CN116502964A - Method and device for monitoring and modeling carbon emission space-time distribution of electric automobile - Google Patents
Method and device for monitoring and modeling carbon emission space-time distribution of electric automobile Download PDFInfo
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Abstract
The embodiment of the specification provides a method and a device for monitoring the space-time distribution of carbon emission of an electric automobile, wherein the method comprises the following steps: simulating a charging demand decision of a user based on the remorse theory, and constructing a charging demand decision model; analyzing trip random characteristics of the user based on a trip chain theory, and constructing a user trip behavior model; based on the charging demand decision model and the user traveling behavior model, randomly sampling by adopting a Monte Carlo method to obtain the charging load space-time distribution of a single electric automobile, superposing the charging loads of all electric automobiles to obtain the total charging load of the electric automobile in a certain time space range, and calculating the carbon emission space-time distribution and the total emission amount of the electric automobile by combining the total charging load of the electric automobile with the average carbon emission factor of the electric power industry in the area.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method and a device for monitoring and modeling the space-time distribution of carbon emission of an electric automobile.
Background
The data report shows that the production quantity and sales amount of new energy automobiles in China are all in the first world in three years from 2018 to 2020. The EV has flexibility and energy storage characteristics after clustering, can be used as a flexible load of a user side in a power grid, can also be used as distributed power equipment, and the EV reasonable charging strategy can help to relieve the power load of the power grid, cut peaks and fill valleys, and can also provide auxiliary services such as frequency modulation, standby and the like for the power grid. However, how to perform effective modeling on reasonable electric vehicle charging load and carbon emission monitoring is an important premise facing current EV development.
In the aspect of time distribution modeling of the charging load of the electric automobile, the EV is classified and analyzed according to different using modes of the EV to generate different charging powers, a starting state of charge (SOC) of the EV and the starting charging time are extracted by using a Monte Carlo method, and the charging load calculation of the electric automobile is realized on a time scale. In the prior art, the Monte Carlo method is utilized to calculate the charging load on a time scale, and the charging time of the user is subdivided into three types by introducing the travel characteristics of the user, so that the EV charging load model is closer to reality. Under the condition of less electric automobile travel data, the travel behavior of the EV is simulated according to the traditional fuel automobile travel data obtained through statistics, probability simulation is carried out on partial random factors, the charging requirements of a plurality of EVs are obtained by using a Monte Carlo method, the defect of less investigation data of the electric automobile is overcome, and the subjectivity is too strong.
In addition, another prior art divides the charging behavior of EVs into unordered home charging, home charging at unordered non-load peak times, "smart" home charging, and unordered public place charging, calculating the EV charging load in each of the four cases. In the prior art, a queuing theory is adopted to solve the charging load calculation problem of the electric vehicle, and a focusing point for solving the charging load problem of the electric vehicle is transferred to a centralized charging station from the EV. In addition, there are prior art proposals for two-stage poisson distribution EV charging station concentration models based on the initial SOC of the EV and the time the EV arrives at the charging station. Another prior art calculates the charging load of an EV at a highway charging station based on highway traffic statistics. The two methods are suitable for centralized charging load calculation. In the prior art, on the basis of EV scale prediction, a private vehicle charging load space-time distribution model with alternating vehicle scale deduction and behavior simulation is built by combining vehicle travel behavior characteristics.
In the aspect of space distribution simulation of charging load and carbon emission of the electric automobile, the prior art uses a traffic start-stop (origin destination, OD) analysis method, namely a departure place-destination analysis in intelligent traffic research, to model and research randomness of the electric automobile in space position. On the basis of OD matrix analysis, traffic flow analysis is established to form positive feedback, so that the position distribution probability of the electric automobile is continuously and finely simulated. In the prior art, roads in cities are divided into different areas, and then the traveling characteristics of the electric automobile are simulated by using an OD analysis method, so that the result shows that the charging load of the EV is constrained by a traffic road network and is influenced by the different areas. The traveling chain theory is introduced in the prior art to simulate traveling characteristics of the EV within one day, so that the charging load of the EV is distributed more finely in space. As for carbon emission monitoring of electric vehicles, measurement and calculation are mainly performed by multiplying the charging load of the electric vehicle by the carbon emission factor of the electric power industry in the area.
At present, more EV charging load and carbon emission modeling work is carried out on a time scale, so that the accuracy of EV load and carbon emission space-time modeling is lower.
Disclosure of Invention
The invention aims to provide a method and a device for monitoring and modeling the carbon emission space-time distribution of an electric automobile, and aims to solve the problems in the prior art.
The invention provides a method for monitoring and modeling the space-time distribution of carbon emission of an electric automobile, which comprises the following steps:
step 1, simulating a charging demand decision of a user based on a remorse theory, and constructing a charging demand decision model;
step 2, analyzing trip random characteristics of the user based on a trip chain theory, and constructing a user trip behavior model;
and 3, based on the charging demand decision model and the user traveling behavior model, randomly sampling by adopting a Monte Carlo method to obtain the charging load space-time distribution of a single electric automobile, superposing the charging loads of all the electric automobiles to obtain the total charging load of the electric automobiles in a certain time space range, and calculating the carbon emission space-time distribution and the emission total amount of the electric automobiles by combining the total charging load of the electric automobiles and the average carbon emission factor of the electric power industry in the region.
