Electric vehicle V2G dispatching method and terminal

Abstract

The present application discloses a V2G scheduling method and terminal for electric vehicles, which receive a V2G scheduling request sent from a target area; determine the vehicles in the target area that accept V2G scheduling based on the V2G scheduling request, and determine a set of candidate vehicles participating in V2G scheduling based on the vehicles in the target area that accept V2G scheduling; match a 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 a set of target vehicles participating in V2G scheduling based on the remaining battery capacity; formulate a scheduling strategy based on a threshold value of the remaining battery capacity of the electric vehicle, 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 power grid, and perform V2G scheduling on the vehicles in the target vehicle set according to the scheduling strategy; the stability and effectiveness of electric vehicles participating in V2G can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle V2G technology, and in particular to an electric vehicle V2G scheduling method and terminal. Background Art

[0002] With the development of building electrification and the widespread use of smart home appliances, as well as global warming and the frequent occurrence of extreme weather, the load intensity in electricity-consuming areas has gradually increased. For example, frequent extremely high temperatures in summer have caused rivers to dry up, significantly reducing hydropower generation. At the same time, the use of air conditioners has greatly increased. However, urban space is limited, making it difficult to expand the power system.

[0003] The current practical solution to the challenges caused by extreme weather is demand-side management and response in the power system. This can alleviate power supply and capacity expansion issues in urban areas, but it is less effective at facilitating capacity expansion for users at the end of the distribution network, such as within a residential complex or a building. There is a need to explore solutions to address the power capacity expansion challenges faced by modern residential renovations and office buildings. Furthermore, the number of flexible loads within the current power grid is increasing. In addition to traditional household appliances like air conditioners and washing machines, the increasing adoption of electric vehicles and their charging stations also increases the uncertainty associated with these flexible loads.

[0004] V2G (Vehicle to Grid) technology, which uses electric vehicles to supply power to the power grid, already exists in existing technologies. However, how to better enable V2G technology to participate in the process of regional flexible load scheduling is a content lacking in existing technologies. It is also a technical problem that urgently needs to be solved in the context of the current surge in social electricity consumption. In particular, existing technologies lack how to effectively dispatch electric vehicles on the road so that they can effectively and stably participate in V2G. This is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] 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.

[0006] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0007] An electric vehicle V2G scheduling method includes the following steps:

[0008] S1. Receive a V2G scheduling request sent by the target area;

[0009] S2. Determine the vehicles that accept V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles that participate in V2G scheduling based on the vehicles that accept V2G scheduling in the target area;

[0010] S3. Matching a corresponding charging station for each vehicle in the candidate vehicle set, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determining a target vehicle set to participate in V2G scheduling based on the remaining battery capacity;

[0011] S4. 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 constraint after the electric vehicle is connected to the power grid, and perform V2G scheduling on the vehicles in the target vehicle set according to the scheduling strategy.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] An electric vehicle V2G dispatching terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned electric vehicle V2G dispatching method is implemented.

[0014] The beneficial effects of the present invention are as follows: during V2G scheduling, a set of candidate vehicles for participating in V2G scheduling is first determined based on the willingness of each vehicle in the target area; then a target vehicle set is determined based on the remaining battery capacity of each vehicle after arriving at the corresponding charging station; finally, a scheduling strategy is formulated based on the remaining battery capacity threshold of the electric vehicle, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraint of the electric vehicle after connecting to the power grid; and V2G scheduling is performed on the vehicles in the target vehicle set according to the scheduling strategy; when performing V2G scheduling, the owner's choice and the remaining battery capacity of the electric vehicle are combined, and in the process of scheduling optimization, when limiting the power output of each electric vehicle to the power grid, the economic efficiency of the electric vehicle, including the charging time and the loss cost of the electric vehicle, is fully considered. This can ensure that on-the-road vehicles can participate in V2G regulation effectively and stably in the long term, rather than randomly selecting charging electric vehicles for grid feedback or simply considering peak and valley control of the power grid without considering the service life of the electric vehicle. This helps to stabilize grid regulation expectations and reduce the possibility of grid fluctuations caused by the participation of electric vehicles in V2G. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of the steps of a V2G scheduling method for electric vehicles according to an embodiment of the present invention;

