Energy distribution method for new energy charging stations in residential areas

Abstract

The present invention proposes an energy allocation method for a new energy charging station in a residential area, which belongs to the field of network security technology. It includes: calculating the steady-state probability and the vehicle waiting probability based on the service intensity of the service desk; calculating the energy allocation index based on the steady-state probability and the vehicle waiting probability; calculating the number of chargeable vehicles based on the simplex method based on the energy allocation index; determining the final charging sequence, and distributing the energy of the charging station according to the number of chargeable vehicles, the charging time, the charging sequence and the number of charging piles turned on. The present invention always guarantees the electricity demand in the residential area in order to ensure smooth, accurate and rapid changes. The energy allocation of the charging station sorts the switching order of the charging piles according to specific needs. At the same time, in the case of energy shortage, each charging pile can be turned on as much as possible at the current power, realizing the command and control of energy. Solve the technical problem of the lack of efficient energy allocation method for new energy charging stations in residential areas in the prior art.

Description

Technical Field

[0001] The invention relates to an energy distribution method for a new energy charging station in a residential area, and belongs to the technical field of network security. Background Art

[0002] In recent years, with the continuous popularization of new energy vehicles, the construction scale of charging stations has also continued to expand. As an important part of charging stations, charging piles are particularly important from the perspective of cost and safety. How to achieve intelligent control of charging piles is particularly important. After entering residential areas, the new energy vehicle charging system will have a great impact on the power consumption environment of the accommodation area. It is necessary to ensure that the new energy vehicle charging system can meet the charging needs of more electric vehicles to the greatest extent while ensuring that the daily power consumption of residents is not affected. It is particularly important to design according to the different electricity consumption habits of residents, the total amount of electric energy, and the total amount of electric vehicle charging demand. Summary of the invention

[0003] A brief overview of the present invention is provided below in order to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important parts of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to a more detailed description discussed later.

[0004] In view of this, in order to solve the technical problem of the lack of efficient energy distribution method for new energy charging stations in residential areas in the prior art, the present invention provides an energy distribution method for new energy charging stations in residential areas.

[0005] Solution 1: Energy distribution method for new energy charging stations in residential areas, including:

[0006] Calculate the steady-state probability and vehicle waiting probability based on the service intensity of the service desk;

[0007] Calculate energy allocation index based on steady-state probability and vehicle waiting probability;

[0008] The number of vehicles that can be charged is calculated based on the simplex method based on the energy allocation index;

[0009] Determine the final charging sequence and distribute the energy of the charging station according to the number of chargeable vehicles, charging time, charging sequence and the number of charging piles opened.

[0010] Preferably, the energy allocation indicators include the average number of vehicles inside and outside the charging pile system, the number of vehicles waiting in line outside the charging pile for charging, the average retention time of vehicles inside and outside the charging station, the average waiting time of vehicles to be charged, and the probability that a charging vehicle needs to wait when it arrives at the charging station.

[0011] Preferably, the method for calculating the energy allocation index based on the steady-state probability and the vehicle waiting probability is:

[0012] Calculate the service intensity of the service desk:

[0013] Among them, ρ * is the service intensity of the service station, λ is the number of vehicles arriving at the system per unit time, μ is the number of vehicles processed by the service station per unit time, and s is the number of service stations;

[0014] Calculate the steady-state probability: The steady-state probability is the probability that the charging pile is idle, and it is also the probability that the new energy vehicle does not have to wait for charging;

[0015] The probability that a vehicle needs to wait is: ρ = 1-P0; the probability that a vehicle needs to wait is the probability that the charging station is busy.

[0016] Preferably, the energy allocation index is calculated based on the steady-state probability and the vehicle waiting probability:

[0017] Average number of vehicles in and out of the charging station system:

[0018] Number of vehicles waiting in line outside the charging piles:

[0019] Average dwell time of cars inside and outside charging stations:

[0020] Average waiting time for cars to be charged:

[0021] The probability that a charging vehicle needs to wait at a charging station:

[0022] Preferably, the method of calculating the number of chargeable vehicles based on the energy allocation index and the simplex method is as follows:

[0023] Given an initial basic feasible solution x (0) , let the initial basis be B;

[0024] Solution Bx B = b, find x B =B -1 b, let x N =0, calculate the objective function value

[0025] For all non-basic variables, calculate the discriminant number Let z k -c k =max{z j -c j}, if z k-c k ≤0, stop the calculation, the current basic feasible solution is the optimal solution; otherwise, proceed to the next step;

[0026] Solution k =p k , we get y k =B -1 p k , if y k ≤0, stop the calculation and the problem does not have a finite optimal solution; otherwise, proceed to the next step;

[0027] Determine the subscript r so that is the base variable, x k For the basis variable, use p k replace Get the new basis matrix B and return the solution Bx B =b.

