CN115833201A - Electric vehicle V2G scheduling method and terminal - Google Patents

Abstract

The present application discloses a V2G scheduling method and terminal for electric vehicles, which receive a V2G scheduling request sent by a target area; determine the vehicles that accept V2G scheduling in the target area according to the V2G scheduling request, and determine the vehicles that accept V2G scheduling in the target area according to the The vehicle determines the set of candidate vehicles participating in V2G scheduling; matches the corresponding charging station for each vehicle in the set of candidate vehicles, and determines the remaining capacity of the battery after each vehicle arrives at the corresponding charging station. The capacity determines the set of target vehicles that participate in V2G scheduling; formulate a scheduling strategy based on the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. Vehicles in the target vehicle set perform V2G scheduling; the stability and effectiveness of electric vehicles participating in V2G can be improved.

Description

A V2G scheduling method and terminal for electric vehicles

technical field

The present invention relates to the technical field of electric vehicle V2G, in particular to an electric vehicle V2G scheduling method and a terminal.

Background technique

With the development of building electrification and the extensive use of smart home appliances, as global warming and extreme weather occur frequently, the load intensity in the power consumption area is gradually increasing. The amount of hydroelectric power generation has been greatly reduced, while the use of air conditioners has been greatly increased. However, due to the limited space in urban areas, it is difficult to expand the power system.

Facing the difficulties caused by extreme weather, the more practical solution at present is to alleviate the problems of power supply and power expansion in urban areas through power system demand-side management and demand-side response. However, for power distribution terminals—such as a certain community Or the power expansion problem of users in a certain building is difficult to play a role. It is necessary to explore a way to solve the problem of power expansion in modern residential areas and office buildings, office buildings and other office spaces. At the same time, the types of flexible loads in the current power grid are increasing. In addition to traditional household equipment such as air conditioners and washing machines, with the popularization of electric vehicles and their charging piles, the uncertainty of flexible loads will also increase. .

There is already a V2G (Vehicle to Grid) technology that uses electric vehicles to supply power to the grid in the existing technology, but how to make the V2G technology better participate in the process of regional flexible load scheduling is what is lacking in the existing technology. However, it is also a technical problem that needs to be solved urgently under the background of the current social power consumption. In particular, the existing technology lacks how to effectively dispatch electric vehicles in transit so that they can effectively and stably participate in V2G. This is an urgent need to be solved. technical problem.

Contents of the invention

The technical problem to be solved by the present invention is to provide an electric vehicle V2G scheduling method and terminal, which can improve the stability and effectiveness of electric vehicles participating in V2G.

In order to solve the above-mentioned technical problems, a kind of technical scheme that the present invention adopts is:

A V2G scheduling method for electric vehicles, comprising the steps of:

S1. Receive the V2G scheduling request sent by the target area;

S2. Determine the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles participating in V2G scheduling according to the vehicles receiving V2G scheduling in the target area;

S3. Match the corresponding charging station for each vehicle in the candidate vehicle set, determine the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determine the target vehicle participating in V2G scheduling according to the remaining battery capacity gather;

S4. Formulate a scheduling strategy according to the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. According to the scheduling strategy, the vehicles in the target vehicle set Perform V2G scheduling.

In order to solve the above-mentioned technical problems, another kind of technical scheme that the present invention adopts is:

An electric vehicle V2G dispatching terminal, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the above-mentioned electric vehicle V2G is realized when the processor executes the computer program The individual steps in a dispatch method.

The beneficial effects of the present invention are: in the process of V2G dispatching, first determine the set of candidate vehicles participating in V2G dispatching according to the wishes of each vehicle in the target area, and then determine the set of target vehicles based on the remaining battery capacity of each vehicle after arriving at the corresponding charging station , and finally formulate a scheduling strategy according to the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraint after the electric vehicle is connected to the grid, and according to the scheduling strategy, the vehicles in the target vehicle set Carry out V2G dispatching; when conducting V2G dispatching, the choice of the vehicle owner is combined with the remaining capacity of the battery of the electric vehicle, and in the process of dispatching optimization, when limiting the output power of each electric vehicle to the grid, the electric vehicle is also fully considered. The economy of vehicles, including the charging time of electric vehicles and the loss cost of electric vehicles, can ensure that vehicles in transit can effectively and stably participate in V2G regulation for a long time, instead of randomly selecting electric vehicles that are being charged for grid feedback or simply considering grid peaks and valleys Regulating and ignoring the service life of electric vehicles will help stabilize grid regulation expectations and reduce the possibility of grid fluctuations caused by the participation of electric vehicle V2G.

