CN114285063A - Short-term carbon emission factor-based intelligent electric vehicle carbon-reducing charging method - Google Patents
Short-term carbon emission factor-based intelligent electric vehicle carbon-reducing charging method Download PDFInfo
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Abstract
The invention relates to an electric automobile carbon-reducing intelligent charging method based on short-term carbon emission factors, which comprises the following steps: performing linear fitting on the total carbon dioxide emission and the total power generation of the power system in the same time period on different days, and taking the slope as a short-term carbon emission factor of the power system in the time period t; establishing an objective function by taking the load variance and the carbon emission of the power system as optimization costs on the basis of data of different time periodsf 1Andf 2(ii) a When the electric vehicle is in an empty state or in a flameout state for more than 40 minutes, the electric vehicle is considered to be capable ofCharging, recording vehicle information of the electric vehicle capable of being charged, and obtaining an electric vehicle data set going to be charged; the method comprises the steps of obtaining charging starting time of an electric automobile data set for charging and charging initial electric quantity of the electric automobile, taking the charging starting time and the charging initial electric quantity as input, taking an objective function as an optimization objective, and obtaining optimal charging starting time and actual charging electric quantity by utilizing a double-layer non-dominated genetic algorithm.
Description
Technical Field
The invention relates to the technical field of carbon emission and intelligent charging of electric automobiles, in particular to a short-term carbon emission factor-based intelligent carbon-reducing charging method for an electric automobile.
Background
In recent years, with the maturity of battery technology, electric vehicles are considered as the most promising substitute for internal combustion engine vehicles in the field of clean transportation, and because of zero exhaust emissions, electric vehicles contribute to the elimination of local pollution, which is important in urban areas with a large population. However, this does not mean that the electric vehicle has no environmental burden at all, and the indirect carbon emission of the pure electric vehicle is derived from carbon dioxide generated by the combustion consumption of fossil energy in the power grid, so that when a large-scale electric vehicle is connected to the power grid, not only a large burden is imposed on the power grid, but also more carbon dioxide emission can be caused. Therefore, it is a topic worth paying attention to orderly incorporate electric vehicles into the power grid for intelligent charging.
The current charging mode is roughly divided into disordered charging and ordered charging, wherein the disordered charging refers to charging of an electric automobile according to the preference of a user, and the time and mode of accessing a power grid are not managed. Because most of electric automobile users charge in the same time, the charging mode is easy to generate a charging peak period and further has influence on the stability of a power grid. The orderly charging refers to the overall planning of the charging behaviors of the electric vehicles connected into a certain area. And performing charging scheduling by taking reduction of load variance of the power grid as an optimization target. Furthermore, more and more objective functions are being considered for ordered charging, such as time of use electricity prices, user costs. The method establishes the electric automobile ordered charging model with the minimum carbon emission and the minimum power grid mean variance as targets.
The carbon emission is taken as an optimization target, carbon emission factors of electric automobiles need to be determined, at present, China has no specific calculation of the carbon emission of the electric automobiles, and the energy source of the electric automobiles is different from that of internal combustion engine automobiles and depends on fossil fuels or renewable energy sources used by a power grid. Therefore, the indirect CO2 emission factor of the pure electric vehicle is calculated according to the power consumption of the vehicle per hundred kilometers and the power grid CO2 emission factor of different provinces. The research of the climate change department by the ecological environment department determines the average carbon emission factor of the power grid baseline in the emission reduction project of China in 2019, but the calculation method is only applied to long-term prediction with a large range. The carbon emission factor is fixed and invariable, which means that the carbon emission factor is not optimized as an evaluation index in the current ordered charging, and the carbon emission of the electric automobile is fixed and invariable whenever the electric automobile is incorporated into a power grid. At present, the efficiency of a generator is mostly used as an evaluation index in an ordered charging model considering carbon emission in China, however, with the incorporation of renewable energy sources such as wind energy, solar energy and the like into a power grid, the source of power grid energy has randomness and uncontrollable property, and the carbon emission when the electric automobile is incorporated into the power grid when green electricity is high is far lower than that when thermal electricity is used for power generation.
The realization of the intelligent charging of the electric automobile based on the short-term carbon emission factor needs to be realized: 1. predicting a short-term carbon emission factor based on the generated energy of the power plant, and providing guidance for intelligent charging of the electric automobile; 2. processing and analyzing the data of the electric automobile, and establishing a charging demand model of the electric automobile; 3. and establishing a multi-objective function with the minimum load variance and the minimum carbon emission of a load curve, and providing a double-layer non-dominated genetic algorithm to optimize the charging behavior of the electric automobile.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide the short-term carbon emission factor-based carbon-reduction intelligent charging method for the electric automobile, which reduces the carbon emission of the electric automobile, ensures the stable operation of a power grid and reduces the burden on the power grid after the large-scale electric automobile is connected into the power grid. On the aspect of the electric vehicle charging scheduling problem, researches aiming at carbon emission are relatively few, and guidance suggestions are provided for the charging behavior of a large-scale electric vehicle connected to a power grid in the future.
