Calculation method of adjustable power on the user side of electric vehicles considering fast charging load scenario

Abstract

The present invention provides a method for calculating adjustable power on the user side of an electric vehicle considering a fast charging load scenario, comprising: establishing an electric vehicle arrival process model based on a non-homogeneous Poisson process setting to estimate the state of charge of the electric vehicle at the start of charging; generating a fast charging load scenario based on a sequential Monte Carlo method to simulate the arrival, queuing and charging process of the electric vehicle; analyzing the feasible domain of the dispatchable capacity of a single electric vehicle in an access period under a charging scenario, and calculating the real-time adjustable power on the user side of the electric vehicle in the charging scenario based on the analysis; analyzing the feasible domain of the dispatchable capacity of a single electric vehicle in an access period under a V2G scenario, and calculating the real-time adjustable power on the user side of the electric vehicle in the V2G scenario based on the analysis; and calculating the real-time adjustable power of a large-scale electric vehicle user-side aggregator by combining the real-time adjustable power on the user side of the electric vehicle in the charging scenario and the V2G scenario.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and electric vehicle charging technology, and in particular to a method for calculating adjustable power at the user side of an electric vehicle considering a fast charging load scenario, and also to a corresponding computing system, a computer terminal and a computer-readable storage medium. Background Art

[0002] Electric vehicles are characterized by high power electronics and low carbon emissions, and are expected to play an important role in the future low-carbon and intelligent transportation system. The emergence of fast charging technology has greatly improved the charging efficiency of electric vehicles. Compared with traditional charging methods, DC fast charging can charge a large amount of electricity to the vehicle in a short time, usually charging to 80% within 30 minutes. This technological advancement makes electric vehicles more convenient in daily use and solves the problem of "range anxiety". However, the requirements for grid load for fast charging have also increased, and how to balance charging speed and grid stability has become a key challenge. At the same time, vehicle-to-grid (V2G) technology allows electric vehicles to not only charge, but also feed back battery power to the grid during peak demand, using the distributed energy storage of a large number of electric vehicles as a buffer between the grid and renewable energy. This two-way charging mechanism not only improves the flexibility of the grid, but also provides users with more options for the use of electric vehicles. The promotion of V2G technology can effectively alleviate the pressure on the grid load and promote the use of renewable energy.

[0003] As a broad form of flexible energy storage, electric vehicles have broad development prospects by utilizing the energy storage characteristics of electric vehicles to participate in the power regulation and auxiliary services of the power grid. By real-time monitoring and calculation of the adjustable power on the user side of electric vehicles, resources can be flexibly allocated according to the load of the power grid and user needs, avoiding the impact of peak charging on the power grid, effectively improving charging efficiency and ensuring the stability of the power grid. At present, research on the calculation of adjustable power on the user side of electric vehicles is relatively lacking in considering fast charging loads and combining vehicle-to-grid interaction (V2G) scenarios. Therefore, it is particularly important to develop a technology for calculating the adjustable power on the user side of electric vehicles that takes into account fast charging load scenarios. At present, no description or report of similar technology to the present invention has been found, and similar information at home and abroad has not yet been collected. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for calculating the adjustable power at the user side of an electric vehicle taking into account the fast charging load scenario, and also provides a corresponding computing system, a computer terminal and a computer-readable storage medium.

[0005] According to one aspect of the present invention, a method for calculating adjustable power at the user side of an electric vehicle considering a fast charging load scenario is provided, comprising:

[0006] A model of electric vehicle arrival process based on non-homogeneous Poisson process is established to estimate the state of charge of electric vehicles at the beginning of charging.

[0007] Generate fast charging load scenarios based on the Sequential Monte Carlo method, including: charging scenarios and V2G scenarios, simulating the arrival, queuing and charging process of electric vehicles;

[0008] Analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the charging scenario, and calculate the real-time adjustable power of the electric vehicle user side under the charging scenario based on the analysis;

[0009] Analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the V2G scenario, and calculate the real-time adjustable power of the electric vehicle user side under the V2G scenario based on the analysis;

[0010] The real-time adjustable power of electric vehicle users in charging scenarios and V2G scenarios is combined to calculate the real-time adjustable power of large-scale electric vehicle user-side aggregators.

[0011] Preferably, the step of establishing an electric vehicle arrival process model based on a non-homogeneous Poisson process to estimate the state of charge of the electric vehicle at the start of charging includes:

[0012] For electric vehicles charging at the station, record the time from 0 to t At this moment, in the interval [0, t The number of electric vehicles that arrived at the fast charging station within ] is , let the random process For a non-homogeneous Poisson process, at the beginning of the counting ;

[0013] is an independent incremental process, that is, when When the random variable With random variables Statistically independent of each other, is the interval [0, t ]Internal satisfaction Four arbitrary moments; when the time interval When it approaches 0, in the time period Calculate the probability of electric vehicles arriving at the station for:

[0014] (1)

[0015] (2)

[0016] Formula (1) represents the probability that there is only one electric car arriving at the charging station;

[0017] Formula (2) represents the probability that two or more electric vehicles arrive at a charging station;

[0018] In the formula, is the intensity of the Poisson process, for Higher-order infinitesimals of ;

[0019] strength A Poisson process with a constant that does not change with time is a homogeneous Poisson process, while A time-varying Poisson process is called a non-homogeneous Poisson process, then:

[0020] (3)

[0021] (4)

[0022] (5)

[0023] Formula (3) indicates that in the time interval (0, t ] the average number of electric vehicles arriving at a charging station within There are only a finite number of first-kind discontinuities in the integration interval, and it is right continuous, then It is expressed as formula (4), that is, the intensity of the Poisson process For the time period The number of electric vehicles arriving at the fast charging station per unit time is the arrival rate of electric vehicles. Changes stepwise over time, within each hour is a constant and right continuous;

[0024] Formula (5) indicates that if the time when the current vehicle arrives at the fast charging station is , then the time interval for the next car to arrive at the fast charging station Follow the distribution ;

[0025] In the formula, For the time interval (0, t ], the average number of electric vehicles arriving at a charging station within is the intensity of the Poisson process;

[0026] Estimate the initial state of charge for electric vehicle charging:

[0027] (6)

[0028] (7)

[0029] (8)

[0030] Formula (6) represents the mileage of an electric vehicle from the last full charge to the start of this charge. Designed for maximum range with electric vehicles Estimating the state of charge of electric vehicles at the start of charging ;

[0031] Formula (7) represents the mileage of an ordinary private electric car from the last full charge to the start of this charge It follows a log-normal distribution, The probability density function of , the probability density function Equivalent to one day's driving mileage The natural logarithm of Obey is the mean, is a normal distribution with variance ;

[0032] According to equations (6) and (7), the initial charging state of charge is obtained The distribution function obeys , formula (8) is the distribution function The calculation formula for .