Description of drawings

FIG. 1 is a flow chart of the steps of an electric vehicle V2G scheduling method according to an embodiment of the present invention;

Fig. 2 is a schematic structural diagram of an electric vehicle V2G dispatching terminal according to an embodiment of the present invention.

Detailed ways

In order to describe the technical content, achieved goals and effects of the present invention in detail, the following descriptions will be made in conjunction with the embodiments and accompanying drawings.

Please refer to Figure 1, a V2G scheduling method for electric vehicles, including steps:

S1. Receive the V2G scheduling request sent by the target area;

S2. Determine the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles participating in V2G scheduling according to the vehicles receiving V2G scheduling in the target area;

S3. Match the corresponding charging station for each vehicle in the candidate vehicle set, determine the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determine the target vehicle participating in V2G scheduling according to the remaining battery capacity gather;

S4. Formulate a scheduling strategy according to the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. According to the scheduling strategy, the vehicles in the target vehicle set Perform V2G scheduling.

It can be seen from the above description that the beneficial effect of the present invention lies in: in the process of V2G scheduling, first determine the set of candidate vehicles participating in V2G scheduling according to the wishes of each vehicle in the target area, and then based on the remaining battery of each vehicle after arriving at the corresponding charging station Determine the capacity of the target vehicle set, and finally formulate a scheduling strategy based on the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. The vehicles in the vehicle set are scheduled for V2G; when performing V2G scheduling, the choice of the vehicle owner is combined with the remaining capacity of the battery of the electric vehicle, and in the process of scheduling optimization, when limiting the output power of each electric vehicle to the grid, The economy of electric vehicles has also been fully considered, including the charging time of electric vehicles and the loss cost of electric vehicles, which can ensure that vehicles in transit can effectively and stably participate in V2G regulation for a long time, instead of randomly selecting electric vehicles that are being charged for grid feedback or simply Considering the peak and valley regulation of the power grid without regard to the service life of electric vehicles will help stabilize the expectation of power grid regulation and reduce the possibility of grid fluctuations caused by the participation of electric vehicle V2G.

Further, the determining the vehicles accepting V2G scheduling in the target area according to the V2G scheduling request includes:

Pushing dispatch request information to vehicles in the target area according to the V2G dispatch request;

receiving dispatch response information sent by vehicles in the target area based on the dispatch request information;

Determining vehicles that accept V2G scheduling in the target area according to the scheduling response information.

It can be seen from the above description that after receiving the V2G scheduling request, the scheduling request information is pushed to each vehicle in the target area, and the vehicle to accept the V2G scheduling is determined based on the scheduling response information of each vehicle. Willingness can improve the stability of electric vehicles participating in V2G.

Further, the V2G scheduling request includes a scheduling period;

The determining the set of candidate vehicles participating in V2G scheduling according to the vehicles accepting V2G scheduling in the target area includes:

Acquiring historical data of charging and discharging of vehicles receiving V2G scheduling in the target area, and determining charging and discharging laws of vehicles receiving V2G scheduling according to the historical data;

determining the probability that the vehicle receiving V2G scheduling will be charged during the scheduling period according to the charging and discharging law;

A set of candidate vehicles participating in V2G scheduling is created, and vehicles receiving V2G scheduling with a probability greater than a first preset value are added to the set of candidate vehicles participating in V2G scheduling.

From the above description, it can be seen that the charging and discharging law of the vehicle is determined by obtaining the historical data of vehicle charging and discharging, and the probability of each vehicle charging during the scheduling period is determined according to the charging and discharging law, and the vehicle whose charging probability is greater than the first preset value is selected as a candidate vehicle , based on the user's real use of electric vehicles, matching the commonly used charging periods of electric vehicles with the high probability periods that can participate in V2G improves the matching degree between vehicles and V2G scheduling, thus helping to further improve the stability of V2G scheduling.

Further, steps are also included:

A set of candidate vehicles participating in V2G scheduling is created, and vehicles receiving V2G scheduling with a probability less than or equal to a first preset value and greater than a second preset value are added to the set of candidate vehicles participating in V2G scheduling.

