Large-scale electric vehicle charging load calculation method and device

Abstract

The invention relates to the field of load prediction of energy optimization, and discloses a large-scale electric vehicle charging load calculation method and device. According to the method, the user behavior and daily mileage problems of common electric vehicles are considered, probability modeling is carried out on the mileage of the electric vehicles in cities and towns, and a daily mileage probability model is obtained; the method comprises the following steps: carrying out probability modeling on initial charging time of an electric vehicle within one day by considering actual requirements of an electric vehicle user to obtain an initial charging time probability model; analyzing charging power behaviors of different electric vehicles, and establishing corresponding charging power probability models; the battery capacity of common electric vehicles on the market is considered, modeling is carried out on electric vehicle battery capacity probability distribution, a battery capacity probability model is obtained, and theoretical support is provided for fusion and interaction of a power distribution network and the electric vehicles.

Description

Technical Field

[0001] The present application relates to the field of energy optimization load forecasting, and specifically to a large-scale electric vehicle charging load calculation method and device. Background Art

[0002] With the rapid development of society, people's awareness of pursuing quality of life has rapidly increased, and transportation energy consumption has continued to rise, exacerbating problems such as energy shortages and environmental pollution. Measures need to be taken to improve the energy consumption structure. The high carbon emissions generated by traditional fuel vehicles are an important issue that needs to be urgently addressed in the context of the dual carbon goals. Compared with traditional internal combustion engine vehicles, electric vehicles driven by electricity can meet people's demand for high-efficiency transportation while also having the advantages of low noise, high efficiency, low emissions, saving fossil energy, and promoting the utilization rate of renewable clean energy.

[0003] Factors such as the type of electric vehicle, battery capacity, charging time and scale affect the charging load at all times, making it random, intermittent and disorderly. In particular, large-scale grid-connected charging of electric vehicles will lead to higher peak loads in the system, which has the same characteristics as distributed power sources and will have an impact on the safe and stable operation of the power grid. Summary of the invention

[0004] The present application provides a large-scale electric vehicle charging load calculation method to solve the problem in the prior art that large-scale access to the grid for charging of electric vehicles will cause the load peak in the system to increase, which will have an impact on the safe and stable operation of the power grid.

[0005] Correspondingly, the present application also provides a large-scale electric vehicle charging load calculation device, an electronic device, and a computer-readable storage medium to ensure the implementation and application of the above method.

[0006] In order to solve the above technical problems, the present application discloses a large-scale electric vehicle charging load calculation method, the method comprising:

[0007] The probability model is constructed by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model;

[0008] Using the Monte Carlo sampling method, the random variables that meet the probability model are sampled multiple times independently, and the mathematical expectation of the random variables obtained by multiple samplings is calculated;

[0009] The mathematical expectation of random variables is used to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes.

[0010] Preferably, the expression of the daily mileage probability model is:

[0011]

[0012] Among them, x s is the random variable of the electric vehicle’s daily mileage, σ s is the average daily mileage, μ s is the daily mileage variance, lnx s For x s The natural logarithm of , exp(·) represents the e-exponential of ·.

[0013] Preferably, the expression of the probability model of the start charging time is:

[0014]

[0015] Among them, x t represents the random variable at the start of charging, μ t represents the mean value of the initial charging time, σ t represents the variance of the initial charging time, and exp(·) represents the e-exponent of ·.

[0016] Preferably, the expression of the charging power probability model is:

[0017]

[0018] Among them, x p A random variable representing the rated charging power, μ p represents the mean value of the rated charging power, σ p represents the variance of the rated charging power, and exp(·) represents the e-exponent of ·.

[0019] Preferably, the expression of the battery capacity probability model is:

[0020]

[0021] Among them, x c Represents the battery capacity of electric vehicles, which ranges from m kW·h to n kW·h.

[0022] Preferably, the expression for obtaining the mathematical expectation of the random variable obtained by multiple samplings is:

[0023]

[0024] Where E(ξ) is the mathematical expectation value of the random variable ξ, N is the number of repeated sampling, ξ1~ξ n is a random variable with n independent sampling sequences.

