CN112613682A - Electric vehicle charging load prediction method - Google Patents
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Abstract
The invention relates to the technical field of urban power grids, and particularly discloses a method for predicting charging load of an electric vehicle, wherein the method comprises the following steps: electric vehicles in a city are divided into a plurality of types, and the distribution conditions of initial charging time and mileage data of the electric vehicles of different types in unit time are respectively obtained; simulating and simulating user habits of different types of electric automobiles according to the initial charging time and the distribution condition of the mileage data of the different types of electric automobiles in unit time, and determining simulation parameters; calculating the initial charge state and charging data of the electric automobile according to the simulation parameters; and calculating the superposed charging power of the electric automobiles of different types according to the charging data of the electric automobiles to form a charging load curve of the electric automobiles. The method for predicting the charging load of the electric automobile can realize accurate prediction of the charging load of the electric automobile.
Description
Technical Field
The invention relates to the technical field of urban power grids, in particular to a method for predicting charging load of an electric vehicle.
Background
The charging load of the electric automobile has strong randomness in time and space, and with the popularization of the electric automobile in future, the charging load of the electric automobile brings more and more influence on the operation of an urban power distribution network, particularly to a medium-sized and small-sized urban power grid. After the charging load of the electric automobile is connected to the power grid in a large scale, on one hand, the influence on the power quality of the power distribution network can bring voltage offset, three-phase imbalance and harmonic pollution to the power distribution network, so that the peak-valley difference is further enlarged to directly influence the reliability of the power grid; on the other hand, after the large-scale access, the network loss of the power distribution network and the service life of the transformer are changed, and the economic operation of the power grid is influenced.
In order to ensure that the urban power grid can normally and reliably operate, the charging load of the future electric vehicle is accurately predicted, the influence of large-scale access of the electric vehicle on the aspects of the power grid structure, the electric energy quality, the load curve, the dispatching control and the like needs to be researched and analyzed, and an adaptive scheme for coordinated development of the power grid and the electric vehicle is formed, so that the popularization and application of the new energy electric vehicle are more effectively promoted. At present, load prediction methods for electric vehicles are mainly divided into short-term load prediction methods for power systems, Monte Carlo simulation methods and other novel load prediction methods for electric vehicles based on load influence factors of the electric vehicles. However, in the current research methods, the charging load of the electric vehicle is not accurately predicted, so that the analysis of the influence on the power distribution network is not accurate. The influence of large-scale access of electric vehicles on medium and small urban power grids is huge, and particularly, a power distribution network directly exchanging energy with the electric vehicles can influence the quality of electric energy transmitted by the power grid and the economic operation of the power grid, so that an adaptive scheme needs to be provided on the basis of correctly analyzing the influence of large-scale access on the power grid, and the power grid can accommodate more electric vehicle charging loads.
Disclosure of Invention
The invention provides a method for predicting a charging load of an electric automobile, which solves the problem that the charging load of the electric automobile cannot be accurately predicted in the related technology.
As an aspect of the present invention, there is provided a method for predicting a charging load of an electric vehicle, including:
electric vehicles in a city are divided into a plurality of types, and the distribution conditions of initial charging time and mileage data of the electric vehicles of different types in unit time are respectively obtained;
simulating and simulating user habits of different types of electric automobiles according to the initial charging time and the distribution condition of the mileage data of the different types of electric automobiles in unit time, and determining simulation parameters;
calculating the initial charge state and charging data of the electric automobile according to the simulation parameters;
and calculating the superposed charging power of the electric automobiles of different types according to the charging data of the electric automobiles to form a charging load curve of the electric automobiles.
Further, the dividing the electric vehicles in the city into a plurality of types and respectively obtaining the distribution of the initial charging time and the mileage data of the electric vehicles of different types in the unit time includes:
dividing electric automobiles in cities into electric private cars, electric buses and electric taxis;
respectively acquiring the holding capacity, the battery type, the charging power, the initial charging time and the daily driving mileage data of an electric private car, an electric bus and an electric taxi;
and analyzing the distribution condition of the initial charging time and the driving mileage data of the electric automobile in unit time according to the acquired data.
