CN111126670A - Electric vehicle charging scheduling method and device, computer equipment and storage medium thereof - Google Patents

Electric vehicle charging scheduling method and device, computer equipment and storage medium thereof Download PDF

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CN111126670A
CN111126670A CN201911201153.1A CN201911201153A CN111126670A CN 111126670 A CN111126670 A CN 111126670A CN 201911201153 A CN201911201153 A CN 201911201153A CN 111126670 A CN111126670 A CN 111126670A
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罗欣儿
余鹏
杜进桥
宋奎铮
申凯月
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Abstract

The application relates to an electric vehicle charging scheduling method and device, and computer equipment and a storage medium thereof. An electric vehicle charging scheduling method comprises the following steps: acquiring a load curve optimization model, wherein the objective function of the load curve optimization model aims at minimizing the mean square error of a load curve of the power distribution network; solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network; determining the charging power of each charging station at each moment according to the target charging load; and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle. The electric vehicle charging scheduling method and device, the computer equipment and the storage medium can solve the problem that the scheduling mode is single in the electric vehicle charging scheduling method in the traditional scheme.

Description

Electric vehicle charging scheduling method and device, computer equipment and storage medium thereof
Technical Field
The present disclosure relates to the field of electric vehicle charging control, and in particular, to a method and an apparatus for electric vehicle charging scheduling, and a computer device and a storage medium thereof.
Background
Electric vehicles have attracted public attention in recent years as a sustainable environment-friendly vehicle. However, the large-scale electric vehicle disorderly distributed network charging has adverse effects on the power distribution network, such as voltage out-of-range, harmonic pollution, three-phase imbalance and the like. Therefore, how to reduce the impact of the charging load of the electric vehicle on the power grid so as to be beneficial to the safe operation of the power grid is a hot spot of current research.
In order to orderly control the charging behavior of the electric vehicle, a great deal of research is conducted by scholars at home and abroad. The method for guiding the user to charge at different time intervals through the time-of-use electricity price becomes the most common method for orderly controlling the charging behavior of the electric vehicle. However, in the conventional scheme, the method for scheduling the charging of the electric vehicle is to perform scheduling on a time scale, the scheduling mode is single, and the scheduling effect cannot be improved any more. In summary, the method for scheduling electric vehicle charging in the conventional scheme has the problem of single scheduling mode.
Disclosure of Invention
Therefore, it is necessary to provide a time-sharing electric vehicle charging scheduling method and apparatus, and a computer device and a storage medium thereof, aiming at the problem that the scheduling method for scheduling electric vehicle charging in the conventional scheme has a single scheduling mode.
The application provides an electric vehicle charging scheduling method, which comprises the following steps:
acquiring a load curve optimization model, wherein the objective function of the load curve optimization model aims at minimizing the mean square error of a load curve of the power distribution network;
solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network;
determining the charging power of each charging station at each moment according to the target charging load;
and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle.
The embodiment provides an electric vehicle charging scheduling method, which comprises the steps of obtaining a load curve optimization model, wherein the target of a target function of the load curve optimization model is the minimum mean square error of a load curve of a power distribution network; solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network; determining the charging power of each charging station at each moment according to the target charging load; and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle. The electric vehicle charging scheduling method provided by the embodiment can not only consider the fluctuation of the power distribution network, but also make different electricity prices for each charging station based on the fluctuation of the power distribution network so as to guide a user to select different charging stations and realize the space scheduling of electric vehicle charging. Therefore, the electric vehicle charging scheduling method provided by the embodiment can solve the problem that the scheduling method for scheduling electric vehicle charging in the traditional scheme is single in scheduling mode.
In one embodiment, the method further comprises:
and establishing a user response model based on the charging power and the electricity price, wherein the user response model comprises a first user response model, a second user response model and a third user response model.
In one embodiment, the first user response model is: t iss＝Tw+Tc，
Figure BDA0002295903590000021
Wherein, TsTime required for the entire charging process, TwIs a rowTeam time, TcFor charging time, C0In order to be charged for the charge fee,
Figure BDA0002295903590000031
charging station for j the electricity price at time t, PcIs the charging power;
the second user response model is: t isc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000032
Wherein, TsTime required for the entire charging process, TcFor charging time, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000033
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtIf the value of (1) is 0, the user does not charge at the time t;
the third user response model is:
Figure BDA0002295903590000034
Tc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000035
wherein, TsTime required for allopatric response, d detour distance of user, E0Power consumption per unit mileage, PCFor charging power, TwFor queuing time, TcFor charging time, TsTime required for charging for off-site response, TcFor charging time at the selected charging station, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000036
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtA value of 0 indicates that the user is not charging at time t.
