A method for power and energy boundary aggregation and allocation of electric vehicle clusters

Abstract

The present application relates to the field of electric vehicle charging and discharging management, and discloses a method for aggregating and allocating the power and energy boundaries of an electric vehicle cluster, including the following steps: S1, dividing electric vehicles into multiple electric vehicle clusters according to the time they leave the charging station; S2, calculating the energy and power boundaries of each electric vehicle cluster; S3, establishing an electric vehicle cluster operation model, including its decision variables and constraints; S4, using a consistency algorithm to allocate the charging and discharging power of the electric vehicle cluster to each electric vehicle in each time period, so as to arrange the charging and discharging plan of each electric vehicle. The method for aggregating and allocating the power and energy boundaries of an electric vehicle cluster established by the present invention can be used in a power grid operation optimization model that takes into account the participation of large-scale electric vehicles, so as to reduce the model calculation time and improve the calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and discharging management, and in particular to a method for power and energy boundary aggregation and distribution of an electric vehicle cluster. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing greenhouse gas emissions has become a global focus. As one of the main sources of greenhouse gas emissions in the transportation sector, electric vehicles (EVs) that replace traditional fuel vehicles have gradually become an effective solution. Electric vehicles can not only reduce dependence on fossil energy, but also significantly reduce greenhouse gas emissions. According to forecasts, by 2030, the number of electric vehicles worldwide is expected to reach 245 million.

[0003] However, despite the significant advantages of electric vehicles in terms of environmental protection, their large-scale charging behavior has brought new challenges to the reliability and economy of the power system. If large-scale electric vehicles are charged simultaneously without coordination, it will lead to problems such as increased energy loss in the power system, increased voltage fluctuations, increased peak-valley load differences, and transformer overload. Studies have shown that when the penetration rate of uncoordinated electric vehicle charging in the power grid reaches 50%, the maximum voltage deviation in the distribution network may increase by more than 30%, and the energy loss in the transmission line will increase by 25%.

[0004] In order to ensure the stable operation of the power system and meet the charging needs of users, research in the field of electric vehicle charging and discharging management has become particularly important. However, most of the existing research is based on centralized control methods. Although it is suitable for considering the power flow constraints in the distribution network, as the number of electric vehicles increases, the optimization model will face the "dimensionality disaster" and solving the optimization problem will be very time-consuming. At the same time, the distribution system operator directly controls the charging and discharging strategy of each electric vehicle, which will impose a huge burden on the communication network. In order to overcome these problems, distributed optimization models have gradually been applied. Although distributed optimization models can handle the optimization problems of large-scale electric vehicles, the power balance constraints of the load bus and the power flow constraints of the distribution network lines are usually ignored when actively managing electric vehicles, which may reduce the possibility of safe and economic operation of the power grid.

[0005] In order to solve the problem of a large increase in variables and constraints brought about by large-scale electric vehicle problems, the ideas of hierarchical optimization and cluster optimization can be adopted. The electric vehicle cluster scheduling model greatly simplifies the computational difficulty by aggregating individual electric vehicles into equivalent clusters for modeling. In addition, by analyzing the regularity of cluster charging demand, the spatiotemporal uncertainty of individual electric vehicles can be reduced.

[0006] Based on the above analysis, the present invention proposes a method for power and energy boundary aggregation and allocation of electric vehicle clusters to establish an electric vehicle cluster operation model, which is applied to the power grid operation optimization model taking into account the participation of large-scale electric vehicles to reduce the optimization model calculation time and improve the calculation efficiency. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a method for power and energy boundary aggregation and allocation of electric vehicle clusters, which effectively manages the charging and discharging behavior of electric vehicles, reduces user charging costs, minimizes network energy loss, and reduces peak-to-valley differences, thereby improving the economy and stability of the power grid.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for power and energy boundary aggregation and allocation of electric vehicle clusters, comprising the following steps:

[0009] Classify EVs into multiple EV clusters based on their charging preferences and usage times;

[0010] Calculate the energy and power boundaries of each EV cluster, including maximum charging power, maximum discharging power, maximum energy, and minimum energy;

[0011] Based on the energy and power boundaries of electric vehicle clusters, an electric vehicle cluster operation model is established, including its decision variables and constraints, and the model is used for existing optimization scheduling problems of various types of electric vehicles;

[0012] Based on the charging and discharging power plan of the electric vehicle cluster in a future scheduling cycle obtained from the optimization scheduling problem, a consistency algorithm is used to allocate the charging and discharging power of the electric vehicle cluster to each electric vehicle in each time period to arrange the charging and discharging plan of each electric vehicle.