The technical scheme for solving the technical problems is as follows:
a short-term carbon emission factor-based intelligent electric vehicle carbon-reducing charging method comprises the following steps:
total carbon dioxide emission and electricity for the same period of time on different days of the area to be studiedThe total power generation of the force system is fitted linearly to obtain a slope beta in a fitted linear equationtAs a short-term carbon emission factor for the t-period power system;
constructing an electric automobile ordered charging mathematical model considering carbon emission: establishing an objective function by taking the load variance and the carbon emission of the power system as optimization costs on the basis of data of different time periodsf 1Andf 2the objective function conforms to the travel constraint and the power grid constraint of the electric automobile, and an electric automobile ordered charging mathematical model considering carbon emission is formed;
the method comprises the steps that collected electric vehicle information mainly comprises a unique number of a vehicle, data collection time, a vehicle state, battery electric quantity SOC and longitude and latitude information; the vehicle state comprises three states of flameout, passenger carrying and empty vehicle, the SOC range of the battery is 0% -100%, and the longitude and latitude information is the geographic position of the electric vehicle at the acquisition moment;
when the electric automobile is in an empty state or a flameout state for more than 40 minutes, the electric automobile is considered to be capable of being charged, the vehicle information of the electric automobile capable of being charged is recorded, and an electric automobile data set going to be charged is obtained;
for the electric automobile in the electric automobile data set going to be charged, obtaining the charging start time according to the distance between the geographical position of the electric automobile starting to go to the charging station and the geographical position of the charging station, the time of the electric automobile starting to go to the charging station and the speed of the electric automobile,
counting and fitting the battery electric quantity when the electric automobile in the electric automobile data set goes to the charging station to obtain the probability density function of the battery electric quantity when the electric automobile goes to the charging stationf(s); by a probability density functionf(s) subtracting the power consumption on the way to the charging station from the expected power obtained at random to obtain the initial charging power of the electric vehicle,
the method comprises the steps that charging starting time and charging initial electric quantity are used as input, the constructed objective function is used as an optimization objective, and the optimal charging starting time and actual charging electric quantity of an electric vehicle data set for charging when the mean value variance of a power grid load curve and carbon emission are minimum are obtained by utilizing a double-layer non-dominated genetic algorithm;
the double-layer non-dominated genetic algorithm has double-layer codes and comprises a time layer and a charging layer, each gene of the charging layer corresponds to each gene of the time layer, the charging layer and the time layer jointly form a chromosome and represent the charging starting time and the actual charging electric quantity of the same vehicle, and the upper and lower layers of genes are subjected to genetic algorithm operation simultaneously or respectively.
The specific process of the double-layer non-dominated genetic algorithm is as follows:
6.1) double layer coding
The upper layer is a time layer, the upper layer gene code represents the charging start time of the electric automobile, and the time is divided every 20min from a point 0, namely the time nodes are 0:20, 0:40, 1:00, …, 23:40 and 0: 00, dividing 24h into 72 sections, numbering each time node in sequence, wherein the numbering is time layer gene coding, namely 1, 2, 3, … and 72, if the charging start time of the electric automobile is not equal to the divided time nodes, calculating the time length between the charging start time of the electric automobile and two adjacent time nodes, and dividing the charging start time of the electric automobile into adjacent time nodes with shorter time length;
counting the charging start time of each electric automobile in the electric automobile data set going to be charged, determining corresponding time layer gene codes according to rules, and then sequencing to obtain the time layer gene codes of the electric automobile data set going to be charged currently, wherein the length of a time layer is the number of the electric automobiles in the electric automobile data set going to be charged;
the lower layer is a charging layer, the gene code of the charging layer is an integer between {50, 100}, the code length is the number of the electric vehicles in the electric vehicle data set going to be charged, a charging percentage code of the empty electric quantity of the battery is formed, the actual charging electric quantity can be obtained according to the gene code of the charging layer and the charging initial electric quantity, and the charging initial electric quantity passes through a probability density functionf(s) subtracting the en-route consumed electric quantity from the electric quantity randomly obtained;
in a time layer, inputting initial time of each vehicle reaching a charging station, wherein the initial time is charging starting time of the electric vehicle, the initial time is obtained by subtracting time spent on the way of going to the charging station from the time of the electric vehicle going to the charging station, in a charging layer, inputting a charging percentage code of residual battery electric quantity randomly generated between {50, 100}, obtaining extra charging electric quantity based on a charging layer gene code and the charging initial electric quantity, and further obtaining actual charging electric quantity corresponding to the charging layer gene code;
6.2) setting population scale n, ditch substitution, maximum iteration times, cross probability and variation probability, simultaneously setting the probability of simultaneous operation of double-layer genes, and calculating the fitness value of each chromosome by using an objective function;
6.3) executing selection operation, cross operation, mutation operation, non-dominant sequencing, calculating congestion distance and elite strategy, and outputting the charging strategy under the optimal condition of the objective function after the convergence condition is reached.
