Pile-road-network infrastructure bearing capacity evaluation method and device, computer equipment

Abstract

This application relates to a method and apparatus for assessing the carrying capacity of pile-road-network infrastructure, as well as computer equipment, and pertains to the field of power grid technology. The method includes: acquiring multi-dimensional parameters of the distribution network grid to be assessed, including grid-side parameters, charging station parameters, and traffic-side parameters; performing multi-dimensional constraint verification on the pile-road-network infrastructure of the distribution network grid based on the multi-dimensional parameters to obtain multi-dimensional verification results; increasing the electric vehicle capacity of the distribution network grid if none of the multi-dimensional verification results exceed the carrying capacity limit, iteratively performing multi-dimensional constraint verification, and adjusting the electric vehicle capacity through a feedback closed loop until the maximum electric vehicle capacity satisfying each dimension constraint is found; and outputting the carrying capacity assessment result of the distribution network grid based on the maximum electric vehicle capacity. This method enables a comprehensive assessment of the carrying capacity of pile-road-network infrastructure.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a method and apparatus for assessing the carrying capacity of pile-road-network infrastructure, and computer equipment. Background Technology

[0002] With the iterative upgrading of automotive technology, the large-scale development of the electric vehicle industry continues to accelerate. Charging infrastructure is a key supporting facility for ensuring the large-scale promotion and application of electric vehicles. Currently, the industry is characterized by explosive growth in charging demand, uneven spatial distribution of facilities, and highly concentrated local power supply loads. The actual operating load of charging stations does not change independently, but is closely related to traffic flow and users' personalized charging behaviors. Under the combined effect of multiple factors, the charging load exhibits significant spatiotemporal fluctuations and is prone to power surges in the power supply network, making operation and management quite difficult.

[0003] As the penetration rate of electric vehicles steadily increases, the coupling and correlation between charging infrastructure, power distribution networks, and transportation networks continue to deepen. Existing single-dimensional assessment technologies and auxiliary load-bearing capacity verification technologies are no longer adequate to meet the industry's development needs. The industry urgently requires independent, high-precision, and multi-dimensional load-bearing capacity assessment technology solutions. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and device for assessing the carrying capacity of pile-road-network infrastructure, as well as computer equipment, to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for assessing the carrying capacity of pile-road-network infrastructure, the method comprising:

[0006] Obtain multi-dimensional parameters of the distribution network grid to be evaluated, including grid-side parameters, charging station parameters, and traffic-side parameters;

[0007] Based on the multidimensional parameters, multidimensional constraint verification is performed on the pile-road-network infrastructure of the power distribution network grid to obtain multidimensional verification results;

[0008] If the multidimensional verification results do not exceed the bearing capacity limit, increase the electric vehicle capacity of the distribution network grid, iteratively perform multidimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found.

[0009] Based on the maximum capacity of the electric vehicle, output the load-bearing capacity assessment result of the power distribution network grid.

[0010] In one embodiment, the multidimensional verification results include power grid supply capacity assessment results, substation service capacity assessment results, and grid maximum allowable vehicle count assessment results; wherein, the step of performing multidimensional constraint verification on the pile-road-network infrastructure of the distribution network grid based on the multidimensional parameters to obtain multidimensional verification results includes:

[0011] The power supply capacity of the power grid is assessed based on the power grid side parameters to obtain the power supply capacity assessment results.

[0012] The service capacity of the charging station is assessed based on the charging station parameters to obtain the assessment results.

[0013] The grid traffic carrying capacity is assessed based on the traffic-side parameters to obtain the grid traffic carrying capacity assessment results.

[0014] In one embodiment, the grid-side parameters include the current grid power supply margin and the minimum grid power supply margin threshold of the distribution network grid; the step of assessing the grid power supply capacity based on the grid-side parameters to obtain the grid power supply capacity assessment result includes:

[0015] Determine whether the current power supply margin of the power grid is greater than or equal to the minimum power supply margin threshold of the power grid;

[0016] If so, the power grid supply capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the power grid supply capacity assessment result is that it exceeds the carrying capacity limit.

[0017] In one embodiment, obtaining the current power supply margin and the minimum power supply margin threshold of the distribution network grid includes:

[0018] Obtain the distribution network capacity, original load, current electric vehicle charging load, and maximum allowable safe load of the power grid for the distribution network grid;

[0019] The current power supply margin of the power grid is obtained as the ratio of a first difference to the capacity of the distribution network, wherein the first difference is the capacity of the distribution network minus the sum of the original load and the current electric vehicle charging load;

[0020] The minimum power supply margin threshold of the power grid is obtained as the ratio of a second difference to the capacity of the distribution network, wherein the second difference is the capacity of the distribution network minus the sum of the original load and the maximum allowable safe load of the power grid.

[0021] In one embodiment, the charging station parameters include the daily access demand of various vehicle types in the power distribution network grid, the number of various types of charging piles, the number of parking spaces required by electric vehicles, and the actual number of parking spaces configured; the step of evaluating the station service capacity based on the charging station parameters to obtain the station service capacity evaluation result includes:

[0022] Determine whether the daily access demand for each type of vehicle is less than or equal to the number of charging piles of the corresponding type, and determine whether the number of parking spaces required for the electric vehicle is less than or equal to the actual number of parking spaces configured.

[0023] If all conditions are met, the assessment result of the station service capacity is that it does not exceed the carrying capacity limit; otherwise, the assessment result of the station service capacity is that it exceeds the carrying capacity limit.

[0024] In one embodiment, the traffic-side parameters include the actual number of vehicles and the maximum allowed number of vehicles in the power distribution grid; the step of assessing the grid's traffic carrying capacity based on the traffic-side parameters to obtain the grid's traffic carrying capacity assessment results includes:

[0025] Determine whether the actual number of vehicles is less than or equal to the maximum allowed number of vehicles;

[0026] If so, the grid traffic carrying capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid traffic carrying capacity assessment result is that it exceeds the carrying capacity limit.

[0027] In one embodiment, the step of increasing the electric vehicle capacity of the distribution network grid when the multi-dimensional verification results do not exceed the bearing capacity limit, iteratively performing multi-dimensional constraint verification, and adjusting the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found includes:

[0028] If the multidimensional verification results do not exceed the bearing capacity limit, increase the electric vehicle capacity of the power distribution network grid, return to obtain the multidimensional parameters of the power distribution network grid to be evaluated, and perform multidimensional constraint verification on the pile-road-network infrastructure of the power distribution network grid according to the multidimensional parameters, so as to obtain the multidimensional verification results again.

[0029] If none of the multidimensional verification results exceed the bearing capacity limit, the electric vehicle capacity is increased and the verification is iterated again. If any constraint verification in the multidimensional verification results exceeds the bearing capacity limit, the electric vehicle capacity of the distribution network grid is reduced and the verification is repeated until the maximum electric vehicle capacity that satisfies each constraint is found.

[0030] Secondly, embodiments of this application provide a device for assessing the carrying capacity of pile-road-network infrastructure, the device comprising:

[0031] The acquisition module is used to acquire multi-dimensional parameters of the distribution network grid to be evaluated, including grid-side parameters, charging station parameters, and traffic-side parameters.

[0032] The verification module is used to perform multi-dimensional constraint verification on the pile-road-network infrastructure of the power distribution network grid based on the multi-dimensional parameters, so as to obtain multi-dimensional verification results;

[0033] The iterative module is used to increase the electric vehicle capacity of the power distribution network grid when the multi-dimensional verification results do not exceed the bearing capacity limit, iteratively perform multi-dimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found.

[0034] The output module is used to output the load-bearing capacity assessment result of the power distribution network grid based on the maximum capacity of the electric vehicle.

[0035] Thirdly, embodiments of this application provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned method.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned method.

