Regional power distribution network reliability evaluation method and system considering vehicle-network interaction

Abstract

The invention discloses a regional power distribution network reliability evaluation method and system considering vehicle-network interaction. The method comprises the steps of S1, data acquisition and processing; s2, simulating behavior data; s3, constructing a dynamic vehicle network interaction model; s4, calculating a reliability evaluation index of the power distribution network; s5, comprehensively evaluating the reliability of the power distribution network; according to the method, the influence of travel behaviors and charge and discharge characteristics of the electric vehicle on the power distribution network is comprehensively considered through a dynamic vehicle network interaction model, a random sample conforming to the operation characteristics of the power distribution network and the electric vehicle is generated through Monte Carlo simulation, and the randomness of travel, charge and discharge behaviors of the electric vehicle is comprehensively considered. The method can dynamically describe the double roles of the electric vehicle as the load and the energy storage resource through the dynamic vehicle network interaction model, effectively improves the reliability evaluation accuracy of the regional power distribution network, and can generate a random sample according with the operation characteristics of the power distribution network and the electric vehicle through Monte Carlo simulation. And the accuracy and robustness of evaluation are effectively improved.

Description

Technical Field

[0001] The present invention relates to a distribution network reliability assessment method, and in particular to a regional distribution network reliability assessment method and system taking vehicle-grid interaction into consideration. Background Art

[0002] With the popularity of electric vehicles (EVs), vehicle-to-grid (V2G) has gradually become an important means to improve the reliability and stability of distribution networks. It not only allows electric vehicles to obtain energy from the grid for charging, but also enables them to feed back the stored energy to the grid during peak grid demand periods, thereby acting as distributed energy storage devices. This two-way energy flow mechanism can effectively alleviate the burden on the grid, balance power supply and demand, and reduce the impact of peak power demand on the grid. Especially in disasters or peak load situations, electric vehicles as mobile energy storage units can provide additional stability for the distribution network.

[0003] Although this method can improve grid stability, it still has the following drawbacks:

[0004] Most of the existing distribution network reliability assessment methods are based on static load data or single-dimensional models, which cannot fully reflect the impact of vehicle-grid interaction on the power grid system, resulting in poor accuracy in regional distribution network reliability assessment.

[0005] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcoming of poor accuracy of regional distribution network reliability assessment in the prior art, and to provide a regional distribution network reliability assessment method and system with better accuracy and considering vehicle-grid interaction.

[0007] To achieve the above objectives, the technical solution of the present invention is:

[0008] In a first aspect, the present invention proposes a distribution network reliability assessment method considering vehicle-grid interaction, the assessment method comprising:

[0009] S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction;

[0010] S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0011] S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that meets the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0012] S4. Calculation of reliability evaluation indexes of distribution network. According to the impact of large-scale access of electric vehicles on the reliability of distribution network, a reliability evaluation index system including system load loss probability, active power loss and voltage deviation rate is constructed. Random samples that meet the operating characteristics of distribution network and electric vehicles are substituted into the reliability evaluation index system to calculate the reliability evaluation index.

[0013] S5. Comprehensive evaluation of distribution network reliability. A distribution network comprehensive evaluation model is constructed with the comprehensive evaluation score of distribution network reliability as the objective function. The distribution network comprehensive evaluation model is solved to obtain the comprehensive evaluation score of distribution network reliability.

[0014] The S1 includes:

[0015] S1.1. Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system;

[0016] The time series data of the load of the distribution network node between time t1 and time t1+T includes:

[0017]

[0018]

[0019] In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; P load (t) is the load of the regional distribution network from time t1 to t1+T, and N is the number of distribution network nodes;

[0020] The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency

[0021] The maximum charging power of the mth electric vehicle in the distribution network include:

[0022]

[0023] In the above formula, is the charging efficiency of the mth electric vehicle;

[0024] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0025]

[0026] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0027] S1.2, data preprocessing, cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0028] S1.3. Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0029] The S2 includes:

[0030] S2.1. Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through the Monte Carlo algorithm;

[0031] S2.2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that meet the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0032] The S3 includes:

[0033] S3.1. Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as the battery SOC and charging time, based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0034]

[0035]

[0036] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are:

[0037] x(t),y(t)∈{0,1};

[0038] x(t)+y(t)≤1;

[0039] S3.2. Construct an electric vehicle total power model. The electric vehicle total power model is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes:

[0040]

[0041] In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0042] S3.3, construct a node voltage model, which is used to perform iterative calculation of the distribution network flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node

[0043] S3.4. Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

[0044] The S4 includes:

[0045] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0046] The system load loss probability includes:

[0047]

[0048] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0;

[0049] The active power loss rate includes:

[0050]

[0051] E i The active power loss in the Xth simulation includes:

[0052]

[0053] In the above formula, ENS% is the system active power loss rate; T is the simulation time period;

[0054] The voltage deviation rate index includes:

[0055]

[0056]

[0057]

[0058] In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV The score for voltage deviation rate assessment;

[0059] In S5, the comprehensive evaluation model includes:

[0060]

[0061] In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1.

[0062] In a second aspect, the present invention proposes a distribution network reliability assessment system considering vehicle-grid interaction, the assessment system comprising:

[0063] The system includes a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability evaluation index calculation module and a distribution network reliability comprehensive evaluation module;

[0064] The data acquisition and processing module is used to obtain data related to the reliability assessment of the distribution network and to construct a time series data set for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction;

[0065] The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through a Monte Carlo algorithm;

[0066] The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. The time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node;

[0067] The distribution network reliability evaluation index calculation module is used to construct a reliability evaluation index system including system load loss probability, active power loss, and voltage deviation rate indicators according to the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability evaluation index system to calculate the reliability evaluation index;

[0068] The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability.

[0069] The specific operations of the data acquisition and processing module include:

[0070] Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system;

[0071] The time series data of the load of the distribution network node between time t1 and time t1+T includes:

[0072]

[0073]

[0074] In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; Pload (t) is the load of the regional distribution network from time t1 to t1+T, and N is the number of distribution network nodes;

[0075] The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency

[0076] The maximum charging power of the mth electric vehicle in the distribution network include:

[0077]

[0078] In the above formula, is the charging efficiency of the mth electric vehicle;

[0079] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0080]

[0081] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0082] Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0083] Construct a time series dataset, consider different operating conditions such as normal and faulty distribution networks, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0084] The specific operations of the simulation behavior data module include:

[0085] Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through a Monte Carlo algorithm;

[0086] Substituting the simulation parameters into a random number generator generates a large number of random samples that conform to the operating characteristics of the distribution network and the electric vehicle, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0087] The specific operations of the dynamic vehicle-grid interaction model building module include:

[0088] Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and the battery SOC, charging time and other factors based on the battery characteristics of the electric vehicle, the type of charging facilities and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0089]

[0090]

[0091] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are:

[0092] x(t),y(t)∈{0,1};

[0093] x(t)+y(t)≤1;

[0094] Construct an electric vehicle total power model, which is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes:

[0095]

[0096] In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0097] Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node

[0098] Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

[0099] In the distribution network reliability evaluation index calculation module:

[0100] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0101] The system load loss probability includes:

[0102]

[0103] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0;

[0104] The active power loss rate includes:

[0105]

[0106] E i The active power loss in the Xth simulation includes:

[0107]

[0108] In the above formula, ENS% is the system active power loss rate; T is the simulation time period;

[0109] The voltage deviation rate index includes:

[0110]

[0111]

[0112]

[0113] In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV Assess the score for voltage deviation rate;

[0114] In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes:

[0115]

[0116] In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1.