[0033] Preferably, the method of generating a fast charging load scenario based on the sequential Monte Carlo method to simulate the arrival, queuing and charging process of electric vehicles includes:

[0034] The sequential Monte Carlo simulation method is used to perform random process simulation on the arrival process, queuing process and charging process of electric vehicles in a certain period of time in chronological order to generate fast charging load scenarios; including:

[0035] S211, set the simulation start time , the current vehicle , set the upper limit of simulation time;

[0036] S212, based on the assumption that the electric vehicle arrival process is a non-homogeneous Poisson process, Generates a random number uniformly distributed in [0,1] , The inverse function of the random function generates the arrival time of the next vehicle. , that is, the time interval and corresponding time for each vehicle to arrive at the fast charging station in turn are obtained;

[0037] S213, Order ,if , then go to S217;

[0038] S214, calculating the waiting time required for the vehicle according to the current charging pile queue situation in the charging station If the waiting time is greater than the upper limit of the waiting time (the waiting time is determined by the actual vehicle user's willingness), the vehicle leaves and the process goes to step S212;

[0039] S215, Order , record the vehicle as An electric car arrives at the station and charges, and the type of the car and the state of charge when it arrives are randomly generated. , determine the power battery capacity according to the type of vehicle And the charging power converted to the AC side of the charging pile , calculate the time required for charging ,in capping the charging of electric vehicle batteries;

[0040] S216: Assign the vehicle to the charging station with the shortest waiting time, update the queue status of the charging station, and update the charging load of the fast charging station , transfer to S212;

[0041] S217, calculate the average charging load in each time interval. As the number of scenarios increases, the average value of the fast charging load in each time period tends to be stable, and this average value is used as the predicted value of the fast charging load.

[0042] Preferably, the feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the charging scenario is analyzed, and based on the analysis, the real-time adjustable power of the electric vehicle user side under the charging scenario is calculated, including:

[0043] During the access time, the charging power that can be increased by the electric vehicle is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle during the access period in the charging scenario includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between zero and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the initial charging power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that when the electric vehicle is finished charging, the battery can reach the owner's expected charging power target;

[0044] Taking the feasible domain of the dispatchable capacity of the electric vehicle during the entire access period at the current moment as the constraint condition, according to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the corresponding real-time adjustable power of the electric vehicle is calculated as:

[0045] (9)

[0046] (10)

[0047] (11)

[0048] Formula (9) is a comprehensive expression of travel constraints, power constraints, and electricity constraints;

[0049] Formula (10) represents the adjustable power of the electric vehicle at the current moment;

[0050] Formula (11) represents the adjustable power of the electric vehicle at the current moment;

[0051] In the formula, Indicates j The minimum power state of the vehicle at the next moment to meet the user's charging needs, Indicates j The charging end time of the vehicle, Indicates j The planned charging power of the vehicle at this time, , Respectively represent j The vehicle's adjustable power and adjustable power in charging mode. Indicates j The expected power of the vehicle at the end of charging, Indicates j The current state of charge of the vehicle. They represent the upper limit of charging power and the upper limit of charging capacity respectively. Indicates the corresponding charging efficiency.

[0052] Preferably, the feasible domain of the dispatchable capacity of a single electric vehicle in the access period in the V2G scenario is analyzed, and based on the analysis, the real-time adjustable power of the electric vehicle user side in the V2G scenario is calculated, including:

[0053] In the V2G scenario, considering the reverse discharge of electric vehicles to the power grid, the charging power that can be increased during the access time is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle in the access period in the V2G scenario includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between the maximum discharge power and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the minimum battery power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging;

[0054] Based on the feasible domain of the dispatchable capacity of electric vehicles in the V2G scenario, according to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the real-time adjustable power corresponding to the electric vehicle in the V2G scenario is calculated as follows:

[0055] (12)

[0056] (13)

[0057] Formula (12) represents the adjustable power of the electric vehicle at the current moment;

[0058] Formula (13) represents the adjustable power of the electric vehicle at the current moment;

[0059] In the formula, Respectively represent j The vehicle's adjustable power and adjustable power in V2G mode.

[0060] Preferably, the method of combining the real-time adjustable power of the electric vehicle user side in the charging scenario and the V2G scenario to calculate the real-time adjustable power of the aggregator on the large-scale electric vehicle user side includes:

[0061] The real-time adjustable power of all electric vehicles connected to the aggregator during the corresponding period is accumulated to obtain the real-time adjustable power of the aggregator during the corresponding period:

[0062] (14)

[0063] (15)

[0064] Formula (14) represents the adjustable power of the electric vehicle aggregator at the current moment;

[0065] Formula (15) represents the power that can be reduced by the EV aggregator at the current moment;

[0066] In the formula, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding moment;

[0067] Combining the adjustable power of the charging scenario and the V2G scenario, based on the real-time adjustable power of the aggregator in the corresponding period, considering the charging mode and V2G mode of the electric vehicle, the real-time adjustable power of the electric vehicle aggregator is calculated as:

[0068] (16)

[0069] (17)

[0070] Formula (16) represents the adjustable power of the electric vehicle aggregator at the current moment in combination with the charging scenario and the V2G scenario;

[0071] Formula (17) represents the power that can be reduced at the current moment by the EV aggregator combining the charging scenario and the V2G scenario;

[0072] in, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding time, where The vehicle is in charging mode. The car is in V2G mode.

[0073] According to another aspect of the present invention, there is provided an electric vehicle user-side adjustable power calculation system considering a fast charging load scenario, comprising:

[0074] Scenario simulation module, which is used to establish an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption and estimate the state of charge of electric vehicles at the beginning of charging; generate fast charging load scenarios based on the sequential Monte Carlo method to simulate the arrival, queuing and charging process of electric vehicles;

[0075] The charging scenario adjustable power calculation module is used to analyze the feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the charging scenario, and calculate the real-time adjustable power of the electric vehicle user side under the charging scenario based on the analysis;

[0076] V2G scenario adjustable power calculation module, which is used to analyze the feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the V2G scenario, and calculate the real-time adjustable power of the electric vehicle user side under the V2G scenario based on the analysis;

[0077] The user-side adjustable power calculation module is used to combine the real-time adjustable power of the electric vehicle user side in the charging scenario and the V2G scenario, and calculate the real-time adjustable power of the large-scale electric vehicle user side aggregator.