The invention provides an electric automobile carbon emission space-time distribution monitoring system, which comprises:
the charging demand decision module is used for simulating the charging demand decision of the user based on the remorse theory and constructing a charging demand decision model;
The user travel behavior module is used for analyzing travel random characteristics of the user based on a travel chain theory and constructing a user travel behavior model;
the processing module is used for randomly sampling by adopting a Monte Carlo method based on the charging demand decision model and the user traveling behavior model to obtain the charging load space-time distribution of a single electric automobile, superposing the charging loads of all the electric automobiles to obtain the total charging load of the electric automobiles in a certain time space range, and calculating the carbon emission space-time distribution and the emission total amount of the electric automobiles by combining the total charging load of the electric automobiles with the average carbon emission factor of the electric power industry in the area.
The embodiment of the invention also provides electronic equipment, which comprises: the method comprises the steps of a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the computer program realizes the method for monitoring and modeling the carbon emission space-time distribution of the electric automobile when being executed by the processor.
The embodiment of the invention also provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and the steps of the method for monitoring and modeling the carbon emission space-time distribution of the electric automobile are realized when the program is executed by a processor.
By adopting the embodiment of the invention, the remorse theory is adopted to simulate the charging behavior decision of the user, so that each charging behavior of the modeled user is closer to reality. The user charging decision model is built based on the regret theory, so that the method is more in line with the characteristic of incomplete rationality during user selection and is more in line with the actual situation. The simulation result better verifies the rationality of the modeling method.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow chart of an electric vehicle carbon emission space-time distribution monitoring modeling method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an electric vehicle charging load space-time distribution modeling framework according to an embodiment of the invention;
FIG. 3 is a schematic representation of a residence time probability distribution of an embodiment of the present invention;
FIG. 4 is a schematic illustration of a single trip probability distribution in accordance with an embodiment of the present invention;
Fig. 5 is a schematic diagram of a regional electric vehicle charging power time distribution according to an embodiment of the present invention;
fig. 6 is a schematic diagram of the charging load expectations of the electric vehicle in each region according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of two number of electric vehicle charging load expectations in accordance with an embodiment of the present invention;
fig. 8 is a schematic diagram of 2500 electric vehicle charging loads according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an electric vehicle charging load predicted by different methods according to embodiments of the present invention;
FIG. 10 is a schematic diagram of an electric vehicle carbon emission space-time distribution monitoring modeling device according to an embodiment of the invention;
fig. 11 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to better simulate the travel characteristics of the EV in one day, the embodiment of the invention selects a travel chain theory which can completely simulate the travel behavior of the EV for research. In the aspect of user behavior habit, the simulation is mainly based on the existing statistical data, and under the trend of high-speed development of electric vehicles in the future, the behavior decision of the user is an important factor influencing EV charging load and carbon emission modeling, so that the embodiment of the invention adopts the regret theory to simulate the charging behavior decision of the user, and each charging behavior of the user is closer to reality.
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the inventive embodiments.
At present, more EV charging load and carbon emission modeling work is carried out on a time scale, but the research on a space scale is relatively insufficient, so that the accuracy of EV load and carbon emission space-time modeling is lower. However, accurate space-time modeling of EV load and carbon emission is an important basis for assessing EV disordered charge characteristics and achieving scientific scheduling and carbon emission reduction. Based on the above findings, the present application proposes a method for monitoring the spatial and temporal distribution of carbon emissions of an electric vehicle, so as to improve the above problems, and the method is described in detail below.
Method embodiment
According to an embodiment of the invention, a method for monitoring and modeling carbon emission space-time distribution of an electric automobile is provided, and fig. 1 is a flowchart of the method for monitoring and modeling carbon emission space-time distribution of an electric automobile according to the embodiment of the invention, as shown in fig. 1, the method for monitoring and modeling carbon emission space-time distribution of an electric automobile according to the embodiment of the invention specifically comprises:
step 101, simulating a charging demand decision of a user based on a remorse theory, and constructing a charging demand decision model; the step 101 specifically includes:
step 11, extracting a travel chain of a current electric automobile, obtaining basic travel information of the electric automobile, and calculating an initial state of charge (SOC) of the electric automobile according to the basic travel information, wherein the basic travel information specifically comprises: the method comprises the steps of starting an electric automobile, starting time, travel chain parking point information, end point positions and the number N of parking points including an end point; specifically: the initial state of charge SOC of the electric vehicle may be calculated from the basic travel information based on equation 1:
wherein μ is an average value of the starting SOCs; sigma is the standard deviation;
step 12, setting a regret initial value;
step 13, calculating remorse degree: calculating the SOC of the electric automobile at the current moment, reading the electricity price at the current moment, calculating the charging time required by the electric automobile to be charged to be full, and the charging time required by the electric automobile to be charged to meet the psychological expected electric quantity of a user, reading the parking time of the electric automobile at the parking point, and calculating the remorse value RU (n, t) of the electric automobile for making a charging decision at the parking point n at the time t according to the remorse function; the calculating the SOC of the present moment of the electric automobile specifically includes:
Calculating the SOC of the electric automobile reaching the current parking point according to the formula 2:
wherein,,is the time from the last stop point; />For the time to reach the current stop standing point; omega d The consumption per kilometer of the road section from the parking point S-1 to the parking point S; l (L) d Is the travel distance of the road section from the parking point S-1 to the parking point S; e (E) b Is the total charge of the battery.
