A multi-level large-scale electric vehicle power control method and system

Abstract

The present invention discloses a multi-level large-scale electric vehicle power control method and system, the method includes constructing a rolling optimization control framework of double-layer power decomposition; clustering the charging piles of electric vehicles according to the type, control accuracy index and linear fitting index of each charging pile; constructing a real-time power deviation decomposition model with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function according to the control cost index and the control accuracy index; constructing the electric vehicle power control priority according to the current SOC and off-grid time of the electric vehicle in the charging pile cluster; determining the corresponding electric vehicle power adjustment amount according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority; and controlling according to the power adjustment amount of each electric vehicle. The present invention can realize the rapid and accurate control of the power of large-scale electric vehicles.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicles participating in power grid regulation, and in particular to a multi-level large-scale electric vehicle power control method and system. Background Art

[0002] With the reform of the national energy structure, the number of electric vehicles has continued to rise. The optimization control of large-scale electric vehicles is an important measure to promote the reform of the national energy structure. The National Energy Administration clearly pointed out that it is necessary to stimulate the response capabilities of third parties such as the demand side, comprehensively promote electric vehicle aggregators and others to participate in the ancillary service market, encourage the demand side to actively participate in system regulation, and reduce the peak-to-valley difference of system operation. At the same time, electric vehicle aggregators participate in the ancillary service market through the optimization control of electric vehicles, which can increase their profits under the condition of meeting the accuracy assessment requirements of the power market. Therefore, electric vehicle aggregators have the need to accurately control the power of large-scale electric vehicles. At present, some scholars have studied the power control of electric vehicles and proposed a power control model based on proportional or quadratic programming. However, the current control method does not meet the control needs. On the one hand, the existing electric vehicle power control method takes a long time to solve under the condition of large-scale electric vehicles, which does not meet the real-time demand of regulation; on the other hand, the existing method ignores the different charging needs and control priorities of multiple types of electric vehicle users, and only considers the constraints of the total power that electric vehicle aggregators need to provide.

[0003] Therefore, there is an urgent need to provide a multi-level large-scale electric vehicle power precision control method or system to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-level large-scale electric vehicle power control method and system, which can achieve rapid and accurate power control of large-scale electric vehicles.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A multi-level large-scale electric vehicle power control method, comprising:

[0007] The charging pile cluster is used as the first layer, and the charging piles built into the charging pile cluster are used as the second layer to build a rolling optimization control framework with double-layer power decomposition;

[0008] Clustering is performed based on the type, control accuracy index, and linear fitting index of each charging pile to obtain different types of charging pile clusters;

[0009] According to the control cost index and control accuracy index, a real-time power deviation decomposition model is constructed with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function;

[0010] According to the current SOC and off-grid time of electric vehicles in the charging pile cluster, the power regulation priority of electric vehicles is established;

[0011] Determine the power adjustment amount of the corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority;

[0012] Control is performed according to the power adjustment amount of each electric vehicle.

[0013] Optionally, clustering the charging piles in the historical control data of the charging piles in each charging pile cluster according to type, control accuracy index and linear fitting index to obtain different types of charging pile clusters specifically includes:

[0014] Using the formula Determine the control accuracy index;

[0015] Using the formula Determine linear fit indices;

[0016] Among them, K k,a is the control accuracy index of the electric vehicle charging pile, with 15 minutes as a control period, T as the total number of control time scales, and P kr,t is the response power of the period t, P kt,t is the target power in period t, R 2 is the linear fitting index, is the power of the response result fitted during period t, is the average value of the response result power.

[0017] Optionally, the real-time power deviation decomposition model is constructed based on the control cost index and the control accuracy index, with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function, and the above also includes:

[0018] Using formula K k,c =b0+b1·ΔP k Determine the control cost indicators;

[0019] Among them, K k,c is the control cost index of the k-type charging pile cluster, ΔP k To control the power, b0 and b1 are the parameters of the linear function.

