sample sets are generated, where both M and N are positive integers;

[0018] According to the N power grid Bootstrap sample sets, respectively predicting interval prediction curves of the output coefficients of the photovoltaic power generation device and the wind power generation device, and predicting interval prediction curves of the power grid load rate;

[0019] According to the N Bootstrap sample sets of the equipment, an interval prediction curve of the comprehensive demand coefficient of the electric vehicle charging load is predicted.

[0020] In one embodiment, the comprehensive carrying capacity of the photovoltaic power generation device and the wind power generation device of a single substation is calculated according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the grid load rate; and the comprehensive carrying capacity of the electric vehicle charging load of a single substation is calculated according to the comprehensive demand coefficient of the electric vehicle charging load, including:

[0021] According to the output coefficients of photovoltaic power generation devices and wind power generation devices, as well as the grid load rate, the maximum incremental installed capacity of photovoltaic power generation devices and wind power generation devices in a single substation is calculated; and according to the comprehensive demand coefficient of electric vehicle charging load, the maximum carrying capacity of electric vehicle charging load in a single substation is calculated;

[0022] The comprehensive carrying capacity of the photovoltaic power generation device and the wind power generation device of a single substation is calculated based on the maximum incremental installed capacity of the photovoltaic power generation device and the wind power generation device of a single substation;

[0023] According to the maximum carrying capacity of the electric vehicle charging load of a single substation, the comprehensive carrying capacity value of the electric vehicle charging load of a single substation is calculated.

[0024] In one embodiment, determining the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area includes:

[0025] The comprehensive carrying capacity value of the independent substation is compared with the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area, and the minimum value of the two is taken as the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area.

[0026] In one embodiment, determining the comprehensive carrying capacity value of the electric vehicle charging load of the first area according to the comprehensive carrying capacity values ​​of the electric vehicle charging loads of all subordinate second areas in the first area includes:

[0027] The comprehensive carrying capacity value of the independent electric vehicle charging load is compared with the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate second areas in the first area, and the minimum value of the two is taken as the comprehensive carrying capacity value of the electric vehicle charging load of the first area.

[0028] In a second aspect, the present application also provides a distribution network carrying capacity evaluation device, comprising:

[0029] A data acquisition module, used to acquire the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the equipment of the electric vehicle charging load in the first area;

[0030] A calculation module, for calculating the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid according to the historical working data of the power grid; and for calculating the comprehensive demand coefficient of the charging load of the electric vehicle according to the historical working data of the equipment;

[0031] The calculation module is also used to calculate the comprehensive carrying capacity value jointly formed by the photovoltaic power generation device and the wind power generation device of a single substation according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid; and to calculate the comprehensive carrying capacity value of the electric vehicle charging load of a single substation according to the comprehensive demand coefficient of the electric vehicle charging load;

[0032] The calculation module is further used to determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area;

[0033] The calculation module is also used to determine the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area based on the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the subordinate second areas within the first area; and to determine the comprehensive carrying capacity value of the electric vehicle charging load in the first area based on the comprehensive carrying capacity value of the electric vehicle charging load in all the subordinate second areas within the first area.

[0034] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0036] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0037] The above-mentioned distribution network carrying capacity evaluation method, device, computer equipment, computer-readable storage medium and computer program product, this application uses a multi-level and multi-dimensional evaluation method to comprehensively consider the combined impact of wind power, photovoltaics and electric vehicles, and improves the accuracy of the distribution network carrying capacity (HC) evaluation. Through the three-level evaluation at the provincial, county and equipment levels, the distribution network's carrying capacity for new energy and electric vehicles can be more comprehensively reflected. The interval prediction technology based on historical data is adopted to improve the flexibility and adaptability of the evaluation results. This method not only improves the accuracy of the evaluation results, but also can adapt to the distribution network carrying capacity evaluation needs in different regions and under different conditions. Through the comprehensive carrying capacity evaluation at the provincial, county and equipment levels, this application helps to improve the utilization rate of new energy, especially when considering the comprehensive impact of multiple renewable energy sources and electric vehicles. By evaluating the carrying capacity of distribution networks at different levels, it helps to optimize the allocation of power resources and ensure the balance of power supply and demand and the efficiency of power transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 An application environment diagram of a method for evaluating the carrying capacity of a distribution network in an embodiment;

[0040] Figure 2 A schematic diagram of a flow chart of a method for evaluating the carrying capacity of a distribution network in one embodiment;

[0041] Figure 3 A schematic diagram of a flow chart of a method for evaluating the carrying capacity of a distribution network in another embodiment;

[0042] Figure 4 It is a framework diagram of a method for evaluating the comprehensive carrying capacity value of a multi-level distribution network in the most detailed embodiment;

[0043] Figure 5 It is a flow chart of a method for evaluating the carrying capacity of a distribution network in the most detailed embodiment;

[0044] Figure 6 It is a structural block diagram of a distribution network carrying capacity evaluation device in one embodiment;

[0045] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The distribution network carrying capacity evaluation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0048] The terminal 102 obtains the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the equipment of the electric vehicle charging load in the first area;

[0049] The server 104 calculates the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid according to the historical working data of the power grid; and calculates the comprehensive demand coefficient of the electric vehicle charging load according to the historical working data of the equipment; calculates the comprehensive carrying capacity value jointly formed by the photovoltaic power generation device and the wind power generation device of a single substation according to the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid; and calculates the comprehensive carrying capacity value of the electric vehicle charging load of a single substation according to the comprehensive demand coefficient of the electric vehicle charging load; and calculates the comprehensive carrying capacity value jointly formed by the photovoltaic power generation device and the wind power generation device of all subordinate substations in the second area according to the comprehensive demand coefficient of the electric vehicle charging load. Carrying capacity value, determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area; determine the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area according to the comprehensive carrying capacity value jointly formed by all subordinate second areas in the first area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the first area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate second areas in the first area.