[0117] Compared with the prior art, the present invention has the following beneficial effects:

[0118] 1. In a regional distribution network reliability assessment method considering vehicle-grid interaction of the present invention, a dynamic vehicle-grid interaction model is constructed. The dynamic vehicle-grid interaction model comprehensively considers the impact of electric vehicle travel behavior and charging and discharging characteristics on the distribution network, and then dynamically describes the dual role of electric vehicles as loads and energy storage resources through probability density functions and behavior simulation, making the assessment more accurate and realistic. Therefore, this design can dynamically describe the dual role of electric vehicles as loads and energy storage resources through a dynamic vehicle-grid interaction model, effectively improving the accuracy of regional distribution network reliability assessment.

[0119] 2. In the reliability assessment method of a regional distribution network considering vehicle-grid interaction of the present invention, a large number of random scenario analyses are performed through Monte Carlo simulation to generate random samples that meet the operating characteristics of the distribution network and electric vehicles, and the randomness of electric vehicle travel, charging and discharging behaviors is comprehensively considered. It not only quantifies the uncertainty of electric vehicle behavior, but also can simulate various possible extreme load scenarios, thereby identifying the potential reliability risks of the system. Therefore, this design can generate random samples that meet the operating characteristics of the distribution network and electric vehicles through Monte Carlo simulation, effectively improving the accuracy and robustness of the assessment.

[0120] 3. In the reliability assessment method of a regional distribution network considering vehicle-grid interaction of the present invention, the distribution network reliability assessment index includes the load loss probability, which is a probabilistic assessment index and can comprehensively quantify the risk of system failure. Therefore, the present design can comprehensively quantify the risk of system failure through probabilistic assessment indicators, effectively expanding the comprehensiveness of reliability assessment.

[0121] 5. Probabilistic assessment and risk analysis in a regional distribution network reliability assessment method considering vehicle-grid interaction. This method innovatively uses probabilistic assessment indicators (such as load loss probability LOLP and other reliability indicators) to provide a comprehensive quantification of system failure risks. This approach can help power companies or system planners more clearly understand the risk level faced by the system and formulate reasonable investment and management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 It is a flow chart of the regional distribution network reliability assessment method considering vehicle-grid interaction in the present invention.

[0123] Figure 2 It is a principle block diagram of the reliability evaluation index system in the present invention.

[0124] Figure 3 It is a structural schematic diagram of the regional distribution network grid structure schematic diagram in the present invention.

[0125] Figure 4 It is a structural diagram of the regional distribution network reliability assessment system considering vehicle-grid interaction in the present invention.

[0126] Figure 5 It is a schematic diagram of the equipment structure in Example 4. DETAILED DESCRIPTION

[0127] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0128] Embodiment 1:

[0129] See also Figure 1 and Figure 2 , a distribution network reliability assessment method considering vehicle-grid interaction, the assessment method comprising:

[0130] S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction;

[0131] S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0132] S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that meets the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0133] S4. Calculation of reliability evaluation indexes of distribution network. According to the impact of large-scale access of electric vehicles on the reliability of distribution network, a reliability evaluation index system including system load loss probability, active power loss and voltage deviation rate is constructed. Random samples that meet the operating characteristics of distribution network and electric vehicles are substituted into the reliability evaluation index system to calculate the reliability evaluation index.

[0134] S5. Comprehensive evaluation of distribution network reliability. A distribution network comprehensive evaluation model is constructed with the comprehensive evaluation score of distribution network reliability as the objective function. The distribution network comprehensive evaluation model is solved to obtain the comprehensive evaluation score of distribution network reliability.

[0135] S1 mainly involves the collection of electric vehicle behavior data and distribution network load data, carrying out distribution network reliability assessment considering vehicle-grid interaction, determining the assessment time t1 and period T, and processing the data to construct an assessment time series data set. S1 includes:

[0136] S1.1. Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system;

[0137] The time series data of the load of the distribution network node between time t1 and time t1+T includes:

[0138]

[0139]

[0140] In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; P load (t) is the load of the regional distribution network from time t1 to time t1+T, and N is the number of distribution network nodes;

[0141] The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency

[0142] The maximum charging power of the mth electric vehicle in the distribution network include:

[0143]

[0144] In the above formula, is the charging efficiency of the mth electric vehicle;