Drawing a real-time space-time distribution map of the electric automobile for charging based on the unique electric automobile number, the acquisition time and the latitude and longitude information in the electric automobile data set for charging;
the space-time distribution diagram records the number of each vehicle, performs space-time statistics by taking the vehicle as a unit, displays all vehicles in the space-time distribution diagram, divides 24h into 72 time nodes by taking 20 minutes as a division node, wherein the position of a point in the diagram represents the geographic position of a route which is acquired when the electric vehicle goes to a charging station, the color of the point represents the current moment of the electric vehicle, each time node is set with different colors, and if the acquisition time is not equal to a certain time node, the time length between two adjacent time nodes of the acquisition time is calculated, and the sampling time is divided into adjacent time nodes with shorter time length;
the electric vehicle charging start time can be obtained from the space-time distribution map.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method and the device have the advantages that the carbon emission of the electric automobile and the mean variance of the power grid are taken as optimization targets, and the charging behavior of the electric automobile is optimized. A double-layer non-dominated genetic algorithm is set, a population is constructed according to the charging behaviors (charging start time and charging percentage) of the electric automobile, the charging behaviors of the electric automobile are optimized, the iteration efficiency and the quality of an optimal solution are improved, and the multi-objective problem of considering carbon emission and power grid load variance is effectively solved.
(2) The conventional orderly charging only considers the time when the electric automobile starts to be charged, but does not consider the charging capacity. The invention provides a double-layer coding mode, wherein the upper layer optimizes the charging start time of an electric automobile, the lower layer optimizes the charging electric quantity, and under the condition of meeting the basic travel requirement of the electric automobile, the charging electric quantity of an electric automobile user at the power utilization peak is reduced, the charging electric quantity at the power utilization valley is improved, and further the carbon emission of a power grid is reduced.
(3) The double-layer non-dominated genetic algorithm combines a double-layer genetic algorithm and a non-dominated genetic algorithm with an elite strategy, and sets the association of double-layer chromosomes in cross variation.
(4) In order to effectively reduce the emission of carbon dioxide, a linear regression model capable of predicting the curve change of the carbon emission factor of a power grid in one day in real time is provided, the model divides one day into 24 time intervals, linear relation between the carbon dioxide emission and the power generation amount is analyzed through a plurality of groups of power plant data in each time interval, short-term carbon emission factors are adopted, the short-term carbon emission factors in each hour are used as evaluation indexes for optimization, a double-layer non-dominated genetic algorithm is provided according to the charging requirement of an electric automobile to solve the problem, and factors such as the charging requirement of the electric automobile and the load variance of the power grid are comprehensively considered. Along with the implementation of policies such as carbon trading and the like, the carbon emission factor curve can effectively reduce the carbon dioxide emission of the electric automobile, and the research blank of the ordered charging of the electric automobile based on the carbon emission in China is filled.
Drawings
FIG. 1 is a schematic flow chart of an intelligent charging method for an electric vehicle based on short-term carbon emission factors according to the present invention;
FIG. 2 is a schematic diagram of a two-layer NSGA II algorithm;
fig. 3 is a spatiotemporal profile of an electric vehicle and charging station.
Detailed Description
Specific examples of the present invention are given below. The specific examples are only for illustrating the present invention in further detail and do not limit the scope of protection of the present application.
The following terms are unified as follows:
s: the battery charge is ready to travel to the charging station,
s': initial charging electric quantity: when the user goes to the charging station, the battery electric quantity s is subtracted by the electric quantity consumed in the way,
battery empty capacity: 1-charging the initial amount of electricity,
extra charge capacity: battery empty charge%
Actual charging capacity: and after the charging is finished, the battery capacity of the electric automobile is the additional charging capacity plus the initial charging capacity.
The invention provides an electric automobile intelligent charging method based on short-term carbon emission factors, which comprises the following steps:
step 1: acquiring and processing data of the power system to obtain the total carbon dioxide emission and the total power generation amount of the power system in different periods:
firstly, the total calorific value of different combustion materials of the thermal power part of the power system per hour is obtained, and the total carbon dioxide emission amount of the power system is calculated according to the carbon emission factor of the common energy provided in the IPCC 2006 revised edition of national greenhouse gas list guideline 2019. Obtaining the total carbon dioxide emission of the power system at different time intervals in one day according to the formula (1):
wherein, DCtM is the total carbon dioxide emission in the power system during the period t, and M is the total number of used fuels.C it For a period t the total consumption of fuel i in the power system,Q i is the lower calorific value of fuel i;EF i CO2 emission factor for fuel i of the year, determined by the manifest guidelines; c is data collected in real time in the power plant, Q is a known constant, and t is a time period divided by every 1h for 24h in 1 dayAnd the total time is 24 periods, namely t = 1-24.
Secondly, the total power generation PG of each time interval of the power system is obtainedtThe total power generation amount includes the power generation amount of the thermal power plant, and also includes the power generation amounts of hydropower, nuclear power, renewable energy sources and the like, and in order to simplify the model, the emission of carbon dioxide generated during the generation and transportation of the energy sources is not considered, and the emission of carbon dioxide generated during the generation and transportation of the energy sources is not considered when the emission amount of the total carbon dioxide is calculated. And summing the data of the power generation amount according to corresponding time intervals to obtain the total power generation amount of each time interval.
