Electric vehicle cluster power decomposition method and system based on charge state consistency rate water injection method

Abstract

The present application relates to electric vehicle charging and discharging scheduling technical field, specifically is a kind of electric vehicle cluster power decomposition method and system based on state of charge consistent rate water injection method, comprising the following steps: S1 obtains electric vehicle cluster scheduling instruction and the state parameter of electric vehicle;S2 determines cluster response direction, and constructs electric vehicle power boundary;S3 distributes the minimum necessary charging power based on S1, and calculates state of charge demand progress consistent rate;S4 carries out secondary distribution of water injection power based on water level optimization;S5 single electric vehicle executes state update and power deviation feedback.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging and discharging scheduling technology, specifically a method and system for decomposing electric vehicle cluster power based on the state-of-charge consistency rate water injection method. Background Technology

[0002] With the continuous advancement of new power system construction, the high proportion of renewable energy integration has led to greater randomness and volatility in grid operation, increasing the demand for flexible load-side adjustment resources. Electric vehicles, possessing the dual attributes of traffic load and mobile energy storage, are an important distributed flexibility resource in new power systems. However, the small capacity of a single electric vehicle, random access times, and significant differences in user travel needs make it difficult for them to directly participate in electricity market transactions. Virtual power plants, acting as intermediaries connecting dispersed users and the electricity market, have become an important form of promoting the large-scale application of vehicle-grid interaction by aggregating massive electric vehicle resources to participate in day-ahead-to-real-time market transactions.

[0003] In the process of electric vehicle clusters participating in virtual power plant dispatching transactions, how to rationally allocate the overall dispatching instructions at the cluster level to the individual vehicle level for execution is a key link to ensure the actual executability of the dispatching scheme; however, existing power allocation and decomposition technologies still have the following shortcomings in practical applications:

[0004] 1) The allocation mechanism lacks consideration for both the heterogeneity of vehicle status and fairness.

[0005] Existing technologies often employ proportional allocation, average allocation, or fixed-weight allocation methods when decomposing cluster power commands. These methods treat electric vehicle clusters as homogeneous energy storage or adjustable loads, ignoring the significant differences between individual vehicles in terms of initial state of charge, remaining dwell time, and the urgency of users needing to recharge. This can easily lead to some vehicles with sufficient power being overcharged / discharged, while vehicles urgently needing recharging cannot obtain enough charging power, severely damaging the user experience and the fairness of participation in dispatching.

[0006] 2) It is difficult to perform closed-loop verification of physical constraints and power trajectory feasibility in real-time allocation.

[0007] The charging and discharging behavior of individual electric vehicles is constrained by multiple hard constraints, such as battery physical capacity and maximum charging and discharging power. Existing allocation methods rarely consider the energy availability of a vehicle in subsequent scheduling periods when calculating the power allocation for a single vehicle. This can easily lead to the excessive postponement of charging tasks, making vehicles face the risk of not being able to fully charge due to exceeding power limits when they are close to leaving the grid.

[0008] 3) There is a conflict between the accurate solution method and the high timeliness requirement of real-time scheduling.

[0009] To achieve better power allocation accuracy, some studies require complex high-dimensional inverse projection decomposition based on high-dimensional geometry, or the use of centralized optimization algorithms to jointly solve the physical constraints of all individual vehicles. However, with the increase in the number of aggregated electric vehicles and the extension of scheduling periods, the independent variables and constraints in the allocation process grow exponentially, facing a severe "curse of dimensionality." Such high-dimensional solution methods have extremely high computational complexity and long computation time, which cannot meet the extremely high timeliness requirements of virtual power plant real-time scheduling for rapid calculation and issuance of individual vehicle control commands.

[0010] In summary, how to achieve a method for decomposing the power of individual electric vehicles in a large-scale, heterogeneous electric vehicle cluster, while ensuring the energy replenishment needs of individual electric vehicles and physical constraints, taking into account the charging state of individual vehicles and scheduling fairness, and having physical feasibility feedback verification, is a bottleneck problem that urgently needs to be solved for the large-scale implementation of vehicle-to-grid interaction technology. Summary of the Invention

[0011] The purpose of this invention is to provide a power decomposition method for electric vehicle clusters based on the state-of-charge consistency rate water injection method. This method addresses the problems in existing electric vehicle cluster power allocation processes, such as the difficulty in balancing the heterogeneity of individual vehicle states with scheduling fairness, the lack of forward-looking verification of the feasibility of individual vehicle cross-time period power trajectories, and the computational burden of high-dimensional centralized optimization allocation methods, which makes it impossible to meet real-time scheduling requirements. This invention provides a fast, fair, and feasible power allocation scheme for virtual power plants to decompose cluster scheduling instructions downwards, for fair and safe execution by individual vehicles, and for efficient adjustment of vehicle-grid interaction.

[0012] To achieve the above objectives, this invention proposes a power decomposition method for electric vehicle clusters based on the state-of-charge consistency rate water injection method, comprising the following steps:

[0013] S1 obtains the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles;

[0014] S2 determines the cluster response direction and constructs the electric vehicle battery power boundary;

[0015] S3 allocation is based on the minimum necessary charging power obtained from S1, and the state of charge requirement schedule consistency rate is calculated.

[0016] S4 performs secondary allocation of water injection power based on water level optimization;

[0017] S5 single electric vehicle (hereinafter referred to as single vehicle) performs status update and power deviation feedback.

[0018] In the above technical solution, S1 obtains the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles, including:

[0019] S1.1 Obtain cluster scheduling instructions;

[0020] S1.2 Obtain the state parameters of the electric vehicle.

[0021] Specifically, S1.1 obtains cluster scheduling instructions;

[0022] Before the start of the current scheduling cycle, obtain the electric vehicle cluster charging scheduling instructions and cluster discharging scheduling instructions issued by the virtual power plant's upper-level scheduling system. The cluster charging scheduling instruction sequence and cluster discharging scheduling instruction sequence within the current scheduling cycle are represented as follows:

[0023] ;

[0024] ;

[0025] In the formula: and Representing the scene respectively The charging and discharging scheduling instructions received by the c-th electric vehicle cluster in time period t; c represents the electric vehicle cluster number; t represents the current scheduling time period; K represents the total number of scheduling time periods within a scheduling cycle; and These represent the c-th electric vehicle cluster in the scenario. The received cluster charging scheduler sequence and cluster discharging scheduler sequence.