[0025] The present application also discloses a large-scale electric vehicle charging load calculation device, the device comprising:

[0026] A model building module is used to build a probability model by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model;

[0027] The electric vehicle load calculation module is used to use the Monte Carlo sampling method to perform multiple independent samplings on random variables that conform to the probability model, and to obtain the mathematical expectation of the random variables obtained by multiple samplings;

[0028] The electric vehicle load calculation module is also used to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes using the mathematical expectation of random variables.

[0029] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, one or more methods described in the present application are implemented.

[0030] The present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, one or more methods described in the present application are implemented.

[0031] This application has the following beneficial effects:

[0032] This application can accurately simulate the charging load of large-scale electric vehicles by probabilistically modeling the mileage, charging time, charging power and battery capacity of electric vehicles, provide scientific and reasonable load forecasting methods, and provide theoretical and data support for orderly charging of electric vehicles and optimal scheduling of power grids.

[0033] This application can better reflect the actual charging needs of electric vehicles through detailed modeling of electric vehicle user behavior and actual needs, improve the accuracy of charging load calculation, and help power companies and distribution network operators more effectively deal with load fluctuations and overload risks brought about by electric vehicle access.

[0034] This application uses a simplified probability distribution model to analyze the charging power behavior of different types of electric vehicles, which can significantly reduce the computational complexity, improve computational efficiency, and provide a fast and effective solution for large-scale electric vehicle load forecasting.

[0035] By modeling the probability distribution of common electric vehicle battery capacities on the market, this application can more comprehensively consider the diversity of electric vehicle battery capacities, enhance the applicability and universality of the model, and have a high reference value and application prospect for the prediction results of electric vehicle charging load.

[0036] Additional aspects and advantages of the present application will be given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0038] Figure 1 A flow chart of a method for calculating large-scale electric vehicle charging load provided in an embodiment of the present application;

[0039] Figure 2 A flow chart of a Monte Carlo sampling electric vehicle load calculation method provided in an embodiment of the present application;

[0040] Figure 3 A probability density diagram of a single-day mileage of an electric vehicle provided in an embodiment of the present application;

[0041] Figure 4 A probability distribution diagram of a single-day driving mileage of an electric vehicle provided in an embodiment of the present application;

[0042] Figure 5 The amount of charging required for multiple electric vehicles after a single day of driving provided in the embodiments of the present application;

[0043] Figure 6 A probability density diagram of the start time of charging of an electric vehicle provided in an embodiment of the present application;

[0044] Figure 7 A distribution diagram of charging start times of multiple electric vehicles provided in an embodiment of the present application;

[0045] Figure 8 A probability density diagram of charging power of multiple electric vehicles provided in an embodiment of the present application;

[0046] Fig. 9 A distribution diagram of the charging time required for multiple electric vehicles after a single day of driving provided in an embodiment of the present application;

[0047] Fig.10 The single-day charging load power results of electric vehicles of different sizes provided in the embodiments of the present application;

[0048] Fig.11 A schematic diagram of the structure of a large-scale electric vehicle charging load calculation device provided in an embodiment of the present application;

[0049] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0051] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0052] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0053] The solution provided in the embodiments of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this. With respect to the technical problems existing in the prior art, the large-scale electric vehicle charging load calculation method and device provided in this application are intended to solve at least one of the technical problems of the prior art.

[0054] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0055] The present application embodiment provides a possible implementation method, such as Figure 1 As shown, a flowchart of a method for calculating charging load of large-scale electric vehicles is provided, and the scheme can be executed by any electronic device, and optionally, can be executed on a server side or a terminal device.

[0056] like Figure 1 As shown in , the method may include the following steps:

[0057] Step 101, constructing a probability model by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily driving mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model.

[0058] In the embodiments of the present application, user behavior and daily mileage of ordinary electric vehicles are taken into consideration, and the mileage of urban electric vehicles is probabilistically modeled to obtain a daily mileage probability model; the actual needs of electric vehicle users are taken into consideration, and the starting charging time of the electric vehicle within a day is probabilistically modeled to obtain a starting charging time probability model; the charging power behavior of different electric vehicles is analyzed, and the corresponding charging power probability model is established; the battery capacity of common electric vehicles on the market is taken into consideration, and the probability distribution of the battery capacity of electric vehicles is modeled to obtain a battery capacity probability model, thereby providing theoretical support for the integrated interaction between the distribution network and electric vehicles.

