Power distribution network acceptance capability assessment method and system considering EV space-time transfer characteristics

Abstract

The invention provides a power distribution network acceptance capability assessment method considering EV space-time transfer characteristics, and belongs to the field of electric vehicle acceptance capability assessment, and the method comprises the steps: building a road network-power grid coupling node aggregation model based on EV charging load space-time distribution; a multi-objective optimization evaluation model of the EV load accepting potential of the power distribution network is constructed by taking the maximum electric vehicle charging load accepting capacity of the power distribution network and the minimum network loss cost of the power distribution network as objective functions, and the time sequence optimal power flow of the power distribution network is iteratively calculated; when it is judged that any one of the maximum deviation rate of the node voltage and the maximum overload rate of the line current exceeds an allowable range, the current EV permeability serves as the maximum critical permeability, and otherwise, the current EV permeability is gradually increased; establishing a comprehensive evaluation index system for the acceptance capability of the power distribution network, and comprehensively scoring each index; the invention further provides a power distribution network admitting ability evaluation system. The problem of insufficient acceptance capability evaluation of the power distribution network during large-scale access of the electric vehicles is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle acceptance capacity assessment, and in particular to a distribution network acceptance capacity assessment method and system considering the spatiotemporal transfer characteristics of EVs. Background Art

[0002] Electric vehicles (EVs) have played a key role in driving carbon emissions reduction and improving energy efficiency, and they also play a vital role in the sustainable development of the transportation industry. However, new loads such as EVs exhibit temporal and spatial stochasticity. As EV penetration continues to increase, power systems face new challenges such as concentrated loads and increased power supply pressure. Especially during peak hours, the grid may struggle to withstand the impact of large-scale charging demand. Therefore, evaluating the distribution network's capacity to accommodate EVs is crucial for optimizing grid operation and rationally planning charging facilities, thereby effectively addressing the power supply security challenges posed by the large-scale integration of EVs.

[0003] Current research methods for evaluating distribution network capacity for electric vehicles (EVs) suffer from the following shortcomings: With the large-scale integration of EVs into distribution networks, the uncertainty of their charging and discharging behavior poses multiple adverse impacts on grid reliability and safety. The complex coupling of power and transportation systems also presents new challenges for analyzing distribution network capacity. Firstly, EV load modeling in EV capacity analysis typically considers only temporal factors, neglecting the impact of spatial patterns of EV travel and driver characteristics on load forecasting. Secondly, existing research primarily focuses on using safety and reliability metrics to quantify the impact of disordered charging loads on distribution network capacity. These approaches often employ a single metric to characterize the impact or simply normalize multiple metrics into a single metric. However, these approaches fail to comprehensively consider the impact on distribution network performance from both subjective and objective perspectives. This results in incomplete evaluation systems and unscientific weighting calculation methods, leading to limitations in the evaluation results.

[0004] In the prior art, the Chinese invention patent application, published with publication number CN112508450A, entitled "A Method for Evaluating the Capacity of Urban Distribution Networks for Electric Vehicles," includes: modeling urban electric vehicle charging loads based on trip chains and the Monte Carlo method; establishing a distribution network capacity evaluation scheme for electric vehicles; standardizing the evaluation scheme's indicator matrix, using Euclidean distance to measure the degree of proximity to the ideal value; and comprehensively evaluating the scheme using gray correlation to describe the closeness of relationships between evaluation objects, group utility to measure the overall closeness of each scheme to the ideal solution, and individual deviation to describe the degree of deviation of the worst indicator among the schemes. The schemes are prioritized based on the comprehensive evaluation criteria; and the optimal scheme is determined based on the prioritized results. This evaluation method considers the influencing factors of time and space in modeling electric vehicle loads. When evaluating the distribution network capacity, it primarily focuses on using six evaluation indicators, including rationality, safety, and economy, to quantify the impact of charging load on the distribution network capacity. However, due to its lack of qualitative and quantitative analysis of the theoretically acceptable capacity of the distribution network for electric vehicles, its assessment of the distribution network's capacity for electric vehicles is insufficient. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to solve the problem of insufficient acceptance capacity assessment of the distribution network when electric vehicles are connected on a large scale.

[0006] The present invention solves the above technical problems through the following technical solutions: a distribution network acceptance capacity evaluation method considering the spatiotemporal transfer characteristics of EVs, the method comprising:

[0007] Construct a road network-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging load;

[0008] Taking the maximum amount of electric vehicle charging load that the distribution network can accommodate and the minimum network loss cost as the objective function, a multi-objective optimization evaluation model for the distribution network's potential to accommodate EV load is constructed, and the optimal time-series power flow of the distribution network is iteratively calculated.

[0009] If either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range, the current EV penetration rate is used as the maximum critical penetration rate. Otherwise, the current EV penetration rate is gradually increased while continuously monitoring the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network.

[0010] A comprehensive evaluation index system for the distribution network's acceptance capacity is established from the distribution network side, the charging network side, and the charging user side, and each indicator is comprehensively scored.

[0011] Beneficial effects: The present invention analyzes the time-space series of the theoretically acceptable electric vehicle charging load power of the distribution network, compares it with the actual travel characteristics of electric vehicles, identifies the potential weak links in the operation of the distribution network after large-scale electric vehicles are connected to the grid for charging, and achieves a qualitative evaluation of the distribution network's ability to accept electric vehicles. On the basis of the qualitative evaluation, the impact of electric vehicle charging loads on the distribution network under different electric vehicle penetration scenarios is analyzed. The maximum penetration rate of EVs is accurately determined based on the two key indicators of the maximum deviation rate of node voltage and the maximum overload rate of line current, which do not exceed the limit, and calculates the scores of various indicators on the distribution network side, the charging network side, and the charging user side under this penetration rate. Through the organic combination of qualitative and quantitative evaluations, a comprehensive evaluation of the distribution network's ability to accept electric vehicles is achieved, which not only improves the scientific nature and precision of the evaluation, but also provides important theoretical support for the optimized operation of the distribution network and the large-scale electric vehicle grid connection planning.