[0028] Preferably, the method for determining the final charging sequence is:

[0029] Different charging modes are systematically encoded. If the normal charging mode is selected, the signal is output as "0", if the accelerated charging mode is selected, the signal is output as "1", and if the long-thread slow charging mode is selected, the signal is output as "2";

[0030] The vehicles in the queue are sorted according to the order of charging pile access in a natural sequence, and the signal data of the charging mode is also sorted into the array according to time. In the same sequence, the vehicle information and the charging mode signal correspond one to one;

[0031] The charging mode signal is detected starting from the first vehicle. When signal "0" is encountered, no action is taken. When signal "1" is encountered, the information of the charging pile is advanced to the first place, and the others are sorted in sequence. When the second signal "1" is encountered, the information of this charging pile is advanced to the second place, and the other sorting remains unchanged. When signal "2" is encountered, the charging pile signal is extracted separately, and the vacant position is filled by the information of the following vehicles.

[0032] Preferably, the method for obtaining the number of charging piles turned on is: dividing the input electric energy by the power of the charging pile.

[0033] Preferably, the charging time is obtained by dividing the charging amount by the charging power.

[0034] Solution 2: An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the energy distribution method for new energy charging stations in residential areas described in Solution 1 are implemented.

[0035] Solution three: A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy distribution method for new energy charging stations in residential areas as described in Solution one.

[0036] The beneficial effects of the present invention are as follows: within the rated power, the present invention supplies as much electricity as needed, whether it is for residential areas or charging stations. When the demands of both exceed the overall demand of the power supply station, the electricity demand in the residential areas is still continuously supplied, but the electricity consumption of the charging station only decreases and does not increase, so as to always ensure the electricity demand of the residential areas, in order to ensure smooth, accurate and rapid changes. The energy distribution of the charging station is based on specific needs, and the switching order of the charging piles is sorted. At the same time, in the case of energy shortage, each charging pile can be turned on as much as possible at the current power, realizing the command and control of energy. The technical problem of the lack of efficient energy distribution method for new energy charging stations in residential areas in the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 Flowchart of energy distribution method for new energy charging stations in residential areas. DETAILED DESCRIPTION

[0039] In order to make the technical solutions and advantages of the embodiments of the present invention more clearly understood, the exemplary embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0040] Example 1: Reference Figure 1 In this embodiment, the energy distribution method of the residential area new energy charging station includes:

[0041] Step 1: Calculate the steady-state probability and vehicle waiting probability based on the service intensity of the service desk;

[0042] Step 2: Calculate the energy allocation index based on the steady-state probability and the vehicle waiting probability;

[0043] Step 3: Calculate the number of vehicles that can be charged based on the energy allocation index using the simplex method;

[0044] Step 4: Determine the final charging sequence and distribute the energy of the charging station according to the number of chargeable vehicles, charging time, charging sequence and the number of charging piles opened.

[0045] Energy allocation indicators include the average number of vehicles inside and outside the charging pile system, the number of vehicles waiting to charge outside the charging pile, the average retention time of vehicles inside and outside the charging station, the average waiting time of vehicles waiting to be charged, and the probability that a charging vehicle needs to wait when it arrives at the charging station;

[0046] Calculate the energy allocation index based on the steady-state probability and vehicle waiting probability:

[0047] Calculate the service intensity of the service desk:

[0048] Among them, ρ * is the service intensity of the service station, λ is the number of vehicles arriving at the system per unit time, μ is the number of vehicles processed by the service station per unit time, and s is the number of service stations;

[0049] Calculate the steady-state probability: The steady-state probability is the probability that the charging pile is idle, and it is also the probability that the new energy vehicle does not have to wait for charging;

[0050] The probability that a vehicle needs to wait is: ρ = 1-P0; the probability that a vehicle needs to wait is the probability that the charging station is busy;

[0051] Calculate the energy allocation index based on the steady-state probability and vehicle waiting probability:

[0052] Average number of vehicles in and out of the charging station system:

[0053] Number of vehicles waiting in line outside the charging piles:

[0054] Average dwell time of cars inside and outside charging stations:

[0055] Average waiting time for cars to be charged:

[0056] The probability that a charging vehicle needs to wait at a charging station:

[0057] Through the above formula, we can quantify the system efficiency when multiple charging piles work together. When multiple charging piles are turned on at the same time, they can meet the charging needs of multiple cars and improve the efficiency of energy use. The following example illustrates:

[0058] When there is only enough energy to open three high-power charging piles, the charging efficiency increases rapidly, and the charging time follows an exponential distribution, which means that 0.4 new energy vehicles can be fully charged every hour on average. The traffic volume follows a Poisson distribution, which means that 0.9 vehicles can be fully charged every hour on average. Now, assuming that each vehicle has been connected to the charging pile, the system has marked the queue order of each vehicle in the order of first come first served, and the system opens the charging piles in sequence to power the vehicles. In an ideal situation, the system capacity and charging vehicles are unlimited, which fully conforms to the M / M / S queuing model, and the following operating indicators can be obtained:

[0059] The steady-state probability of the entire charging station:

[0060] Number of vehicles waiting in line outside the charging piles:

[0061] Average residence time of vehicles inside and outside the charging station: L s =L q +sρ * =1.7+2.25=3.95;

[0062] Average waiting time for cars to be charged:

[0063] Average dwell time of cars inside and outside charging stations:

[0064] The probability that a charging vehicle needs to wait at a charging station:

[0065] The number of chargeable vehicles is calculated based on the simplex method based on the energy allocation index, and the method is: first determine the objective function and the predetermined boundary, find the optimal solution based on the objective function and the predetermined boundary, and iterate until the objective function achieves the maximum value or the minimum value.

[0066] The optimization solution is solved by iteration to find the optimal feasible solution:

[0067] maxf(x i )

[0068] g(x i )≤a

[0069] h(x i )≤b ....

[0071] x i >0

[0072] Among them, f(x i) is the general formula to be solved, g(x i )、h(x i ) is a linear constraint function, a and b are linear constraint conditions, x i It is an unknown solution.

[0073] When considering the calculation of the simplex method, it is necessary to first change the constraints to the standard form:

[0074] min c T x

[0075] stAx=b

[0076] x≥0

[0077] in, and

[0078] Constraint matrix A = [p1, p2, · · ·, p n ] can be decomposed into [B,N], where B is the basis matrix and N is the non-basis matrix. Correspondingly, the objective function coefficient c T It can be written as Get the basic feasible solution: In x (0) The objective function value at:

[0079]

[0080] set up From Ax=b we get x B =B -1 B -1 Nx N , the target value at the electric field x

[0081]

[0082] Among them, R is the standard set under non-basic variables,

[0083] By selecting the non-basic variable x j value, so that ∑ j∈R (z j -c j )x j >0, thus a new basic feasible solution is obtained which reduces the objective function.

[0084] Select x k , so that z k -c k =max j∈R {z j -c j}, let z k -c k >0;xk After changing from zero to a positive number, we get the solution of the equation system Ax=b: in, y k =B -1 p k ,Right now:

[0085]

[0086] At the newly obtained point, the objective function value is:

[0087] f=f0-(z k -c k )x k

[0088] So, x k The larger the value, the more the f value decreases; but k The value is limited by feasibility and cannot be infinite;

[0089] When ik >0, to ensure Value

[0090] In order to make x B ≥0, should be Get x k Then, let the original basis change vector The feasible solution is:

[0091] Because the original base The m columns in are linearly independent, and all of them contain p k , due to y k =B -1 p k , so That is, p k Is a vector group A linear combination of rk ≠0, so use p k Replace p Br After that, the vector group is also linearly independent. Therefore, the columns corresponding to the positive components of the new feasible solution x are linearly independent, so x is a basic feasible solution. Repeating the above process can further improve the feasible solution until all z j -c i All are non-positive numbers.

[0092] For the discriminant number of the simplex method, if in the minimization problem, for a basic feasible solution, then z j -c j ≤0, then this solution is the optimal solution, where In linear programming problems, z is usually called j -c j is the discriminant number or test number. In the iterative process, let z k -c k =max{z j -c j};

[0093] Each iteration will result in one of the following three situations:

[0094] 1.z k -c k ≤0. At this time, the current basic feasible solution is the optimal solution.