[0013] Preferably, the calculating of the energy and power boundaries of each electric vehicle cluster comprises:

[0014] Collect the maximum charging and discharging power, capacity, and range information of each EV in the cluster;

[0015] Use the following formula to calculate the maximum charging power and maximum discharging power of the electric vehicle cluster:

[0016]

[0017] in, is the maximum charging power of electric vehicle cluster c at time t, is the maximum discharge power of electric vehicle cluster c at time t, X n,t is the state variable of electric vehicle n in electric vehicle cluster c when it is connected to the charging station at time t, X n,t=1 means access to a charging station, which can aggregate power and energy. n,t =1 only indicates the connection status between the electric vehicle and the charging station, X n,t =0 means that the charging station is not connected and its power and energy cannot be aggregated. n,t The value of is obtained from the trip information of the electric vehicle. is the maximum charging power of electric vehicle n, is the maximum discharge power of electric vehicle n;

[0018] The maximum and minimum energy boundaries of the electric vehicle cluster are calculated using the following formula:

[0019]

[0020] in, is the maximum energy boundary of the virtual battery of electric vehicle cluster c at time t, is the minimum energy boundary of the virtual battery of electric vehicle cluster c at time t, is the battery capacity of electric vehicle n, is the maximum allowable state of charge of electric vehicle n, is the minimum allowable state of charge of electric vehicle n, is the maximum energy boundary of the virtual battery of the electric vehicle cluster kc at time t. It means that if the electric vehicles in the cluster are charged at the rated power immediately after connecting to the charging station, the virtual battery of the electric vehicle cluster can have the maximum energy at time t. is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It means that if the electric vehicles in the cluster are not charged immediately after connecting to the charging station, and are charged at rated power at a certain time before leaving the grid, then the virtual battery of the electric vehicle cluster must have a minimum energy boundary at time t to meet the expected power when the electric vehicles leave. and are the time when electric vehicle n enters and leaves the charging station, It represents the initial charge of electric vehicle n when it is connected to the charging station. represents the expected charge of electric vehicle n when it leaves the charging station, is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It indicates the minimum energy left in the virtual battery of the electric vehicle cluster at time t, assuming that the electric vehicles in the cluster are discharged at rated power immediately after being connected to the charging station;

[0021] The following formula is used to calculate the energy change of each electric vehicle cluster at each moment due to the different time when each electric vehicle in the cluster connects to or leaves the power station:

[0022]

[0023] in, is the energy change of the virtual battery of electric vehicle cluster c at time t due to the connection or departure of electric vehicles.

[0024] Preferably, based on the energy and power boundaries of the electric vehicle cluster, an electric vehicle cluster operation model is established, including:

[0025] Define the variable for charging power of electric vehicle clusters Electric vehicle cluster discharge power variable Electric vehicle cluster charging state variables Discharge state variables of electric vehicle clusters Virtual battery energy variables of electric vehicle clusters

[0026] Establish operating constraints for electric vehicle clusters, including:

[0027]

[0028] Among them, SC is the electric vehicle cluster set, ST is the scheduling time set;

[0029]

[0030] Based on the above variables and constraints, an electric vehicle cluster operation model is established, and the model can be used for various electric vehicle cluster charging and discharging scheduling optimization models.