In one embodiment, the objective function of the load curve optimization model is as follows:
Figure BDA0002295903590000037
wherein f is1Mean square error, p, of the distribution network load curve at time iiRepresenting the sum of the charging load of the electric automobile and the basic load of the distribution network at the moment i,
Figure BDA0002295903590000038
represents the average of the system load over the day, and k represents the division of 24 hours a day into k time segments.
In one embodiment, in the objective function,
Figure BDA0002295903590000039
wherein p isiRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, p0iRepresenting the base load of the distribution network at time i, j representing the current charging station, pevjRepresenting the charging load of j charging stations, xijRepresents a charging flag;
Figure BDA00022959035900000310
wherein the content of the first and second substances,
Figure BDA00022959035900000311
represents the average value of the system load in the current day, piRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, k representing that 24 hours a day is divided into k time periods, and i representing the current moment.
In one embodiment, the constraint conditions met by the objective function include power flow constraint of a power distribution network, line operation constraint and electric vehicle charging constraint.
In one embodiment, the power flow constraint of the power distribution network is:
Figure BDA0002295903590000041
wherein, PDGi,tActive power, Q, injected by the distributed power supply at node i during the period tDGi,tReactive power, Q, injected by the distributed power supply at node i during the t periodCi,tFor the access capacity, G, of the capacitor bank at node i of the system during the period tij,tIs the conductance value between the system node i and the node j in the period t, Bij,tIs the susceptance value between system node i and node j, ei,tReal part of voltage, f, of system node i for a period of ti,tThe imaginary part of the voltage of the system node i in the period t;
the line operating constraints are: i isl≤Ipll＝1,.....,Li，gp∈GpWherein, IlIs the current flowing through the element; i isplThe maximum allowable current for the element; l isiIs the number of elements; in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
the electric automobile charging constraint is as follows: SOCS<SOCE1 or less, wherein, SOCSState of charge, SOC, for electric vehicle users before chargingEAnd (4) charging the electric quantity state of the electric automobile user after the charging is finished.
In one embodiment, the time-of-use electricity price model is:
Figure BDA0002295903590000042
determining the electricity price corresponding to the charging power, wherein rho represents the electricity price, and rho represents the electricity price1、ρ2、ρ3、ρ4Respectively represent different electricity rates, P represents the charging power, P representsmaxRepresenting the maximum charging power of the day, PminRepresenting the charging power minimum for the day.
An electric vehicle charging scheduling device, comprising:
the optimization model obtaining module is used for obtaining a load curve optimization model, and the target of the target function of the load curve optimization model is that the mean square error of the load curve of the power distribution network is minimum;
the calculation module is used for solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, and the target charging load is used for minimizing the volatility of the power distribution network;
the charging power calculation module is used for determining the charging power of each charging station at each moment according to the target charging load;
and the electricity price calculation module is used for determining the electricity price of each charging station at each moment based on the time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric automobile.
A computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, causes the processor to perform the steps of the method for scheduling charging of an electric vehicle as described above.
A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the electric vehicle charging scheduling method as described above.
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Fig. 1 is a schematic flowchart of an electric vehicle charging scheduling method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of an electric vehicle charging scheduling device according to an embodiment of the present application.
Fig. 3 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The problem that a device for acquiring noise time domain waveforms of a power distribution room is lacked in the traditional scheme is solved, and based on the problem, the application provides an electric vehicle charging scheduling method and device, computer equipment and a storage medium thereof.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, the present application provides a method for scheduling charging of an electric vehicle, including:
s100, obtaining a load curve optimization model, wherein the target of the target function of the load curve optimization model is that the mean square error of the load curve of the power distribution network is minimum.