[0050] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0051] In an exemplary embodiment, Figure 2 As shown in the figure, a distribution network carrying capacity evaluation method is provided. Figure 1 The server in the example is used to illustrate, including the following steps S202 to S210. Among them:

[0052] Step S202, obtaining the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the electric vehicle charging load in the first area.

[0053] Specifically, the first region refers to a level in the evaluation method, and in this embodiment, it is a provincial region. All relevant historical working data in the first region are collected, including data of photovoltaic power generation devices, wind power generation devices, grid loads, and electric vehicle charging loads.

[0054] Among them, the historical working data of photovoltaic power generation devices and wind power generation devices include but are not limited to power generation, power generation time series, power generation efficiency, etc. These data are helpful in evaluating the output coefficients of these power generation devices.

[0055] The historical working data of the power grid load includes the load demand of the power grid, load change trend, load peak value, etc., which are used to calculate the power grid load rate.

[0056] The historical working data of the electric vehicle charging load includes the charging demand of the electric vehicle, the charging time series, the charging efficiency, etc., which are used to calculate the comprehensive demand coefficient of the electric vehicle charging load.

[0057] The above historical working data includes daily, hourly and even 15-minute data in the past period of time (such as several years) to facilitate detailed analysis and prediction. These historical working data are the basis for evaluating the carrying capacity of the distribution network. By analyzing these data, the power coefficient, load rate and comprehensive demand coefficient can be calculated to evaluate the carrying capacity of the distribution network.

[0058] Step S204, respectively calculating the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid according to the historical working data of the power grid; and calculating the comprehensive demand coefficient of the electric vehicle charging load according to the historical working data of the equipment.

[0059] Among them, the output coefficient of photovoltaic power generation devices (DPV) and wind power generation devices (DWG) reflects the ratio between the actual power generation of photovoltaic and wind power generation devices and their maximum theoretical power generation within a specific time. The calculation of these coefficients requires the use of historical working data of the power grid, including the power output and operating time of the power generation devices. The calculation formula of the output coefficient may involve the ratio of actual power generation to installed capacity, and may be affected by factors such as weather and equipment performance.

[0060] The grid load rate refers to the ratio between the actual load of the grid in a specific period of time and the maximum load capacity of the grid. By analyzing the historical working data of the grid, the load demand of the grid in different time periods can be determined and the grid load rate can be calculated. This ratio helps to evaluate the carrying capacity of the grid in different situations.

[0061] Among them, the comprehensive demand coefficient of electric vehicle charging load reflects the ratio between the total charging demand of electric vehicle charging facilities in a specific time and their total capacity. The calculation of this coefficient requires the use of historical equipment working data of electric vehicle charging load, including the power output, charging time series and charging efficiency of charging facilities. The calculation of the comprehensive demand coefficient involves the ratio of the sum of all electric vehicle charging demands in a specific time period to the total capacity of charging facilities.

[0062] When calculating the above four coefficients, interval prediction technology (such as the Bootstrap method) can be used to process historical working data to obtain more accurate output coefficients, load rates and demand coefficients. At the same time, the randomness and uncertainty of the data can be taken into account to provide prediction results within a certain confidence interval (for example, 95%).

[0063] Step S206, based on the output coefficients of the photovoltaic power generation devices and the wind power generation devices, and the grid load rate, calculate the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and the wind power generation devices of a single substation; and based on the comprehensive demand coefficient of the electric vehicle charging load, calculate the comprehensive carrying capacity value of the electric vehicle charging load of a single substation.

[0064] Specifically, the combined carrying capacity of photovoltaic and wind power generation devices reflects the maximum capacity of renewable energy generation that a single substation can carry, taking into account the output coefficient of photovoltaic and wind power generation devices and the load rate of the power grid. To calculate this value, it is necessary to comprehensively consider the output characteristics of photovoltaic and wind power generation, as well as the load of the power grid.

[0065] The output coefficients of photovoltaic and wind power generation represent the ratio between the actual power generation of these power generation devices in a specific time and their maximum theoretical power generation. These coefficients take into account the impact of factors such as weather and equipment performance on power generation. The grid load rate represents the ratio between the actual load of the grid in a specific time and the maximum load capacity of the grid. This ratio helps to evaluate the carrying capacity of the grid under different circumstances.

[0066] Among them, the comprehensive demand coefficient of electric vehicle charging load reflects the ratio between the total charging demand of electric vehicle charging facilities within a specific time and their total capacity. To calculate this value, it is necessary to use the historical working data of the equipment of electric vehicle charging load, including the power output of the charging facilities, charging time series and charging efficiency.

[0067] Step S208, determining the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determining the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area.

[0068] Specifically, first, the comprehensive carrying capacity values ​​of the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area are summarized, which means that the carrying capacity values ​​of each substation need to be added together to obtain the total carrying capacity of the photovoltaic and wind power generation devices in the entire second area.

[0069] After summarizing the comprehensive carrying capacity values ​​of subordinate substations, it is necessary to determine the total comprehensive carrying capacity value of photovoltaic power generation devices and wind power generation devices in the second area. This value reflects the maximum capacity of new energy power generation that the second area can carry, taking into account the output coefficients of photovoltaic and wind power generation devices and the load rate of the power grid.