[0078] According to a third aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, can be used to execute any one of the methods described above in the present invention, or to run the system described above in the present invention.

[0079] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the methods described above in the present invention, or to run the system described above in the present invention.

[0080] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0081] The present invention uses a non-homogeneous Poisson process to model the process of electric vehicles arriving at fast-charging stations, and generates fast-charging load scenarios based on the sequential Monte Carlo method. It can effectively characterize the impact of the time-varying arrival rate of electric vehicles on the load prediction of fast-charging stations. This method can better reflect the timing characteristics of the arrival process, queuing process and charging process of electric vehicles.

[0082] The feasible domain of dispatchable capacity proposed in the present invention includes multiple constraints of power constraint, electricity constraint and travel constraint, and provides a calculation method of adjustable power, which is more in line with the actual charging of electric vehicles. The calculation method of adjustable power on the user side of electric vehicles is practical.

[0083] The method for calculating the adjustable power of an electric vehicle user at the fast charging load scenario provided by the present invention takes into account both the charging scenario and the V2G scenario. That is, in addition to the electric vehicle charging-only scenario, the electric vehicle reverse discharge scenario is also considered. This method is more in line with the current application needs of vehicle-grid interaction technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0085] Figure 1 The present invention is a flowchart of a method for calculating adjustable power at the user side of an electric vehicle considering a fast charging load scenario in a preferred embodiment of the present invention.

[0086] Figure 2 It is a schematic diagram of the feasible domain of dispatchable capacity of a single electric vehicle within the access period in a charging scenario in a preferred embodiment of the present invention.

[0087] Figure 3 Schematic diagram of the feasible domain of dispatchable capacity of a single electric vehicle during the access period in the V2G scenario.

[0088] Figure 4 This is a schematic diagram of the component modules of an electric vehicle user-side adjustable power calculation system considering a fast charging load scenario in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0089] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0090] In view of the problem that the prior art lacks a technology for calculating the adjustable power of electric vehicles on the user side taking into account fast charging load scenarios, an embodiment of the present invention provides a method for calculating the adjustable power of electric vehicles on the user side taking into account fast charging load scenarios. The method can effectively calculate the adjustable power of electric vehicles on the user side under fast charging load scenarios.

[0091] Specifically, Figure 1 As shown, the method for calculating the adjustable power at the user side of an electric vehicle considering the fast charging load scenario provided in this embodiment may include the following operations:

[0092] S1, establish an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption to estimate the state of charge of the electric vehicle at the beginning of charging;

[0093] S2, generates fast charging load scenarios based on the sequential Monte Carlo method, including: charging scenarios and V2G scenarios, simulating the arrival, queuing and charging processes of electric vehicles;

[0094] S3, based on the feasible domain analysis of the dispatchable capacity of a single electric vehicle during the access period under the charging scenario, calculate the real-time adjustable power of the electric vehicle user side under the charging scenario;

[0095] S4, based on the feasible domain analysis of the dispatchable capacity of a single electric vehicle during the access period in the vehicle-to-grid interaction (V2G) scenario, calculate the real-time adjustable power of the electric vehicle user side in the vehicle-to-grid interaction (V2G) scenario;

[0096] S5, combines the real-time adjustable power of the electric vehicle user side in the charging scenario and the vehicle-to-grid interaction (V2G) scenario, and calculates the real-time adjustable power of the aggregator on the large-scale electric vehicle user side.

[0097] In some preferred embodiments, the above S1, establishing an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption to estimate the state of charge of the electric vehicle at the start of charging, may further include the following operations:

[0098] S11, for electric vehicles charging at the station, record the time from 0 to t At this moment, in the interval [0, t The number of electric vehicles that arrived at the fast charging station within ] is , let the random process For a non-homogeneous Poisson process, at the beginning of the counting ;

[0099] S12, is an independent incremental process, that is, when When the random variable With random variables Statistically independent of each other, is the interval [0,t ]Internal satisfaction Four arbitrary moments; when the time interval When it approaches 0, in the time period Calculate the probability of electric vehicles arriving at the station for:

[0100] (1)

[0101] (2)

[0102] Formula (1) represents the probability that there is only one electric car arriving at the charging station;

[0103] Formula (2) represents the probability that two or more electric vehicles arrive at a charging station;

[0104] In the formula, is the intensity of the Poisson process, for Higher-order infinitesimals of ;

[0105] S13, Strength A Poisson process with a constant that does not change with time is a homogeneous Poisson process, while A time-varying Poisson process is called a non-homogeneous Poisson process, then:

[0106] (3)

[0107] (4)

[0108] (5)

[0109] Formula (3) indicates that in the time interval (0, t ] the average number of electric vehicles arriving at a charging station within There are only a finite number of first-kind discontinuities in the integration interval, and it is right continuous, then It is expressed as formula (4), that is, the intensity of the Poisson process For the time period The number of electric vehicles arriving at the fast charging station per unit time is the arrival rate of electric vehicles. In order to more accurately describe the process of electric vehicles arriving at the fast charging station, we assume Changes stepwise over time, within each hour is a constant and right continuous;

[0110] Formula (5) indicates that if the time when the current vehicle arrives at the fast charging station is , then the time interval for the next car to arrive at the fast charging station Follow the distribution ; In the formula, For the time interval (0, t ], the average number of electric vehicles arriving at a charging station within is the intensity of the Poisson process;

[0111] S14, estimating the initial state of charge of the electric vehicle:

[0112] (6)

[0113] (7)

[0114] (8)

[0115] Formula (6) represents the mileage of an electric vehicle from the last full charge to the start of this charge. Designed for maximum range with electric vehicles Estimating the state of charge of electric vehicles at the start of charging ;

[0116] Formula (7) represents the mileage of an ordinary private electric car from the last full charge to the start of this charge It follows a log-normal distribution, The probability density function is , the probability density function Equivalent to the natural logarithm of the mileage per day Obey is the mean, is a normal distribution with variance ;

[0117] According to equations (6) and (7), the initial charging state of charge can be obtained: The distribution function obeys ;

[0118] Formula (8) is the distribution function The calculation formula for .