[0026] In any given scheduling period, the cluster charging scheduling instruction and the cluster discharging scheduling instruction satisfy the following:

[0027] ;

[0028] ;

[0029] The above constraints mean that both the cluster charging scheduling command and the discharging scheduling command are non-negative values, and neither can be greater than zero at the same time, so as to ensure that the electric vehicle cluster only executes charging response or discharging response in any scheduling period; when both are zero, the cluster does not execute charging and discharging regulation in the current period.

[0030] S1.2 Obtain the state parameters of the electric vehicle;

[0031] Obtain the state parameters of each participating vehicle within the electric vehicle cluster. For the i-th electric vehicle, its vehicle state parameter vector... Represented as:

[0032] ;

[0033] In the formula: and These refer to the vehicle's registration and departure times, respectively. Duration of vehicle stay; This refers to the initial state of charge of the vehicle upon grid connection. To meet users' expected off-site power requirements; Allowing users to charge beyond their desired capacity; and These are the minimum and maximum allowable states of charge of the battery, respectively. This refers to the vehicle's battery capacity. and These are the maximum charging power and the maximum discharging power, respectively. and These are the charging efficiency and the discharging efficiency, respectively.

[0034] In the above technical solution, S2 determines the cluster response direction and constructs the power boundary, which includes:

[0035] S2.1 Determine the direction of the cluster response;

[0036] The electric vehicle's battery capacity boundary is constructed as follows:

[0037] S2.2 Calculate the minimum and maximum acceptable battery power for a single vehicle.

[0038] S2.3 Calculate the charging and discharging power limit for a single vehicle;

[0039] S2.4 Calculate the minimum required charging power for a single vehicle.

[0040] Specifically, in S2, determining the cluster response direction and constructing the power boundary includes:

[0041] S2.1 Determine the direction of the cluster response;

[0042] During the current time period, the cluster response direction is determined to be either a charging response or a discharging response based on the cluster scheduling instruction. When the cluster charging scheduling instruction is greater than zero, a charging response is executed; when the cluster discharging scheduling instruction is greater than zero, a discharging response is executed.

[0043] Specifically, the electrical boundary in S2 is constructed as follows:

[0044] S2.2 Calculate the minimum and maximum acceptable battery power for a single vehicle.

[0045] For vehicles that leave the network within the current scheduling cycle, the expected off-site power demand is taken as the power demand for the current cycle; for vehicles that remain stationed across scheduling cycles, the minimum power demand at the end of the current cycle is calculated by working backward from the remaining stay time in the next scheduling cycle.

[0046] ;

[0047] In the formula: This represents the cutoff power demand of the i-th electric vehicle during the current scheduling cycle; This indicates the expected off-site power demand; Indicates the minimum permissible state of charge of the battery; Indicates battery capacity; Indicates charging efficiency; Indicates the maximum charging power; Indicates the extended off-network time; K represents the total number of scheduling periods; Indicates the duration of a single scheduling period.

[0048] Based on the cutoff demand for electricity and the vehicle's remaining dwell time, calculate the vehicle's dynamic minimum acceptable electricity level for time period t:

[0049] ;

[0050] In the formula: This represents the minimum acceptable energy level of the i-th electric vehicle during time period t; This indicates the cut-off electricity demand within the current scheduling cycle; Indicates charging efficiency; Indicates the maximum charging power; This represents the equivalent off-network time within the current scheduling cycle; Indicates the duration of a single scheduling period.

[0051] The maximum acceptable battery level for a vehicle during time period t is:

[0052] ;

[0053] In the formula: This represents the maximum acceptable battery level of the i-th electric vehicle during time period t; Indicates the maximum permissible state of charge of the battery; Indicates battery capacity; Indicates the expected off-site power consumption; This indicates that the user is allowed additional charging beyond the desired off-site charge level.

[0054] S2.3 Calculate the charging and discharging power limit for a single vehicle;

[0055] Based on the vehicle's online status, maximum charging and discharging power limits, and considering the highest acceptable battery level, lowest acceptable battery level, and current battery level for the current time period, the maximum feasible time for the vehicle in the current time period is calculated.

[0056] Charging power and maximum feasible discharging power:

[0057] ;

[0058] ;

[0059] In the formula, and These represent the maximum feasible charging power and the maximum feasible discharging power of a single vehicle during the current time period, respectively. and These represent the vehicle's highest and lowest acceptable battery levels for the current time period; , These refer to the vehicle's charging efficiency and discharging efficiency, respectively. , These are the vehicle's maximum charging power and maximum discharging power, respectively. The electric vehicle is in online status; This represents the current battery level. The scheduling time interval; This indicates that the non-negative value is taken, meaning that if the value inside the parentheses is greater than zero, the original value is taken, and if it is less than or equal to zero, zero is taken; i represents the individual electric vehicle serial number; t represents the current scheduling time period serial number; Indicates the scene number.

[0060] S2.4 Calculate the minimum required charging power for a single vehicle;

[0061] Based on the deviation between the vehicle's dynamic minimum acceptable battery level for the next time period and its current battery level, and in conjunction with the vehicle's maximum charging power limit and charging efficiency, the minimum necessary charging power that must be enforced in the current time period to ensure basic transportation needs is determined as follows:

[0062] ;

[0063] In the formula, This indicates the minimum necessary charging power for a single vehicle during the current time period; Indicates the vehicle's maximum charging power limit; This indicates the minimum acceptable battery level for the bicycle in the next time period; This indicates the battery level of the bicycle in the current time period; Indicates charging efficiency; Indicates the duration of a single scheduling period; This represents the operator for taking non-negative values.

[0064] The core of this step lies in calculating the charging and discharging power limits and minimum necessary charging power for a single vehicle based on its highest and lowest acceptable battery levels. Relying solely on power allocation within a single time period can easily lead to excessively delayed charging tasks, making it difficult to meet the minimum guaranteed off-grid battery level requirement even when operating at maximum charging power as the vehicle approaches its departure point. Therefore, this invention calculates the minimum necessary charging power for a single vehicle in advance before allocating cluster power, achieving a safe minimum guarantee for the off-grid battery level of electric vehicles under market fluctuations.