[0070] Similarly, the comprehensive carrying capacity values ​​of the electric vehicle charging loads of all subordinate substations in the second region are summarized. This means that the carrying capacity values ​​of the electric vehicle charging loads of each subordinate region or substation need to be added together to obtain the total carrying capacity of the electric vehicle charging loads of the entire second region. After summarizing the comprehensive carrying capacity values ​​of the electric vehicle charging loads of the subordinate regions, it is necessary to determine the total comprehensive carrying capacity value of the electric vehicle charging loads in the second region. This value reflects the maximum capacity of electric vehicle charging that the second region can carry, taking into account the comprehensive demand coefficient of the electric vehicle charging load.

[0071] The above step S208 reflects the application of the multi-level evaluation method, that is, the step-by-step summary and determination process from the equipment level to the county level. In this way, the consistency and accuracy of the evaluation results at different levels can be ensured.

[0072] Step S210, determining the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area based on the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in all subordinate second areas within the first area; and determining the comprehensive carrying capacity value of the electric vehicle charging load in the first area based on the comprehensive carrying capacity value of the electric vehicle charging load in all subordinate second areas within the first area.

[0073] Specifically, after summarizing the comprehensive carrying capacity values ​​of the subordinate second region, it is necessary to determine the total comprehensive carrying capacity values ​​of the photovoltaic power generation devices and wind power generation devices in the first region. This value reflects the maximum capacity of new energy power generation that the first region can carry, taking into account the output coefficients of the photovoltaic and wind power generation devices and the grid load rate.

[0074] After summarizing the comprehensive carrying capacity values ​​of the electric vehicle charging loads in the subordinate second area, it is necessary to determine the total comprehensive carrying capacity value of the electric vehicle charging loads in the first area. This value reflects the maximum capacity of electric vehicle charging that the first area can carry, taking into account the comprehensive demand coefficient of the electric vehicle charging loads.

[0075] It reflects the application of a multi-level assessment method, that is, a step-by-step summary and determination process from the equipment level to the county level, and then to a higher level region (such as the provincial level). In this way, the consistency and accuracy of the assessment results at different levels can be ensured.

[0076] In the above-mentioned distribution network carrying capacity evaluation method, this application comprehensively considers the combined impact of wind power, photovoltaics and electric vehicles through a multi-level and multi-dimensional evaluation method, thereby improving the accuracy of the distribution network carrying capacity (HC) evaluation. Through the three-level evaluation at the provincial, county and equipment levels, the distribution network's carrying capacity for new energy and electric vehicles can be more comprehensively reflected. The interval prediction technology based on historical data is adopted to improve the flexibility and adaptability of the evaluation results. This method not only improves the accuracy of the evaluation results, but also can adapt to the distribution network carrying capacity evaluation needs in different regions and under different conditions. Through the comprehensive carrying capacity evaluation at the provincial, county and equipment levels, this application helps to improve the utilization rate of new energy, especially when considering the comprehensive impact of multiple renewable energy sources and electric vehicles. By evaluating the carrying capacity of distribution networks at different levels, it helps to optimize the allocation of power resources and ensure the balance of power supply and demand and the efficiency of power transmission.

[0077] In an exemplary embodiment, the historical working data of the equipment includes the charging load of the equipment and the capacity of the equipment; and the comprehensive demand coefficient of the charging load of the electric vehicle is calculated according to the historical working data of the equipment, including:

[0078] Determine a first sum of the equipment charging loads at each moment in a preset time period on the interval prediction curve of the comprehensive demand coefficient, and a second sum of the equipment capacities in the area;

[0079] The ratio between the first sum and the second sum is used as the comprehensive demand coefficient of the electric vehicle charging load.

[0080] Specifically, the equipment historical operation data includes the equipment charging load and equipment capacity of the electric vehicle charging facility. The equipment charging load refers to the power output of the electric vehicle charging facility within a specific time, and the second sum of the equipment capacity reflects the total capacity of the charging facility.

[0081] The interval prediction curve predicts the changing trend of electric vehicle charging demand in the future by analyzing historical data. This curve provides a confidence interval (such as 95%), indicating the possible range of electric vehicle charging demand at a specific moment.

[0082] In the preset period, the charging load of the equipment at each moment is added to obtain the first sum, which reflects the total power output of the electric vehicle charging facilities in the period. In the preset area, the capacity of each device is added to obtain the second sum, which reflects the sum of the equipment capacity of the electric vehicle charging infrastructure in the area. The ratio between the first sum and the second sum is used as the comprehensive demand coefficient of the electric vehicle charging load. This coefficient reflects the ratio between the average charging demand and the total capacity of the electric vehicle charging facilities in the preset period.

[0083] The comprehensive demand coefficient can help evaluate the efficiency of electric vehicle charging facilities and the grid's carrying capacity for electric vehicle charging demand. By calculating this coefficient, we can better understand and predict the impact of electric vehicle charging load on the grid, providing a scientific basis for grid planning and management.

[0084] In this embodiment, by calculating the equipment charging load and the comprehensive demand coefficient of the electric vehicle charging facility, it is beneficial to improve the accuracy of the evaluation result of the distribution network carrying capacity.

