CN117648520A - New energy automobile charging load analysis-based carbon emission calculation method and system - Google Patents
New energy automobile charging load analysis-based carbon emission calculation method and system Download PDFInfo
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Abstract
The invention relates to a carbon emission calculation method and system based on new energy automobile charging load analysis. According to the invention, firstly, charging scenes of different new energy automobiles are considered, including residential communities, unit parks, public parking lots, high-speed service areas and the like. And then, sequentially constructing a new energy automobile charging load characteristic analysis model according to different dates and meteorological conditions. And the model is used for describing the number of charging vehicles and the charging demand time in each period of the whole day, and obtaining a load characteristic curve of a target new energy charging scene based on Monte Carlo simulation. Finally, the carbon emission amount is calculated based on the load characteristic curve, the charging type, and the related carbon emission coefficient. The invention realizes more scientific and accurate carbon emission calculation and provides powerful support for energy management and emission reduction path analysis of the new energy charging pile.
Description
Technical Field
The invention belongs to the field of electric vehicle charging load analysis and energy system management, and particularly relates to a new energy vehicle charging load characteristic analysis and carbon emission calculation method and system.
Background
With the continuous development and popularization of new energy automobile markets, carbon emission is one focus of attention in the energy field. In the past, carbon emissions of new energy vehicles have generally been evaluated in consideration of fuel type and carbon emission factor per unit mileage. However, in real life, the running condition of the vehicle is affected by many factors, for example, holidays and weather are suitable, so that the travel of the vehicle is increased, otherwise, the travel of the vehicle is reduced when the working day is not working and the weather is bad. The traditional method ignores factors influencing the driving mileage of the automobile, and only can estimate the carbon emission in a general sense.
As the popularity of charging facilities and the demand for charge load management increase, carbon emission calculation based on load profile analysis becomes a more scientific and accurate method.
At present, a load curve and a load prediction model are generally adopted for carbon emission calculation based on new energy automobile charging load analysis. These methods infer future charge load demands by using historical data and statistical models. However, these methods have the following disadvantages: the traditional method ignores charging requirements and feature analysis under different scenes and lacks in-depth research on scene features. Meanwhile, due to the uncertainty of historical data and models, the load characteristic analysis and prediction accuracy of the traditional method is low, and only coarse-granularity carbon emission estimation can be achieved.
Disclosure of Invention
In view of the above, the invention provides a carbon emission calculation method based on new energy automobile charging load feature analysis, which introduces three influencing factors of charging pile position, date type and meteorological conditions, divides a new energy automobile charging scene, then constructs a charging load feature analysis model aiming at various charging scenes, simulates the number of charging automobiles and charging demand duration in different time periods throughout the day, finally realizes accurate estimation of carbon emission under various influencing factors, and solves the problem of insufficient accuracy of the traditional new energy automobile carbon emission calculation result.
In order to solve the technical problems, the invention is realized as follows:
in a first aspect, the invention provides a carbon emission calculation method based on new energy automobile charging load characteristic analysis,
s1, dividing charging scenes of a new energy automobile according to the positions, date types and meteorological conditions of the charging piles, and acquiring historical data of the charging piles in various charging scenes;
s2, constructing a new energy automobile charging load characteristic analysis model aiming at a target charging scene, wherein the charging load characteristic analysis model is used for describing the number of charging automobiles and the charging demand duration in each period of the whole day;
s3, based on a new energy automobile charging load characteristic analysis model, simulating and extracting the number of charging vehicles and the charging demand duration in different time periods by utilizing Monte Carlo, and obtaining a daily load characteristic curve of electric automobile charging in a target charging scene according to charging power information of a charging pile;
and S4, calculating to obtain the daily carbon emission in the target charging scene based on the daily load characteristic curve, the charging type and the carbon emission coefficient of the electric automobile.
Further, the charging pile position comprises four main scenes, namely a residential area, a unit park, a public parking lot and a high-speed service area;
date types include workdays, holidays, and holidays;
meteorological conditions include temperature and rainfall, the temperature is divided into: { Cold, comfort, hot }, rainfall conditions are divided into: { sunny and rainy };
the charging pile history data comprises charging train number information, charging duration, charging power information and charging type of each train number, wherein the charging type comprises conventional thermal power and renewable energy green electricity.