[0020] Optionally, according to the control cost index and the control accuracy index, a real-time power deviation decomposition model is constructed with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function, which specifically includes the following formula:

[0021]

[0022] Among them, t is the current optimization period, τ is the future optimization period, and λ a is the weight coefficient of control accuracy, k is the charging pile cluster number, ΔP k (t) is the power adjustment amount of the charging pile cluster and is the decision variable; c is the weight coefficient for adjusting the cost index, H is the calculation period for rolling optimization, and are the upper and lower limits of the electric vehicle cluster power adjustment, P set (t) is the target power that the electric vehicle aggregator needs to track; P pre (t) is the total power when the electric vehicle aggregator does not perform power control, f(t) is the optimization objective function for the t period, is the objective function for optimizing the time period τ. The specific calculation formula is the same as f(t). k A collection of electric vehicle clusters that are subject to regulation.

[0023] Optionally, the electric vehicle power regulation priority is constructed according to the current SOC and off-grid time of the electric vehicle in the charging pile cluster, specifically including the following formula:

[0024]

[0025] Among them, i is the number of electric vehicles, ω i (t) is the power regulation priority of the ith electric vehicle, is the SOC priority coefficient of the i-th electric vehicle, is the power control priority time priority coefficient of the i-th electric vehicle, β1 and β2 are the weight coefficients of SOC and time priority respectively, S i (t) is the SOC of the i-th electric vehicle at time t, is the off-grid time of the ith electric vehicle.

[0026] Optionally, the power adjustment amount of the corresponding electric vehicle is determined according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority, specifically including the following formula:

[0027]

[0028] Among them, ΔP i (t) is the power adjustment of the i-th electric vehicle, is the optimization variable, P i u,max (t) and P i d,max(t) are the upper and lower limits of the power adjustment of the i-th electric vehicle, respectively; α1 and α2 are the control accuracy and control priority weight, respectively, and α1 is much larger than α2, f k (t) is the objective function of the decomposition of the power adjustment of the charging pile cluster during period t, is the objective function for decomposing the power adjustment of the charging pile cluster during the τ period. The specific calculation formula is the same as f k (t)Consistent.

[0029] A multi-level large-scale electric vehicle power control system, comprising:

[0030] A control framework construction module is used to construct a rolling optimization control framework with double-layer power decomposition, taking the charging pile cluster as the first layer and the charging piles built into the charging pile cluster as the second layer;

[0031] The charging pile cluster classification module is used to cluster each charging pile according to its type, control accuracy index and linear fitting index to obtain different types of charging pile clusters;

[0032] A real-time power deviation decomposition model building module is used to build a real-time power deviation decomposition model based on the control cost index and the control accuracy index, with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function;

[0033] The electric vehicle power control priority building module is used to build the electric vehicle power control priority according to the current SOC and off-grid time of the electric vehicles in the charging pile cluster;

[0034] The power adjustment amount determination module of the electric vehicle is used to determine the power adjustment amount of the corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority;

[0035] The tracking control module is used to perform control according to the power adjustment amount of each electric vehicle.

[0036] A multi-level large-scale electric vehicle power control system includes: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the multi-level large-scale electric vehicle power control method is implemented.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] A multi-level large-scale electric vehicle power control method and system provided by the present invention constructs a rolling optimization control framework of double-layer power decomposition; collects historical control data of charging piles, and clusters charging piles according to types, control accuracy indicators and linear fitting indicators; according to control cost indicators and control accuracy indicators, constructs a real-time power deviation decomposition model with the power adjustment amount of the charging pile cluster as a decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function, and decomposes the real-time deviation into different types of charging pile clusters based on the real-time power deviation decomposition model; for each electric vehicle on the network, a priority coefficient is constructed according to the electric vehicle power state and off-grid time; for each charging pile cluster, the priority coefficient is considered to optimize the power adjustment amount of each electric vehicle under the cluster, and the target power is accurately tracked. The method of the present invention has universality, can realize large-scale electric vehicle power control through multi-level control, shorten the control strategy generation time, meet the real-time demand of control, and has practical significance in the participation of electric vehicle clusters in power grid control. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic flow chart of a multi-level large-scale electric vehicle power control method provided by the present invention;

[0041] Figure 2 It is a schematic diagram of the rolling optimization control framework structure of double-layer power decomposition;

[0042] Figure 3 This is a schematic diagram of the structure of a multi-level large-scale electric vehicle power control system provided by the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments 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.