Further, the unit time includes 24 hours.
Further, the simulating and simulating the user habits of the different types of electric vehicles according to the distribution conditions of the initial charging time and the mileage data of the different types of electric vehicles in unit time, and determining simulation parameters includes:
according to the Monte Carlo method and the distribution condition of the initial charging time and the traveling mileage data of different types of electric automobiles in unit time, simulation is carried out on the user habits of the different types of electric automobiles, and the simulation times, the holding capacity of the simulated electric automobiles and the maximum traveling mileage corresponding to the types of the electric automobiles are determined.
Further, when the user habits of the electric private car are simulated, different travel chains and dates, corresponding initial charging time and daily travel mileage are extracted for simulation;
when the electric bus and the electric taxi are simulated, corresponding initial charging time and daily driving mileage are randomly extracted for simulation.
Further, the calculating of the initial state of charge and the charging data of the electric vehicle according to the simulation parameters includes:
calculating the initial charge state of the electric automobile according to the simulation parameters;
and calculating the time consumption of the electric automobile and the charging time range.
Further, the calculation formula for calculating the initial state of charge of the electric vehicle according to the simulation parameters is as follows:
wherein, SOC represents the state of charge of the battery when the electric automobile starts to charge, and SOC2Representing the state of charge of the battery at the completion of the previous charge, d representing the mileage traveled this time, dmRepresents the maximum mileage of the electric vehicle.
Further, the calculation formula for calculating the time consumed by charging the electric vehicle and the time range of charging is as follows:
Tend＝Tstart+T，
wherein, the time length consumed by charging the T electric automobile represents CiThe battery capacity of the i-th type electric automobile is shown, the SOC shows the battery charge state of the electric automobile at the beginning of the charging, and etaiRepresents the charging efficiency, P, of the i-th electric vehicleiIndicates charging power, T, of the i-th electric vehicleendIndicates the end time of charging, T, of the electric vehiclestartIndicating the charging start time of the electric vehicle.
Further, the calculating the superposed charging powers of the electric vehicles of different types according to the charging data of the electric vehicles to form a charging load curve of the electric vehicle includes:
calculating the charging power of each node, wherein the unit time is divided into a plurality of nodes, and each node calculates the charging power of the electric automobile once;
and superposing the charging power of various types of electric automobiles.
Further, the calculation formula for calculating the charging power of each node is as follows:
wherein, Pi,kRepresents the sum of charging power P of the ith type electric vehicle at the kth nodei,tk-1,kIndicating that the charging start time of the electric automobile meeting the condition in the ith type of electric automobile is greater than the kth-1 node tk-1The charging power of the vehicle of (1);
the calculation formula for superposing the charging power of the electric automobiles of various types is as follows:
Pk＝Pcar,k+Pbus,k+Ptaxi,k，
wherein, Pcar,kRepresents the sum of the charging power of the electric private car at the kth node, Pbus,kRepresents the sum of the charging powers of the electric buses at the kth node, Ptaxi,kRepresents the sum of charging power of the electric taxi at the kth node, PkThe charging power of the plurality of types of electric vehicles is added and summed by the kth node.
According to the method for predicting the charging load of the electric automobile, the urban electric automobile is divided into the electric private car, the electric bus and the electric taxi, and the battery parameters, the initial charging time, the daily driving mileage and the traveling habits of the user are respectively researched. The travel habits of the users of the electric private cars with large quantity in the city are refined and divided into holidays and working days, and due to the fact that the difference of the travel distribution conditions of the two time periods is very large, the charging load prediction of the electric private cars is better through separate research. On the basis of the data, a Monte Carlo method is adopted, battery parameters and charging efficiency of various electric vehicles are combined, the traveling habits in working days and holidays are simulated, the charging load of the urban electric vehicles is predicted, and the method has the advantage of more accurate prediction result.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flowchart of a method for predicting a charging load of an electric vehicle according to the present invention.
Fig. 2 is a distribution diagram of the initial departure time of the electric private car, the bus and the taxi user provided by the invention.