[0119] In some preferred embodiments, the above S2, generating a fast charging load scenario based on the sequential Monte Carlo method to simulate the arrival, queuing and charging process of electric vehicles, may further include the following operations:

[0120] S21, generating intraday fast charging load scenarios based on the sequential Monte Carlo method. Based on the generation of fast charging load scenarios based on the sequential Monte Carlo method, according to the real-time dispatching requirements of the power grid, it is divided into two specific scenarios: 1) Charging scenario: electric vehicles only charge but do not discharge, that is, the charging power of electric vehicles is between zero and the maximum charging power; 2) V2G scenario: electric vehicle V2G technology refers to the technology of electric vehicles supplying electricity to the power grid. In the V2G scenario, electric vehicles can both charge and discharge the power grid in reverse, that is, the charging power of electric vehicles is between the maximum discharge power and the maximum charging power. In the scenario where electric vehicles arrive at the fast charging station for charging, since the arrival process, queuing process and charging process of electric vehicles are all time-series related, in order to accurately describe the electric vehicle charging scenario, the sequential Monte Carlo simulation method is used to obtain the fast charging station load scenario. The sequential Monte Carlo method generates a random process in a period of time according to the time sequence. It is a random simulation method that can reflect the time series characteristics of the random process. The intraday fast charging load scenario based on the sequential Monte Carlo method is generated, and specifically includes the following steps:

[0121] S211, set the simulation start time , the current vehicle , set the upper limit of simulation time ,The simulation time is calculated accurately to the minute;

[0122] S212, based on the assumption that the electric vehicle arrival process is a non-homogeneous Poisson process, Generates a random number uniformly distributed in [0,1] , The inverse function of the random function generates the arrival time of the next vehicle. , that is, the time interval and corresponding time for each vehicle to arrive at the fast charging station in turn are obtained;

[0123] S213, Order ,if , then go to S217;

[0124] S214, calculating the waiting time required for the vehicle according to the current charging pile queue situation in the charging station , if the waiting time is greater than the upper limit of the waiting time (the waiting time is determined by the actual vehicle user's willingness), the vehicle leaves and proceeds to S212;

[0125] S215, Order , record the vehicle as An electric car arrives at the station and charges, and the type of the car and the state of charge when it arrives are randomly generated. , determine the power battery capacity according to the vehicle type And the charging power converted to the AC side of the charging pile , calculate the time required for charging ,in capping the charging of electric vehicle batteries;

[0126] S216: Assign the vehicle to the charging station with the shortest waiting time, update the queue status of the charging station, and update the charging load of the fast charging station , transfer to S212;

[0127] S217, calculate the average charging load in each time interval. As the number of scenarios increases, the average value of the fast charging load in each time period tends to be stable, and this average value is used as the predicted value of the fast charging load.

[0128] In some preferred implementations, the above S3, the analysis of the feasible domain of dispatchable capacity of a single electric vehicle in the access period in the charging scenario, and the calculation of the real-time adjustable power of the electric vehicle user side in the charging scenario, may further include the following operations:

[0129] S31, analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the charging scenario. The feasible domain of dispatchable capacity of an electric vehicle in the entire access charging period is as follows: Figure 2 As shown; the specific analysis method is:

[0130] During the access time, the charging power that can be increased by the electric vehicle is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle during the access period in the charging scenario includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between zero and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the initial charging power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that when the electric vehicle is finished charging, the battery can reach the owner's expected charging power target;

[0131] S32, for the electric vehicle at the current moment, the feasible domain of its dispatchable capacity during the entire access period can be used as a constraint to participate in the optimization problem analysis, but real-time dispatch requires the real-time adjustable power at that moment. If its charging power is between zero and the maximum charging power, the intelligent charging technology can be used to enable the electric vehicle to have the ability to adjust the charging power up and down. According to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the corresponding adjustable power of the electric vehicle is calculated as:

[0132] (9)

[0133] (10)

[0134] (11)

[0135] Formula (9) is a comprehensive expression of travel constraints, power constraints, and electricity constraints, where: Indicates j The current state of charge of the vehicle. Indicates j The minimum power state (i.e. guaranteed power) of the vehicle at the next moment to meet the user's charging needs. Indicates j The charging end time corresponding to the vehicle;

[0136] Formula (10) represents the adjustable power of the electric vehicle at the current moment;

[0137] Formula (11) represents the adjustable power of the electric vehicle at the current moment;

[0138] In the formula, Indicates j The planned charging power of the vehicle at this time, , Respectively represent j The vehicle's adjustable power and adjustable power in charging mode. Indicates j The expected power of the vehicle at the end of charging, They represent the upper limit of charging power and the upper limit of charging capacity respectively. Indicates the corresponding charging efficiency.

[0139] In some preferred implementations, the above S4, the analysis of the feasible domain of dispatchable capacity of a single electric vehicle in the access period in the vehicle-to-grid interaction (V2G) scenario, and the calculation of the real-time adjustable power of the electric vehicle user side in the vehicle-to-grid interaction (V2G) scenario, may further include the following operations:

[0140] S41, analyzes the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the V2G scenario. In the V2G scenario, electric vehicles can discharge the grid in reverse, and the charging power can be adjusted below 0, thereby improving the real-time power adjustment capability of electric vehicles. The feasible domain of dispatchable capacity of electric vehicles in the entire access period is as follows: Figure 3 As shown; the specific analysis method is:

[0141] In the V2G scenario, considering the reverse discharge of electric vehicles to the power grid, the charging power that can be increased during the access time is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle in the access period in the V2G scenario includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between the maximum discharge power and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the minimum battery power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging;

[0142] S42, with Figure 2 In contrast, since the reverse discharge capability of electric vehicles is considered in the V2G scenario, region ① is the newly added dispatchable capacity feasible domain considering the reverse discharge capability. As long as the electric vehicle can reach its expected power at the time of departure, the electric vehicle can discharge at the maximum discharge power. According to the current power state of the electric vehicle, the end time of charging and the expected power at the end of charging, the corresponding adjustable power of the electric vehicle in the V2G scenario is calculated as:

[0143] (12)

[0144] (13)

[0145] Formula (12) represents the adjustable power of the electric vehicle at the current moment;

[0146] Formula (13) represents the adjustable power of the electric vehicle at the current moment;

[0147] In the formula, Respectively represent j The power that can be increased and decreased in the V2G mode is the same as that in the charging scenario in terms of the increased power. The key is that the lower limit of the decreased power is modified due to the consideration of the reverse discharge capability of V2G. In addition, it should be noted that after considering the charging and discharging efficiency of electric vehicles, the decreased power in the discharge scenario and the decreased power in the charging scenario need to be discussed separately, corresponding to the calculation of formula (13).