[0044] The purpose of the present invention is to provide a multi-level large-scale electric vehicle power control method and system, which performs cluster analysis on charging piles connected to electric vehicles based on the response characteristics of the charging piles, and controls them in clusters to reduce the solution time of the control strategy. The priority of power control is established by considering the off-grid time of electric vehicles and the charging demand to improve the rationality of power control.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] like Figure 1 As shown, the present invention provides a multi-level large-scale electric vehicle power control method, comprising:

[0047] S101, taking the charging pile cluster as the first layer and the charging piles built in the charging pile cluster as the second layer, construct a rolling optimization control framework with double-layer power decomposition, and Figure 2 As shown;

[0048] S102, clustering is performed according to the type, control accuracy index and linear fitting index of each charging pile to obtain charging pile clusters of different types;

[0049] The historical control data includes, but is not limited to, the power instruction issuing time, actual power (power before control), target power, and response result power.

[0050] The high-precision piles and low-precision piles are divided according to the control accuracy; the response result power is taken as the dependent variable, the actual power and the target power are taken as the independent variables, and the linear power characteristic model is constructed to calculate the linear model fitting degree, and the piles that can be linearly fitted and the piles that cannot be linearly fitted are divided accordingly. Finally, the control accuracy and power characteristics are comprehensively considered to obtain four types of charging pile clusters, namely high-precision linearly fitted piles, low-precision linearly fitted piles, high-precision non-linearly fitted piles, and low-precision non-linearly fitted piles.

[0051] Types of charging piles include ordered piles, public piles and bus piles.

[0052] S102 specifically includes:

[0053] Using the formula Determine the control accuracy index;

[0054] From the above formula, we can see that the response accuracy of the electric vehicle charging pile is affected by the target power and the response result power, and K k,a The closer the value is to 1, the higher the control accuracy is. Based on the response accuracy index, electric vehicle charging piles can be divided into two categories: K k,a ∈[0.9,1.1] is one type of electric vehicle charging pile, K k,aCharging piles with other values ​​are in another category.

[0055] In addition to considering the response accuracy, the response of the electric vehicle charging pile can also consider whether the response of the charging pile can be fitted by a linear function. The response result power, target power, and actual power are all included in the linear model, and the response result power is defined as the output and the other two as inputs.

[0056]

[0057] Where: P is the power of the response result fitted during the t period; kc,t is the power of the electric vehicle charging pile before responding; a0, a1 and a2 are the parameters of the linear function respectively.

[0058] Using the formula Determine linear fit indices;

[0059] Among them, K k,a is the control accuracy index of the electric vehicle charging pile, with 15 minutes as a control period, T as the total number of control time scales, and P kr,t is the response power of the period t, P kt,t is the target power in period t, R 2 is the linear fitting index, is the power of the response result fitted during period t, is the average power value of the response result.

[0060] Linear fit index R 2 The closer the value is to 1, the better the linear fit is. 2 Charging piles with a charge rate greater than 0.8 are grouped into one category, and the other charging piles are grouped into another category.

[0061] Finally, consider the type of EV charging station.

[0062] Based on the above three technical indicators, electric vehicle charging piles can be divided into 2*2*3=12 clusters, realizing the clustering of charging piles.

[0063] S103, based on the control cost index and the control accuracy index, construct a real-time power deviation decomposition model with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function;

[0064] Before S103, it also included:

[0065] Using formula K k,c =b0+b1·ΔP k Determine the control cost indicators;

[0066] Among them, K k,cis the control cost index of the k-type charging pile cluster, ΔP k To control the power, b0 and b1 are the parameters of the linear function.

[0067] The rolling optimization control method is used to perform the first-level power decomposition considering the control accuracy and control cost.

