CN110429596B - Reliability assessment method of distribution network considering the spatiotemporal distribution of electric vehicles - Google Patents

Abstract

The invention relates to a distribution network reliability evaluation method taking into account the temporal and spatial distribution of electric vehicles, and belongs to the field of smart grids. The method includes: S1: constructing a charging/discharging behavior model based on the travel of electric vehicle users; S2: constructing a distribution network reliability evaluation model considering the electric vehicle to the power grid, including: improving the distribution network Monte Carlo reliability evaluation simulation time The propulsion method is to calculate the electric vehicles participating in the V2G call and the reliability indicators of the distribution network during the fault period. On the basis of the charging and discharging behavior model of the electric vehicle, the invention improves the traditional Monte Carlo simulation time advancement method of the distribution network, avoids the invalid calculation amount generated by directly using the traditional method, and improves the efficiency of the simulation.

Description

Reliability assessment method of distribution network considering the spatiotemporal distribution of electric vehicles

technical field

The invention belongs to the technical field of smart grids, and relates to a distribution network reliability evaluation method that takes into account the temporal and spatial distribution of electric vehicles.

Background technique

As a means of transportation powered by electric energy, electric vehicles are a kind of load with random distribution characteristics to the power grid. With the development of electric vehicle technology, the battery capacity of electric vehicles is gradually increasing. Researchers use electric vehicle energy storage batteries as emergency power sources. When the distribution network fails, V2G technology is used to provide power to the faulty island to improve the reliability of the distribution network.

Common research on distribution network reliability, such as Liu Wenxia et al. in the literature "Quantitative Analysis of the Impact of Electric Vehicle Load on Distribution Network Reliability [J]. Journal of Electric Power Systems and Automation, 2013, 25(4): 1-6 Using the probabilistic method, the charging model and V2G model of electric vehicles in different scenarios are established, and the Monte Carlo method is used to evaluate the reliability changes of a large number of Electric Vehicles (EV) connected to the distribution network. Bai Hao et al. in the literature "Reliability Evaluation Research of Combined Power Generation System for Distribution Network with Electric Vehicles [J]. Journal of Electrotechnical Technology, 2015, 30(11): 127-137" , distributed power generation and energy storage equipment are regarded as an organic whole, and the ability of the co-generation system to restore power supply when the distribution network fails is calculated, and the reliability of the distribution network is evaluated based on the Monte Carlo method. In the document "Reliability Studies of Distribution Systems Integrated With ElectricVehicles Under Battery-Exchange Mode[J].IEEE Transactions on Power Delivery, 2016,31(16):2473-2482", Farzin H et al. considered the vehicle characteristics of different users , using the analytical method to calculate the impact of large-scale electric vehicles connected to the power grid on the reliability of the power grid, and mainly for the electric vehicles using the battery swap mode. In the document "Reliability assessment of power generation and transmission system under the large-scale access environment of plug-in hybrid electric vehicles [J]. Power Grid Technology, 2013, 37(4): 899-905", in the traditional analysis of reliable Based on the reliability evaluation method, an extended state method is proposed to calculate the reliability index of large-scale electric vehicles connected to the distribution network. Mozafar M R et al. considered the specifications of lithium-ion batteries as well as vehicle The length of time parked in the workplace, the reliability index was calculated using the shortest path analysis method. Xu N Z et al. adopted Sequential Monte Carlo in the document "ReliabilityEvaluation of Distribution Systems Including Vehicle-to-Home and Vehicle-to-Grid[J].IEEE Transactions on Power Systems,2015,31(1):1-10" Luo method, using V2G and V2H (Vehicle to Home) technology to supply power to distribution network users in the case of power failure and island mode operation, the results prove that this method can improve the reliability and effectiveness of distribution network. Li Haijuan et al. considered in the literature "Reliability Evaluation of Distribution Network with Wireless Charging of Electric Vehicles [J]. Journal of Electrotechnical Technology, 2015(s1): 244-250", considering wireless charging electric vehicles and other types of energy supplementary forms of electric vehicles According to the different load characteristics of the vehicle, the Monte Carlo method is used to calculate the reliability index of the distribution network. Wang Xiang et al. in the document "A Reliability Evaluation Algorithm of Distribution Network Considering Breaking Probability and Load Transfer Probability in V2G Mode [J]. Power Grid Technology, 2014, 38(8): 2213-2219", considering the downstream The influence of reliability between components and upstream nodes, main network and branch network, the reliability index after EV access to distribution network is calculated by analytical method. The above literature is mainly aimed at the reliability evaluation of the electric vehicle cluster as a whole connected to the fixed nodes of the distribution network, ignoring the spatial distribution characteristics of electric vehicles.

Through further development, Chen Ya in the document "Reliability and Benefit Evaluation of Electric Vehicles Connecting to Distribution Network [D].2015.", taking into account the driving space and time factors of electric vehicles, using the Latin hypercube sampling method to evaluate Considering the reliability of the distribution network connected by EVs, this study involves the influence of the spatial distribution of electric vehicles on the reliability of the distribution network. Fahimeh Moslemi in the paper "Investigating the Impact of Electric Vehicles on Reliability of the Distribution System [J]. Indian Journal of Fundamental and Applied Life Sciences. 2015, 5(S3): 2311-2318" studied the electric vehicles in 6 different regions According to the mobility characteristics of mobility, each area is corresponding to each node of the test distribution network, and the reliability of electric vehicles before and after they are connected to the grid is evaluated. Further, Ge Shaoyun et al. used the game theory method in the literature "Comprehensive reliability analysis of the distribution network and the urban road network associated network [J]. Chinese Journal of Electrical Engineering, 2016(6): 1568-1577", the electric The behavior of cars on the road is regarded as a mutual game process, and the reliability of the distribution network and the coupled road network is evaluated by an analytical method, and the distribution network and the road network are regarded as a unified whole. In order to take into account the mutual coupling relationship between the distribution network and the road network and maintain a good calculation speed, Kai H et al. on Smart Grid, 2018, 9(1): 88-100", the road network is represented by a vertical coordinate system, and with the sampling of travel locations, the spatial distribution simulation of electric vehicle travel is completed, and corresponding to different power outage scenarios are given. Different V2G power recovery strategies are discussed, and finally the reliability index is calculated by Monte Carlo simulation method.