[0148] In some preferred implementations, the above S5, combining the real-time adjustable power of the electric vehicle user side in the charging scenario and the vehicle-to-grid interaction (V2G) scenario, and calculating the real-time adjustable power of the aggregator on the large-scale electric vehicle user side, may further include the following operations:

[0149] S51, aggregation of adjustable power on the user side of large-scale electric vehicles. Specifically: the real-time adjustable power of a single electric vehicle is relatively small, but from the perspective of the aggregator, making good use of the real-time adjustable power of large-scale electric vehicles can have a great impact on the power grid. The real-time adjustable power of all electric vehicles connected to the aggregator in the corresponding period is accumulated, and the real-time adjustable power of the aggregator in the corresponding period can be obtained as:

[0150] (14)

[0151] (15)

[0152] Formula (14) represents the adjustable power of the electric vehicle aggregator at the current moment;

[0153] Formula (15) represents the power that can be reduced by the EV aggregator at the current moment;

[0154] In the formula, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding moment;

[0155] S52, combined with the calculation of the adjustable power of the charging scenario and the V2G scenario. Specifically: Based on S51, considering the charging mode and V2G mode of the electric vehicle, the real-time adjustable power of the electric vehicle aggregator is calculated as:

[0156] (16)

[0157] (17)

[0158] Formula (16) represents the adjustable power of the electric vehicle aggregator at the current moment in combination with the charging scenario and the V2G scenario;

[0159] Formula (17) represents the power that can be reduced at the current moment by the EV aggregator combining the charging scenario and the V2G scenario;

[0160] in, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding time, where The vehicle is in charging mode. The car is in V2G mode.

[0161] Based on the same inventive concept, an embodiment of the present invention further provides an electric vehicle user-side adjustable power calculation system that takes into account fast charging load scenarios.

[0162] Specifically, Figure 4As shown, the electric vehicle user-side adjustable power calculation system considering the fast charging load scenario provided by this embodiment may include the following modules:

[0163] Scenario simulation module, which is used to establish an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption and estimate the state of charge of electric vehicles at the beginning of charging; generate fast charging load scenarios based on the sequential Monte Carlo method to simulate the arrival, queuing and charging process of electric vehicles;

[0164] The module for calculating the adjustable power in charging scenarios is based on the feasible domain analysis of the dispatchable capacity of a single electric vehicle in the access period under the charging scenario, and calculates the real-time adjustable power of the electric vehicle user side under the charging scenario;

[0165] V2G scenario adjustable power calculation module, which is based on the feasible domain analysis of the dispatchable capacity of a single electric vehicle during the access period in the vehicle-to-grid interaction (V2G) scenario, and calculates the real-time adjustable power of the electric vehicle user side in the vehicle-to-grid interaction (V2G) scenario;

[0166] The user-side adjustable power calculation module combines the real-time adjustable power of electric vehicle users in charging scenarios and vehicle-to-grid interaction (V2G) scenarios to calculate the real-time adjustable power of large-scale electric vehicle user-side aggregators.

[0167] The working contents of each functional module in the above system are further described in detail below.

[0168] The work content of the scenario simulation module mainly includes the following two parts:

[0169] 1. Establish an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption to estimate the state of charge of the electric vehicle at the start of charging, including the following work contents:

[0170] Step S11: For electric vehicles that arrive at the station for charging, record the time from 0 to t At this moment, in the interval [0, t The number of electric vehicles that arrived at the fast charging station within ] is , let the random process For a non-homogeneous Poisson process, at the beginning of the counting ;

[0171] Step S12: is an independent incremental process, that is, when When the random variable With random variables Statistically independent of each other, is the interval [0, t ]Internal satisfaction Four arbitrary moments; when the time interval When it approaches 0, in the time period Calculate the probability of electric vehicles arriving at the station.

[0172] (1)

[0173] (2)

[0174] Formula (1) represents the probability that only one electric vehicle arrives at the charging station, and formula (2) represents the probability that two or more electric vehicles arrive at the charging station, where: is the intensity of the Poisson process.

[0175] Step S13: Strength A Poisson process with a constant that does not change with time is a homogeneous Poisson process, while A Poisson process that varies with time is called an inhomogeneous Poisson process.

[0176] (3)

[0177] (4)

[0178] (5)

[0179] Formula (3) indicates that in the time interval (0, t ] the average number of electric vehicles arriving at a charging station within There are only a finite number of first-kind discontinuities in the integration interval, and it is right continuous, then It is expressed as formula (4), that is, the intensity of the Poisson process For the time period The number of electric vehicles arriving at the fast charging station per unit time is the arrival rate of electric vehicles. In order to more accurately describe the process of electric vehicles arriving at the fast charging station, assume Changes stepwise over time, within each hour is a constant and right continuous. Formula (5) indicates that if the time when the current vehicle arrives at the fast charging station is , then the time interval for the next car to arrive at the fast charging station Follow the distribution ; In the formula, For the time interval (0, t ], the average number of electric vehicles arriving at a charging station within is the intensity of the Poisson process;

[0180] Step S14: estimating the initial state of charge of the electric vehicle.

[0181] (6)

[0182] (7)

[0183] (8)

[0184] Formula (6) represents the mileage of an electric vehicle from the last full charge to the start of this charge. Designed for maximum range with electric vehicles Estimating the state of charge of electric vehicles at the start of charging Formula (7) represents the mileage of an ordinary private electric car from the last full charge to the start of this charge It obeys the lognormal distribution, and its probability density function is , which is equivalent to the natural logarithm of the mileage per day Obey is the mean, is a normal distribution with variance According to equations (6) and (7), the initial charging state of charge can be obtained The distribution function is obeyed, and formula (8) is its calculation formula.