[0016] Figure 2 Schematic diagram of the structure of an electric vehicle V2G dispatching terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0018] Please refer to Figure 1 , a V2G dispatching method for electric vehicles, comprising the steps of:

[0019] S1. Receive a V2G scheduling request sent by the target area;

[0020] S2. Determine the vehicles that accept V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles that participate in V2G scheduling based on the vehicles that accept V2G scheduling in the target area;

[0021] S3. Matching a corresponding charging station for each vehicle in the candidate vehicle set, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determining a target vehicle set to participate in V2G scheduling based on the remaining battery capacity;

[0022] S4. 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 constraint after the electric vehicle is connected to the power grid, and perform V2G scheduling on the vehicles in the target vehicle set according to the scheduling strategy.

[0023] As can be seen from the above description, the beneficial effects of the present invention are as follows: during V2G scheduling, a set of candidate vehicles for V2G scheduling is first determined based on the willingness of each vehicle in the target area. Then, a target vehicle set is determined based on the remaining battery capacity of each vehicle after arriving at the corresponding charging station. Finally, a scheduling strategy is formulated based on the remaining battery capacity threshold of the electric vehicle, the charging time of the electric vehicle, the loss cost of the electric vehicle participating in V2G, and the active power constraint of the electric vehicle after connecting to the power grid. V2G scheduling is performed on the vehicles in the target vehicle set according to the scheduling strategy. During V2G scheduling, the owner's choice is combined with the remaining battery capacity of the electric vehicle. In addition, during the scheduling optimization process, when limiting the power output of each electric vehicle to the grid, the economic efficiency of the electric vehicle, including the charging time and loss cost of the electric vehicle, is fully considered. This ensures that on-the-road vehicles can participate in V2G regulation effectively and stably over the long term, rather than randomly selecting charging electric vehicles for grid feedback or simply considering peak and valley control without considering the service life of the electric vehicle. This helps stabilize grid regulation expectations and reduces the possibility of grid fluctuations caused by the participation of electric vehicles in V2G.

[0024] Furthermore, determining the vehicles in the target area that accept V2G scheduling according to the V2G scheduling request includes:

[0025] Pushing dispatch request information to vehicles in the target area according to the V2G dispatch request;

[0026] receiving dispatch response information sent by vehicles in the target area based on the dispatch request information;

[0027] The vehicles that accept V2G dispatch in the target area are determined according to the dispatch response information.

[0028] From the above description, it can be seen that after receiving the V2G scheduling request, the scheduling request information is pushed to each vehicle in the target area, and the vehicle accepting the V2G scheduling is determined based on the scheduling response information of each vehicle. When performing V2G scheduling, the wishes of each vehicle owner are fully considered, which can improve the stability of electric vehicles participating in V2G.

[0029] Furthermore, the V2G scheduling request includes a scheduling period;

[0030] The determining of a set of candidate vehicles participating in the V2G scheduling based on the vehicles that accept the V2G scheduling in the target area includes:

[0031] Acquire historical charging and discharging data of vehicles that accept V2G scheduling in the target area, and determine the charging and discharging patterns of the vehicles that accept V2G scheduling based on the historical data;

[0032] Determining the probability that the vehicle receiving the V2G dispatch will be charged during the dispatch period according to the charging and discharging rule;

[0033] A set of candidate vehicles participating in V2G scheduling is created, and vehicles with a probability of accepting V2G scheduling greater than a first preset value are added to the set of candidate vehicles participating in V2G scheduling.

[0034] From the above description, it can be seen that the charging and discharging patterns of vehicles are determined by obtaining historical data on vehicle charging and discharging, and the probability of each vehicle charging during the scheduling period is determined based on the charging and discharging pattern. Vehicles with a charging probability greater than a first preset value are selected as candidate vehicles. Based on the actual usage patterns of electric vehicles by users, the commonly used charging time periods of electric vehicles are matched with the time periods with a high probability of their participation in V2G, thereby improving the matching degree between vehicles and V2G scheduling, which helps to further improve the stability of V2G scheduling.