Step 14, comparing the remorse value: if the remorse value of the electric automobile for charging decision at the standing point n at the time t is smaller than RUmin, replacing RUmin with the remorse value, otherwise, turning to the next standing point, and executing step 13, wherein RUmin represents the current minimum remorse value.
Step 102, analyzing trip random characteristics of a user based on a trip chain theory, and constructing a user trip behavior model;
step 103, based on the charging demand decision model and the user traveling behavior model, randomly sampling by adopting a Monte Carlo method to obtain the charging load space-time distribution of a single electric automobile, superposing all the charging loads of the electric automobile, obtaining the total charging load of the electric automobile in a certain time space range, and calculating the carbon emission space-time distribution and the emission total amount of the electric automobile by combining the total charging load of the electric automobile and the average carbon emission factor of the electric industry in the area. . Step 103 specifically includes:
Step 31, setting the total amount of the electric automobile;
step 32, reading data information of a j-th electric automobile, wherein the data information comprises the land location of the electric automobile and the starting time of a journey;
step 33, extracting a travel chain of the j-th electric automobile through a Monte Carlo method based on the user travel behavior model, and reading travel chain information;
step 34, when the electric automobile reaches the h parking point, judging whether the current parking point needs to be charged or not based on the charging demand decision model, if the decision information is that charging is needed, updating the residual SOC information of the automobile and the charging load information of the land where the automobile is charged, and if the decision information is that charging is not needed, the residual SOC information of the electric automobile is unchanged, and the charging load information of the land where the parking point of the automobile is located is kept unchanged;
step 35, when the vehicle goes to the next parking point, repeating step 34 until the travel of the vehicle is finished and the travel returns to the land where the end point of the travel chain is located, after the whole travel behavior is finished, judging whether to need charging decision according to the set SOC threshold value, updating the vehicle state of charge information SOC, and updating the charging load information of the land;
Step 36, reading information of the next electric automobile, and repeating the steps 32 to 35 until the charging behaviors of all the electric automobiles are simulated;
step 37, counting the total charging load of the electric automobile in each land;
and step 38, multiplying the total charging load of the electric vehicle by the carbon emission factor of the electric power industry in each region to obtain the carbon emission of the electric vehicle in each region.
The following describes the above technical solution of the embodiment of the present invention in detail.
1. Regret theory: the charging behavior of the user is affected by various factors, such as the state of charge of the electric vehicle at the current time, the electricity price at the current time, the residence time of the user at the current location, and the like. In order to better simulate the situation after the electric automobile fully develops in the future, the embodiment of the invention assumes that the charging piles at any positions are abundant, namely the charging behavior of the user is not influenced by the positions of the charging piles.
Because the user is irrational to the current decision of whether to charge or not, the embodiment of the invention introduces the regret theory to build the charging decision model of the electric automobile. The regret theory is a common theory in economics, mainly researches the psychological and scheme selection of users under the condition of uncertainty and risk, and is well applied to the fields of power grid optimization scheduling, risk assessment and the like. When the remorse theory is applied to the mind of the user of the electric automobile, the remorse theory considers that the current decision of the traveler on the charging action depends not only on the benefits of the selected scheme but also on the benefits of other non-selected schemes, such as the possibility that the charging is selected at another point in time at another place, and the judgment of whether the charging is needed at the current moment is affected. If the unselected scheme B is less effective than the currently selected scheme A, the user will feel happy, otherwise will feel remorse, and remorse will show correlation with the difference in benefit from the selected scheme A and the abandoned scheme. According to subjective feeling of travelers, when the benefit brought by the selected scheme A is better than that of the abandoned scheme, the regret value is a negative number, when the benefit brought by the selected scheme A is worse than that of the abandoned scheme, the regret value is a positive number, and the larger the benefit difference between schemes is, the larger the absolute value of the regret value is. Therefore, the regret value can quantify the subjective feeling of the user and becomes the basis of scheme selection.
For an efficient selection scheme, the regret value needs to be calculated. And calculating absolute differences of benefits of various schemes, and comparing the absolute values to simulate the remorse value. When a user selects to charge at a certain place at a certain time, the scheme selection is influenced by the current charge state, the charge duration and the real-time electricity price of the vehicle. At this time, the user desired utility may be expressed as:
wherein: p is p a A selection probability for the selected scheme; u (t) a ) For utility functions, expressing utility embodied by the selected scheme, U (t a ) There are a variety of expression forms. Utility function U (t) a ) The exponential form of (a) is as follows:
U(t a )＝[1-exp(θt a )]/θ (2)
where θ is a risk aversion parameter. The linear utility function is expressed as follows:
U(t a )＝αt a +βc a +χd a +ε (3)
wherein: alpha, beta, χ are the charge time, the current state of charge of the electric automobile and the coefficient of the real-time electricity price respectively; epsilon is a dimensionless parameter, and the feeling of remorse is represented by a remorse utility function RU (t), as shown in formula (4):
2. charging demand judgment based on regret theory
The charge demand judgment of the user at the current moment is influenced by the charge state of the electric automobile at the current moment, the electricity price at the current moment and the residence time of the user at the current position. The lower the state of charge of the electric automobile at the current moment, the higher the anxiety of the user, the greater the charging demand, and the lower the remorse degree of the selected charging scheme, and the higher the remorse degree is conversely. The charging time of the user at the current moment of the current position and the residence time of the user at the current position also influence the remorse degree of the user selecting the charging scheme, and the longer the charging time, the higher the remorse degree, and the lower the remorse degree, conversely. The higher the electricity price at the current charging moment, the higher the remorse of the charging behavior selected by the user, and conversely, the lower.