To sum up, the research on the reliability assessment of distribution network considering electric vehicles is similar to the research on charging load prediction of electric vehicles. It involves the spatiotemporal distribution of electric vehicles. Therefore, there is an urgent need for a distribution network reliability assessment method that considers both the temporal and spatial distribution of electric vehicles.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a distribution network reliability assessment method that takes into account the temporal and spatial distribution of electric vehicles, first simulating the travel of electric vehicles in the road network, and then using an improved Monte Carlo simulation method to improve computational efficiency , simulates the charging and discharging behavior of electric vehicles in the distribution network, and finally evaluates the impact of electric vehicles participating in V2G on the reliability of the distribution network and electric vehicles, thereby improving computational efficiency.

To achieve the above object, the present invention provides the following technical solutions:

A distribution network reliability evaluation method considering the spatiotemporal distribution of electric vehicles, which specifically includes the following steps:

S1: Build a charging/discharging behavior model based on the travel of electric vehicle users;

S2: Build a distribution network reliability evaluation model considering V2G, including: improving the Monte Carlo reliability evaluation simulation time advancement method of the distribution network, and calculating the electric vehicles participating in the V2G call during the fault period and the distribution network reliability index.

Further, in the step S1, constructing a charging behavior model based on the travel of electric vehicle users specifically includes the following steps:

S101: Classify electric vehicles into 6 types, including models A, B, C, D, E, and F. The living and working conditions of model A are full and part, respectively, and the living and working conditions of model B are full and part, respectively. The slow charging conditions of the ground and working places are full and none, respectively, the residential and working slow charging conditions of the C model are part and part, the residential and working slow charging conditions of the D model are part and null respectively, and the slow charging conditions of the E model are part and null respectively. The slow charging conditions of the residence and work are none and part respectively, and the slow charging conditions of the residence and work of the E model are none and none respectively; among them, full means that the owner has a self-provided slow charging device, which can fully meet their charging needs. part means that the place of residence or work has a public slow charging device, but it is likely to be occupied by other vehicles, and none means that there is no slow charging device; the probability of slow charging is defined as p _a , which means that when the electric vehicle needs to be charged at a slow speed, it can the probability of charging;

S102: Build a charging behavior model during the normal operation period of the distribution network or a charging behavior model during the distribution network failure period.

Further, in the step S102, constructing a charging behavior model during the normal operation period of the distribution network specifically includes the following steps:

1) According to the quasi-dynamic travel simulation of electric vehicles, the temporal and spatial distribution of electric vehicle travel is obtained;

If the state of charge of the electric vehicle satisfies the formula (1) when it arrives at the place of residence or work, and the slow charging pile is not occupied, the electric vehicle will be charged slowly:

表示电动汽车i日最大耗电量，设置比例系数1.5防止突发状况导致汽车在路途中将电量耗尽；Among them, Si represents the charging threshold of EV _i , SoC _i (t) represents the state of charge of EV i at time t, EN _i represents the battery capacity of EV i,

Indicates the maximum power consumption of the electric vehicle i-day, and the proportional coefficient is set to 1.5 to prevent the car from running out of power on the road due to unexpected situations;

If the battery can be fully charged before the next travel segment of the electric vehicle, the charging time is expressed as:

表示电动汽车慢速充电功率；Among them, EC _i represents the remaining battery power of electric vehicle i,

Indicates the slow charging power of electric vehicles;

If the battery is not fully charged before the next travel segment of the electric vehicle, the charging time indicates:

TCR=TL-TS (3)

The battery level is expressed as:

表示离开时刻电动汽车i的电池电量，

表示开始充电时刻电动汽车i的电池电量；Among them, TL represents the departure time of the electric vehicle, TS represents the charging time of the electric vehicle,

represents the battery power of the electric vehicle i at the time of departure,

Represents the battery power of electric vehicle i at the time of starting charging;

2) If the electric vehicle needs to be charged at a slow speed, but the charging pile is occupied, the electric vehicle needs to be charged quickly on the next trip; if there are multiple fast charging stations in the travel path, one will be randomly selected for power supplementation. If there is no charging station on the travel route, select the fast charging station closest to the departure place for charging, and then start from the fast charging station to the next destination;

The fast charging mode is set to direct full charge, and the charging time indicates:

表示电动汽车快速充电功率；in,

Indicates the fast charging power of electric vehicles;

3) For the F type vehicle, since it can only perform fast charging, the charging behavior is performed in the way that the slow charging pile is occupied and requires fast charging in step 2).

Further, in the step S102, constructing the charging behavior model during the fault period of the distribution network specifically includes the following steps:

1) If the electric vehicle is being charged at a slow speed, the charging will be interrupted. If the power outage has ended before departure, the charging will continue; if the battery capacity of the electric vehicle meets the charging conditions of formula (1) before departure, the electric vehicle will select the nearest fast charging pile. Recharge the electric energy before driving to the destination; if the destination is still in a power outage state, the electric vehicle waits for the end of the power outage at the destination; if the formula (1) is not satisfied, the original travel chain travel plan is continued without charging; For the situation that the electric vehicle needs to find a fast charging pile due to a power outage due to the slow charging of the electric vehicle, the electric vehicle needs to travel a longer distance and consume more time;

2) If the electric vehicle is undergoing fast charging and the battery capacity meets the charging conditions of formula (1), the electric vehicle leaves the charging station and selects another fast charging station closest to the current location for charging, which is the same as when the slow charging is interrupted. .

Further, in the step S1, constructing a discharge behavior model based on the travel of electric vehicle users specifically includes the following steps:

S111: Calculate the dischargeable probability and discharge energy;

When the location of the electric vehicle is in the full state, regardless of whether the electric vehicle has a charging demand, it is considered that it is always connected to the grid, and the electric vehicle can be used as a backup power source to discharge to the grid; in the part state, there is a probability p _a connected to the grid; when the distribution network occurs When a fault occurs and the electric vehicle needs to perform V2G operation, for the electric vehicle connected to the power grid, if its battery state of charge satisfies the formula (6), the electric vehicle will perform the V2G power supply operation;

SoC _i >S _y % (6)

Among them, S _y represents the V2G discharge threshold, and the present invention assumes that the V2G power supply can use up to 25% of the battery power of the electric vehicle;

S112: Calculate the earliest dischargeable time and discharge end time;

The earliest discharge time of an electric vehicle is recorded as TDS, the discharge end time is recorded as TDE, the fault start time is recorded as TGS, and the fault end time is recorded as TGE. The four moments have the following three relationships:

Scenario 1): Before the electric vehicle participates in V2G, the node where the electric vehicle is located has experienced a power outage, TDS is the time when the electric vehicle arrives at the node, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold time, that is, the electric vehicle has stopped discharging before the power outage is over;

Scenario 2): The power outage occurs after the electric vehicle reaches a certain node, then TDS is the time of failure, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold;

Scenario 3): A power outage occurs after the electric vehicle reaches a certain node, then TDS is the time when the electric vehicle arrives at the node, and the discharge end time is the end of the outage, that is, the fault has recovered before the electric vehicle leaves or reaches the discharge threshold time;

Due to the actual scheduling situation, the electric vehicle may not discharge the whole process during the TDS-TDE period. The discharge power is recorded as P _d , and the remaining battery power of the electric vehicle i after the discharge is completed is expressed as:

表示电动汽车i放电前的电池剩余电量，C_g(t)为0-1变量，当电动汽车放电时为1，其余时刻为0。in,

Represents the remaining power of the battery before the electric vehicle i is discharged, C _g (t) is a 0-1 variable, 1 when the electric vehicle is discharged, and 0 at other times.

Further, in the step S2, the improved method for advancing the simulation time of the Monte Carlo reliability assessment of the distribution network specifically includes:

1): Simulate the operation and failure of power grid components;

The components other than electric vehicles in the distribution network simulate their operation and faults according to the time advancement method in the reliability assessment based on Monte Carlo, form the time series event set of each component of the distribution network, and record the time TGS _i and repair time of each fault. TGE _i ;

2): Simulate the timing behavior of electric vehicles during normal operation of the distribution network;

For each electric vehicle, according to the charging behavior model based on the travel of electric vehicle users, the travel and charging behavior of each electric vehicle is simulated in a time-series manner; after the calculation of the first vehicle is completed, the cycle of the next vehicle is performed. , until the last vehicle of the day is calculated, and the travel and charging simulation of each electric vehicle in the day is realized;

3): Simulate the timing behavior of electric vehicles during the fault period of the distribution network;

For the case where the fault does not span the sky (faults between 0-24 hours), the simulation is based on the "major cycle" in days and the "small cycle" in a single vehicle, and records in 1) The fault occurrence time and repair time are matched to the time of the day, taking into account the impact of the fault on the charging of electric vehicles and the role of V2G in providing power for the unloaded area; for a certain electric vehicle i In terms of the sequence events of one day, the sequence events of the day are sequentially advanced according to the simulation time of ①～⑥, among which: ① means that the electric vehicle departs from the residence; ② means that the electric vehicle arrives at the work place; Participate in V2G; ④ Indicates that the V2G discharge ends because the electric vehicle meets the constraints; ⑤ Indicates that the electric vehicle leaves the work place and returns to the place of residence; ⑥ The electric vehicle is active in the place of residence;

For the case of fault spanning days (the fault period spans 0 hours), the cycle period is extended from one day to the number of days the fault spans.

Further, in the step S2, calculating the electric vehicles participating in the V2G call power during the fault period specifically includes: after the travel simulation of all electric vehicles is completed, sorting all the electric vehicles that can participate in the V2G in the isolated island according to the earliest time that can participate in the V2G; The former is called first, and the latter is called later, until the load power shortage is met; the number of electric vehicles N _dEV called in each period is expressed as formula (8):

Among them, P _d,i (t) represents the discharge power of electric vehicle i at time t, P _DG (t) is the power emitted by the distributed generator at time t, and L _g,i (t) represents the discharge power of load point i at time t power; if all electric vehicles that can participate in V2G are discharged and still cannot meet the power shortage, N _dEV is the maximum available quantity; The remaining power of the battery is obtained after the discharge energy is subtracted from the electric power; the condition for the electric vehicle to participate in V2G is that the electric vehicle has sufficient battery power, so there is still enough energy after providing power to the grid, which will not affect the recent travel demand, so this simulation method The results are consistent with the synchronous real-time update results.

Further, in the step S2, the calculation of the reliability index of the distribution network specifically includes: during the fault period of the distribution network, according to the situation of the joint power supply of the electric vehicle and the distributed power source, the parameters of the power outage time and the loss of load are calculated in a sequential manner, and the parameters are calculated according to the time series. As the simulation time goes on, it is gradually accumulated, and after the simulation time is reached, each reliability index is calculated according to the total number of parameters and formulas (9) to (11); during the normal operation period of the distribution network, there is no reliability index calculation process;

The distribution network reliability indicators include:

①System average interruption frequency index (SAIFI) per year, unit: times/(user·year);

表示电力用户k在仿真时间内停电总次数，M表示电力用户总数；in,

Represents the total number of power outages of power user k in the simulation time, and M represents the total number of power users;

②System average interruption duration index (SAIDI) unit: h/(user·year);

表示电力用户k在仿真时间内停电总时长；in,

Represents the total duration of power outage for power user k in the simulation time;

③System power shortage indicator (expected energy not supplied, EENS), unit: MWh/year;

Among them, P _ct (t) represents the load shedding power of the system at time t, and P _ct (t)=0 represents the normal operation of the distribution network.

The beneficial effect of the present invention is that: in order to evaluate the reliability of the distribution network including electric vehicles, the present invention fully considers the contradictory relationship between calculation accuracy and calculation accuracy, and the influence of traffic congestion on route selection. On the basis of the charging and discharging behavior model of the electric vehicle, the present invention improves the traditional Monte Carlo simulation time advancement method of the distribution network, avoids the ineffective calculation amount generated by directly using the traditional method, and improves the efficiency of the simulation.

Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.