[0059] Step 102, using the Monte Carlo sampling method, perform multiple independent samplings on random variables that conform to the probability model, and calculate the mathematical expectation of the random variables obtained by the multiple samplings.

[0060] Among them, the random variable corresponding to the daily mileage probability model is the daily mileage probability, the random variable corresponding to the starting charging time probability model is the starting charging time, the random variable corresponding to the charging power probability model is the charging power, and the random variable corresponding to the battery capacity probability model is the battery capacity.

[0061] Step 103, using the mathematical expectation of random variables to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes.

[0062] In the embodiment of the present application, the basic idea of ​​the Monte Carlo sampling method is described as follows: first, a probability model of a random process in the charging behavior of an electric vehicle is established to obtain the parameter value of the probability density function, and then a pseudo-random number that obeys the probability distribution is generated. The corresponding numerical value is extracted from the pseudo-random number to simulate the random process, and the simulation process is repeated many times. Then, the numerical value obtained by the simulation is used to calculate the approximate value of the problem. The accuracy of the solution can be expressed by the standard error of the approximate value.

[0063] Among them, the Monte Carlo simulation process solves the simulation calculation problem of disordered charging load of large-scale electric vehicles. By utilizing its mathematical characteristics, the method is implemented in Matlab using the programming language.

[0064] In the embodiment of the present application, the probability model is constructed by considering the influencing parameters of the electric vehicle charging load, including the daily mileage probability model, the starting charging time probability model, the charging power probability model and the battery capacity probability model; then the Monte Carlo sampling method is used to perform multiple independent samplings on the random variables that meet the probability model, and the mathematical expectation of the random variables obtained by multiple samplings is obtained; the mathematical expectation of the random variables is used to simulate the charging load equivalent to the charging behavior of electric vehicles of different scales. In order to deal with the impact and overload problems caused by the access of electric loads represented by large-scale electric vehicles to the distribution network, a probability model and calculation method are provided, which has a strong reference significance for reducing the safety hazards caused by the disorderly charging of a large number of electric vehicles to the system.

[0065] In an optional embodiment, the daily mileage of different users varies greatly due to their travel habits. For example, the daily mileage of private cars is less, but the number is large and the scale is large, while the daily mileage of taxis is large but the scale is not large. The embodiment of the present application simplifies the travel rules of various types of cars, taking the behavior of typical household electric car users as a representative, and equivalently simulates the daily mileage distribution function of different types of electric vehicles. In addition, considering that electric vehicles are currently a new type of transportation, their sample size is still small compared to traditional fuel vehicles. The embodiment of the present application assumes that the use habits of electric vehicle users are consistent with those of ordinary fuel vehicle users, and pre-processes the data on the use behavior rules of household electric vehicles, and concludes that the daily mileage obeys the normal distribution of the natural logarithm. Therefore, the expression of the daily mileage probability model is:

[0066]

[0067] Among them, x s is the random variable of the electric vehicle’s daily mileage, σ s is the average daily mileage, μ s is the daily mileage variance, lnx s For x s The natural logarithm of , exp(·) represents the e-exponential of ·.

[0068] Since the driving range of most electric vehicles on the market can generally meet the user's urban travel needs for three days or even longer, the electric vehicle battery is not fully charged when the user goes out for the first time every day. Assuming that the power of the electric vehicle before each trip is a random distribution that can meet the travel needs of the day, after finding the probability distribution of the electric vehicle's daily mileage, the probability distribution of the electric vehicle battery charge state at the time of the last trip can be found.