The initial state of charge of the electric automobile obeys gaussian normal distribution:
wherein: mu is the average value of the initial SOC; sigma is the standard deviation.
The state of charge of the electric automobile reaching the current stop standing point is as follows:
wherein:is the time from the last stop point; />For the time to reach the current stop standing point; omega d The consumption per kilometer of the road section from the parking point S-1 to the parking point S; l (L) d Is the travel distance of the road section from the parking point S-1 to the parking point S; e (E) b Is the total charge of the battery.
The time length of charging when the user is:
wherein:the method comprises the steps of providing the charge quantity of the electric automobile when a user arrives at a parking point currently; />The method comprises the steps of (1) charging a battery when a user leaves a current parking point; />Is the charging power; η is the charging efficiency. The single charge duration of the user at a certain stop point needs to be smaller than the stop time, i.e +.>
Based on psychological demands of users for saving charging cost, charging electricity prices directly affect charging decisions of users, and peak-to-valley electricity prices are different from charging decisions of users under average electricity prices. When the average electricity rate is considered, the electricity rate is not within a range that affects the user charging decision, and when the peak-to-valley electricity rate is considered, the effect of the electricity rate on the user charging decision is as follows:
α i,t ＝max{0,β i,t (y i,t -y i+1,t′ )} (8)
β i,t ＝-(1-K SOC,t )C (9)
wherein: alpha i,t A remorse value influencing factor related to electricity price, which is generated by selecting time t and charging at the i residence point; y is i,t And y i+1,t′ The charge electricity price at the parking point i at time t and the charge electricity price at the parking point i+1 at time t', respectively. Beta i,t Is a parameter of importance degree of the reactive electricity price attribute related to the state of charge (SOC) of the electric automobile at the moment t.
For potential chargeable points, calculating the remorse value, and selecting the parking point charge with the minimum remorse value for the chargeable points based on the remorse minimization principle.
The single electric automobile charging demand judging step based on the regret theory is as follows:
(1) Extracting a travel path: and extracting a travel chain of the electric automobile to obtain basic travel information of the electric automobile, wherein the basic travel information comprises an electric automobile starting point, starting time, travel chain parking point information, end point positions, the number N of parking points including the end point, and calculating the starting SOC of the electric automobile according to a formula (5).
(2) Setting a regret initial value: 1000 is selected as an initial value of the remorse degree, and the whole operation process is to obtain the minimum remorse degree, so that the initial value of the remorse degree cannot be set too small, otherwise, the judgment result is affected.
(3) Calculating remorse degree: calculating the SOC of the electric automobile at the current moment, reading the electricity price at the current moment, calculating the charging time required by the electric automobile to be charged to be full, and the charging time required by the electric automobile to be charged to meet the psychological expected electric quantity of a user, reading the parking time of the electric automobile at the parking point, and then calculating the remorse degree value RU (n, t) of the electric automobile for making a charging decision at the parking point n at the time t according to the remorse degree function.
(4) Comparing the regret values: and (3) if the remorse value of the charging decision of the electric automobile at the standing point n at the time t is smaller than RUmin, replacing, otherwise, turning to the next standing point, and performing the calculation of the step (3).
The remorse theory can be well related to daily charging decisions, quantitative representation is carried out on remorse, remorse degree of charging decisions which a user possibly feels can be quantitatively and intuitively displayed in a data form, and the method is more practical. In addition, the parameters are fewer, the calculation is relatively simple, and the method is suitable for large-scale charge simulation analysis.
3. EV charging load and carbon emission space-time modeling taking into account user behavior decisions.
The charging load distribution of the electric automobile depends on the traveling behavior and the charging behavior of a user, a user charging behavior decision model based on the previous regret and each influence factor thereof are combined with a user traveling behavior model of a traveling chain, random sampling is carried out by a Monte Carlo method, so that the charging load space-time distribution of a single electric automobile is obtained, then all the electric automobile charging loads are overlapped to obtain the electric automobile charging load in a certain time space range, and finally the carbon emission of the electric automobile in the region is obtained by multiplying the electric automobile charging load by the carbon emission factor of the electric industry in the region, so that the carbon emission monitoring of the electric automobile is realized.
The charging power of all electric vehicles in the area can be represented by formula (10).
As shown in fig. 2, the main steps of the analysis are as follows:
(1) The total electric automobile amount is set, the data can adopt statistical data and prediction data provided by related institutions or government planning data, the data can also be obtained through prediction by a mathematical method, and a specified value can also be directly set when the model is verified.
(2) And reading data information of the jth electric automobile, wherein the data information comprises the land parcel position where the jth electric automobile is located and the moment when the journey starts.
(3) And extracting a travel chain of the jth electric automobile by a Monte Carlo method, and reading travel chain information.
(4) When the electric automobile reaches the h parking point, a charging decision judging method based on the regret theory is adopted to judge whether the current parking point needs to be charged. If the decision information is that charging is needed, updating the residual SOC information of the vehicle and updating the charging load information of the land block to which the vehicle belongs when charging. If the decision information is that charging is not needed, the residual SOC information of the electric automobile is unchanged, and the charging load information of the land where the parking point of the automobile is kept unchanged.
