An evaluation method and system for carrying capacity of hybrid distribution network based on electric vehicle characteristics

Abstract

The disclosure provides a hybrid power distribution network carrying capacity evaluation method and system based on electric vehicle characteristics, belonging to the technical field of power distribution network planning. The method comprises: based on the influencing factors related to the charging behavior of the electric vehicle in the hybrid power distribution network, establishing a comprehensive evaluation model of the hybrid power distribution network; determining the comprehensive weight of each index calculated by the comprehensive evaluation model; based on the various indexes, establishing a carrying capacity evaluation cloud model of the hybrid power distribution network; combining the comprehensive weight and the carrying capacity evaluation cloud model, obtaining the carrying capacity comprehensive cloud model of the hybrid power distribution network; comparing the digital characteristics calculated by the carrying capacity comprehensive cloud model with the digital characteristics of the preset hybrid power distribution network grade, and determining the hybrid power distribution network grade reflecting the carrying capacity level of the hybrid power distribution network. Compared with the prior art, the method and system provided by the disclosure can more objectively evaluate the carrying capacity of the hybrid power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network planning technology, and in particular relates to a method and system for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles. Background Technology

[0002] In recent years, with the continuous advancement of "dual-carbon" goals and the construction of new power systems, the usage rate of electric vehicles in power distribution networks has rapidly increased. Traditional power distribution networks are mainly AC systems, and their planning is primarily aimed at operating scenarios with relatively stable load changes and relatively fixed power flow. When dealing with the high proportion and rapid changes in power flow from electric vehicles, they face challenges such as low efficiency, complex control, and high expansion costs. To meet these challenges, AC / DC hybrid power distribution networks, or simply hybrid power distribution networks, have been developed. Because AC / DC hybrid power distribution networks can effectively reduce energy conversion losses and improve power flow regulation and voltage support capabilities when connecting DC distributed power sources or electric vehicle charging loads, they provide new technical conditions to alleviate the operational pressure caused by the concentrated access of electric vehicles. Therefore, AC / DC hybrid power distribution networks are considered a key form of the next generation of smart power distribution networks.

[0003] However, hybrid AC / DC distribution networks still face challenges when dealing with electric vehicles (EVs), a major form of electricity consumption. Due to the high charging power levels and concentrated nature of EVs, and their significant influence from user travel behavior, they exhibit obvious temporal fluctuations and spatial uneven distribution characteristics. When EV charging loads concentrate in local areas and specific time periods, it can easily lead to problems such as voltage shifts at the balance nodes of the AC / DC distribution network, increased load rates on lines and distribution transformers, and in severe cases, even affect the safety and reliability of the AC / DC distribution network. Therefore, scientifically assessing the carrying capacity of AC / DC distribution networks for EVs has become a core issue in the planning and operation of hybrid AC / DC distribution networks. However, due to the significant randomness and uncertainty of EV charging behavior, the system's operating state varies considerably under different time and spatial scenarios. Therefore, constructing an assessment method that can reflect the true carrying capacity of hybrid AC / DC distribution networks has become a key technical problem that urgently needs to be solved in current distribution network planning. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles.

[0005] The first aspect of this invention proposes a method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles, the method comprising: Based on the influencing factors related to the charging behavior of the electric vehicles in the hybrid distribution network, a comprehensive evaluation model for the hybrid distribution network is established; wherein, the comprehensive evaluation model includes a distribution network reliability calculation model for calculating distribution network reliability indicators, a distribution network efficiency calculation model for calculating distribution network efficiency indicators, a distribution network quality calculation model for calculating distribution network quality indicators, a charging network efficiency model for calculating charging network efficiency indicators, and a charging service reliability model for calculating charging service reliability indicators; The comprehensive weights of each indicator calculated using the comprehensive evaluation model are determined; the indicators include the distribution network reliability indicator, the distribution network efficiency indicator, the distribution network quality indicator, the charging network efficiency indicator, and the charging service reliability indicator. Based on the aforementioned indicators, a cloud model for evaluating the carrying capacity of the hybrid distribution network is established. By combining the comprehensive weights and the carrying capacity evaluation cloud model, the carrying capacity comprehensive cloud model of the hybrid distribution network is obtained; The digital characteristics calculated by the integrated cloud model of carrying capacity are compared with the digital characteristics of the preset hybrid distribution network level to determine the hybrid distribution network level that reflects the carrying capacity level of the hybrid distribution network.

