Delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation

Abstract

The present invention proposes a delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation, which belongs to the technical field of charging scheduling. It includes: constructing a charging load model for electric vehicles to obtain the total demand for electric vehicle charging samples; based on Monte Carlo simulation and the total demand for electric vehicle charging samples, the total charging demand for electric vehicles in residential areas is obtained. The present invention constructs a charging load model for electric vehicles to obtain the total demand for electric vehicle charging samples; based on Monte Carlo simulation, a more specific total demand for electric vehicle charging samples is calculated; secondly, a load regulation based on the power grid load curve is constructed to distribute the calculated total demand for electric vehicle charging samples to the trough section of the power grid load in the time dimension, so as to achieve the purpose of "peak shaving and valley filling"; reduce the fluctuation of the power grid load to maintain the smooth operation of the power grid. The problem of the lack of a method for distributing the total demand for electric vehicle charging samples to the trough section of the power grid load in the time dimension is solved.

Description

Technical Field

[0001] The invention relates to a delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation, and belongs to the technical field of charging scheduling. Background Art

[0002] In recent years, with the continuous increase of new energy vehicles, the capacitor load pressure and electricity safety issues in residential areas have become the main technical difficulties. If we calculate based on 52 million new energy vehicles in 2030 and 30 degrees of charging and discharging per day, more than 500 billion degrees of electricity will be consumed each year, which is about half of the current electricity consumption of urban residents. The power grid system will face a huge impact; some old communities are planned earlier, the power capacity is low, and the charging piles are in a 24-hour non-stop operation state. The charging pile lines are continuously running with power. Aging lines, loose joints, and damaged insulation will cause electrical circuits to ignite, short circuits and other faults, which can easily cause electrical fires.

[0003] Therefore, in order to improve the adverse effects of disorderly charging of electric vehicles in residential areas on the power grid, it is particularly important to allocate the total demand of electric vehicle charging samples to the low-peak section of the power grid load in the time dimension to achieve the purpose of "peak shaving and valley filling", reduce the fluctuation of the power grid load, and maintain the smooth operation of the power grid. Summary of the invention

[0004] 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.

[0005] In view of this, in order to solve the technical problem in the prior art of lacking a specific method for allocating the total demand for electric vehicle charging samples to the low-peak section of the grid load in the time dimension, the present invention provides a delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation.

[0006] Solution 1: A delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation, including:

[0007] Construct an electric vehicle charging load model to obtain the total demand for electric vehicle charging samples;

[0008] Based on Monte Carlo simulation and the total demand of electric vehicle charging samples, the total charging demand of electric vehicles in residential areas is obtained.

[0009] Preferably, the method for constructing the charging load model of electric vehicles is: assuming that the time when the last vehicle returns to the community every day is the charging start time, then the probability density distribution function is:

[0010]

[0011] Where x represents time, μ s represents the average difference in the vehicle charging start time, taking 17.47, σ s represents the standard deviation of the vehicle charging start time, which is 3.41;

[0012] The charging capacity of an electric vehicle is determined by the rated battery capacity and SOC, so the charging capacity Qc of an electric vehicle is:

[0013] Qc=Q1-Q0

[0014] Q1=Q*SOC1

[0015] Q0=Q*SOC0

[0016] Among them, Q is the capacity of the car battery, SOC0 is the SOC at the beginning of charging, and SOC1 is the SOC at the end of charging;

[0017] The SOC0 at the beginning of charging follows a normal distribution:

[0018]

[0019] Among them, μ is 0.46576, σ is 0.17924;

[0020] The total charging demand EQ of electric vehicles is obtained by cumulative summation:

[0021]

[0022] Among them, N is the total number of electric vehicles in the residential area, Q ci is the charging capacity of the i-th electric vehicle;

[0023] Since the private charging piles in residential areas can charge for a long time and the charging piles are fully charged, the capacity of the charging sample of the electric vehicle is Q c 'for:

[0024] Q c =Q*(1-SOC0)

[0025] Q c '=Q*(1-SOC0) into The total demand E of the electric vehicle charging sample is obtained Q 'for:

[0026]

[0027] Preferably, the method for obtaining the total charging demand of electric vehicles in residential areas based on Monte Carlo simulation and the total demand of electric vehicle charging samples comprises the following steps:

[0028] Step a, extracting N random samples of electric vehicles;

[0029] Step b, based on the Monte Carlo simulation probability density distribution function, SOC0 obeys the normal distribution to extract the initial charging time and SOC0 of N samples, and solve the large-scale charging load simulation of electric vehicles;

[0030] Step c, sorting the samples from small to large according to their initial charging time;

[0031] Step d, completing the matching of the electric vehicle and the charging pile;

[0032] Step e: Establish a state matrix model of the charging pile;

[0033] Step f, according to the original transformer information of the community, the actual load of each transformer is accumulated to calculate the maximum load M of the community power grid, the historical data of the community is collected, the power load prediction model J of the community infrastructure and the power load model R of the residents' daily life are established, and the load regulation based on the power grid load curve is constructed to distribute the total demand of the electric vehicle charging sample to the low section of the power grid load in the time dimension;

[0034] Comparison of total demand E for electric vehicle charging samples Q ' and the size of the maximum residual load MJR of the community power grid, if E Q '>MJR, introduce the dispatch load increment P d :

[0035]

[0036] Among them, P d is the dispatch load increment; P e is the output power of the charging pile; N Ti N is the number of electric vehicles that have not been charged at the charging pile in the Tth time period; max is the upper limit of the number of electric vehicles that are not charged at the charging pile in the Tth time period;

[0037] The total demand E of the electric vehicle charging sample is introduced Q ' and dispatch load increment P d The grid load capacity P r for:

[0038] P r =E Q '+P d

[0039] At this time, the total load power P of the AC charging pile group f For: P f =P r -P b , where P b The parameter means the basic load rate of the residential area when the charging pile group is not connected to the power grid;

[0040] Construct load regulation based on the grid load curve to distribute the total demand of electric vehicle charging samples to the low-peak section of the grid load in the time dimension;

[0041]

[0042] Among them, P d is the dispatch load increment; P e is the output power of the charging pile; N Ti N is the number of electric vehicles that have not been charged at the charging pile in the Tth time period; max is the upper limit of the number of electric vehicles that are not charged at the charging pile in the Tth time period;

[0043] Step g: Update the state matrix of the charging pile every 30 minutes, and repeat step f until the charging needs of all electric vehicles are met.

[0044] The beneficial effects of the present invention are as follows: the present invention constructs an electric vehicle charging load model to obtain the total demand for electric vehicle charging samples; calculates a more specific total demand for electric vehicle charging samples based on Monte Carlo simulation; secondly, constructs a load regulation based on the power grid load curve, and distributes the calculated total demand for electric vehicle charging samples to the low-peak section of the power grid load in the time dimension, so as to achieve the purpose of "peak shaving and valley filling"; reduces the fluctuation of the power grid load to maintain the smooth operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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:

[0046] Figure 1 This is a flow chart of the delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation. DETAILED DESCRIPTION

[0047] 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.

[0048] Example 1: Reference Figure 1 The present embodiment describes a delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation, including:

[0049] Step 1: Construct an electric vehicle charging load model to obtain the total demand for electric vehicle charging samples. The method is: the charging capacity of the car is affected by the initial SOC, and the charging start time is affected by the time the vehicle returns to the community. According to the analysis of travel statistics, the last car returns to the community with regularity. After normalizing the statistical data, it can be found that the time when the private car returns to the community obeys the normal distribution of N (17.47, 3.41). Therefore, assuming that the time when the last car returns to the community every day is the charging start time, the probability density distribution function is:

[0050]

[0051] Where x represents time, μ s represents the average difference in the vehicle charging start time, taking 17.47, σ s represents the standard deviation of the vehicle charging start time, which is 3.41;