Description of drawings

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be preferably described in detail below with reference to the accompanying drawings, wherein:

Figure 1 is a schematic diagram of the charging route;

Figure 2 is a flowchart of the charging behavior during the distribution network fault period;

Figure 3 is a schematic diagram of the start and end time of charging and discharging;

Fig. 4 is the simulation flow chart of power grid components;

Fig. 5 is the simulation flow chart of the non-fault period of a single vehicle;

Fig. 6 is the simulation flow chart of the failure period of a single vehicle that can participate in V2G;

Figure 7 is a schematic diagram of the power shortage in the isolated island;

Fig. 8 is a schematic diagram of "settlement after failure" V2G;

Figure 9 is a schematic diagram of a traffic network;

Figure 10 is a schematic diagram of the IEEE-RBTS Bus6 test system;

Figure 11 is the fault circuit diagram;

Figure 12 shows the charging and discharging power of electric vehicles;

Figure 13 is a diagram of the V2G and distributed power output of electric vehicles.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

Please refer to Figure 1 to Figure 13, which is a distribution network reliability assessment method that takes into account the spatiotemporal distribution of electric vehicles, including:

S1: Build a charging/discharging behavior model based on the travel of electric vehicle users;

(1) Build a charging behavior model based on the travel of electric vehicle users, which includes the following steps:

S101: Classify electric vehicles by vehicle type, in order to accurately calculate the charging and discharging behavior of electric vehicles, classify household electric vehicles. Judging from the several charging methods suggested by Tesla, it is recommended to perform slow charging in the place of residence and work, and fast charging in the way. However, not all electric vehicles are fully equipped with slow charging devices in their residential and work parking spaces. For electric vehicles that do not have slow charging piles, the parking spaces with charging piles may be used by other vehicles when charging is required. Occupied, at this time, it is necessary to supplement the electric energy in other ways to meet the travel demand. Therefore, household electric vehicles are divided into 6 types, as shown in Table 1:

Table 1 Classification of electric vehicles

In the above table, full means that the owner has a self-provided slow charging device, which can fully meet their charging needs; part means that there is a public slow charging device in the place of residence or work, but it is likely to be occupied by other vehicles, and none means there is no slow charging device. Defining the chargeable probability p _a of the slow charge, it represents the probability that the EV can be charged when the EV needs to be charged at a slow speed. For Type A and B vehicles, since the residence has self-provided charging facilities, the rechargeable probability p _a at the residence is 1. For Type F vehicles, the slow charging probability p _a at the workplace and residence is Both are 0, that is, only fast charging can be performed. Whether each vehicle can be slow-charged is simulated by drawing a uniformly distributed random number.

S102: Build a charging behavior model during normal operation of the distribution network or a charging behavior model during a distribution network failure.

A. The construction of the charging behavior model during the normal operation period of the distribution network specifically includes:

1) According to the quasi-dynamic travel simulation of electric vehicles, the temporal and spatial distribution of electric vehicle travel is obtained. If the state of charge of the electric vehicle satisfies the formula (1) when it arrives at the place of residence or work, and the slow charging pile is not occupied, the electric vehicle will be charged slowly:

表示电动汽车i日最大耗电量，设置比例系数1.5防止突发状况导致汽车在路途中将电量耗尽；Among them, Si represents the charging threshold of EV _i , SoC _i (t) represents the state of charge of EV i at time t, EN _i represents the battery capacity of EV i,

Indicates the maximum power consumption of the electric vehicle i-day, and the proportional coefficient is set to 1.5 to prevent the car from running out of power on the road due to unexpected situations;

If the battery can be fully charged before the next travel segment of the electric vehicle, this embodiment assumes that the EVs all adopt the constant power charging mode, and the charging time is expressed as:

表示电动汽车慢速充电功率；Among them, EC _i represents the remaining battery power of electric vehicle i,

Indicates the slow charging power of electric vehicles;

If the battery is not fully charged before the next travel segment of the electric vehicle, the charging time indicates:

TCR=TL-TS (3)

The battery level is expressed as:

表示离开时刻电动汽车i的电池电量，

表示开始充电时刻电动汽车i的电池电量。Among them, TL represents the departure time of the electric vehicle, TS represents the charging time of the electric vehicle,

represents the battery power of the electric vehicle i at the time of departure,

Indicates the battery level of the electric vehicle i at the time of starting charging.

2) If the electric vehicle needs to be charged at a slow speed, but the charging pile is occupied, the electric vehicle needs to be charged quickly on the next trip; if there are multiple fast charging stations in the travel path, one will be randomly selected for power supplementation. If there is no charging station on the travel route, select the fast charging station closest to the departure place for charging, and then start from the fast charging station to the next destination. That is, the situation shown in FIG. 1 .

The fast charging mode is set to direct full charge, and the charging time indicates:

表示电动汽车快速充电功率。in,

Indicates the electric vehicle fast charging power.

3) For the F type vehicle, since it can only perform fast charging, the charging behavior is performed in the way that the slow charging pile is occupied and requires fast charging in step 2).

B. Construction of the charging line model during the fault period of the distribution network specifically includes:

1) If the electric vehicle is being charged at a slow speed, the charging will be interrupted. If the power outage has ended before departure, the charging will continue; if the battery capacity of the electric vehicle meets the charging conditions of formula (1) before departure, the electric vehicle will select the nearest fast charging pile. Recharge the electric energy before driving to the destination; if the destination is still in a power outage state, the electric vehicle waits for the end of the power outage at the destination; if the formula (1) is not satisfied, the original travel chain travel plan is continued without charging; For the situation that the electric vehicle needs to find a fast charging pile due to a power outage due to the slow charging of the electric vehicle, the electric vehicle needs to travel a longer distance and consume more time;

2) If the electric vehicle is undergoing fast charging and the battery capacity meets the charging conditions of formula (1), the electric vehicle leaves the charging station and selects another fast charging station closest to the current location for charging, which is the same as when the slow charging is interrupted. . The two types of charging processes during the distribution network fault period can be represented as Figure 2.

(2) Build a discharge behavior model based on the travel of electric vehicle users, which includes the following steps:

S111: Calculate the dischargeable probability and discharge energy;

When the location of the electric vehicle is in the full state, regardless of whether the electric vehicle has a charging demand, it is considered that it is always connected to the grid, and the electric vehicle can be used as a backup power source to discharge to the grid; in the part state, there is a probability p _a connected to the grid; when the distribution network occurs When a fault occurs and the electric vehicle needs to perform V2G operation, for the electric vehicle connected to the power grid, if its battery state of charge satisfies the formula (6), the electric vehicle will perform the V2G power supply operation;

SoC _i >S _y % (6)

Among them, S _y represents the V2G discharge threshold. In this embodiment, it is assumed that the V2G power supply can use up to 25% of the battery power of the electric vehicle.