It can be known from the above description that the set of candidate vehicles is set, and the candidate vehicles in the set of candidate vehicles can be scheduled when the set of candidate vehicles is insufficient, which ensures the reliability and robustness of V2G scheduling.

Further, the matching the corresponding charging station for each vehicle in the set of candidate vehicles includes:

Each vehicle in the candidate vehicle set is matched with a corresponding charging station based on the principle that the overall dispatch distance of all vehicles in the candidate vehicle set is the smallest.

Further, the charging pile allocation model constructed based on the above principles is:

h _k ≤ H _k

In the formula, M represents the total number of vehicles in the candidate vehicle set, K represents the total number of charging stations in the target area, and D _k,j represents the distance between the jth vehicle assigned to the kth charging station and the charging station , H _k represents the maximum number of electric vehicles that the kth charging station can accommodate, and hk represents the number of electric vehicles allocated to the kth charging station.

It can be known from the above description that each vehicle in the candidate vehicle set is matched with a corresponding charging station based on the principle that the overall dispatching distance of all vehicles in the candidate vehicle set is the smallest, which can ensure optimal allocation of charging stations.

Further, the determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station includes:

Determining the time T _i required for each vehicle to reach the corresponding charging station and the distance D _i from the corresponding charging station;

The remaining battery capacity E _i of each vehicle after arriving at the corresponding charging station is:

E _i ＝E _i0 -f _i (T _i , D _i )

In the formula, E _i0 represents the current remaining battery capacity of the i-th vehicle, and the f _i function represents the average power curve of the battery pack of the i-th vehicle.

It can be seen from the above description that the remaining battery capacity after the vehicle arrives at the corresponding charging station is determined according to the time required for the vehicle to reach the corresponding charging station, the distance from the corresponding charging station, and the static power curve of the battery pack of the vehicle, which ensures that all Determine the accuracy of the remaining capacity of the vehicle battery.

Further, the formulation of the scheduling strategy based on the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraint after the electric vehicle is connected to the grid includes:

The scheduling strategy formulated is as follows:

In the formula, M represents the total number of vehicles in the candidate vehicle set, E _i,max represents the upper limit of the remaining battery capacity of the i-th vehicle, E _th represents the lower limit of the remaining battery capacity of the vehicle, and P _i represents the participation of the i-th vehicle in V2G scheduling to provide The power to the grid, T _i represents the duration of the i-th vehicle participating in V2G scheduling, δ _i represents the charging efficiency of the i-th vehicle, T _i represents the time required for the i-th vehicle to reach its corresponding charging station, and the corresponding The distance between the charging stations D _i represents the distance between the i-th vehicle and its corresponding charging station, the f _i function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, and K _i represents the i-th The nominal number of cycles of the vehicle, W _i represents the selling price of the i-th vehicle, G represents the total output power of M electric vehicles participating in V2G, Z represents the electricity cost and battery loss cost of M electric vehicles participating in V2G, U _min Indicates the minimum grid-connected voltage of the vehicle connected to the charging station for V2G, U _max indicates the maximum reverse input voltage that the corresponding charging station of the vehicle can withstand, U _i indicates the vehicle output voltage when the i-th vehicle participates in V2G, P _f indicates The output power of the conventional power supply side in the target area, P _max-min represents the power difference between the maximum load and the minimum load in the target area, and P _ave represents the average load of the vehicle during the V2G scheduling period.

From the above description, it can be seen that when optimizing the strategy, not only the output power of electric vehicles and the active power constraints on the grid are considered, but also the battery charge and discharge cycle loss caused by the battery participating in V2G is considered, so that when optimizing the scheduling of electric vehicles in the target area, full consideration is given to electric vehicles. The loss economy of vehicles ensures that the overall loss of vehicles participating in V2G in the target area is low, and in the dispatching process, fully considers the power loss of electric vehicles in the process of going to the charging station, so that after going to the charging station or in the process of V2G Electric vehicles with low power are excluded to prevent excessive loss of electric vehicle batteries due to V2G and shorten the service life of electric vehicles, so as to ensure that electric vehicles under the owner's name can participate in V2G regulation relatively stably.

Please refer to FIG. 2 , an electric vehicle V2G dispatching terminal, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, the above-mentioned Various steps in the electric vehicle V2G dispatching method.