[0068] S103 specifically includes the following formula:

[0069]

[0070] Among them, t is the current optimization period, λ a is the weight coefficient of control accuracy, k is the charging pile cluster number, ΔP k (t) is the power adjustment amount of the charging pile cluster and is the decision variable; c is the weight coefficient for adjusting the cost index, H is the calculation period for rolling optimization, and are the upper and lower limits of the electric vehicle cluster power adjustment, P set (t) is the target power that the electric vehicle aggregator needs to track; P pre (t) is the total power when the electric vehicle aggregator does not perform power control, f(t) is the optimization objective function for the t period, is the objective function for optimizing the time period τ. The specific calculation formula is the same as f(t). k A collection of electric vehicle clusters that are subject to regulation.

[0071] Considering that different markets have different control accuracy requirements, the control accuracy coefficient is introduced to relax the second constraint in the above formula:

[0072]

[0073] Since the dimensions of control accuracy and control cost are different, in order to quantify the impact of different factors on the optimization target, the different items in the objective function are normalized based on formulas (5)-(6):

[0074]

[0075]

[0076]

[0077] In summary, the decomposition of the first-layer real-time deviation power can be expressed as the following optimization problem:

[0078]

[0079] S104, constructing a power control priority of the electric vehicle according to the current SOC and off-grid time of the electric vehicle in the charging pile cluster;

[0080] The priority coefficient is constructed according to the cluster to which the electric vehicles on the grid belong, the power status and the off-grid time; the smaller the priority coefficient is defined, the higher the charging priority is, the larger the absolute value of the power adjustment of the corresponding electric vehicle is, and the priority is allocated to adjust the power. The power status of the electric vehicle is represented by the state of charge (SOC), and the rule for constructing the priority coefficient is: when the cluster power adjustment is positive and the electric vehicle needs to charge more, more charging power increase is allocated to the electric vehicle with a small SOC, and the smaller its priority coefficient is; the closer the current time is to the off-grid time of the electric vehicle, the greater the randomness of the off-grid. In order to achieve precise power control, this type of electric vehicle will not be called first, and the larger its priority coefficient is, the lower the priority of regulation.

[0081] After electric vehicles are no longer in use, they are connected to a charging gun for charging. Due to the different travel needs of different electric vehicle users, the time when electric vehicles are connected to the grid for charging, the time when they are off the grid for use, and the expected power when off the grid are different. The regulation of electric vehicles needs to take the above factors into consideration.

[0082] S104 specifically includes the following formula:

[0083]

[0084] Among them, i is the number of the electric vehicle, ω i (t) is the power regulation priority of the ith electric vehicle, is the SOC priority coefficient of the i-th electric vehicle, is the power control priority time priority coefficient of the i-th electric vehicle, β1 and β2 are the weight coefficients of SOC and time priority respectively, S i (t) is the SOC of the i-th electric vehicle at time t, is the off-grid time of the ith electric vehicle.

[0085] The priority coefficient of electric vehicle regulation is composed of SOC priority coefficient and time priority coefficient. k When (t) < 0, the aggregator has a demand to reduce the charging power, so it will give priority to charging less electric vehicles with large SOC and allocate more charging power reduction; when ΔP kWhen (t)>0, the aggregator has a demand to increase the charging power, and gives priority to allocating more charging power increase to electric vehicles with small SOC. On the other hand, considering the time priority, the closer the current time is to the time when the electric vehicle is off the grid, the greater the uncertainty of the electric vehicle. In order to better achieve control, the priority of such electric vehicle control is reduced, and the larger the priority coefficient is. At the same time, formula (8) limits the maximum value of the priority coefficient. When the priority coefficient is greater than the set value, the difference in priority is negligible.

[0086] S105, determining a power adjustment amount of a corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority;

[0087] The second-level power decomposition is performed using a rolling optimization control method. For the charging piles in each cluster, the charging priority of each vehicle in the cluster is considered, and the power adjustment amount of the electric vehicle is optimized using a quadratic programming method to achieve target power tracking.

[0088] S105 specifically includes the following formula:

[0089]

[0090] Among them, ΔP i (t) is the power adjustment of the i-th electric vehicle, is the optimization variable, P i u,max (t) and P i d,max (t) are the upper and lower limits of the power adjustment of the i-th electric vehicle, respectively; α1 and α2 are the control accuracy and control priority weight, respectively, and α1 is much larger than α2, f k (t) is the objective function of the decomposition of the power adjustment of the charging pile cluster during period t, is the objective function for decomposing the power adjustment of the charging pile cluster during the τ period. The specific calculation formula is the same as f k (t)Consistent.