[0065] In the above technical solution, S3, which allocates the minimum necessary charging power and calculates the consistency rate of the state of charge requirement schedule, specifically includes:

[0066] The minimum necessary charging power is used as the base charging power for allocation. Based on the current battery level of a single vehicle, the minimum necessary charging power, charging efficiency, and scheduling time interval, the equivalent battery state of a single vehicle after allocating the base power is calculated. The specific formula is as follows:

[0067] ;

[0068] In the formula, The equivalent electrical state after allocating base power to a single vehicle; This indicates the current battery level of the bicycle. Indicates charging efficiency; Indicates the minimum required charging power; Indicates the scheduling time interval.

[0069] Based on the equivalent energy status, the dynamic minimum acceptable energy level, and the maximum acceptable energy level, the consistency rate of the charge status demand progress for each vehicle in the current time period is calculated. The specific formula is as follows:

[0070] ;

[0071] In the formula, This indicates the consistency rate of the charging status and demand progress of each bicycle in the current time period. Indicates the equivalent electrical quantity status; Indicates the dynamic minimum acceptable power level; Indicates the maximum acceptable battery level; To prevent extremely small positive numbers with a denominator of zero.

[0072] The core of this step lies in prioritizing the allocation of the minimum necessary charging power to each vehicle and calculating the consistency rate of vehicle state-of-charge demand progress. Because different electric vehicles vary in battery capacity, current charge level, and dwell time, it is impossible to directly compare their charging urgency. Therefore, this invention standardizes the current charge level of each vehicle within a dynamically acceptable range by calculating the consistency rate index of individual vehicle state-of-charge demand progress, thereby objectively measuring the charging urgency and discharge capacity of different vehicles.

[0073] In the above technical solution, the secondary allocation of water injection power based on water level optimization in step S4 includes:

[0074] S4.1: Determine the direction of the cluster response and calculate the charging and discharging power limit and minimum necessary charging power for a single vehicle;

[0075] S4.2: Allocate the minimum necessary charging power and calculate the state-of-charge requirement schedule consistency rate;

[0076] S4.3: Secondary allocation of water injection power based on water level optimization;

[0077] S4.4: Single vehicle execution status update and power deviation feedback.

[0078] Specifically, S4.1 calculates the cluster's water level.

[0079] The optimization objective is to minimize the deviation between the total actual power allocated to all participating vehicles and the cluster scheduling instructions. The optimal water level of the cluster in the current time period is determined by this optimization, specifically including:

[0080] In charging mode, the charging water level is obtained through optimization:

[0081] ;

[0082] In discharge mode, the discharge water level is obtained by optimization:

[0083] ;

[0084] In the formula, and These are the charging water level and the discharging water level, respectively. and These are respectively the cluster charging scheduling command and the discharging scheduling command; and These are the final actual charging power and discharging power allocated to each vehicle; For the set of electric vehicles participating in the response.

[0085] S4.2 Calculate the change in power distribution for a single vehicle;

[0086] The deviation between the vehicle's state of charge demand progress consistency rate index and the charging / discharging level is compared, and combined with the vehicle's maximum feasible charging or discharging power, the power change allocated to the vehicle is determined, specifically including:

[0087] In charging mode, the change in charging power allocated to a single vehicle is calculated using the following formula:

[0088] ;

[0089] In discharge mode, the change in the discharge power allocated to a single vehicle is calculated using the following formula:

[0090] ;

[0091] In the formula, and These represent the changes in charging and discharging power allocated to a single vehicle, respectively. and These represent the maximum feasible charging and discharging power of a single vehicle, respectively. and These represent the cluster charging level and discharging level, respectively. This indicator represents the consistency rate of demand progress for a single vehicle's state of charge. and These represent the vehicle's highest and lowest acceptable battery level for the current time period, respectively. and These represent the charging and discharging efficiencies, respectively. Indicates the scheduling time interval; This represents the operator for taking non-negative values.

[0092] In discharge mode, for vehicles that have been allocated the minimum necessary charging power, their base charging power is maintained and they do not participate in the discharge power allocation.

[0093] During the water injection power allocation process, if the allocated power of a certain vehicle reaches its maximum feasible charging and discharging power or energy boundary, the charging and discharging power of that vehicle is fixed, and the remaining unallocated power of the cluster continues to be allocated to the other unrestricted vehicles; when the cluster scheduling instruction is completely decomposed, or all participating vehicles reach the constraint boundary, the allocation stops.

[0094] S4.3 calculates the final actual power of a single vehicle;

[0095] The final allocated power is calculated by algebraically superimposing the minimum necessary charging power of a single vehicle with the change in the secondary allocation power of the single vehicle, specifically including:

[0096] In charging mode, the final actual allocated charging power is calculated using the following formula:

[0097] ;

[0098] In discharge mode, the final actual distributed discharge power is calculated using the following formula:

[0099] ;

[0100] In the formula, and These represent the final actual charging power and discharging power allocated to a single vehicle, respectively. Indicates the minimum required charging power; and These represent the changes in charging and discharging power of a single vehicle under secondary distribution, respectively.

[0101] The core of this step lies in establishing a cluster water-filling power allocation mechanism that balances computational efficiency and distribution fairness. Using precise multidimensional convex optimization algorithms to solve for a massive number of vehicles can lead to the curse of dimensionality, resulting in high computational complexity and an inability to meet real-time scheduling requirements. Simple average or proportional allocation, on the other hand, ignores the differences in vehicle states, leading to unfair allocation. Therefore, this invention simplifies multidimensional optimization to a comparison of the consistency rate of individual vehicles and the deviation of water levels by solving for one-dimensional cluster charging and discharging water levels. Under the premise of strictly ensuring hard constraints on individual vehicle charging and discharging power and energy, it achieves rapid and fair allocation of cluster commands at the level of massive numbers of vehicles.

[0102] In the above technical solution, the S5 single-vehicle execution status update and power deviation feedback specifically include:

[0103] Based on the cluster scheduling instructions and the sum of the final actual power allocated to all participating vehicles, the charging power deviation and discharging power deviation of the cluster in the current time period are calculated. This is used to perform deviation feedback for cluster power decomposition. The specific calculation formula is as follows:

[0104] ;

[0105] ;

[0106] In the formula, and These represent the charging power deviation and discharging power deviation of the cluster during the current time period, respectively. and These represent the cluster charging scheduling command and the discharging scheduling command, respectively. and These represent the final actual charging power and discharging power allocated to a single vehicle, respectively. For the set of electric vehicles participating in the response.