The above electric vehicle V2G dispatching method and terminal in this application can be applied to the V2G optimal dispatching of electric vehicles in transit, and will be described through specific implementation methods as follows:

Embodiment one

Please refer to Figure 1, a V2G scheduling method for electric vehicles, including steps:

S1. Receive the V2G scheduling request sent by the target area;

S2. Determine the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles participating in V2G scheduling according to the vehicles receiving V2G scheduling in the target area;

Specifically, the determining the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request includes:

Pushing dispatch request information to vehicles in the target area according to the V2G dispatch request;

receiving dispatch response information sent by vehicles in the target area based on the dispatch request information;

determining the vehicles receiving V2G dispatch in the target area according to the dispatch response information;

Wherein, the V2G scheduling request includes a scheduling period;

The determining the set of candidate vehicles participating in V2G scheduling according to the vehicles accepting V2G scheduling in the target area includes:

Acquiring historical data of charging and discharging of vehicles receiving V2G scheduling in the target area, and determining charging and discharging laws of vehicles receiving V2G scheduling according to the historical data;

determining the probability that the vehicle receiving V2G scheduling will be charged during the scheduling period according to the charging and discharging rule;

Create a set of candidate vehicles participating in V2G scheduling, and add vehicles that accept V2G scheduling with a probability greater than a first preset value into the set of candidate vehicles participating in V2G scheduling;

In a specific application scenario, the vehicle battery IoT communication monitoring module can be loaded on the battery block of the electric vehicle. The vehicle battery IoT communication monitoring module can communicate with the cloud scheduling module. The cloud scheduling module is responsible for the V2G unified scheduling of each area. The scheduling module can communicate with the charging station IoT communication module, wherein the vehicle battery IoT communication monitoring module has the following functions: (1) monitor and collect the SOC (State of Charge, battery remaining capacity), charge and discharge voltage and current of the battery and battery temperature and other information; (2) communicate with the cloud dispatching module; (3) communicate with the engine of the electric vehicle to obtain user instructions or feedback information to the user through the engine;

When the cloud scheduling module receives the V2G scheduling request sent by the target area, the cloud scheduling module pushes the request information of whether to participate in V2G scheduling to the vehicle battery IoT communication monitoring module of each vehicle in the target area, and the use process of electric vehicles in the target area Among them, the vehicle battery IoT communication monitoring module will continue to collect the SOC, charge and discharge voltage and current, and battery temperature of the battery in the electric vehicle. After receiving the push information from the motion scheduling module, the user has two choices. Option 1: Not participating in V2G in the target area; Option 2: Participating in V2G in the target area;

If the user chooses not to participate in the V2G in the target area through his own vehicle, then for this part of the user's vehicle, the cloud dispatching module cannot obtain information such as the SOC of the battery, the charging and discharging voltage and current, and the battery temperature through the vehicle battery IoT communication monitoring module;

And if the user chooses to participate in the V2G in the target area, the cloud scheduling module can obtain the state information of the battery SOC, charging and discharging voltage and current, and battery temperature through the vehicle battery IoT communication monitoring module, and can also obtain the electric vehicle of this part of the user. The historical data of charge and discharge is statistically analyzed by the cloud scheduling module to generate the charging and discharging rules of electric vehicles for this part of users. The following is an example of the annual charging data used by a real electric vehicle:

Table 1: Historical data of EV charging

charging period frequency 22:00-06:00 32 06:00-10:00 5 10:00-14:00 1 14:00-18:00 7 18:00-22:00 12

Table 1 shows the charging history data of an electric vehicle. It can be seen that the user’s main charging period is from 10:00 pm to 6:00 am the next day, followed by 6:00 pm to 10:00 pm; The significance of the charge and discharge law of electric vehicles is that it can determine the specific period of time when the electric vehicles owned by the user can participate in V2G regulation through the user's usage pattern. In this embodiment, based on the user's real use of electric vehicles, the common charging period of electric vehicles is matched with the high probability period that can participate in V2G, so as to help improve the stability of the system control scheme;

Among them, the target area usually refers to the administrative planning area where the electric vehicle users are located. Of course, the target area can also be divided according to the distribution range of charging stations, or according to the degree of flexible load aggregation, such as residential areas or urban business areas. Areas where flexible loads are more concentrated;

After the cloud scheduling module periodically collects the electric vehicle battery information of users who agree to participate in V2G regulation in the target area, the following statistics are performed:

(1) According to the time period, the vehicles with charging history in the time period are counted;

(2) For the period of time, the vehicles with charging history in the period are counted, and the average probability of charging the vehicle in the period is calculated.