[0085] In an exemplary embodiment, the process of obtaining the interval prediction curve of the comprehensive demand coefficient includes:

[0086] The non-parametric Bootstrap resampling method is used to resample the historical working data of the power grid and the historical working data of the equipment respectively;

[0087] By performing M iterations of replacement sampling and repeating the iteration process N times, N power grid Bootstrap sample sets and N device Bootstrap sample sets are generated, where M and N are both positive integers;

[0088] According to N power grid Bootstrap sample sets, the interval prediction curves of the output coefficients of photovoltaic power generation devices and wind power generation devices are predicted respectively, and the interval prediction curve of the power grid load rate is predicted;

[0089] According to the Bootstrap sample set of N devices, the interval prediction curve of the comprehensive demand coefficient of the electric vehicle charging load is predicted.

[0090] Bootstrap is a statistical resampling technique used to estimate the distribution of a statistic from the original data set. The non-parametric Bootstrap method does not require any assumptions about the distribution of the data, so it is called "non-parametric".

[0091] Resampling the historical working data of the power grid and the historical working data of the equipment means randomly extracting samples from these data sets to construct new sample sets. These sample sets are used to simulate the statistical characteristics of the original data sets. In each iteration, the samples are replaced by random sampling from the original data sets to form a new sample set. This means that each sample can be extracted multiple times or not.

[0092] Performing M iterations of replacement sampling means performing M random draws on each sample set to form M new sample sets. These sample sets will be used for subsequent statistical analysis. Repeat the above iteration process N times to generate N power grid bootstrap sample sets and N device bootstrap sample sets. Both M and N are positive integers, indicating the number of iterations.

[0093] Based on N power grid Bootstrap sample sets, statistical analysis methods (such as calculating the mean, median, confidence interval, etc.) are used to predict the interval prediction curves of the output coefficients of photovoltaic power generation devices and wind power generation devices, as well as the interval prediction curves of the power grid load rate. These curves provide the range of possible changes in the output coefficient and load rate in a certain period of time in the future.

[0094] Based on the Bootstrap sample set of N devices, the statistical analysis method is also used to predict the interval prediction curve of the comprehensive demand coefficient of the electric vehicle charging load. This curve provides the range of possible changes in the electric vehicle charging load in a certain period of time in the future.

[0095] These interval prediction curves provide predictions of the comprehensive demand coefficients of photovoltaic and wind power output factors, grid load factors and electric vehicle charging demand in the future. These predictions are accompanied by confidence intervals and can be used to evaluate the carrying capacity of the distribution network.

[0096] In this embodiment, the Bootstrap method is used to resample the historical data to generate prediction curves, which are helpful for evaluating and predicting the carrying capacity of the distribution network under different situations and can improve the accuracy and reliability of the evaluation results because it takes into account the randomness and uncertainty of the data.

[0097] In an exemplary embodiment, Figure 3 As shown, according to the output coefficients of photovoltaic power generation devices and wind power generation devices, as well as the grid load rate, the comprehensive carrying capacity of photovoltaic power generation devices and wind power generation devices of a single substation is calculated; and according to the comprehensive demand coefficient of electric vehicle charging load, the comprehensive carrying capacity of electric vehicle charging load of a single substation is calculated, including:

[0098] Step S302, calculating the maximum incremental installed capacity of the photovoltaic power generation device and the wind power generation device of a single substation according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid; and calculating the maximum carrying capacity of the electric vehicle charging load of a single substation according to the comprehensive demand coefficient of the electric vehicle charging load;

[0099] Step S304, based on the maximum incremental installed capacity of the photovoltaic power generation devices and wind power generation devices in a single substation, calculate the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in a single substation; based on the maximum carrying capacity of the electric vehicle charging load in a single substation, calculate the comprehensive carrying capacity value of the electric vehicle charging load in a single substation.

[0100] Specifically, based on the output coefficients of photovoltaic and wind power generation devices and the load rate of the power grid, the maximum installed capacity that can be added to the photovoltaic and wind power generation devices of a single substation without exceeding the carrying capacity of the power grid can be calculated. This calculation takes into account the output characteristics of the power generation device and the load of the power grid.

[0101] Based on the comprehensive demand factor of electric vehicle charging load, the maximum charging capacity that a single substation can carry without exceeding the grid's carrying capacity can be calculated. This calculation takes into account the ratio between electric vehicle charging demand and the total capacity of charging facilities.

[0102] Based on the maximum incremental installed capacity of photovoltaic power generation devices and wind power generation devices in a single substation, the comprehensive carrying capacity of photovoltaic power generation devices and wind power generation devices in a single substation can be calculated. This value reflects the maximum capacity of new energy power generation that the substation can carry, taking into account the output coefficient of photovoltaic and wind power generation devices and the load rate of the power grid.

[0103] Based on the maximum carrying capacity of electric vehicle charging load at a single substation, the comprehensive carrying capacity of electric vehicle charging load at a single substation can be calculated. This value reflects the maximum charging capacity that electric vehicle charging facilities can carry when considering the comprehensive demand factor.

[0104] The calculation of these comprehensive carrying capacity values ​​is part of a multi-level assessment approach that will be performed at multiple levels, including the device level, county level, and province level, to fully assess the carrying capacity of the distribution network.

[0105] In this embodiment, by calculating the comprehensive carrying capacity value of photovoltaic power generation, wind power generation and electric vehicle charging load, the accuracy of the evaluation result of the distribution network carrying capacity can be improved.

[0106] In an exemplary embodiment, determining the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area includes:

[0107] The comprehensive carrying capacity value of the independent substation in the second area is compared with the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area, and the minimum value between the two is taken as the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area.