Step 2: linearly fitting the total carbon dioxide emission amount of the thermal power plants (the number of the thermal power plants is changed according to the size of a survey area (namely, an electric automobile activity area needing to be counted), and each thermal power plant has own total power generation amount) in the same period with the total power generation amount of the power system, and obtaining a slope beta in a fitted linear equationtNamely short-term carbon emission factor (CO 2/KWh) of the power system in the t period, and the straight line intercept of the fitting equation
The calculation of the carbon emission factor is ignored, the calculation of the carbon emission factor is not influenced, the fitting equation is established by calculating an average carbon emission factor based on a linear regression model and feeding back the charging behavior of an electric vehicle driver, the short-term carbon emission factor is the ratio beta of the total carbon dioxide emission amount and the total power generation amount of the power grid per hour, and the fitting formula is shown as a formula (2):
the formula (2) is a linear regression equation obtained by taking the total power generation amount as an independent variable and taking the total carbon dioxide emission amount as a dependent variable. Wherein, Δ DCt and Δ PGt respectively represent the total carbon dioxide emission and the total power generation amount in the power system at the t periodt-1Is the total carbon dioxide emission in the power system during the t-1 period,
is the total power generation amount in the power system during the period t,
is the total power generation amount in the t-1 period in the power system.
Analyzing the generated energy of the power plant and the calorific value of energy in each period, obtaining the total carbon emission and the generated energy of the power plant in each hour through the carbon emission factors of the common combustion fuels of the power plants such as coal, natural gas and the like, and establishing a linear regression equation to predict a 24h carbon emission factor curve.
And step 3: collecting electric vehicle data
The collected information of the electric automobile mainly comprises a unique number of the automobile, data collection time, automobile speed, automobile State, battery electric quantity (SOC) and longitude and latitude information. Each vehicle has a unique number, the vehicle data acquisition time is accurate to seconds, and each vehicle is acquired every 30 seconds on average. The vehicle states are divided into three states of flameout, passenger carrying and empty vehicle (without passenger carrying), and the passenger carrying and the empty vehicle represent that the vehicle is running. The battery state of charge SOC ranges from 0% to 100%. The longitude and latitude information is the geographic position of the electric automobile at the acquisition moment.
And 4, step 4: and screening the data to obtain the information data of the electric automobile predicted to be charged.
Supposing that when the electric automobile is in an empty state or a flameout state for more than 40 minutes (namely, a network appointment rest state), the electric automobile at the moment is considered to be charged, a driver can go to a nearby charging station to rest and supply, the vehicle information of the electric automobile capable of being charged is recorded, an electric automobile data set going to be charged is obtained, and a real-time space-time distribution map of the electric automobile going to be charged is drawn based on the unique number, the acquisition time and the longitude and latitude information of the electric automobile in the data set. As shown in fig. 3, the map records the number of each vehicle, performs space-time statistics on the vehicle as a unit, displays all vehicles in a space-time distribution diagram, where the position of the point in fig. 3 represents the geographical position of a route that the electric vehicle travels through when heading to a charging station, the color of the point represents the current time of the electric vehicle, and takes 20 minutes as a division node, divides 24h into 72 time nodes, each time node is set with a different color, and if the collection time is not equal to a certain time node, calculates the time duration between two adjacent time nodes of the collection time, and divides the sampling time into adjacent time nodes with shorter time duration, for example, when the collection time is greater than 1:00 is less than 1: 10 minutes, the acquisition time is 1: and the time length of the time node 00 is less than the time length of the time node 1:20, and the acquisition time is divided into 1:00, when the acquisition time is more than or equal to 1: 10 is less than 1:20 hours, the acquisition time is 1: the time length of the time node 00 is greater than the time length of the time node 1:20, and the acquisition time is divided into 1: for 20 minutes.
The battery electric quantity when the electric automobile goes to the charging station is subjected to statistical fitting to obtain the probability density function of the battery electric quantity when the electric automobile goes to the charging stationf(s), f(s) is a function of s that can randomly generate a value for s:
in the formula: mu is the expected value of the battery capacity of the electric vehicle reaching the charging station, sigma is the standard deviation of the battery capacity of the electric vehicle reaching the charging station, and the known value s is the battery capacity of the electric vehicle when the electric vehicle goes to the charging station. However, the battery power generated at this time is not the power generated when the electric vehicle reaches the charging station, and a part of the power is consumed while the electric vehicle travels to the charging station. When the vehicle travels to a charging station, the battery electric quantity s minus the en-route consumed electric quantity is the charging initial electric quantity s', and the calculation formula is as follows:
sj' is initial charge of electric vehicle j, Djrα is the amount of battery power consumed per kilometer (KWh/km) for the distance between the geographic location of the electric vehicle j when it starts traveling to the charging station and the geographic location of the charging station r.