[0052] The charging capacity of an electric vehicle is determined by the rated battery capacity and SOC, so the charging capacity Qc of an electric vehicle is:

[0053] Qc=Q1-Q0

[0054] Q1=Q*SOC1

[0055] Q0=Q*SOC0

[0056] Among them, Q is the capacity of the car battery, SOC0 is the SOC at the beginning of charging, and SOC1 is the SOC at the end of charging;

[0057] The SOC0 at the beginning of charging follows a normal distribution:

[0058]

[0059] Among them, μ is 0.46576, σ is 0.17924;

[0060] The total charging demand EQ of electric vehicles is obtained by cumulative summation:

[0061]

[0062] Among them, N is the total number of electric vehicles in the residential area, Q ci is the charging capacity of the i-th electric vehicle;

[0063] Since the private charging piles in residential areas can charge for a long time and the charging piles are fully charged, the capacity of the charging sample of the electric vehicle is Q c 'for:

[0064] Q c =Q*(1-SOC0)

[0065] Q c '=Q*(1-SOC0) into The total demand E of the electric vehicle charging sample is obtained Q 'for:

[0066]

[0067] Step 2: Based on the Monte Carlo simulation and the total demand of the electric vehicle charging samples, the total charging demand of the electric vehicles in the residential area is obtained, and the method includes the following steps:

[0068] Step a, extracting N random samples of electric vehicles;

[0069] Step b, based on the Monte Carlo simulation probability density distribution function, SOC0 obeys the normal distribution to extract the initial charging time and SOC0 of N samples, and solve the large-scale charging load simulation of electric vehicles;

[0070] Step c, sorting the samples from small to large according to their initial charging time;

[0071] Step d: Matching electric vehicles and charging piles through communication protocol standardization, intelligent identification technology, and data exchange technology and control;

[0072] Step e: Establish a state matrix model of the charging pile;

[0073] The state matrix model of the charging pile is S = (P idle , P charging , P fault ), where P idle is the probability that the charging pile is in an idle state, P charging is the probability that the charging pile is in the state of charging the electric vehicle, P fault is the probability that the charging pile is in a fault state;

[0074] Step f, according to the original transformer information of the community, the actual load of each transformer is accumulated to calculate the maximum load M of the community power grid, the historical data of the community is collected, the power load prediction model J of the community infrastructure and the power load model R of the residents' daily life are established, and the load regulation based on the power grid load curve is constructed to distribute the total demand of the electric vehicle charging sample to the low section of the power grid load in the time dimension;

[0075] Comparison of total demand E for electric vehicle charging samplesQ ' and the size of the maximum residual load MJR of the community power grid, if E Q '>MJR, introduce the dispatch load increment P d :

[0076]

[0077] Among them, P d is the dispatch load increment; P e is the output power of the charging pile; N Ti N is the number of electric vehicles that have not been charged at the charging pile in the Tth time period; max is the upper limit of the number of electric vehicles that are not charged at the charging pile in the Tth time period;

[0078] The total demand E of the electric vehicle charging sample is introduced Q ' and dispatch load increment P d The grid load capacity P r for:

[0079] P r =E Q '+P d

[0080] At this time, the total load power P of the AC charging pile group f For: P f =P r -P b , where P b The parameter means the basic load rate of the residential area when the charging pile group is not connected to the power grid;

[0081] Construct load regulation based on the grid load curve to distribute the total demand of electric vehicle charging samples to the low-peak section of the grid load in the time dimension;

[0082]

[0083] Among them, P d is the dispatch load increment; P e is the output power of the charging pile; N Ti N is the number of electric vehicles that have not been charged at the charging pile in the Tth time period; max is the upper limit of the number of electric vehicles that are not charged at the charging pile in the Tth time period;

[0084] Step g: Update the state matrix of the charging pile every 30 minutes, and repeat step f until the charging needs of all electric vehicles are met.