[0108] In an exemplary embodiment, determining the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity values ​​of the electric vehicle charging loads of all subordinate substations in the second area includes:

[0109] The comprehensive carrying capacity value of the independent electric vehicle charging load in the second area is compared with the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area, and the minimum value of the two is taken as the comprehensive carrying capacity value of the electric vehicle charging load in the second area.

[0110] Specifically, when determining the comprehensive carrying capacity value of the photovoltaic power generation device and the wind power generation device in the second area, it is necessary to compare the comprehensive carrying capacity value of each independent substation with the comprehensive carrying capacity value formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area. The purpose of this step is to ensure that the evaluation result does not exceed the carrying capacity limit of any subordinate substation.

[0111] A conservative approach is adopted to determine the regional level carrying capacity by selecting the minimum value of the two as the comprehensive carrying capacity value of the photovoltaic power generation device and the wind power generation device in the second area. This approach can prevent the overload of the power grid and ensure the stable and safe operation of the power grid.

[0112] Similarly, for the electric vehicle charging load, it is also necessary to compare the comprehensive carrying capacity value of the independent electric vehicle charging load in the second area with the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area, and take the minimum value of the two as the comprehensive carrying capacity value of the electric vehicle charging load in the second area.

[0113] In this embodiment, this method embodies the step-by-step aggregation and determination method from the device level to the regional level, ensuring the consistency and accuracy of the evaluation results at different levels. It can ensure that the power grid planning and operation of the second area will not exceed the carrying capacity of any subordinate substation or electric vehicle charging facility, thereby improving the safety and reliability of the power grid.

[0114] In an exemplary embodiment, the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area is determined according to the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the second area under the first area, including:

[0115] The comprehensive carrying capacity value of the independent substation in the first area is compared with the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the subordinate second area in the first area, and the minimum value between the two is taken as the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the first area.

[0116] Determining the comprehensive carrying capacity value of the electric vehicle charging load in the first area according to the comprehensive carrying capacity values ​​of the electric vehicle charging load in all subordinate second areas in the first area includes:

[0117] The comprehensive carrying capacity value of the independent electric vehicle charging load in the first area is compared with the comprehensive carrying capacity value of the electric vehicle charging load in all subordinate second areas within the first area, and the minimum value of the two is taken as the comprehensive carrying capacity value of the electric vehicle charging load in the first area.

[0118] Specifically, the comprehensive carrying capacity values ​​of all the photovoltaic power generation devices, wind power generation devices and electric vehicle charging loads in the subordinate second areas in the first area are calculated. These values ​​are obtained based on the independent evaluation results of each second area. For photovoltaic power generation devices and wind power generation devices, the comprehensive carrying capacity value of the independent substation in the first area is compared with the comprehensive carrying capacity value of the subordinate second area. The minimum value of the two is selected as the comprehensive carrying capacity value of the photovoltaic power generation device and wind power generation device in the first area.

[0119] Similarly, for the electric vehicle charging load, the comprehensive carrying capacity value of the independent electric vehicle charging load in the first area is also compared with the comprehensive carrying capacity value of the subordinate second area, and the minimum value of the two is selected.

[0120] In this embodiment, by selecting the minimum value as the final comprehensive carrying capacity value, this method embodies the principle of conservatism, ensuring that the power grid planning and operation of the first area will not exceed the carrying capacity limit of any subordinate area, thereby ensuring the safety and stability of the power grid. This process ensures the consistency of the carrying capacity assessment results from subordinate areas to higher-level areas, so that the assessment results of each level are based on the maximum practical carrying capacity. In practical applications, this method can help grid operators and planners understand the carrying capacity of the power grid at different levels, so as to better allocate resources, upgrade the power grid and manage loads. Through this method, the risks in the operation of the power grid can be effectively managed to avoid power grid failures or instability caused by exceeding the carrying capacity.

[0121] The most detailed embodiment of this application is:

[0122] When constructing the JHC (comprehensive carrying capacity value, or comprehensive carrying capacity) evaluation method, a province is taken as the final evaluation object. Figure 4 As shown, the distribution network is divided into three levels: provincial level, county level and equipment level, and JHC assessment is performed at each level. The JHC assessment method of this application follows the following basic principles:

[0123] 1) Provincial JHC assessment principles: Based on the constraints of new energy utilization rate, carry out JHC calculation for the whole province.

[0124] 2) County-level JHC assessment principles: The electricity generated by distributed DPV (photovoltaic power generation devices) and distributed DWG (photovoltaic wind power devices) is consumed within the county-level area, and county-level JHC calculations are carried out.

[0125] 3) Equipment-level JHC evaluation principle: Under the N-1 safety constraint, calculate the JHC of equipment at 220 kV substations and below at each voltage level. Among them, the 110 kV and below equipment is subject to the constraint of no reverse overload; the 220 kV substation is subject to the constraint that DG (i.e. distributed DPV (photovoltaic power generation device) and distributed DWG (photovoltaic wind power device)) power generation is not transmitted, and the centralized transmission power does not cause reverse overload of the equipment. "N-1 constraint" refers to the ability of the system to continue to operate normally even if any single component fails.

[0126] 4) Principles for selecting typical scenarios: To ensure the reliability of the calculation results and reduce the impact of extreme scenarios, the output coefficients, load factors of DPV and DWG, and the comprehensive demand coefficients of electric vehicles (EVs) will be selected based on interval prediction results within a 95% confidence interval.

[0127] Specifically, the elements of the JHC assessment method in a multi-level distribution network are:

[0128] (1) Comprehensive demand coefficient: The comprehensive demand coefficient of charging infrastructure in a certain area at time t is:

[0129]

[0130] Where P EV,i,t is the charging load of device i at time t; S EV,i is the capacity of device i; τ EV (t) is the demand factor of equipment i at time t.