And 5: an electric automobile ordered charging mathematical model considering carbon emission is constructed,
establishing an objective function by taking the load variance and the carbon emission of the power system as optimization costs on the basis of data of different time periodsf 1Andf 2：
the objective function complies with travel constraints and power grid constraints of the electric vehicle:
furthermore, PLtThe time period t does not contain the original power grid load of the electric automobile, PjtIs the charging power, P, of the electric vehicle j in the period of tavrFor the average load of the electric network including electric vehicles, betatIs a short-term carbon emission factor of the power system during the period t, EtmaxFor the maximum number of electric vehicles that can be accommodated by the charging station in the current region in the period of t,E t the number of electric vehicles in the electric vehicle group for charging the current area in the time period t, and the SOCjIs the actual charging capacity, P, of the electric vehicle jtIs the charging power sum of the electric automobile in a period of t, s'jAnd charging the initial electric quantity of the electric automobile j. s'jminThe minimum initial charging capacity which can be tolerated by the user of the electric automobile. SOCminAnd SOCmaxAnd respectively charging the maximum and minimum battery electric quantity of the electric automobile. PtminAnd PtmaxThe maximum value and the minimum value of the total power of the electric vehicle which can be accessed by the power grid are respectively.
Wherein: the objective function (7) represents the objective function with the minimum power grid load variance on the aspect of the power grid, and represents the fluctuation situation of the power grid load, and the smaller the mean square error is, the smaller the power grid load fluctuation is. The objective function (8) represents the objective function on the part of the user with minimum carbon emissions. And the formula (9) is the average load of the power grid. The constraints (10) are electric vehicles that can be accommodated by the charging station in the area for a certain period of time. And the constraint (11) represents the upper limit and the lower limit of the charging capacity of the electric automobile. And the constraint (12) is the upper limit and the lower limit of the power of the electric automobile which can be accessed by the power grid in the period. And the constraint (13) is that the electric quantity of the electric vehicle is larger than the minimum value when the electric vehicle goes to the charging station.
Step 6, optimizing by using a non-dominated sorting double-layer genetic algorithm (NSTLGA-II) with elite strategy
The method comprises the following specific steps:
step 6.1: the charging capacity of the electric automobile cannot be determined by the traditional ordered charging codes. Only the time of onset can be determined, and for this purpose, the invention extends the standard genotype coding to another layer, proposing a two-layer coding approach. A two-layer coding form is proposed: time layer and charging layer. The upper and lower genes are operated by genetic algorithm at the same time or respectively, and the specific coding mode is as follows:
the upper layer code is a time layer, the layer of gene code represents the charging start time of the electric automobile, and is divided into sections every 20min from the 0 point, namely the time nodes are 0:20, 0:40, 1:00, …, 23:40 and 0: 00, dividing 24h into 72 sections, numbering each time node in sequence to obtain time layer gene codes, namely 1, 2, 3, … and 72, wherein 1 corresponds to 0:20 of the time node at which the electric automobile starts to charge, and if the charging start time of the electric automobile is not equal to the divided time node, calculating the time length between two adjacent time nodes of the charging start time of the electric automobile, and dividing the charging start time of the electric automobile into the adjacent time nodes with shorter time length. The length of the time layer is the number of the electric automobile groups going to be charged, and the specific code of the time layer gene corresponds to the charging starting time of each electric automobile and is used as the first layer code of the genetic algorithm.
And counting the charging start time of each electric automobile in the electric automobile data set going to be charged, determining the corresponding time layer gene codes according to rules, and then sequencing to obtain the time layer gene codes of the electric automobile data set going to be charged currently. The temporal layer coding takes the form of integer coding, recorded once every 20 minutes. Specifically, the time-layer code length is E, where E represents the number of electric vehicles in the electric vehicle group going to be charged, and for example, the first-layer gene code {2, 7, … } represents that the vehicle is charged at a speed of 0:40,2: 20, … each begin charging. The code means that the code is recorded once every 20 minutes, namely when the code number is 2, the code actually represents 2 x 20 minutes, namely 0:40 start charging.
The second layer is encoded as a charging layer. The charge capacity of the vehicle is not constant and is one of the optimization objectives, the charge capacity also being the optimization objective, but its fundamental meaning is to reduce carbon emissions, i.e. to reduce the charge capacity when the carbon emission factor is large, and vice versa. The gene codes of the charging layer are integers between {50, 100}, the code length is the number of electric automobiles, and the number constitutes one percentage of the empty capacity of the battery, namely the empty capacity of the battery = (1-initial charging capacity). The intelligent charging system can directly obtain the initial charging electric quantity of each electric automobile, the initial charging electric quantity is obtained through a formula (6), and the required additional charging electric quantity can be calculated according to the gene codes of the charging layer and the battery empty electric quantity, namely the additional charging electric quantity = the battery empty electric quantity and the gene codes of the charging layer. For example, if the initial charge is 30% and the charge level is 60%, it means that 60% of the empty battery is filled up in 70%, and therefore, the vehicle needs to be charged up to 42% (0.6 × 0.7) of the full capacity so that the charged battery capacity reaches 72%. The charging layer reduces the carbon emission and charging station crowding caused by charging time, and ensures that the electric automobile reaches more than 50% of full capacity after charging is finished so as to ensure the next journey, so that one charging layer gene code corresponds to an actual charging electric quantity, namely, the charging layer is a lower layer gene of a chromosome and represents the charging electric quantity of an electric automobile fleet, and each gene in the lower layer is the charging electric quantity of an electric automobile. Each gene of the charging layer corresponds to each gene of the time layer, and the charging layer and the time layer jointly form a chromosome which represents the charging starting time and the charging electric quantity of the same vehicle. The genetic code of the charging layer is defined between 50, 100, which means that the primary charging behavior of the electric vehicle can be at least 50% of the total charge.