(5) And (4) the vehicle goes to the next parking point, and the step (4) is repeated until the travel of the vehicle is finished and the vehicle returns to the land where the end point of the travel chain is located. After the whole trip behavior is finished, the user finally judges whether the charging decision is needed or not based on the set SOC threshold value, the vehicle state of charge information SOC is updated, and the charging load information of the land is updated.
(6) And (5) reading information of the next electric automobile, and repeating the steps (2) to (5) until the charging behaviors of all the electric automobiles are simulated.
(7) And then counting the charging load of the electric automobile in each land.
(7) And finally multiplying the carbon emission factors of the power industry in each region to obtain the carbon emission of the electric automobile in each region.
Simulation and result analysis:
1 parameter set-up and fitting analysis thereof
The embodiment of the invention mainly utilizes the statistical data of the resident trip investigation of the NHTS of the United states department of transportation 2009 to carry out simulation analysis, names the resident point of the trip chain as a Work (W) of returning Home (Home, H) and Other matters (Other, O) according to the purpose of trip activities. The single trip in the travel chain includes six types, home to workplace "H-W", workplace to home "W-H", home to other activity location "H-O", other activity location to home location "O-H", workplace to other activity location "W-O", and other activity location to workplace "O-W". The strokes 'H-W' and 'W-H' are relatively concentrated under the constraint of general working time, and normal distribution can be adopted for fitting. Other strokes are influenced by spontaneous behaviors of users, the starting time of the strokes is relatively dispersed, normal distribution, weibull distribution and Gamma distribution are used for fitting, and the distribution form with the best fitting effect is selected through the determination coefficient R and the correction determination coefficient R', and the results are shown in Table 1.
TABLE 1 Single Stroke Start time fitting Effect
The driving time period probability distribution parameter setting process is as follows. The continuous driving time of the user considering the fatigue driving requirement should be less than two hours, so the embodiment of the invention assumes that the single driving duration does not exceed 120 minutes. The driving duration probability distribution curve can be fitted by adopting a lognormal distribution function, and the probability density function is as follows:
fitting resulted in a parameter μ=3.040, σ=0.761.
The different area residence time probability distribution parameter setting process is as follows. According to the behavior habit of the user, the parking time of the electric automobile is different in different areas, and the parking time of the electric automobile influences the charging time of the electric automobile. The probability distribution of the parking time of the electric automobile is shown in fig. 3.
As can be seen from the parking time probability distribution map of the electric automobile in different areas, the parking time probability distribution of the electric automobile is not clear, so that the fitting effect of different distribution forms is evaluated by adopting the decision coefficient R and the correction decision coefficient R'.
Table 2 effects of fitting parking time lengths of electric vehicles in different areas
The probability distribution of parking time of the electric automobile in the H area is best as Weibull distribution effect:
the fitting yields the parameters k= 1.156, λ= 198.343.
The parking time of the electric automobile in the W area has better generalized extremum distribution fitting effect:
the fitting yields the parameters σ= 168.784, μ= 439.467, ζ=0.234.
The parking time of the electric automobile in the O area has a good generalized extremum distribution fitting effect, and fitting parameters are sigma= 45.678, mu= 69.568 and zeta=0.644.
The driving range is a key factor affecting the power consumption of the electric vehicle, and the probability distribution of the single driving range of the electric vehicle is approximately normal distribution, as shown in fig. 4:
its fitting parameter μ= 52.456, σ= 2.341.
The battery capacity of the electric automobile directly influences the endurance and the charging capacity of the electric automobile. According to the embodiment of the invention, the existing electric automobiles in the market are classified into 3 types according to the sales data of the market: the first type of electric automobile has large battery capacity and longer charging time; the second type of electric automobile has larger battery capacity, longer endurance and longer charging time; the third type of electric automobile has smaller battery capacity and short endurance time. The parameter settings are as in table 3.
Table 3 electric vehicle duty cycle and battery capacity
2 simulation results
For convenience of analysis, the total amount of electric vehicles in the area is 1000 in the embodiment of the invention. According to the method of the embodiment of the invention, the charging load time distribution curve of the regional electric automobile is shown in fig. 5. Because of the difference of sampling results of each operation based on Monte Carlo electric vehicle charging load modeling, the EV charging load curve obtained through simulation is not a determined curve, and the embodiment of the invention provides the charging load upper and lower limit banded region and the average load curve. In order to make the simulation result clearer, only the average charging power curve is reserved in the subsequent simulation result in the embodiment of the invention.
As shown in fig. 6, the expected charging load curves of the electric vehicles in the respective regions show that the charging load characteristics of the electric vehicles in the different functional regions are different. The charging load of the H area, i.e. the residence, is mainly concentrated in the evening to the next morning, and after 16 hours, the charging load of the electric vehicle starts to rise sharply, and reaches the load peak around 21 hours. The charging load of the electric automobile in the W area, namely the working area, is mainly concentrated in the daytime, and the charging load of the electric automobile in the working area is in a peak stage between 9 hours and 14 hours, which corresponds to the daily working time of a user, and the user stops the electric automobile in a parking lot to start charging after arriving at a working place. The O region includes a plurality of possibilities, has high randomness, and has no obvious peak time period of the charging load in the O region. The charging total load curve can show that the main peak of the charging load is still concentrated from evening to the next morning, and meanwhile, after the user uses the electric automobile to reach the working area W, the electric automobile is charged in the area for a certain time to return home from the working area, so that another smaller charging load peak is caused in the period from 8 am to 14 pm, the electric automobile is matched with the actual situation, and the model is reasonable.