[0037] The aforementioned method, device, computer equipment, storage medium, and computer program products for assessing the carrying capacity of the pile-road-network infrastructure acquire multi-dimensional parameters of the distribution network grid to be assessed. These multi-dimensional parameters include grid-side parameters, charging station parameters, and traffic-side parameters. Based on these parameters, the pile-road-network infrastructure of the distribution network grid undergoes multi-dimensional constraint verification to obtain multi-dimensional verification results. If none of the multi-dimensional verification results exceed the carrying capacity limit, the multi-dimensional constraint verification is iteratively executed by increasing the electric vehicle capacity. The electric vehicle capacity is adjusted through a feedback closed loop until the maximum electric vehicle capacity that satisfies all constraints is found. Based on the maximum electric vehicle capacity, the carrying capacity assessment results of the distribution network grid are output. This achieves a coordinated carrying capacity assessment of the pile-road-network system, solving the problems of incomplete and inaccurate single-dimensional assessments. It can accurately determine the carrying capacity limit of infrastructure, providing a scientific basis for infrastructure planning and electric vehicle promotion, improving resource utilization efficiency, and avoiding infrastructure overload or resource waste. Attached Figure Description

[0038] Figure 1 This is a schematic diagram illustrating the factors influencing the bearing capacity of a pile-road-network infrastructure in one embodiment;

[0039] Figure 2This is a flowchart illustrating a method for assessing the carrying capacity of pile-road-network infrastructure in one embodiment;

[0040] Figure 3 This is a flowchart illustrating the bearing capacity assessment method for pile-road-network infrastructure in another embodiment;

[0041] Figure 4 This is a flowchart illustrating the method for assessing the carrying capacity of pile-road-network infrastructure in yet another embodiment;

[0042] Figure 5 This is a flowchart illustrating the method for assessing the carrying capacity of pile-road-network infrastructure in another embodiment;

[0043] Figure 6 This is a schematic diagram of the architecture of a pile-road-network infrastructure bearing capacity assessment method in one embodiment;

[0044] Figure 7 This is a schematic diagram of test data for a pile-road-network infrastructure bearing capacity assessment method in one embodiment;

[0045] Figure 8 This is a structural block diagram of a pile-road-network infrastructure bearing capacity assessment device in one embodiment;

[0046] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] Currently, research on electric vehicle charging capacity in this field mainly focuses on two major technical branches. The first is electric vehicle capacity assessment technology on the distribution network side. This type of technology focuses solely on the distribution network, relying on inherent electrical parameters such as the rated capacity of distribution network equipment and the current-carrying limit of lines to calculate the grid's capacity to accept electric vehicle charging loads. It is a fundamental technical means for capacity assessment in this field. The second is charging station planning and optimization technology. This type of technology aims to output scientifically reasonable charging station spatial layouts and equipment configuration schemes. While it involves some capacity analysis during application, it is only used as an auxiliary step in verifying the feasibility of planning schemes and does not conduct in-depth analysis of capacity. Although these two existing technologies have promoted technological iteration in the charging infrastructure industry and provided basic technical support for comprehensive capacity assessment research in multi-system coupled scenarios, significant technical shortcomings still exist.

[0049] Current technologies for assessing the carrying capacity of electric vehicles in power distribution networks focus solely on the power grid system, neglecting key constraints such as traffic network capacity and charging station service capacity. They also ignore the cumulative effects of multiple factors, making it difficult to reflect the true carrying capacity under the coupling of multiple systems (charging stations, roads, and the network) within a region. Furthermore, existing technologies cannot directly identify which dimension among the power grid, charging stations, and transportation is the core bottleneck restricting overall carrying capacity. This is crucial for subsequent urban planning and charging station / network configuration. Moreover, in complex scenarios involving dense charging station layouts, dynamic traffic flow changes, and limited distribution network margins, current technologies fail to provide a quantifiable and interpretable comprehensive assessment method for the carrying capacity of the coupled charging-road-network system, making it difficult to determine key indicators such as the types and quantities of electric vehicles that can be safely and efficiently carried within a region, and their maximum charging power. Although relevant charging station planning technologies include content related to carrying capacity assessment, they are only used as an auxiliary sub-link and verification method in the planning process. They do not build an independent quantitative system or design a special assessment method for carrying capacity assessment, and cannot output standardized carrying capacity boundary indicators. They can only provide simple reference verification for planning, thus failing to meet the industry's core need for independent and accurate quantification of carrying capacity.

[0050] In response, this application provides a method and apparatus for assessing the carrying capacity of pile-road-network infrastructure, as well as a computer device, which can realize a comprehensive assessment of the carrying capacity of pile-road-network infrastructure.

[0051] The method for assessing the carrying capacity of pile-road-network infrastructure provided in this application can be applied to power distribution network grids. In power distribution network grids, the carrying capacity of pile-road-network infrastructure is affected by multiple factors, such as… Figure 1 As shown, it mainly consists of three parts: the power grid side, the charging station side, and the transportation side.

[0052] like Figure 2 As shown, in some embodiments, a method for assessing the carrying capacity of pile-road-network infrastructure is provided, which is then applied to... Figure 1 The following explanation is based on the distribution network grid in the example, including the following S202 to S208.

[0053] S202, obtain the multi-dimensional parameters of the distribution network grid to be evaluated. The multi-dimensional parameters include grid-side parameters, charging station parameters, and traffic-side parameters.

[0054] A distribution network grid refers to the smallest evaluation unit in which a distribution network is divided according to preset rules (such as regional function and power supply range), such as an L2 grid. Its scope can be flexibly determined based on urban administrative divisions and distribution network topology. Once the grid is delineated, the evaluation boundaries are clearly defined, providing a range basis for subsequent parameter acquisition and carrying capacity assessment. In application, the distribution network grid to be evaluated can be determined through urban distribution network management platforms and geographic information systems, clarifying its boundary range and corresponding distribution network structure information. Based on this, various parameters related to the pile-road-network infrastructure within the grid are collected.

[0055] Grid-side parameters refer to relevant parameters that affect the distribution network's ability to carry charging loads, including but not limited to distribution network capacity, original load, current electric vehicle charging load, grid power supply margin, minimum grid power supply margin threshold, and maximum allowable safe load of the grid. Their core function is to reflect the distribution network's ability to accept charging loads within the safe operating boundary. These parameters can be collected in real time through the distribution network SCADA system or extracted from historical data from the power dispatching platform to ensure the timeliness and accuracy of the parameters.

[0056] Charging station parameters refer to relevant parameters that affect the service capacity of charging stations, including but not limited to the daily access demand of various types of vehicles, the number of various types of charging piles, the number of parking spaces required by electric vehicles, the actual number of parking spaces configured, the per-unit value curve of charging characteristics, and the energy storage configuration capacity. These parameters are used to reflect whether the charging station can meet the charging and parking needs of various types of electric vehicles. The data can be obtained through the charging station operation and management platform and the energy storage monitoring system.

[0057] Traffic-side parameters refer to relevant parameters that affect the support capacity of the traffic network, including but not limited to the actual number of vehicles within the grid, the maximum number of vehicles allowed, road density, road segment capacity, and land use nature. They are used to reflect the limit of the number of vehicles that the traffic system can carry under a certain service level. Data can be obtained through urban traffic monitoring cameras, traffic flow survey equipment, or intelligent transportation platforms.

[0058] S204. Based on multidimensional parameters, perform multidimensional constraint verification on the pile-road-network infrastructure of the distribution network grid to obtain multidimensional verification results.

[0059] The pile-road-network infrastructure refers to the comprehensive infrastructure system composed of the power distribution network (network), charging stations (piles), and road traffic network (road). Its carrying capacity is jointly constrained by the power grid, charging stations, and traffic. Therefore, multi-dimensional constraint verification needs to be carried out separately for these three dimensions. By combining the corresponding multi-dimensional parameters, it is determined whether each dimension exceeds its own carrying capacity limit. Finally, a multi-dimensional verification result covering the evaluation of each dimension is obtained to ensure the comprehensiveness and pertinence of the verification and avoid the bias caused by single-dimensional evaluation.

[0060] S206. If the multi-dimensional verification results do not exceed the bearing capacity limit, increase the electric vehicle capacity of the distribution network grid, iteratively perform multi-dimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found.

[0061] The multi-dimensional verification results all do not exceed the carrying capacity limit, indicating that the current distribution network grid's infrastructure (pile-road-network) can support more electric vehicles. Therefore, it is necessary to gradually increase the electric vehicle capacity to simulate the scenario of future growth in the number of electric vehicles. After each increase in capacity, S202 (obtaining the corresponding multi-dimensional parameters based on the increased electric vehicle capacity) must be executed again, followed by S204 (performing a multi-dimensional constraint verification based on the newly obtained multi-dimensional parameters) to determine whether each dimension still meets the carrying capacity requirements after the increase. Through a feedback loop of increasing capacity, verification, and adjustment, if the constraints are still met after the increase, the capacity continues to increase; if any dimension exceeds the constraint, the electric vehicle capacity of the distribution network grid is reduced, and S204 (performing a multi-dimensional constraint verification based on the newly obtained multi-dimensional parameters) is executed again to determine whether each dimension still meets the carrying capacity requirements after the capacity reduction. This iterative process is repeated until the maximum electric vehicle capacity that simultaneously meets the constraints of the power grid, power stations, and transportation is found. This value is the comprehensive carrying capacity limit under the current infrastructure conditions. The newly added electric vehicle capacity can be a preset increase, and the reduced electric vehicle capacity can be a preset decrease. Both can also be dynamically determined based on the multi-dimensional parameters of the distribution network grid. For example, the future demand curve of the distribution network grid can be obtained through multi-dimensional parameters to determine the newly added or reduced electric vehicle capacity. The specific adjustments can be made flexibly according to the actual situation.