[0031] Preferably, the method of using a consensus algorithm to distribute the charging and discharging power of the electric vehicle cluster to each electric vehicle in each time period includes:

[0032] Define the state variable λ n,t , whose initial value is given by the following formula:

[0033]

[0034] in, is the initial value of the state variable, is the initial charging power of electric vehicle n at time t, is the initial discharge power of electric vehicle n at time t, and its calculation formula is as follows:

[0035]

[0036] Among them, NEV c,t is the number of electric vehicles in electric vehicle cluster c at time t;

[0037] Iterative update λ n,tuntil the total power error requirement is met:

[0038]

[0039] in, is the value of the state variable at the qth iteration, d nm,t is the weight between electric vehicles n and m, ε is the step size parameter, is the total power error, calculated as follows:

[0040]

[0041] Among them, z n,t and z m,t The number of electric vehicles that can communicate with electric vehicle n or m at time t is equal to

[0042]

[0043] Get the updated Redistribute the charging and discharging power of each electric vehicle in the electric vehicle cluster. The distribution formula is as follows:

[0044]

[0045] in, is the charging power of electric vehicle n at time t, is the discharge power of electric vehicle n at time t;

[0046] To adjust the power value that exceeds the limit:

[0047]

[0048]

[0049] in, is the maximum charging power of electric vehicle n, is the maximum discharge power of electric vehicle n;

[0050] Based on the above method, the allocation starts from the first moment of the scheduling cycle. When the power allocation error at that moment meets the requirements, the state of charge of each electric vehicle at that moment is calculated, and the power allocation at the next moment is performed. The calculation formula of the state of charge of the electric vehicle at each moment is as follows:

[0051]

[0052] Allocation starts from the first moment of the scheduling cycle and ends at the last moment.

[0053] The present invention also provides an electric vehicle cluster power and energy boundary aggregation and distribution device, comprising:

[0054] A data collection module, used for collecting electric vehicle data and dividing the electric vehicles into multiple electric vehicle clusters;

[0055] The cluster boundary calculation module is used to calculate the energy and power boundaries of each electric vehicle cluster and establish an electric vehicle cluster operation model;

[0056] The power distribution calculation module is used to distribute the charging and discharging power of each electric vehicle cluster.

[0057] The present invention also provides a computer device, comprising 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, the above method is implemented.

[0058] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0059] The present invention provides a method for power and energy boundary aggregation and allocation of electric vehicle clusters. It has the following beneficial effects:

[0060] 1. The present invention divides electric vehicles into different clusters and aggregates large-scale electric vehicles into equivalent clusters, which greatly reduces the variables and constraints of the optimization model, significantly reduces the computational complexity, and improves the solution speed and efficiency. Compared with directly scheduling a large number of individual electric vehicles, the cluster scheduling method is more efficient and can quickly solve the optimal scheduling plan to meet the needs of large-scale electric vehicles to access the power grid.

[0061] 2. The electric vehicle cluster operation model established by the present invention can be applied to various electric vehicle optimization and scheduling problems, whether it is household electric vehicle charging management or the scheduling optimization of public charging piles, and can be effectively applied, with wide adaptability and application prospects. In particular, the distribution network operation optimization scheduling taking into account the participation of electric vehicles, by optimizing the scheduling of electric vehicle charging and discharging behavior, avoids the impact of large-scale electric vehicle simultaneous charging on the power system, reduces energy loss, and improves the economy of the power system;

[0062] 3. When distributing the charging and discharging power, the present invention fully considers the individual constraints of each electric vehicle, including the maximum charging and discharging power and state of charge (SOC) limit of the battery, and avoids the battery from working in extreme conditions. Reasonable charging and discharging strategies not only improve the charging and discharging efficiency of the battery, but also reduce the loss and aging speed of the battery, extend the service life of the battery, and reduce the maintenance cost of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0064] Figure 2 It is a schematic diagram of the structure of the device of the present invention;

[0065] Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0066] Among them, 10, data collection module; 20, cluster boundary calculation module; 30, power allocation calculation module; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for aggregating and allocating power and energy boundaries of an electric vehicle cluster, comprising the following steps:

[0069] S1. Divide electric vehicles into multiple electric vehicle clusters based on their charging preferences and usage time

[0070] First, EVs are grouped into different EV clusters (EVCs) based on the charging preferences and specific usage times of EV users. This step is the basis of the entire optimization process, because different charging preferences and usage times will directly affect the charging needs of EVs and the impact on the power grid. For example, some EVs may only charge at night when electricity prices are low, while other EVs may support vehicle-to-grid (V2G) functions and feed electricity back to the grid when the grid load is peak.