FIG. 3 is a distribution diagram of mileage of a private car during working days and resting days according to the present invention.
FIG. 4 is a schematic flow chart of the Monte Carlo method provided by the present invention.
Fig. 5 is a load prediction curve of the electric private car, the electric taxi and the electric bus provided by the invention.
Fig. 6 is a total load curve of the electric vehicle provided by the invention.
Fig. 7 is a schematic diagram of an IEEE33 node system according to the present invention.
Fig. 8 is a classification diagram of the ordered charging strategy provided by the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this embodiment, a method for predicting a charging load of an electric vehicle is provided, and fig. 1 is a flowchart of a method for predicting a charging load of an electric vehicle according to an embodiment of the present invention, as shown in fig. 1, including:
s110, dividing electric automobiles in a city into a plurality of types, and respectively acquiring the initial charging time and the distribution condition of the mileage data of the electric automobiles of different types in unit time;
in some embodiments, the dividing the electric vehicles in the city into a plurality of types and respectively obtaining the distribution of the initial charging time and the mileage data of the electric vehicles of different types in a unit time may specifically include:
dividing electric automobiles in cities into electric private cars, electric buses and electric taxis;
respectively acquiring the holding capacity, the battery type, the charging power, the initial charging time and the daily driving mileage data of an electric private car, an electric bus and an electric taxi;
and analyzing the distribution condition of the initial charging time and the driving mileage data of the electric automobile in unit time according to the acquired data.
In the embodiment of the present invention, the unit time may be specifically 24 hours, that is, one day.
It should be understood that the holding capacity, the battery type and the charging power, the initial charging time and the daily driving mileage of each type of electric vehicle can be respectively investigated, and the distribution of the initial charging time and the daily driving mileage of the electric vehicle in one day can be analyzed by adopting least square fitting.
In the embodiment of the invention, urban electric automobiles are divided into electric private cars, electric bus cars and electric taxis, 4.29 thousands of private cars, 0.88 thousands of taxis and 0.06 thousands of buses of a certain electric bus are taken.
Fig. 2 shows the initial departure times of the electric private car, the electric bus and the electric taxi.
According to the 2019 resident travel-in survey, the departure times of the three types of cars are concentrated in the peak time of morning and evening according to statistical data analysis, wherein 36.6% of the initial travel behaviors of private cars occur in the morning peak 7: 00-9: 00 and late peak 17: 00-19: 00; the bus accounts for 34.5% of the whole day when going out in the early peak time period and 28.3% when going out in the late peak time period; the taxi occupies 34.9% of the whole day when going out in the early peak time period, and occupies 12% of the taxi when going out in the late peak time period.
And analyzing the early and late peaks of the line time, and calculating parameters approximately following normal distribution through a distribution curve. The calculation parameters are shown in table 1.
TABLE 1 Normal distribution parameters of travel time of various electric vehicles
The mileage of the private car on the weekdays and the holidays was investigated, as shown in fig. 3.
And calculating parameters of which the daily driving mileage approximately obeys normal distribution. The calculation parameters are shown in table 2.
TABLE 2 distribution parameters of daily and holiday driving mileage of private car
Investigating the daily driving mileage of the taxi, and calculating to obtain 0: 00-10: 00 and 15: 00 to 24: in the time period of 00, the daily driving mileage of the taxi meets N (335,225); in the following 10: 00-15: in the time period of 00, the daily driving mileage of the taxi is in accordance with N (265, 225).
The daily driving mileage of the bus is investigated, and data analysis shows that the average driving mileage of the bus is within the range of 140 plus 200km and the characteristic of random distribution is embodied, so that the daily driving mileage of the bus is assumed to be uniformly distributed [140,200 ].
S120, simulating and simulating user habits of the different types of electric automobiles according to the initial charging time and the distribution condition of the mileage data of the different types of electric automobiles in unit time, and determining simulation parameters;
specifically, according to the monte carlo method and in combination with the initial charging time and the distribution condition of the mileage data of different types of electric vehicles in unit time, simulation can be performed on the user habits of the different types of electric vehicles, and the number of times of simulation, the holding capacity of the simulated electric vehicles and the maximum mileage number corresponding to the type of the electric vehicles are determined.