[0035] Furthermore, the method further comprises the steps of:

[0036] A set of candidate vehicles participating in V2G scheduling is created, and vehicles whose probability of accepting V2G scheduling is 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.

[0037] From the above description, it can be seen that by setting up a candidate vehicle set, when the candidate vehicle set is insufficient, the candidate vehicles in the candidate vehicle set can be scheduled, thereby ensuring the reliability and robustness of V2G scheduling.

[0038] Furthermore, matching a corresponding charging station for each vehicle in the candidate vehicle set includes:

[0039] A corresponding charging station is matched for each vehicle in the candidate vehicle set based on the principle that the overall dispatch distance of all vehicles in the candidate vehicle set is minimized.

[0040] Furthermore, the charging pile allocation model constructed based on the above principle is:

[0041]

[0042] h k ≤H k

[0043] Where 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 H represents the distance between the jth vehicle assigned to the kth charging station and the charging station. k represents the maximum number of electric vehicles that the k-th charging station can accommodate, and hk represents the number of electric vehicles allocated to the k-th charging station.

[0044] As can be seen from the above description, matching a corresponding charging station to each vehicle in the candidate vehicle set based on the principle of minimizing the overall dispatch distance of all vehicles in the candidate vehicle set can ensure optimal allocation of charging stations.

[0045] Furthermore, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station includes:

[0046] Determine the time T required for each vehicle to reach the corresponding charging station i and the distance D between the corresponding charging station i ;

[0047] The remaining battery capacity E of each vehicle after arriving at the corresponding charging station i for:

[0048] E i =E i0 -f i (T i ,D i )

[0049] Where, E i0 represents the current remaining battery capacity of the i-th vehicle, f i The function represents the average power curve of the battery pack of the i-th vehicle.

[0050] From the above description, it can be seen that the remaining battery capacity of the vehicle after arriving at the corresponding charging station is determined based on the time required for the vehicle to reach the corresponding charging station, the distance between the vehicle and the corresponding charging station, and the vehicle's battery pack quiet power curve, thereby ensuring the accuracy of the determined remaining battery capacity of the vehicle.

[0051] Furthermore, the scheduling strategy is formulated 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 power grid, including:

[0052] The scheduling strategy developed is as follows:

[0053]

[0054] Where 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 Indicates the lower limit of the vehicle's battery remaining capacity, P i represents the power provided to the grid by the i-th vehicle participating in V2G scheduling, 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 distance D between it and the corresponding charging station i represents the distance between the i-th vehicle and its corresponding charging station, f i The function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, K i represents the nominal cycle number of the i-th vehicle, W i represents the 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 for a vehicle to access a charging station for V2G, U max Indicates the maximum reverse input voltage that the charging station corresponding to the vehicle can withstand, U i represents 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 Indicates the power difference between the maximum load and the minimum load in the target area, P ave Indicates the average load of vehicles during the V2G scheduling period.

[0055] From the above description, it can be seen that when optimizing the strategy, not only the power output of the electric vehicle and the active power constraints on the grid are taken into account, but also the battery charge and discharge cycle loss caused by the battery participating in V2G is taken into account. Therefore, when optimizing the scheduling of electric vehicles in the target area, the loss economy of the electric vehicle is fully considered to ensure that the overall loss of vehicles participating in V2G in the target area is low. In addition, in the scheduling process, the power loss of the electric vehicle in the process of going to the charging station is fully considered, so that electric vehicles with too low power after going to the charging station or during the V2G process are excluded, so as to prevent excessive loss of the electric vehicle battery due to V2G and shorten the service life of the electric vehicle, thereby ensuring that the electric vehicles under the name of the car owner participate in V2G regulation relatively stably.

[0056] Please refer to Figure 2 , an electric vehicle V2G scheduling terminal, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements each step in the above-mentioned electric vehicle V2G scheduling method when executing the computer program.