[0095] 2.z k -c k >0 and y k ≤0. For this situation, the original problem has no solution.

[0096] 3.z k -c k >0 and y k There are values ​​greater than 0 in , which means that a new basic feasible solution can be found.

[0097] For the non-degenerate case, in each iteration x k > 0, the objective function value decreases. Since the number of basic feasible solutions is limited, the optimal solution can be reached after a finite number of iterations.

[0098] The specific calculation steps of the simplex method are:

[0099] Given an initial basic feasible solution x (0) , let the initial basis be B;

[0100] Solution Bx B = b, find x B =B -1 b, let x N =0, calculate the objective function value

[0101] For all non-basic variables, calculate the discriminant number Let z k -c k =max{z j -c j}, if z k -c k ≤0, stop the calculation, the current basic feasible solution is the optimal solution; otherwise, proceed to the next step;

[0102] Solution k =p k , we get y k =B -1 pk , if y k ≤0. Then stop the calculation, and the problem does not have a finite optimal solution; otherwise, proceed to the next step;

[0103] Determine the subscript r so that is the base variable, x k For the basis variable, use p k replace Get the new basis matrix B and return the solution Bx B =b.

[0104] The charging piles at the charging station are AC household charging piles with three power levels, namely 4kW, 7kW and 11kW. When the total power input at the charging station is 80W, the average charging residence time is 1.2h, 0.9h and 0.7h, and the probabilities of waiting are 0.6, 0.5 and 0.4 respectively. It is required to be fully charged within 7 hours, and there should be no more than 4 vehicles waiting for charging. The simplex method is used to calculate the maximum number of vehicles that can be charged.

[0105] A 4kW charging station can charge 1 new energy vehicle, a 7kW charging station can charge 1 new energy vehicle, and an 11kW charging station can charge 1 new energy vehicle.

[0106] min-x1-x2-x3

[0107] 4x1+7x2+11x3≤8

[0108] 0.6x1+0.5x2+0.4x3≤4

[0109] 1.2x1≤7

[0110] 0.9x1≤7

[0111] 0.7x1≤7

[0112] The running results show that the optimal value of the function is about -9, but the minimum value is sought, that is, its opposite is the maximum value, so it can handle up to 9 cars, and the optimal 7kW charging pile can charge 4 cars, the 11kW charging pile can charge 4 cars, and the 4kW charging pile will not charge at all. It can be concluded that the algorithm can solve certain optimization problems and solve different practical optimization problems according to different constraints.

[0113] Allocating energy at the charging station according to the number of chargeable vehicles, by: determining a final charging sequence, and allocating energy at the charging station according to the number of chargeable vehicles, charging time, charging sequence, and the number of charging piles opened;

[0114] The method to determine the final charging sequence is:

[0115] Different charging modes are systematically encoded. If the normal charging mode is selected, the signal is output as "0", if the accelerated charging mode is selected, the signal is output as "1", and if the long-thread slow charging mode is selected, the signal is output as "2";

[0116] The vehicles in the queue are sorted according to the order of charging pile access in a natural sequence, and the signal data of the charging mode is also sorted into the array according to time. In the same sequence, the vehicle information and the charging mode signal correspond one to one;

[0117] Start detecting the charging mode signal from the first vehicle. If the signal is "0", no action will be taken. If the signal is "1", the information of the charging pile will be moved to the first place, and the others will be sorted in sequence. If the second signal is "1", the information of this charging pile will be moved to the second place, and the other orders will remain unchanged. If the signal is "2", the charging pile signal will be extracted separately, and the vacant position will be filled by the following vehicle information.

[0118] Number of charging piles turned on: The maximum number of charging piles turned on is obtained by dividing the input electric energy by the power of the charging pile;

[0119] The power of the charging pile is equal to the total power minus the safety zone, minus the residential power consumption, minus the independent part;

[0120] The safety zone is the flexible space of the transformer and is set according to the actual situation;

[0121] Residential electricity consumption refers to the real-time electricity consumption of residents;

[0122] The independent part is the independent power-consuming part in the charging station;

[0123] Charging time: The charging time can be obtained by dividing the charging capacity by the charging power;

[0124] The following is a specific example of using the present invention to achieve energy distribution:

[0125] There are 10 cars connected to the charging pile in sequence. The 5th and 9th cars need to be charged quickly, and the 8th car needs to be charged for a long time, so the order of charging modes should be as follows: [0; 0; 0; 0; 1; 0; 2; 1; 0; 0]