[0062] S208 outputs the load-bearing capacity assessment results of the power distribution grid based on the maximum capacity of electric vehicles.

[0063] The carrying capacity assessment results include, but are not limited to, the comprehensive carrying capacity limit of the distribution network grid (i.e., the maximum capacity of electric vehicles), the single-dimensional carrying capacity limit under various constraints (such as the maximum capacity that the power grid can carry, the maximum capacity that the power station can carry, and the maximum capacity that the transportation can carry), and the bottleneck analysis conclusions (identifying the core dimensions that restrict the comprehensive carrying capacity, such as insufficient power supply margin of the power grid and insufficient configuration of charging piles), providing data support and decision-making basis for the planning, upgrading and transformation of the distribution network grid's pile-road-network infrastructure.

[0064] The carrying capacity assessment method for pile-road-network infrastructure provided in the above embodiments obtains multi-dimensional parameters of the distribution network grid to be assessed, including grid-side parameters, charging station parameters, and traffic-side parameters. Based on these multi-dimensional parameters, the method performs multi-dimensional constraint verification on the pile-road-network infrastructure of the distribution network grid to obtain multi-dimensional verification results. If none of the multi-dimensional verification results exceed the carrying capacity limit, the method iteratively executes multi-dimensional constraint verification by increasing the electric vehicle capacity and adjusting the electric vehicle capacity through a feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found. Based on the maximum electric vehicle capacity, the carrying capacity assessment result of the distribution network grid is output. This method achieves a coordinated carrying capacity assessment of pile-road-network, solving the problems of incomplete and inaccurate single-dimensional assessment. It can accurately determine the carrying capacity limit of infrastructure, providing a scientific basis for infrastructure planning and electric vehicle promotion, improving resource utilization efficiency, and avoiding infrastructure overload or resource waste.

[0065] In some embodiments, the multidimensional verification results include the power grid supply capacity assessment results, the station service capacity assessment results, and the grid maximum allowable vehicle number assessment results. For example, Figure 3 As shown in S204, based on multidimensional parameters, multidimensional constraint verification is performed on the pile-road-network infrastructure of the distribution network grid to obtain multidimensional verification results, including the following S302 to S306.

[0066] S302, assess the power supply capacity of the power grid based on the grid-side parameters to obtain the assessment results.

[0067] The power supply capacity of a power grid refers to the capacity of a distribution network to handle electric vehicle charging loads while meeting safe operating criteria such as voltage stability, line current carrying capacity, and equipment thermal stability. Its assessment is based on grid-side parameters to determine whether the current distribution network can safely accommodate existing and potential charging loads. Grid-side parameters encompass key information such as the distribution network's capacity, load, and margin. By analyzing and calculating these parameters, the power supply potential and safety boundaries of the distribution network can be clearly defined, leading to the power grid power supply capacity assessment result. This result directly reflects the grid's carrying capacity status.

[0068] S304. Evaluate the service capacity of the charging station based on its parameters to obtain the evaluation results.

[0069] Charging station service capacity refers to the ability of a charging station to meet the charging and parking needs of various electric vehicles. Its assessment is primarily based on charging station parameters to determine whether the station's resource allocation matches the demand from electric vehicles. These parameters include key information such as vehicle type requirements, charging pile configuration, and the number of parking spaces. By analyzing these parameters, the supply and demand balance of charging piles and parking spaces can be clarified, determining whether there is a resource shortage or underutilization at the station, thus obtaining the station service capacity assessment result. This result directly reflects the carrying capacity status of the charging station.

[0070] S306, conduct grid traffic carrying capacity assessment based on traffic-side parameters to obtain grid traffic carrying capacity assessment results.

[0071] Grid traffic carrying capacity refers to the maximum number of vehicles that a transportation system within an assessed power grid can support at a given service level. Its core assessment is based on traffic-side parameters to determine whether the current road network can support the operation of existing and newly added electric vehicles. These parameters include key information such as the actual number of vehicles and the maximum permitted number of vehicles. By comparing and analyzing these parameters, the congestion risk and traffic potential of the road network can be clearly identified, leading to the grid traffic carrying capacity assessment result, which directly reflects the carrying capacity status in the traffic dimension.

[0072] The pile-road-network infrastructure carrying capacity assessment method provided in the above embodiments decomposes the multi-dimensional constraint verification into three independent but related assessment links: power grid, power station, and transportation. Each link is assessed based on parameters of the corresponding dimension, ensuring the accuracy and comprehensiveness of the multi-dimensional verification results. At the same time, the specific connotations of the assessment results of each dimension are clarified, making subsequent iterative verification and carrying capacity limit determination more targeted, avoiding the ambiguity of multi-dimensional verification, and further improving the operability and reliability of the entire assessment method, laying the foundation for subsequent detailed assessments of each dimension.

[0073] In some embodiments, the grid-side parameters include the current grid power supply margin and the minimum grid power supply margin threshold of the distribution network grid. In step S302, the grid power supply capacity is assessed based on the grid-side parameters to obtain the grid power supply capacity assessment result, including: determining whether the current grid power supply margin is greater than or equal to the minimum grid power supply margin threshold; if so, the grid power supply capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid power supply capacity assessment result is that it exceeds the carrying capacity limit.

[0074] The current power supply margin of the power grid is a core parameter characterizing the limit of additional charging load that the distribution network can bear under the premise of meeting safe operation criteria. Essentially, it is the rigid bearing limit of the distribution network infrastructure for the increase in charging load; the larger the value, the more additional charging load the distribution network can accept. The minimum power supply margin threshold of the power grid is the minimum requirement for the safe operation of the distribution network. It is a critical value determined based on the thermal stability limits of distribution network equipment (current-carrying capacity of transformers and lines), node voltage deviation control requirements, short-circuit capacity matching, and other physical characteristics. Falling below this threshold will lead to safety hazards such as overload of distribution network equipment and voltage drops. Determining the relationship between these two values ​​is the core logic for assessing the power grid's power supply capacity and can directly reflect the current safe bearing status of the power grid.

[0075] When the current power supply margin of the power grid is greater than or equal to the minimum power supply margin threshold, it indicates that the distribution network still has sufficient margin to accommodate new charging loads under the current load conditions, and the power grid operation is within the safety boundary. Therefore, the power supply capacity assessment result is that it does not exceed the carrying capacity limit. When the current power supply margin of the power grid is less than the minimum power supply margin threshold, it indicates that the current load of the distribution network is close to or exceeds the safe operating boundary and cannot accommodate new charging loads. Continuing to add new loads will cause safety problems such as equipment overload and voltage drop. Therefore, the power supply capacity assessment result is that it exceeds the carrying capacity limit. For example, the local voltage may drop below the lower limit of the qualified range, causing the charging pile to fail to start due to insufficient voltage; the line current may exceed the current carrying capacity limit, and the conductor temperature may rise above the safety threshold, accelerating insulation aging and even causing short circuit faults; the transformer winding temperature may exceed the thermal stability upper limit, triggering overload protection tripping, ultimately causing the power grid operation to enter a risky state and affecting the overall power supply reliability of the region.

[0076] The infrastructure carrying capacity assessment method for pile-road-network provided in the above embodiments clarifies the core parameters and specific judgment logic for power grid power supply capacity assessment, transforming the abstract power grid power supply capacity into quantifiable and comparable margin parameters, making the power grid-level assessment more operable and objective. At the same time, by combining the physical characteristics of the distribution network to determine the judgment criteria, the scientificity and accuracy of the assessment results are ensured, enabling precise identification of power grid carrying capacity bottlenecks and providing a clear direction for subsequent power grid infrastructure upgrades and renovations, thus avoiding power grid safety risks caused by blindly adding electric vehicles.