[0131] (2) Power supply classification:

[0132] Non-adjustable output power sources, including wind power generation and photovoltaic power generation, can be centralized or distributed; adjustable output power sources include traditional centralized power sources (such as thermal power generators) and other distributed power sources.

[0133] (3) Load classification:

[0134] Electric vehicle charging load; other conventional loads.

[0135] (4) Interval prediction: The non-parametric Bootstrap resampling technique is used to estimate the parameters of the data set through a data set containing M actual measured values. When a data point is selected from the original data set, there is a 1 / M probability of being selected again and included in the training set. By performing M iterations of replacement sampling, a Bootstrap sample set is created. This process is repeated N times to generate N Bootstrap sample sets, each with its estimated value. Analyzing these sample sets helps to reflect the probability distribution and random properties of the estimated values. Applying the Bootstrap method in the sampling process can effectively alleviate the heteroscedasticity in time series data such as wind power generation, photovoltaic power generation and load power generation. The present invention uses this method to resample historical data and obtains 24-hour wind energy, photovoltaic output coefficient curves, grid load rate coefficient curves and electric vehicle comprehensive demand coefficient curves at 95% confidence level for subsequent reference.

[0136] (5) Calculate the comprehensive bearing capacity (JHC):

[0137] ① Equipment-level evaluation:

[0138] Under the conditions of maximum power demand and minimum adjustable power output within 24 hours, the comprehensive carrying capacity of the distribution network for DPV, DWG and EV is:

[0139]

[0140] Where: P l,max Represents the maximum load value in the area, P g,min Represents the minimum output value of the adjustable power supply; C WT,new and C PV,new Represent the incremental installed capacity allowed for photovoltaic and wind power, C EV Indicates the additional charging capacity allowed for electric vehicles; C WT,now , C PV,now Respectively represent the installed capacity of photovoltaic and wind power that have been connected to the grid; τ WT (t) represents the output coefficient of the wind power generation device, τ PV (t) represents the output coefficient of the photovoltaic power generation device, τ EV (t) represents the comprehensive demand coefficient of electric vehicles, τ l (t) represents the proportional coefficient of load to maximum load, and its value range is [0,1]; S A is the total capacity of the power grid equipment, η Ax , η As It is the maximum positive grid load rate and maximum reverse grid load rate allowed by the grid equipment.

[0141] Assuming that the newly added capacity of DPV and DWG is 0, the maximum forward grid load rate moment t1 is selected, and the carrying capacity of the equipment for newly added EV access is calculated by the following formula:

[0142]

[0143] Assuming that the newly added access capacity of EV is 0, the maximum reverse grid load rate moment t2 is selected, and the carrying capacity of the device to carry the newly added DG access is calculated by the following formula:

[0144]

[0145] In a top-down order, the capacity of power equipment at each level to carry EV and DG is connected respectively. When the capacity of the upper-level equipment to carry DG / EV is less than that of the lower-level equipment, the measured value of the lower-level equipment's carrying capacity is used as the weight to distribute the upper-level equipment's carrying capacity to the lower-level equipment as the actual value of the lower-level equipment's carrying capacity. The calculation formula is as follows:

[0146]

[0147] Where: S x ' j,i is the actual bearing capacity of the lower-level equipment; S s ' j is the actual carrying capacity of the upper-level equipment in the system; S xj,i , S xj,j Calculate the carrying capacity of the lower-level equipment.

[0148] ② County carrying capacity assessment:

[0149] Taking the distribution substation in the county as the smallest unit, the actual carrying capacity of each distribution substation for EVs within the county is accumulated as the capacity of the county to carry new EV access. The calculation formula is as follows:

[0150]

[0151] Where: S x ' y,EV is the actual carrying capacity of the county for EVs; S p ' b,EV,j is the actual carrying capacity of distribution transformer j for EVs in the county.

[0152] Taking the local consumption of DG as a constraint and assuming that the capacity of new EV access is 0, the county's carrying capacity for DG is calculated according to the following formula:

[0153]

[0154] Where: P xyl,max represents the maximum load value in the county, Pxyg,min Represents the minimum output value of the adjustable power supply in the county; C xyWT,now , C xyPV,now Respectively represent the installed capacity of photovoltaic power generation devices and wind power generation devices connected to the grid in the county; τ xyWT , τ xyPV and τ xyl They represent the wind power and photovoltaic power generation output coefficients, as well as the ratio of load to maximum load, respectively, and their value range is [0,1].

[0155] The county's carrying capacity for new EV access is:

[0156]

[0157] Where: S' pb,DG,j It is the carrying capacity of the distribution substation within the county for DG. By summing up the carrying capacity values ​​of all counties within the scope, the carrying capacity of the county equipment level for DG can be obtained. The minimum value of the DG carrying capacity under the constraints of the county equipment level carrying capacity and the absorption capacity is taken to obtain the carrying capacity of the county for DG.

[0158] ③ Provincial carrying capacity assessment:

[0159] Taking the province's new energy utilization rate and peak-shaving capacity as constraints, an assessment of the province's carrying capacity was carried out based on an 8,760-hour time-series production simulation method.

[0160] First, assuming that the newly added DG installed capacity in the province is 0, and taking the peak load-shaving capacity of the province as a constraint, the EV carrying capacity calculation is carried out to obtain the maximum access capacity of the newly added EVs;

[0161] Secondly, assuming that the new EV capacity in the province is 0, the DG carrying capacity is calculated based on the new energy utilization rate and peak-shaving capacity to obtain the maximum DG access capacity allowed.