It can be understood that the mean square error of the load curve of the power distribution network can reflect the fluctuation of the power distribution network. The smaller the mean square error of the power distribution network is, the smaller the fluctuation of the corresponding power distribution network is. The objective function of the load curve optimization model is that the mean square error of the load curve of the power distribution network is minimum so as to achieve the aim of minimizing the fluctuation of the corresponding power distribution network.
S200, solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network.
And the target charging load is the charging load corresponding to the minimum value of the mean square error of the load curve of the power distribution network. The target charging load is used to minimize the volatility of the distribution network, i.e., to minimize the mean square error of the distribution network.
And S300, determining the charging power of each charging station at each moment according to the target charging load.
It is understood that the charging load is equal to the charging power. And the charging power corresponding to the target charging load is the planned charging power sent to each charging pile by the power distribution network. It can be understood that each charging station can obtain different target charging loads, and due to the fluctuation of the distribution network, the target charging load of one charging station at different moments is different, and the charging power of the corresponding different moments is also different.
And S400, determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric automobile.
The time-of-use electricity price model refers to that charging power in an area range corresponds to an electricity price, and the charging power in the area range refers to an interval of the charging power. It can be understood that, based on the time-of-use electricity price model, the electricity price can be found according to the charging power corresponding to the target charging load, and the found electricity price corresponds to the charging power. For example, the charging power is 1KW, and the interval that 1KW was located at this moment is (0KW, 2KW), (0KW, 2KW) the price of electricity that corresponds is 1 yuan per minute, so the price of electricity that the charging power of 1KW corresponds at this moment is 1 yuan per minute promptly.
It can be understood that the target charging load of each charging station is different, and the obtained electricity prices are different, so that the user can select different charging stations according to different electricity prices, rather than waiting at one charging station and only selecting waiting time. Therefore, the electric vehicle charging scheduling method provided by the embodiment can not only consider the fluctuation of the power distribution network, but also formulate different electricity prices for each charging station based on the fluctuation of the power distribution network so as to guide a user to select different charging stations and realize the space scheduling of electric vehicle charging.
The embodiment provides an electric vehicle charging scheduling method, which comprises the steps of obtaining a load curve optimization model, wherein the target of a target function of the load curve optimization model is the minimum mean square error of a load curve of a power distribution network; solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network; determining the charging power of each charging station at each moment according to the target charging load; and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle. The electric vehicle charging scheduling method provided by the embodiment can not only consider the fluctuation of the power distribution network, but also make different electricity prices for each charging station based on the fluctuation of the power distribution network so as to guide a user to select different charging stations and realize the space scheduling of electric vehicle charging. Therefore, the electric vehicle charging scheduling method provided by the embodiment can solve the problem that the scheduling method for scheduling electric vehicle charging in the traditional scheme is single in scheduling mode.
The electric vehicle charging scheduling method further comprises the following steps:
and S500, establishing a user response model based on the charging power and the electricity price, wherein the user response model comprises a first user response model, a second user response model and a third user response model.
Wherein the first user response model is: t iss＝Tw+Tc，
Figure BDA0002295903590000081
Wherein, TsTime required for the entire charging process, TwFor queuing time, TcFor charging time, C0In order to be charged for the charge fee,
Figure BDA0002295903590000082
charging station for j the electricity price at time t, PcIs the charging power. And the user selects a charging station nearby for charging when responding to the first user response model, and needs to wait for charging in a queue until the electric vehicle is fully charged.
The second user response model is: t isc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000083
Wherein, TsTime required for the entire charging process, TcFor charging time, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000084
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtA value of 0 indicates that the user is not charging at time t. In other words, when the user responds to the second user response model, the user selects a nearby charging station for charging, and the user can charge the electric vehicle without waiting.
The third user response model is:
Figure BDA0002295903590000085
Tc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000086
wherein, TsTime required for allopatric response, d detour distance of user, E0Power consumption per unit mileage, PCFor charging power, TwFor queuing time, TcFor charging time, TsTime required for charging for off-site response, TcFor charging time at the selected charging station, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000087
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtA value of 0 indicates that the user is not charging at time t. I.e. the user responds to the thirdWhen the user responds to the model, the user needs to detour to select a relatively far charging station for charging, and can directly charge the electric automobile without waiting after arriving at the far charging station.