S112: Calculate the earliest dischargeable time and discharge end time;

As shown in Figure 3, the earliest discharge time of an electric vehicle is recorded as TDS, the discharge end time is recorded as TDE, the fault start time is recorded as TGS, and the fault end time is recorded as TGE. The four moments have the following three relationships:

Scenario 1): Before the electric vehicle participates in V2G, the node where the electric vehicle is located has experienced a power outage, TDS is the time when the electric vehicle arrives at the node, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold time, that is, the electric vehicle has stopped discharging before the power outage is over;

Scenario 2): The power outage occurs after the electric vehicle reaches a certain node, then TDS is the time of failure, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold;

Scenario 3): A power outage occurs after the electric vehicle reaches a certain node, then TDS is the time when the electric vehicle arrives at the node, and the discharge end time is the end of the outage, that is, the fault has recovered before the electric vehicle leaves or reaches the discharge threshold time;

Due to the actual scheduling situation, the electric vehicle may not discharge the whole process during the TDS-TDE period. The discharge power is recorded as P _d , and the remaining battery power of the electric vehicle i after the discharge is completed is expressed as:

表示电动汽车i放电前的电池剩余电量，C_g(t)为0-1变量，当电动汽车放电时为1，其余时刻为0。in,

Represents the remaining power of the battery before the electric vehicle i is discharged, C _g (t) is a 0-1 variable, 1 when the electric vehicle is discharged, and 0 at other times.

S2: Build a distribution network reliability evaluation model considering V2G, including: improving the Monte Carlo reliability evaluation simulation time advancement method of the distribution network, and calculating the electric vehicles participating in the V2G call during the fault period and the distribution network reliability index.

(1) Improve the simulation time advancement method of Monte Carlo reliability assessment of distribution network;

In order to solve the problem of how to advance the simulation time, in this embodiment, the components of the distribution network and the electric vehicle are processed in different time advancing manners, so as to avoid invalid calculation amount for the electric vehicle whose state has not changed. details as follows:

1): Simulate the operation and failure of power grid components;

The components other than electric vehicles in the distribution network simulate their operation and faults according to the time advancement method in the reliability assessment based on Monte Carlo, form the time series event set of each component of the distribution network, and record the time TGS _i and repair time of each fault. TGE _i . As shown in Figure 4. If the distribution network does not contain electric vehicles, each component in it can use the outage rate function and the repair rate function to describe the state of work and outage. The time advancement method in the reliability assessment based on Monte Carlo is as follows:

Step 1: Initialize simulation time H=0, fault time TTF=0.

Step 2: Generate the normal working duration TTF and fault repair time TTR of all components, and arrange them in sequence to form the running state duration time series of each component in the total simulation time:

where λ _y,i and μ _y,i are the outage rate and repair rate of component i, respectively; σ is a random number between 0 and 1 that obeys a uniform distribution.

Step 3: Synthesize the running state sequence of each element, determine the original with the smallest TTF (T _TTFmin ), identify it as the faulty original, and advance the analog clock to T _TTFmin : H=H+T _TTFmin .

2): Simulate the timing behavior of electric vehicles during normal operation of the distribution network;

For each electric vehicle, according to the charging behavior model based on the travel of electric vehicle users, the travel and charging behaviors of each electric vehicle are simulated in a time-series manner in units of days. The simplified simulation process is shown in Figure 5. When the EV performs the next activity, the simulation time is directly advanced to the start time of the activity, as shown in the dark process in Figure 5. After the calculation of the first car is completed, the cycle of the next car is carried out until the last car of the day is calculated, and the travel and charging simulation of each electric car in a day is realized.

3): Simulate the timing behavior of electric vehicles during the fault period of the distribution network;

For the case where the fault does not span the sky (faults between 0-24 hours), the simulation is based on the "major cycle" in days and the "small cycle" in a single vehicle, and records in 1) The fault occurrence time and repair time are matched to the time of the day, taking into account the impact of the fault on the charging of electric vehicles and the role of V2G in providing power for the unloaded area; for a certain electric vehicle i For example, its one-day time series events are shown in Figure 6. According to the simulation time of ①～⑥, where: ① means that the electric vehicle starts from the place of residence; ② means that the electric vehicle arrives at the work place; ③ means that the electric vehicle has a power outage at the place where the electric vehicle is located, and the electric vehicle begins to participate in V2G; ④ means that the electric vehicle Satisfy the constraints and the V2G discharge ends; ⑤ means that the electric vehicle leaves the workplace and returns to the place of residence; ⑥ the electric vehicle moves to the place of residence.

For the case of fault spanning days (the fault period spans 0 hours), the cycle period is extended from one day to the number of days the fault spans.

(2) Calculate the electric vehicle's participation in the V2G call during the fault period;

Since a single vehicle is cycled in the simulation, V2G is actually multiple electric vehicles supplying power to the island at the same time. In this embodiment, the method of "settlement after failure" is used to calculate the electricity of multiple electric vehicles participating in V2G at the same time. For the above electric vehicle i, the fault period is just in the time interval of parking, and the time interval in which it can participate in V2G is TGS-TGE.

During the simulation cycle, each electric vehicle only calculates the earliest available V2G time TDS and the maximum discharge time, and does not calculate the change of battery energy. For each fault point, there will be different numbers of electric vehicles that can provide V2G services in the time period from TGS to TGE, and the earliest start time and maximum discharge duration of each electric vehicle that can participate in V2G are different. The gray part in Figure 7 represents the power shortage after the distributed power generation in the isolated island, and this part needs to be supplemented by electric vehicles participating in V2G.

After the travel simulation of all electric vehicles is completed, all electric vehicles that can participate in V2G in the island are sorted according to the earliest time they can participate in V2G; those with the highest order are called first, and those with the lowest order are called later, until the load power shortage is met, as shown in Figure 8 The number of electric vehicles N _dEV called in each period is expressed as formula (8):

Among them, P _d,i (t) represents the discharge power of electric vehicle i at time t, P _DG (t) is the power emitted by the distributed generator at time t, and L _g,i (t) represents the discharge power of load point i at time t power; if all electric vehicles that can participate in V2G are discharged and still cannot meet the power shortage, N _dEV is the maximum available quantity; The remaining power of the battery is obtained by subtracting the discharge energy from the electric power; the condition for the electric vehicle to participate in V2G is that the electric vehicle battery has sufficient power, so there is still enough energy after providing power to the grid, which will not affect the recent travel demand, so this simulation method The results are consistent with the synchronous real-time update results.