[0077] In some embodiments, such as Figure 4 As shown, the current power supply margin and minimum power supply margin threshold of the distribution network grid are obtained, including the following S402 to S406.

[0078] S402 retrieves the distribution network capacity, original load, current electric vehicle charging load, and maximum allowable safe load of the distribution network grid.

[0079] Distribution network capacity refers to the maximum total load that the entire distribution network can carry. It is a fundamental parameter of the distribution network's power supply capacity and is determined by the capacity of core equipment such as transformers and lines. Original load refers to all types of electrical loads within the distribution network grid, excluding electric vehicle charging loads (such as residential, industrial, and commercial electricity). It is the basic load of the distribution network, and its size directly affects the remaining capacity available to carry charging loads. Current electric vehicle charging load refers to the total load consumed by all electric vehicles charging within the current distribution network grid, and is an important component of the current distribution network load. The maximum permissible safe load of the power grid is the upper limit of safe operating load determined based on the thermal stability limits of distribution network equipment, node voltage deviation control requirements, and short-circuit capacity matching. It is a core constraint parameter for the safe operation of the distribution network; exceeding this load will lead to abnormal operation of the distribution network. All four types of parameters must be obtained through authoritative channels such as the distribution network SCADA system and power dispatching platform to ensure their accuracy and timeliness.

[0080] S404, obtain the current power supply margin of the power grid as the ratio of the first difference to the distribution network capacity, where the first difference is the distribution network capacity minus the sum of the original load and the current electric vehicle charging load.

[0081] The first difference essentially represents the remaining capacity of the distribution network that can currently support new charging loads. It is the remaining portion of the distribution network capacity after deducting the existing original load and the current electric vehicle charging load. The larger this difference is, the greater the power supply potential of the distribution network. By comparing the first difference with the distribution network capacity, we obtain the current power supply margin of the power grid. This ratio is presented as a percentage, which can intuitively reflect the proportion of the remaining power supply capacity of the distribution network to the total power supply capacity, facilitating quantitative evaluation and comparative analysis.

[0082] The current power supply margin of the power grid can be expressed by the formula:

[0083]

[0084] Among them, M res P represents the current power supply margin of the power grid. c For distribution network capacity; P L The original load; P EV This is for the current charging load of electric vehicles.

[0085] S406, the minimum power supply margin threshold of the power grid is obtained as the ratio of the second difference to the distribution network capacity, where the second difference is the distribution network capacity minus the sum of the original load and the maximum allowable safe load of the power grid.

[0086] The second difference is the minimum remaining capacity that the distribution network must reserve to ensure safe operation. It is the remaining part of the distribution network capacity after deducting the original load and the maximum allowable safe load of the power grid. This difference ensures that the distribution network can still meet the safe operation criteria when the load fluctuates (such as a surge in charging load). The second difference is compared with the distribution network capacity to obtain the minimum power supply margin threshold of the power grid. This threshold is the critical standard for the safe operation of the distribution network. If it is lower than this threshold, the distribution network will be unable to cope with load fluctuations, causing safety hazards.

[0087] The verification criteria for assessing the power supply capacity of the power grid can be expressed as follows:

[0088]

[0089] Among them, M res M represents the current power supply margin of the power grid. res,min P is the minimum power supply margin threshold of the power grid. c For distribution network capacity; P L The original load; P EV,max This is the maximum permissible safe load of the power grid.

[0090] The infrastructure carrying capacity assessment method for pile-road-network provided in the above embodiments clarifies the specific steps and calculation logic for obtaining the current power supply margin and the minimum power supply margin threshold of the power grid. It transforms abstract margin parameters into quantifiable and calculable indicators, solving the problems of difficulty in obtaining margin parameters and inconsistent calculation standards. At the same time, the calculation process combines the core parameters and safety constraints of the distribution network, ensuring the scientificity and accuracy of the margin parameters, providing a solid foundation for power grid power supply capacity assessment, and further improving the operability and reliability of the entire assessment method.

[0091] In some embodiments, the charging station parameters include the daily access demand of various vehicle types in the power distribution network grid, the number of various types of charging piles, the number of parking spaces required by electric vehicles, and the actual number of parking spaces configured. In step S304 above, the charging station service capacity is assessed based on the charging station parameters to obtain the assessment result, including: determining whether the daily access demand of various vehicle types is less than or equal to the number of charging piles of the corresponding type, and determining whether the number of parking spaces required by electric vehicles is less than or equal to the actual number of parking spaces configured; if both conditions are met, the charging station service capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the charging station service capacity assessment result is that it exceeds the carrying capacity limit.

[0092] As we understand it, charging stations are physical entities connecting the power grid and electric vehicles. The efficiency with which a single station can meet user charging needs directly determines the service experience and operational efficiency of the charging pile-road-network system. Station service capacity assessment can include charging pile quantity matching analysis and parking space matching assessment. Charging pile quantity matching can be understood as whether the charging pile configuration meets the actual service needs of different vehicle models. Parking space matching can be understood as whether the number of parking spaces meets the charging demand for electric vehicles.

[0093] Among them, "multiple vehicle types" refers to common electric vehicle types within the power distribution grid, including but not limited to cars, buses, and taxis. Different vehicle types have different charging needs and charging methods, so it is necessary to distinguish between vehicle types to count the daily access demand. This number refers to the total number of vehicles of various types that need to enter the charging station for charging each day, which can be obtained through historical operation data of the charging station and statistics on the number of electric vehicles in the region. "Multiple charging piles" refers to the total number of charging piles configured at the charging station for different vehicle types, including fast charging piles, slow charging piles, and supercharging piles. Fast charging piles are suitable for short-term charging of commercial vehicles, slow charging piles meet the needs of residents for long-term parking and charging, and supercharging piles are mainly deployed in scenarios where rapid charging is urgently needed. It is necessary to ensure that the number of each type of charging pile matches the daily access demand of the corresponding vehicle type. The number of parking spaces required for electric vehicles refers to the total number of parking spaces needed for all electric vehicles entering the charging station for charging each day. This needs to be differentiated by vehicle type. For example, parking spaces for cars need to meet standard dimensions, while dedicated parking spaces for buses and taxis need to reserve space for charging gun operation and their dimensions need to be appropriately expanded. The actual number of parking spaces configured refers to the total number of parking spaces actually built at the charging station that can be used for parking and charging electric vehicles. This also needs to be differentiated by vehicle type to ensure that different vehicle types have suitable parking spaces.

[0094] In the application, determine the daily access demand D for various vehicle types. EV Is it less than or equal to the number N of the corresponding type of charging pile? pile That is, to determine whether D is satisfied. EV ≤N pile If this condition is not met, meaning the total service capacity of charging piles is lower than the daily access demand of a particular vehicle type, then the charging station will face a significant capacity bottleneck when serving that type of vehicle. During peak hours, vehicle queues will occur, and the average waiting time may exceed a considerable amount of time. This will not only reduce user satisfaction but may also cause some vehicles to go to other charging stations due to excessive waiting, leading to a decline in overall service efficiency. In this case, the charging station's service capacity assessment result will be deemed unsatisfactory, meaning the charging station's service capacity assessment result will not exceed its carrying capacity limit.

[0095] Furthermore, determine the number D of parking spaces required for electric vehicles. park Is it less than or equal to the actual number of parking spaces N? park That is, to determine whether D is satisfied. park ≤N parkIf this condition is not met, i.e., the actual number of parking spaces is less than the required number, some charging vehicles will not be able to park in time during peak hours, and some vehicles may occupy the municipal roads around the charging station while waiting. This will not only affect road traffic order, but also prevent vehicles from connecting to charging piles in time even if they arrive at the charging station, directly limiting the overall service capacity of the station. In this case, the parking space matching assessment will be deemed unsuccessful, i.e., the station service capacity assessment result will be deemed not to exceed the carrying capacity limit.

[0096] When the daily access demand for each type of vehicle is less than or equal to the number of charging piles of the corresponding type, and the number of parking spaces required by electric vehicles is less than or equal to the number of parking spaces actually configured, it indicates that the charging piles and parking spaces of the charging station can meet the current charging and parking needs of electric vehicles, the resource utilization is reasonable, and there is no situation where vehicles are queuing to charge or there are no parking spaces. Therefore, the service capacity assessment result of the station is that it does not exceed the carrying capacity limit.