Further, constructing a new energy automobile charging load characteristic analysis model aiming at the target charging scene further comprises the following steps,
s21, acquiring a total number function of the electric vehicles under a target charging sceneCharging probability function of electric vehicle，/>Time is;
s22, according to the total number function of the vehiclesAnd a charging probability function of an electric vehicle +.>Integrating to obtainThe number of vehicles to be charged is selected in the time period +.>Further obtaining the time distribution of the number of the charged vehicles in the whole day under the target charging scene;
s23, acquiring the distribution of the charging demand time length of the electric automobile under a target charging scene: the charging demand time length of the electric automobile in different time periods is subjected to Gaussian distribution, and the charging time length in different time periods is different in Gaussian distribution parameters.
Further, the method comprises the steps of,the actual historical data of all charging piles in the target charging scene are obtained through statistical fitting;
charging probability functionThe probability density function is adopted, and the probability density function is obtained by statistical fitting of actual historical data or is approximately expressed by a Gaussian mixture model when the historical data is insufficient.
Further, the method comprises the steps of,the approximation expressed by the mixture gaussian model is as follows:
in the method, in the process of the invention,for the desired charging time, +.>For the variance of the charging time>Are weight coefficients of different distributions. The model parameters described above are available from the maximum expectation method based on limited historical data.
Further, the electric vehicle charging demand duration distribution is specifically expressed as follows:
in the method, in the process of the invention,for time variable, representing different time periods, +.>And->Respectively +.>The charge demand duration distribution expectations and variances in the time period are respectively defined by +.>Historical statistics of the charge demand duration in a time period are derived,/-)>Is the charge demand duration.
Further, the daily carbon emission in the target sceneThe expression is as follows:
in the method, in the process of the invention,is carbon emission factor, < >>Indicating energy type>The electric vehicle charge amount obtained based on the daily load characteristic curve of the electric vehicle charge is represented.
In a second aspect, the present invention provides a carbon emission computing system based on new energy automobile charging load feature analysis, comprising:
the load data acquisition module is deployed on each charging pile in the target charging scene and is used for acquiring charging vehicles, charging loads, charging time and charging types of each charging pile in the target charging scene, recording target analysis scene types, dates and meteorological conditions, and constructing a historical database;
the charging load characteristic analysis module is used for acquiring the quantity and time distribution of the charging vehicles of the electric vehicle and the multi-state charging demand duration distribution of the electric vehicle in different time periods, and acquiring a charging daily load characteristic curve of the electric vehicle in a target scene by utilizing Monte Carlo simulation;
the carbon emission calculation module is used for calculating the daily carbon emission in the target analysis scene;
the system can be used for detecting the charging load characteristic analysis and carbon emission calculation method of the new energy automobile, and the online deployment system is applied to daily charging load characteristic prediction and charging scheduling strategy optimization in a target scene.
In a third aspect, the present invention provides a readable storage medium having stored thereon a program or instructions which when executed by a processor, implement the steps of the method as described in the first aspect.
Compared with the prior art, the invention has the following advantages and outstanding technical effects:
1) The system fully considers the charging load characteristics under different scenes, including residential communities, unit parks, public parking lots and high-speed service areas, and is helpful for more accurately predicting and evaluating the charging requirements and carbon emission conditions under different scenes.
2) According to the method, the influence of the date type and the meteorological conditions on the charging requirement is considered, classification is carried out according to the factors, a corresponding charging load characteristic analysis model is constructed, and the prediction accuracy is further improved.
3) The charging load characteristic analysis model can be used for describing the number of charging vehicles and the charging demand duration in each period of the whole day, and analyzing the charging demands and the carbon emission conditions in different periods with finer granularity.
4) According to the invention, the Monte Carlo simulation technology is utilized to more accurately acquire the charging daily load characteristic curve of the electric automobile under specific scenes, specific dates and weather conditions, so that the charging requirements and the carbon emission conditions are better evaluated.