[0107] Based on the final actual power allocated to the bicycle, the bicycle's battery status for the next time period is updated according to the following state transition formula. :

[0108] ;

[0109] In the formula, This indicates the bicycle's battery status for the next time period; This indicates the current battery level. and These represent the final actual charging and discharging power allocated to a single vehicle, respectively. and These represent the charging and discharging efficiencies, respectively. Indicates the scheduling time interval.

[0110] An electronic device includes one or more processors for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the aforementioned electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method.

[0111] A storage medium storing a computer program, wherein the computer program is configured to execute the aforementioned electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method at runtime.

[0112] The core of this step lies in implementing the update of individual vehicle battery status and feedback of cluster power deviation. Because individual vehicles are subject to physical constraints during actual execution, deviations can easily occur between the actual allocated power and the overall cluster command. Therefore, this invention provides deviation feedback by calculating the cluster power deviation in real time and uses state transition equations to update the individual vehicle's battery status for the next time period, ensuring the algorithm's control loop remains open under changes in individual vehicle status.

[0113] By adopting the above technical solution, the present invention can produce the following beneficial effects:

[0114] 1. Improved the fairness of decomposing cluster scheduling instructions to the individual vehicle level.

[0115] This invention calculates the consistency rate of the state of charge demand of individual electric vehicles, standardizes the current power of heterogeneous vehicles within a dynamically acceptable power range, and accurately measures the urgency of charging and the discharge capacity of different vehicles. When allocating power, vehicles with low consistency rates are given priority to receive charging power, while vehicles with high consistency rates are given priority to receive discharging power. This effectively avoids the charging unfairness caused by traditional proportional allocation and improves the user experience in participating in the scheduling.

[0116] 2. It ensures the physical feasibility and travel safety of the goal of off-grid charging for bicycles.

[0117] Before the secondary power allocation, this invention calculates the battery margin of each vehicle for the next time period and determines the minimum necessary charging power for the current time period. This power is used as a safety margin for mandatory allocation, which effectively avoids the risk that virtual power plants will over-delay the charging task in pursuit of economy, resulting in vehicles not being fully charged when they leave the grid, and effectively protects the basic travel needs of car owners.

[0118] 3. Significantly improved the computational efficiency of the power allocation algorithm to adapt to real-time scheduling.

[0119] This invention proposes a water injection power allocation algorithm based on water level optimization. With the goal of minimizing the total allocated power and the deviation from the cluster command, it simplifies the high-dimensional centralized convex optimization solution process under large-scale vehicles into a fast optimization and deviation comparison of only one-dimensional scalar, namely the charging and discharging water levels. This effectively avoids the "curse of dimensionality" and greatly shortens the calculation time of command decomposition, which can effectively adapt to the control requirements of virtual power plants in the real-time spot market. Attached Figure Description

[0120] Figure 1 This invention relates to a flowchart of the overall process for decomposing the power of a single vehicle based on the state-of-charge consistency rate water injection method.

[0121] Figure 2 This is a schematic diagram of the single-vehicle power decomposition method based on the state-of-charge consistency rate water injection method involved in the present invention;

[0122] Figure 3 This is a comparative analysis chart showing the results of different single-vehicle power decomposition methods in the embodiments of the present invention (wherein, Figure 3 (a) in the figure is a comparison chart of cluster power command tracing results. Figure 3 (b) in the figure is the trajectory of the change in the state of charge of a single vehicle under the average decomposition method. Figure 3 (c) in the figure is a trajectory diagram of the change in the state of charge of a single vehicle under the method of the present invention. Figure 3 (d) in the figure is the power distribution result of a single vehicle under the method of the present invention. Detailed Implementation

[0123] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0124] See attached document Figure 1-3 The present invention provides a method for power decomposition of electric vehicle clusters based on the state-of-charge consistency rate water injection method:

[0125] S1: Obtain the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles;

[0126] S2: Determine the cluster response direction and construct the electric vehicle battery power boundary;

[0127] S3: Allocate the minimum necessary charging power based on S1 and calculate the state of charge requirement schedule consistency rate;

[0128] S4: Secondary allocation of water injection power based on water level optimization;

[0129] S5: Individual electric vehicle (hereinafter referred to as individual vehicle) performs status update and power deviation feedback.

[0130] In the above technical solution, S1 obtains the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles, including:

[0131] S1.1 Obtain cluster scheduling instructions;

[0132] S1.2 Obtain the state parameters of the electric vehicle;

[0133] Specifically, S1.1 obtains cluster scheduling instructions;

[0134] Before the start of the current scheduling cycle, obtain the electric vehicle cluster charging scheduling instructions and cluster discharging scheduling instructions issued by the virtual power plant's upper-level scheduling system. The cluster charging scheduling instruction sequence and cluster discharging scheduling instruction sequence within the current scheduling cycle are represented as follows:

[0135] ;

[0136] ;

[0137] In the formula: and Representing the scene respectively The charging and discharging scheduling instructions received by the c-th electric vehicle cluster in time period t; c represents the electric vehicle cluster number; t represents the current scheduling time period; K represents the total number of scheduling time periods within a scheduling cycle; and These represent the c-th electric vehicle cluster in the scenario. The received cluster charging scheduler sequence and cluster discharging scheduler sequence.

[0138] In any given scheduling period, the cluster charging scheduling instruction and the cluster discharging scheduling instruction satisfy the following:

[0139] ;

[0140] ;

[0141] The above constraints mean that both the cluster charging scheduling command and the discharging scheduling command are non-negative values, and neither can be greater than zero at the same time, so as to ensure that the electric vehicle cluster only executes charging response or discharging response in any scheduling period; when both are zero, the cluster does not execute charging and discharging regulation in the current period.

[0142] S1.2 Obtain the state parameters of the electric vehicle;

[0143] Obtain the state parameters of each participating vehicle within the electric vehicle cluster. For the i-th electric vehicle, its vehicle state parameter vector... Represented as:

[0144] ;

[0145] In the formula: and These refer to the vehicle's registration and departure times, respectively. Duration of vehicle stay; This refers to the initial state of charge of the vehicle upon grid connection. To meet users' expected off-site power requirements; Allowing users to charge beyond their desired capacity; and These are the minimum and maximum allowable states of charge of the battery, respectively. This refers to the vehicle's battery capacity. and These are the maximum charging power and the maximum discharging power, respectively. and These are the charging efficiency and the discharging efficiency, respectively.