[0097] The charging-road-network infrastructure carrying capacity assessment method provided in the above embodiments clarifies the core parameters and specific judgment logic for assessing the service capacity of charging stations, differentiates between vehicle models to conduct matching assessments of charging piles and parking spaces, fits actual operating scenarios, and ensures the relevance and accuracy of the assessment results. At the same time, through dual matching judgments, it comprehensively covers the core needs of charging station services, can accurately identify bottlenecks in the allocation of charging station resources, provides a scientific basis for the optimized layout of charging stations and the supplementary allocation of charging piles and parking spaces, improves the efficiency of charging station services, and avoids problems such as resource waste or unmet needs.

[0098] In some embodiments, traffic-side parameters include the actual number of vehicles in the distribution network grid and the maximum allowed number of vehicles. In step S306 above, the grid traffic carrying capacity assessment is performed based on the traffic-side parameters to obtain the grid traffic carrying capacity assessment result, including: determining whether the actual number of vehicles is less than or equal to the maximum allowed number of vehicles; if so, the grid traffic carrying capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid traffic carrying capacity assessment result is that it exceeds the carrying capacity limit.

[0099] The actual number of vehicles refers to the total number of vehicles actually driving and parked during peak hours each day within the power distribution network grid, including both electric and non-electric vehicles. Its magnitude directly reflects the current load status of the traffic network and can be obtained in real-time through urban traffic monitoring cameras, traffic flow survey equipment, or intelligent transportation platforms. The maximum permissible number of vehicles is the maximum number of vehicles a transportation system can carry at a given service level, determined by a comprehensive assessment of regional road density, road segment capacity, land use, parking resources, and traffic control conditions within a given grid unit. It is a core indicator of the traffic network's carrying capacity. Road density refers to road mileage per square kilometer; residential areas generally need to reach a certain level, while commercial areas need to reach a higher level. Road segment capacity is determined by road grade; for example, a two-way four-lane arterial road has a medium design capacity, while a two-way six-lane road has a higher capacity. Land use affects vehicle generation rate; commercial areas have a relatively higher vehicle generation rate, while residential areas have a relatively lower one. Determining the relationship between the actual number of vehicles and the maximum permissible number of vehicles is the core logic for assessing traffic carrying capacity.

[0100] In application, it can be determined that the actual number of vehicles is Q. peak Is it less than or equal to the maximum allowed number of vehicles Q? max That is, to determine whether Q is satisfied. peak ≤Q max If this condition is not met, for example, if the actual number of vehicles exceeds the maximum allowed number, it indicates that the current traffic carrying capacity of the area has exceeded a reasonable threshold, and significant operational deterioration will occur: the average vehicle speed may drop to a low level (indicating severe congestion), queue lengths at major intersections will exceed a considerable distance, and parking search times will exceed a considerable time. This will not only significantly increase the time users spend traveling to charging stations (for example, a journey that would normally take a short time may become much longer), but will also trigger a chain reaction of traffic congestion in the area. In this case, the traffic carrying capacity assessment result will be deemed unsuccessful, meaning the grid traffic carrying capacity assessment result exceeds the carrying capacity limit. If this condition is met, it indicates that the current traffic network can normally carry the operation of existing vehicles, and there is no significant congestion, increased delays, or insufficient resource supply. The traffic system is in good operating condition, therefore the grid traffic carrying capacity assessment result does not exceed the carrying capacity limit.

[0101] The pile-road-network infrastructure carrying capacity assessment method provided in the above embodiments clarifies the core parameters and specific judgment logic of grid traffic carrying capacity assessment, and determines the maximum allowable number of vehicles in combination with the actual characteristics of the traffic network, ensuring the scientificity and rationality of the assessment standard. At the same time, by comparing the actual number of vehicles with the maximum allowable number of vehicles, the carrying capacity status of the traffic network can be intuitively reflected, traffic carrying capacity bottlenecks can be accurately identified, and a scientific basis can be provided for the optimization and upgrading of the traffic network and traffic flow control, ensuring the coordinated development of the traffic system and the growth of electric vehicles, and avoiding the impact of traffic congestion on the overall operating efficiency of the pile-road-network infrastructure.

[0102] In some embodiments, the aforementioned S206, where the multidimensional verification results do not exceed the bearing capacity limit, involves increasing the electric vehicle capacity of the distribution network grid, iteratively performing multidimensional constraint verification, and adjusting the electric vehicle capacity through a feedback loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found. This includes: increasing the electric vehicle capacity when the multidimensional verification results do not exceed the bearing capacity limit, returning to obtain the multidimensional parameters of the distribution network grid to be evaluated, and performing multidimensional constraint verification on the pile-road-network infrastructure of the distribution network grid based on the multidimensional parameters to re-obtain the multidimensional verification results; if the multidimensional verification results do not exceed the bearing capacity limit, then continuing to increase the electric vehicle capacity and continuing iterative verification; if any constraint verification in the multidimensional verification results exceeds the bearing capacity limit, then decreasing the electric vehicle capacity and returning to re-verify until the maximum electric vehicle capacity that satisfies each dimension constraint is found.

[0103] The multi-dimensional verification results all do not exceed the carrying capacity limit, indicating that the current distribution network grid's pile-road-network infrastructure still has carrying capacity potential. The scenario of future electric vehicle growth can be simulated by increasing the electric vehicle capacity. When increasing the electric vehicle capacity, adjustments can be made gradually according to a preset increase (e.g., adding 50 or 100 electric vehicles each time) to avoid excessive increases leading to assessment deviations. After adding capacity, the charging load, parking demand, and traffic flow of electric vehicles will all increase accordingly. Therefore, it is necessary to re-execute S202 to obtain the multi-dimensional parameters of the distribution network grid (including the added grid-side parameters, charging station-side parameters, and traffic-side parameters), and re-execute the multi-dimensional constraint verification of S204 to obtain the multi-dimensional verification results after the added capacity, and determine whether each dimension still meets the carrying capacity requirements after the added capacity.

[0104] The newly acquired multidimensional verification results cover the latest assessment of the power grid supply capacity, station service capacity, and grid traffic carrying capacity after the addition of electric vehicle capacity. Determining whether they all exceed the carrying capacity limit is the key basis for deciding whether to continue to increase or decrease the capacity, ensuring the scientific and targeted nature of the iteration process, and avoiding inefficiency or inaccurate assessment caused by blindly adjusting the capacity.

[0105] If the re-acquired multi-dimensional verification results do not exceed the carrying capacity limit, it indicates that the current new capacity is still within the carrying capacity of the infrastructure. The electric vehicle capacity can continue to be increased according to the preset increase, and the above steps can be repeated to continue iterative verification until the carrying capacity limit is found. If the verification result of any dimension (power grid, station, transportation) in the re-acquired multi-dimensional verification results exceeds the carrying capacity limit, it indicates that the current new capacity has exceeded the carrying capacity of that dimension. The electric vehicle capacity needs to be appropriately reduced (such as reduced to the capacity after the previous increase, or adjusted according to the preset reduction). Then return to steps S202 and S204, re-acquire multi-dimensional parameters and perform verification to avoid the distortion of the evaluation results due to excessive capacity.

[0106] The above-mentioned feedback loop of increasing capacity, verifying and continuing to increase, or increasing capacity, verifying, decreasing capacity and verifying again, is iteratively adjusted until a maximum new electric vehicle capacity value that can satisfy the constraints of the power grid, charging stations and transportation is found. This value is the comprehensive carrying capacity limit of the current distribution network grid pile-road-network infrastructure. At this point, any further increase in capacity will cause the verification result of at least one dimension to exceed the carrying capacity limit.

[0107] The carrying capacity assessment method for pile-road-network infrastructure provided in the above embodiments clarifies the specific steps of iterative verification and the feedback closed-loop logic. By gradually adjusting the capacity of newly added electric vehicles and repeatedly verifying, the comprehensive carrying capacity limit can be accurately found, solving the problem of the difficulty in quantifying the carrying capacity limit. At the same time, the iterative process closely follows the actual scenario, gradually approaching the limit value, ensuring the accuracy and reliability of the assessment results and avoiding deviations caused by single or one-time assessments. The design of the feedback closed loop enables the assessment process to have self-adjustment capabilities, which can promptly correct the direction of capacity adjustment, improve assessment efficiency, and provide accurate quantitative basis for the planning of pile-road-network infrastructure and the promotion of electric vehicles.