5) According to the method, through analysis and evaluation of the multi-scene charging load of the electric vehicle, carbon emission calculation is realized, and powerful support is provided for energy management and emission reduction path analysis of the new energy charging pile.
6) In summary, the method and the system provided by the invention can more accurately predict and evaluate the charging requirements and the carbon emission conditions in different scenes, and provide an effective method and system for new energy automobile charging management and environmental protection.
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FIG. 1 is a flow chart of a new energy automobile charging load characteristic analysis and carbon emission calculation method provided by the embodiment of the invention;
FIG. 2 is a diagram of a new energy vehicle charging load characteristic analysis and carbon emission calculation system provided by an embodiment of the invention;
FIG. 3 is a graph showing a time distribution of the charge number of a certain bus parking lot according to an embodiment of the present invention;
fig. 4 is a diagram showing a multi-state charge demand duration distribution of an electric vehicle.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the method for calculating carbon emission based on new energy automobile charging load feature analysis according to the embodiment of the invention includes:
s1, dividing four main scenes according to the positions of the charging piles, wherein the four main scenes comprise residential communities, unit parks, public parking lots and high-speed service areas. Three different date types, three different temperature conditions and rainfall conditions are considered in each category of scene, the dates consider the following classification: { weekday, holiday }, weatherThe conditions mainly consider two conditions of temperature and rainfall, and the temperature can be divided into: { Cold, comfort, hot }, rainfall conditions can be divided into: { sunny and rainy }. Wherein the cold corresponding temperature range isComfort temperature range is +.>The heat temperature range is->. The method comprises the steps of obtaining historical data of the charging pile under different scenes, different dates, different temperature conditions and rainfall conditions, wherein the historical data comprise charging train number information, charging duration of each train number, charging power information, charging type and the like, and the charging type comprises conventional thermal power and renewable energy green electricity.
S2, sequentially constructing a new energy automobile charging load characteristic analysis model according to different scenes, different dates, different temperature conditions and rainfall conditions. The charging load characteristic analysis model is used for describing the number of charging vehicles and the charging demand duration in each period of the whole day. The time distribution of the number of the charging vehicles is determined by historical data of all charging piles; in order to better describe the charging requirements of the electric vehicle in different time periods of the day, the charging duration distribution in each time period taking the hour as a unit is adopted, and the charging duration distribution is obtained by fitting historical data of the charging pile.
Further, the step S2 specifically includes:
s21, acquiring a total number function of the electric vehicles under a target charging sceneCharging probability function of electric vehicle，/>Time is; />Changes over time, typically affected by scene, date type, and weather conditions. />The change over time can be statistically fitted from the actual historical data of all charging piles in the target charging scenario. Each electric vehicle is->Charging probability +.>Can be determined by probability density function, < >>And is also statistically fitted from the actual historical data. As the actual historical data volume is insufficient, it is difficult to fit +.>Time distribution rule, each electric vehicle is in +.>Probability density function of time charging>The approximation can be expressed by a mixed Gaussian model:
in the method, in the process of the invention,for the desired charging time, +.>For the variance of the charging time>Are weight coefficients of different distributions. Based on limited historical data, by maximumThe above model parameters can be obtained by the expectation method.
S22, according to the total number of vehiclesAnd charging probability per vehicle->Can get at->The number of selectively charged vehicles in a time period +.>And further obtaining the time distribution of the number of the charging cars in the whole day.
S23, acquiring multi-state charging duration distribution considering time. Can be expressed as follows:
in the method, in the process of the invention,as time variable, different time periods are represented, here the day is divided into 24 time periods at one hour intervals, +.>And->Respectively +.>The charge demand duration distribution expectations and variances in the time period are respectively defined by +.>Historical statistics of charge demand duration over a period of timeAccording to which (I) is (are)>Is the charge demand duration.