[0006] A second aspect of the present invention provides a hybrid power distribution network carrying capacity assessment system based on electric vehicle characteristics. The system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the method as described in the first aspect of the present invention.

[0007] In summary, this invention proposes a more scientific, objective, and engineering-aligned method and system for assessing the carrying capacity of AC / DC hybrid distribution networks. This method and system comprehensively consider the temporal and spatial distribution characteristics of electric vehicle charging loads, the power regulation characteristics of voltage source converters, and the operational constraints of the distribution network. It constructs a multi-level, multi-dimensional comprehensive carrying capacity assessment index system to adapt to the actual operating characteristics of electric vehicle charging loads, which are highly random and subject to large fluctuations in operating scenarios. This objectively improves the adaptability and engineering guidance value of the AC / DC hybrid distribution network carrying capacity assessment results for electric vehicle access scenarios, and provides effective technical support for optimizing the layout of electric vehicle charging facilities and for the planning and operation management of AC / DC hybrid distribution networks. Attached Figure Description

[0008] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a hybrid power distribution network carrying capacity assessment method based on electric vehicle characteristics, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a hybrid power distribution network carrying capacity assessment system based on electric vehicle characteristics, according to an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Appendix Figure 1 The present invention discloses a method 100 for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles, the method 100 comprising: Step 101: Based on the influencing factors related to the charging behavior of the electric vehicle in the hybrid distribution network, establish a comprehensive evaluation model for the hybrid distribution network; wherein, the comprehensive evaluation model includes a distribution network reliability calculation model for calculating distribution network reliability indicators, a distribution network efficiency calculation model for calculating distribution network efficiency indicators, a distribution network quality calculation model for calculating distribution network quality indicators, a charging network efficiency model for calculating charging network efficiency indicators, and a charging service reliability model for calculating charging service reliability indicators; Step 102: Determine the comprehensive weight of each indicator calculated using the comprehensive evaluation model; the indicators include the distribution network reliability indicator, the distribution network efficiency indicator, the distribution network quality indicator, the charging network efficiency indicator, and the charging service reliability indicator. Step 103: Based on the aforementioned indicators, establish a cloud model for evaluating the carrying capacity of the hybrid distribution network; Step 104: Combining the comprehensive weight and the carrying capacity evaluation cloud model, obtain the comprehensive cloud model of the carrying capacity of the hybrid distribution network; Step 105: Compare the digital features calculated by the integrated cloud model of carrying capacity with the digital features of the preset hybrid distribution network level to determine the hybrid distribution network level that reflects the carrying capacity level of the hybrid distribution network.

[0012] The comparison process combines the digital features calculated by the integrated cloud model of carrying capacity with the digital features of preset hybrid distribution network levels into the same coordinate system. These digital features are then compared using cloud mapping, resulting in a more intuitive evaluation of the carrying capacity level of the hybrid distribution network. These preset hybrid distribution network level digital features are typically derived from empirical values ​​or calculations based on empirical values. When evaluating a specific hybrid distribution network level, the spatial distribution and digital features of the integrated cloud model of carrying capacity in different preset hybrid distribution network levels are observed. The preset hybrid distribution network level closest to this model can be considered the hybrid distribution network level. In an optional embodiment, the closest preset hybrid distribution network level can also be determined by calculating the Euclidean distance, cosine similarity, and other methods between the digital features calculated by the integrated cloud model of carrying capacity and the digital features of the preset hybrid distribution network level.