[0185] 2. Generate fast charging load scenarios based on the Sequential Monte Carlo method to simulate the arrival, queuing and charging process of electric vehicles, including the following work content:

[0186] Step S21, description of the generation of intraday fast charging load scenarios. In the scenario where electric vehicles arrive at fast charging stations for charging, since the arrival process, queuing process, and charging process of electric vehicles are all time-series related, in order to accurately describe the electric vehicle charging scenario, the sequential Monte Carlo simulation method is used to obtain the fast charging station load scenario. The sequential Monte Carlo simulation method simulates a random process over a period of time in chronological order, and is a random simulation method that can reflect the time series characteristics of random processes.

[0187] Step S22: Setting the simulation start time , the current vehicle , set the upper limit of simulation time , the simulation time is calculated accurately to the minute.

[0188] Step S23: Randomly generate the arrival time of the next vehicle Based on the assumption that the arrival process of electric vehicles is a non-homogeneous Poisson process, a random number uniformly distributed in [0,1] is generated. , and take , the time interval and corresponding time for each vehicle to arrive at the fast charging station in turn can be obtained.

[0189] Step S24: ,if , then go to step S28;

[0190] Step S25: Calculate the waiting time required for the vehicle based on the current charging pile queue situation in the charging station If the waiting time is greater than the upper limit of the waiting time (the waiting time is determined by the actual vehicle user's willingness), the vehicle leaves and the process goes to step S23;

[0191] Step S26: , record the vehicle as An electric car arrives at the station and charges, and the type of the car and the state of charge when it arrives are randomly generated. , determine the power battery capacity according to the vehicle type And the charging power converted to the AC side of the charging pile , calculate the time required for charging ,in Charging caps for electric vehicle batteries.

[0192] Step S27: Assign the vehicle to the charging station with the shortest waiting time, update the queue status of the charging station, and update the charging load of the fast charging station. , proceed to step S23;

[0193] Step S28, calculate the average charging load in each time interval. As the number of scenarios increases, the average value of the fast charging load in each time period tends to be stable, and this average value is used as the predicted value of the fast charging load.

[0194] The charging scenario adjustable power calculation module includes the following work contents:

[0195] Step S31, analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the charging scenario. During the access time, the charging power that can be increased by the electric vehicle is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of dispatchable capacity of a single electric vehicle in the access period under the charging scenario includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between zero and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the initial charging power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging; Under the condition of meeting the travel constraint, power constraint and power constraint, the feasible domain of dispatchable capacity of the corresponding electric vehicle in the entire access charging time period can be obtained as follows: Figure 2 shown.

[0196] Step S32: For the electric vehicle at the current moment, the feasible domain of its dispatchable capacity within the entire access period can be used as a constraint to participate in the optimization problem analysis, but real-time scheduling requires the real-time adjustable power at that moment. If its charging power is between zero and the maximum charging power, the intelligent charging technology can be used to enable the electric vehicle to have the ability to adjust the charging power up and down. The adjustable power corresponding to the electric vehicle is calculated according to the current power status of the electric vehicle, the charging end time and the expected power at the end of charging.

[0197] (9)

[0198] (10)

[0199] (11)

[0200] Formula (9) is a comprehensive expression of travel constraints, power constraints, and electricity constraints, where: Indicates j The current state of charge of the vehicle. Indicates j The minimum power state (i.e. guaranteed power) of the vehicle at the next moment to meet the user's charging needs. Indicates j The charging end time of the vehicle.

[0201] Formula (10) represents the power that can be adjusted up by the electric vehicle at the current moment, and formula (11) represents the power that can be adjusted down by the electric vehicle at the current moment, where: Indicates j The planned charging power of the vehicle at this time, , Respectively represent j The vehicle's adjustable power and adjustable power in charging mode. Indicates j The expected power of the vehicle at the end of charging, They represent the upper limit of charging power and the upper limit of charging capacity respectively. Indicates the corresponding charging efficiency.

[0202] The V2G scenario adjustable power calculation module includes the following work contents:

[0203] Step S41, analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the V2G scenario. In the V2G scenario, the electric vehicle can be considered to reverse discharge to the power grid, and the reduced charging power can be below 0, thereby improving the real-time power adjustment capability of the electric vehicle. In the V2G scenario, considering that the electric vehicle reversely discharges to the power grid, the charging power that can be increased during the access time is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of dispatchable capacity of a single electric vehicle in the V2G scenario during the access period includes the following constraints: ① Power constraint: the charging power of the electric vehicle is between the maximum discharge power and the maximum charging power; ② Power constraint: the battery power of the electric vehicle is between the minimum battery power and the maximum battery power; ③ Travel constraint: the charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging; Under the condition of satisfying the travel constraint, power constraint, and power constraint, the feasible domain of dispatchable capacity of the corresponding electric vehicle in the entire access time period can be obtained as follows: Figure 3 shown.

[0204] Step S42, and Figure 2 In contrast, since the reverse discharge capability of electric vehicles is considered in the V2G scenario, region ① is the newly added dispatchable capacity feasible domain considering the reverse discharge capability. As long as the electric vehicle can reach its expected power at the time of departure, the electric vehicle can discharge at the maximum discharge power. According to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the corresponding adjustable power of the electric vehicle in the V2G scenario is calculated.

[0205] (12)

[0206] (13)

[0207] Formula (12) represents the power that can be adjusted up by the electric vehicle at the current moment, and formula (13) represents the power that can be adjusted down by the electric vehicle at the current moment, where: Respectively represent j The power that can be increased and decreased in the V2G mode is the same as that in the charging scenario in terms of the increased power. The key is that the lower limit of the decreased power is modified due to the consideration of the reverse discharge capability of V2G. In addition, it should be noted that after considering the charging and discharging efficiency of electric vehicles, the decreased power in the discharge scenario and the decreased power in the charging scenario need to be discussed separately, corresponding to the calculation of formula (13).

[0208] The user-side adjustable power calculation module includes the following work contents:

[0209] Step S51, aggregation of adjustable power on the user side of large-scale electric vehicles. The real-time adjustable power of a single electric vehicle is relatively small, but from the perspective of the aggregator, making good use of the real-time adjustable power of large-scale electric vehicles can have a great impact on the power grid. The real-time adjustable power of all electric vehicles connected to the aggregator in the corresponding period is accumulated to obtain the real-time adjustable power of the aggregator in the corresponding period.

[0210] (14)

[0211] (15)

[0212] Formula (14) represents the power that can be adjusted upward by the electric vehicle aggregator at the current moment, and formula (15) represents the power that can be adjusted downward by the electric vehicle aggregator at the current moment, where: They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, Indicates the number of electric vehicles connected to the aggregator at the corresponding moment.