[0127] According to the final charging sequence, the number of vehicles that charge for a long time, and the specific charging pile numbers, as shown in Table 1, the information of the vehicles that need to be advanced is recorded, and the order of the vehicles in front is adjusted backward step by step, and finally the information of the advanced vehicles is placed in front, and the queue is advanced. After that, if there are advanced vehicles, the vehicles will be arranged after the previous advanced vehicles, and sorted in time. Next, the long-term charging vehicles are eliminated, and they can be directly recorded, and the vehicles behind are advanced one. The final order is that the advanced vehicles are sorted in sequence, followed by the regular charging in sequence, and finally all blank.

[0128] Table 1 Queuing results

[0129]

[0130] After the queue is completed, the order of opening the charging piles is formed. At this time, the fluctuating power has been input into the charging station in real time, and the power distribution part begins to be called. Because the maximum power of a household charging pile is generally 11kW, the highest power is used for charging. The input electric energy divided by the power of the charging pile can get the maximum number of charging piles that can be opened. When calculating, round down to prevent the power from exceeding the standard. Since the amount of charging required by the vehicle has been counted, the specific charging time can be obtained by dividing the charging amount by the charging power, which is convenient for using time to control the switch of the charging pile. The charging station starts to allocate electricity and the rechargeable charging piles start to supply power, and the unpowered charging piles wait for power supply. At this time, if the input power becomes larger, more charging piles at the tail will be opened, and if the input power becomes smaller, some charging piles at the tail will be closed; until the vehicles at the front of the queue begin to be fully charged, the fully charged charging piles will be cut off, and the charging position will be transferred to the charging piles at the tail to continue charging, and so on, until all vehicles are fully charged. For example, the following example: the dynamic power input at quarter-hour intervals is

[0131] 100; 110; 110; 110; 110; 90; 80; 120; 130; 100; 100; 110; 90 (kW); The average power consumption of each charging pile is 30kW. Each car is expected to charge for 5 quarters of an hour. There are 10 cars in total. The charging queue order is 5; 9; 1; 2; 3; 4; 6; 8; 10; 0;

[0132] The number represents the charging pile number, and "0" means that there is no vehicle charging or other services are selected. As shown in Table 2, the real-time charging effect of the charging pile is as follows: It can be seen from the table that the operating result of the first quarter of an hour is that there are 3 charging stations that can charge normally and the expedited No. 5 and No. 9 are charged first. In the following 7 quarters of an hour, only 3 charging piles are turned on regardless of 100kW or 110kW. By the eighth quarter of an hour, only 2 charging piles are turned on, so charging pile No. 4 stops working, but then the power becomes 4 charging piles, so charging pile No. 6 has been charging; starting at the eleventh quarter of an hour, it starts to supply energy and charge the charging piles behind. In the whole process, charging pile No. 7 has never been charged, indicating that it is a long-threaded charging vehicle and does not participate in the conventional charging process. Through the present invention, the working status and charging time of each charging pile can be clearly monitored, while ensuring the full utilization of energy, so as to achieve the most efficient processing method under the condition of limited energy.

[0133] Table 2 Partial results of energy distribution of charging piles

[0134]

[0135] Furthermore, the pid negative feedback regulation algorithm is used for energy scheduling; the pid negative feedback regulation algorithm is used to make the regulation process faster and more accurate. The pid negative feedback regulation algorithm is jointly regulated through three control methods: proportional, integral, and derivative. The error between the target value and the actual value is used as the basis for calculation, and the specific incremental values ​​of the integral part and the derivative part are calculated. At the same time, they are recorded and superimposed as the basis for the next time. Hundreds of cycles are performed in a short period of time, quickly approaching and finally completely consistent with the target value, completing accurate and rapid numerical regulation. The advantage of such regulation is to protect the transformer and reduce the heating of the pipeline caused by power mutation.

[0136] Proportional control controls the output by calculating the proportion of the current error. It is used to quickly respond to errors and control the overall direction of the algorithm when the error is large. The mathematical expression is: u(t) = K p ·e(t), where each parameter has the following meaning.

[0137] Integral control calculates the control output through the past integral value, which is used to make up for the shortcomings of the proportional algorithm, eliminate steady-state errors, and prevent over-regulation of the proportional algorithm. The mathematical expression is: Among them, the meaning of each parameter is.