[0069] In an optional embodiment, the electric vehicle user will choose a time that he thinks is appropriate for charging, and the charging time is random. The last driving return time of the electric vehicle every day is the charging start time. In the embodiment of the present application, it is assumed that the last driving return time of the electric vehicle every day follows a normal distribution, and the expression of the starting charging time probability model is:

[0070]

[0071] Among them, x t represents the random variable at the start of charging, μ t represents the mean value of the initial charging time, σ t represents the variance of the initial charging time, and exp(·) represents the e-exponent of ·.

[0072] In an optional embodiment, the charging process of an electric vehicle has fast, conventional and slow charging modes. Considering the development of large-scale household electric vehicles, only the conventional charging mode is considered in the embodiment of the present application. In this charging mode, the uniform charging time period is relatively long, and the charging behavior is non-uniform. The actual charging stage and the end charging stage account for a relatively low proportion in the entire charging process. Therefore, the charging power probability model is simplified, and the impact of the start and end stages on the overall calculation process is ignored. Specifically, for a single electric vehicle, it is charged at a constant power, which is simplified to a combination of two step-like functions and a horizontal straight line under the time and power coordinate axes. In addition, considering the differences in conventional charging power of different models of electric vehicles on the market, assuming that the charging power of large-scale electric vehicles obeys the standard normal distribution, the expression of the charging power probability model is:

[0073]

[0074] Among them, x p A random variable representing the rated charging power, μ p represents the mean value of the rated charging power, σ p represents the variance of the rated charging power, and exp(·) represents the e-exponent of ·.

[0075] Optionally, the mean of the rated charging power is the common charging power on the market, and the variance of the rated charging power is 10% of the common charging power.

[0076] In an optional embodiment, the capacity of electric vehicles currently on the market varies slightly depending on their models. According to statistical theory, it is assumed that the range of electric vehicle battery capacity is distributed between m kW·h and n kW·h. The range of large-scale electric vehicle battery capacity can be simulated by a uniform distribution within a certain range. Therefore, the expression of the battery capacity probability model is:

[0077]

[0078] Among them, x c Represents the battery capacity of electric vehicles, which ranges from m kW·h to n kW·h.

[0079] In an optional embodiment, the expression for obtaining the mathematical expectation of the random variable obtained by multiple samplings is:

[0080]

[0081] Where E(ξ) is the mathematical expectation value of the random variable ξ, N is the number of repeated sampling, ξ1~ξ n is a random variable with n independent sampling sequences.

[0082] The Monte Carlo simulation method can be called a random simulation method, or a random sampling or statistical test method. In the embodiment of the present application, the Monte Carlo method is used to perform multiple independent samplings on the four random variables of mileage, starting charging time, charging power, and battery capacity described in equations (1)-(4), and the mathematical expectation of each random variable is obtained according to equation (5) as its final random variable distribution. The following proves that the simulation accuracy of the method in the embodiment of the present application depends only on the number of samples and the variance.

[0083] Proof: From Kolmogorov's law of large numbers, we know that when E(ξ)=I, the mathematical expectation value converges to I with probability 1. That is:

[0084]

[0085] According to the central limit theorem, for any λ α >0 has:

[0086]

[0087] It can be seen that the inequality:

[0088]

[0089] Approximately, the probability is 1-α, where α is called the confidence level and 1-α is the confidence level. σ represents the mean square error of the random variable ξ. The above formula shows that the order of the speed of convergence to I is O(N -0.5 ). If σ is not zero, the error of the Monte Carlo method is:

[0090]

[0091] The normal difference λ in the above formula α There is a one-to-one correspondence with the confidence level α, and the corresponding relationship can be expressed by the integral relationship:

[0092]

[0093] By analyzing the error formula, we can see that the error of the Monte Carlo method is determined by the mean square error of the random variable. To reduce the error, we need to increase the number of samples N or reduce the variance σ. 2 .

[0094] In the embodiments of the present application, it is assumed that the charging behavior of each electric vehicle has the characteristics of independence, and the daily mileage, starting charging time, charging power and battery capacity of each electric vehicle are independent of each other, and are randomly distributed in a known probability distribution diagram. Therefore, Monte Carlo random sampling can be performed to extract random numbers in each step, so as to obtain the charging load characteristics of a single electric vehicle, and accumulate the total charging load curve when large-scale electric vehicles are connected to the grid for charging.