S3, selecting the time distribution of the number of the electric vehicle charging vehicles corresponding to the day to be analyzed according to the specific scene, the date type and the meteorological conditionsAnd electric automobile multi-state charge demand duration distribution +.>. And simulating and extracting the charge train number and the charge demand duration in different time periods by utilizing Monte Carlo. Monte Carlo simulation is a numerical calculation method based on random sampling. The invention uses Monte Carlo simulation to simulate the randomness of the charge train number and the charge demand duration. And (3) accurately calculating the charging load of each sample by combining the charging power information of the charging pile, and accumulating to obtain the charging daily load characteristic curve of the electric automobile in the target scene.
And S4, calculating the daily carbon emission based on the daily load characteristic curve, the charging type and the carbon emission coefficient corresponding to the charging type.
Based on daily load characteristic curve calculation process, acquiring charge quantity of electric automobile，/>Representing the energy type, including the conventional thermal power and the renewable energy green power, the daily carbon emission in the target scene can be represented as follows:
in the method, in the process of the invention,is carbon emission factor, < >>Is daily carbon emission.
Implementations of the invention may be realized by means of a computer program, which may employ a programming language including, but not limited to Python, MATLAB.
Referring to fig. 2, the invention provides a carbon emission computing system based on new energy automobile charging load characteristic analysis. Comprising the following steps:
the load data acquisition module is deployed on each charging pile in the target analysis scene and is used for acquiring charging vehicles, charging loads, charging time and charging types of each charging pile in the target analysis scene, recording the types, dates and meteorological conditions of the target analysis scene, and constructing a historical database;
the charging load characteristic analysis module is used for acquiring time distribution of the number of charging vehicles of the electric vehicle and multi-state charging demand duration distribution of the electric vehicle in different time periods, and acquiring a charging daily load characteristic curve of the electric vehicle in a target scene by utilizing Monte Carlo simulation;
the carbon emission calculation module is used for calculating the daily carbon emission in the target analysis scene;
the verification and prediction module is used for verifying the new energy automobile charging load characteristic analysis and carbon emission calculation method, and the online deployment system is applied to daily carbon emission calculation under a target scene.
The embodiment of the invention also provides a readable storage medium, wherein a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the process of the embodiment of the carbon emission calculation method and the system based on the new energy automobile charging load characteristic analysis is realized, and the same technical effect can be achieved, so that the repetition is avoided, and the description is omitted.
Taking a certain bus parking lot as an example, the load characteristics of all charging piles in the parking lot are analyzed. Fig. 3 shows the charge fraction time distribution of an electric vehicle throughout the day. It can be seen that the charge times are mainly concentrated at night and noon. Fig. 4 shows a multi-state charge demand duration distribution diagram of an electric vehicle. The influence of different moments in the day on the charging behavior can be better considered by counting the charging demand duration distribution of the electric automobile in different time periods. For example, in the bus parking lot example, buses have explicit operating time and route constraints, so their charging time is mostly concentrated at night. The daytime charging is concentrated at rest moments such as noon, and the charging time span is shorter. Therefore, the charge duration distribution at night and in the daytime of the bus station shows great variability, and the time-sharing consideration is needed. As can be seen in fig. 4, the average value of the multi-state charge demand duration distribution of the electric vehicle is significantly higher at night than at daytime. The actual scene is compounded more, and the accuracy of load characteristic analysis and prediction is improved. It is emphasized that the specific multi-state charging demand duration distribution parameters of the electric automobile need to be obtained by fitting historical data under specific scenes, dates and meteorological conditions.
According to the method, the charging requirements and the characteristic analysis under different scenes are fully considered, the time distribution of the charging times of the electric vehicle and the time distribution of the charging requirements of the electric vehicle under different time periods are obtained, the charging daily load characteristic curve of the electric vehicle is obtained through simulation by using a Monte Carlo method, and more scientific and accurate carbon emission calculation is realized.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present invention is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of embodiments, it will be clear to those skilled in the art that the above example method may be implemented by means of software plus necessary general purpose hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (8)
1. The carbon emission calculation method based on the new energy automobile charging load analysis is characterized by comprising the following steps of:
s1, dividing charging scenes of a new energy automobile according to the positions, date types and meteorological conditions of the charging piles, and acquiring historical data of the charging piles in various charging scenes;
s2, constructing a new energy automobile charging load characteristic analysis model aiming at a target charging scene, wherein the charging load characteristic analysis model is used for describing the number of charging automobiles and the charging demand duration in each period of the whole day;
s3, based on a new energy automobile charging load characteristic analysis model, simulating and extracting the number of charging vehicles and the charging demand duration in different time periods by utilizing Monte Carlo, and obtaining a daily load characteristic curve of electric automobile charging in a target charging scene according to charging power information of a charging pile;
and S4, calculating to obtain the daily carbon emission in the target charging scene based on the daily load characteristic curve, the charging type and the carbon emission coefficient of the electric automobile.