[0071] In this embodiment, step S1 specifically includes:

[0072] 1. Data collection:

[0073] First, relevant data needs to be collected from each electric vehicle (EV) user. This data includes but is not limited to the following:

[0074] Arrival time : The time it takes for an electric vehicle to arrive at a charging station.

[0075] Departure time : The time when the electric vehicle leaves the charging station.

[0076] Initial power status : The state of charge of the electric vehicle battery upon arrival.

[0077] Expected power state : The battery charge state that the user expects to be when leaving the device.

[0078] Minimum power state : The minimum power state allowed by the battery.

[0079] Maximum power state : The maximum charge state allowed by the battery.

[0080] Battery capacity : The total energy capacity of the battery.

[0081] Maximum charging power : The maximum charging power allowed by the charging device.

[0082] Maximum discharge power : The maximum discharge power allowed by the charging device.

[0083] Maximum apparent power : The maximum apparent power allowed by the charging device.

[0084] 2. Determination of classification standards:

[0085] Based on the collected data, electric vehicle users are classified based on usage time.

[0086] The specific method is: extract the departure time of all electric vehicles and classify them according to the preset time period (for example, one segment per hour). Assuming that the departure time is mainly concentrated between 6:00 and 10:00, it can be subdivided into four small clusters: EVC 1 , EVC 2 , EVC 3 and EVC 4 .

[0087] 3. Allocate result storage:

[0088] The classification results are stored in a database or memory for subsequent calculation and scheduling optimization. Each cluster contains its corresponding electric vehicle list and its detailed parameter information.

[0089] Through the above steps, this embodiment can effectively classify electric vehicles into multiple electric vehicle clusters according to their usage time, laying a foundation for subsequent energy and power boundary calculation and charge and discharge scheduling optimization.

[0090] S2. Calculate the energy and power boundaries of each electric vehicle cluster, including maximum charging power, maximum discharging power, maximum energy, and minimum energy

[0091] After the electric vehicles are grouped, the energy and power boundaries are calculated for each cluster. This step is crucial for subsequent scheduling optimization because it determines the maximum and minimum amount of resources available to each cluster at different points in time. By aggregating the parameters of all electric vehicles in the cluster (such as battery capacity and charge and discharge rate), the overall capacity of the cluster can be obtained, thereby maximizing its economic benefits while ensuring the stable operation of the power grid.

[0092] In this embodiment, step S2 specifically includes:

[0093] 1. Data Collection:

[0094] The following key parameters are collected for each electric vehicle in the cluster:

[0095] Maximum charging power : The maximum charging power of each electric vehicle n.

[0096] Maximum discharge power : The maximum discharge power of each electric vehicle n.

[0097] Battery capacity : The battery capacity of each electric vehicle n.

[0098] Arrival time : The time it takes for each electric vehicle n to arrive at the charging station.

[0099] Departure time : The time when each electric vehicle n leaves the charging station.

[0100] Initial SOC : The initial charge state of each electric vehicle n when it arrives at the charging station.

[0101] Maximum SOC : The maximum state of charge allowed for each electric vehicle n.

[0102] Minimum SOC : The minimum state of charge allowed for each electric vehicle n.

[0103] Expected SOC : The expected state of charge of each electric vehicle n when it leaves the charging station.

[0104] 2. State variable definition:

[0105] Define a binary state variable X n,t , indicating whether electric vehicle n is in the charging station at time t:

[0106]

[0107] 3. Calculation of maximum charging power and maximum discharging power:

[0108] Use the following formula to calculate the maximum charging power and maximum discharging power of the electric vehicle cluster:

[0109]

[0110] in:

[0111] is the maximum charging power of electric vehicle cluster c at time t.

[0112] is the maximum discharge power of electric vehicle cluster c at time t.

[0113] X n,t is the state variable of electric vehicle n in electric vehicle cluster c when it is connected to the charging station at time t, X n,t =1 means access to the charging station, and the power boundary and energy boundary can be aggregated. n,t =1 only indicates the connection status between the electric vehicle and the charging station, X n,t =0 means that the charging station is not connected, and its power boundary and energy boundary cannot be aggregated. n,t The value of is obtained from the trip information of the electric vehicle.