[0095] Based on the above, the embodiment of the present application provides a Monte Carlo sampling electric vehicle load calculation method, which calculates the charging load of large-scale electric vehicles in the target distribution network area, such as Figure 2 As shown, first initialize the number of electric vehicles N in the distribution network area EV , probability distribution model mean and variance and other parameters. Then calculate each electric vehicle charging load time series and get N in a cumulative way EVThe charging load curve of electric vehicles. Specifically, the number of electric vehicles is initialized to k = 0, and the cumulative number of electric vehicles is calculated as k = k + 1 = 1, that is, the charging load time series of the first electric vehicle is calculated. The first step is to randomly select the mileage of electric vehicles and calculate the power consumption and charging amount of the vehicle according to the driving efficiency; the second step is to randomly select the charging time according to the power distribution at the starting charging time; the third step is to randomly select the charging power of the electric vehicle and calculate the charging time of the vehicle; the fourth step is to calculate the charging load time series of a single electric vehicle and accumulate the charging load curve. Then, it is determined whether the cumulative number of electric vehicles this time reaches the maximum total number of electric vehicles. If not, the next cumulative number of electric vehicles is calculated, and the charging load time series of the next electric vehicle is calculated; if so, it is determined whether the model converges. If it converges, the simulation ends and the charging load curve obtained by this accumulation is used as the final charging load curve of large-scale electric vehicles. Otherwise, the number of electric vehicles is initialized and the simulation calculation continues.

[0096] In order to verify the effectiveness of the method designed in the embodiment of the present application, the method is implemented based on computer technology, and the following example scenario is set: According to the survey data of a city in southern my country, the mean σ of the logarithmic normal distribution of the daily mileage is set in the example s is 3.2, and the variance μ s is 0.88; the starting charging time of electric vehicles is assumed to follow a normal distribution, with a mean μ t is 18.6, and the variance σ t is 3.4; the mean μ of the probability distribution of charging power p is 8.5, and the variance σ p is 2.2; the electric vehicle driving efficiency is 7.5km / kWh; the battery capacity ranges from 30 to 50kWh. Taking five different scales as examples, the charging load calculation values ​​are simulated.

[0097] (1) Daily mileage probability results

[0098] according to Figure 3 As a result, when the log-normal distribution mean of the daily mileage of electric vehicles (which can be referred to as trams) is set to 3.2 km, after conversion to the natural log-normal distribution, the mean of the actual mileage is about 12.61 km, which is in line with the daily driving characteristics of ordinary electric vehicle users in the city. Figure 4 The data show that the probability of electric vehicles with a daily mileage distribution of less than 100 km is about 95%, which is consistent with the daily travel and work characteristics of most urban users. Therefore, it can be considered that the log-normal distribution and the currently set mean and variance parameters can well simulate the single-day mileage probability model of urban electric vehicle users with a high confidence level.

[0099] Figure 5The results of the daily mileage of electric vehicles based on Monte Carlo simulations were shown, taking 1,000 electric vehicle users as an example. By calculating the driving efficiency of electric vehicles, it was found that the amount of charging required after the car was driven on the same day was concentrated within 10kWh, which is also consistent with the basic facts of electric vehicle battery capacity and urban driving habits.

[0100] (2) Probability results of the starting charging time

[0101] According to the behavioral characteristics of the starting charging time of electric vehicle users established in the previous article and the normal distribution mean and variance of the return time set in the example, the probability density of the starting time of electric vehicle charging in a single day and the distribution results of the starting time of electric vehicle charging under the current scale are as follows: Figure 6 , Figure 7 shown.

[0102] from Figure 6 and Figure 7 It can be seen that the most densely distributed charging start time period is from 13:00 to 24:00, and the most sparse period is from 4:00 to 9:00 in the morning. Most users choose to charge between 17:00 and 21:00 in the afternoon. This is mainly because electric vehicles are usually in working condition in the morning due to various travel needs, and the number of people who choose to charge during this period is relatively small. During the afternoon working hours, the trams are idle, and users can use this time period to charge. After get off work in the afternoon is the peak charging period, and most users choose to charge when they go home after get off work. Since different users have different off-get off work times, the charging start time is also different. This distribution feature is consistent with the basic behavioral characteristics of users in large cities, which verifies the rationality and accuracy of the model.