[0012] Preferably, the road network-grid coupling node aggregation model is:

[0013]

[0014] Among them, CD k D is the coupling node after the power of the road network node is distributed to the distribution system. m For all distribution network nodes, D m ={m|m=1,2,...,M}, M is the total number of distribution network nodes, R z is the set of road network nodes that can provide charging stations / piles, k is the number of coupling nodes, and the upper limit of the number of coupling nodes k is equal to the total number of distribution network nodes. Indicates that R z Mapping to D m This relationship, dist(i,R n )、dist(j,R n ) are coupling nodes i and j are distance network nodes R n The nearest distribution network node, R n For all road network nodes, R n ={n|n=1,2,...,N}, Coupling node CD k The peak value of electric vehicle charging load at the corresponding road network node within the power supply range, is the coupled distribution network node D m The original foundation load, is the upper limit of the load potential that the distribution transformer at coupling node i can accept.

[0015] Beneficial effects: In response to the problem that research on the spatiotemporal distribution characteristics of electric vehicle charging load and the coupling relationship with the distribution network is still insufficient, the present invention proposes a collaborative operation model of the coupling system, analyzes the main influencing factors of the distribution network on the electric vehicle acceptance capacity, and establishes a power supply area division model of the coupled distribution unit to reflect the spatiotemporal distribution characteristics of the charging load in the road network and the coupling relationship with the distribution network, thereby improving the applicability of the distribution network for the evaluation of the electric vehicle acceptance capacity.

[0016] Preferably, the objective function is expressed as:

[0017]

[0018] Among them, P all is the sum of the charging power of electric vehicles that the system can accept during the scheduling period under study, T is the system operation time period, is the charging power of the electric vehicle at coupling node i during period t, N EV is the number of electric vehicle charging nodes in the system, is the network loss cost of the distribution network, is the line network loss, r ij is the line resistance, I in,t is the line current at time t.

[0019] Preferably, the multi-objective optimization evaluation model also includes equality constraints and inequality constraints. The equality constraints are the power flow balance equations of each node, and the inequality constraints include node voltage constraints, node charging power constraints, substation transmission power constraints, and line transmission power constraints.

[0020] Preferably, the expression of the power flow balance equation of each node is:

[0021]

[0022] Among them, U j,t is the voltage of node j at time t, U i,t is the voltage of node i at time t, R ij 、X ij Respectively represent the resistance and reactance of branch ij, P ij,t , Q ij,t are the active power and reactive power flowing through the branch ij at time t, I ij,t is the current flowing through branch ij at time t, p j,t ,q j,t They represent the active power and reactive power injected into node j at time t, P jk,t , Q jk,t are the active power and reactive power flowing from node j to node k at time t, respectively. Node k is the child node of node j.

[0023] Beneficial effects: In view of the problem that traditional quantitative methods are difficult to fully reflect the potential weak links of the distribution network when electric vehicles are connected to the distribution system, the present invention proposes a multi-objective optimization evaluation model for the theoretical acceptance capacity of the distribution system for electric vehicles based on the theoretical basis of optimal power flow, conducts a qualitative evaluation of the electric vehicle acceptance capacity, and reflects the weak links in the operation and acceptance capacity of the distribution network from the time and space dimensions.

[0024] Preferably, the expression of the maximum deviation rate of the node voltage is:

[0025]

[0026] Among them, U limit % is the critical value of node voltage deviation, U i,t is the voltage of node i at time t, U N is the node voltage rating;

[0027] The expression of the maximum overload rate of line current is:

[0028] I limit =θ ij P′ ij ×100%

[0029] Among them, I limit is the maximum value of the line current, θ ij P is the ratio between the load actually accepted by the power line and the rated load of the line. ij ′ is the rated transmission power of the line.

[0030] Preferably, a comprehensive evaluation index system for distribution network acceptance capacity is established from the distribution network side, the charging network side, and the charging user side, including:

[0031] The distribution network side evaluation index A1 is constructed from three aspects: technical rationality, safety and reliability, and coordination and efficiency. The indicators at the technical rationality level include the voltage level qualification rate A1. 11 , reactive power configuration failure rate A 12 , the indicators of safety and reliability include single-circuit line safe operation status A 21 、Minimum line N-1 ratio A 23 , short-time load rate A 23 , the indicators of coordination efficiency include load fluctuation rate A 31 、System load rate A 32 ;

[0032] Construct the charging network side evaluation index B1, which includes the average utilization rate of charging facilities B 11 , Charging station utilization balance B 12 ;

[0033] Construct the charging user side evaluation index C1, which includes the charging consumption time C 11 , Charging satisfaction C 12 .

[0034] Beneficial effects: In response to the problem that the current distribution network's evaluation results on electric vehicle acceptance capacity are difficult to fully reflect the actual operation level of the distribution network, the present invention proposes a comprehensive evaluation method for the distribution network's electric vehicle acceptance capacity from the perspectives of the charging network, charging users and the distribution network, constructs a more systematic evaluation framework, and better quantifies the operation service level of the power-transportation coordinated distribution network.

[0035] Preferably, the process of comprehensively scoring each indicator includes:

[0036] Based on the hierarchical indicator system, a judgment matrix is established through a scaling system table; the maximum eigenvalue λ of the judgment matrix is obtained max and its corresponding eigenvector W, based on the maximum eigenvalue λ max Perform consistency check on the judgment matrix to check whether the judgment matrix passes the check. If it passes, normalize the eigenvector W to obtain the required subjective weight α j ,If it fails to pass the verification, modify the element values in the judgment matrix and ,calculate again;

[0037] The indicators are normalized and normalized, and after processing, a data matrix can be formed to calculate the information entropy E of the indicators. j ;Indicator-based information entropy E j Calculate the objective weight β of each indicator j ; Calculate the combined weight W based on the subjective weight and objective weight of each indicator j :

[0038]

[0039] Select "excellent, good, poor" as the evaluation level of indicators, divide all indicators into cost type, benefit type and intermediate type, calculate the fuzzy score of each indicator, and calculate the comprehensive fuzzy score of each evaluation object based on the comprehensive weight matrix of each evaluation object at the criterion layer and the corresponding fuzzy score matrix.

[0040] Preferably, the comprehensive fuzzy score of each evaluation object is:

[0041] F A =W A1 M A1 +W A2 M A2 +W A3 M A3

[0042] FB =W B1 M B1

[0043] F C =W C1 M C1

[0044]

[0045] Among them, F A 、F B 、F C are the fuzzy scores of the corresponding criteria layers of distribution network, charging network and charging users, respectively. total is the final score; ψ is the variable decision coefficient, W A1 、W A2 、W A3 、W B1 、W C1 are the combined weight matrices of each evaluation object at the criterion level, M A1 、M A2 、M A3 、M B1 、M C1 are the fuzzy scoring matrices of each evaluation object at the criterion layer.