[0091] The optimization result includes not only the control instructions for the current period, but also the control instructions for the future. In actual execution, only the current control instructions are executed. This control method can improve the effect of the control strategy. The setting of the weight coefficient directly affects the control accuracy of the electric vehicle cluster. When α1 is much larger than α2, the control accuracy of the electric vehicle cluster is mainly considered, while also taking into account the priority of user regulation.

[0092] By solving the above optimization problem, the control strategy of each electric vehicle can be obtained, and the precise control of the power of large-scale electric vehicles can be achieved.

[0093] S106, performing control according to the power adjustment amount of each electric vehicle.

[0094] Under the condition that the power plan and spare capacity of traditional electric vehicles are known, the present invention takes into account the control accuracy, control time and user needs, and proposes a large-scale electric vehicle power precision control technology. Considering the charging characteristics of electric vehicle charging piles, a clustering method of electric vehicle charging piles is proposed to provide a basis for the control of electric vehicles in clusters. The first-level power decomposition model decomposes the deviation power by taking into account the control cost and control accuracy. In terms of user energy demand, the uncertainty of the electric energy demand and vehicle use time of electric vehicles is taken into account, and the priority coefficient of electric vehicle power control is proposed to support the power control of a single electric vehicle. In the second-level power decomposition model, the control accuracy and control priority are taken into account, and each cluster can solve the control power of a single electric vehicle in parallel, which greatly shortens the solution time of the control strategy. Based on the method of the present invention, large-scale electric vehicle power control can be realized quickly and accurately, which has very important application value for scientific research institutions and the industrial and commercial sectors to realize real-time power control technology of electric vehicles and use electric vehicles to participate in the power auxiliary service market.

[0095] like Figure 2 As shown, the present invention provides a multi-level large-scale electric vehicle power control system, comprising:

[0096] The control framework construction module 201 is used to construct a rolling optimization control framework with double-layer power decomposition, taking the charging pile cluster as the first layer and the charging piles built into the charging pile cluster as the second layer;

[0097] The charging pile cluster classification module 202 is used to cluster each charging pile according to its type, control accuracy index and linear fitting index to obtain different types of charging pile clusters;

[0098] A real-time power deviation decomposition model building module 203 is used to build a real-time power deviation decomposition model based on the control cost index and the control accuracy index, with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function;

[0099] The electric vehicle power regulation priority building module 204 is used to build the electric vehicle power regulation priority according to the current SOC and off-grid time of the electric vehicles in the charging pile cluster;

[0100] The electric vehicle power adjustment amount determination module 205 is used to determine the power adjustment amount of the corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority;

[0101] The tracking control module 206 is used to perform control according to the power adjustment amount of each electric vehicle.

[0102] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, the present invention also provides a multi-level large-scale electric vehicle power control system, including: at least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, the multi-level large-scale electric vehicle power control method is implemented.

[0103] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0104] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multi-level large-scale electric vehicle power control method, characterized in that: include: The charging pile cluster is used as the first layer, and the charging piles built into the charging pile cluster are used as the second layer to build a rolling optimization control framework with double-layer power decomposition; Clustering is performed based on the type, control accuracy index, and linear fitting index of each charging pile to obtain different types of charging pile clusters; According to the control cost index and control accuracy index, a real-time power deviation decomposition model is constructed with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function; According to the current SOC and off-grid time of electric vehicles in the charging pile cluster, the power regulation priority of electric vehicles is established; Determine the power adjustment amount of the corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority; Control is performed according to the power adjustment amount of each electric vehicle; The clustering is performed according to the type, control accuracy index and linear fitting index of each charging pile to obtain different types of charging pile clusters, specifically including: Using the formula Determine the control accuracy index; Using the formula Determine linear fit indices; Among them, K k,a is the control accuracy index of the electric vehicle charging pile, with 15 minutes as a control period, T as the total number of control time scales, and P kr,t is the response power of the period t, P kt,t is the target power in period t, R 2 is the linear fitting index, is the power of the response result fitted during period t, is the average value of the response result power.