[0114] is the maximum charging power of electric vehicle n.

[0115] is the maximum discharge power of electric vehicle n.

[0116] 4. Calculation of maximum energy and minimum energy:

[0117] The maximum and minimum energy boundaries of the electric vehicle cluster are calculated using the following formulas:

[0118]

[0119]

[0120] in:

[0121] and are the maximum energy boundary and minimum energy boundary of the virtual battery of electric vehicle cluster c at time t, respectively.

[0122] is the battery capacity of electric vehicle n.

[0123] and are the maximum and minimum allowable state of charge of the electric vehicle n.

[0124] is the maximum energy boundary of the virtual battery of the electric vehicle cluster kc at time t. It means that if the electric vehicles in the cluster are charged at the rated power immediately after connecting to the charging station, the virtual battery of the electric vehicle cluster can have the maximum energy at time t.

[0125] is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It means that if the electric vehicles in the cluster are not charged immediately after connecting to the charging station, and are charged at rated power at some time before leaving the grid, then the virtual battery of the electric vehicle cluster must have a minimum energy boundary at time t to ensure that the electric vehicles have the expected amount of power when they leave.

[0126] and are the time when electric vehicle n enters and leaves the charging station, respectively.

[0127] It indicates the initial charge of electric vehicle n when it is connected to the charging station.

[0128] It represents the expected amount of electricity that electric vehicle n has when it leaves the charging station.

[0129] is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It indicates the minimum energy remaining in the virtual battery of the electric vehicle cluster at time t, assuming that the electric vehicles in the cluster are discharged at rated power immediately after being connected to the charging station.

[0130] 5. Calculation of energy change:

[0131] The following formula is used to calculate the energy change of each electric vehicle cluster at each moment due to the different time when each electric vehicle in the cluster connects to or leaves the power station:

[0132]

[0133] in, is the energy change of the virtual battery of electric vehicle cluster c at time t due to the connection or departure of electric vehicles.

[0134] Through the above steps, this embodiment can accurately calculate the energy and power boundaries of each electric vehicle cluster in each time period, providing accurate data support for subsequent power allocation and scheduling optimization.

[0135] S3. Establish an electric vehicle cluster operation model based on the energy and power boundaries of the electric vehicle cluster

[0136] The model is based on the precise calculation of the energy and power boundaries of each cluster, and by defining key variables such as charging and discharging power and the state variables of electric vehicles, it achieves optimal control of the charging and discharging behavior of the electric vehicle cluster. The constraints included in the model ensure the technical feasibility and safety of the charging and discharging process, while taking into account the energy balance of the battery and the stability of the grid operation.

[0137] In this embodiment, step S3 specifically includes the following contents:

[0138] 1. Define decision variables:

[0139] Charging power variables for electric vehicle clusters : represents the charging power of cluster c at time t.

[0140] Discharge power variables of electric vehicle clusters : represents the discharge power of cluster c at time t.

[0141] State of charge variables for electric vehicle clusters : Indicates whether cluster c is charging at time t.

[0142] Discharge state variables of electric vehicle clusters : Indicates whether cluster c is discharging at time t.

[0143] Virtual battery energy variables for electric vehicle clusters : represents the energy state of cluster c at time t.

[0144] 2. Establish operating constraints:

[0145] Charge and discharge power constraints:

[0146]

[0147] in, and They represent the maximum charging and discharging powers of cluster c at time t, respectively.

[0148] Charge and discharge state constraints:

[0149]

[0150] in, and is a binary variable indicating whether cluster c is in a charging or discharging state at time t.

[0151] Energy balance constraints:

[0152]

[0153] in, is the energy state of cluster c at time t, η c,ch and η c,dis are the charging and discharging efficiencies respectively, Δt is the time interval, is the energy change caused by the electric vehicle connecting or leaving.

[0154] Energy boundary constraints:

[0155]

[0156] in, and are the minimum and maximum energy boundaries of cluster c at time t, and is the charging energy boundary, is the discharge energy boundary.