[0013] In one optional embodiment, the power distribution network reliability calculation model includes a line overload rate model, a distribution transformer overload rate change model, and a converter capacity utilization model. The line overload rate model is as follows: , , , , ; in, Indicates whether the AC lines of the hybrid distribution network at time t are heavy-load power flow lines. This indicates whether the DC lines of the hybrid distribution network at time t are heavily loaded with power flow. This refers to the number of AC line branches in the hybrid distribution network. Let ACB be the number of DC branches in the hybrid distribution network, ACB be the set of AC branches in the hybrid distribution network, and DCB be the set of DC branches in the hybrid distribution network. Let be the branch power flow overload probability of the hybrid distribution network at time t. Let be the active power flow value of branch b of the hybrid distribution network at time t. The rated transmission power of the lines in the hybrid distribution network. The line overload rate index is used to reflect the line overload level of the hybrid distribution network. The proportion of heavily loaded lines in the hybrid distribution network at time t. The proportion of heavy-load lines in the hybrid distribution network is preset, and time t is a sampling time after the load of the electric vehicle is connected to the hybrid distribution network; The model for the change in overload rate of the distribution transformer is as follows: , , ; in, The average overload probability of the distribution transformers in the hybrid distribution network is preset. Let be the average overload probability of the distribution transformer at time t. This indicates the load status of the q-th distribution transformer at time t. The total number of the distribution transformers. Let q be the load of the q-th distribution transformer at time t. The rated load of the qth distribution transformer is... The overload rate change index is used to reflect the degree of overload rate change of the distribution transformers in the hybrid distribution network. The converter capacity utilization model is as follows: , = ; in, The rated capacity of the converter in the i-th hybrid distribution network is... The active power output from the AC side of the converter of the i-th hybrid distribution network is [value]. Let be the reactive power output from the AC side of the converter of the i-th hybrid distribution network. This is a representative value indicating whether the converter of the i-th hybrid distribution network is within the safe range. The converter capacity utilization rate is an index that reflects the degree of capacity utilization of the converters in the hybrid distribution network. This represents the total number of converters in the hybrid distribution network. Among them, the reliability index of the distribution network for: = + .

[0014] In one optional embodiment, the power distribution network efficiency calculation model includes a system load rate model and a converter conversion efficiency model; The system load rate model is as follows: , ; in, The average daily load factor of the hybrid power distribution network after the electric vehicle load is connected is given. The preset daily average load factor of the hybrid distribution network, The system load factor index reflects the load level of the hybrid distribution network. Let be the total load of the hybrid distribution network at time t. The maximum load of the hybrid distribution network is preset; The converter conversion efficiency model is as follows: , = ; in, The efficiency of the preset i-th converter in the hybrid distribution network is given. The active power absorbed by the converter of the i-th hybrid distribution network from the AC side. The power output to the DC side by the converter of the i-th hybrid distribution network is given. The converter conversion efficiency index is used to reflect the conversion efficiency of the converters in the hybrid distribution network. Among them, the power distribution network efficiency index : = + .

[0015] In one optional embodiment, the distribution network quality calculation model includes an AC voltage deviation rate model, a DC voltage deviation rate model, and a system average network loss model. The AC voltage offset model is as follows: , = ; in, This represents the effective value of the AC voltage at the balancing node of the i-th hybrid distribution network. This indicates the AC rated voltage of the hybrid distribution network. The AC voltage deviation rate is an index that reflects the AC stability of the slack node in the hybrid distribution network. Let be the AC voltage offset rate of the balancing node of the i-th hybrid distribution network. The total number of balancing nodes in the hybrid distribution network; The DC voltage offset model is as follows: , = ; in, This represents the effective value of the DC voltage at the balancing node of the i-th hybrid distribution network. This indicates the rated DC voltage of the hybrid distribution network. The DC voltage deviation rate is an index that reflects the DC stability of the slack nodes in the hybrid distribution network. Let be the DC voltage offset rate of the balancing node of the i-th hybrid distribution network; With the The same applies, both being the total number of balancing nodes in the hybrid distribution network; The average network loss model of the system is as follows: , ; in, Let be the active power injected into the balancing node of the i-th hybrid distribution network at time t. The active power injected at time t by other slack nodes that are different from the slack node of the i-th hybrid distribution network. The average network loss of the hybrid distribution network is preset. Let be the average network loss of the hybrid distribution network at time t. This refers to the set of balancing nodes in the hybrid distribution network. The system average network loss index is used to reflect the network loss level of the hybrid distribution network. Among them, the quality indicators of the distribution network for: = .

[0016] In one optional embodiment, the charging network efficiency model includes an average charging time model; The average charging time model is as follows: ; The charging network is a network of charging piles connected to the hybrid power distribution network. The number of electric vehicles that were charged using the charging network at the time of sampling. Let n be the average queuing time of the nth electric vehicle on the charging network. The actual charging time of the nth electric vehicle on the charging network. The charging network efficiency index is used to reflect the charging efficiency of the charging network.