In order to analyze the influence of the electric vehicles in the area, the number of the electric vehicles is set to 2500, other parameters are unchanged, and the comparison results are shown in fig. 7 and 8.
As can be seen from fig. 7 and 8, the charging load is greatly increased as a whole due to the change in the number of electric vehicles, but the charging loads of two curves corresponding to 1000 electric vehicles and 2500 electric vehicles are expected to be similar, and the times of the charging load peaks and valleys are basically consistent. The peak-valley difference of the power grid load is increased when a large number of electric vehicles are connected to the network.
Then, the charging load prediction result obtained by the model of the embodiment of the present invention is compared with the charging load prediction result obtained by the method used in the prior art, which is the same as 1000 electric vehicles, and the comparison result is shown in fig. 9. The load curves obtained by the two methods have similarity, the load curves have similar ascending trend at about 14, and the time and the load peak value of the load peak are close. The load curve obtained by the method of the embodiment of the invention has a load peak between 8 hours and 11 hours, but the method of the prior art does not have, because the embodiment of the invention considers the charging behavior of the electric automobile at the middle stop standing point based on the travel chain theory, the model established by the embodiment of the invention is more practical. In addition, the result is totally similar to the space-time distribution of the charging load of the electric automobile in a certain typical area obtained by research in the prior art, and the rationality of the result of the embodiment of the invention is also reflected.
And finally, multiplying the charging load of the electric vehicle by the carbon emission factor of the electric power industry in the region to obtain a carbon emission day curve of the electric vehicle, wherein the shape of the curve is similar to that of the charging load of the electric vehicle. The grid emission factor is 0.5810tCO 2 MWh, the carbon emission of the electric automobile in the case is 62.75 tons. And integrating the curve with time to obtain the total carbon emission of the electric automobile.
In summary, accurate modeling of the load and carbon emission space-time characteristics of the electric vehicle has become an important foundation for effective interaction between a large-scale electric vehicle and a power grid. The embodiment of the invention mainly considers the influence of user behavior decision to model the charging load and the carbon emission space-time distribution of the electric automobile. Firstly, a regret theory is introduced to simulate a charging demand decision of a user, then a trip chain theory is introduced to analyze trip random characteristics of the user, and then an uncertain parameter is randomly extracted by a Monte Carlo method to obtain charging load space-time distribution of the electric automobile, and finally, average carbon emission factors of the electric power industry in the region are multiplied to obtain the carbon emission space-time distribution and emission total amount of the electric automobile. Compared with the method that a person can completely rationally select the optimal charging scheme from a plurality of schemes, the method has the advantages that the user charging decision model is built based on the remorse theory, so that the method is more in line with the characteristic of incomplete rationality during user selection, and is more in line with actual conditions. The simulation result better verifies the rationality of the modeling method.
System embodiment
According to an embodiment of the present invention, there is provided an electric vehicle carbon emission space-time distribution monitoring and modeling system, and fig. 10 is a schematic diagram of the electric vehicle carbon emission space-time distribution monitoring and modeling system according to the embodiment of the present invention, as shown in fig. 10, where the electric vehicle carbon emission space-time distribution monitoring and modeling system according to the embodiment of the present invention specifically includes:
the charging demand decision module 100 is configured to simulate a charging demand decision of a user based on a remorse theory, and construct a charging demand decision model;
the user trip behavior module 110 is configured to analyze trip random characteristics of a user based on a trip chain theory, and construct a user trip behavior model; the method is particularly used for:
step 11, extracting a travel chain of a current electric automobile, obtaining basic travel information of the electric automobile, and calculating an initial state of charge (SOC) of the electric automobile according to the basic travel information, wherein the basic travel information specifically comprises: the method comprises the steps of starting an electric automobile, starting time, travel chain parking point information, end point positions and the number N of parking points including an end point; specifically: the initial state of charge SOC of the electric vehicle may be calculated from the basic travel information based on equation 1:
Wherein μ is an average value of the starting SOCs; sigma is the standard deviation;
step 12, setting a regret initial value;
step 13, calculating remorse degree: calculating the SOC of the electric automobile at the current moment, reading the electricity price at the current moment, calculating the charging time required by the electric automobile to be charged to be full, and the charging time required by the electric automobile to be charged to meet the psychological expected electric quantity of a user, reading the parking time of the electric automobile at the parking point, and calculating the remorse value RU (n, t) of the electric automobile for making a charging decision at the parking point n at the time t according to the remorse function; the calculating the SOC of the present moment of the electric automobile specifically includes:
calculating the SOC of the electric automobile reaching the current parking point according to the formula 2:
wherein,,to stop from lastThe time of departure of the standing point; />For the time to reach the current stop standing point; omega d The consumption per kilometer of the road section from the parking point S-1 to the parking point S; l (L) d Is the travel distance of the road section from the parking point S-1 to the parking point S; e (E) b Is the total charge of the battery.
Step 14, comparing the remorse value: if the remorse value of the electric automobile for charging decision at the standing point n at the time t is smaller than RUmin, replacing RUmin with the remorse value, otherwise, turning to the next standing point, and executing step 13, wherein RUmin represents the current minimum remorse value.