In one embodiment of the present application, the objective function of the load curve optimization model is:
Figure BDA0002295903590000091
wherein f is1Mean square error, p, of the distribution network load curve at time iiRepresenting the sum of the charging load of the electric automobile and the basic load of the distribution network at the moment i,
Figure BDA0002295903590000092
represents the average of the system load over the day, and k represents the division of 24 hours a day into k time segments. In one embodiment, k may be selected 96.
In the objective function, the target function is,
Figure BDA0002295903590000093
wherein p isiRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, p0iRepresenting the base load of the distribution network at time i, j representing the current charging station, pevjRepresenting the charging load of j charging stations, xijRepresenting a charging flag.
Figure BDA0002295903590000094
Wherein the content of the first and second substances,
Figure BDA0002295903590000095
represents the average value of the system load in the current day, piRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, k representing that 24 hours a day is divided into k time periods, and i representing the current moment.
In one embodiment of the application, the constraint conditions met by the objective function comprise power flow constraint of a power distribution network, line operation constraint and electric vehicle charging constraint.
The power flow constraint of the power distribution network is as follows:
Figure BDA0002295903590000096
wherein, PDGi,tActive power, Q, injected by the distributed power supply at node i during the period tDGi,tReactive power, Q, injected by the distributed power supply at node i during the t periodCi,tFor the access capacity, G, of the capacitor bank at node i of the system during the period tij,tIs the conductance value between the system node i and the node j in the period t, Bij,tIs the susceptance value between system node i and node j, ei,tReal part of voltage, f, of system node i for a period of ti,tIs the imaginary voltage of the system node i during the period t.
The line operating constraints are: i isl≤Ipll＝1,.....,Li，gp∈GpWherein, IlIs the current flowing through the element; i isplThe maximum allowable current for the element; l isiIs the number of elements; in the formula, gpRepresenting the current network structure; gpAll allowed radial network configurations are represented.
The electric automobile charging constraint is as follows: SOCS<SOCE1 or less, wherein, SOCSState of charge, SOC, for electric vehicle users before chargingEAnd (4) charging the electric quantity state of the electric automobile user after the charging is finished.
In one embodiment of the present application, the time-of-use electricity price model is:
Figure BDA0002295903590000101
determining the electricity price corresponding to the charging power, wherein rho represents the electricity price, and rho represents the electricity price1、ρ2、ρ3、ρ4Respectively represent different electricity rates, P represents the charging power, P representsmaxRepresenting the maximum charging power of the day, PminRepresenting the charging power minimum for the day. For example, the charging power at a certain moment of a certain charging station is P, where P2≤P<P, then the corresponding electricity price at this time is rho3。
Referring to fig. 2, the present application further provides an electric vehicle charging scheduling device 10, including:
an optimization model obtaining module 100, configured to obtain a load curve optimization model, where a target function of the load curve optimization model is a minimum mean square error of a load curve of a power distribution network;
a calculation module 200, configured to solve an optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, where the target charging load is used to minimize volatility of the power distribution network;
a charging power calculation module 300, configured to determine, according to the target charging load, charging power of each charging station at each time;
and the electricity price calculating module 400 is configured to determine, according to the charging power and based on a time-of-use electricity price model, an electricity price at each time of each charging station, so as to implement electric vehicle charging scheduling.
The structure of the distance measuring device based on the graph information enhancement technology is shown in fig. 2, and the working principle of the electric vehicle charging scheduling device 10 is as described in the embodiment of the electric vehicle charging scheduling method, and is not described herein again.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a reclosing control method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring a load curve optimization model, wherein the objective function of the load curve optimization model aims at minimizing the mean square error of a load curve of the power distribution network;
solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network;
determining the charging power of each charging station at each moment according to the target charging load;
and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
and establishing a user response model based on the charging power and the electricity price, wherein the user response model comprises a first user response model, a second user response model and a third user response model.
Wherein the first user response model is: t iss＝Tw+Tc，
Figure BDA0002295903590000121
Wherein, TsTime required for the entire charging process, TwFor queuing time, TcFor charging time, C0In order to be charged for the charge fee,
Figure BDA0002295903590000122
charging station for j the electricity price at time t, PcIs the charging power;
the second user response model is: t isc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000123
Wherein, TsTime required for the entire charging process, TcFor charging time, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000124
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtIf the value of (1) is 0, the user does not charge at the time t;
the third user response model is:
Figure BDA0002295903590000125
Tc<Ts≤Tdmax-Ta，
Figure BDA0002295903590000126
wherein, TsTime required for allopatric response, d detour distance of user, E0Power consumption per unit mileage, PCFor charging power, TwFor queuing time, TcFor charging time, TsTime required for charging for off-site response, TcFor charging time at the selected charging station, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure BDA0002295903590000127
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtA value of 0 indicates that the user is not charging at time t.