Step 6.2: initializing parameters: setting population scale n, generation ditches, maximum iteration times, crossover probability, mutation probability and probability p of simultaneous operation of double-layer genes, determining whether the double-layer genes are crossed and mutated simultaneously or separately according to the probability p, and calculating the fitness value of each chromosome according to an objective function.
Initializing a population: and inputting the charging start time and the initial charging electric quantity, wherein the charging start time is obtained according to the distance between the geographic coordinate when the electric vehicle starts to move to the charging station and the geographic position of the charging station and the time when the electric vehicle starts to move to the charging station. The initial charging electric quantity is used for determining the additional charging electric quantity, the additional charging electric quantity of the electric automobile is obtained through the charging layer gene code and the initial charging electric quantity, when the charging layer gene code is determined, the initial charging electric quantity is obtained based on the formula (6), and the initial charging electric quantity + the additional charging electric quantity is the actual charging electric quantity, so that one charging layer gene code corresponds to one actual charging electric quantity.
In the time layer, the initial time of each vehicle arriving at the charging station is input, the initial time is obtained by subtracting the time spent on the way to the charging station from the time of the electric vehicle going to the charging station in the space-time distribution, and the calculation formula is shown as a formula (15). T isjThe charging time of the electric vehicle j. T issjThe time when the electric vehicle j starts to travel to the charging station. DjrThe distance between the geographical position of the electric vehicle j and the geographical position of the charging station r when the electric vehicle j starts to go to the charging station, and speed is the speed of the electric vehicle.
In the charging layer, a charging percentage code of the empty capacity of the battery randomly generated between {50, 100} is inputted, and a charging initial capacity, which is the charging initial capacity + an additional charging capacity, which is the actual charging capacity, is obtained based on equation (6). The calculation formula is shown as formula (16), SOCjThe actual charging capacity of the electric vehicle j is theta, and theta is the charging percentage of the electric vehicle j in the charging layer, namely the gene coding% of the charging layer.
Step 6.3: and executing selection operation, cross operation, mutation operation, non-dominated sorting, calculation of congestion distance and elite strategy, and outputting the charging strategy under the optimal condition of the objective function after the convergence condition is reached.
(1) Selecting operation: the selection adopts a roulette selection method, the probability of each individual being selected is in direct proportion to the fitness of the individual, and the first generation offspring population is randomly generated according to the probability. Because the probability of selecting the individuals with better fitness is higher, the individuals with better fitness in the parent individuals can be selected for many times, and the population sizes of the first generation filial population and the parent population are kept consistent; the fitness value is an objective function.
(2) And (3) cross operation: uniform crossing and two-point crossing are adopted. When the double-layer simultaneous crossing is carried out, the uniform crossing can effectively ensure the diversity of the population, and is specific: traversing each individual in the parent 1, and exchanging the position of the gene in the corresponding parent 2 according to the cross probability Ps to obtain filial generations. Another child is obtained in a similar manner. Because the genetic algorithm has a double-layer coding form, better individuals can be reserved while ensuring that double-layer genes can be iterated independently. In a single-layer intersection, two-point intersection can make the charging layer and the time layer have more combinations, and reduce the intersection time, specifically: two crossover points are then set in the two parents, and partial gene crossover is then performed to give the offspring a and b. The invention sets the probability p of the simultaneous operation of double-layer genes and the probability 1-p of the single operation of double layers, and the specific examples are shown in the figure.
(3) Mutation operation: judging whether the dyeing individuals in the second generation filial generation population need to be mutated according to the preset mutation probability, and if not, carrying out the next operation; if needed, randomly selecting two mutation positions a and b on the chromosome, and then exchanging parts of the two positions to generate a mutated chromosome until the mutation probability of the chromosomes in the population reaches a set value; the population output by the mutation operation is called a third generation offspring population; the same principle as the crossover operator is that the mutation operation is set with the gene simultaneous operation probability p to mutate the double-layer genotypes simultaneously or independently.
(4) Non-dominant ordering: comparing dominant and non-dominant relationships between individuals, preferably, selecting a chromosome from a population of size n, comparing the chromosome with the dominant and non-dominant relationships of all other individuals in the population, and if no chromosome is superior to the current chromosome, then the chromosome is marked as a non-dominant chromosome. And circulating until all chromosomes in the population are traversed, namely, each chromosome is compared with other chromosomes in a dominant and non-dominant relationship, and the obtained non-dominant chromosome set is a first-level non-dominant layer. Then, ignoring those chromosomes that have been labeled as non-dominant chromosomes, repeating the above steps for the remaining chromosomes in the population, a second level of non-dominant hierarchy is obtained, and so on until the chromosomes of the entire population are all layered.