[0213] Step S52: Calculate the adjustable power in combination with the charging scenario and the V2G scenario. Based on step S51, the real-time adjustable power of the electric vehicle aggregator is calculated by considering the charging mode and the V2G mode of the electric vehicle.

[0214] (16)

[0215] (17)

[0216] Formula (16) represents the power that can be increased by the electric vehicle aggregator combining the charging scenario and the V2G scenario at the current moment, and Formula (17) represents the power that can be decreased by the electric vehicle aggregator combining the charging scenario and the V2G scenario at the current moment, where: They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding time, where The vehicle is in charging mode. The car is in V2G mode.

[0217] It should be noted that the steps in the method provided by the present invention can be implemented by using the corresponding components in the system, and those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, and can also refer to the technical solution of the method to implement the composition of the system, that is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, which will not be elaborated here.

[0218] An embodiment of the present invention further provides a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any method of the above-mentioned embodiments of the present invention, or to run any system of the above-mentioned embodiments of the present invention.

[0219] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviated: RAM), such as static random access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0220] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0221] The processor is used to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0222] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0223] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute the method of any one of the above-mentioned embodiments of the present invention, or to run the system of any one of the above-mentioned embodiments of the present invention.

[0224] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0225] The above-mentioned embodiment of the present invention provides a method for calculating the adjustable power on the user side of an electric vehicle considering the fast charging load scenario. It uses a non-homogeneous Poisson process to model the process of an electric vehicle arriving at a fast charging station, and generates a fast charging load scenario based on the sequential Monte Carlo method. It can effectively characterize the impact of the time-varying arrival rate of electric vehicles on the load prediction of fast charging stations. This method can better reflect the timing characteristics of the arrival process, queuing process and charging process of electric vehicles. The proposed dispatchable capacity feasible domain includes multiple constraints of power constraints, power constraints and travel constraints, and provides a method for calculating the adjustable power, which is more in line with the actual charging of electric vehicles. The method for calculating the adjustable power on the user side of electric vehicles is practical. The method for calculating the adjustable power on the user side of electric vehicles considering the fast charging load scenario takes into account both charging scenarios and V2G scenarios, that is, in addition to the electric vehicle charging scenario, the electric vehicle reverse discharge scenario is also considered. This method is more in line with the current application needs of vehicle-grid interaction technology.

[0226] All matters not covered in the above embodiments of the present invention are well known in the art.

[0227] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for calculating the adjustable power of an electric vehicle user side considering a fast charging load scenario, characterized in that: include: A model of electric vehicle arrival process based on non-homogeneous Poisson process is established to estimate the state of charge of electric vehicles at the beginning of charging. Generate fast charging load scenarios based on the Sequential Monte Carlo method, including: charging scenarios and V2G scenarios, simulating the arrival, queuing and charging process of electric vehicles; Analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the charging scenario, and calculate the real-time adjustable power of the electric vehicle user side under the charging scenario based on the analysis; Analyze the feasible domain of dispatchable capacity of a single electric vehicle in the access period under the V2G scenario, and calculate the real-time adjustable power of the electric vehicle user side under the V2G scenario based on the analysis; Combine the real-time adjustable power of electric vehicle users in charging scenarios and V2G scenarios to calculate the real-time adjustable power of aggregators on the large-scale electric vehicle user side; The method of establishing an electric vehicle arrival process model based on a non-homogeneous Poisson process setting to estimate the state of charge of the electric vehicle at the start of charging includes: For electric vehicles charging at the station, record the time from 0 to t At this moment, in the interval [0, t The number of electric vehicles that arrived at the fast charging station within ] is , let the random process For a non-homogeneous Poisson process, at the beginning of the counting ; is an independent incremental process, that is, when When the random variable With random variables Statistically independent of each other, is the interval [0, t ]Internal satisfaction Four arbitrary moments; when the time interval When it approaches 0, in the time period Calculate the probability of electric vehicles arriving at the station for: （1） （2） Formula (1) represents the probability that there is only one electric car arriving at the charging station; Formula (2) represents the probability that two or more electric vehicles arrive at a charging station; In the formula, is the intensity of the Poisson process, for Higher-order infinitesimals of ; strength A Poisson process with a constant that does not change with time is a homogeneous Poisson process, while A time-varying Poisson process is called a non-homogeneous Poisson process, then: （3） （4） （5） Formula (3) indicates that in the time interval (0, t ] the average number of electric vehicles arriving at a charging station within There are only a finite number of first-kind discontinuities in the integration interval, and it is right continuous, then It is expressed as formula (4), that is, the intensity of the Poisson process For the time period The number of electric vehicles arriving at the fast charging station per unit time is the arrival rate of electric vehicles. Changes stepwise over time, within each hour is a constant and right continuous; Formula (5) indicates that if the time when the current vehicle arrives at the fast charging station is , then the time interval for the next car to arrive at the fast charging station Follow the distribution ; In the formula, For the time interval (0, t ], the average number of electric vehicles arriving at a charging station within is the intensity of the Poisson process; Estimate the initial state of charge for electric vehicle charging: （6） （7） （8） Formula (6) represents the mileage of an electric vehicle from the last full charge to the start of this charge. Designed for maximum range with electric vehicles Estimating the state of charge of electric vehicles at the start of charging ; Formula (7) represents the mileage of an ordinary private electric car from the last full charge to the start of this charge It follows a log-normal distribution, The probability density function of , the probability density function Equivalent to The natural logarithm of Obey is the mean, is a normal distribution with variance ; According to equations (6) and (7), the initial charging state of charge is obtained The distribution function obeys , formula (8) is the distribution function The calculation formula for .