[0046] Beneficial effects: The comprehensive evaluation method of the present invention adopts a subjective and objective evaluation method that combines information entropy with fuzzy comprehensive evaluation. This method realizes the accurate quantitative evaluation of the operating status and acceptance capacity of the distribution network after the connection of electric vehicles by constructing an evaluation index set, an evaluation result set and an index weight set. Compared with the traditional evaluation method, the present invention has the following significant advantages: First, the introduction of information entropy ensures the objectivity of the index weight and effectively avoids the deviation of subjective assignment; second, the fuzzy comprehensive evaluation method can deal with the uncertainty and ambiguity in the evaluation process, and improves the reliability of the evaluation results; finally, the evaluation system combining subjective and objective realizes the organic unity of quantitative and qualitative analysis, and provides a more comprehensive and scientific decision-making basis for the evaluation of the distribution network acceptance capacity.

[0047] The present invention also provides a distribution network acceptance capacity evaluation system considering the spatiotemporal transfer characteristics of EVs, the system comprising:

[0048] A road-grid coupling model building module, which is used to build a road-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging loads;

[0049] The qualitative evaluation module is used to build a multi-objective optimization evaluation model for the distribution network's potential to accommodate EV loads, taking the maximum amount of electric vehicle charging load that the distribution network can accommodate and the minimum network loss cost as the objective function, and iteratively calculate the distribution network's time-series optimal power flow;

[0050] A quantitative assessment module is used to determine if either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range. The module then uses the current EV penetration rate as the maximum critical penetration rate. Otherwise, the module gradually increases the current EV penetration rate while continuously monitoring the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network.

[0051] The indicator scoring module is used to establish a comprehensive evaluation indicator system for the distribution network's acceptance capacity from the distribution network side, the charging network side, and the charging user side, and to comprehensively score each indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of a method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a comprehensive evaluation index system in a distribution network acceptance capacity evaluation method considering the spatiotemporal transfer characteristics of EVs provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the following describes the technical solutions of the present invention clearly and completely with reference to specific embodiments and the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0055] Example 1

[0056] See also Figure 1 This embodiment provides a method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs. The method includes the following steps:

[0057] Step 1: Construct a road network-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging load.

[0058] Based on the definition of the urban distribution network topology, system parameters, equipment parameters, and system load, the number of electric vehicles corresponding to the electric vehicle penetration rate is determined based on the definition of electric vehicle penetration rate and the population in the region. Based on the travel chain and Monte Carlo method, the charging load of all electric vehicles in the target area is modeled. The charging time and charging load of each user are calculated separately to predict the spatiotemporal distribution of the electric vehicle charging load. The electric vehicle penetration rate, also known as the new energy vehicle penetration rate, refers to the percentage of new energy vehicle sales in total vehicle sales during a specific time period, reflecting the popularity of new energy vehicles.

[0059] The specific process of modeling all electric vehicle charging loads in the target area based on the trip chain and Monte Carlo method includes:

[0060] Step 1.1: Construct an electric vehicle travel chain model. The expression of the electric vehicle travel chain model is:

[0061]

[0062] In formula (1), G TC is the set of spatiotemporal characteristics of electric vehicle travel, s i is the user's starting point, d i is the user's destination, t0 is the starting time of the user's trip, For users starting from the starting point s i Drive to destination i driving time, For users at destination d i The residence time, is the travel distance of the i-th trip, starting point s in the travel chain i and destination d i Represented by H, W, C, R, and O respectively.

[0063] Step 1.2: Construct an electric vehicle energy consumption model to determine the remaining battery power and state of charge when the electric vehicle arrives at the destination. The expression of the electric vehicle energy consumption model is:

[0064]

[0065] In formula (2), For electric vehicles from the starting point i Drive to destination i The total power consumption, is the driving distance of the i-th trip, e0 is the power consumption per unit mileage, For electric vehicles to reach their destination i The electric power at that time, E0 is the starting power of the electric vehicle, For electric vehicles to reach their destination i The state of charge at the time, B ev is the battery capacity.

[0066] Step 1.3: Use the Monte Carlo method to model all electric vehicles in the target area, calculate the charging time and charging load of each user, and then obtain the total spatiotemporal distribution of charging demand, that is, the spatiotemporal distribution of charging load of all electric vehicles in the target area.

[0067] Constructing a road network-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging loads means using the spatiotemporal distribution data of electric vehicle charging loads to divide the corresponding power supply areas in the coupled distribution network according to the basic power load of each node in the distribution network, the power consumption plan of the power grid, and the capacity of the distribution transformer. The model is then aggregated to form distribution network evaluation units and a road network-grid coupling node aggregation model is constructed. The expression of the road network-grid coupling node aggregation model is:

[0068]

[0069] In formula (3), CD k D is the coupling node after the power of the road network node is distributed to the distribution system. m For all distribution network nodes, D m ={m|m=1,2,...,M}, M is the total number of distribution network nodes, R z is the set of road network nodes that can provide charging stations / piles, k is the number of coupling nodes, and the upper limit of the number of coupling nodes k is equal to the total number of distribution network nodes. Indicates that R z Mapping to D m This relationship, dist(i,R n ) is the coupling node i is the distance to the network node R n The nearest distribution network node, dist(j,R n ) is the coupling node j is the distance network node R n The nearest distribution network node, R n For all road network nodes, there are R n ={n|n=1,2,...,N}, Coupling node CD k The peak value of electric vehicle charging load at the corresponding road network node within the power supply range, is the coupled distribution network node D m The original foundation load, is the upper limit of the load potential that the distribution transformer at coupling node i can accept.

[0070] In response to the problem that research on the spatiotemporal distribution characteristics of electric vehicle charging load and the coupling relationship with the distribution network is still insufficient, the present invention proposes a collaborative operation model of the coupled system, analyzes the main influencing factors of the distribution network on the electric vehicle acceptance capacity, and establishes a power supply area division model of the coupled distribution unit to reflect the spatiotemporal distribution characteristics of the charging load in the road network and the coupling relationship with the distribution network, thereby improving the applicability of the distribution network for evaluating the electric vehicle acceptance capacity.