Preferably, taking the data in Table 1 as an example, for this car, the charging probability at 22:00-06:00 is 56%, the charging probability at 18:00-22:00 is 21%, and at 14:00 -18:00 charging probability is 12%, 14:00-18:00 charging probability is 9%, and the rest of the charging time is 2%;

Afterwards, the period with the highest charging probability is taken as the vehicle's preferred V2G period, and the period with the second highest charging probability is used as the vehicle's backup V2G period. For example, for the electric vehicle in Table 1, its preferred V2G period is 22:00-06: 00, the backup V2G time slot is 18:00-22:00;

(3) Continuously count the distance information of charging piles near each vehicle or the GPS information of vehicles, as well as the real-time usage of charging stations;

(4) Continuously count the current SOC of each vehicle.

After obtaining the above information, the cloud scheduling module will generate the corresponding vehicle preferred V2G time period list, that is, list the vehicles with a higher probability of being in the charging state in each time period, and the vehicle preferred V2G time period list is as follows:

Table 2: List of preferred V2G time slots for vehicles

When making a selection, a probability threshold P1 can be set, and the electric vehicles with a statistically charged probability P greater than the probability threshold P1 during the scheduling period are added to the candidate vehicle set, in order to avoid some vehicles temporarily being unable to participate in the scheduling , you can add a set of alternative vehicles, the probability P of the vehicles in this set is less than or equal to P1, but greater than P2, where P2<P1;

That is, in another optional embodiment, a set of candidate vehicles participating in V2G scheduling is created, and vehicles accepting V2G scheduling with a probability less than or equal to the first preset value and greater than the second preset value are added to the participating V2G scheduling. A set of candidate vehicles for scheduling;

Selecting a vehicle set by adding equipment can avoid the occurrence of some special or unexpected situations that cause the vehicles in the candidate vehicle set to participate in V2G scheduling to be unable to participate; at this time, the candidate vehicle can be selected from the candidate vehicle set for V2G scheduling ;

When the vehicles willing to participate in V2G dispatching in the specific statistical target area, the preferred statistical method can push information to the vehicle battery IoT communication monitoring module through the cloud dispatching module, and the vehicle battery IoT communication monitoring module transmits the information to the vehicle or the owner's Mobile phone, according to the owner's choice to determine whether the vehicle participates in V2G dispatching;

According to the statistical results, if the vehicle owner is willing, match the period when the grid side is expected to need V2G to participate in the dispatch and the matching degree of the V2G period of the vehicle that is willing to participate in V2G. For example, the current power grid expects electric vehicles to participate in V2G dispatching from 22:00 to 06:00. According to Table 2, it can be determined that car A and car H (with the consent of the car owner) can participate in dispatching, and at the same time, it can also traverse the backup V2G of other vehicles. time period, match the time period when the grid side is expected to need V2G to participate in dispatching with the spare V2G time period of these vehicles, and obtain a total of M electric vehicles that can participate in V2G. At the same time, the stability of V2G is increased;

S3. Match the corresponding charging station for each vehicle in the candidate vehicle set, determine the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determine the target vehicle participating in V2G scheduling according to the remaining battery capacity gather;

When matching the corresponding charging station for each vehicle, the current distance of all M vehicles from the nearest charging station and the current usage of these charging stations are counted in the cloud scheduling module, and the corresponding charging stations for M vehicles are planned and determined so that for V2G;

After the vehicles are respectively matched to the corresponding charging station, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station includes:

Determining the time T _i required for each vehicle to reach the corresponding charging station and the distance D _i from the corresponding charging station;

The remaining battery capacity E _i of each vehicle after arriving at the corresponding charging station is:

E _i ＝E _i0 -f _i (T _i , D _i )

In the formula, E _i0 represents the current remaining battery capacity of the i-th vehicle, and the f _i function represents the average power curve of the battery pack of the i-th vehicle. This power curve is usually measured when the electric vehicle leaves the factory. The running time T _i , distance D _i , and the running speed estimated according to the time T _i and distance D _i are used to calculate the remaining SOC value E _i of the battery after the i-th vehicle arrives at the corresponding charging station;

The reason for estimating the remaining SOC value of each vehicle is that the vehicle with a low remaining SOC value cannot provide V2G after arriving at the charging station, because its own low SOC value will further reduce the SOC value if it participates in V2G to supply power to the grid However, it will affect the subsequent use of the car by the car owner. Therefore, it is necessary to set the SOC residual threshold E _th . Vehicles lower than the SOC residual threshold E _th will be prioritized for vehicle charging instead of participating in V2G grid regulation;

That is, judge the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and exclude it from the target vehicle set if the remaining battery capacity is lower than the SOC remaining threshold value E _th ;

S4. Formulate a scheduling strategy according to the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. According to the scheduling strategy, the vehicles in the target vehicle set Perform V2G scheduling.