[0146] Furthermore, S2 includes:

[0147] S2.1 Determine the direction of the cluster response:

[0148] During the current time period, the virtual power plant determines its response direction based on the received cluster overall dispatch instructions. When the cluster overall charging dispatch instructions are greater than zero, the cluster executes a charging response; when the cluster discharging dispatch instructions are greater than zero, the cluster executes a discharging response.

[0149] S2.2 Calculate the minimum and maximum acceptable battery power for a single vehicle:

[0150] For vehicles leaving the network within the current scheduling cycle, the expected off-site power demand is used as the cutoff power demand for the current cycle. For vehicles staying across scheduling cycles, the minimum power demand at the end of the current cycle is calculated by working backward from the remaining dwell time in the next scheduling cycle, thus determining the cutoff power demand at the end of the current cycle. Based on the obtained cutoff power demand and the vehicle's remaining dwell time, the dynamic minimum acceptable power demand for the vehicle in the current time period is calculated. Simultaneously, based on the vehicle's maximum state of charge, battery capacity, and the additional charging space allowed by the user, the maximum acceptable power demand for a single vehicle in the current time period is calculated.

[0151] S2.3 Calculation of single-vehicle charging and discharging power limits:

[0152] Based on the vehicle's online status, maximum charging and discharging power limits, and combined with the highest acceptable power, lowest acceptable power, and current power level for the current period, the maximum feasible charging power and maximum feasible discharging power of the vehicle for the current period are calculated.

[0153] S2.4 Calculate the minimum required charging power for a single vehicle:

[0154] Based on the deviation between the vehicle's dynamic minimum acceptable battery level for the next time period and its current battery level, the minimum necessary charging power that the vehicle must perform in the current time period is calculated. When the vehicle's current battery level cannot meet the dynamic minimum acceptable battery level requirement for the next time period, the charging power gap is calculated and converted into the minimum necessary charging power required in the current time period, taking into account charging efficiency and maximum charging power limits. When the vehicle's current battery level already meets the dynamic minimum acceptable battery level requirement for the next time period, the minimum necessary charging power is determined to be zero.

[0155] Furthermore, S3 includes:

[0156] The calculated minimum necessary charging power is used as the base power and allocated preferentially to the corresponding vehicles, and the current battery status of each vehicle is updated accordingly. The updated equivalent battery status is used to characterize the new battery benchmark of the vehicle after the minimum travel charging needs have been forcibly met.

[0157] Based on the equivalent state of charge of a single vehicle, its relative position within the dynamic minimum acceptable charge range and the maximum acceptable charge range is calculated, i.e., the state of charge demand consistency rate. This rate objectively measures the charging urgency and discharge adjustment potential of different online vehicles. The smaller the consistency rate, the closer the vehicle's charge is to the dynamic minimum acceptable charge, and the stronger the urgency of charging; the larger the value, the more sufficient the vehicle's charge and the greater the discharge adjustment space.

[0158] Furthermore, S4 includes:

[0159] S4.1 Determine the cluster water level:

[0160] The optimization objective is to minimize the deviation between the total power allocated to all participating responding vehicles and the overall cluster scheduling command issued by the upper layer. Within the numerical range of 0 to 1, the unified charging and discharging control level of the cluster in the current time period is optimized and solved.

[0161] S4.2 Calculate the change in power distribution per vehicle:

[0162] The consistency rate of the charging status requirement of a single vehicle is compared with the unified control level of the cluster. In charging mode, the charging power change is allocated to the single vehicle with the lower consistency rate first. In discharging mode, the discharging power change is allocated to the single vehicle with the higher consistency rate first. For single vehicles that have been allocated the minimum necessary charging power, their base charging power is maintained and they do not participate in the discharging allocation.

[0163] During the water injection power allocation process, if the allocated power of a certain vehicle reaches its maximum feasible charging and discharging power or energy boundary, the charging and discharging power of that vehicle is fixed, and the remaining unallocated power of the cluster continues to be allocated to the other unrestricted vehicles; when the cluster scheduling instruction is completely decomposed, or all participating vehicles reach the constraint boundary, the allocation stops.

[0164] S4.3 Calculate the final actual power of a single vehicle:

[0165] The calculated minimum necessary charging power is superimposed with the power change obtained from water injection allocation to finally obtain the net power actually performed by the vehicle. In charging mode, the minimum necessary charging power and the charging power increment are added in the same direction; in discharging mode, since the minimum necessary charging power and the discharge adjustment are in opposite directions, the final actual net discharge power is determined by the difference between the discharge power change and the minimum necessary charging power.

[0166] Furthermore, S5 includes:

[0167] Based on the cluster scheduling instructions and the sum of the final actual allocated power of all participating vehicles, the charging power deviation and discharging power deviation of the cluster in the current time period are calculated and fed back to the upper-level system as deviation feedback signals, so as to realize closed-loop tracking and deviation settlement of physical execution deviations.

[0168] Based on the net power actually generated by the vehicle in the current time period and the corresponding charging and discharging efficiency, the increase or decrease in power consumption during the current time period is calculated, and then the power status of the vehicle in the next time period is updated to provide a power benchmark for power allocation in the next time period, ensuring the control closed loop of the algorithm under changes in vehicle status.

[0169] Example:

[0170] Cluster power decomposition process based on a virtual power plant scenario for electric vehicles in a certain province;

[0171] This embodiment takes electric vehicles aggregated in a virtual power plant of electric vehicle type in a certain province as the research object. The scheduling cycle is set to 24 hours, the scheduling time interval is 15 minutes, and each scheduling cycle includes 96 scheduling time periods. In this embodiment, four online response electric vehicles (denoted as EV1, EV2, EV3, and EV4) in the same cluster within the evening peak adjustment window (18:00-24:00) are selected as the specific analysis objects to demonstrate the specific execution process and effect of the algorithm of the present invention at the single vehicle level.

[0172] The power decomposition of the electric vehicle cluster is completed through cluster scheduling and individual vehicle data acquisition, cluster response direction judgment and individual vehicle power limit calculation, minimum necessary charging power allocation and state of charge demand progress consistency rate calculation, secondary allocation of water injection power based on water level optimization, deviation closed-loop feedback and state update.

[0173] 1. Electric vehicle cluster scheduling and individual vehicle data acquisition

[0174] Before the start of the current scheduling period, the system acquires the cluster charging and discharging scheduling instructions issued by the virtual power plant, and obtains the individual vehicle status parameters of the participating electric vehicles. These individual vehicle status parameters include the vehicle's grid entry time, grid exit time, online status, current battery level, expected off-grid battery level, battery capacity, maximum charging power, maximum discharging power, charging efficiency, and discharging efficiency.