[0103] (3) Charging power probability results

[0104] Depend on Figure 8 It can be seen that the average charging power of electric vehicles under conventional charging methods in the current market is about 8.6kW. Due to differences in different car brands and models, the charging power also varies, but the overall distribution is around 8.6kW, of which the proportion of charging power distribution between 6.5kW and 10.5kW exceeds 96%. This is in line with the technical level and market status under conventional charging mode.

[0105] According to the probability distribution characteristics of charging power, the charging time required for multiple electric vehicles after driving on the same day is calculated using the charging power of electric vehicles. Fig. 9 As shown. It can be seen that the charging time is mostly concentrated within 2 hours. Since the variance of the charging power probability model is small, the distribution of the required charging amount and the charging time is also similar, but there are differences in units and values. This is also in line with the current distribution characteristics of charging power, that is, the mean is about 8.6kW and the variance does not exceed 0.9.

[0106] (4) Battery capacity probability results

[0107] According to the survey, the battery capacity of most common household electric vehicles on the market is distributed between 30 and 50 kw·h. The probability density function of the battery capacity probability model established according to the above formula (4) is:

[0108]

[0109] For most users, considering the battery life and range anxiety issues, they will not choose to charge after the battery is exhausted, but will start charging when they return on the same day to meet the travel needs of the next day.

[0110] (5) Charging load calculation results

[0111] According to the charging load calculation method established in the above embodiment, random sampling analysis and calculation are performed on the charging load of electric vehicle users with a scale of 100, 500, 1000, 5000 and 10000 respectively, and the charging load under the scenario of electric vehicle users charging autonomously is studied. The daily load curves of electric vehicles of different scales for disorderly autonomous charging within a scheduling cycle are obtained as follows: Fig.10 shown.

[0112] from Fig.10 It can be seen that even for electric vehicles of different sizes, their daily charging load curves will show obvious time peak and valley characteristics. Between 15:00 and 24:00, a large number of electric vehicles are charged together, and the system load reaches its peak; and between 4:00 and 11:00, the load charging demand is the lowest, which is closely related to the living habits of the user group. Users usually charge in the afternoon and evening after returning home, while the early morning and morning are mainly used for travel and work, so the charging demand is relatively low.

[0113] Specifically, the charging load trends of electric vehicle users of different sizes show the following characteristics:

[0114] 1) Peak period analysis:

[0115] The peak charging period is between 15:00 and 24:00. The system load increases significantly during this period, especially between 18:00 and 22:00, reaching the peak of the day. This phenomenon is mainly due to the fact that users return home to charge after work, and electric vehicles are idle during this period, which is suitable for charging. As the scale of electric vehicle users increases, the absolute value of the charging peak also increases significantly. For example, the peak load under the scale of 10,000 users is significantly higher than that under the scale of 100 users, indicating that the impact of large-scale electric vehicle access on the grid load is more obvious.

[0116] 2) Valley period analysis:

[0117] The charging trough period is between 4:00 and 11:00. During this period, the system load is low and the demand for electric vehicle charging is low. This is mainly because users mainly travel in the early morning and morning, and electric vehicles are mostly in driving state. Although the load of electric vehicle users of different sizes is different during the trough period, the overall trend is consistent, showing a significant reduction in charging demand.

[0118] 3) Load fluctuation analysis:

[0119] The peak-to-valley difference of the charging load curve is extremely large, which poses a challenge to the safety and stability of the power grid. Especially in the case of large-scale, peak load may cause large power fluctuations in the power grid, affecting the normal use of other power-consuming equipment. For example, the peak-to-valley difference under the scale of 10,000 users is significantly higher than that of 100 users, indicating that the large-scale electric vehicle charging load has a greater impact on the power grid. If it is not optimized and dispatched, it may cause system instability.