When the user habits of the electric private car are simulated, different travel chains and dates, corresponding initial charging time and daily travel mileage are extracted for simulation;
when the electric bus and the electric taxi are simulated, corresponding initial charging time and daily driving mileage are randomly extracted for simulation.
S130, calculating the initial charge state and the charging data of the electric automobile according to the simulation parameters;
specifically, the initial state of charge of the electric vehicle is calculated according to the simulation parameters, and the calculation formula is as follows:
wherein, SOC represents the state of charge of the battery when the electric automobile starts to charge, and SOC2Representing the state of charge of the battery at the completion of the previous charge, d representing the mileage traveled this time, dmRepresenting the maximum driving mileage of the electric automobile;
calculating the time consumption and the charging time range of the electric automobile, wherein the calculation formula is as follows:
Tend＝Tstart+T，
wherein, the time length consumed by charging the T electric automobile represents CiThe battery capacity of the i-th type electric automobile is shown, the SOC shows the battery charge state of the electric automobile at the beginning of the charging, and etaiRepresents the charging efficiency, P, of the i-th electric vehicleiIndicates charging power, T, of the i-th electric vehicleendIndicates the end time of charging, T, of the electric vehiclestartIndicating the charging start time of the electric vehicle.
S140, calculating the superposed charging power of the electric automobiles of different types according to the charging data of the electric automobiles to form a charging load curve of the electric automobiles,
specifically, the charging power of each node is calculated, wherein the unit time is divided into a plurality of nodes, each node calculates the charging power of the electric vehicle once, and the calculation formula is as follows:
wherein, Pi,kRepresents the sum of charging power P of the ith type electric vehicle at the kth nodei,tk-1,kIndicating that the charging start time of the electric automobile meeting the condition in the ith type of electric automobile is greater than the kth-1 node tk-1The charging power of the vehicle of (1);
the charging power of various types of electric automobiles is superposed, and the calculation formula is as follows:
Pk＝Pcar,k+Pbus,k+Ptaxi,k，
wherein, Pcar,kRepresents the sum of the charging power of the electric private car at the kth node, Pbus,kRepresents the sum of the charging powers of the electric buses at the kth node, Ptaxi,kIndicating charging power of electric taxi at kth nodeAnd, PkThe charging power of the plurality of types of electric vehicles is added and summed by the kth node.
As shown in fig. 4, a schematic flow chart of the monte carlo method is provided, a user trip habit is simulated by using the monte carlo method, parameters including electric vehicle starting time, electric vehicle charging power, electric vehicle daily driving mileage and the like are randomly extracted, the simulation times are determined to be 10000 times, the electric vehicle holding capacity and the maximum driving mileage corresponding to the electric vehicle type are input, statistical data is collected once per minute, that is, a load is calculated once per minute, and 1440 load calculation nodes are counted.
The load curves of electric private cars, electric taxis and electric buses are shown in fig. 5.
The loads of the electric private car, the electric taxi and the electric bus are superposed to form an electric car load curve as shown in fig. 6.
After the load of the electric automobile is superposed with the conventional load, the form of a daily load curve is found and is obviously changed, the daily maximum load and the peak-valley difference are not obviously improved, and therefore, the load brought by the electric automobile does not influence the overall load curve.
According to the method for predicting the charging load of the electric automobile, provided by the embodiment of the invention, the urban electric automobile is divided into the electric private car, the electric bus and the electric taxi, and the battery parameters, the initial charging time, the daily driving mileage and the traveling habits of the user are respectively researched. The travel habits of the users of the electric private cars with large quantity in the city are refined and divided into holidays and working days, and due to the fact that the difference of the travel distribution conditions of the two time periods is very large, the charging load prediction of the electric private cars is better through separate research. On the basis of the data, a Monte Carlo method is adopted, battery parameters and charging efficiency of various electric vehicles are combined, the traveling habits in working days and holidays are simulated, the charging load of the urban electric vehicles is predicted, and the method has the advantage of more accurate prediction result.