[0175] 2. Cluster response direction determination and single-vehicle power limit calculation

[0176] During the current scheduling period, the virtual power plant determines its response direction based on the received cluster overall scheduling command. When the cluster overall charging scheduling command is greater than zero, the cluster executes a charging response; when the cluster discharging scheduling command is greater than zero, the cluster executes a discharging response.

[0177] Simultaneously, based on the online status of each vehicle, its maximum charging and discharging power limits, and the current highest and lowest acceptable battery levels, the maximum feasible charging power and maximum feasible discharging power for each vehicle in the current time period are calculated. Furthermore, focusing on EV3 and EV4 vehicles that joined the network later and have lower battery levels, the minimum necessary charging power that each vehicle must perform in the current time period is calculated based on the dynamic minimum acceptable battery level requirements for the next time period.

[0178] 3. Calculation of the consistency rate between minimum necessary charging power allocation and state-of-charge demand schedule

[0179] The calculated minimum necessary charging power is used as the base power and allocated preferentially to the corresponding vehicle, and the current battery status of the vehicle is updated accordingly.

[0180] Based on the equivalent state of charge of each vehicle, its relative position within the dynamic minimum and maximum acceptable charge ranges is calculated, i.e., the state of charge demand consistency rate. EV3 and EV4, which have urgent charging needs, show lower consistency rates, indicating their pressing charging requirements. EV1, with ample charge, shows higher consistency rates, indicating its greater discharge margin.

[0181] 4. Secondary allocation of water injection power based on water level optimization

[0182] The optimization objective is to minimize the deviation between the total power allocated to all participating responding vehicles and the overall cluster scheduling command issued by the upper layer. Within the numerical range of 0 to 1, the unified charging and discharging control level of the cluster in the current time period is optimized and solved.

[0183] By comparing the consistency rate of individual vehicle state-of-charge demand progress with the unified control level of the cluster, in charging mode, EV3 and EV4 with lower consistency rates are given priority to receive additional charging power increments for rapid energy replenishment. In discharging mode, EV1 with higher consistency rates takes on the main discharge power increments, while EV3 and EV4, which have already been allocated the minimum necessary charging power, maintain their base charging power and do not participate in discharge allocation.

[0184] Finally, the minimum necessary charging power allocated to a single vehicle is added together with the power change obtained from water injection to calculate the actual net power actually performed by the vehicle.

[0185] 5. Deviation closed-loop feedback and status update

[0186] Based on the overall scheduling instructions of the cluster and the sum of the final actual power allocated to all participating vehicles, the charging power deviation and discharging power deviation of the cluster in the current time period are calculated and fed back to the upper-level system as deviation feedback signals to achieve closed-loop tracking and deviation settlement of physical execution deviations.

[0187] Meanwhile, based on the actual net power and corresponding charging and discharging efficiency of each vehicle in the current period, the state transition equation is used to update the power status of EV1 to EV4 in the next period, providing an energy benchmark for power allocation in the next period and ensuring the control closed loop of the algorithm under vehicle state changes.

[0188] To demonstrate the effectiveness and superiority of the electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method proposed in this invention, numerical examples can be conducted based on actual data, and combined with the appendix... Figure 1 —3. Analyze the results.

[0189] Figure 1 This is a flowchart illustrating the overall process of the electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method, which is the subject of this invention. Figure 1As can be seen, this invention first obtains basic data such as cluster charging and discharging scheduling instructions, vehicle entry and exit times, expected departure charge, charging and discharging power, and charging and discharging efficiency; then, it determines the charging or discharging response direction based on the cluster scheduling instructions and calculates the dynamic minimum and maximum acceptable charge of a single vehicle; based on this, it calculates the maximum feasible charging and discharging power by combining the upper and lower limits of the vehicle's charge, and determines the minimum necessary charging power according to the dynamic minimum charge requirement for the next time period; then, it prioritizes the allocation of the minimum necessary charging power as the base power, updates the equivalent charge state of a single vehicle, and calculates the consistency rate of the charge state requirement progress; further, with the goal of minimizing the deviation between the cluster scheduling instructions and the total power allocated to a single vehicle, it optimizes and determines the unified charging and discharging control level, and completes the water injection power allocation based on the deviation between the consistency rate of the charge state requirement progress and the level; finally, it obtains the final actual charging and discharging power of a single vehicle, updates the charge state for the next time period, calculates the cluster charging and discharging power deviation, and forms the cluster power decomposition feedback result. Each step has a clear input-output relationship, forming a complete process from data acquisition, boundary construction, power limit calculation, base power allocation, water level optimization to execution feedback, realizing the accurate, feasible and fair decomposition of cluster scheduling instructions to the single vehicle level.

[0190] Figure 2 This is a schematic diagram of the single-vehicle power decomposition method based on the state-of-charge consistency rate water injection method involved in this invention. Figure 2 As can be seen, this invention first calculates the consistency rate of the charging state demand progress of individual vehicles and sorts them from low to high, prioritizing vehicles with lower consistency rates and stronger urgency for charging. Secondly, when some vehicles are constrained by power limits or reach their target charging state and generate unabsorbed power, the algorithm sequentially transfers the remaining unallocated power to subsequent vehicles, ensuring that the overall cluster command is completely decomposed. Furthermore, by setting a minimum guaranteed charging state as a hard constraint, priority is given to allocating guaranteed charging power to vehicles with low battery levels, ensuring basic battery capacity for individual vehicle travel.

[0191] Figure 3 A comparative analysis chart showing the results of different single-vehicle power decomposition methods; from Figure 3 As can be seen in (a) of the present invention, the actual execution power of the method can track the cluster net power command relatively well; in contrast, the traditional power averaging method has a large power execution deviation. Figure 3 (b) and Figure 3 A comparison of the state of charge change curves in (c) shows that, within the control window of 18:00–24:00, the state of charge of the water injection decomposition method of the present invention ( Figure 3(b) can differentiate the charging progress based on the current demand progress of the vehicles and the dynamic off-grid charge boundary. During charging, it prioritizes compensating vehicles with low progress, and during discharging, it prioritizes calling up vehicles with high progress, so that EV1 to EV4 can all reach the desired off-grid state of charge at the time of leaving the grid. In contrast, the traditional average decomposition method ( Figure 3 (c) In the discharge phase, the lack of active protection for vehicles with low charge states makes it easier for EV2, with low charge, to approach the dynamic minimum charge boundary after discharge. During the charging phase, the average decomposition method fails to adequately differentiate the remaining dwell time and charge gap of different vehicles, resulting in a relatively delayed recharging process for vehicles with low charge states that arrive later. Therefore, EV3 and EV4 ultimately fail to reach their desired charge state, indicating insufficient scheduling fairness. Figure 3 As can be seen in (d), during the initial stage of charging from 19:30 to 21:00, EV3 and EV4, which urgently needed to replenish their energy, achieved higher net charging power, demonstrating the targeted nature of the water injection allocation.