[0145] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0146]

[0147] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0148] S1.2, data preprocessing, cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0149] S1.3. Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0150] The S2 adopts the Monte Carlo algorithm, sets simulation parameters (simulation times K, random variables and their value ranges), simulates the number of electric vehicles, electric vehicle travel behavior, charging and discharging behavior, substitutes the electric vehicle power, node voltage and other indicators of the regional distribution network calculated in step S3, and substitutes the results of all simulation times into step S5 to calculate the comprehensive weight. The S2 includes:

[0151] S2.1. Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through the Monte Carlo algorithm;

[0152] S2.2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that meet the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0153] The S3 mainly establishes an electric vehicle behavior model, an electric vehicle total power model, a node voltage, and a regional distribution network source-load state model, and is used for numerical calculation of the process of carrying out a distribution network reliability assessment for vehicle-grid interaction. The S3 includes:

[0154] S3.1. Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as the battery SOC and charging time, based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0155]

[0156]

[0157] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are:

[0158] x(t),y(t)∈{0,1};

[0159] x(t)+y(t)≤1;

[0160] S3.2. Construct an electric vehicle total power model. The electric vehicle total power model is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes:

[0161]

[0162] In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0163] S3.3, construct a node voltage model, which is used to perform iterative calculation of the distribution network flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node

[0164] S3.4. Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

[0165] The S4 includes:

[0166] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0167] The system load loss probability is calculated by analyzing the probability that the distribution network cannot meet the load demand due to faults or load fluctuations under different operating conditions, and using methods such as Monte Carlo simulation to obtain the system load loss probability index value, including:

[0168]

[0169] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0;

[0170] The active power loss rate is a statistical measure of the total amount of active power that cannot be normally supplied to the load due to factors such as distribution network failure and unreasonable charging and discharging of electric vehicles within a certain period of time. It is calculated by integrating the power data of each period, including:

[0171]

[0172] E i The active power loss in the Xth simulation includes:

[0173]

[0174] In the above formula, ENS% is the system active power loss rate; T is the simulation time period;

[0175] The voltage deviation rate index is obtained by using the distribution network simulation software to calculate the distribution network flow to obtain the deviation between the voltage of each node in the distribution network and the rated voltage, and the voltage deviation rate index is calculated. The average deviation measures the degree of voltage deviation of the regional distribution network, including:

[0176]

[0177]

[0178]

[0179] In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV Assess the score for voltage deviation rate;

[0180] Construct a reliability evaluation index system, integrate the above-mentioned system load loss probability, active power loss, voltage deviation rate and other indicators, and construct a distribution network reliability evaluation index system. This system should be able to comprehensively and objectively evaluate the reliability of the distribution network under the condition of vehicle-grid interaction;

[0181] In S5, the comprehensive evaluation model combines the distribution network reliability evaluation index system and adopts multi-index comprehensive evaluation methods such as hierarchical analysis method and fuzzy comprehensive evaluation method to construct a distribution network comprehensive evaluation model. The model should be able to organically integrate reliability indicators of different dimensions in order to comprehensively evaluate the overall reliability of the distribution network, including:

[0182]

[0183] In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1;

[0184] Set corresponding weights for different indicators. The weights can be determined based on expert experience, hierarchical analysis method and other methods to ensure that w1+w2+w3=1. For the system load loss probability indicator that has a greater impact on the power supply stability of the distribution network, a relatively high weight can be given; and for the voltage deviation rate indicator, an appropriate weight can be given according to the degree of its impact on the user's power experience;

[0185] The comprehensive evaluation calculation is to substitute the various index values ​​obtained through Monte Carlo simulation into the comprehensive evaluation model of the distribution network, and perform weighted calculation according to the set index weights to obtain the comprehensive reliability evaluation result of the distribution network under the condition of vehicle-grid interaction.