[0071] Step 2: Taking the maximum amount of electric vehicle charging load that the distribution network can accept and the minimum network loss cost as the optimization objective function, a multi-objective optimization evaluation model for the distribution network's potential to accept EV load is constructed. The optimal time-series power flow of the distribution network is iteratively calculated, and the distribution network's acceptance capacity is qualitatively evaluated to identify the weak links in the distribution network's acceptance capacity.

[0072] The multi-objective optimization evaluation model includes objective functions and constraints. The optimization objective functions are to maximize the amount of electric vehicle charging load that the distribution network can accommodate and to minimize the network loss cost of the distribution network. The expression of the objective function is:

[0073]

[0074] In formula (4) and formula (5), P all is the sum of the charging power of electric vehicles that the system can accept during the scheduling period under study, T is the system operation time period, is the charging power of the electric vehicle at coupling node i during period t, N EV is the number of electric vehicle charging nodes in the system, is the network loss cost of the distribution network, is the line network loss, r ij is the line resistance, I ij,t is the line current at time t.

[0075] Constraints include equality constraints and inequality constraints. The equality constraints are the power flow balance equations of each node. The expressions of the power flow balance equations of each node are:

[0076]

[0077] In formula (6), U j,t is the voltage of node j at time t, U i,t is the voltage of node i at time t, R ij 、X ij Respectively represent the resistance and reactance of branch ij, P ij,t , Q ij,t are the active power and reactive power flowing through the branch ij at time t, I ij,t is the current flowing through branch ij at time t, p j,t ,q j,t They represent the active power and reactive power injected into node j at time t, P jk,t , Q jk,t are the active power and reactive power flowing from node j to node k at time t, respectively. Node k is the child node of node j.

[0078] The inequality constraints include node voltage constraints, node charging power constraints, substation transmission power constraints, and line transmission power constraints. The expression of the node voltage constraint is:

[0079] U min ≤U i,t ≤U max (7)

[0080] In formula (7), U i,t is the voltage of node i at time t, U max 、U min are the upper and lower limits of the node voltage respectively.

[0081] The expression of node charging power constraint is:

[0082]

[0083] In formula (8), is the charging power of the electric vehicle at coupling node i during time period t, is the upper limit of the electric vehicle charging load that the charging facility at coupling node i can accommodate.

[0084] The expression of substation transmission power constraint is:

[0085]

[0086] In formula (9), are respectively the active power and reactive power of the power supply transformer μ, They are the upper limit of active power and reactive power of power supply transformer u respectively.

[0087] The expression of line transmission power constraint is:

[0088]

[0089] In formula (10), P ij,t is the active power flowing through the first end of branch ij at time t, It is the upper limit of the active power flowing through the head end of branch ij at time t.

[0090] The distribution network's time-series optimal power flow is solved using an improved non-inferiority sorting multi-objective genetic algorithm. The algorithm obtains the optimal solution to the objective function, which is the spatiotemporal sequence of the electric vehicle load that the distribution network can accommodate (theoretically, the maximum number of EVs that can be accommodated at different nodes at different times). The specific process of solving the optimal solution to the objective function includes:

[0091] Step 2.1: Randomly generate an initial population of size N and randomly initialize each individual x iThe decision variables are charging power allocation and charging time arrangement. Charging power allocation refers to the charging power of electric vehicles at each node in different time periods, which determines the load distribution of the distribution network. Charging time arrangement refers to the time arrangement of starting and ending charging of electric vehicles in different time periods.

[0092] Step 2.2, calculate the fitness value f of each individual i (x i ), i = 1, 2, ..., M, and use non-inferiority sorting to divide the population into different non-inferiority levels, the lower the level, the better the individual.

[0093] Step 2.3. In each non-inferior level, calculate the crowding distance of each individual. For each objective function, the calculation method is:

[0094]

[0095] In formula (11), are the maximum and minimum values of the kth objective function, respectively, x i+1 、x i-1 are adjacent individuals sorted by objective function value.

[0096] Step 2.4: Use the roulette wheel selection mechanism to select individuals to enter the next generation population based on their non-inferiority rating and crowding distance.

[0097] Step 2.5: Perform crossover mutation on the selected individuals to generate new individuals and increase the diversity of the population.

[0098] Step 2.6: Merge the current population P and the newly generated offspring population Q to obtain the merged population R, perform non-inferiority sorting and crowding calculation on the merged population R, and select the first N individuals from the merged population R as the new population P. new , retain individuals with high fitness and high crowding. The new population P new It is the optimal solution set selected during the algorithm iteration process, representing the set of candidate solutions that meet the multi-objective optimization conditions (maximizing the distribution network's ability to accommodate EV loads and minimizing network loss costs). new Every solution X in i Corresponding to a set of formula variable values, the charging load of node i in time period t, and the current of line ij in time period t.

[0099] Step 2.7: Repeat steps 2.2 to 2.6 until the iteration stopping condition is met.

[0100] Steps 1 and 2 analyze the time-space series of the theoretically acceptable charging load power of electric vehicles in the distribution network, compare it with the actual travel characteristics of electric vehicles, identify potential weak links, and achieve a qualitative assessment of the distribution network's ability to accommodate electric vehicles, providing directional guidance for subsequent analysis. The following quantitative assessment of the distribution network's acceptance capacity is carried out, selecting the maximum node voltage deviation rate and the maximum line current overload rate as key indicators for evaluating the distribution network's ability to accommodate electric vehicles, and determining the maximum critical penetration rate of electric vehicles. The specific process includes:

[0101] Step 3: When either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range, the current EV penetration rate is used as the maximum critical penetration rate. Otherwise, the current EV penetration rate is gradually increased, and the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network are continuously monitored.

[0102] The maximum node voltage deviation rate refers to the maximum deviation percentage between the actual voltage and the rated voltage of all nodes in the power system. The expression of the maximum node voltage deviation rate is:

[0103]

[0104] In formula (12), U limit % is the critical value of node voltage deviation, U i,t is the voltage of node i at time t, U N is the node voltage rating.

[0105] The maximum overload rate of line current refers to the maximum percentage by which the actual current flowing through the line exceeds its safe current carrying capacity. The expression for the maximum overload rate of line current is:

[0106] I limit =θ ij P′ ij ×100% (13)

[0107] In formula (13), I limit is the maximum value of the line current, θ ij It is the ratio between the actual load accepted by the power line and the rated load of the line, reflecting the load condition of the power line at a certain moment. ij ′ is the rated transmission power of the line.