[0017] In one optional embodiment, the charging service reliability model includes a charging satisfaction rate model; The charging satisfaction rate model is as follows: , ; In the formula, This refers to the total number of electric vehicles that have a charging need for the charging network. Let V be the indicator that the charging demand of the z-th electric vehicle is met, and let V be the set of ordinal numbers of the electric vehicles whose charging demand is met. Let be the satisfaction rate of the z-th electric vehicle after its charging demand is met at time t. The satisfaction rate can be calculated using a five-point scale, a ten-point scale, or a percentage scale. The appropriate calculation method can be selected based on the subsequent mathematical expression and normalization. The charging service reliability index is used to reflect the reliability of the charging network's charging service. Here, z is the ordinal number of the electric vehicle; if the z-th electric vehicle belongs to the set of electric vehicles whose charging demand is met, represented by V, then... The value is 1. Otherwise, the value is 0.

[0018] In an optional embodiment, determining the comprehensive weight of each indicator calculated using the comprehensive evaluation model includes: Establish an indicator comprehensive weight model, which is expressed as follows: , ; in, In order to meet the indicators The corresponding comprehensive weighting coefficient, w {1,2,3,4,5}, To be consistent with the aforementioned indicators The corresponding subjective weighting coefficient, To be consistent with the aforementioned indicators The corresponding objective weighting coefficients, The corresponding relative importance, and The corresponding relative importance.

[0019] The subjective weighting coefficients can be determined using the commonly used analytic hierarchy process (AHP), thus correlating the volatility of the indicators with the overall operational level of the hybrid distribution network. Alternatively, the objective weighting coefficients can be determined using the common entropy weighting method. Finally, by combining subjective judgment with objective data through the aforementioned comprehensive weighting, the shortcomings of a single method can be overcome, and the importance of each indicator can be reflected more comprehensively.

[0020] Among them, the indicators When being calculated, it can be processed using conventional mathematical transformations such as normalization and percentage differentiation as needed, so as to facilitate the combined calculation of index parameters.

[0021] In one optional embodiment, the carrying capacity evaluation cloud model is: , , ; in, For set { The variance, w {1,2,3,4,5}, For the aforementioned indicators entropy, For the aforementioned indicators Expectations For the aforementioned indicators hyperentropy, set { Right now{ .

[0022] In an optional embodiment, the step of combining the comprehensive weights and the carrying capacity evaluation cloud model to obtain the comprehensive cloud model of the carrying capacity of the hybrid distribution network includes: The criterion layer model of the comprehensive cloud carrying capacity model is established, and the criterion layer model is as follows: , , ; in, The expectation of the criterion layer model, The entropy of the criterion layer model, The hyperentropy of the criterion layer model; The target layer model of the comprehensive cloud carrying capacity model is established as follows: , , ; in, The expectation of the target layer model, The entropy of the target layer model. The hyperentropy of the target layer model, and the digital features calculated by the comprehensive cloud model of carrying capacity are set { , , , , }

[0023] Figure 2 The structure of a hybrid power distribution network carrying capacity assessment system 200 based on electric vehicle characteristics according to a second aspect of the present invention is shown. The system 200 includes: at least one processor 201; and a memory 202 communicatively connected to the at least one processor. The memory 202 stores instructions executable by the at least one processor 201, which, when executed by the at least one processor 201, enable the at least one processor 201 to implement the following method steps: Based on the influencing factors related to the charging behavior of the electric vehicles in the hybrid distribution network, a comprehensive evaluation model for the hybrid distribution network is established; wherein, the comprehensive evaluation model includes a distribution network reliability calculation model for calculating distribution network reliability indicators, a distribution network efficiency calculation model for calculating distribution network efficiency indicators, a distribution network quality calculation model for calculating distribution network quality indicators, a charging network efficiency model for calculating charging network efficiency indicators, and a charging service reliability model for calculating charging service reliability indicators; The comprehensive weights of each indicator calculated using the comprehensive evaluation model are determined; the indicators include the distribution network reliability indicator, the distribution network efficiency indicator, the distribution network quality indicator, the charging network efficiency indicator, and the charging service reliability indicator. Based on the aforementioned indicators, a cloud model for evaluating the carrying capacity of the hybrid distribution network is established. By combining the comprehensive weights and the carrying capacity evaluation cloud model, the carrying capacity comprehensive cloud model of the hybrid distribution network is obtained; The digital characteristics calculated by the integrated cloud model of carrying capacity are compared with the digital characteristics of the preset hybrid distribution network level to determine the hybrid distribution network level that reflects the carrying capacity level of the hybrid distribution network.