[0186] Embodiment 2:

[0187] See also Figure 4 , a distribution network reliability assessment system considering vehicle-grid interaction, the system comprising a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability assessment index calculation module and a distribution network reliability comprehensive assessment module;

[0188] The data acquisition and processing module is used to obtain data related to the reliability assessment of the distribution network and to construct a time series data set for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction;

[0189] The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through a Monte Carlo algorithm;

[0190] The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. The time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node;

[0191] The distribution network reliability evaluation index calculation module is used to construct a reliability evaluation index system including system load loss probability, active power loss, and voltage deviation rate indicators according to the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability evaluation index system to calculate the reliability evaluation index;

[0192] The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability.

[0193] The specific operations of the data acquisition and processing module include:

[0194] Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system;

[0195] The time series data of the load of the distribution network node between time t1 and time t1+T includes:

[0196]

[0197]

[0198] In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; P load (t) is the load of the regional distribution network from time t1 to time t1+T, and N is the number of distribution network nodes;

[0199] The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency

[0200] The maximum charging power of the mth electric vehicle in the distribution network include:

[0201]

[0202] In the above formula, is the charging efficiency of the mth electric vehicle;

[0203] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0204]

[0205] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0206] Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0207] Construct a time series dataset, consider different operating conditions such as normal and faulty distribution networks, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0208] The specific operations of the simulation behavior data module include:

[0209] Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through a Monte Carlo algorithm;

[0210] Substituting the simulation parameters into a random number generator generates a large number of random samples that conform to the operating characteristics of the distribution network and the electric vehicle, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0211] The specific operations of the dynamic vehicle-grid interaction model building module include:

[0212] Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and the battery SOC, charging time and other factors based on the battery characteristics of the electric vehicle, the type of charging facilities and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0213]

[0214]

[0215] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are:

[0216] x(t),y(t)∈{0,1};

[0217] x(t)+y(t)≤1;

[0218] Construct an electric vehicle total power model, which is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes:

[0219]

[0220] In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0221] Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node

[0222] Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

[0223] In the distribution network reliability evaluation index calculation module:

[0224] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0225] The system load loss probability includes:

[0226]

[0227] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0;

[0228] The active power loss rate includes:

[0229]

[0230] E i The active power loss in the Xth simulation includes:

[0231]

[0232] In the above formula, ENS% is the system active power loss rate; T is the simulation time period;

[0233] The voltage deviation rate index includes:

[0234]

[0235]

[0236]

[0237] In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV , is the voltage deviation rate evaluation score;

[0238] In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes:

[0239]

[0240] In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1.

[0241] Embodiment 3:

[0242] According to step S1, the source and data status of the distribution network data are determined to form a reliability assessment time series data set;

[0243] Select as Figure 3 The regional distribution network shown in the figure has 5 nodes and 30 electric vehicles. The battery capacity and charging and discharging efficiency of electric vehicles are known. The typical daily load peak period from 8:00 to 14:00 is selected to carry out the distribution network reliability assessment. The load of each node in the distribution network, the behavior of electric vehicles, the voltage level and other data are known. A distribution network reliability assessment time series data set considering the interaction between the vehicle and the grid is constructed, as shown in Table 1:

[0244] Table 1 Distribution network node data

[0245] time Node 1 Node 2 Node 3 Node 4 Node 5 8:00 0.05 0.16 0.09 0.02 0.18 9:00 0.11 0.06 0.17 0.13 0.02 10:00 0.14 0.03 0.06 0.09 0.11 11:00 0.08 0.14 0.10 0.16 0.03 12:00 0.17 0.09 0.07 0.02 0.12 13:00 0.03 0.19 0.13 0.05 0.15 14:00 0.10 0.04 0.15 0.08 0.10 ;

[0246] According to S2, the Monte Carlo algorithm parameters are set, the simulation times are 1000, and the behavior, charging and discharging status of 30 electric vehicles in the distribution network are simulated. The electric vehicle parameters are shown in Table 2 and substituted into S3 to calculate the electric vehicle load power and node voltage;

[0247] Table 2 Electric vehicle parameters

[0248] Serial number Battery capacitykW.h Charging efficiency % Discharge efficiency % Number of cars 1 30 0.85 0.8 20 2 60 0.9 0.85 30 3 80 0.95 0.9 20 ;

[0249] According to S3, combined with electric vehicle parameters, the electric vehicle behavior data simulated by the Monte Carlo algorithm, the distribution network structure is shown in Figure 3 , substitute the electric vehicle load power, node voltage model, etc. in step S3, obtain the distribution network electric vehicle load power, node voltage, and the number of times the regional load demand is not met under 1000 simulation states, and substitute them into S4.