[0108] If either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range, the current EV penetration rate is used as the maximum critical penetration rate to determine the maximum critical penetration rate of the distribution network for EVs, and the process proceeds to step 4. Otherwise, the current EV penetration rate is increased, for example, by 5% increments, and the process returns to step 1 and repeats steps 1 through 3 until either indicator exceeds the allowable range. The maximum critical penetration rate of the distribution network for EVs is then determined, and the process proceeds to step 4.

[0109] Aiming at the problem that traditional quantitative methods are unable to fully reflect the potential weak links of the distribution network when electric vehicles are connected to the existing distribution system, the present invention proposes a multi-objective optimization evaluation model for the theoretical acceptance capacity of the distribution system for electric vehicles based on the theoretical basis of optimal power flow. The model conducts a qualitative evaluation of the electric vehicle acceptance capacity and reflects the weak links in the operation and acceptance capacity of the distribution network from the temporal and spatial dimensions.

[0110] Based on qualitative assessments, the impact of EV charging loads on the distribution network under different EV penetration scenarios was analyzed. The maximum EV penetration rate was accurately determined, using the two key indicators, maximum node voltage deviation rate and maximum line current overload rate, as criteria for maintaining consistent limits. By integrating qualitative and quantitative assessments, a comprehensive assessment of the distribution network's capacity to accommodate EVs was achieved. This not only enhanced the scientific nature and sophistication of the assessment but also provided important theoretical support for optimizing distribution network operation and planning large-scale EV integration.

[0111] Step 4: Establish a comprehensive evaluation index system for the distribution network's acceptance capacity from the distribution network side, the charging network side, and the charging user side, and give a comprehensive score to each indicator.

[0112] See also Figure 2 , a comprehensive evaluation index system for distribution network acceptance capacity is established from the distribution network side, charging network side, and charging user side, including:

[0113] Step 4.1.1, construct the distribution network side evaluation index A1 from the three levels of technical rationality, safety and reliability, and coordination and efficiency. The indicators at the technical rationality level include the voltage level qualification rate A1. 11 , reactive power configuration failure rate A 12 , the indicators of safety and reliability include single-circuit line safe operation status A 21 、Minimum line N-1 ratio A 22 , short-time load rate A 23 , the indicators of coordination efficiency include load fluctuation rate A 31 、System load rate A 32 .

[0114] Among them, the voltage level qualification rate A 11It refers to the ratio of the number of nodes in the distribution network that meet the voltage level requirements to the total number of nodes in the distribution network. The voltage level qualification rate A 11 The expression is:

[0115]

[0116] In formula (14), N V is the number of nodes in the distribution network that meet the voltage level requirements, and N is the total number of nodes in the distribution network.

[0117] Reactive power configuration failure rate A 12 It refers to the ratio of the number of nodes in the distribution network that cannot meet the reactive configuration requirements after the power factor is compensated as much as possible by reactive power to the total number of nodes. The reactive configuration failure rate A 12 The expression is:

[0118]

[0119] In formula (15), N q is the number of nodes in the distribution network that meet the reactive power configuration requirements, and N is the total number of nodes in the distribution network.

[0120] Single-circuit line safe operation status A 21 It refers to the ratio of the number of lines whose actual current exceeds the maximum current safety operating range of the line to the total number of lines after the distribution network is connected to the electric vehicle charging station. 21 The expression is:

[0121]

[0122] In formula (16), L out is the number of lines in the distribution network that exceed the safe operating range of the maximum current that can be tolerated by the line, and L is the total number of lines.

[0123] Minimum line N-1 ratio A 22 It refers to the ratio of the minimum number of lines in the distribution network that meet the N-1 safety criterion to the total number of lines. The minimum line N-1 ratio A 22 The expression is:

[0124]

[0125] In formula (17), L N-1 is the minimum number of lines in the distribution network that meets the N-1 safety criterion, and L is the total number of lines.

[0126] Short-time load rate A 23 It refers to the ratio of the average load to the maximum load in the distribution network in a short period of time. The short-term load rate A 23 The expression is:

[0127]

[0128] In formula (18), P av is the average load in the distribution network within a short period of time, P max It is the maximum load in the distribution network in a short period of time.

[0129] Load fluctuation rate A 31 It reflects the change of the peak-to-valley difference of the distribution network load after the electric vehicle charging load is connected. The load fluctuation rate A 31 The expression is:

[0130]

[0131] In formula (19), RP 0 and RP are the daily average load fluctuation rates of the system before and after electric vehicle load access, They are the peak and valley values of system load in a day, It is the total system load for each period of the day.

[0132] System load rate A 32 It shows the change of peak load capacity of distribution system after electric vehicle charging load is connected, and the system load rate A 32 The expression is:

[0133]

[0134] In formula (20), RL 0 and RL are the system daily average load rates before and after electric vehicle load access, respectively. is the total system load for each period of the day, The peak load of the system in a day.

[0135] Step 4.1.2: Construct the charging network side evaluation index B1, which includes the average utilization rate of charging facilities B 11 , Charging station utilization balance B 12 .

[0136] Among them, the average utilization rate of charging facilities is B 11 It reflects the utilization efficiency of charging resources and the saturation of their service capacity. The average utilization rate of charging facilities is B 11 The expression is:

[0137]

[0138] In formula (21), N CS is the total number of nodes in the charging network, It is defined as the charging duration of the EV served by the nth charging station in time period t.

[0139] Charging station utilization balance B 12 It reflects the uniformity of the utilization rate of each charging station in the region, and the balance degree of charging station utilization B 12 The expression is:

[0140]

[0141] In formula (22), It is defined as the EV charging duration served by the nth charging station in time period t, It is defined as the total charging time of an EV user at the nth charging station in a day.

[0142] Step 4.1.3: Construct the charging user side evaluation index C1, which includes the charging consumption time C 11 , Charging satisfaction C 12 .

[0143] Among them, the charging time C 11 Reflects the time required for EV to reach the target power from the start of charging, charging consumption time C 11 The expression is:

[0144]

[0145] In formula (23) and formula (24), N EV is the total number of EVs that need charging, is the waiting time of the i-th EV, is the charging time of the i-th EV, is the dynamic weight coefficient of vehicle charging power, is the actual charging power of vehicle n in time period t, P base is the rated reference power of the charging pile, and η is the charging efficiency factor.