[0162] Compare the cumulative results of the carrying capacity of DG / EV in all counties and districts with the carrying capacity calculated based on the 8760-hour time series simulation, and take the minimum value as the final result of the provincial JHC. The calculation formula is as follows:

[0163]

[0164] Where: S' xy,DG,j is the carrying capacity of all county-level administrative units in the province for DG; S sy,DG,new It is the carrying capacity of DG calculated by the province based on 8760 hours of time series production simulation; S' sy,DG,new It is the final provincial carrying capacity for DG selected after comparison.

[0165] S' xy,EV,jis the carrying capacity of all county-level administrative units in the province for DG; S sy,EV,new It is the carrying capacity of DG calculated by the province based on 8760 hours of time series production simulation; S' sy,EV,new It is the final provincial carrying capacity for DG selected after comparison.

[0166] ④ Principles for linking carrying capacity at all levels:

[0167] When considering the development scale of EV and DG, local governments need to determine the scale of EV / DG grid connection in each region based on the "first come, first served" or allocation principle determined by the energy regulatory authorities, while meeting the equipment carrying capacity of the provincial, county and voltage levels.

[0168] like Figure 5 As shown, using the above evaluation method, the JHC of the distribution network is evaluated at three levels.

[0169] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0170] Based on the same inventive concept, the embodiment of the present application also provides a distribution network carrying capacity assessment device for implementing the distribution network carrying capacity assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network carrying capacity assessment device embodiments provided below can refer to the limitations of the distribution network carrying capacity assessment method above, and will not be repeated here.

[0171] In an exemplary embodiment, Figure 6 As shown, a distribution network carrying capacity evaluation device is provided, comprising:

[0172] The data acquisition module 602 is used to acquire the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the equipment of the electric vehicle charging load in the first area;

[0173] The calculation module 604 is used to calculate the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid according to the historical working data of the power grid; and calculate the comprehensive demand coefficient of the charging load of the electric vehicle according to the historical working data of the equipment;

[0174] The calculation module 604 is further used to calculate the comprehensive carrying capacity value jointly formed by the photovoltaic power generation device and the wind power generation device of a single substation according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the grid load rate; and calculate the comprehensive carrying capacity value of the electric vehicle charging load of a single electric substation according to the comprehensive demand coefficient of the electric vehicle charging load;

[0175] The calculation module 604 is further used to determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area;

[0176] The calculation module 604 is also used to determine the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area based on the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in all subordinate second areas within the first area; and to determine the comprehensive carrying capacity value of the electric vehicle charging load in the first area based on the comprehensive carrying capacity value of the electric vehicle charging load in all subordinate second areas within the first area.

[0177] In an exemplary embodiment, the historical working data of the equipment includes the equipment charging load and the equipment capacity; the calculation module 604 is also used to determine the first sum of the equipment charging load at each moment in a preset time period on the interval prediction curve of the comprehensive demand coefficient, and the second sum of the equipment capacity in the area; the ratio between the first sum and the second sum is used as the comprehensive demand coefficient of the electric vehicle charging load.

[0178] In an exemplary embodiment, the data acquisition module 602 is also used to adopt a non-parametric Bootstrap resampling method to resample the historical working data of the power grid and the historical working data of the equipment respectively; by performing M iterations of replacement sampling and repeating N iterations, N power grid Bootstrap sample sets and N equipment Bootstrap sample sets are generated, and both M and N are positive integers; based on the N power grid Bootstrap sample sets, interval prediction curves of the output coefficients of photovoltaic power generation devices and wind power generation devices are predicted, and an interval prediction curve of the power grid load rate is predicted; based on the N equipment Bootstrap sample sets, an interval prediction curve of the comprehensive demand coefficient of the electric vehicle charging load is predicted.

[0179] In an exemplary embodiment, the calculation module 604 is also used to calculate the maximum incremental installed capacity of the photovoltaic power generation device and the wind power generation device of a single substation based on the output coefficient of the photovoltaic power generation device, the wind power generation device, and the grid load rate; and to calculate the maximum carrying capacity of the electric vehicle charging load of a single substation based on the comprehensive demand coefficient of the electric vehicle charging load; to calculate the comprehensive carrying capacity value of the photovoltaic power generation device and the wind power generation device of a single substation based on the maximum incremental installed capacity of the photovoltaic power generation device and the wind power generation device of a single substation; and to calculate the comprehensive carrying capacity value of the electric vehicle charging load of a single substation based on the maximum carrying capacity of the electric vehicle charging load of a single substation.

[0180] In an exemplary embodiment, the calculation module 604 is also used to compare the comprehensive carrying capacity value of the independent substation with the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area, and take the minimum value between the two as the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area.

[0181] In an exemplary embodiment, the calculation module 604 is also used to compare the comprehensive carrying capacity value of the independent electric vehicle charging load with the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate second areas in the first area, and take the minimum value of the two as the comprehensive carrying capacity value of the electric vehicle charging load of the first area.

[0182] Each module in the above-mentioned distribution network carrying capacity assessment device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0183] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the equipment of the electric vehicle charging load in the first area. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution network carrying capacity evaluation method is implemented.

[0184] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0185] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above method when executing the computer program.