[0024] The comparison process combines the digital features calculated by the integrated cloud model of carrying capacity with the digital features of preset hybrid distribution network levels into the same coordinate system. These digital features are then compared using cloud mapping, resulting in a more intuitive evaluation of the carrying capacity level of the hybrid distribution network. These preset hybrid distribution network level digital features are typically derived from empirical values ​​or calculations based on empirical values. When evaluating a specific hybrid distribution network level, the spatial distribution and digital features of the integrated cloud model of carrying capacity in different preset hybrid distribution network levels are observed. The preset hybrid distribution network level closest to this model can be considered the hybrid distribution network level. In an optional embodiment, the closest preset hybrid distribution network level can also be determined by calculating the Euclidean distance, cosine similarity, and other methods between the digital features calculated by the integrated cloud model of carrying capacity and the digital features of the preset hybrid distribution network level.

[0025] In one optional embodiment, the power distribution network reliability calculation model includes a line overload rate model, a distribution transformer overload rate change model, and a converter capacity utilization model. The line overload rate model is as follows: , , , , ; in, Indicates whether the AC lines of the hybrid distribution network at time t are heavy-load power flow lines. This indicates whether the DC lines of the hybrid distribution network at time t are heavily loaded with power flow. This refers to the number of AC line branches in the hybrid distribution network. Let ACB be the number of DC branches in the hybrid distribution network, ACB be the set of AC branches in the hybrid distribution network, and DCB be the set of DC branches in the hybrid distribution network. Let be the branch power flow overload probability of the hybrid distribution network at time t. Let be the active power flow value of branch b of the hybrid distribution network at time t. The rated transmission power of the lines in the hybrid distribution network. This refers to the line's heavy load rate. The proportion of heavily loaded lines in the hybrid distribution network at time t. The proportion of heavy-load lines in the hybrid distribution network is preset, and time t is a sampling time after the load of the electric vehicle is connected to the hybrid distribution network; The model for the change in overload rate of the distribution transformer is as follows: , , ; in, The average overload probability of the distribution transformers in the hybrid distribution network is preset. Let be the average overload probability of the distribution transformer at time t. This indicates the load status of the q-th distribution transformer at time t. The total number of the distribution transformers. Let q be the load of the q-th distribution transformer at time t. The rated load of the qth distribution transformer is... This is an indicator of the change in overload rate of distribution transformers. The converter capacity utilization model is as follows: , = ; in, The rated capacity of the converter in the i-th hybrid distribution network is... The active power output from the AC side of the converter of the i-th hybrid distribution network is [value]. Let be the reactive power output from the AC side of the converter of the i-th hybrid distribution network. This is a representative value indicating whether the converter of the i-th hybrid distribution network is within the safe range. As an indicator of converter capacity utilization, This represents the total number of converters in the hybrid distribution network. Among them, the reliability index of the distribution network for: = + .

[0026] In one optional embodiment, the power distribution network efficiency calculation model includes a system load rate model and a converter conversion efficiency model; The system load rate model is as follows: , ; in, The average daily load factor of the hybrid power distribution network after the electric vehicle load is connected is given. The preset daily average load factor of the hybrid distribution network, System load factor Let be the total load of the hybrid distribution network at time t. The maximum load of the hybrid distribution network is preset; The converter conversion efficiency model is as follows: , = ; in, The efficiency of the preset i-th converter in the hybrid distribution network is given. The active power absorbed by the converter of the i-th hybrid distribution network from the AC side. The power output to the DC side by the converter of the i-th hybrid distribution network is given. The converter conversion efficiency index; Among them, the power distribution network efficiency index : = + .

[0027] In one optional embodiment, the distribution network quality calculation model includes an AC voltage deviation rate model, a DC voltage deviation rate model, and a system average network loss model. The AC voltage offset model is as follows: , = ; in, This represents the effective value of the AC voltage at the balancing node of the i-th hybrid distribution network. This indicates the AC rated voltage of the hybrid distribution network. This refers to the AC voltage offset rate index. Let be the AC voltage offset rate of the balancing node of the i-th hybrid distribution network. The total number of balancing nodes in the hybrid distribution network; The DC voltage offset model is as follows: , = ; in, This represents the effective value of the DC voltage at the balancing node of the i-th hybrid distribution network. This indicates the rated DC voltage of the hybrid distribution network. This refers to the DC voltage offset rate indicator. Let be the DC voltage offset rate of the balancing node of the i-th hybrid distribution network; With the The same applies, both being the total number of balancing nodes in the hybrid distribution network; The average network loss model of the system is as follows: , ; in, Let be the active power injected into the balancing node of the i-th hybrid distribution network at time t. The active power injected at time t by other slack nodes that are different from the slack node of the i-th hybrid distribution network. The average network loss of the hybrid distribution network is preset. Let be the average network loss of the hybrid distribution network at time t. This refers to the set of balancing nodes in the hybrid distribution network. This refers to the average network loss index of the system. Among them, the quality indicators of the distribution network for: = .