(5) And (3) calculating a crowding distance: and calculating the average distance of the target functions at two sides of each target function, namely the crowding degree, wherein the target functions at two sides refer to the target functions of the left and right adjacent chromosomes of the chromosome in the same level non-dominant chromosome set. For example, when calculating the crowdedness of the chromosome u, the average distance of the corresponding objective functions of the chromosome u +1 and the chromosome u-1 in the same stage is calculated; the congestion degree calculation formula is as follows:
Cuindicating the degree of crowding of the chromosome u,
represents the kth objective function value of chromosome u +1,
the k-th objective function value of the chromosome u-1, in this example, O is 2, and represents two objective functions of the power system load variance and the carbon emission, respectively.
(6) Elite strategy: and merging the parent and the offspring, performing non-dominant sorting and crowding degree comparison to obtain non-dominant chromosome sets with different grades, and filling the non-dominant chromosome set with higher priority into the next generation of population until the number of the next generation of population reaches n.
(7) And stopping optimization when the maximum iteration times are reached, and outputting an optimal solution, namely the optimal charging starting time and the actual charging electric quantity of each electric automobile.
Nothing in this specification is said to apply to the prior art. EV is electric vehicle abbreviation.
Claims (7)
1. The intelligent charging method for reducing carbon of the electric automobile based on the short-term carbon emission factor is characterized by comprising the following steps:
linearly fitting the total carbon dioxide emission amount of the region to be researched in the same period of time on different days and the total power generation amount of the power system to obtain a slope beta in a fitted linear equationtAs a short-term carbon emission factor for the t-period power system;
constructing an electric automobile ordered charging mathematical model considering carbon emission: establishing an objective function by taking the load variance and the carbon emission of the power system as optimization costs on the basis of data of different time periodsf 1Andf 2the objective function conforms to the travel constraint and the power grid constraint of the electric automobile, and an electric automobile ordered charging mathematical model considering carbon emission is formed;
the method comprises the steps that collected electric vehicle information mainly comprises a unique number of a vehicle, data collection time, a vehicle state, battery electric quantity SOC and longitude and latitude information; the vehicle state comprises three states of flameout, passenger carrying and empty vehicle, the SOC range of the battery is 0% -100%, and the longitude and latitude information is the geographic position of the electric vehicle at the acquisition moment;
when the electric automobile is in an empty state or a flameout state for more than 40 minutes, the electric automobile is considered to be capable of being charged, the vehicle information of the electric automobile capable of being charged is recorded, and an electric automobile data set going to be charged is obtained;
for the electric automobile in the electric automobile data set going to be charged, obtaining the charging start time according to the distance between the geographical position of the electric automobile starting to go to the charging station and the geographical position of the charging station, the time of the electric automobile starting to go to the charging station and the speed of the electric automobile,
counting and fitting the battery electric quantity when the electric automobile in the electric automobile data set goes to the charging station to obtain the probability density function of the battery electric quantity when the electric automobile goes to the charging stationf(s); by a probability density functionf(s) subtracting the power consumption on the way to the charging station from the expected power obtained at random to obtain the initial charging power of the electric vehicle,
the method comprises the steps that charging starting time and charging initial electric quantity are used as input, the constructed objective function is used as an optimization objective, and the optimal charging starting time and actual charging electric quantity of an electric vehicle data set for charging when the mean value variance of a power grid load curve and carbon emission are minimum are obtained by utilizing a double-layer non-dominated genetic algorithm;
the double-layer non-dominated genetic algorithm has double-layer codes and comprises a time layer and a charging layer, each gene of the charging layer corresponds to each gene of the time layer, the charging layer and the time layer jointly form a chromosome and represent the charging starting time and the actual charging electric quantity of the same vehicle, and the upper and lower layers of genes are subjected to genetic algorithm operation simultaneously or respectively.
2. The charging method according to claim 1, wherein the specific process of the two-layer non-dominated genetic algorithm is:
6.1) double layer coding
The upper layer is a time layer, the upper layer gene code represents the charging start time of the electric automobile, and the time is divided every 20min from a point 0, namely the time nodes are 0:20, 0:40, 1:00, …, 23:40 and 0: 00, dividing 24h into 72 sections, numbering each time node in sequence, wherein the numbering is time layer gene coding, namely 1, 2, 3, … and 72, if the charging start time of the electric automobile is not equal to the divided time nodes, calculating the time length between the charging start time of the electric automobile and two adjacent time nodes, and dividing the charging start time of the electric automobile into adjacent time nodes with shorter time length;
counting the charging start time of each electric automobile in the electric automobile data set going to be charged, determining corresponding time layer gene codes according to rules, and then sequencing to obtain the time layer gene codes of the electric automobile data set going to be charged currently, wherein the length of a time layer is the number of the electric automobiles in the electric automobile data set going to be charged;
the lower layer is a charging layer, the gene code of the charging layer is an integer between {50, 100}, the code length is the number of the electric vehicles in the electric vehicle data set going to be charged, a charging percentage code of the empty electric quantity of the battery is formed, the actual charging electric quantity can be obtained according to the gene code of the charging layer and the charging initial electric quantity, and the charging initial electric quantity passes through a probability density functionf(s) subtracting the en-route consumed electric quantity from the electric quantity randomly obtained;
in a time layer, inputting initial time of each vehicle reaching a charging station, wherein the initial time is charging starting time of the electric vehicle, the initial time is obtained by subtracting time spent on the way of going to the charging station from the time of the electric vehicle going to the charging station, in a charging layer, inputting a charging percentage code of residual battery electric quantity randomly generated between {50, 100}, obtaining extra charging electric quantity based on a charging layer gene code and the charging initial electric quantity, and further obtaining actual charging electric quantity corresponding to the charging layer gene code;
6.2) setting population scale n, ditch substitution, maximum iteration times, cross probability and variation probability, simultaneously setting the probability of simultaneous operation of double-layer genes, and calculating the fitness value of each chromosome by using an objective function;
6.3) executing selection operation, cross operation, mutation operation, non-dominant sequencing, calculating congestion distance and elite strategy, and outputting the charging strategy under the optimal condition of the objective function after the convergence condition is reached.