[0192] To quantitatively compare the scheduling fairness of different decomposition methods, based on fixed virtual power plant cluster charging and discharging commands, the power decomposition method results are evaluated from two dimensions: the balance of demand schedule under state of charge and the fairness of the distribution of control transfer burden. The calculation results are shown in Table 1.

[0193] Table 1. Comparison of scheduling fairness indicators for different single-vehicle power decomposition methods

[0194] Jain Index of Demand Progress Based on State of Charge 0.9208 0.9577 Standard deviation of state of charge demand schedule 0.1767 0.1216 The progress of state of charge demand is extremely poor. 0.7810 0.5761 The burden of control transfer is the Gini coefficient. 0.4151 0.3592

[0195] As shown in Table 1, compared with the traditional average decomposition method, the Jain fairness index of the state-of-charge demand progress of the present invention, based on the state-of-charge consistency rate water-filling decomposition method, increased from 0.9208 to 0.9577. The standard deviation and range of the state-of-charge demand progress decreased by 31.18% and 26.24%, respectively, indicating that the overall balance of vehicle demand progress has improved, and both the overall and extreme differences between vehicles have decreased. Simultaneously, the Gini coefficient for the burden of control transfer decreased from 0.4151 to 0.3592, a decrease of 13.46%, indicating that the degree to which the burden of charging delay and trajectory adjustment is concentrated on a few vehicles has been alleviated.

[0196] The above results demonstrate that, while ensuring the hard constraints of energy replenishment for individual vehicle trips and the power demand for off-grid use, the present invention can significantly reduce the computational complexity of power allocation by optimizing the water level, effectively avoiding the "curse of dimensionality" of high-dimensional centralized solutions. At the same time, it takes into account both the accurate tracking of the overall cluster command and the fairness of regulation at the level of a large number of heterogeneous individual vehicles, providing a fast, fair, and physically feasible power allocation scheme for virtual power plants to issue commands, execute vehicle safety, and efficiently regulate vehicle-grid interaction in the real-time spot market.

Claims

1. A method for power decomposition of electric vehicle clusters based on the state-of-charge consistency rate water injection method, characterized in that, S1 obtains the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles; S2 determines the cluster response direction and constructs the electric vehicle battery power boundary; S3 allocation is based on the minimum necessary charging power obtained from S1, and the state of charge requirement schedule consistency rate is calculated. S4 performs secondary allocation of water injection power based on water level optimization; S5 single electric vehicle performs status updates and power deviation feedback; The secondary allocation of water injection power based on water level optimization in S4 includes: S4.1: Determine the direction of the cluster response and calculate the charging and discharging power limit and minimum necessary charging power for a single vehicle; S4.2: Allocate the minimum necessary charging power and calculate the state-of-charge requirement schedule consistency rate; S4.3: Secondary allocation of water injection power based on water level optimization; S4.4: Single vehicle execution status update and power deviation feedback; S4.1 calculates the cluster water level as follows; The optimization objective is to minimize the deviation between the total actual power allocated to all participating vehicles and the cluster scheduling instructions. The optimal water level of the cluster in the current time period is determined, including: In charging mode, the charging water level is obtained through optimization: ； In discharge mode, the discharge water level is obtained by optimization: ； In the formula, and These are the charging water level and the discharging water level, respectively. and These are cluster charging and discharging scheduling commands, respectively. and These represent the final actual charging and discharging power allocated to each vehicle; For the set of electric vehicles participating in the response; S4.2 calculates the change in power distribution per vehicle as follows: The deviation between the vehicle's state-of-charge demand schedule consistency rate and the charging / discharging level is compared, and combined with the vehicle's maximum feasible charging or discharging power, the power change allocated to the vehicle is determined, including: In charging mode, the change in charging power allocated to a single vehicle is calculated using the following formula: ； In discharge mode, the change in the discharge power allocated to a single vehicle is calculated using the following formula: ； In the formula, and These represent the changes in charging and discharging power allocated to a single vehicle, respectively. and These represent the maximum feasible charging and discharging power of a single vehicle, respectively. and These represent the cluster charging level and discharging level, respectively. This indicator represents the consistency rate of demand progress for a single vehicle's state of charge. and These represent the vehicle's highest and lowest acceptable battery level for the current time period, respectively. and These represent the charging and discharging efficiencies, respectively. Indicates the scheduling time interval; This represents the operator for taking non-negative values; In discharge mode, for a single vehicle that has been allocated the minimum necessary charging power, its base charging power is maintained and it does not participate in the discharge power allocation. During the water injection power allocation process, if the allocated power of a certain vehicle reaches its maximum feasible charging and discharging power or energy boundary, the charging and discharging power of that vehicle is fixed, and the remaining unallocated power of the cluster continues to be allocated to the other unrestricted vehicles; when the cluster scheduling instruction is completely decomposed, or all participating vehicles reach the constraint boundary, the allocation stops.

2. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 1, characterized in that, S1 obtains the electric vehicle cluster scheduling instructions and the status parameters of the electric vehicles, including: S1.1 Obtain cluster scheduling instructions; Before the start of the current scheduling cycle, obtain the electric vehicle cluster charging scheduling instructions and cluster discharging scheduling instructions issued by the virtual power plant upper-level scheduling system; S1.2 Obtain the state parameters of the electric vehicle; Obtain the status parameters of each participating vehicle in the electric vehicle cluster.

3. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 1, characterized in that, S2 determines the cluster response direction and constructs power boundaries, including: S2.1 Determine the direction of the cluster response; The electric vehicle's battery capacity boundary is constructed as follows: S2.2 Calculate the minimum and maximum acceptable battery power for a single vehicle. S2.3 Calculate the charging and discharging power limit for a single vehicle; S2.4 Calculate the minimum required charging power for a single vehicle.

4. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 3, characterized in that, S2.2 Calculate the minimum and maximum acceptable battery power for a single vehicle; For vehicles that leave the network within the current scheduling cycle, the expected off-site power demand is taken as the power demand for the current cycle; for vehicles that remain stationed across scheduling cycles, the minimum power demand at the end of the current cycle is calculated by working backward from the remaining stay time in the next scheduling cycle. ； In the formula: This represents the cutoff power demand of the i-th electric vehicle during the current scheduling cycle; This indicates the expected off-site power demand; Indicates the minimum permissible state of charge of the battery; Indicates battery capacity; Indicates charging efficiency; Indicates the maximum charging power; Indicates the extended off-network time; K represents the total number of scheduling periods; Indicates the duration of a single scheduling period; Based on the cutoff demand for electricity and the vehicle's remaining dwell time, calculate the vehicle's dynamic minimum acceptable electricity level for time period t: ； In the formula: This represents the minimum acceptable energy level of the i-th electric vehicle during time period t; This indicates the cut-off electricity demand within the current scheduling cycle; Indicates charging efficiency; Indicates the maximum charging power; This represents the equivalent off-network time within the current scheduling cycle; Indicates the duration of a single scheduling period; The maximum acceptable battery level for a vehicle during time period t is: ； In the formula: This represents the maximum acceptable battery level of the i-th electric vehicle during time period t; Indicates the maximum permissible state of charge of the battery; Indicates battery capacity; Indicates the expected off-site power consumption; This indicates that the user is allowed additional charging beyond the desired off-site charge level.

5. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 3, characterized in that, S2.3 calculates the single-vehicle charging and discharging power limit as follows: Based on the vehicle's online status, maximum charging and discharging power limits, and considering the highest acceptable battery level, lowest acceptable battery level, and current battery level for the current period, the maximum feasible charging power and maximum feasible discharging power of the vehicle for the current period are calculated: ； ； In the formula, and These represent the maximum feasible charging power and the maximum feasible discharging power of a single vehicle during the current time period, respectively. and These represent the vehicle's highest and lowest acceptable battery levels for the current time period; , These refer to the vehicle's charging efficiency and discharging efficiency, respectively. , These are the vehicle's maximum charging power and maximum discharging power, respectively. The electric vehicle is in online status; This represents the current battery level. The scheduling time interval; This indicates that the non-negative value is taken, meaning that if the value inside the parentheses is greater than zero, the original value is taken, and if it is less than or equal to zero, zero is taken; i represents the individual electric vehicle serial number; t represents the current scheduling time period serial number; Indicates the scene number.

6. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 3, characterized in that, S2.4 calculates the minimum required charging power for a single vehicle; Based on the deviation between the vehicle's dynamic minimum acceptable battery level for the next time period and its current battery level, and in conjunction with the vehicle's maximum charging power limit and charging efficiency, the minimum necessary charging power that must be enforced in the current time period to ensure basic transportation needs is determined as follows: ； In the formula, This indicates the minimum necessary charging power for a single vehicle during the current time period; Indicates the vehicle's maximum charging power limit; This indicates the minimum acceptable battery level for the bicycle in the next time period; This indicates the battery level of the bicycle in the current time period; Indicates charging efficiency; Indicates the duration of a single scheduling period; This represents the operator for taking non-negative values.

7. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 1, characterized in that, The S3 method, which allocates the minimum necessary charging power and calculates the state-of-charge requirement schedule consistency rate, includes: The minimum necessary charging power is used as the base charging power for allocation, and the equivalent energy state of a vehicle after the base power is allocated is calculated based on the current energy of the vehicle, the minimum necessary charging power, the charging efficiency, and the scheduling time interval. ； In the formula, The equivalent electrical state after allocating base power to a single vehicle; This indicates the current battery level of the bicycle. Indicates charging efficiency; Indicates the minimum required charging power; Indicates the scheduling time interval; Based on the equivalent power status, the dynamic minimum acceptable power, and the maximum acceptable power, the consistency rate of the charging status demand progress of each vehicle in the current time period is calculated. ； In the formula, This indicates the consistency rate of the charging status and demand progress of each bicycle in the current time period. Indicates the equivalent electrical quantity status; Indicates the dynamic minimum acceptable power level; Indicates the maximum acceptable battery level; To prevent extremely small positive numbers with a denominator of zero.

8. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 1, characterized in that, S4.3 calculates the final actual power of a single vehicle as follows: The final allocated power is calculated by algebraically superimposing the minimum necessary charging power of a single vehicle with the change in the secondary allocation power of the single vehicle, including: In charging mode, the final actual allocated charging power is calculated as follows: ； In discharge mode, the final actual distributed discharge power is calculated as follows: ； In the formula, and These represent the final actual charging power and discharging power allocated to a single vehicle, respectively. Indicates the minimum required charging power; and These represent the changes in charging and discharging power of a single vehicle under secondary distribution, respectively.

9. The electric vehicle cluster power decomposition method based on the state-of-charge consistency rate water injection method according to claim 1, characterized in that, The S5 single-vehicle execution status update and power deviation feedback include: Based on the cluster scheduling instructions and the sum of the final actual power allocated to all participating vehicles, the charging power deviation and discharging power deviation of the cluster in the current time period are calculated to perform deviation feedback for cluster power decomposition. The calculation formula is as follows: ； ； In the formula, and These represent the charging power deviation and discharging power deviation of the cluster during the current time period, respectively. and These represent the cluster charging scheduling command and the discharging scheduling command, respectively. and These represent the final actual charging power and discharging power allocated to a single vehicle, respectively. For the set of electric vehicles participating in the response; Based on the final actual power allocated to the bicycle, the bicycle's power status for the next time period is updated according to the following state transition formula. : ； In the formula, This indicates the bicycle's battery status for the next time period; This indicates the current battery level. and These represent the final actual charging and discharging power allocated to a single vehicle, respectively. and These represent the charging and discharging efficiencies, respectively. Indicates the scheduling time interval.

10. An electronic device comprising one or more processors for storing one or more programs, characterized in that, When the one or more programs are executed by the one or more processors, the one or more processors execute the electric vehicle cluster power decomposition system based on the state-of-charge consistency rate water injection method of any one of the preceding claims 1-9.

11. A storage medium storing a computer program, characterized in that, in, The computer program is configured to execute, at runtime, the electric vehicle cluster power decomposition system based on the state-of-charge consistency rate water injection method of any one of claims 1-9.

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