[0028] In one optional embodiment, the charging network efficiency model includes an average charging time model; The average charging time model is as follows: ; The charging network is a network of charging piles connected to the hybrid power distribution network. The number of electric vehicles that were charged using the charging network at the time of sampling. Let n be the average queuing time of the nth electric vehicle on the charging network. The actual charging time of the nth electric vehicle on the charging network. This refers to the efficiency index of the charging network.

[0029] In one optional embodiment, the charging service reliability model includes a charging satisfaction rate model; The charging satisfaction rate model is as follows: , ; In the formula, This refers to the total number of electric vehicles that have a charging need for the charging network. Let V be the indicator that the charging demand of the z-th electric vehicle has been met, and let V be the set of ordinal numbers of the electric vehicles whose charging demands have been met. Let Z be the satisfaction rate after the charging demand of the z-th electric vehicle is met at time t. The charging service reliability index reflects the service reliability of the charging station. Here, z is the ordinal number of the electric vehicle; if the z-th electric vehicle belongs to the set of electric vehicles whose charging demand is met (represented by V), i.e., z is in set V, then... The value is 1. Otherwise, the value is 0.

[0030] In an optional embodiment, determining the comprehensive weight of each indicator calculated using the comprehensive evaluation model includes: Establish an indicator comprehensive weight model, which is expressed as follows: , ; in, In order to meet the indicators The corresponding comprehensive weighting coefficient, w {1,2,3,4,5}, To be consistent with the aforementioned indicators The corresponding subjective weighting coefficient, To be consistent with the aforementioned indicators The corresponding objective weighting coefficients, The corresponding relative importance, and The corresponding relative importance.

[0031] The subjective weighting coefficients can be determined using the commonly used analytic hierarchy process (AHP), thus correlating the volatility of the indicators with the overall operational level of the hybrid distribution network. Alternatively, the objective weighting coefficients can be determined using the common entropy weighting method. Finally, by combining subjective judgment with objective data through the aforementioned comprehensive weighting, the shortcomings of a single method can be overcome, and the importance of each indicator can be reflected more comprehensively.

[0032] In one optional embodiment, the carrying capacity evaluation cloud model is: , , ; in, For set { The variance, w {1,2,3,4,5}, For the aforementioned indicators entropy, For the aforementioned indicators Expectations For the aforementioned indicators hyperentropy.

[0033] In an optional embodiment, the step of combining the comprehensive weights and the carrying capacity evaluation cloud model to obtain the comprehensive cloud model of the carrying capacity of the hybrid distribution network includes: The criterion layer model of the comprehensive cloud carrying capacity model is established, and the criterion layer model is as follows: , , ; in, The expectation of the criterion layer model, The entropy of the criterion layer model, The hyperentropy of the criterion layer model; The target layer model of the comprehensive cloud carrying capacity model is established as follows: , , ; in, The expectation of the target layer model, The entropy of the target layer model. The hyperentropy of the target layer model, and the digital features calculated by the comprehensive cloud model of carrying capacity are set { , , , , }

[0034] In summary, this invention proposes a more scientific and engineering-aligned method and system for assessing the carrying capacity of AC / DC hybrid distribution networks. This method and system comprehensively consider the temporal and spatial distribution characteristics of electric vehicle charging loads, the power regulation characteristics of voltage source converters, and the operational constraints of the distribution network. It constructs a multi-level, multi-dimensional comprehensive carrying capacity assessment index system to adapt to the actual operating characteristics of electric vehicle charging loads, which are highly random and fluctuate greatly in operating scenarios. This objectively improves the adaptability and engineering guidance value of the AC / DC hybrid distribution network carrying capacity assessment results for electric vehicle access scenarios, and provides effective technical support for the optimization of electric vehicle charging facility layout, and the planning and operation management of AC / DC hybrid distribution networks.