3. The charging method according to claim 1, wherein the crossover operation adopts a uniform crossover and a two-point crossover, a double-layer gene simultaneous operation probability p and a double-layer individual operation probability 1-p are set, and when the double-layer genes are crossed simultaneously, the uniform crossover is adopted, specifically: traversing each individual in the parent 1, and exchanging the position of the gene in the corresponding parent 2 according to the cross probability Ps to obtain filial generations; another child is obtained in a similar manner; in the single-layer crossing, two-point crossing is adopted, specifically: two cross points are immediately arranged in the two parents, and then partial genes are exchanged to obtain offspring a and b;
mutation operation: judging whether the dyeing individuals in the second generation filial generation population need to be mutated according to the preset mutation probability, and if not, carrying out the next operation; if needed, randomly selecting two mutation positions a and b on the chromosome, and then exchanging parts of the two positions to generate a mutated chromosome until the mutation probability of the chromosomes in the population reaches a set value; the population output by the mutation operation is called a third generation offspring population; the same principle as the crossover operator, the gene simultaneous operation probability p is set in the mutation operation, and the double-layer genotypes are mutated simultaneously or independently;
the congestion degree is calculated by equation (17):
Cuindicating the degree of crowding of the chromosome u,
represents the kth objective function value of chromosome u +1,
the first to represent chromosome u-1oAnd (4) an objective function value.
4. The charging method according to claim 1, wherein a real-time space-time distribution map of the electric vehicle going to be charged is drawn based on the electric vehicle unique number, the acquisition time and the latitude and longitude information in the electric vehicle data set going to be charged;
the space-time distribution diagram records the number of each vehicle, performs space-time statistics by taking the vehicle as a unit, displays all vehicles in the space-time distribution diagram, divides 24h into 72 time nodes by taking 20 minutes as a division node, wherein the position of a point in the diagram represents the geographic position of a route which is acquired when the electric vehicle goes to a charging station, the color of the point represents the current moment of the electric vehicle, each time node is set with different colors, and if the acquisition time is not equal to a certain time node, the time length between two adjacent time nodes of the acquisition time is calculated, and the sampling time is divided into adjacent time nodes with shorter time length;
the electric vehicle charging start time can be obtained from the space-time distribution map.
5. The charging method of claim 1, wherein the objective function is:
the travel constraint and the power grid constraint of the electric automobile are as follows:
in the formula, PLtThe time period t does not contain the original power grid load of the electric automobile, PjtIs the charging power, P, of the electric vehicle j in the period of tavrFor the average load of the electric network including electric vehicles, betatIs a short-term carbon emission factor of the power system during the period t, EtmaxFor the maximum number of electric vehicles that can be accommodated by the charging station in the current region in the period of t,E t the number of electric vehicles in the data set of the electric vehicles going to the charging area at the current area in the time period of t, and the SOCjIs the actual charging capacity, P, of the electric vehicle jtIs the charging power sum of the electric automobile in a period of t, s'jThe initial charging quantity of the electric vehicle j is obtained; s'jminThe initial charging quantity is tolerable for the user of the electric automobile; SOCminAnd SOCmaxRespectively charging the maximum and minimum battery electric quantity of the electric automobile; ptminAnd PtmaxThe maximum value and the minimum value of the total power of the electric vehicle which can be accessed by the power grid are respectively.
6. The charging method according to claim 1, wherein t is a period divided every 1h by 24h for 1 day, and the fitting formula is formula (2):
wherein, Δ DCtAnd Δ PGtRespectively representing the total carbon dioxide emission and the total power generation amount, DC, in the power system in the period tt-1Is the total carbon dioxide emission in the power system during the t-1 period,
is the total power generation amount in the power system during the period t,
and delta is the straight line intercept of a fitting equation, wherein delta is the total power generation amount in the t-1 period in the power system.
7. The charging method according to claim 1, wherein the data acquisition time for acquiring the vehicle information of the electric vehicle is accurate to seconds, and is acquired every 30 seconds on average.
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