[0057] The electric vehicle V2G scheduling method and terminal described in this application can be applied to V2G optimization scheduling of electric vehicles on the road, and are described below through specific implementation methods:

[0058] Example 1

[0059] Please refer to Figure 1 , a V2G dispatching method for electric vehicles, comprising the steps of:

[0060] S1. Receive a V2G scheduling request sent by the target area;

[0061] S2. Determine the vehicles that accept V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles that participate in V2G scheduling based on the vehicles that accept V2G scheduling in the target area;

[0062] Specifically, determining the vehicles in the target area that accept V2G scheduling according to the V2G scheduling request includes:

[0063] Pushing dispatch request information to vehicles in the target area according to the V2G dispatch request;

[0064] receiving dispatch response information sent by vehicles in the target area based on the dispatch request information;

[0065] Determining vehicles that accept V2G dispatch in the target area according to the dispatch response information;

[0066] The V2G scheduling request includes a scheduling period;

[0067] The determining of a set of candidate vehicles participating in the V2G scheduling based on the vehicles that accept the V2G scheduling in the target area includes:

[0068] Acquire historical charging and discharging data of vehicles that accept V2G scheduling in the target area, and determine the charging and discharging patterns of the vehicles that accept V2G scheduling based on the historical data;

[0069] Determining the probability that the vehicle receiving the V2G dispatch will be charged during the dispatch period according to the charging and discharging rule;

[0070] Creating a set of candidate vehicles for participating in V2G scheduling, and adding vehicles with a probability of accepting V2G scheduling greater than a first preset value to the set of candidate vehicles for participating in V2G scheduling;

[0071] In a specific application scenario, a vehicle battery IoT communication monitoring module can be installed at the battery block of an electric vehicle. The vehicle battery IoT communication monitoring module can communicate with a cloud scheduling module. The cloud scheduling module is responsible for V2G unified scheduling in each area. The cloud scheduling module can communicate with the charging station IoT communication module. The vehicle battery IoT communication monitoring module has the following functions: (1) monitoring and collecting information such as the battery's SOC (State of Charge, remaining battery capacity), charge and discharge voltage and current, and battery temperature; (2) communicating with the cloud scheduling module; (3) communicating with the electric vehicle's onboard computer to obtain user instructions or feedback information to the user through the onboard computer;

[0072] 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 the V2G scheduling to the vehicle battery IoT communication monitoring module of each vehicle in the target area. During the use of electric vehicles in the target area, the vehicle battery IoT communication monitoring module will continuously collect the battery SOC, charge and discharge voltage and current, and battery temperature of the electric vehicle. After receiving the push information from the motion scheduling module, the user has two options: option 1: do not participate in the V2G in the target area; option 2: participate in the V2G in the target area;

[0073] If the user chooses not to participate in V2G in the target area through his or her vehicle computer, the cloud scheduling module will not be able to obtain battery SOC, charge and discharge voltage and current, battery temperature and other information for this part of the user's vehicle through the vehicle battery IoT communication monitoring module;

[0074] If the user chooses to participate in V2G in the target area, the cloud scheduling module can obtain battery status information such as SOC, charge and discharge voltage and current, and battery temperature through the vehicle battery IoT communication monitoring module. At the same time, it can also obtain the historical charging and discharging data of the user's electric vehicle. The cloud scheduling module statistically analyzes and generates the charging and discharging patterns of the user's electric vehicle. The following is an example of annual charging data for a real electric vehicle:

[0075] Table 1: Electric vehicle charging history data

[0076] 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

[0077] Table 1 shows the charging history data for an electric vehicle. It can be seen that the user's primary charging period is from 10 pm to 6 am the next day, followed by 6 pm to 10 pm. The significance of statistically analyzing a user's electric vehicle charging and discharging patterns is that it can be used to determine the specific time periods when their electric vehicles can participate in V2G control based on their usage patterns. Compared to existing technologies that randomly select or temporarily test which vehicles are eligible for V2G control, this implementation matches the user's actual usage patterns of electric vehicles with the time periods when they are most likely to participate in V2G, thereby helping to improve the stability of the system control scheme.