[0157] 3. Establishment of running model:

[0158] Based on the above variables and constraints, an operation model of an electric vehicle cluster is established. The model aims to optimize the charging and discharging scheduling of the electric vehicle cluster to achieve the following goals:

[0159] Minimize charging costs: Reduce user costs by choosing to charge during periods of low electricity prices.

[0160] Minimize energy loss: Reduce power transmission loss by properly allocating charging and discharging power.

[0161] Smooth load curve: Reduce the peak and valley load differences of the power grid by adjusting the charging and discharging behavior of electric vehicles.

[0162] 4. Mathematical expression of the model:

[0163] Objective function:

[0164] F=min(w 1 f cost +w 2 f dlv +w 3 f loss )

[0165] Among them, f cost is the charging cost, f dlv is the daily load variance, f loss is the energy loss, w 1 ,w 2 ,w 3 is the weight coefficient.

[0166] Sub-objective function definition:

[0167] User charging cost (f cost ):

[0168]

[0169] Among them, ρ t is the electricity price at time t, is the charging power minus the discharging power of all EVCs at bus j, and NT is the total number of time periods in the scheduling cycle.

[0170] Daily load variance (f dlv ):

[0171]

[0172] in, is the active power demand of other loads at bus j, P avg is the average load during the dispatch period, and the calculation formula is:

[0173]

[0174] Network energy loss (f loss ):

[0175]

[0176] Among them, r ij is the resistance of line (i,j), i ij,t is the square of the current in line (i, j) at time t, and SL is the set of all lines.

[0177] 5. Constraints:

[0178] The charging and discharging power constraints, state constraints and energy constraints are as described above.

[0179] The following constraints are also included:

[0180] Bus power balancing constraints:

[0181]

[0182] in, is the active power of all EVCs at bus j, is the reactive power of all EVCs at bus j, P ij,t is the active power of line (i, j), Q ij,t is the reactive power of line (i, j), r ij is the resistance of line (i, j), x ij is the reactance of line (i, j).

[0183] Node voltage and DC current constraints:

[0184]

[0185] Among them, u j,t is the square of the voltage amplitude of bus j at time t.

[0186] Bus voltage amplitude constraint:

[0187]

[0188] Line current amplitude constraint:

[0189]

[0190] in, u j and are the lower and upper limits of the voltage amplitude of bus j, is the upper limit of the current of line (i, j).

[0191] Through the above steps, this embodiment can establish a comprehensive electric vehicle cluster operation model for optimizing the charging and discharging scheduling of electric vehicle clusters. The model can effectively reduce user costs, reduce energy loss, smooth grid load, and improve the economy and stability of the grid.

[0192] S4. Based on the charging and discharging power plan of the electric vehicle cluster in the next scheduling cycle obtained from the optimization scheduling problem, the charging and discharging power of the electric vehicle cluster is allocated to each electric vehicle in each time period using a consistency algorithm to arrange the charging and discharging plan of each electric vehicle.

[0193] This step ensures that the charging and discharging behaviors within the EV cluster can meet the overall power demand while taking into account the individual constraints of each EV, achieving dual optimization for the grid and EV users.

[0194] In this embodiment, step S4 specifically includes:

[0195] 1. Define state variables:

[0196] Define the state variable λ n,t , its initial value It is given by the following formula:

[0197]

[0198] in, is the initial value of the state variable, is the initial charging power of electric vehicle n at time t, is the initial discharge power of electric vehicle n at time t. and The calculation formula is:

[0199]

[0200] Among them, NEV c,t is the number of electric vehicles in electric vehicle cluster c at time t.

[0201] 2. Iteratively update state variables:

[0202] Iteratively update λ using the consensus algorithm n,t The value of until the total power error requirement is met. The iterative update formula is as follows:

[0203]

[0204] in, is the value of the state variable at the qth iteration, d nm,t is the weight between electric vehicles n and m, ε is the step size parameter, is the total power error. nm,t and The calculation formula is:

[0205]

[0206] Among them, z n,t and z m,t The number of electric vehicles that can communicate with electric vehicle n or m at time t is equal to

[0207]

[0208] in, and are the target charging and discharging powers of cluster c at time t, and are the charging and discharging powers of electric vehicle n at time t, respectively.