The processing module 120 is configured to randomly sample by using a monte carlo method based on the charge demand decision model and the user travel behavior model, obtain a charge load space-time distribution of a single electric vehicle, superimpose all the charge loads of the electric vehicle, obtain a total charge load of the electric vehicle in a certain time space range, and calculate a carbon emission space-time distribution and a total emission amount of the electric vehicle by combining the total charge load of the electric vehicle and an average carbon emission factor of the electric power industry in the region. The method is particularly used for:
step 31, setting the total amount of the electric automobile;
step 32, reading data information of a j-th electric automobile, wherein the data information comprises the land location of the electric automobile and the starting time of a journey;
step 33, extracting a travel chain of the j-th electric automobile through a Monte Carlo method based on the user travel behavior model, and reading travel chain information;
step 34, when the electric automobile reaches the h parking point, judging whether the current parking point needs to be charged or not based on the charging demand decision model, if the decision information is that charging is needed, updating the residual SOC information of the automobile and the charging load information of the land where the automobile is charged, and if the decision information is that charging is not needed, the residual SOC information of the electric automobile is unchanged, and the charging load information of the land where the parking point of the automobile is located is kept unchanged;
Step 35, when the vehicle goes to the next parking point, repeating step 34 until the travel of the vehicle is finished and the travel returns to the land where the end point of the travel chain is located, after the whole travel behavior is finished, judging whether to need charging decision according to the set SOC threshold value, updating the vehicle state of charge information SOC, and updating the charging load information of the land;
step 36, reading information of the next electric automobile, and repeating the steps 32 to 35 until the charging behaviors of all the electric automobiles are simulated;
step 37, counting the total charging load of the electric automobile in each land;
and step 38, multiplying the total charging load of the electric vehicle by the carbon emission factor of the electric power industry in each region to obtain the carbon emission of the electric vehicle in each region.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood by referring to the description of the method embodiment, which is not repeated herein.
Device embodiment 1
An embodiment of the present invention provides an electronic device, as shown in fig. 11, including: memory 110, processor 112, and a computer program stored on the memory 110 and executable on the processor 112, which when executed by the processor 112, performs the steps as described in the method embodiments.
Device example two
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a program for carrying out information transmission, which when executed by the processor 112, carries out the steps described in the method embodiments.
The computer readable storage medium of the present embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, etc.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The method for monitoring and modeling the carbon emission space-time distribution of the electric automobile is characterized by comprising the following steps of:
step 1, simulating a charging demand decision of a user based on a remorse theory, and constructing a charging demand decision model;
step 2, analyzing trip random characteristics of the user based on a trip chain theory, and constructing a user trip behavior model;
And 3, based on the charging demand decision model and the user traveling behavior model, randomly sampling by adopting a Monte Carlo method to obtain the charging load space-time distribution of a single electric automobile, superposing the charging loads of all the electric automobiles to obtain the total charging load of the electric automobiles in a certain time space range, and calculating the carbon emission space-time distribution and the emission total amount of the electric automobiles by combining the total charging load of the electric automobiles and the average carbon emission factor of the electric power industry in the region.
2. The method according to claim 1, wherein step 1 specifically comprises:
step 11, extracting a travel chain of a current electric automobile, obtaining basic travel information of the electric automobile, and calculating an initial state of charge (SOC) of the electric automobile according to the basic travel information, wherein the basic travel information specifically comprises: the method comprises the steps of starting an electric automobile, starting time, travel chain parking point information, end point positions and the number N of parking points including an end point;
step 12, setting a regret initial value;
step 13, calculating the SOC of the electric automobile at the current moment, reading the electricity price at the current moment, calculating the charging time required by the electric automobile to be charged to be full and the charging time required by the electric automobile to be charged to meet the psychological expected electric quantity of a user, reading the parking time of the electric automobile at the parking point, and calculating the remorse value RU (n, t) of the electric automobile for making a charging decision at the parking point n at the time t according to the remorse function;
And 14, if the remorse value of the electric automobile for charging decision at the standing point n at the time t is smaller than RUmin, replacing the RUmin with the remorse value, otherwise, turning to the next standing point, and executing the step 13, wherein RUmin represents the current minimum remorse value.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
the calculating the initial state of charge (SOC) of the electric automobile according to the basic travel information specifically comprises the following steps:
based on formula 1, calculating the initial state of charge (SOC) of the electric automobile according to the basic travel information:
wherein μ is an average value of the starting SOCs; sigma is the standard deviation;
the calculating the SOC of the current moment of the electric automobile specifically comprises the following steps:
calculating the SOC of the electric automobile reaching the current parking point according to the formula 2:
wherein,,is the time from the last stop point; />For the time to reach the current stop standing point; omega d For each kilometer of the road section from the parking spot S-1 to the parking spot SAn amount of; l (L) d Is the travel distance of the road section from the parking point S-1 to the parking point S; e (E) b Is the total charge of the battery.
4. The method according to claim 1, wherein step 3 specifically comprises:
step 31, setting the total amount of the electric automobile;
Step 32, reading data information of a j-th electric automobile, wherein the data information comprises the land location of the electric automobile and the starting time of a journey;
step 33, extracting a travel chain of the j-th electric automobile through a Monte Carlo method based on the user travel behavior model, and reading travel chain information;
step 34, when the electric automobile reaches the h parking point, judging whether the current parking point needs to be charged or not based on the charging demand decision model, if the decision information is that charging is needed, updating the residual SOC information of the automobile and the charging load information of the land where the automobile is charged, and if the decision information is that charging is not needed, the residual SOC information of the electric automobile is unchanged, and the charging load information of the land where the parking point of the automobile is located is kept unchanged;
step 35, when the vehicle goes to the next parking point, repeating step 34 until the travel of the vehicle is finished and the travel returns to the land where the end point of the travel chain is located, after the whole travel behavior is finished, judging whether to need charging decision according to the set SOC threshold value, updating the vehicle state of charge information SOC, and updating the charging load information of the land;
Step 36, reading information of the next electric automobile, and repeating the steps 32 to 35 until the charging behaviors of all the electric automobiles are simulated;
step 37, counting the total charging load of the electric automobile in each land;
and step 38, multiplying the total charging load of the electric vehicle by the carbon emission factor of the electric power industry in each region to obtain the carbon emission of the electric vehicle in each region.