[0078] The target area usually refers to the administrative planning area where 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 concentration of flexible loads, such as residential areas or urban business districts where flexible loads are relatively concentrated.

[0079] When the cloud scheduling module periodically collects the electric vehicle battery information of users who agree to participate in V2G regulation in the target area, it performs the following statistics:

[0080] (1) Count the vehicles with charging history in each time period;

[0081] (2) For each time period, count the vehicles that have a charging history in that time period and calculate the average probability of the vehicle charging in that time period.

[0082] Preferably, taking the data in Table 1 as an example, for this car, the probability of charging between 22:00 and 06:00 is 56%, the probability of charging between 18:00 and 22:00 is 21%, the probability of charging between 14:00 and 18:00 is 12%, the probability of charging between 14:00 and 18:00 is 9%, and the charging time in the remaining time periods is 2%;

[0083] Afterwards, the time period with the highest charging probability is used as the preferred V2G time period for the vehicle, and the time period with the second highest charging probability is used as the backup V2G time period for the vehicle. For example, for the electric vehicle in Table 1, its preferred V2G time period is 22:00-06:00, and its backup V2G time period is 18:00-22:00;

[0084] (3) Continuously collect information about the distance to the charging piles near each vehicle or the vehicle's GPS information, as well as the real-time usage of the charging station;

[0085] (4) Continuously count the current SOC of each vehicle.

[0086] After obtaining the above information, the cloud scheduling module will generate a corresponding list of preferred V2G time periods for vehicles, that is, listing the vehicles with a higher probability of being in a charging state in each time period. The preferred V2G time period list for vehicles is as follows:

[0087] Table 2: List of preferred V2G time slots for vehicles

[0088]

[0089]

[0090] When making a selection, a probability threshold P1 can be set, and electric vehicles whose probability P of charging during the scheduling period is greater than the probability threshold P1 are added to the candidate vehicle set. In order to avoid some vehicles being unable to participate in the scheduling due to temporary matters, an alternative vehicle set can be added. The probability P of the vehicles in this set of vehicles charging during the scheduling period is less than or equal to P1, but greater than P2, where P2 <P1;

[0091] That is, in another optional embodiment, a set of candidate vehicles participating in V2G scheduling is created, and vehicles whose probability of accepting V2G scheduling is 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;

[0092] By adding a vehicle selection set, it is possible to avoid the situation where some special or unexpected circumstances may cause vehicles in the candidate vehicle set that are supposed to participate in V2G scheduling to be unable to participate; in this case, alternative vehicles can be selected from the candidate vehicle set for V2G scheduling;

[0093] When counting vehicles willing to participate in V2G dispatch within a specific target area, the preferred statistical method is to push information from the cloud dispatch module to the vehicle battery IoT communication monitoring module, which then transmits the information to the vehicle computer or the owner's mobile phone, and determines whether the vehicle participates in V2G dispatch based on the owner's choice;

[0094] According to the statistical results, if the car owner is willing, the time period expected by the grid side to participate in V2G scheduling is matched with the V2G time period of the vehicles willing to participate in V2G. For example, the current grid expects electric vehicles to participate in V2G scheduling from 22:00 to 06:00. According to Table 2, it can be determined that car A and car H (with the owner's consent) can participate in scheduling. At the same time, the spare V2G time periods of the remaining vehicles can be traversed, and the time period expected by the grid side to participate in V2G scheduling is matched with the spare V2G time periods of these vehicles. A total of M electric vehicles that can participate in V2G are obtained. In this case, the V2G willingness of electric vehicles in the target area can be maximized while increasing the stability of V2G.

[0095] S3. Matching a corresponding charging station for each vehicle in the candidate vehicle set, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determining a target vehicle set to participate in V2G scheduling based on the remaining battery capacity;

[0096] When matching each vehicle with a corresponding charging station, the cloud scheduling module counts the distances of all M vehicles to the nearest charging stations and the current usage of these charging stations, and plans to determine the corresponding charging stations for each of the M vehicles to facilitate V2G.