[0146] Charging satisfaction C 12 It reflects the proportion of users who have achieved the target charging power among all users waiting to be charged, and the charging satisfaction C 12 The expression is:

[0147]

[0148] The present invention constructs a more systematic evaluation framework from the perspectives of charging networks, charging users and distribution networks to better quantify the operational service level of the power-transportation coordinated distribution network.

[0149] The process of comprehensively scoring each indicator includes:

[0150] Step 4.2: Determine the weight of each indicator. This includes the following steps:

[0151] Step 4.2.1: Based on the hierarchical indicator system, establish a judgment matrix through the scaling system table. The characteristics that the judgment matrix needs to meet are:

[0152] I ab I ba =1 (27)

[0153] In formula (27), I ab is the importance of indicator a to indicator b, I ba is the importance of indicator b to indicator a.

[0154] Step 4.2.2: Find the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W, based on the maximum eigenvalue λ max Perform consistency check on the judgment matrix. The consistency check method is:

[0155]

[0156] In formula (28), c CI is the consistency index, c RI is the random consistency index, c CR is the consistency deviation, and n is the order of the judgment matrix.

[0157] Step 4.2.3: Check whether the judgment matrix passes the verification. If it passes, normalize the eigenvector W to obtain the required subjective weight α j ,If it fails to pass the verification, modify the element values in the judgment matrix and ,calculate again.

[0158] Step 4.2.4: Forward and standardize the indicators. After processing, the data matrix Z = (z ij ) m×n , calculate the information entropy E of the index j :

[0159]

[0160] In formula (29), m is the number of objects to be evaluated, n is the number of evaluation indicators, and z ij is the element in the i-th row and j-th column of the original data matrix Z, representing the original value of the i-th sample under the j-th indicator, p ij For element z ij The value after positive and normalization, that is, p ijis the standard value of the i-th sample under the j-th indicator. Through this step, the data can be converted into a comparable form so that the value of each indicator is at the same level.

[0161] Step 4.2.5: Information entropy E based on indicators j Calculate the objective weight β of the indicator j :

[0162]

[0163] Step 4.2.6: Calculate the combined weight W based on the subjective and objective weights of each indicator. j :

[0164]

[0165] Step 4.3: Perform fuzzy scoring on each indicator.

[0166] Step 4.3.1. Determine the evaluation set: select "excellent, good, poor" as the evaluation level V of the indicator s , indicating whether the distribution network’s acceptance capacity needs to be further improved after large-scale EV access.

[0167] Step 4.3.2, indicator classification: Cost indicators include: reactive power configuration failure rate A 12 , Single-circuit line safe operation status A 21 , load fluctuation rate A 31 , Charging time C 11 ; Benefit indicators include: voltage level qualification rate A 11 、Minimum line N-1 ratio A 22 、System load rate A 32 , Charging satisfaction C 12 ; Intermediate indicators include: short-term load rate A 23 , average utilization rate of charging facilities B 11 , Charging station utilization balance B 12 .

[0168] Step 4.3.3, construct the fuzzy score set: assume that the scores of the three-level comments when the membership degree is 1 are M1, M2, and M3 respectively, and the corresponding score levels of M1, M2, and M3 are set to 100, 85, and 70 respectively. The fuzzy score of the jth indicator is calculated as follows:

[0169]

[0170] In formula (32), For the jth indicator in the evaluation level V s The corresponding membership value, M sis the corresponding score of the comment set, s is equal to 1, 2, and 3, representing the three evaluation levels of excellent, good, and poor respectively.

[0171] Step 4.4: Comprehensive scoring method: Based on the combined weight matrix W of each evaluation object at the criterion level A1 、W A2 、W A3 、W B1 、W C1 And the corresponding fuzzy rating matrix M A1 、M A2 、M A3 、M B1 、M C1 , calculate the comprehensive fuzzy score of each evaluation object.

[0172] F A =W A1 M A1 +W A2 M A2 +W A3 M A3 (33)

[0173] F B =W B1 M B1 (34)

[0174] F C =W C1 M C1 (35)

[0175]

[0176] In formula (36), F A 、F B 、F C are the fuzzy scores of the corresponding criteria layers of distribution network, charging network and charging users, respectively. total is the final score; ψ is the variable decision coefficient, and the specific value can be determined according to the focus of different operational decision makers.

[0177] This paper addresses the problem that current distribution network EV acceptance capacity assessments fail to fully reflect the network's actual operational performance. By considering the impacts of charging networks, charging users, and the distribution network, this paper proposes a comprehensive evaluation method for distribution network EV acceptance capacity. This method considers both subjective and objective factors affecting distribution network performance and uses an objective weighting method combining information entropy and fuzzy theory to comprehensively score the distribution network's acceptance capacity, improving the accuracy of this assessment.

[0178] The comprehensive evaluation method of the present invention adopts a subjective and objective evaluation method that combines information entropy with fuzzy comprehensive evaluation. This method realizes the accurate quantitative evaluation of the operating status and acceptance capacity of the distribution network after the connection of electric vehicles by constructing an evaluation index set, an evaluation result set and an index weight set. Compared with the traditional evaluation method, the present invention has the following significant advantages: first, the introduction of information entropy ensures the objectivity of the index weight and effectively avoids the deviation of subjective assignment; second, the fuzzy comprehensive evaluation method can deal with the uncertainty and ambiguity in the evaluation process, and improve the reliability of the evaluation results; finally, the evaluation system combining subjective and objective realizes the organic unity of quantitative and qualitative analysis, and provides a more comprehensive and scientific decision-making basis for the evaluation of the distribution network acceptance capacity.

[0179] Example 2

[0180] This embodiment provides a distribution network acceptance capacity assessment system that considers the spatiotemporal transfer characteristics of EVs. The system includes:

[0181] The road-grid coupling model construction module is used to construct a road-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging loads. The road-grid coupling node aggregation model is:

[0182]

[0183] Among them, CD k D is the coupling node after the power of the road network node is distributed to the distribution system. m For all distribution network nodes, D m ={m|m=1,2,...,M}, M is the total number of distribution network nodes, R z is the set of road network nodes that can provide charging stations / piles, k is the number of coupling nodes, and the upper limit of the number of coupling nodes k is equal to the total number of distribution network nodes. Indicates that R z Mapping to D m This relationship, dist(i,R n )、dist(j,R n ) are coupling nodes i and j are distance network nodes R n The nearest distribution network node, R n For all road network nodes, R n ={n|n=1,2,...,N}, Coupling node CD k The peak value of electric vehicle charging load at the corresponding road network node within the power supply range, is the coupled distribution network node D m The original foundation load, is the upper limit of the load potential that the distribution transformer at coupling node i can accept.