[0120] 4) Reasonableness analysis:

[0121] Through a detailed analysis of the charging load of electric vehicle users of different sizes, the following conclusions can be drawn:

[0122] a) Consistency of simulation results: The charging load calculation method proposed in the present invention can well simulate the charging load of electric vehicles at different scales. Whether it is a scale of 100 users or a scale of 10,000 users, the trend of the daily load curve is basically the same, showing obvious peak-valley characteristics. This shows that the proposed method has good applicability and consistency in different application scenarios.

[0123] b) Data trend verification: Through in-depth analysis of the charging load curve, it can be seen that the charging behavior and load characteristics of electric vehicles are highly correlated with the daily living habits of users. The distribution of peak and valley time periods and load fluctuations are consistent with the actual situation. This verifies the rationality and accuracy of the proposed method in capturing the characteristics of electric vehicle charging behavior.

[0124] c) Reference value for power grid dispatch: By analyzing the charging load of large-scale electric vehicle users, it can provide important reference data for the optimization and dispatch of the power grid. Especially during the peak period of electric vehicle charging load, the power grid operator can take load balancing measures in advance according to the forecast results to reduce the impact of the charging peak on the power grid and ensure the stable operation of the system.

[0125] In summary, the large-scale electric vehicle charging load calculation method proposed in the embodiment of the present application shows high reliability and rationality at different scales. This method can not only accurately simulate the charging load of electric vehicles, but also provide scientific decision support for the optimal dispatching of power grids, and has important practical application value.

[0126] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a large-scale electric vehicle charging load calculation device, such as Fig.11 As shown, the device comprises:

[0127] The model building module 1101 is used to build a probability model by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily driving mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model;

[0128] The electric vehicle load calculation module 1102 is used to perform multiple independent sampling of random variables that meet the probability model using the Monte Carlo sampling method, and to obtain the mathematical expectation of the random variables obtained by the multiple samplings;

[0129] The electric vehicle load calculation module 1102 is also used to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes by using the mathematical expectation of random variables.

[0130] In the embodiment of the present application, the probability model is constructed by considering the influencing parameters of the electric vehicle charging load, including the daily mileage probability model, the starting charging time probability model, the charging power probability model and the battery capacity probability model; then the Monte Carlo sampling method is used to perform multiple independent samplings on the random variables that meet the probability model, and the mathematical expectation of the random variables obtained by multiple samplings is obtained; the mathematical expectation of the random variables is used to simulate the charging load equivalent to the charging behavior of electric vehicles of different scales. In order to deal with the impact and overload problems caused by the access of electric loads represented by large-scale electric vehicles to the distribution network, a probability model and calculation method are provided, which has a strong reference significance for reducing the safety hazards caused by the disorderly charging of a large number of electric vehicles to the system.

[0131] The large-scale electric vehicle charging load calculation device provided in the embodiment of the present application can achieve Figures 1 to 10 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0132] The large-scale electric vehicle charging load calculation device of the embodiment of the present application can execute the large-scale electric vehicle charging load calculation method provided in the embodiment of the present application, and its implementation principle is similar. The actions performed by each module and unit in the large-scale electric vehicle charging load calculation device in each embodiment of the present application correspond to the steps in the large-scale electric vehicle charging load calculation method in each embodiment of the present application. For the detailed functional description of each module of the large-scale electric vehicle charging load calculation device, please refer to the description in the corresponding large-scale electric vehicle charging load calculation method shown in the previous text, which will not be repeated here.

[0133] Based on the same principle as the method shown in the embodiment of the present application, the embodiment of the present application also provides an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the large-scale electric vehicle charging load calculation method shown in any optional embodiment of the present application by calling a computer program. Compared with the prior art, the large-scale electric vehicle charging load calculation method provided by the present application considers the influencing parameters of the electric vehicle charging load to construct a probability model, including a daily mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model; then, the Monte Carlo sampling method is used to perform multiple independent samplings on random variables that conform to the probability model, and the mathematical expectation of the random variables obtained by multiple samplings is obtained; the mathematical expectation of the random variables is used to simulate the charging load equivalent to the charging behavior of electric vehicles of different scales. In order to deal with the impact and overload problems caused by the access of electric loads represented by large-scale electric vehicles to the distribution network, a probability model and a calculation method are provided, which has a strong reference significance for reducing the safety hazards caused by the disorderly charging of a large number of electric vehicles to the system.