[0097] After each vehicle is matched to a corresponding charging station, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station includes:

[0098] Determine the time T required for each vehicle to reach the corresponding charging station i and the distance D between the corresponding charging station i ;

[0099] The remaining battery capacity E of each vehicle after arriving at the corresponding charging station i for:

[0100] E i =E i0 -f i (T i ,D i )

[0101] Where, E i0 represents the current remaining battery capacity of the i-th vehicle, f i The function represents the average power curve of the battery pack of the i-th vehicle. The power curve is usually measured when the electric vehicle leaves the factory. The power curve is calculated based on the running time T of the electric vehicle. i , distance D i , and according to time T i and distance D iThe estimated running speed is used to calculate the remaining SOC value E of the battery after the i-th vehicle arrives at the corresponding charging station i ;

[0102] The reason for estimating the remaining SOC value of each vehicle is that vehicles with low remaining SOC values cannot provide V2G after arriving at the charging station. If their own low SOC values participate in V2G power supply to the grid, the SOC value will be further reduced and affect the owner's subsequent use of the vehicle. Therefore, it is necessary to set the SOC remaining threshold E th , lower than the SOC remaining threshold E th For vehicles, priority is given to vehicle charging rather than participating in V2G grid regulation;

[0103] That is, the remaining battery capacity of each vehicle after arriving at the corresponding charging station is determined. If the remaining battery capacity is lower than the SOC remaining threshold E th , then exclude it from the target vehicle set;

[0104] S4. 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 constraint after the electric vehicle is connected to the power grid, and perform V2G scheduling on the vehicles in the target vehicle set according to the scheduling strategy.

[0105] Example 2

[0106] This embodiment further defines how to match a corresponding charging station for each vehicle in the candidate vehicle set, specifically:

[0107] Ideally, all M vehicles choose the nearest charging station for V2G. However, a more common scenario is that N of the M vehicles are closest to a single charging station. In this case, the current usage of that charging station and its K nearby charging stations is counted, where K must be greater than or equal to M to ensure there are enough charging stations to accommodate M vehicles. Preferably, K ≥ M + 5.

[0108] In this case, a charging station allocation optimization algorithm can be set to determine the corresponding charging station allocation for each of the M vehicles;

[0109] 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 minimized;

[0110] The charging pile allocation model constructed based on the above principles is:

[0111]

[0112] h k ≤H k

[0113] Where 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 H represents the distance between the jth vehicle assigned to the kth charging station and the charging station. k represents the maximum number of electric vehicles that the k-th charging station can accommodate, and hk represents the number of electric vehicles allocated to the k-th charging station.

[0114] Example 3

[0115] This embodiment further defines how to formulate a dispatching 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. Specifically:

[0116] The scheduling strategy developed is as follows:

[0117]

[0118] Where 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 Indicates the lower limit of the vehicle's battery remaining capacity, P i represents the power provided to the grid by the i-th vehicle participating in V2G scheduling, 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 distance D between it and the corresponding charging station i represents the distance between the i-th vehicle and its corresponding charging station, f i The function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, K i represents the nominal cycle number of the i-th vehicle, W i represents the 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 for a vehicle to access a charging station for V2G, U max Indicates the maximum reverse input voltage that the charging station corresponding to the vehicle can withstand, U i represents 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 Indicates the power difference between the maximum load and the minimum load in the target area, P ave represents the average load of vehicles during the V2G dispatch period, Pave The average load power can be obtained based on historical data statistics of the target area, or based on the load power level statistics during the period when M electric vehicles actually participated in V2G;

[0119] In the above optimization strategy, E i，max ≥E i ≥E th，1 The purpose of setting ≤i≤M is to set the SOC of M vehicles below the SOC remaining threshold E th The vehicles are excluded from V2G, and the P i Assign a value of 0 to prevent vehicles with too 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 The significance of setting 1≤i≤M is that once the SOC value of the V2G vehicle is found to be too low, it will also be controlled to stop participating in V2G and its P i Assign a value of 0 to exit V2G;