(3) Calculate the reliability index of the distribution network;

During the fault period of the distribution network, according to the joint power supply of electric vehicles and distributed power sources, the parameters of the power outage time and the loss of load are calculated in a sequential manner, and are gradually accumulated as the simulation time goes on. Formulas (9) to (11) calculate each reliability index; during the normal operation period of the distribution network, there is no reliability index calculation process;

The distribution network reliability indicators include:

①System average interruption frequency index (SAIFI) per year, unit: times/(user·year);

表示电力用户k在仿真时间内停电总次数，M表示电力用户总数；in,

Represents the total number of power outages of power user k in the simulation time, and M represents the total number of power users;

②System average interruption duration index (SAIDI) unit: h/(user·year);

表示电力用户k在仿真时间内停电总时长；in,

Represents the total duration of power outage for power user k in the simulation time;

③System power shortage indicator (expected energy not supplied, EENS), unit: MWh/year;

Among them, P _ct (t) represents the load shedding power of the system at time t, and P _ct (t)=0 represents the normal operation of the distribution network.

After the electric vehicle has the V2G power supply function, it is both a power source and a load, and the charging and discharging behavior is closely related to its own spatiotemporal characteristics. Based on this, the reliability index of electric vehicles is defined to evaluate the ability of electric vehicles to participate in V2G as a backup power source. Since an electric vehicle is a load that can be moved and can be interrupted for the power grid, when the electric vehicle is being charged and the power is already sufficient, the power outage will have less impact on it. grid system.

Simulation study analysis:

(1) Parameter setting

The road network structure adopts an example model as shown in Figure 9. Nodes 1, 4, 5, 6, 8, 10, 11, and 15 are equipped with fast charging stations. The electric vehicle discharge power is set to 5kW, the simulation start time assumes that all electric vehicle battery SoCs are 100%, the car ownership is 10324, and the simulation time is 100 years. The distribution network test system is shown in Figure 10, taking the main feeder F4 in the improved IEEE-RBTS Bus6 test system as an example. It is assumed that the isolating switch operates 100% of the time every time. It is also equipped with two distributed power sources, each of which is a wind turbine with a capacity of 1MW. The corresponding relationship between the physical locations of road network nodes and distribution network load points is shown in Table 2.

Table 2 Correspondence table of road network and distribution network nodes

(2) The charging and discharging power of the electric vehicle during the fault period

One day in the simulation process, as shown in Figure 11, the front-end line of the distribution network node 18 (road network node 1) fails and causes a power outage accident. The type power supply is used as an emergency power supply to supply power to other loads except electric vehicles in the island. For intuitive display, it is assumed that the load of node 18 during this period is a constant load of 1000kW (excluding the electric vehicle charging load).

The electric vehicle charging and discharging power distribution at node 18 on the day is shown in Figure 12. A positive value indicates the charging load, and a negative value indicates the discharging power. The fault start time is 8:00 am, the fault end time is 13:00 pm, and the fault duration is 5h. It can be seen from Figure 12 that the charging power of electric vehicles at the same node is smaller than the discharging power, because the electric vehicles that need to be charged account for a small proportion of all electric vehicles, while a considerable proportion of electric vehicles can provide power to the distribution network. And most of the electric vehicles are parked during this period. The output of electric vehicles and distributed power sources is shown in Figure 13. The electric vehicle V2G and the distributed power supply can fully meet the electricity load of the island node 18 under the combined action.

(3) The influence of electric vehicle V2G technology on the reliability of distribution network

First, the electric vehicle is used as a common load and does not supply reverse power to the power grid. At this time, the electric vehicle, as the load with the lowest power supply level, is first cut off when the power grid fails. Then consider the situation when the penetration rate of electric vehicles in the power supply area is 40% and can supply power to the distribution network in reverse. At this time, the electric vehicles with charging needs are first cut off, and the electric vehicles that meet the V2G conditions supply power to the power supply island. The system reliability of the two cases is shown in Table 3. Due to the special load of electric vehicles, according to the above settings, the reliability indicators of the distribution network do not include the charging load of electric vehicles.

Table 3 Impact of V2G technology on distribution network reliability

It can be seen from the above evaluation results that under the condition of using the V2G technology of electric vehicles, the electric vehicle energy storage battery can supply power to the fault island, the three reliability indicators of the distribution network system are greatly reduced, and the power supply of the distribution network is reliable. Sex was significantly improved. It shows that EV as a backup power source has a strong energy supply capacity in the distribution network. In actual operation, if the energy stored by the electric vehicle is fully utilized, the value of the power outage index can be effectively reduced and the operation level can be improved.

(4) Analysis of the influence of the penetration rate of electric vehicles on the reliability of the distribution network

At present, electric vehicles are in the stage of vigorous promotion all over the world, and the penetration rate of electric vehicles will continue to increase with the development of technology. Therefore, it is of great significance to study the influence of electric vehicle penetration rate on reliability indicators. Table 4 shows the simulation results of the reliability index of the distribution network and electric vehicles.

Table 4 Impact of electric vehicle penetration on distribution network reliability

It can be seen from the above results that for the distribution network system, the increase in the penetration rate of electric vehicles actually increases the capacity of the backup power supply of the distribution network. Therefore, as the number of electric vehicles participating in V2G increases, the three indexes of distribution network reliability are all equal. improved, and by a larger margin. In terms of the trend of changes in the reliability index of the power grid, when the number of electric vehicles is less, the increase in the penetration rate of electric vehicles makes the reliability index change more significantly, and the reliability index changes significantly when the penetration rate increases from 20% to 60%. As the permeability increased from 60% to 100%.

(5) Analysis of the influence of electric vehicle discharge threshold on the reliability of distribution network

The electric vehicle V2G discharge threshold is equivalent to the activation condition of the backup power supply. The lower the threshold, the easier it is to be activated, and the more significant the improvement of the grid reliability. The simulation results of the reliability indicators of the distribution network are shown in Table 5.

Table 5 Influence of electric vehicle discharge threshold on distribution network reliability

It can be seen from the simulation results that the increase of the V2G discharge threshold has a significant effect on the reliability of the power grid, especially when the discharge threshold is changed from 75% to 65%, the change rates of the two reliability indicators of the power grid both exceed 30%. The change is significant for the impact on charging reliability.

(6) Analysis of the influence of electric vehicle battery capacity on the reliability of distribution network

The battery capacity of electric vehicles can be said to be the most important factor restricting the development of electric vehicles. For the distribution network, when the electric vehicle travels consumes a certain amount of electric energy, the larger the battery capacity, the more electric energy the electric vehicle can store. On the one hand, more electric vehicles can participate in the V2G power supply, and on the other On the one hand, a single electric vehicle participating in V2G can provide more energy to the grid. Table 6 shows the simulation results of the distribution network reliability index.