[0250] According to S4, substitute the calculation results simulated 1000 times in S3 into LOLP%%.ENS% in S4. In the calculation model, LOLP%, ENS%, The values ​​are 35%, 15%, and 50% respectively, and are substituted into S5.

[0251] According to S5, LOLP%, ENS%, Considering that reliability assessment focuses more on the load supply capacity of electric vehicles for the regional distribution network, w1, w2, w3 are set to 0.3, 0.4, 0.3, and substituted into the comprehensive assessment model to obtain a comprehensive assessment Score = 0.3×(1-0.35)+0.4×(1-0.15)+0.3×(1-0.50)=0.685. It can be seen that the reliability capacity of the distribution network in this region through vehicle-grid interaction is relatively low.

[0252] Embodiment 4:

[0253] See also Figure 5 , a regional distribution network reliability assessment device considering vehicle-grid interaction, the device comprising a processor and a memory;

[0254] The memory is used to store computer program code and transmit the computer program code to the processor;

[0255] The processor is used to execute the regional distribution network reliability assessment method considering vehicle-grid interaction described in Example 1 according to the instructions in the computer program code.

[0256] A computer medium having a computer program stored thereon, wherein when the computer program is executed by a processor, a method for evaluating the reliability of a regional distribution network taking into account vehicle-grid interaction as described in Example 1 is implemented.

[0257] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A distribution network reliability assessment method considering vehicle-grid interaction, characterized by: The evaluation methods include: S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction; S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm; S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that meets the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node. S4. Calculation of reliability evaluation indexes of distribution network. According to the impact of large-scale access of electric vehicles on the reliability of distribution network, a reliability evaluation index system including system load loss probability, active power loss and voltage deviation rate is constructed. Random samples that meet the operating characteristics of distribution network and electric vehicles are substituted into the reliability evaluation index system to calculate the reliability evaluation index. S5. Comprehensive evaluation of distribution network reliability. A distribution network comprehensive evaluation model is constructed with the comprehensive evaluation score of distribution network reliability as the objective function. The distribution network comprehensive evaluation model is solved to obtain the comprehensive evaluation score of distribution network reliability.

2. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 1, characterized in that: The S1 includes: S1.

1. Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system; The time series data of the load of the distribution network node between time t1 and time t1+T includes: In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; P load (t) is the load of the regional distribution network from time t1 to t1+T, and N is the number of distribution network nodes; The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency The maximum charging power of the mth electric vehicle in the distribution network include: In the above formula, is the charging efficiency of the mth electric vehicle; The maximum discharge power of the electric vehicle at the mth node of the distribution network include: In the above formula, is the discharge efficiency of the mth electric vehicle; S1.2, data preprocessing, cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data; S1.

3. Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

3. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 2, characterized in that: The S2 includes: S2.

1. Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through the Monte Carlo algorithm; S2.

2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that meet the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

4. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 3, characterized in that: The S3 includes: S3.

1. Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as the battery SOC and charging time, based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes: In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are: x(t),y(t)∈{0,1}; x(t)+y(t)≤1; S3.

2. Construct an electric vehicle total power model. The electric vehicle total power model is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes: In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles; S3.3, construct a node voltage model, which is used to perform iterative calculation of the distribution network flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node S3.

4. Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

5. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 4, characterized in that: The S4 includes: The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators; The system load loss probability includes: In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0; The active power loss rate includes: E i The active power loss in the Xth simulation includes: In the above formula, ENS% is the system active power loss rate; T is the simulation time period; The voltage deviation rate index includes: In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV The score for voltage deviation rate assessment; In S5, the comprehensive evaluation model includes: In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1.