[0108] In some embodiments, the method further includes: statistically analyzing the number of charging stations, parking spaces, and distribution network equipment information within the distribution network grid to be evaluated; determining the operating power and status data of charging piles within the grid based on the number of charging stations, parking spaces, and distribution network equipment information; constructing a daily charging characteristic per-unit operating curve based on the operating power and status data; and scaling the daily charging characteristic per-unit operating curve according to the preset planned annual charging demand to obtain the future charging demand curve of the distribution network grid.

[0109] The number of charging stations within the distribution network grid to be evaluated refers to the total number of all charging stations that have been built and put into operation within the grid, including public charging stations and dedicated charging stations (such as bus charging stations and taxi charging stations). This number directly affects the coverage and carrying capacity of charging services within the grid and can be obtained through statistics from the charging station operation management platform and urban distribution network planning data. The number of parking spaces refers to the total number of parking spaces actually configured at all charging stations within the grid that can be used for electric vehicle parking and charging. Statistics need to be compiled separately for different vehicle types (cars, buses, and taxis), as the size and usage requirements of parking spaces vary for different vehicle types. For example, car parking spaces need to conform to standard dimensions, while dedicated parking spaces for buses and taxis need to reserve space for charging gun operation and have appropriately expanded dimensions. This data can be obtained through on-site surveys and extraction of charging station operation data. Distribution network equipment information refers to the core equipment information of the distribution network within the grid related to charging load carrying capacity, including but not limited to transformer capacity, line current carrying capacity, distribution network topology, and SCADA monitoring equipment information. This information is the basis for subsequently determining the upper limit of charging pile operating power and analyzing the impact of charging load on the power grid, and can be obtained through the distribution network management platform and power dispatching system.

[0110] The operating power of a charging pile refers to the electrical power output of the charging pile during actual operation. Different types of charging piles have different operating power levels: fast charging piles have a medium to high power level (suitable for short-term charging of commercial vehicles), slow charging piles have relatively low power (meeting the needs of residents charging during long-term parking), and supercharging piles have a high power level (mainly deployed in scenarios requiring rapid charging, such as highway service areas). By combining the number of charging stations and parking spaces, the total number and distribution of various types of charging piles within the grid can be determined. Combined with information on power distribution network equipment (such as transformer capacity and line current carrying capacity), the maximum allowable operating power of each type of charging pile can be determined, avoiding overloading of the power distribution network equipment due to excessively high charging pile operating power. The status data of a charging pile refers to the real-time working status information of the charging pile during operation, including but not limited to the daily average failure rate, the percentage of effective service time, the distribution of charging time, and the distribution of idle time. This data can be collected in real time through the charging station operation management platform and the charging pile monitoring system, reflecting the operating efficiency and availability of the charging piles, providing data support for the subsequent construction of charging characteristic curves.

[0111] The per-unit operating curve of charging characteristics refers to a curve reflecting the temporal fluctuation characteristics of charging load within a day, obtained by normalizing the actual operating power of charging piles to a certain standard value (such as the rated power of charging piles or the maximum charging load of the grid). Its core function is to intuitively present the intraday variation pattern of charging load within the grid. When constructing this curve, it is necessary to select typical daily samples and distinguish the load differences between weekdays and weekends: the charging load on weekdays exhibits a bi-peak load characteristic, with a higher proportion of load during the morning peak (e.g., 7:00-9:00) and evening peak (e.g., 17:00-19:00), mainly due to the charging needs of commercial vehicles and residents after commuting; the charging load on weekends exhibits a single-peak load characteristic, with the peak load concentrated in the daytime period (e.g., 10:00-16:00), mainly due to the charging needs of residents after leisure travel. By combining the charging pile's operating power data (actual power at different times of the day) and status data (effective service time and idle time), the charging load for each time period is normalized, and finally, the daily charging characteristic normalized value operation curve is plotted. Based on this, the overall operation status of the charging station can be explored, providing a basis for carrying capacity analysis.

[0112] The pre-set annual charging demand refers to the total charging load in a future planning year (such as 3 years or 5 years later) determined based on regional electric vehicle ownership growth plans, charging service demand forecasts, urban development plans, etc. This demand needs to be comprehensively determined in conjunction with factors such as population growth, industrial development, and transportation planning within the grid to ensure the scientific and reasonable nature of the forecast.

[0113] Scaling refers to scaling the vertical axis (per-unit power) of a daily charging characteristic per-unit curve based on the ratio of the planned annual total charging demand to the current total charging demand. This scales the curve while maintaining its temporal fluctuation pattern, adjusting only the load magnitude to obtain the future charging demand curve for the planned year. This curve accurately reflects the intraday variation and total demand of the charging load within the grid in the future planned year, providing core load data for the iterative verification of S206 mentioned above, ensuring that the assessment of new electric vehicle capacity during the iteration process aligns with future realities.

[0114] The infrastructure carrying capacity assessment method for charging piles, roads, and networks provided in the above supplementary embodiments determines the operating power and status data of charging piles by statistically analyzing information on charging stations, parking spaces, and power distribution network equipment within the grid. It then constructs a daily charging characteristic per-unit value operating curve and performs scaling processing to obtain a future charging demand curve, filling the gap in the original assessment method regarding future charging load prediction. This curve accurately depicts the temporal fluctuation characteristics and total demand of the charging load in the future planning year, providing a scientific and realistic load basis for iterative verification and avoiding carrying capacity assessment deviations caused by inaccurate future load predictions. Furthermore, by constructing a curve based on typical daily load characteristics, it closely reflects the actual charging behavior of electric vehicle users, further improving the scientific rigor, accuracy, and practicality of the entire assessment method, and providing more comprehensive data support for the long-term planning and upgrading of infrastructure for charging piles, roads, and networks.

[0115] In some embodiments, such as Figure 5 and Figure 6 As shown, a comprehensive evaluation method for the carrying capacity of pile-road-network infrastructure with superimposed multidimensional constraints is provided, which includes the following steps S502 to S516.

[0116] S502, Identify the distribution network grid to be evaluated.

[0117] Identify the distribution network grid to be evaluated, i.e., the L2 grid, and obtain its range and related distribution network structure information to delineate the evaluation boundary.

[0118] S504 retrieves the grid-side parameters, charging station parameters, and traffic-side parameters of the distribution network grid.

[0119] Among them, grid-side parameters include, but are not limited to, distribution network capacity, original load, current electric vehicle charging load, maximum allowable safe load of the grid, current grid power supply margin, and minimum grid power supply margin threshold, reflecting the upper limit of load acceptance of the distribution network within the safety boundary, which can be collected in real time through the distribution network SCADA system or extracted from historical data of the power dispatching platform; charging station parameters include, but are not limited to, the number of charging stations in the grid, the number of parking spaces (by vehicle type), the number of various types of charging piles (fast charging / slow charging / supercharging), the daily access demand of various vehicle types, the number of parking spaces required for electric vehicles, the per-unit value curve of charging characteristics, and the energy storage configuration capacity, reflecting the resource allocation and service capacity of the charging station, which can be obtained through the charging station operation and management platform and the energy storage monitoring system; traffic-side parameters include, but are not limited to, the actual number of vehicles during peak hours in the grid, the maximum allowable number of vehicles, road density, road segment capacity, and land use nature, reflecting the carrying capacity of the traffic network, which can be obtained through statistics from the urban traffic monitoring platform and traffic flow survey equipment.

[0120] S506 uses grid-side parameters, charging station parameters, and traffic-side parameters to perform multi-dimensional constraint verification on the power supply capacity, station service capacity, and traffic carrying capacity of the distribution network grid, and determines whether they exceed the carrying capacity limit.

[0121] The analysis includes three main components: Power grid supply capacity analysis and transportation network support analysis. The first component analyzes the power grid's supply capacity based on grid parameters, calculating the current power grid margin and comparing it to the minimum power grid margin threshold to assess whether the distribution network meets safety requirements such as voltage stability, line current carrying capacity, and equipment thermal stability. The second component analyzes the charging station service capacity based on charging station parameters, comparing the daily access demand of various vehicle types with the corresponding number of charging piles, and the number of parking spaces required by electric vehicles with the actual number of parking spaces configured to assess whether the stations can meet the charging and parking needs of different vehicle types. The third component analyzes the transportation network support capacity based on transportation parameters, comparing the actual number of vehicles during peak hours with the maximum allowed number of vehicles in the grid to assess whether the current transportation network can support the existing electric vehicle operation needs. In other words, the analysis determines whether the following conditions are met: power grid supply margin ≥ minimum power grid margin threshold; daily access demand of various vehicle types ≤ corresponding number of charging piles; required parking spaces ≤ actual configured parking spaces; and actual number of vehicles ≤ maximum allowed number of vehicles. If all conditions are met, it indicates that the current pile-road-network infrastructure has a certain bearing capacity and has not exceeded the bearing capacity limit. Then, it can proceed to S508 to carry out the new capacity calculation. If any dimension is not met, it indicates that the current infrastructure has exceeded the bearing capacity limit and can no longer support new electric vehicles. The calculation process ends and proceeds to S514 to output the bearing capacity assessment results.