[0085] 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. Furthermore, the processor is used to implement the steps of the above-mentioned delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation when executing the computer program stored in the memory.

[0086] 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.

[0087] 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.

[0088] Embodiment 3: Computer readable storage medium embodiment.

[0089] 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 delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation can be implemented.

[0090] 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.

[0091] 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 didactic 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 delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation, characterized in that: include: Construct an electric vehicle charging load model to obtain the total demand for electric vehicle charging samples; Based on Monte Carlo simulation and the total demand of electric vehicle charging samples, the total charging demand of electric vehicles in residential areas is obtained; The method of constructing the charging load model of electric vehicles is: assuming that the time when the last vehicle returns to the community every day is the charging start time, then the probability density distribution function is: Where x represents time, μ s represents the average difference in the vehicle charging start time, taking 17.47, σ s represents the standard deviation of the vehicle charging start time, which is 3.41; The charging capacity of an electric vehicle is determined by the rated battery capacity and SOC, so the charging capacity Qc of an electric vehicle is: Qc=Q1-Q0 Q1=Q*SOC1 Q0=Q*SOC0 Among them, Q is the capacity of the car battery, SOC0 is the SOC at the beginning of charging, and SOC1 is the SOC at the end of charging; The SOC0 at the beginning of charging follows a normal distribution: Among them, μ is 0.46576, σ is 0.17924; The total charging demand EQ of electric vehicles is obtained by cumulative summation: Where N is the total number of electric vehicles in the residential area, Q ci is the charging capacity of the i-th electric vehicle; Since the private charging piles in residential areas can charge for a long time and the charging piles are fully charged, the capacity of the charging sample of the electric vehicle is Q c 'for: Q c '=Q*(1-SOC0) Q c '=Q*(1-SOC0) into The total demand E of the electric vehicle charging sample is obtained Q 'for: Based on the Monte Carlo simulation and the total demand of the electric vehicle charging samples, the method for obtaining the total charging demand of the electric vehicles in the residential area includes the following steps: Step a, extracting N random samples of electric vehicles; Step b, based on the Monte Carlo simulation probability density distribution function, SOC0 obeys the normal distribution to extract the initial charging time and SOC0 of N samples, and solve the large-scale charging load simulation of electric vehicles; Step c, sorting the samples from small to large according to their initial charging time; Step d, completing the matching of the electric vehicle and the charging pile; Step e: Establish a state matrix model of the charging pile; Step f, according to the original transformer information of the community, the actual load of each transformer is accumulated to calculate the maximum load M of the community power grid, the historical data of the community is collected, the power load prediction model J of the community infrastructure and the power load model R of the residents' daily life are established, and the load regulation based on the power grid load curve is constructed to distribute the total demand of the electric vehicle charging sample to the low section of the power grid load in the time dimension; Step g: Update the state matrix of the charging pile every 30 minutes, and repeat step f until the charging needs of all electric vehicles are met.

2. The delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation according to claim 1 is characterized in that: Comparison of total demand E for electric vehicle charging samples Q ' and the size of the maximum residual load MJR of the community power grid, if E Q '>MJR, introduce the dispatch load increment P d : Among them, P d is the dispatch load increment; P e is the output power of the charging pile; N Ti N is the number of electric vehicles that have not been charged at the charging pile in the Tth time period; max is the upper limit of the number of electric vehicles that are not charged at the charging pile in the Tth time period; The total demand E of the electric vehicle charging sample is introduced Q ' and dispatch load increment P d The grid load capacity P r for: P r =E Q '+P d At this time, the total load power P of the AC charging pile group f For: P f =P r -P b , where P b The parameter means the basic load rate of the residential area when the charging pile group is not connected to the power grid.

3. 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 delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation as claimed in claim 1 or 2 when executing the computer program.

4. 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 delayed scheduling charging method for charging piles in residential areas based on Monte Carlo simulation as described in claim 1 or 2 is implemented.

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