2. The method for calculating the adjustable power of an electric vehicle user side considering the fast charging load scenario according to claim 1 is characterized in that: The sequential Monte Carlo method is used to generate a fast charging load scenario, simulating the arrival, queuing and charging process of electric vehicles, including: The sequential Monte Carlo simulation method is used to perform random process simulation on the arrival process, queuing process and charging process of electric vehicles in a certain period of time in chronological order to generate fast charging load scenarios; including: S211, set the simulation start time , the current vehicle , set the upper limit of simulation time; S212, based on the assumption that the electric vehicle arrival process is a non-homogeneous Poisson process, Generates a random number uniformly distributed in [0,1] , The inverse function of the random function generates the arrival time of the next vehicle. , that is, the time interval and corresponding time for each vehicle to arrive at the fast charging station in turn are obtained; S213, Order ,if , then go to S217; S214, calculating the waiting time required for the vehicle according to the current charging pile queue situation in the charging station If the waiting time is greater than the upper limit of the waiting time, the vehicle leaves and proceeds to S212; S215, Order The vehicle is recorded as An electric car arrives at the station and charges, and the type of the car and the state of charge when it arrives are randomly generated. , determine the power battery capacity according to the type of vehicle And the charging power converted to the AC side of the charging pile , calculate the time required for charging ,in capping the charging of electric vehicle batteries; S216: Assign the vehicle to the charging station with the shortest waiting time, update the queue status of the charging station, and update the charging load of the fast charging station , transfer to S212; S217, calculate the average charging load in each time interval. As the number of scenarios increases, the average value of the fast charging load in each time period tends to be stable, and this average value is used as the predicted value of the fast charging load.

3. The method for calculating the adjustable power of an electric vehicle user side considering the fast charging load scenario according to claim 1 is characterized in that: The feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the charging scenario is analyzed, and based on the analysis, the real-time adjustable power of the electric vehicle user side under the charging scenario is calculated, including: During the access time, the charging power that can be increased by the electric vehicle is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle during the access period in the charging scenario includes the following constraints: Power constraint: The charging power of electric vehicles is between zero and maximum charging power; Power constraint: The battery power of an electric vehicle is between the initial charge power and the maximum battery power; Travel constraints: The charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging; Taking the feasible domain of the dispatchable capacity of the electric vehicle during the entire access period at the current moment as the constraint condition, according to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the corresponding real-time adjustable power of the electric vehicle is calculated as: （9） （10） （11） Formula (9) is a comprehensive expression of travel constraints, power constraints, and electricity constraints; Formula (10) represents the adjustable power of the electric vehicle at the current moment; Formula (11) represents the adjustable power of the electric vehicle at the current moment; In the formula, Indicates j The minimum power state of the vehicle at the next moment to meet the user's charging needs, Indicates j The charging end time of the vehicle, Indicates j The planned charging power of the vehicle at this time, , Respectively represent j The vehicle's adjustable power and adjustable power in charging mode. Indicates j The expected power of the vehicle at the end of charging, Indicates j The current state of charge of the vehicle. They represent the upper limit of charging power and the upper limit of charging capacity respectively. Indicates the corresponding charging efficiency.

4. The method for calculating the adjustable power of an electric vehicle user side considering the fast charging load scenario according to claim 1 is characterized in that: The feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the V2G scenario is analyzed, and based on the analysis, the real-time adjustable power of the electric vehicle user side under the V2G scenario is calculated, including: In the V2G scenario, considering the reverse discharge of electric vehicles to the grid, the charging power that can be increased during the access time is the adjustable power, and the charging power that can be reduced is the adjustable power. The feasible domain of the dispatchable capacity of a single electric vehicle in the access period in the V2G scenario includes the following constraints: Power constraint: The charging power of electric vehicles is between the maximum discharge power and the maximum charging power; Power Constraint: The battery power of an electric vehicle is between the minimum battery power and the maximum battery power; Travel constraints: The charging process must meet user needs, that is, to ensure that the battery can reach the owner's expected charging power target when the electric vehicle is finished charging; Based on the feasible domain of the dispatchable capacity of electric vehicles in the V2G scenario, according to the current power state of the electric vehicle, the charging end time and the expected power at the end of charging, the real-time adjustable power corresponding to the electric vehicle in the V2G scenario is calculated as follows: （12） （13） Formula (12) represents the adjustable power of the electric vehicle at the current moment; Formula (13) represents the adjustable power of the electric vehicle at the current moment; In the formula, Respectively represent j The vehicle's adjustable power and adjustable power in V2G mode.

5. The method for calculating the adjustable power of an electric vehicle user side considering the fast charging load scenario according to claim 1 is characterized in that: The real-time adjustable power of the electric vehicle user side in the charging scenario and the V2G scenario is combined to calculate the real-time adjustable power of the aggregator on the large-scale electric vehicle user side, including: The real-time adjustable power of all electric vehicles connected to the aggregator during the corresponding period is accumulated to obtain the real-time adjustable power of the aggregator during the corresponding period: （14） （15） Formula (14) represents the adjustable power of the electric vehicle aggregator at the current moment; Formula (15) represents the power that can be reduced by the EV aggregator at the current moment; In the formula, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding moment; Combining the adjustable power of the charging scenario and the V2G scenario, based on the real-time adjustable power of the aggregator in the corresponding period, considering the charging mode and V2G mode of the electric vehicle, the real-time adjustable power of the electric vehicle aggregator is calculated as: （16） （17） Formula (16) represents the adjustable power of the electric vehicle aggregator at the current moment in combination with the charging scenario and the V2G scenario; Formula (17) represents the power that can be reduced at the current moment by the EV aggregator combining the charging scenario and the V2G scenario; in, They represent the real-time up- and down-adjustable power of the aggregator at the corresponding moment, represents the number of electric vehicles connected to the aggregator at the corresponding time, where The vehicle is in charging mode. The car is in V2G mode.

6. An electric vehicle user-side adjustable power calculation system considering fast charging load scenarios, characterized in that: include: Scenario simulation module, which is used to establish an electric vehicle arrival process model based on the non-homogeneous Poisson process assumption and estimate the state of charge of the electric vehicle at the start of charging; Generate fast charging load scenarios based on the Sequential Monte Carlo method, including: charging scenarios and V2G scenarios, simulating the arrival, queuing and charging process of electric vehicles; A charging scenario adjustable power calculation module, which is used to analyze the feasible domain of the dispatchable capacity of a single electric vehicle in the access period under the charging scenario, and calculate the real-time adjustable power of the electric vehicle user side under the charging scenario based on the analysis; A V2G scenario adjustable power calculation module, which is used to analyze the feasible domain of the dispatchable capacity of a single electric vehicle in the access period in the V2G scenario, and calculate the real-time adjustable power of the electric vehicle user side in the V2G scenario based on the analysis; The user-side adjustable power calculation module is used to combine the real-time adjustable power of the electric vehicle user side in the charging scenario and the V2G scenario, and calculate the real-time adjustable power of the large-scale electric vehicle user side aggregator.

7. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it can be used to perform the method described in any one of claims 1 to 5, or to run the system described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method described in any one of claims 1 to 5, or to run the system described in claim 6.

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