2. The carbon emission calculation method based on the new energy automobile charging load analysis according to claim 1, characterized in that:
the charging pile position comprises four main scenes, namely a residential community, a unit park, a public parking lot and a high-speed service area;
date types include workdays, holidays, and holidays;
meteorological conditions include temperature and rainfall, the temperature is divided into: { Cold, comfort, hot }, rainfall conditions are divided into: { sunny and rainy };
the charging pile history data comprises charging train number information, charging duration, charging power information and charging type of each train number, wherein the charging type comprises conventional thermal power and renewable energy green electricity.
3. The carbon emission calculation method based on the new energy automobile charging load analysis according to claim 2, characterized in that:
the construction of the new energy automobile charging load characteristic analysis model aiming at the target charging scene further comprises the following steps,
s21, acquiring a total number function of the electric vehicles under a target charging sceneCharging probability function of electric vehicle，/>Time is;
s22, according to the total number function of the vehiclesAnd a charging probability function of an electric vehicle +.>Integrating to obtainThe number of vehicles to be charged is selected in the time period +.>Further obtaining the time distribution of the number of the charged vehicles in the whole day under the target charging scene;
s23, acquiring the distribution of the charging demand time length of the electric automobile under a target charging scene: the charging demand time length of the electric automobile in different time periods is subjected to Gaussian distribution, and the charging time length in different time periods is different in Gaussian distribution parameters.
4. The carbon emission calculation method based on new energy automobile charging load analysis according to claim 3, characterized in that:
the actual historical data of all charging piles in the target charging scene are obtained through statistical fitting;
charging probability functionThe probability density function is adopted, and the probability density function is obtained by statistical fitting of actual historical data or is approximately expressed by a Gaussian mixture model when the historical data is insufficient.
5. The method for calculating carbon emissions based on new energy automobile charging load analysis according to claim 4, wherein:
the approximation expressed by the mixture gaussian model is as follows:
in the method, in the process of the invention,for the desired charging time, +.>For the variance of the charging time>Are weight coefficients of different distributions.
6. The carbon emission calculation method based on new energy automobile charging load analysis according to claim 3, characterized in that:
the electric automobile charging demand duration distribution is specifically expressed as follows:
in the method, in the process of the invention,for time variable, representing different time periods, +.>And->Respectively +.>Charging demand duration distribution expectations and variances within a time period, +.>Is the charge demand duration.
7. The carbon emission calculation method based on the new energy automobile charging load analysis according to claim 1 or 2 or 3 or 4 or 5 or 6, characterized in that:
daily carbon emission in target sceneThe expression is as follows:
in the method, in the process of the invention,is carbon emission factor, < >>Indicating energy type>The electric vehicle charge amount obtained based on the daily load characteristic curve of the electric vehicle charge is represented.
8. The carbon emission computing system based on the new energy automobile charging load analysis is characterized by comprising:
the load data acquisition module is deployed on each charging pile in the target charging scene and is used for acquiring charging vehicles, charging loads, charging time and charging types of each charging pile in the target charging scene, recording target analysis scene types, dates and meteorological conditions, and constructing a historical database;
the charging load characteristic analysis module is used for acquiring the quantity and time distribution of the charging vehicles of the electric vehicle and the multi-state charging demand duration distribution of the electric vehicle in different time periods, and acquiring a charging daily load characteristic curve of the electric vehicle in a target scene by utilizing Monte Carlo simulation;
and the carbon emission calculation module is used for calculating the daily carbon emission in the target analysis scene.
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