After the charging load of the urban electric vehicle is accurately predicted, the influence of the charging load of the electric vehicle on the operation of a power grid can be analyzed according to a prediction result. The electric automobile directly exchanges energy with the power distribution network, so that the influence on the power distribution network is the largest. In general, two aspects of the influence research of the electric automobile on the power quality of the power distribution network are load curve peak-valley difference and voltage deviation.
Peak to valley difference Δ P of load curveLThe calculation formula of (2) is:
ΔPL＝max(Pi-Pj)，
wherein, PiIndicating the load value at time i.
The peak-to-valley difference rate mu of the load curve is calculated by the formula:
and (4) surveying to obtain urban conventional load information, and superposing the predicted electric automobile load and the conventional load. The peak-to-valley difference and peak-to-valley difference rate of the curve were calculated.
The voltage deviation is Δ ViThe deviation value of the actual voltage of the node and the nominal voltage of the system is indicated, and the mathematical expression is as follows:
wherein, ViRepresenting the actual voltage, V, of the i-th nodeNRepresenting the nominal voltage of the system.
The nodes are divided into residential areas, working areas and business areas according to regional functions, charging load models of the functional areas are established, and charging loads of the electric vehicles are calculated respectively. And proportionally connecting the charging load to each node of the feeder line, carrying out load flow calculation hour by hour, obtaining the voltage distribution condition of the nodes in 24 hours and calculating the voltage deviation.
As shown in fig. 7, taking IEEE33 node system as an example, charging loads are connected to feeder nodes, and voltage distribution at each feeder node is calculated. As shown, nodes 2-5, 8-13, 17-19, 23-25, and 27-33 are residential areas, nodes 20-22 are work areas, nodes 6-7 and 26 are business areas, and nodes 14-16 are other functional areas. Simulation results show that voltage deviation of a residential area at night is large, because the peak of residential life electricity consumption at night is superposed with the charging load of the electric automobile, and voltage deviation of a working area and a commercial area at day is large.
According to the predicted charging load of the electric automobile, planning and designing of the electric automobile can be achieved, as shown in fig. 8, planning mainly comprises three aspects, namely ordered charging and discharging, a V2G technology and an electric automobile charging and discharging management system. a. The orderly charging of the orderly charging and discharging electric automobile means that the charging behavior of the electric automobile is effectively guided and controlled on the premise of ensuring the travel demand of a user, so that the aim of reducing the negative influence of the charging load on a power grid is fulfilled. b. The power battery of the electric automobile is used as the buffer of the power grid and the renewable energy source, and when the load of the power grid is high, the electric automobile releases electric energy to the power grid; when the load of the power grid is low, the electric automobile is used for storing redundant electric quantity of the power grid, and waste of electric energy is avoided. Through the V2G technical mode, the electric automobile user can buy electricity from the power grid when the electricity price is low, and sell electricity to the power grid when the electricity price is high, thereby achieving the win-win effect. c. The electric vehicle charging and discharging management system of the electric vehicle and power grid interactive charging and discharging control network is divided into a distributed interactive charging strategy and a centralized charging strategy.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A method for predicting charging load of an electric vehicle is characterized by comprising the following steps:
electric vehicles in a city are divided into a plurality of types, and the distribution conditions of initial charging time and mileage data of the electric vehicles of different types in unit time are respectively obtained;
simulating and simulating user habits of different types of electric automobiles according to the initial charging time and the distribution condition of the mileage data of the different types of electric automobiles in unit time, and determining simulation parameters;
calculating the initial charge state and charging data of the electric automobile according to the simulation parameters;
and calculating the superposed charging power of the electric automobiles of different types according to the charging data of the electric automobiles to form a charging load curve of the electric automobiles.
2. The method for predicting the charging load of the electric vehicle according to claim 1, wherein the step of dividing the electric vehicles in the city into a plurality of types and respectively acquiring distribution conditions of initial charging time and mileage data of the electric vehicles of different types in unit time comprises the following steps:
dividing electric automobiles in cities into electric private cars, electric buses and electric taxis;
respectively acquiring the holding capacity, the battery type, the charging power, the initial charging time and the daily driving mileage data of an electric private car, an electric bus and an electric taxi;
and analyzing the distribution condition of the initial charging time and the driving mileage data of the electric automobile in unit time according to the acquired data.