[0209] 3. Calculate the updated power allocation:

[0210]

[0211] in, is the charging power of electric vehicle n at time t, is the discharge power of electric vehicle n at time t.

[0212] 4. Adjust the power value that exceeds the limit:

[0213] To ensure that the calculated power value is within the allowable range, adjust the formula as follows:

[0214]

[0215] in, is the maximum charging power of electric vehicle n, is the maximum discharge power of electric vehicle n.

[0216] 5. Calculate the state of charge of electric vehicles:

[0217] When the power allocation error at that moment meets the requirements, the state of charge of each electric vehicle at that moment is calculated, and the power allocation at the next moment is performed. The calculation formula of the state of charge of the electric vehicle at each moment is as follows:

[0218]

[0219] 6. Loop execution:

[0220] The allocation starts from the first moment of the scheduling cycle and ends at the last moment. This process ensures that the charging and discharging power allocation of electric vehicles in each time period meets the target requirements of the cluster while taking into account the limitations of individual vehicles.

[0221] Through the above steps, this embodiment can achieve accurate charging and discharging power distribution of the electric vehicle cluster in a future scheduling cycle, ensure that the needs of the power grid and users are met, and maximize the overall benefits.

[0222] The electric vehicle cluster power and energy boundary aggregation and allocation device described below and the electric vehicle cluster power and energy boundary aggregation and allocation method described above can correspond to each other.

[0223] Please see attached Figure 2 The present invention also provides an electric vehicle cluster power and energy boundary aggregation and distribution device, comprising:

[0224] A data collection module 10 is used to collect electric vehicle data and divide the electric vehicles into a plurality of electric vehicle clusters;

[0225] The cluster boundary calculation module 20 is used to calculate the energy and power boundary of each electric vehicle cluster and establish an electric vehicle cluster operation model;

[0226] The power distribution calculation module 30 is used to distribute the charging and discharging power of each electric vehicle cluster.

[0227] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0228] Please see attached Figure 3The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is executed.

[0229] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0230] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0231] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for power and energy boundary aggregation and allocation of electric vehicle clusters, characterized in that: The following steps are involved: Classify EVs into multiple EV clusters based on their charging preferences and usage times; Calculate the energy and power boundaries of each EV cluster, including maximum charging power, maximum discharging power, maximum energy, and minimum energy; Based on the energy and power boundaries of electric vehicle clusters, an electric vehicle cluster operation model is established, including its decision variables and constraints, and the model is used for existing optimization scheduling problems of various types of electric vehicles; Based on the charging and discharging power plan of the electric vehicle cluster in a future scheduling cycle obtained from the optimization scheduling problem, a consistency algorithm is used to allocate the charging and discharging power of the electric vehicle cluster to each electric vehicle in each time period to arrange the charging and discharging plan of each electric vehicle; The calculation of the energy and power boundary of each electric vehicle cluster includes: Collect the maximum charging and discharging power, capacity, and range information of each EV in the cluster; Use the following formula to calculate the maximum charging power and maximum discharging power of the electric vehicle cluster: in, is the maximum charging power of electric vehicle cluster c at time t, is the maximum discharge power of electric vehicle cluster c at time t, X n,t is the state variable of electric vehicle n in electric vehicle cluster c when it is connected to the charging station at time t, X n,t =1 means access to the charging station, and the power boundary and energy boundary can be aggregated. n,t =1 only indicates the connection status between the electric vehicle and the charging station, X n,t =0 means that the charging station is not connected, and its power boundary and energy boundary cannot be aggregated. n,t The value of is obtained from the trip information of the electric vehicle. is the maximum charging power of electric vehicle n, is the maximum discharge power of electric vehicle n; The maximum and minimum energy boundaries of the electric vehicle cluster are calculated using the following formula: in, is the maximum energy boundary of the virtual battery of electric vehicle cluster c at time t, is the minimum energy boundary of the virtual battery of electric vehicle cluster c at time t, is the battery capacity of electric vehicle n, is the maximum allowable state of charge of electric vehicle n, is the minimum allowable state of charge of electric vehicle n, is the maximum energy boundary of the virtual battery of the electric vehicle cluster kc at time t. It means that if the electric vehicles in the cluster are charged at the rated power immediately after connecting to the charging station, the virtual battery of the electric vehicle cluster can have the maximum energy at time t. is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It means that if the electric vehicles in the cluster are not charged immediately after connecting to the charging station, and are charged at rated power at a certain time before leaving the grid, then the virtual battery of the electric vehicle cluster must have a minimum energy boundary at time t to meet the expected power when the electric vehicles leave. and are the time when electric vehicle n enters and leaves the charging station, It represents the initial charge of electric vehicle n when it is connected to the charging station. represents the expected charge of electric vehicle n when it leaves the charging station, is the minimum energy boundary of the virtual battery of the electric vehicle cluster c at time t. It indicates the minimum energy left in the virtual battery of the electric vehicle cluster at time t, assuming that the electric vehicles in the cluster are discharged at rated power immediately after being connected to the charging station; The following formula is used to calculate the energy change of each electric vehicle cluster at each moment due to the different time when each electric vehicle in the cluster connects to or leaves the power station: in, is the energy change of the virtual battery of electric vehicle cluster c at time t due to the connection or departure of electric vehicles.