[0122] S508 conducts multi-dimensional support capability assessments, calculates the available new capacity, sets targets for new electric vehicle capacity, and increases the electric vehicle capacity of the distribution network grid.

[0123] After passing multi-dimensional verification, it is determined that the current infrastructure still has carrying capacity potential. Based on current data, the initial potential for new capacity needs to be calculated, and a reasonable target for new electric vehicle capacity should be set (e.g., adding 50-200 electric vehicles each time according to a preset increase, or setting an initial target of 10% of the planned annual demand). Furthermore, by combining the number of charging stations, parking spaces, and distribution network equipment information obtained from S504, a per-unit daily charging characteristic curve can be constructed. This curve can then be scaled according to the preset planned annual charging demand to obtain the future charging demand curve, providing a basis for load verification after the new capacity is added. The set target for new capacity must take into account the theoretical carrying capacity potential of the power grid, charging stations, and transportation, avoiding an initial target that is too large, leading to too many iterations, or too small, leading to inefficiency.

[0124] S510 calculates and updates the grid-side parameters, depot-side parameters, and traffic-side parameters of the distribution network grid based on the new electric vehicle capacity target.

[0125] The addition of electric vehicle (EV) capacity will directly impact charging load, charging demand, and traffic flow within the grid, necessitating a recalculation of key parameters across all dimensions. On the grid side, the current EV charging load is updated based on the charging load of the new EVs, and the current grid power margin is recalculated. On the charging station side, the daily access demand for each type of EV and the number of parking spaces required for EVs are updated based on the vehicle type distribution of the new EVs. On the traffic side, the actual number of vehicles during peak hours is updated based on the operational demands of the new EVs. Simultaneously, based on future charging demand curves, the load time-series fluctuations after the addition of capacity are simulated to ensure that the updated parameters accurately reflect the actual operating conditions under the new scenario.

[0126] S512, based on the updated grid-side parameters, charging station parameters, and traffic-side parameters, performs multi-dimensional constraint verification on the power supply capacity, station service capacity, and grid traffic carrying capacity of the distribution network grid to determine whether they exceed the carrying capacity limit.

[0127] The newly added electric vehicle capacity is adjusted based on the multi-dimensional verification results, forming a feedback closed-loop iteration. The updated parameters are then re-verified using multi-dimensional checks to determine if each dimension still meets the carrying capacity requirements: if the grid power supply margin ≥ the minimum grid power supply margin threshold, the daily access demand for various vehicle types ≤ the corresponding number of charging piles, the required parking spaces ≤ the actual configured parking spaces, and the actual number of vehicles ≤ the maximum allowed number of vehicles, it indicates that the current new capacity target is still within the infrastructure carrying capacity range, and the new capacity target can be further increased; that is, return to S508 and repeat the parameter update and verification steps. If any dimension fails the verification, it indicates that the current new capacity target has exceeded the carrying capacity limit of that dimension, and S514 needs to be executed.

[0128] S514 reduces the electric vehicle capacity of the distribution grid.

[0129] Lower the target capacity for electric vehicles, such as by halving it or reducing it by a preset amount, and then return to S510 to recalculate and update the parameters and verify them until the maximum capacity of electric vehicles that simultaneously meets the constraints of all dimensions is found.

[0130] S516 summarizes the calculation results and outputs the load-bearing capacity assessment results of the distribution network grid.

[0131] The results of the iterative process are summarized to form a complete carrying capacity assessment, including but not limited to the maximum carrying capacity limit of the distribution network grid infrastructure (i.e., the maximum new electric vehicle capacity), the single-dimensional carrying capacity bottleneck analysis of the grid side / station side / traffic side, and infrastructure optimization and upgrading suggestions such as grid expansion, charging pile construction, and traffic flow control. This provides a scientific basis for subsequent infrastructure planning, renovation, and electric vehicle promotion.

[0132] Based on the above method, a calculation and analysis were performed on a power distribution network grid in District B of City A. The calculation results are as follows: Figure 7 As shown in the figure, this application achieves precise quantitative output of basic data and carrying capacity indicators through specific data verification. Specifically, it can accurately extract and integrate core basic data such as the number of parking spaces, charging stations, and total charging pile capacity based on the actual basic conditions of the grid, forming standardized evaluation input. Simultaneously, it directly outputs carrying capacity values ​​under different constraint scenarios through quantitative calculation, clearly defining the threshold number of electric vehicles that a regional grid can safely support under single constraints (charging stations, power grid, transportation network) and comprehensive constraints. This solves the problems of vague carrying capacity indicators and lack of quantitative basis in traditional assessments, providing an intuitive and practical numerical reference for subsequent decision-making. Furthermore, the calculated data reflects the differences and synergistic effects of multiple constraints on carrying capacity, i.e., through… Figure 7 A comparison of carrying capacity values ​​under different constraints demonstrates that this application can clearly distinguish the constraint strength of charging station resource supply, power grid carrying capacity, and transportation network traffic service level on regional carrying capacity. Example data shows significant differences in carrying capacity values ​​under different single constraints; in this example, the charging station side is the core constraint, while the power grid and transportation sides are secondary constraints. The carrying capacity value after integrating multi-dimensional constraints achieves a synergistic balance among various constraints, avoiding resource misallocation that may result from single-constraint assessments, and providing direct evidence for identifying core constraint bottlenecks through quantitative differences. Furthermore, this application provides standardized carrying capacity boundaries, offering precise data support for multi-scenario practical applications. In other words, the carrying capacity output results of this application provide carrying capacity boundaries, possessing strong practical guidance and directly serving aspects such as charging station layout planning, power distribution facility expansion, and traffic guidance strategies. Compared to the simple carrying capacity verification in related technologies, this significantly improves the scientific rigor and rationality of planning work.

[0133] In summary, the method provided in this application focuses on the core objective of comprehensive carrying capacity assessment. It can quantitatively assess the number of electric vehicles that a regional grid can safely support under the condition of comprehensively considering multiple constraints of the power grid, charging stations, and transportation. By constructing an independent carrying capacity assessment system, it effectively breaks through the limitations of single-dimensional analysis, distinguishes the functional boundaries between carrying capacity assessment and charging station planning, and realizes the output of carrying capacity under the coupling of multiple systems such as piles, roads, and networks.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0135] Based on the same inventive concept, this application also provides a pile-road-network infrastructure bearing capacity assessment device for implementing the pile-road-network infrastructure bearing capacity assessment method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more pile-road-network infrastructure bearing capacity assessment device embodiments provided below can be found in the limitations of the pile-road-network infrastructure bearing capacity assessment method described above, and will not be repeated here.

[0136] In some embodiments, such as Figure 8 As shown, a pile-road-network infrastructure carrying capacity assessment device 800 is provided, comprising: an acquisition module 801, used to acquire multi-dimensional parameters of the distribution network grid to be assessed, including grid-side parameters, charging station parameters, and traffic-side parameters; a verification module 802, used to perform multi-dimensional constraint verification on the pile-road-network infrastructure of the distribution network grid according to the multi-dimensional parameters, to obtain multi-dimensional verification results; an iteration module 803, used to increase the electric vehicle capacity of the distribution network grid, iteratively perform multi-dimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found, provided that the multi-dimensional verification results do not exceed the carrying capacity limit; and an output module 804, used to output the carrying capacity assessment results of the distribution network grid according to the maximum electric vehicle capacity.

[0137] In some embodiments, the multidimensional verification results include power grid power supply capacity assessment results, charging station service capacity assessment results, and grid maximum allowable vehicle number assessment results; wherein, the verification module is also used to perform power grid power supply capacity assessment based on power grid side parameters to obtain power grid power supply capacity assessment results; perform charging station service capacity assessment based on charging station parameters to obtain charging station service capacity assessment results; and perform grid traffic carrying capacity assessment based on traffic side parameters to obtain grid traffic carrying capacity assessment results.