[0184] The qualitative evaluation module is used to build a multi-objective optimization evaluation model for the distribution network's potential to accept EV loads, taking the maximum amount of electric vehicle charging load that the distribution network can accept and the minimum network loss cost as the objective function, and iteratively calculate the distribution network's time-series optimal power flow. The expression of the objective function is:

[0185]

[0186] Among them, P all is the sum of the charging power of electric vehicles that the system can accept during the scheduling period under study, T is the system operation time period, is the charging power of the electric vehicle at coupling node i during period t, N EV is the number of electric vehicle charging nodes in the system, is the network loss cost of the distribution network, is the line network loss, r ij is the line resistance, I ij,t is the line current at time t.

[0187] The multi-objective optimization evaluation model also includes equality constraints and inequality constraints. The equality constraints are the power flow balance equations of each node, and the inequality constraints include node voltage constraints, node charging power constraints, substation transmission power constraints, and line transmission power constraints.

[0188] The expression of the power flow balance equation at each node is:

[0189]

[0190] Among them, U j,t is the voltage of node j at time t, U i,t is the voltage of node i at time t, R ij 、X ij Respectively represent the resistance and reactance of branch ij, P ij,t , Q ij,t are the active power and reactive power flowing through the branch ij at time t, I ij,t is the current flowing through branch ij at time t, p j,t ,q j,t They represent the active power and reactive power injected into node j at time t, P jk,t , Q jk,t are the active power and reactive power flowing from node j to node k at time t, respectively. Node k is the child node of node j.

[0191] The quantitative assessment module is used to determine if either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range. The module then uses the current EV penetration rate as the maximum critical penetration rate. Otherwise, the module gradually increases the current EV penetration rate and continuously monitors the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network.

[0192] The expression of the maximum deviation rate of node voltage is:

[0193]

[0194] Among them, U limit % is the critical value of node voltage deviation, U i,t is the voltage of node i at time t, U N is the node voltage rating;

[0195] The expression of the maximum overload rate of line current is:

[0196] I limit =θ ij P′ ij ×100%

[0197] Among them, I limit is the maximum value of the line current, θ ij P is the ratio between the load actually accepted by the power line and the rated load of the line. ij ′ is the rated transmission power of the line.

[0198] The indicator scoring module is used to establish a comprehensive evaluation indicator system for the distribution network's acceptance capacity from the distribution network side, the charging network side, and the charging user side, and to comprehensively score each indicator.

[0199] Establishing a comprehensive evaluation index system for distribution network acceptance capacity from the distribution network side, charging network side, and charging user side includes:

[0200] The distribution network side evaluation index A1 is constructed from three aspects: technical rationality, safety and reliability, and coordination and efficiency. The indicators at the technical rationality level include the voltage level qualification rate A1. 11 , reactive power configuration failure rate A 12 , the indicators of safety and reliability include single-circuit line safe operation status A 21 、Minimum line N-1 ratio A 22 , short-time load rate A 23 , the indicators of coordination efficiency include load fluctuation rate A 31 、System load rate A 32 ;

[0201] Construct the charging network side evaluation index B1, which includes the average utilization rate of charging facilities B 11, Charging station utilization balance B 12 ;

[0202] Construct the charging user side evaluation index C1, which includes the charging consumption time C 11 , Charging satisfaction C 12 .

[0203] The process of comprehensively scoring each indicator includes:

[0204] Based on the hierarchical indicator system, a judgment matrix is established through a scaling system table; the maximum eigenvalue λ of the judgment matrix is obtained max and its corresponding eigenvector W, based on the maximum eigenvalue λ max Perform consistency check on the judgment matrix to check whether the judgment matrix passes the check. If it passes, normalize the eigenvector W to obtain the required subjective weight α j ,If it fails to pass the verification, modify the element values in the judgment matrix and ,calculate again;

[0205] The indicators are normalized and normalized, and after processing, a data matrix can be formed to calculate the information entropy E of the indicators. j ;Indicator-based information entropy E j Calculate the objective weight β of each indicator j ; Calculate the combined weight W based on the subjective weight and objective weight of each indicator j :

[0206]

[0207] Select "excellent, good, poor" as the evaluation level of indicators, divide all indicators into cost type, benefit type and intermediate type, calculate the fuzzy score of each indicator, and calculate the comprehensive fuzzy score of each evaluation object based on the comprehensive weight matrix of each evaluation object at the criterion layer and the corresponding fuzzy score matrix.

[0208] The comprehensive fuzzy score of each evaluation object is:

[0209] F A =W A1 M A1 +W A2 M A2 +W A3 M A3

[0210] F B =W B1 M B1

[0211] F C =W C1 M C1

[0212]

[0213] Among them, F A 、F B 、F C are the fuzzy scores of the corresponding criteria layers of distribution network, charging network and charging users, respectively. total is the final score; ψ is the variable decision coefficient, W A1 、W A2 、W A3 、W B1 、W C1 are the combined weight matrices of each evaluation object at the criterion level, M A1 、M A2 、M A3 、M B1 、M C1 are the fuzzy scoring matrices of each evaluation object at the criterion layer.

[0214] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distribution network acceptance capacity assessment method considering the spatiotemporal transfer characteristics of EVs, characterized by: Methods include: Construct a road network-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging load; Taking the maximum amount of electric vehicle charging load that the distribution network can accommodate and the minimum network loss cost as the objective function, a multi-objective optimization evaluation model for the distribution network's potential to accommodate EV load is constructed, and the optimal time-series power flow of the distribution network is iteratively calculated. If either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range, the current EV penetration rate is used as the maximum critical penetration rate. Otherwise, the current EV penetration rate is gradually increased while continuously monitoring the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network. A comprehensive evaluation index system for the distribution network's acceptance capacity is established from the distribution network side, the charging network side, and the charging user side, and each indicator is comprehensively scored.