2. The method for power and energy boundary aggregation and allocation of electric vehicle clusters according to claim 1 is characterized in that: Based on the energy and power boundaries of the electric vehicle cluster, an electric vehicle cluster operation model is established, including: Define the variable for charging power of electric vehicle clusters Electric vehicle cluster discharge power variable Electric vehicle cluster charging state variables Discharge state variables of electric vehicle clusters Virtual battery energy variables of electric vehicle clusters Establish operating constraints for electric vehicle clusters, including: Among them, SC is the electric vehicle cluster set, ST is the scheduling time set; Based on the above variables and constraints, an electric vehicle cluster operation model is established, and the model can be used for various electric vehicle cluster charging and discharging scheduling optimization models.

3. The method for power and energy boundary aggregation and allocation of electric vehicle clusters according to claim 1, characterized in that: The method of using a consensus algorithm to distribute the charging and discharging power of the electric vehicle cluster to each electric vehicle in each time period includes: Define the state variable λ n,t , whose initial value is given by the following formula: in, is the initial value of the state variable, is the initial charging power of electric vehicle n at time t, is the initial discharge power of electric vehicle n at time t, and its calculation formula is as follows: Among them, NEV c,t is the number of electric vehicles in electric vehicle cluster c at time t; Iterative update λ n,t until the total power error requirement is met: in, is the value of the state variable at the qth iteration, d nm,t is the weight between electric vehicles n and m, ε is the step size parameter, is the total power error, calculated as follows: Among them, z n,t and z m,t The number of electric vehicles that can communicate with electric vehicle n or m at time t is equal to Get the updated Redistribute the charging and discharging power of each electric vehicle in the electric vehicle cluster. The distribution formula is as follows: in, is the charging power of electric vehicle n at time t, is the discharge power of electric vehicle n at time t; To adjust the power value that exceeds the limit: in, is the maximum charging power of electric vehicle n, is the maximum discharge power of electric vehicle n; Based on the above method, the allocation starts from the first moment of the scheduling cycle. When the power allocation error at that moment meets the requirements, the state of charge of each electric vehicle at that moment is calculated, and the power allocation at the next moment is performed. The calculation formula of the state of charge of the electric vehicle at each moment is as follows: Allocation starts from the first moment of the scheduling cycle and ends at the last moment.

4. An electric vehicle cluster power and energy boundary aggregation and distribution device, used to implement the method described in any one of claims 1 to 3, characterized in that: include: A data collection module, used for collecting electric vehicle data and dividing the electric vehicles into multiple electric vehicle clusters; The cluster boundary calculation module is used to calculate the energy and power boundaries of each electric vehicle cluster and establish an electric vehicle cluster operation model; The power distribution calculation module is used to distribute the charging and discharging power of each electric vehicle cluster.

5. A computer device 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, the method according to any one of claims 1 to 3 is implemented.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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