[0134] In an optional embodiment, an electronic device is also provided, such as Fig.12 As shown, Fig.12 The electronic device 1200 shown may be a server, including: a processor 1201 and a memory 1203. The processor 1201 and the memory 1203 are connected, such as through a bus 1202. Optionally, the electronic device 1200 may further include a transceiver 1204. It should be noted that in actual applications, the transceiver 1204 is not limited to one, and the structure of the electronic device 1200 does not constitute a limitation on the embodiments of the present application.

[0135] Processor 1201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 1201 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0136] The bus 1202 may include a path to transmit information between the above components. The bus 1202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 1202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0137] The memory 1203 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0138] The memory 1203 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 1201. The processor 1201 is used to execute the application code stored in the memory 1203 to implement the contents shown in the above method embodiment.

[0139] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Fig.12 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0140] The server provided in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected by wired or wireless communication, and this application does not limit this.

[0141] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0142] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0143] It should be noted that the computer-readable storage medium mentioned above in the present application can also be a computer-readable signal medium or a combination of a computer-readable storage medium and a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0144] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0145] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0146] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the large-scale electric vehicle charging load calculation method and apparatus provided in the above-mentioned various optional implementations.

[0147] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0148] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the model building module can also be described as "a model building module for building a probability model considering the influencing parameters of the electric vehicle charging load".

[0150] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for calculating charging load of large-scale electric vehicles, characterized in that: The method comprises: A probability model is constructed by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model; Using the Monte Carlo sampling method, a random variable that meets the probability model is sampled multiple times independently, and the mathematical expectation of the random variables obtained by the multiple samplings is calculated; The mathematical expectation of the random variable is used to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes.

2. The method for calculating charging load of large-scale electric vehicles according to claim 1, characterized in that: The expression of the daily mileage probability model is: Among them, x s is the random variable of the electric vehicle’s daily mileage, σ s is the average daily mileage, μ s is the variance of daily mileage, lnx s For x s The natural logarithm of , exp(·) represents the e-exponential of ·.

3. The method for calculating charging load of large-scale electric vehicles according to claim 1, characterized in that: The expression of the probability model of the starting charging time is: Among them, x t The random variable representing the starting charging time, μ t represents the mean value of the initial charging time, σ t represents the variance of the initial charging time, and exp(·) represents the e-exponent of ·.

4. The method for calculating charging load of large-scale electric vehicles according to claim 1, characterized in that: The expression of the charging power probability model is: Among them, x p A random variable representing the rated charging power, μ p represents the mean value of the rated charging power, σ p represents the variance of the rated charging power, and exp(·) represents the e-exponent of ·.

5. The method for calculating charging load of large-scale electric vehicles according to claim 1, characterized in that: The expression of the battery capacity probability model is: Among them, x c Represents the battery capacity of electric vehicles, which ranges from m kW·h to n kW·h.

6. The method for calculating charging load of large-scale electric vehicles according to claim 1, characterized in that: The expression for obtaining the mathematical expectation of the random variable obtained by multiple samplings is: Where E(ξ) is the mathematical expectation value of the random variable ξ, N is the number of repeated sampling, ξ1~ξ n is a random variable with n independent sampling sequences.

7. A large-scale electric vehicle charging load calculation device, characterized in that: The device comprises: A model building module is used to build a probability model by considering the influencing parameters of the electric vehicle charging load; the probability model includes a daily mileage probability model, a starting charging time probability model, a charging power probability model and a battery capacity probability model; The electric vehicle load calculation module is used to perform multiple independent samplings on random variables that meet the probability model using the Monte Carlo sampling method, and to obtain the mathematical expectation of the random variables obtained by the multiple samplings; The electric vehicle load calculation module is also used to simulate the charging load equivalent to the charging behavior of electric vehicles of different sizes using the mathematical expectation of the random variables.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images