5. An electric automobile carbon emission space-time distribution monitoring modeling system, which is characterized by comprising:
the charging demand decision module is used for simulating the charging demand decision of the user based on the remorse theory and constructing a charging demand decision model;
the user travel behavior module is used for analyzing travel random characteristics of the user based on a travel chain theory and constructing a user travel behavior model;
the processing module is used for randomly sampling by adopting a Monte Carlo method based on the charging demand decision model and the user traveling behavior model to obtain the charging load space-time distribution of a single electric automobile, superposing the charging loads of all the electric automobiles to obtain the total charging load of the electric automobiles in a certain time space range, and calculating the carbon emission space-time distribution and the emission total amount of the electric automobiles by combining the total charging load of the electric automobiles with the average carbon emission factor of the electric power industry in the area.
6. The system of claim 5, wherein the charge demand decision module is specifically configured to:
step 11, extracting a travel chain of a current electric automobile, obtaining basic travel information of the electric automobile, and calculating an initial state of charge (SOC) of the electric automobile according to the basic travel information, wherein the basic travel information specifically comprises: the method comprises the steps of starting an electric automobile, starting time, travel chain parking point information, end point positions and the number N of parking points including an end point;
step 12, setting a regret initial value;
step 13, calculating the SOC of the electric automobile at the current moment, reading the electricity price at the current moment, calculating the charging time required by the electric automobile to be charged to be full and the charging time required by the electric automobile to be charged to meet the psychological expected electric quantity of a user, reading the parking time of the electric automobile at the parking point, and calculating the remorse value RU (n, t) of the electric automobile for making a charging decision at the parking point n at the time t according to the remorse function;
and 14, if the remorse value of the electric automobile for charging decision at the standing point n at the time t is smaller than RUmin, replacing RUmin with the remorse value, otherwise, turning to the next standing point, and executing the step 13, wherein RUmin represents the current minimum remorse value.
7. The system of claim 6, wherein the charge demand decision module is specifically configured to:
based on formula 1, calculating the initial state of charge (SOC) of the electric automobile according to the basic travel information:
wherein μ is an average value of the starting SOCs; sigma is the standard deviation;
calculating the SOC of the electric automobile reaching the current parking point according to the formula 2:
wherein,,is the time from the last stop point; />For the time to reach the current stop standing point; omega d The consumption per kilometer of the road section from the parking point S-1 to the parking point S; l (L) d Is the travel distance of the road section from the parking point S-1 to the parking point S; e (E) b Is the total charge of the battery.
8. The system of claim 5, wherein the processing module is specifically configured to:
step 31, setting the total amount of the electric automobile;
step 32, reading data information of a j-th electric automobile, wherein the data information comprises the land location of the electric automobile and the starting time of a journey;
step 33, extracting a travel chain of the j-th electric automobile through a Monte Carlo method based on the user travel behavior model, and reading travel chain information;
step 34, when the electric automobile reaches the h parking point, judging whether the current parking point needs to be charged or not based on the charging demand decision model, if the decision information is that charging is needed, updating the residual SOC information of the automobile and the charging load information of the land where the automobile is charged, and if the decision information is that charging is not needed, the residual SOC information of the electric automobile is unchanged, and the charging load information of the land where the parking point of the automobile is located is kept unchanged;
Step 35, when the vehicle goes to the next parking point, repeating step 34 until the travel of the vehicle is finished and the travel returns to the land where the end point of the travel chain is located, after the whole travel behavior is finished, judging whether to need charging decision according to the set SOC threshold value, updating the vehicle state of charge information SOC, and updating the charging load information of the land;
step 36, reading information of the next electric automobile, and repeating the steps 32 to 35 until the charging behaviors of all the electric automobiles are simulated;
step 37, counting the total charging load of the electric automobile in each land;
and step 38, multiplying the total charging load of the electric vehicle by the carbon emission factor of the electric power industry in each region to obtain the carbon emission of the electric vehicle in each region.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the method for monitoring and modeling the spatial and temporal distribution of carbon emissions of an electric vehicle according to any one of claims 1 to 4.
10. A computer-readable storage medium, wherein a program for realizing information transfer is stored on the computer-readable storage medium, and the program when executed by a processor realizes the steps of the method for monitoring and modeling the carbon emission space-time distribution of the electric vehicle according to any one of claims 1 to 4.
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| CN117648520A (en) * | 2024-01-29 | 2024-03-05 | 北京理工大学 | New energy automobile charging load analysis-based carbon emission calculation method and system |
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| CN117648520A (en) * | 2024-01-29 | 2024-03-05 | 北京理工大学 | New energy automobile charging load analysis-based carbon emission calculation method and system |
| CN117648520B (en) * | 2024-01-29 | 2024-05-07 | 北京理工大学 | New energy automobile charging load analysis-based carbon emission calculation method and system |
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