Table 6 Influence of electric vehicle battery capacity on distribution network reliability

The simulation results show that when the battery capacity of the electric vehicle increases from 50kWh to 60kWh, the reliability index of the power grid is significantly improved, and as the battery capacity continues to increase, the degree of improvement of the reliability index gradually decreases.

(7) Analysis of the influence of road impedance on the reliability of distribution network

The emergence of electric vehicles has strengthened the coupling relationship between the road network and the distribution network. From the perspective of the road network, the increase in the number of vehicles on the road will aggravate the congestion and increase the road resistance. On the other hand, the change of road resistance will directly affect the travel and charging and discharging behavior of electric vehicles, and then affect the reliability of the power grid and the charging of electric vehicles. The road resistance depends on the number of vehicles in the road network, so this embodiment is measured by the total number of vehicles road blockage. Table 7 shows the change of reliability index under the condition that the number of electric vehicles remains unchanged, and the total number of vehicles continues to increase on the basis of the above number.

Table 7 Influence of road impedance on reliability of distribution network

It can be seen from the above results that with the increase of the number of vehicles in the road network, the congestion degree of the road network intensifies, but the impact on the distribution network is relatively small. .

Electric vehicles, as power sources and loads with spatial and temporal distribution characteristics, make distribution network reliability assessment not only need to consider the traditional power system network, but also the random movement characteristics of electric vehicles in the road network. In order to evaluate the reliability of the distribution network including electric vehicles, the present invention fully considers the contradictory relationship between the calculation accuracy and the calculation accuracy, and considers the influence of traffic congestion on route selection. On the basis of the charging and discharging behavior model of electric vehicles, the traditional Monte Carlo simulation time advancement method of distribution network is improved, which avoids the ineffective calculation amount caused by directly using the traditional method and improves the efficiency of simulation. Finally, the reliability indexes of the power grid and the electric vehicle are obtained through the calculation of the simulation example, which verifies the effectiveness of the invented method.

The calculation example results show that the increase of the penetration rate of electric vehicles and the increase of the battery capacity can improve the reliability of the distribution network, while the reduction of the discharge threshold is beneficial to the improvement of the reliability of the distribution network, and the increase of the road network resistance is conducive to the reliability of the power grid. The adverse effects of sex are small. In addition, the influencing factors of reliability are not proportional to the reliability index, especially when the number of electric vehicles is small or the battery capacity is low, increasing the number and battery capacity of electric vehicles can effectively improve the reliability index.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.

Claims (6)

1. a distribution network reliability assessment method taking into account the spatiotemporal distribution of electric vehicles, is characterized in that, the method specifically comprises the following steps: S1: Build a charging/discharging behavior model based on the travel of electric vehicle users; S2: Build a distribution network reliability evaluation model that considers electric vehicles to the grid (Vehicle-to-grid, V2G), including: improving the Monte Carlo reliability evaluation simulation time advancement method of the distribution network, calculating the participation of electric vehicles in V2G during the fault period Calling power and distribution network reliability indicators; In the step S1, constructing a charging behavior model based on the travel of electric vehicle users specifically includes the following steps: S101: Classify electric vehicles into 6 types, including models A, B, C, D, E, and F. The living and working conditions of model A are full and part, respectively, and the living and working conditions of model B are full and part, respectively. The slow charging conditions of the ground and working places are full and none, respectively, the residential and working slow charging conditions of the C model are part and part, the residential and working slow charging conditions of the D model are part and null respectively, and the slow charging conditions of the E model are part and null respectively. The slow charging conditions of the residence and work are none and part respectively, and the slow charging conditions of the E model’s residence and work are none and none respectively; among them, full means that the owner has a self-provided slow charging device, which can fully meet their charging needs. part means that the place of residence or work has a public slow charging device, but it is likely to be occupied by other vehicles, and none means that there is no slow charging device; the probability of slow charging is defined as p _a , which means that when the electric vehicle needs to be charged at a slow speed, it can the probability of charging; S102: Build a charging behavior model during normal operation of the distribution network or a charging behavior model during a distribution network failure; The construction of the charging behavior model during the normal operation period of the distribution network includes the following steps: 1) According to the quasi-dynamic travel simulation of electric vehicles, the temporal and spatial distribution of electric vehicle travel is obtained; If the state of charge of the electric vehicle satisfies the formula (1) when it arrives at the place of residence or work, and the slow charging pile is not occupied, the electric vehicle will be charged slowly:

Among them, Si represents the charging threshold of EV _i , SoC _i (t) represents the state of charge of EV i at time t, EN _i represents the battery capacity of EV i,

Indicates the maximum power consumption of the electric vehicle i-day, and the proportional coefficient is set to 1.5 to prevent the car from running out of power on the road due to unexpected situations; If the battery can be fully charged before the next travel segment of the electric vehicle, the charging time is expressed as:

Among them, EC _i represents the remaining battery power of electric vehicle i,

Indicates the slow charging power of electric vehicles; If the battery is not fully charged before the next travel segment of the electric vehicle, the charging time indicates: TCR=TL-TS (3) The battery level is expressed as:

Among them, TL represents the departure time of the electric vehicle, TS represents the charging time of the electric vehicle,

represents the battery power of the electric vehicle i at the time of departure,

Represents the battery power of electric vehicle i at the time of starting charging; 2) If the electric vehicle needs to be charged at a slow speed, but the charging pile is occupied, the electric vehicle needs to be charged quickly on the next trip; if there are multiple fast charging stations in the travel path, one will be randomly selected for power supplementation. If there is no charging station on the travel route, select the fast charging station closest to the departure place for charging, and then start from the fast charging station to the next destination; The fast charging mode is set to direct full charge, and the charging time indicates:

in,

Indicates the fast charging power of electric vehicles; 3) For the F type vehicle, since it can only perform fast charging, the charging behavior is performed in the way that the slow charging pile is occupied and requires fast charging in step 2). 2. The distribution network reliability assessment method considering the spatiotemporal distribution of electric vehicles according to claim 1, is characterized in that, in described step S102, constructing distribution network fault period charging behavior model specifically comprises the following steps: 1) If the electric vehicle is being charged at a slow speed, the charging will be interrupted. If the power outage has ended before departure, the charging will continue; if the battery capacity of the electric vehicle meets the charging conditions of formula (1) before departure, the electric vehicle will select the nearest fast charging pile. Recharge the electric energy before driving to the destination; if the destination is still in a power outage state, the electric vehicle waits for the end of the power outage at the destination; if the formula (1) is not satisfied, the original travel chain travel plan is continued without charging; 2) If the electric vehicle is undergoing fast charging and the battery capacity meets the charging conditions of formula (1), the electric vehicle leaves the charging station and selects another fast charging station closest to the current location for charging, which is the same as when the slow charging is interrupted. . 3. The distribution network reliability assessment method considering the spatiotemporal distribution of electric vehicles according to claim 1, is characterized in that, in described step S1, constructing the discharge behavior model based on electric vehicle user travel specifically comprises the following steps: S111: Calculate the dischargeable probability and discharge energy; When the location of the electric vehicle is in the full state, regardless of whether the electric vehicle has a charging demand, it is considered that it is always connected to the grid, and the electric vehicle can be used as a backup power source to discharge to the grid; in the part state, there is a probability p _a connected to the grid; when the distribution network occurs When a fault occurs and the electric vehicle needs to perform V2G operation, for the electric vehicle connected to the power grid, if its battery state of charge satisfies the formula (6), the electric vehicle will perform the V2G power supply operation; SoC _i >S _y % (6) Among them, S _y represents the V2G discharge threshold; S112: Calculate the earliest dischargeable time and discharge end time; The earliest discharge time of an electric vehicle is recorded as TDS, the discharge end time is recorded as TDE, the fault start time is recorded as TGS, and the fault end time is recorded as TGE. The four moments have the following three relationships: Scenario 1): Before the electric vehicle participates in V2G, the node where the electric vehicle is located has experienced a power outage, TDS is the time when the electric vehicle arrives at the node, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold time, that is, the electric vehicle has stopped discharging before the power outage is over; Scenario 2): The power outage occurs after the electric vehicle reaches a certain node, then TDS is the time of failure, and TDE is the time when the electric vehicle leaves or reaches the discharge threshold; Scenario 3): A power outage occurs after the electric vehicle reaches a certain node, then TDS is the time when the electric vehicle arrives at the node, and the discharge end time is the end of the outage, that is, the fault has recovered before the electric vehicle leaves or reaches the discharge threshold time; Denote the discharge power as P _d , and the remaining battery power of the electric vehicle i after discharge is expressed as:

in,

Represents the remaining power of the battery before the electric vehicle i is discharged, C _g (t) is a 0-1 variable, 1 when the electric vehicle is discharged, and 0 at other times. 4. The distribution network reliability evaluation method considering the spatiotemporal distribution of electric vehicles according to claim 3, is characterized in that, in the described step S2, improving the distribution network Monte Carlo reliability evaluation simulation time advancement mode specifically comprises: 1): Simulate the operation and failure of power grid components; The components other than electric vehicles in the distribution network simulate their operation and faults according to the time advancement method in the reliability assessment based on Monte Carlo, form the time series event set of each component of the distribution network, and record the time TGS _i and repair time of each fault. TGE _i ; 2): Simulate the timing behavior of electric vehicles during normal operation of the distribution network; For each electric vehicle, according to the charging behavior model based on the travel of electric vehicle users, the travel and charging behavior of each electric vehicle is simulated in a time-series manner; after the calculation of the first vehicle is completed, the cycle of the next vehicle is performed. , until the last vehicle of the day is calculated, and the travel and charging simulation of each electric vehicle in the day is realized; 3): Simulate the timing behavior of electric vehicles during the fault period of the distribution network; For the case where the fault does not span the sky, the simulation will match the fault occurrence time and repair time recorded in 1) to the current day on the basis of “major cycle” in days and “small cycle” in single vehicle. At the time of time, consider the impact of the fault on the charging of electric vehicles and the role of V2G in providing power for the unloaded area; for an electric vehicle i that has a power outage at the work place and meets the conditions for participating in V2G, its one-day sequence events are as follows ① The simulation time of ~⑥ is advanced in sequence, among which: ① means that the electric vehicle starts from the residence; ② means that the electric vehicle arrives at the work place; ③ means that the electric vehicle is in a power outage accident, and the electric vehicle starts to participate in V2G; ④ means that the electric vehicle meets the constraints condition and the V2G discharge ends; ⑤ means that the electric vehicle leaves the workplace and returns to the place of residence; ⑥ the place where the electric vehicle is active; For the case of fault spanning days, expand the cycle period from one day to the number of days the fault spans. 5. The method for evaluating the reliability of distribution network according to claim 4, characterized in that, in the step S2, calculating the electric vehicle's participation in V2G invoking power during the fault period, specifically comprising: all electric vehicles After the travel simulation is over, all electric vehicles that can participate in V2G in the isolated island are sorted according to the earliest time they can participate in V2G; those with the highest order are called first, and those with the lowest order are called later, until the load power shortage is met; The number of cars N _dEV is expressed as equation (8):

Among them, P _d,i (t) represents the discharge power of electric vehicle i at time t, P _DG (t) is the power emitted by the distributed generator at time t, and L _g,i (t) represents the discharge power of load point i at time t power; if all electric vehicles that can participate in V2G are discharged and still cannot meet the power shortage, N _dEV is the maximum available quantity; The remaining power of the battery is obtained by subtracting the discharge energy from the electric power; the condition for electric vehicles to participate in V2G is that the electric vehicle has sufficient battery power, so there is still enough energy to supply power to the grid, which will not affect the recent travel demand. 6. The distribution network reliability assessment method considering the time-space distribution of electric vehicles according to claim 5, characterized in that, in the step S2, calculating the distribution network reliability index specifically comprises: distribution network failure period, According to the joint power supply of electric vehicles and distributed power sources, the parameters of power outage time and load loss are calculated according to the time series, and gradually accumulated as the simulation time goes on. 11) Calculate each reliability index; during the normal operation period of the distribution network, there is no reliability index calculation process; The distribution network reliability indicators include: ①System average interruption frequency index (SAIFI) per year, unit: times/(user·year);

in,

Represents the total number of power outages of power user k during the simulation time, M represents the total number of power users, and Y represents the number of simulation years; ②System average interruption duration index (SAIDI) unit: h/(user·year);

in,

Represents the total duration of power outage for power user k in the simulation time; ③System power shortage indicator (expected energy not supplied, EENS), unit: MWh/year;

Among them, P _ct (t) represents the load shedding power of the system at time t, and P _ct (t)=0 represents the normal operation of the distribution network.

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