Embodiment two

This embodiment further defines how to match a corresponding charging station for each vehicle in the candidate vehicle set, specifically:

Ideally, M vehicles all choose the nearest charging station for V2G, but a more common situation is that N vehicles in M vehicles are all closest to a certain charging station. In this case, count the charging The current usage of the station and its nearby K charging stations, where K must be greater than or equal to M to ensure that there are enough charging piles to accommodate M vehicles; preferably, K≥M+5;

In this case, an optimization algorithm for charging station allocation can be set to determine the allocation of charging stations corresponding to M vehicles;

In an optional embodiment, each vehicle in the candidate vehicle set may be matched with a corresponding charging station based on the principle that the overall dispatch distance of all vehicles in the candidate vehicle set is the smallest;

The charging pile allocation model constructed based on the above principles is:

h _k ≤ H _k

In the formula, M represents the total number of vehicles in the candidate vehicle set, K represents the total number of charging stations in the target area, and D _k,j represents the distance between the jth vehicle assigned to the kth charging station and the charging station , H _k represents the maximum number of electric vehicles that the kth charging station can accommodate, and hk represents the number of electric vehicles allocated to the kth charging station.

Embodiment Three

This embodiment further defines how to formulate a scheduling strategy based on the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid, specifically:

The scheduling strategy formulated is as follows:

In the formula, M represents the total number of vehicles in the candidate vehicle set, E _i,max represents the upper limit of the remaining battery capacity of the i-th vehicle, E _th represents the lower limit of the remaining battery capacity of the vehicle, and P _i represents the participation of the i-th vehicle in V2G scheduling to provide The power to the grid, T _i represents the duration of the i-th vehicle participating in V2G scheduling, δ _i represents the charging efficiency of the i-th vehicle, T _i represents the time required for the i-th vehicle to reach its corresponding charging station, and the corresponding The distance between the charging stations D _i represents the distance between the i-th vehicle and its corresponding charging station, the f _i function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, and K _i represents the i-th The nominal number of cycles of the vehicle, W _i represents the selling price of the i-th vehicle, G represents the total output power of M electric vehicles participating in V2G, Z represents the electricity cost and battery loss cost of M electric vehicles participating in V2G, U _min Indicates the minimum grid-connected voltage of the vehicle connected to the charging station for V2G, U _max indicates the maximum reverse input voltage that the corresponding charging station of the vehicle can withstand, U _i indicates the vehicle output voltage when the i-th vehicle participates in V2G, P _f indicates The output power of the conventional power supply side in the target area, P _max-min represents the power difference between the maximum load and the minimum load in the target area, Pa _ave represents the average load of the vehicle during the V2G scheduling period, and Pa _ave can be based on the historical data of the target area The average load power obtained by statistics, or statistics based on the load power level of M electric vehicles actually participating in the V2G period;

In the above optimization strategy, the significance of setting E _{i, max} ≥ E _i ≥ E _{th, 1} ≤ i ≤ M is to exclude the vehicles whose SOC is lower than the SOC residual threshold E _th among the M vehicles from V2G, and make these vehicles The corresponding P _i is assigned a value of 0 to prevent vehicles with low SOC values from participating in the V2G process; at the same time, in the V2G process, E _{i, max} ≥ E _i -P _i ≥ E _th , 1≤i≤M set The significance is that once the SOC value of the V2G vehicle is found to be too low, it is also controlled to stop participating in V2G, and its _Pi is assigned a value of 0 to exit V2G;