6. A distribution network reliability assessment system considering vehicle-grid interaction, characterized by: The system includes a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability evaluation index calculation module and a distribution network reliability comprehensive evaluation module; The data acquisition and processing module is used to obtain data related to the reliability assessment of the distribution network and to construct a time series data set for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction; The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through a Monte Carlo algorithm; The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. The time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node; The distribution network reliability evaluation index calculation module is used to construct a reliability evaluation index system including system load loss probability, active power loss, and voltage deviation rate indicators according to the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability evaluation index system to calculate the reliability evaluation index; The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability.

7. A distribution network reliability assessment system considering vehicle-grid interaction according to claim 6, characterized in that: The specific operations of the data acquisition and processing module include: Acquire data related to the reliability assessment of the distribution network, wherein the data related to the reliability assessment of the distribution network includes regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node load, maximum charging power of each node in the distribution network, and maximum discharge power of each node in the distribution network. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system; The time series data of the load of the distribution network node between time t1 and time t1+T includes: In the above formula, is the load of regional distribution network node i from time t1 to t1+T, in MW; P load (t) is the load of the regional distribution network from time t1 to t1+T, and N is the number of distribution network nodes; The electric vehicle characteristics include the mth electric vehicle capacity Charging efficiency And discharge efficiency The maximum charging power of the mth electric vehicle in the distribution network include: In the above formula, is the charging efficiency of the mth electric vehicle; The maximum discharge power of the electric vehicle at the mth node of the distribution network include: In the above formula, is the discharge efficiency of the mth electric vehicle; Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data; Construct a time series dataset, consider different operating conditions such as normal and faulty distribution networks, integrate the pre-processed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

8. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 7, characterized in that: The specific operations of the simulation behavior data module include: Setting simulation parameters, including the number of simulations K, random variables and the range of values ​​of random variables, and simulating the number of electric vehicles, travel behavior of electric vehicles, and charging and discharging behavior through a Monte Carlo algorithm; Substituting the simulation parameters into a random number generator generates a large number of random samples that conform to the operating characteristics of the distribution network and the electric vehicle, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

9. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 8, characterized in that: The specific operations of the dynamic vehicle-grid interaction model building module include: Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and the battery SOC, charging time and other factors based on the battery characteristics of the electric vehicle, the type of charging facilities and the power supply capacity of the power grid. The electric vehicle behavior model includes: In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in a charging state. If x(t) is 1, it means that the electric vehicle is in a charging state, otherwise it is in a non-charging state or a traveling state. y(t) is the mark value of whether the electric vehicle is in a discharging state. If y(t) is 1, it means that the electric vehicle is in a discharging state, otherwise it is in a non-discharging state or a traveling state. The expressions of x(t) and y(t) are: x(t),y(t)∈{0,1}; x(t)+y(t)≤1; Construct an electric vehicle total power model, which is used to combine the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The electric vehicle total power model includes: In the above formula, P EV (t) is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles; Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state mark I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load status mark I takes 1.

10. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 9, characterized in that: In the distribution network reliability evaluation index calculation module: The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators; The system load loss probability includes: In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I = 1, otherwise I = 0; The active power loss rate includes: E i The active power loss in the Xth simulation includes: In the above formula, ENS% is the system active power loss rate; T is the simulation time period; The voltage deviation rate index includes: In the above formula, ΔV i (t) is the voltage deviation of the distribution network node i at time t, is the simulated voltage of the distribution network node i at time t, V nominal The rated voltage of each node in the distribution network. The rated voltage of distribution networks of the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, ΔV nominal is the maximum voltage deviation required by the distribution network, Score ΔV Assess the score for voltage deviation rate; In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes: In the above formula, Score is the comprehensive evaluation score; w1, w2, w3 are indicator weights, and w1+w2+w3=1.

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