[0186] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0187] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0188] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0189] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0190] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0191] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the carrying capacity of a distribution network, characterized in that: The method comprises: Obtaining historical grid operation data of photovoltaic power generation devices, wind power generation devices, and grid loads in the substation in the first area, and historical equipment operation data of electric vehicle charging loads in the first area; According to the historical working data of the power grid, the output coefficients of the photovoltaic power generation device and the wind power generation device, as well as the power grid load rate are calculated respectively; and according to the historical working data of the equipment, the comprehensive demand coefficient of the electric vehicle charging load is calculated; The comprehensive carrying capacity of the photovoltaic power generation device and the wind power generation device of a single substation is calculated based on the output coefficient of the photovoltaic power generation device and the wind power generation device, as well as the load rate of the power grid; and the comprehensive carrying capacity of the electric vehicle charging load of a single substation is calculated based on the comprehensive demand coefficient of the electric vehicle charging load; Determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area; The comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area is determined based on the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the subordinate second areas within the first area; and the comprehensive carrying capacity value of the electric vehicle charging load in the first area is determined based on the comprehensive carrying capacity value of the electric vehicle charging load in all the subordinate second areas within the first area.

2. The method according to claim 1, characterized in that The equipment historical working data includes equipment charging load and equipment capacity; the comprehensive demand coefficient of the electric vehicle charging load is calculated based on the equipment historical working data, including: Determine a first sum of the equipment charging loads at each moment in a preset time period on the interval prediction curve of the comprehensive demand coefficient, and a second sum of the equipment capacities in the area; The ratio between the first sum and the second sum is used as the comprehensive demand coefficient of the electric vehicle charging load.

3. The method according to claim 2, characterized in that The process of obtaining the interval prediction curve of the comprehensive demand coefficient includes: The non-parametric Bootstrap resampling method is used to resample the historical working data of the power grid and the historical working data of the equipment respectively; By performing M iterations of replacement sampling and repeating the iteration process N times, N power grid Bootstrap sample sets and N device Bootstrap sample sets are generated, where both M and N are positive integers; According to the N power grid Bootstrap sample sets, respectively predicting interval prediction curves of the output coefficients of the photovoltaic power generation device and the wind power generation device, and predicting interval prediction curves of the power grid load rate; According to the N Bootstrap sample sets of the equipment, an interval prediction curve of the comprehensive demand coefficient of the electric vehicle charging load is predicted.

4. The method according to claim 1, characterized in that: The comprehensive carrying capacity value of the photovoltaic power generation device and the wind power generation device of a single substation is calculated according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the grid load rate; And according to the comprehensive demand coefficient of electric vehicle charging load, the comprehensive carrying capacity value of electric vehicle charging load of a single substation is calculated, including: According to the output coefficients of photovoltaic power generation devices and wind power generation devices, as well as the grid load rate, the maximum incremental installed capacity of photovoltaic power generation devices and wind power generation devices in a single substation is calculated; and according to the comprehensive demand coefficient of electric vehicle charging load, the maximum carrying capacity of electric vehicle charging load in a single substation is calculated; The comprehensive carrying capacity of the photovoltaic power generation device and the wind power generation device of a single substation is calculated based on the maximum incremental installed capacity of the photovoltaic power generation device and the wind power generation device of a single substation; According to the maximum carrying capacity of the electric vehicle charging load of a single substation, the comprehensive carrying capacity value of the electric vehicle charging load of a single substation is calculated.

5. The method according to claim 1, characterized in that Determining the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area includes: The comprehensive carrying capacity value of the independent substation is compared with the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area, and the minimum value of the two is taken as the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area.

6. The method according to claim 1, characterized in that Determining the comprehensive carrying capacity value of the electric vehicle charging load in the first area according to the comprehensive carrying capacity values ​​of the electric vehicle charging loads in all subordinate second areas in the first area includes: The comprehensive carrying capacity value of the independent electric vehicle charging load is compared with the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate second areas in the first area, and the minimum value of the two is taken as the comprehensive carrying capacity value of the electric vehicle charging load of the first area.

7. A distribution network carrying capacity assessment device, characterized in that: The device comprises: A data acquisition module, used to acquire the historical working data of the photovoltaic power generation device, the wind power generation device and the power grid load in the substation in the first area, and the historical working data of the equipment of the electric vehicle charging load in the first area; A calculation module, for calculating the output coefficients of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid according to the historical working data of the power grid; and for calculating the comprehensive demand coefficient of the charging load of the electric vehicle according to the historical working data of the equipment; The calculation module is also used to calculate the comprehensive carrying capacity value jointly formed by the photovoltaic power generation device and the wind power generation device of a single substation according to the output coefficient of the photovoltaic power generation device and the wind power generation device, and the load rate of the power grid; and to calculate the comprehensive carrying capacity value of the electric vehicle charging load of a single substation according to the comprehensive demand coefficient of the electric vehicle charging load; The calculation module is further used to determine the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices in the second area according to the comprehensive carrying capacity value jointly formed by the photovoltaic power generation devices and wind power generation devices of all subordinate substations in the second area; and determine the comprehensive carrying capacity value of the electric vehicle charging load in the second area according to the comprehensive carrying capacity value of the electric vehicle charging load of all subordinate substations in the second area; The calculation module is also used to determine the comprehensive carrying capacity value of the photovoltaic power generation devices and wind power generation devices in the first area based on the comprehensive carrying capacity value jointly formed by all the photovoltaic power generation devices and wind power generation devices in the subordinate second areas within the first area; and to determine the comprehensive carrying capacity value of the electric vehicle charging load in the first area based on the comprehensive carrying capacity value of the electric vehicle charging load in all the subordinate second areas within the first area.

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 according to 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 according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images