[0035] It should be noted that, in the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways, and can be interchanged to achieve the same or similar functions. For example, the embodiments described above are merely illustrative. For instance, the division of units or steps is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or steps may be combined or integrated into another system or step, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0036] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0037] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0038] It should be noted that the embodiments provided in this invention are all illustrative, and different embodiments can be arbitrarily and reasonably combined. For the sake of brevity, not all possible combinations of the various technical features in the above embodiments are described; however, as long as such combinations do not contradict each other, they should all be considered to fall within the scope of this specification. Furthermore, it should be understood that the systems and methods disclosed in the embodiments provided in this invention can be implemented in other ways or with modifications. Any substitutions made in hardware or software, or any modifications made without departing from the concept of this invention, are within the protection scope of this invention.

Claims

1. A method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles, characterized in that, Includes the following steps: Based on the influencing factors related to the charging behavior of the electric vehicles in the hybrid distribution network, a comprehensive evaluation model for the hybrid distribution network is established; wherein, the comprehensive evaluation model includes a distribution network reliability calculation model for calculating distribution network reliability indicators, a distribution network efficiency calculation model for calculating distribution network efficiency indicators, a distribution network quality calculation model for calculating distribution network quality indicators, a charging network efficiency model for calculating charging network efficiency indicators, and a charging service reliability model for calculating charging service reliability indicators; The comprehensive weights of each indicator calculated using the comprehensive evaluation model are determined; the indicators include the distribution network reliability indicator, the distribution network efficiency indicator, the distribution network quality indicator, the charging network efficiency indicator, and the charging service reliability indicator. Based on the aforementioned indicators, a cloud model for evaluating the carrying capacity of the hybrid distribution network is established. By combining the comprehensive weights and the carrying capacity evaluation cloud model, the carrying capacity comprehensive cloud model of the hybrid distribution network is obtained; The digital characteristics calculated by the integrated cloud model of carrying capacity are compared with the digital characteristics of the preset hybrid distribution network level to determine the hybrid distribution network level that reflects the carrying capacity level of the hybrid distribution network.

2. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 1, characterized in that: The power distribution network reliability calculation model includes a line overload rate model, a distribution transformer overload rate change model, and a converter capacity utilization model. The line overload rate model is as follows: ， ， ， ， ； in, Indicates whether the AC lines of the hybrid distribution network at time t are heavy-load power flow lines. This indicates whether the DC lines of the hybrid distribution network at time t are heavily loaded with power flow. This refers to the number of AC line branches in the hybrid distribution network. Let ACB be the number of DC branches in the hybrid distribution network, ACB be the set of AC branches in the hybrid distribution network, and DCB be the set of DC branches in the hybrid distribution network. Let be the branch power flow overload probability of the hybrid distribution network at time t. Let be the active power flow value of branch b of the hybrid distribution network at time t. The rated transmission power of the lines in the hybrid distribution network. This refers to the line's heavy load rate. The proportion of heavily loaded lines in the hybrid distribution network at time t. The proportion of heavy-load lines in the hybrid distribution network is preset, and time t is a sampling time after the load of the electric vehicle is connected to the hybrid distribution network; The model for the change in overload rate of the distribution transformer is as follows: ， ， ； in, The average overload probability of the distribution transformers in the hybrid distribution network is preset. Let be the average overload probability of the distribution transformer at time t. This indicates the load status of the q-th distribution transformer at time t. The total number of the distribution transformers. Let q be the load of the q-th distribution transformer at time t. The rated load of the qth distribution transformer is... This is an indicator of the change in overload rate of distribution transformers. The converter capacity utilization model is as follows: , = ； in, The rated capacity of the converter in the i-th hybrid distribution network is... The active power output from the AC side of the converter of the i-th hybrid distribution network is [value]. Let be the reactive power output from the AC side of the converter of the i-th hybrid distribution network. This is a representative value indicating whether the converter of the i-th hybrid distribution network is within the safe range. As an indicator of converter capacity utilization, This represents the total number of converters in the hybrid distribution network. Among them, the reliability index of the distribution network for: = + 。 3. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 2, characterized in that: The power distribution network efficiency calculation model includes a system load rate model and a converter conversion efficiency model. The system load rate model is as follows: ， ； in, The average daily load factor of the hybrid power distribution network after the electric vehicle load is connected is given. The preset daily average load factor of the hybrid distribution network, System load factor Let be the total load of the hybrid distribution network at time t. The maximum load of the hybrid distribution network is preset; The converter conversion efficiency model is as follows: ， = ； in, The efficiency of the preset i-th converter in the hybrid distribution network is given. The active power absorbed by the converter of the i-th hybrid distribution network from the AC side. The power output to the DC side by the converter of the i-th hybrid distribution network is given. The converter conversion efficiency index; Among them, the power distribution network efficiency index : = + 。 4. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 3, characterized in that: The distribution network quality calculation model includes an AC voltage deviation rate model, a DC voltage deviation rate model, and a system average network loss model. The AC voltage offset model is as follows: ， = ； in, This represents the effective value of the AC voltage at the balancing node of the i-th hybrid distribution network. This indicates the AC rated voltage of the hybrid distribution network. This refers to the AC voltage offset rate index. Let be the AC voltage offset rate of the balancing node of the i-th hybrid distribution network. The total number of balancing nodes in the hybrid distribution network; The DC voltage offset model is as follows: ， = ； in, This represents the effective value of the DC voltage at the balancing node of the i-th hybrid distribution network. This indicates the rated DC voltage of the hybrid distribution network. This refers to the DC voltage offset rate indicator. Let be the DC voltage offset rate of the balancing node of the i-th hybrid distribution network; With the The same applies, both being the total number of balancing nodes in the hybrid distribution network; The average network loss model of the system is as follows: ， ； in, Let be the active power injected into the balancing node of the i-th hybrid distribution network at time t. The active power injected at time t by other slack nodes that are different from the slack node of the i-th hybrid distribution network. The average network loss of the hybrid distribution network is preset. Let be the average network loss of the hybrid distribution network at time t. This refers to the set of balancing nodes in the hybrid distribution network. This refers to the average network loss index of the system. Among them, the quality indicators of the distribution network for: = 。 5. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 4, characterized in that: The charging network efficiency model includes an average charging time model; The average charging time model is as follows: ； The charging network is a network of charging piles connected to the hybrid power distribution network. The number of electric vehicles that were charged using the charging network at the time of sampling. Let n be the average queuing time of the nth electric vehicle on the charging network. The actual charging time of the nth electric vehicle on the charging network. This refers to the efficiency index of the charging network.

6. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 5, characterized in that: The charging service reliability model includes a charging satisfaction rate model. The charging satisfaction rate model is as follows: ， ； In the formula, This refers to the total number of electric vehicles that have a charging need for the charging network. Let V be the indicator that the charging demand of the z-th electric vehicle has been met, and let V be the set of ordinal numbers of the electric vehicles whose charging demands have been met. Let Z be the satisfaction rate after the charging demand of the z-th electric vehicle is met at time t. This refers to the reliability index of the charging service.

7. The method for assessing the carrying capacity of a hybrid distribution network based on the characteristics of electric vehicles according to claim 6, characterized in that: The determination of the comprehensive weights of each indicator calculated using the comprehensive evaluation model includes: Establish an indicator comprehensive weight model, which is expressed as follows: ， ； in, In order to meet the indicators The corresponding comprehensive weighting coefficient, w {1,2,3,4,5}, To be consistent with the aforementioned indicators The corresponding subjective weighting coefficient, To be consistent with the aforementioned indicators The corresponding objective weighting coefficients, The corresponding relative importance, and The corresponding relative importance.

8. The method for assessing the carrying capacity of a hybrid power distribution network based on the characteristics of electric vehicles according to claim 7, characterized in that: The cloud model for carrying capacity evaluation is as follows: ， ， ； in, For set { The variance, w {1,2,3,4,5}, For the aforementioned indicators entropy, For the aforementioned indicators Expectations For the aforementioned indicators hyperentropy.

9. The method for assessing the carrying capacity of a hybrid distribution network based on the characteristics of electric vehicles according to claim 8, characterized in that: The process of combining the comprehensive weights and the carrying capacity evaluation cloud model to obtain the comprehensive cloud model of the carrying capacity of the hybrid distribution network includes: The criterion layer model of the comprehensive cloud carrying capacity model is established, and the criterion layer model is as follows: ， ， ； in, The expectation of the criterion layer model, The entropy of the criterion layer model, The hyperentropy of the criterion layer model; The target layer model of the comprehensive cloud carrying capacity model is established as follows: ， ， ； in, The expectation of the target layer model, The entropy of the target layer model. The hyperentropy of the target layer model, and the digital features calculated by the comprehensive cloud model of carrying capacity are set { , , , , } 10. A hybrid power distribution network carrying capacity assessment system based on electric vehicle characteristics, the system comprising: At least one processor; And a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one processor, the instructions being executed by at least one processor to enable the at least one processor to implement the method as described in any one of claims 1-9.

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