[0120] The above method can ensure that the electric vehicles under the name of the car owner participate in V2G regulation in a relatively stable manner. At the same time, when optimizing the strategy, not only the power output of the electric vehicle 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. When optimizing the dispatch of electric vehicles in this area, the loss economy of the electric vehicle is fully considered to ensure that the overall loss of vehicles participating in V2G in the target area is low. In addition, during the dispatch process, the power loss of electric vehicles on the way to the charging station is fully considered, so that electric vehicles with too low power after going to the charging station or during the V2G process are excluded, to prevent excessive loss of the electric vehicle battery due to V2G and shorten the service life of the electric vehicle;

[0121] During the V2G control process, with the help of current Internet of Things technology, the cloud scheduling module can continuously push information to the car owner's mobile phone, allowing the car owner to obtain the vehicle's V2G information visually.

[0122] Example 4

[0123] Please refer to Figure 2 , an electric vehicle V2G scheduling terminal, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, each step of an electric vehicle V2G scheduling method described in any one of embodiments 1 to 3 is implemented.

[0124] In summary, the electric vehicle V2G scheduling method and terminal provided by the present invention, first, compared to the existing practice of V2G for electric vehicles charged at charging stations, this application fully combines the owner's choice with the remaining SOC of the electric vehicle battery, and selects suitable electric vehicles to participate in V2G in a manner that reduces electric vehicle losses. Compared to the existing practice of not making any distinction, this application fully considers that V2G will occupy the number of charge and discharge cycles of electric vehicles, which is conducive to extending the service life of electric vehicles.

[0125] At the same time, during the dispatching process, this application focuses more on dispatching electric vehicles that are far away from charging stations or are not in a charging state, rather than being limited to vehicles that are charging in charging stations. This is more conducive to increasing the supply of V2G power, smoothing out grid load fluctuations, and improving the stability of the grid;

[0126] Finally, in the dispatch optimization process, in addition to simply considering active voltage and power balance, the economic efficiency of electric vehicles is also fully considered when limiting the power output of each electric vehicle to the grid. This includes the time it takes to reach the charging station, the amount of power consumed upon reaching the charging station, and the impact of V2G on battery economy. Furthermore, considering the voltage and power limitations during the V2G process, compared to existing technologies, active power balance and economic efficiency limitations can further ensure that vehicles on the road can effectively and stably participate in V2G regulation over the long term, rather than randomly selecting charging electric vehicles for grid feedback or simply considering grid peak and valley regulation while ignoring the service life of electric vehicles.

[0127] Therefore, the present application focuses more on the scheduling of electric vehicles on the road, so as to make the V2G scheduling of the power grid in the target area more stable. Its purpose is not only to focus on the energy output of V2G to the power grid, but also to focus on determining the stability of V2G participation in scheduling, and effectively prevent electric vehicles from randomly participating in V2G power grid scheduling and causing power grid fluctuations. Compared with the existing technology that only focuses on active power balance in V2G scheduling, the present application also focuses on the impact of V2G on the technical and economic costs of electric vehicles, especially the scheduling optimization of electric vehicles from charging stations and the cost of the number of cycles of electric vehicle battery use occupied by the power output of V2G, preventing the power output of V2G from occupying more battery use cycles, thereby balancing the economic use of electric vehicles and the balance of V2G scheduling, and ensuring that electric vehicles on the road effectively and stably participate in V2G regulation, which helps to stabilize the expectations of power grid regulation and reduce the possibility of power grid fluctuations caused by the participation of electric vehicles in V2G.