[0138] In some embodiments, the grid-side parameters include the current grid power supply margin and the minimum grid power supply margin threshold of the distribution grid; the verification module is also used to determine whether the current grid power supply margin is greater than or equal to the minimum grid power supply margin threshold; if so, the grid power supply capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid power supply capacity assessment result is that it exceeds the carrying capacity limit.

[0139] In some embodiments, the acquisition module is further configured to acquire the distribution network capacity, original load, current electric vehicle charging load, and maximum allowable safe load of the distribution network grid; acquire the current power supply margin of the distribution network as the ratio of a first difference to the distribution network capacity, wherein the first difference is the distribution network capacity minus the sum of the original load and the current electric vehicle charging load; and acquire the minimum power supply margin threshold of the distribution network as the ratio of a second difference to the distribution network capacity, wherein the second difference is the distribution network capacity minus the sum of the original load and the maximum allowable safe load of the distribution network.

[0140] In some embodiments, the charging station parameters include the daily access demand of various vehicle types in the power distribution network, the number of various types of charging piles, the number of parking spaces required by electric vehicles, and the actual number of parking spaces configured. The verification module is also used to determine whether the daily access demand of various vehicle types is less than or equal to the number of charging piles of the corresponding type, and whether the number of parking spaces required by electric vehicles is less than or equal to the actual number of parking spaces configured. If both conditions are met, the station service capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the station service capacity assessment result is that it exceeds the carrying capacity limit.

[0141] In some embodiments, traffic-side parameters include the actual number of vehicles in the distribution network grid and the maximum allowed number of vehicles; the verification module is also used to determine whether the actual number of vehicles is less than or equal to the maximum allowed number of vehicles; if so, the grid traffic carrying capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid traffic carrying capacity assessment result is that it exceeds the carrying capacity limit.

[0142] In some embodiments, the iteration module is further configured to increase the electric vehicle capacity of the distribution network grid if none of the multidimensional verification results exceed the bearing capacity limit, return to obtain the multidimensional parameters of the distribution network grid to be evaluated, and perform multidimensional constraint verification on the pile-road-network infrastructure of the distribution network grid according to the multidimensional parameters to obtain the multidimensional verification results again; if none of the multidimensional verification results exceed the bearing capacity limit, the capacity of the newly added electric vehicles is increased and the iterative verification continues; if any constraint verification in the multidimensional verification results exceeds the bearing capacity limit, the capacity of the electric vehicles in the distribution network grid is reduced and the verification is returned for re-verification until the maximum capacity of electric vehicles that satisfies the constraints of each dimension is found.

[0143] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0144] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned method.

[0145] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0146] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned method.

[0147] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method.

[0148] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the aforementioned method.

[0149] 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, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0150] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0151] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0152] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the carrying capacity of pile-road-network infrastructure, characterized in that, The method includes: Obtain multi-dimensional parameters of the distribution network grid to be evaluated, including grid-side parameters, charging station parameters, and traffic-side parameters; Based on the multidimensional parameters, multidimensional constraint verification is performed on the pile-road-network infrastructure of the power distribution network grid to obtain multidimensional verification results; If the multidimensional verification results do not exceed the bearing capacity limit, increase the electric vehicle capacity of the distribution network grid, iteratively perform multidimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found. Based on the maximum capacity of the electric vehicle, output the load-bearing capacity assessment result of the power distribution network grid.

2. The method according to claim 1, characterized in that, The multi-dimensional verification results include power grid supply capacity assessment results, substation service capacity assessment results, and grid maximum allowable vehicle number assessment results; wherein, the multi-dimensional constraint verification of the pile-road-network infrastructure of the distribution network grid based on the multi-dimensional parameters to obtain multi-dimensional verification results includes: The power supply capacity of the power grid is assessed based on the power grid side parameters to obtain the power supply capacity assessment results. The service capacity of the charging station is assessed based on the charging station parameters to obtain the assessment results. The grid traffic carrying capacity is assessed based on the traffic-side parameters to obtain the grid traffic carrying capacity assessment results.

3. The method according to claim 2, characterized in that, The grid-side parameters include the current grid power supply margin and the minimum grid power supply margin threshold of the distribution network grid; The step of assessing the power supply capacity of the power grid based on the grid-side parameters to obtain the assessment result includes: Determine whether the current power supply margin of the power grid is greater than or equal to the minimum power supply margin threshold of the power grid; If so, the power grid supply capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the power grid supply capacity assessment result is that it exceeds the carrying capacity limit.

4. The method according to claim 3, characterized in that, Obtaining the current power supply margin and the minimum power supply margin threshold of the distribution network grid includes: Obtain the distribution network capacity, original load, current electric vehicle charging load, and maximum allowable safe load of the power grid for the distribution network grid; The current power supply margin of the power grid is obtained as the ratio of a first difference to the capacity of the distribution network, wherein the first difference is the capacity of the distribution network minus the sum of the original load and the current electric vehicle charging load; The minimum power supply margin threshold of the power grid is obtained as the ratio of a second difference to the capacity of the distribution network, wherein the second difference is the capacity of the distribution network minus the sum of the original load and the maximum allowable safe load of the power grid.

5. The method according to claim 2, characterized in that, The charging station parameters include the daily access demand of various vehicle types, the number of various types of charging piles, the number of parking spaces required for electric vehicles, and the actual number of parking spaces configured in the power distribution network grid; the step of evaluating the station service capacity based on the charging station parameters to obtain the station service capacity evaluation results includes: Determine whether the daily access demand for each type of vehicle is less than or equal to the number of charging piles of the corresponding type, and determine whether the number of parking spaces required for the electric vehicle is less than or equal to the actual number of parking spaces configured. If all conditions are met, the assessment result of the station service capacity is that it does not exceed the carrying capacity limit; otherwise, the assessment result of the station service capacity is that it exceeds the carrying capacity limit.

6. The method according to claim 2, characterized in that, The traffic-side parameters include the actual number of vehicles and the maximum allowed number of vehicles in the power distribution network grid; the step of assessing the grid's traffic carrying capacity based on the traffic-side parameters to obtain the grid's traffic carrying capacity assessment results includes: Determine whether the actual number of vehicles is less than or equal to the maximum allowed number of vehicles; If so, the grid traffic carrying capacity assessment result is that it does not exceed the carrying capacity limit; otherwise, the grid traffic carrying capacity assessment result is that it exceeds the carrying capacity limit.

7. The method according to claim 1, characterized in that, When the multi-dimensional verification results do not exceed the bearing capacity limit, the electric vehicle capacity of the distribution network grid is increased, multi-dimensional constraint verification is iteratively performed, and the electric vehicle capacity is adjusted through feedback closed loop until the maximum electric vehicle capacity that satisfies each constraint is found, including: If the multidimensional verification results do not exceed the bearing capacity limit, increase the electric vehicle capacity of the power distribution network grid, return to obtain the multidimensional parameters of the power distribution network grid to be evaluated, and perform multidimensional constraint verification on the pile-road-network infrastructure of the power distribution network grid according to the multidimensional parameters, so as to obtain the multidimensional verification results again. If none of the multidimensional verification results exceed the bearing capacity limit, the electric vehicle capacity is increased and the verification is iterated again. If any constraint verification in the multidimensional verification results exceeds the bearing capacity limit, the electric vehicle capacity of the distribution network grid is reduced and the verification is repeated until the maximum electric vehicle capacity that satisfies each constraint is found.

8. A device for assessing the bearing capacity of pile-road-network infrastructure, characterized in that, The device includes: The acquisition module is used to acquire multi-dimensional parameters of the distribution network grid to be evaluated, including grid-side parameters, charging station parameters, and traffic-side parameters. The verification module is used to perform multi-dimensional constraint verification on the pile-road-network infrastructure of the power distribution network grid based on the multi-dimensional parameters, so as to obtain multi-dimensional verification results; The iterative module is used to increase the electric vehicle capacity of the power distribution network grid when the multi-dimensional verification results do not exceed the bearing capacity limit, iteratively perform multi-dimensional constraint verification, and adjust the electric vehicle capacity through feedback closed loop until the maximum electric vehicle capacity that satisfies each dimension constraint is found. The output module is used to output the load-bearing capacity assessment result of the power distribution network grid based on the maximum capacity of the electric vehicle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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

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