2. A multi-level large-scale electric vehicle power control method according to claim 1, characterized in that: According to the control cost index and the control accuracy index, a real-time power deviation decomposition model is constructed with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function, which also includes: Using formula K k,c =b0+b1·ΔP k Determine the control cost indicators; Among them, K k,c is the control cost index of the k-type charging pile cluster, ΔP k To control the power, b0 and b1 are the parameters of the linear function.

3. A multi-level large-scale electric vehicle power control method according to claim 2, characterized in that: According to the control cost index and the control accuracy index, a real-time power deviation decomposition model is constructed with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function, which specifically includes the following formula: Among them, t is the current optimization period, τ is the future optimization period, and λ a is the weight coefficient of control accuracy, k is the charging pile cluster number, ΔP k (t) is the power adjustment amount of the charging pile cluster and is the decision variable; c is the weight coefficient for adjusting the cost index, H is the calculation period for rolling optimization, and are the upper and lower limits of the electric vehicle cluster power adjustment, P set (t) is the target power that the electric vehicle aggregator needs to track; P pre (t) is the total power when the electric vehicle aggregator does not perform power control, f(t) is the optimization objective function for the t period, is the objective function for optimizing the time period τ. The specific calculation formula is the same as f(t). k A collection of electric vehicle clusters that are subject to regulation.

4. A multi-level large-scale electric vehicle power control method according to claim 3, characterized in that: The electric vehicle power control priority is constructed according to the current SOC and off-grid time of the electric vehicle in the charging pile cluster, and specifically includes the following formula: Among them, i is the number of the electric vehicle, ω i (t) is the power regulation priority of the ith electric vehicle, is the SOC priority coefficient of the i-th electric vehicle, is the power control priority time priority coefficient of the i-th electric vehicle, β1 and β2 are the weight coefficients of SOC and time priority respectively, S i (t) is the SOC of the i-th electric vehicle at time t, is the off-grid time of the ith electric vehicle.

5. A multi-level large-scale electric vehicle power control method according to claim 4, characterized in that: The power adjustment amount of the corresponding electric vehicle is determined according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority, and specifically includes the following formula: Among them, ΔP i (t) is the power adjustment of the i-th electric vehicle, and is the optimization variable. and are the upper and lower limits of the power adjustment of the i-th electric vehicle respectively; α1 and α2 are the control accuracy and control priority weight respectively, and α1 is much larger than α2, f k (t) is the objective function of the decomposition of the power adjustment of the charging pile cluster during period t, is the objective function for decomposing the power adjustment of the charging pile cluster during the τ period. The specific calculation formula is the same as f k (t)Consistent.

6. A multi-level large-scale electric vehicle power control system, implementing a multi-level large-scale electric vehicle power control method as described in any one of claims 1-5, characterized in that: The multi-level large-scale electric vehicle power control system includes: A control framework construction module is used to construct a rolling optimization control framework with double-layer power decomposition, taking the charging pile cluster as the first layer and the charging piles built into the charging pile cluster as the second layer; The charging pile cluster classification module is used to cluster charging piles according to the charging pile type, control accuracy index and linear fitting index of each charging pile to obtain different types of charging pile clusters; A real-time power deviation decomposition model building module is used to build a real-time power deviation decomposition model based on the control cost index and the control accuracy index, with the power adjustment amount of the charging pile cluster as the decision variable and the power instruction decomposition to obtain the maximum benefit as the objective function; The electric vehicle power control priority building module is used to build the electric vehicle power control priority according to the current SOC and off-grid time of the electric vehicles in the charging pile cluster; The power adjustment amount determination module of the electric vehicle is used to determine the power adjustment amount of the corresponding electric vehicle according to the determined power adjustment amount of the charging pile cluster and the corresponding electric vehicle power control priority; The tracking control module is used to perform control according to the power adjustment amount of each electric vehicle.

7. A multi-level large-scale electric vehicle power control system, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a multi-level large-scale electric vehicle power control method as described in any one of claims 1-5 is implemented.

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