The objective function of the load curve optimization model is as follows:
Figure BDA0002295903590000128
wherein f is1Mean square error, p, of the distribution network load curve at time iiRepresenting the sum of the charging load of the electric automobile and the basic load of the distribution network at the moment i,
Figure BDA0002295903590000129
represents the average of the system load over the day, and k represents the division of 24 hours a day into k time segments.
In the objective function, the target function is,
Figure BDA0002295903590000131
wherein p isiRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, p0iRepresenting the base load of the distribution network at time i, j representing the current charging station, pevjRepresenting the charging load of j charging stations, xijRepresents a charging flag;
Figure BDA0002295903590000132
wherein the content of the first and second substances,
Figure BDA0002295903590000133
represents the average value of the system load in the current day, piRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, k representing that 24 hours a day is divided into k time periods, and i representing the current moment.
The constraint conditions met by the objective function comprise power distribution network power flow constraint, line operation constraint and electric vehicle charging constraint.
Wherein, the power flow constraint of the power distribution network is as follows:
Figure BDA0002295903590000134
wherein, PDGi,tActive power, Q, injected by the distributed power supply at node i during the period tDGi,tReactive power, Q, injected by the distributed power supply at node i during the t periodCi,tFor the access capacity, G, of the capacitor bank at node i of the system during the period tij,tIs the conductance value between the system node i and the node j in the period t, Bij,tIs the susceptance value between system node i and node j, ei,tReal part of voltage, f, of system node i for a period of ti,tThe imaginary part of the voltage of the system node i in the period t;
the line operating constraints are: i isl≤Ipll＝1,.....,Li，gp∈GpWherein, IlIs the current flowing through the element; i isplThe maximum allowable current for the element; l isiIs the number of elements; in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
the electric automobile charging constraint is as follows: SOCS<SOCE1 or less, wherein, SOCSState of charge, SOC, for electric vehicle users before chargingEAnd (4) charging the electric quantity state of the electric automobile user after the charging is finished.
The time-of-use electricity price model is as follows:
Figure BDA0002295903590000141
determining the electricity price corresponding to the charging power, wherein rho represents the electricity price, and rho represents the electricity price1、ρ2、ρ3、ρ4Respectively represent different electricity rates, P represents the charging power, P representsmaxRepresenting the maximum charging power of the day, PminRepresenting the charging power minimum for the day.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (11)

1. An electric vehicle charging scheduling method is characterized by comprising the following steps:
acquiring a load curve optimization model, wherein the objective function of the load curve optimization model aims at minimizing the mean square error of a load curve of the power distribution network;
solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, wherein the target charging load is used for minimizing the volatility of the power distribution network;
determining the charging power of each charging station at each moment according to the target charging load;
and determining the electricity price of each charging station at each moment based on a time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric vehicle.
2. The method of claim 1, wherein the method further comprises:
and establishing a user response model based on the charging power and the electricity price, wherein the user response model comprises a first user response model, a second user response model and a third user response model.
3. The method of claim 2, wherein the first user response model is: t iss＝Tw+Tc，
Figure FDA0002295903580000011
Wherein, TsTime required for the entire charging process, TwFor queuing time, TcFor charging time, C0In order to be charged for the charge fee,
Figure FDA0002295903580000012
charging station for j the electricity price at time t, PcIs the charging power;
the second user response model is: t isc<Ts≤Tdmax-Ta，
Figure FDA0002295903580000013
Wherein, TsTime required for the entire charging process, TcFor charging time, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure FDA0002295903580000014
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtIf the value of (1) is 0, the user does not charge at the time t;
the third user response model is:
Figure FDA0002295903580000015
Tc<Ts≤Tdmax-Ta，
Figure FDA0002295903580000016
wherein, TsTime required for allopatric response, d detour distance of user, E0Power consumption per unit mileage, PCFor charging power, TwFor queuing time, TcFor charging time, TsTime required for charging for off-site response, TcFor charging time at the selected charging station, Tdmax-TaThe maximum parking time acceptable to the user, CiIn order to be charged for the charge fee,
Figure FDA0002295903580000017
charging station for j the electricity price at time t, PcFor charging power, FtFor charging flag bit, FtA value of 1 then represents user charging at time t, FtA value of 0 indicates that the user is not charging at time t.