Through the above method, it can ensure that the electric vehicles under the owner’s name can participate in V2G regulation relatively stably. At the same time, when optimizing the strategy, not only the output power of electric vehicles and the active power constraints on the grid are considered, but also the battery charge and discharge cycle loss caused by the battery’s participation in V2G is considered, so that When optimizing the dispatch of electric vehicles in this area, fully consider the loss economy of electric vehicles to ensure that the overall loss of vehicles participating in V2G in the target area is low. And in the scheduling process, fully consider the power loss of electric vehicles in the process of going to the charging station, so as to exclude the electric vehicles with low power after going to the charging station or during the V2G process, so as to prevent the battery of the electric vehicle from being overcharged by V2G. More loss, shorten the service life of electric vehicles;

In the process of V2G control, with the help of the current Internet of Things technology, the cloud scheduling module can continuously push information to the mobile phone of the car owner, so that the car owner can obtain the vehicle V2G information visually.

Embodiment Four

Please refer to FIG. 2 , an electric vehicle V2G dispatching terminal, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the computer program when executing the computer program. Various steps in a V2G scheduling method for electric vehicles described in any one of Example 1 to Example 3.

To sum up, the present invention provides a V2G dispatching method and terminal for electric vehicles. Firstly, compared with the prior art method of performing V2G on electric vehicles charged at charging stations, this application fully integrates the choice of car owners and electric vehicles. Combined with the situation of the remaining SOC of the vehicle battery, a suitable electric vehicle is selected to participate in V2G in a way that reduces the loss of electric vehicles. Compared with the practice of not screening in the prior art, this application fully considers that V2G will occupy electric vehicles. The number of charging and discharging cycles of the car is beneficial to prolong the service life of the electric car;

At the same time, in the dispatching process of this application, more emphasis is placed on the dispatching of electric vehicles that are far away from the charging station or are not in the charging state, rather than limited to the vehicles in the charging state in the charging station, which is more conducive to increasing the power supply of V2G. Suppress power grid load fluctuations and improve grid stability;

Finally, in the process of scheduling optimization, in addition to simply considering the active voltage and power balance, when limiting the output power of each electric vehicle to the grid, the economy of electric vehicles is also fully considered, including the time of arrival at the charging station, arrival The power consumed by the charging station and the impact of V2G on the battery economy, and considering the voltage limit and power limit in the V2G process, compared with the existing technology, the active power balance limit and economic limit can further ensure that the car on the way can be effectively used for a long time Stable participation in V2G regulation, instead of randomly selecting charging electric vehicles for grid feedback or simply considering the peak and valley regulation of the grid without regard to the service life of electric vehicles;

Therefore, this application focuses more on the dispatch of electric vehicles on the way, so that the stability of the V2G dispatch of the power grid in the target area is higher. The purpose is not only to focus on the energy output by V2G to the power grid, but to pay more attention to determining the participation of V2G in dispatch It can effectively prevent electric vehicles from randomly participating in V2G grid dispatching and causing grid fluctuations. Compared with the prior art that only focuses on active power balance during V2G dispatching, this application also focuses on the impact of V2G on the technical and economic costs of electric vehicles. , especially the scheduling optimization of the distance between the electric vehicle and the charging station and the cost of the cycle times of the electric vehicle battery occupied by the electricity output by V2G, so as to prevent the electricity output by V2G from occupying more battery cycles, so as to balance the use economy of electric vehicles and the balance of V2G scheduling It can ensure that electric vehicles on the road can effectively and stably participate in V2G regulation, help to stabilize power grid regulation expectations, and reduce the possibility of grid fluctuations caused by the participation of electric vehicles in V2G.

The above description is only an embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent transformations made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in related technical fields, are all included in the same principle. Within the scope of patent protection of the present invention.

Claims (9)