2. The distribution network acceptance capacity assessment method considering EV spatiotemporal transfer characteristics according to claim 1, characterized in that: The road network-grid coupling node aggregation model is: Among them, CD k D is the coupling node after the power of the road network node is distributed to the distribution system. m For all distribution network nodes, D m ={m|m=1,2,...,M}, M is the total number of distribution network nodes, R z is the set of road network nodes that can provide charging stations / piles, k is the number of coupling nodes, and the upper limit of the number of coupling nodes k is equal to the total number of distribution network nodes. Indicates that R z Mapping to D m This relationship, dist(i,R n )、dist(j,R n ) are coupling nodes i and j are distance network nodes R n The nearest distribution network node, R n For all road network nodes, R n ={n|n=1,2,...,N}, Coupling node CD k The peak value of electric vehicle charging load at the corresponding road network node within the power supply range, is the coupled distribution network node D m The original foundation load, is the upper limit of the load potential that the distribution transformer at coupling node i can accept.

3. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 1, characterized in that: The expression of the objective function is: Among them, P all is the sum of the charging power of electric vehicles that the system can accept during the scheduling period under study, T is the system operation time period, is the charging power of the electric vehicle at coupling node i during period t, N EV is the number of electric vehicle charging nodes in the system, is the network loss cost of the distribution network, is the line network loss, r ij is the line resistance, I ij,t is the line current at time t.

4. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 1, wherein: The multi-objective optimization evaluation model also includes equality constraints and inequality constraints. The equality constraints are the power flow balance equations of each node, and the inequality constraints include node voltage constraints, node charging power constraints, substation transmission power constraints, and line transmission power constraints.

5. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 5, characterized in that: The expression of the power flow balance equation at each node is: Among them, U j,t is the voltage of node j at time t, U i,n is the voltage of node i at time t, R ij 、X ij Represents the resistance and reactance of branch ij, P ij,t , Q ij,t are the active power and reactive power flowing through the branch ij at time t, I ij,t is the current flowing through branch ij at time t, p j,t ,q j,t They represent the active power and reactive power injected into node j at time t, P jk,t , Q jk,t are the active power and reactive power flowing from node j to node k at time t, respectively. Node k is the child node of node j.

6. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 1, characterized in that: The expression of the maximum deviation rate of node voltage is: Among them, U limit % is the critical value of node voltage deviation, U i,t is the voltage of node i at time t, U N is the node voltage rating; The expression of the maximum overload rate of line current is: I limit =θ ij P ij ′×100% Among them, I limit is the maximum value of the line current, θ ij P is the ratio between the load actually accepted by the power line and the rated load of the line. ij ′ is the rated transmission power of the line.

7. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 1, characterized in that: Establishing a comprehensive evaluation index system for distribution network acceptance capacity from the distribution network side, charging network side, and charging user side includes: The distribution network side evaluation index A1 is constructed from three aspects: technical rationality, safety and reliability, and coordination and efficiency. The indicators at the technical rationality level include the voltage level qualification rate A1. 11 , reactive power configuration failure rate A 12 , the indicators of safety and reliability include single-circuit line safe operation status A 21 、Minimum line N-1 ratio A 22 , short-time load rate A 23 , the indicators of coordination efficiency include load fluctuation rate A 31 , system load rate A 32 ; Construct the charging network side evaluation index B1, which includes the average utilization rate of charging facilities B 11 , Charging station utilization balance B 12 ; Construct the charging user side evaluation index C1, which includes the charging consumption time C 11 , Charging satisfaction C 12 .

8. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 1, characterized in that: The process of comprehensively scoring each indicator includes: Based on the hierarchical indicator system, a judgment matrix is established through a scaling system table; the maximum eigenvalue λ of the judgment matrix is obtained max and its corresponding eigenvector W, based on the maximum eigenvalue λ max Perform consistency check on the judgment matrix to check whether the judgment matrix passes the check. If it passes, normalize the eigenvector W to obtain the required subjective weight α j ,If it fails to pass the verification, modify the element values in the judgment matrix and ,calculate again; The indicators are normalized and normalized, and after processing, a data matrix can be formed to calculate the information entropy E of the indicators. j ;Indicator-based information entropy E j Calculate the objective weight β of each indicator j ; Calculate the combined weight W based on the subjective weight and objective weight of each indicator j : "Excellent, good, and poor" are selected as the evaluation levels of indicators, and all indicators are divided into cost-type, benefit-type, and intermediate types. The fuzzy score of each indicator is calculated. Based on the comprehensive weight matrix of each evaluation object at the criterion layer and the corresponding fuzzy score matrix, the comprehensive fuzzy score of each evaluation object is calculated.

9. The method for evaluating the distribution network acceptance capacity considering the spatiotemporal transfer characteristics of EVs according to claim 8, characterized in that: The comprehensive fuzzy score of each evaluation object is: F A =W A1 M A1 +W A2 M A2 +W A3 M A3 F B =W B1 M B1 F C =W C1 M C1 Among them, F A 、F B 、F c are the fuzzy scores of the corresponding criteria layers of distribution network, charging network and charging users, respectively. total is the final score, ψ is the variable decision coefficient, W A1 、W A2 、W A3 、W B1 、W C1 are the combined weight matrices of each evaluation object at the criterion level, M A1 、M A2 、M A3 、M B1 、M C1 are the fuzzy scoring matrices of each evaluation object at the criterion layer.

10. A distribution network acceptance capacity assessment system considering EV spatiotemporal transfer characteristics, characterized by: The system includes: A road-grid coupling model building module, which is used to build a road-grid coupling node aggregation model based on the spatiotemporal distribution of EV charging loads; The qualitative evaluation module is used to build a multi-objective optimization evaluation model for the distribution network's potential to accommodate EV loads, taking the maximum amount of electric vehicle charging load that the distribution network can accommodate and the minimum network loss cost as the objective function, and iteratively calculate the distribution network's time-series optimal power flow; A quantitative assessment module is used to determine if either the maximum node voltage deviation rate or the maximum line current overload rate exceeds the allowable range. The module then uses the current EV penetration rate as the maximum critical penetration rate. Otherwise, the module gradually increases the current EV penetration rate while continuously monitoring the maximum node voltage deviation rate and the maximum line current overload rate of the distribution network. The indicator scoring module is used to establish a comprehensive evaluation indicator system for the distribution network's acceptance capacity from the distribution network side, the charging network side, and the charging user side, and to comprehensively score each indicator.

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