[0138] Differential control calculates the control output through the differential value of the error, which is used to predict error changes and eliminate overshoot and oscillation. The mathematical expression is: Among them, the meaning of each parameter is.

[0139] The three different controls, proportional control, integral control and differential control, complement each other and eliminate each other's errors. Therefore,

[0140]

[0141] The specific implementation steps of the pid algorithm are:

[0142] First, calculate the error: e(k) = r(k) - y(k), where r(k) is the set value and y(k) is the system output;

[0143] Secondly, calculate the proportional phase, calculate the integral term, and calculate the differential term:

[0144] P(k)=K p ·e(k)

[0145] I(k)=I(k-1)+K i ·e(k)·T

[0146]

[0147] Where T is the sampling time;

[0148] Finally, the control output is calculated: u(k) = P(k) + I(k) + D(k);

[0149] By looping the above algorithm process and iterating step by step, the target value can be quickly approached to achieve the purpose of rapid adjustment. In actual application, the process of adjusting the power from 300kW to 310kW will become smooth due to the PID algorithm, and the system can complete the adjustment in a very short time.

[0150] Embodiment 2: The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit, etc. Moreover, the processor is used to implement the steps of the above-mentioned energy distribution method of the residential area new energy charging station when executing the computer program stored in the memory.

[0151] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0152] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0153] Embodiment 3: Computer readable storage medium embodiment.

[0154] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned method for energy distribution of new energy charging stations in residential areas can be implemented.

[0155] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0156] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A method for distributing energy in a residential area new energy charging station, characterized in that: include: The steady-state probability is calculated based on the service intensity of the service desk, including the following methods: Calculate the service intensity of the service desk: Among them, ρ * is the service intensity of the service station, λ is the number of vehicles arriving at the system per unit time, μ is the number of vehicles processed by the service station per unit time, and S is the number of service stations; Calculate the steady-state probability: The steady-state probability is the probability that the charging pile is idle, and it is also the probability that the new energy vehicle does not have to wait for charging; The method for calculating energy allocation index based on steady-state probability is as follows: Number of vehicles waiting in line outside the charging piles: Average dwell time of cars inside and outside charging stations: Average waiting time for cars to be charged: The probability that a charging vehicle needs to wait at a charging station: The number of vehicles that can be charged is calculated based on the energy distribution index and the simplex method, which specifically includes the following methods: The constraints are constructed based on the energy allocation index, and the number of rechargeable vehicles is calculated based on the simplex method with the goal of maximizing the number of rechargeable vehicles. Determine the final charging sequence and allocate the energy of the charging station according to the number of chargeable vehicles, charging time, charging sequence and the number of charging piles opened, including the following methods: Systematic encoding of different charging modes: if the normal charging mode is selected, the output signal is "0", if the accelerated charging mode is selected, the output signal is "1", and if the long-thread slow charging mode is selected, the output signal is "2"; The vehicles in the queue are sorted according to the order of charging pile access in a natural sequence, and the signal data of the charging mode is also sorted into the array according to time. In the same sequence, the vehicle information and the charging mode signal correspond one to one; The charging mode signal is detected starting from the first vehicle. When signal "0" is encountered, no action is taken. When signal "1" is encountered, the vehicle information is advanced to the first position, and the others are sorted in sequence. When the second signal "1" is encountered, the vehicle information is advanced to the second position, and the other positions remain unchanged. When signal "2" is encountered, the vehicle information is extracted separately, and the vacant position is filled by the following vehicle information.

2. The energy distribution method for a new energy charging station in a residential area according to claim 1 is characterized in that: Energy allocation indicators include the number of vehicles waiting in line outside the charging piles for charging, the average retention time of vehicles inside and outside the charging station, the average waiting time of vehicles waiting to be charged, and the probability that charging vehicles need to wait when they arrive at the charging station.

3. The energy distribution method for a new energy charging station in a residential area according to claim 1 is characterized in that: The method to obtain the number of charging piles turned on is: the input electrical energy divided by the power of the charging pile.

4. The energy distribution method for a new energy charging station in a residential area according to claim 1, characterized in that: How to get the charging time: Divide the charging capacity by the charging power.

5. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the energy distribution method for new energy charging stations in residential areas as claimed in claim 1 when executing the computer program.

6. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the energy distribution method for new energy charging stations in residential areas as claimed in claim 1 is implemented.

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