[0128] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A V2G dispatching method for electric vehicles, characterized in that: Including steps: S1. Receive a V2G scheduling request sent by the target area; S2. Determine the vehicles that accept V2G scheduling in the target area according to the V2G scheduling request, and determine a set of candidate vehicles that participate in V2G scheduling based on the vehicles that accept V2G scheduling in the target area; S3. Matching a corresponding charging station for each vehicle in the candidate vehicle set, determining the remaining battery capacity of each vehicle after arriving at the corresponding charging station, and determining a target vehicle set to participate in V2G scheduling based on the remaining battery capacity; S4. 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 constraint after the electric vehicle is connected to the power grid, and perform V2G scheduling on the vehicles in the target vehicle set according to the scheduling strategy; The V2G scheduling request includes a scheduling period; The determining of a set of candidate vehicles participating in the V2G scheduling based on the vehicles that accept the V2G scheduling in the target area includes: Acquire historical charging and discharging data of vehicles that accept V2G scheduling in the target area, and determine the charging and discharging patterns of the vehicles that accept V2G scheduling based on the historical data; Determining the probability that the vehicle receiving the V2G dispatch will be charged during the dispatch period according to the charging and discharging rule; Creating a set of candidate vehicles for participating in V2G scheduling, and adding vehicles with a probability of accepting V2G scheduling greater than a first preset value to the set of candidate vehicles for participating in V2G scheduling; The scheduling strategy formulated 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 developed is as follows: Where M represents the total number of vehicles in the candidate vehicle set, E i Indicates the remaining battery capacity of each vehicle after it arrives at the corresponding charging station. represents the upper limit of the remaining battery capacity of the i-th vehicle, Indicates the lower limit of the vehicle's remaining battery capacity. represents the power provided to the grid by the i-th vehicle participating in V2G scheduling, t i represents the duration of time that the i-th vehicle participates in V2G scheduling, 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, D i represents the distance between the i-th vehicle and its corresponding charging station, f i The function represents the average power curve of the battery pack of the i-th vehicle, Q represents the electricity price of the charging station, represents the nominal number of cycles of the i-th vehicle, 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, Indicates the minimum grid connection voltage for vehicles to access charging stations for V2G. Indicates the maximum reverse input voltage that the charging station corresponding to the vehicle can withstand. represents the vehicle output voltage when the i-th vehicle participates in V2G, Indicates the output power of the conventional power supply side in the target area, Indicates the power difference between the maximum load and the minimum load in the target area. Indicates the average load of vehicles during the V2G scheduling period.

2. The electric vehicle V2G scheduling method according to claim 1, characterized in that: Determining the vehicles in the target area that accept V2G scheduling 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; The vehicles that accept V2G dispatch in the target area are determined according to the dispatch response information.

3. The electric vehicle V2G scheduling method according to claim 1, characterized in that: After creating a set of candidate vehicles for participating in V2G scheduling and adding vehicles with a probability of accepting V2G scheduling greater than a first preset value to the set of candidate vehicles for participating in V2G scheduling, the method further includes the following steps: A set of candidate vehicles participating in V2G scheduling is created, and vehicles whose probability of accepting V2G scheduling is 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.

4. An electric vehicle V2G scheduling method according to any one of claims 1 to 3, characterized in that: Matching a corresponding charging station for each vehicle in the candidate vehicle set includes: A corresponding charging station is matched for each vehicle in the candidate vehicle set based on the principle that the overall dispatch distance of all vehicles in the candidate vehicle set is minimized.

5. The electric vehicle V2G scheduling method according to claim 4, characterized in that: The charging pile allocation model constructed based on the above principles is: Where 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, represents the maximum number of electric vehicles that the kth charging station can accommodate, represents the number of electric vehicles assigned to the kth charging station, Min (·) indicates minimization.

6. An electric vehicle V2G scheduling method according to any one of claims 1 to 3, characterized in that: Determining the remaining battery capacity of each vehicle after it arrives at the corresponding charging station includes: Determining the time required for each vehicle to reach the corresponding charging station and the distance between the vehicle and the corresponding charging station; The remaining battery capacity E of each vehicle after arriving at the corresponding charging station i for: E i =E i0 -f i (T i ,D i ) Where, E i0 represents the current remaining battery capacity of the i-th vehicle, f i The function represents the average power curve of the battery pack of the i-th vehicle.

7. An electric vehicle V2G dispatching terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the electric vehicle V2G scheduling method according to any one of claims 1 to 6 is implemented.

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