3. The electric vehicle charging load prediction method according to claim 2, wherein the unit time includes 24 hours.
4. The method for predicting the charging load of the electric vehicle according to claim 2, wherein the simulating and simulating the user habits of the different types of electric vehicles according to the distribution of the initial charging time and the mileage data of the different types of electric vehicles in unit time and determining the simulation parameters comprises:
according to the Monte Carlo method and the distribution condition of the initial charging time and the traveling mileage data of different types of electric automobiles in unit time, simulation is carried out on the user habits of the different types of electric automobiles, and the simulation times, the holding capacity of the simulated electric automobiles and the maximum traveling mileage corresponding to the types of the electric automobiles are determined.
5. The method for predicting the charging load of an electric vehicle according to claim 4,
when the user habits of the electric private car are simulated, extracting different travel chains and dates and corresponding initial charging time and daily travel mileage for simulation;
when the electric bus and the electric taxi are simulated, corresponding initial charging time and daily driving mileage are randomly extracted for simulation.
6. The method for predicting the charging load of the electric vehicle according to claim 2, wherein the calculating the initial state of charge and the charging data of the electric vehicle according to the simulation parameters comprises:
calculating the initial charge state of the electric automobile according to the simulation parameters;
and calculating the time consumption of the electric automobile and the charging time range.
7. The method for predicting the charging load of the electric vehicle according to claim 6, wherein the calculation formula for calculating the initial state of charge of the electric vehicle according to the simulation parameters is as follows:
wherein, SOC represents the state of charge of the battery when the electric automobile starts to charge, and SOC2Representing the state of charge of the battery at the completion of the previous charge, d representing the mileage traveled this time, dmRepresents the maximum mileage of the electric vehicle.
8. The method for predicting the charging load of the electric vehicle according to claim 6, wherein the calculation formula for calculating the time taken for charging the electric vehicle and the charging time range is as follows:
Tend＝Tstart+T，
wherein, the time length consumed by charging the T electric automobile represents CiThe battery capacity of the i-th type electric automobile is shown, the SOC shows the battery charge state of the electric automobile at the beginning of the charging, and etaiRepresents the charging efficiency, P, of the i-th electric vehicleiIndicates charging power, T, of the i-th electric vehicleendIndicates the end time of charging, T, of the electric vehiclestartIndicating the charging start time of the electric vehicle.
9. The method for predicting the charging load of the electric vehicle according to claim 2, wherein the step of calculating the superimposed charging powers of different types of electric vehicles according to the charging data of the electric vehicles to form a charging load curve of the electric vehicle comprises the following steps:
calculating the charging power of each node, wherein the unit time is divided into a plurality of nodes, and each node calculates the charging power of the electric automobile once;
and superposing the charging power of various types of electric automobiles.
10. The method for predicting the charging load of the electric vehicle according to claim 9, wherein the calculation formula for calculating the charging power of each node is as follows:
wherein, Pi,kRepresents the sum of charging power P of the ith type electric vehicle at the kth nodei,tk-1,kIndicating that the charging start time of the electric automobile meeting the condition in the ith type of electric automobile is greater than the kth-1 node tk-1The charging power of the vehicle of (1);
the calculation formula for superposing the charging power of the electric automobiles of various types is as follows:
Pk＝Pcar,k+Pbus,k+Ptaxi,k，
wherein, Pcar,kRepresents the sum of the charging power of the electric private car at the kth node, Pbus,kRepresents the sum of the charging powers of the electric buses at the kth node, Ptaxi,kRepresents the sum of charging power of the electric taxi at the kth node, PkThe charging power of the plurality of types of electric vehicles is added and summed by the kth node.
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| CN115465141A (en) * | 2022-09-16 | 2022-12-13 | 上海挚达科技发展有限公司 | Electric vehicle charging and discharging control method and device, electronic equipment and storage medium |
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