4. The method of claim 1, wherein the objective function of the load curve optimization model is:
Figure FDA0002295903580000021
wherein f is1Mean square error, p, of the distribution network load curve at time iiRepresenting the sum of the charging load of the electric automobile and the basic load of the distribution network at the moment i,
Figure FDA0002295903580000022
represents the average of the system load over the day, and k represents the division of 24 hours a day into k time segments.
5. The method of claim 4, wherein, in the objective function,
Figure FDA0002295903580000023
wherein p isiRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, p0iRepresenting the base load of the distribution network at time i, j representing the current charging station, pevjRepresenting the charging load of j charging stations, xijRepresents a charging flag;
Figure FDA0002295903580000024
wherein the content of the first and second substances,
Figure FDA0002295903580000025
represents the average value of the system load in the current day, piRepresenting the sum of the charging load of the electric automobile and the basic load of the power distribution network at the moment i, k representing that 24 hours a day is divided into k time periods, and i representing the current moment.
6. The method of claim 2, wherein the constraints satisfied by the objective function include power distribution network flow constraints, line operation constraints, and electric vehicle charging constraints.
7. The method of claim 6, wherein the distribution network flow constraint is:
Figure FDA0002295903580000026
wherein, PDGi,tActive power, Q, injected by the distributed power supply at node i during the period tDGi,tReactive power, Q, injected by the distributed power supply at node i during the t periodCi,tFor the access capacity, G, of the capacitor bank at node i of the system during the period tij,tIs the conductance value between the system node i and the node j in the period t, Bij,tIs the susceptance value between system node i and node j, ei,tReal part of voltage, f, of system node i for a period of ti,tThe imaginary part of the voltage of the system node i in the period t;
the line operating constraints are: i isl≤Ipll＝1,.....,Li，gp∈GpWherein, IlIs the current flowing through the element; i isplThe maximum allowable current for the element; l isiIs the number of elements; in the formula, gpRepresenting the current network structure; gpRepresents all allowed radial network configurations;
the electric automobile charging constraint is as follows: SOCS<SOCE1 or less, wherein, SOCSState of charge, SOC, for electric vehicle users before chargingEAnd (4) charging the electric quantity state of the electric automobile user after the charging is finished.
8. The method of claim 1, wherein the time of use electricity price model is:
Figure FDA0002295903580000031
determining the electricity price corresponding to the charging power, wherein rho represents the electricity price, and rho represents the electricity price1、ρ2、ρ3、ρ4Respectively represent different electricity rates, P represents the charging power, P representsmaxRepresenting the maximum charging power of the day, PminRepresenting the charging power minimum for the day.
9. The utility model provides an electric automobile scheduling device that charges which characterized in that includes:
the optimization model obtaining module is used for obtaining a load curve optimization model, and the target of the target function of the load curve optimization model is that the mean square error of the load curve of the power distribution network is minimum;
the calculation module is used for solving the optimal solution of the load curve optimization model based on a particle swarm optimization algorithm to obtain a target charging load, and the target charging load is used for minimizing the volatility of the power distribution network;
the charging power calculation module is used for determining the charging power of each charging station at each moment according to the target charging load;
and the electricity price calculation module is used for determining the electricity price of each charging station at each moment based on the time-of-use electricity price model according to the charging power so as to realize the charging scheduling of the electric automobile.
10. A computer device comprising a memory and a processor, the memory having a computer program stored therein, wherein the computer program, when executed by the processor, causes the processor to perform the steps of the electric vehicle charging scheduling method according to any one of claims 1 to 8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the electric vehicle charging scheduling method according to any one of claims 1 to 8.
CN201911201153.1A 2019-11-29 2019-11-29 Electric vehicle charging scheduling method and device, computer equipment and storage medium thereof Pending CN111126670A (en)

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