1. A V2G scheduling method for electric vehicles, characterized in that it comprises steps: S1. Receive the V2G scheduling request sent by the target area; S2. Determine the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles participating in V2G scheduling according to the vehicles receiving V2G scheduling in the target area; S3. Match the corresponding charging station for each vehicle in the candidate vehicle set, determine the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determine the target vehicle participating in V2G scheduling according to the remaining battery capacity gather; S4. Formulate a scheduling strategy according to the remaining capacity threshold of the electric vehicle battery, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraints of the electric vehicle after it is connected to the grid. According to the scheduling strategy, the vehicles in the target vehicle set Perform V2G scheduling. 2. A V2G scheduling method for electric vehicles according to claim 1, wherein said determining the vehicles receiving V2G scheduling in the target area according to the V2G scheduling request comprises: Pushing dispatch request information to vehicles in the target area according to the V2G dispatch request; receiving dispatch response information sent by vehicles in the target area based on the dispatch request information; Determining vehicles that accept V2G scheduling in the target area according to the scheduling response information. 3. A V2G scheduling method for electric vehicles according to claim 1, wherein the V2G scheduling request includes a scheduling period; The determining the set of candidate vehicles participating in V2G scheduling according to the vehicles accepting V2G scheduling in the target area includes: Acquiring historical data of charging and discharging of vehicles receiving V2G scheduling in the target area, and determining charging and discharging laws of vehicles receiving V2G scheduling according to the historical data; determining the probability that the vehicle receiving V2G scheduling will be charged during the scheduling period according to the charging and discharging rule; A set of candidate vehicles participating in V2G scheduling is created, and vehicles receiving V2G scheduling with a probability greater than a first preset value are added to the set of candidate vehicles participating in V2G scheduling. 4. A kind of electric vehicle V2G scheduling method according to claim 3, is characterized in that, also comprises the step: A set of candidate vehicles participating in V2G scheduling is created, and vehicles receiving V2G scheduling with a probability less than or equal to a first preset value and greater than a second preset value are added to the set of candidate vehicles participating in V2G scheduling. 5. A V2G scheduling method for electric vehicles according to any one of claims 1 to 4, wherein said matching a corresponding charging station for each vehicle in the candidate vehicle set comprises: Each vehicle in the candidate vehicle set is matched with a corresponding charging station based on the principle that the overall dispatch distance of all vehicles in the candidate vehicle set is the smallest. 6. A V2G scheduling method for electric vehicles according to claim 5, wherein the charging pile allocation model constructed based on the principle is:

h _k ≤ H _k In the formula, M represents the total number of vehicles in the candidate vehicle set, K represents the total number of charging stations in the target area, and D _k,j represents the distance between the jth vehicle assigned to the kth charging station and the charging station , H _k represents the maximum number of electric vehicles that the kth charging station can accommodate, and h _k represents the number of electric vehicles allocated to the kth charging station. 7. A V2G scheduling method for electric vehicles according to any one of claims 1 to 4, wherein said determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station comprises: Determining the time T _i required for each vehicle to reach the corresponding charging station and the distance D _i from the corresponding charging station; The remaining battery capacity E _i of each vehicle after arriving at the corresponding charging station is: E _i ＝E _i0 -f _i (T _i , D _i ) In the formula, E _i0 represents the current remaining battery capacity of the i-th vehicle, and the f _i function represents the average power curve of the battery pack of the i-th vehicle. 8. A V2G dispatching method for electric vehicles according to any one of claims 1 to 4, characterized in that, according to the electric vehicle battery remaining capacity threshold, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and After the electric vehicle is connected to the grid, the active power constraint formulation and dispatching strategy includes: The scheduling strategy formulated is as follows:

In the formula, M represents the total number of vehicles in the candidate vehicle set, E _i,max represents the upper limit of the remaining battery capacity of the i-th vehicle, E _th represents the lower limit of the remaining battery capacity of the vehicle, and P _i represents the participation of the i-th vehicle in V2G scheduling to provide The power to the grid, T _i represents the duration of the i-th vehicle participating in V2G scheduling, δ _i represents the charging efficiency of the i-th vehicle, T _i represents the time required for the i-th vehicle to reach its corresponding charging station, and the corresponding The distance between the charging stations D _i represents the distance between the i-th vehicle and its corresponding charging station, the f _i function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, and K _i represents the i-th The nominal number of cycles of the vehicle, W _i represents the selling price of the i-th vehicle, G represents the total output power of M electric vehicles participating in V2G, Z represents the electricity cost and battery loss cost of M electric vehicles participating in V2G, U _min Indicates the minimum grid-connected voltage of the vehicle connected to the charging station for V2G, U _max indicates the maximum reverse input voltage that the corresponding charging station of the vehicle can withstand, U _i indicates the vehicle output voltage when the i-th vehicle participates in V2G, P _f indicates The output power of the conventional power supply side in the target area, P _max-min represents the power difference between the maximum load and the minimum load in the target area, and P _ave represents the average load of the vehicle during the V2G scheduling period. 9. An electric vehicle V2G dispatching terminal, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the computer program, it realizes The various steps in the V2G scheduling method for electric vehicles according to any one of claims 1 to 8.

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