A distributed event-triggered charging scheduling method for electric vehicles under a transformer hierarchy

Abstract

The present invention belongs to the field of electric vehicle charging scheduling and discloses a distributed event-triggered charging scheduling method for electric vehicles under a transformer hierarchy. The method takes into account the trade-offs between power generation cost, transformer load cost, electric vehicle battery loss and user preference, and constructs a transformer-electric vehicle two-layer distributed architecture. Due to the large number and wide distribution of electric vehicles, the information exchange during the electric vehicle charging process is highly dependent on the information network. In order to reduce unnecessary waste of communication resources during information transmission, the present invention introduces an event trigger mechanism. The electricity price information of electric vehicles and transformers can be transmitted on demand, reducing the communication burden. The effectiveness of the charging scheduling is verified by simulation. The proposed distributed event-triggered charging scheduling method minimizes system costs and solves the charging strategy optimization problem.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle charging scheduling, and specifically relates to an electric vehicle distributed charging scheduling method based on an event-triggered consistency algorithm under a transformer hierarchical structure. Background Art

[0002] According to data from the International Energy Agency, global transportation sector carbon dioxide emissions are increasing annually, making transportation the second-largest sector globally, accounting for 25%. Light vehicles, including passenger cars, contribute the largest share of carbon emissions, accounting for 45% of transportation sector emissions. New energy vehicles (NEVs), due to their impressive fuel efficiency and ability to effectively reduce carbon emissions, have become a global transportation trend. Relevant surveys show that the number of NEVs in the automotive market has shown a significant growth trend in recent years, and the future development of NEVs is highly dependent on charging technology. Disorderly charging behavior has significant adverse effects on the power grid, including imbalances between EV charging demand and grid supply, local overloads, excessive losses, and significant voltage deviations. Therefore, to coordinate EV charging, mitigate these negative impacts, and achieve lower operating costs and improved system stability through reasonable coordinated allocation, EV charging scheduling warrants attention.

[0003] Centralized scheduling is a common traditional charging scheduling method. Centralized control typically requires a central controller to collect information such as demand and charging prices for each electric vehicle in the charging network. This not only increases the computing pressure on the control center but also significantly increases system operating costs. Furthermore, the safety and reliability of centralized charging architectures are susceptible to single points of failure. This means that if a single charging station fails, the safety and reliability of the entire charging system will be affected.

[0004] Compared to the complex computation and communication burdens of centralized charging, distributed scheduling is independent of central control and requires only information exchange between adjacent units to complete a given task. In charging scheduling, neighboring vehicles exchange relevant information, and then use a consensus algorithm to optimize the charging schedule for the entire system. This undoubtedly reduces computational complexity and the system's communication burden, and improves system stability to a certain extent.

[0005] Whether centralized or distributed, charging coordination relies heavily on information networks, and information transmission plays a crucial role in the entire scheduling process. Traditional information transmission methods generally use periodic sampling. From the perspective of system analysis and design, system analysis and design are relatively simple, and periodic sampling is easily accepted. However, from the perspective of network resource utilization, this sampling method, which is independent of system processes, also wastes resources such as communication bandwidth and computing time. This has prompted research on event-driven control methods for electric vehicle charging systems. Event-triggered control refers to updating a controlled variable only when it exceeds a given critical value. This can reduce the number of communications between electric vehicles in the system and the number of controller updates, thereby reducing system operating power consumption and improving resource utilization. Furthermore, for general electric vehicle charging scheduling, factors such as power generation costs, transformer load costs, electric vehicle battery loss, and user preferences should be comprehensively considered.

[0006] As mentioned above, flexible charging requirements, user privacy protection, and reduced charging costs are crucial for the widespread and large-scale charging scheduling of electric vehicles. Centralized control systems struggle to meet the flexible charging needs of large numbers of electric vehicles, and single points of failure can severely impact system security. Information exchange relies on information networks, and traditional periodic sampling mechanisms can waste communication resources. Current research has yet to address the challenges of ensuring the safe and stable operation of a charging scheduling system for large numbers of electric vehicles while minimizing system costs while taking into account various factors in an environment with limited communication resources. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention adopts a two-layer architecture of transformer-electric vehicle. In order to protect user privacy and avoid the high maintenance costs of centralized control, a distributed consistency algorithm is used when individuals in the system interact with each other. Furthermore, considering that the traditional periodic sampling mechanism will cause resource waste, in order to alleviate communication pressure and solve the network congestion problem, the present invention adopts an event trigger mechanism and adds an event trigger condition when individuals interact with neighbors on electricity prices. On the basis of ensuring the effective completion of the charging scheduling task, the on-demand transmission of charging price information between electric vehicles is realized, avoiding the waste of communication resources, effectively solving the congestion problem in the communication network, and ensuring the safe and stable operation of the entire electric vehicle charging scheduling system.

[0008] In order to achieve the above object, the present invention is achieved through the following technical solutions:

[0009] The present invention is a distributed event-triggered charging scheduling method for electric vehicles under a transformer hierarchy, specifically comprising the following steps:

[0010] Step 1: Set system parameters, including transformer group Where M represents the number of transformers. Electric vehicle group under the transformer where N m Indicates the number of electric vehicles in the group and the charging time range Parameter β, The maximum average charging power of electric vehicles Γ mn ,parameter Parameter δ, iteration step θ. Among them, the value range of β is 0<β<1. Given the inelastic demand dq of the system in the charging time range, the individual electricity price threshold ∈ stop , system electricity price threshold ε stop , m represents the transformer number, m=1,2,…,M, represents the transformer number, τ represents the charging time,

[0011] Step 2: Set the connection coefficient a according to the communication topology between each transformer mn , n=1,2,…,M, where

[0012] Similarly, the connection coefficients between each group of electric vehicles In addition, the coefficient b io,m To represent the case where the transformer m is directly connected to the electric vehicle below it, And for each group of electric vehicles there is at least one b io,m =1;

[0013] Step 3: Calculate the power value d of each transformer partition at different times according to the given inelastic demand dq, and thus the total background demand of each transformer at time τ can be obtained. For the initial time τ0, that is, τ=τ0, calculate the initial value of the electricity price of each group of electric vehicles Set k = 0, ε > ε stop ;

[0014] Step 4: Transformer m converts the price ρ m (k) Transmit to the electric vehicles directly connected to it, and define a virtual variable ρ for the electric vehicles under each group of transformers mi,τ (k), Each electric vehicle sends its own trigger electricity price through the communication network To the adjacent electric vehicle, and set the initial value of the trigger electricity price to Define the state error as

[0015] Step 5: Assume that the individual trigger price of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0016] Step 6: Update the state value ρ through the event-triggered consistency algorithm mi,τ (k);

[0017] Step 7: Calculate individual electricity price error Update k=k+1, and judge whether the individual electricity price error ∈1 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 5; otherwise go to step 8;

[0018] Step 8, Transformer The average electricity value of the electric vehicle group under m,τ ;

[0019] Step 9: The electric vehicles under each transformer calculate the optimal charging strategy locally based on the obtained price information.

[0020] Step 10: Define a dummy variable X for each set of electric vehicles under the transformer. mi,τ (t), Each electric vehicle sends variable trigger values ​​through the communication network To the adjacent electric car, and define the trigger value of the variable as the initial value Set ∈2>∈ stop , k=0; define the state error as

[0021] Step 11: Assume that the individual power trigger value of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0022] Step 12: Update the state value X through the event-triggered consistency algorithm mi,τ (k);

[0023] Step 13: Calculate individual state error Update k=k+1, and judge whether the individual state error ∈2 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 11; otherwise go to step 14;

[0024] Step 14: The electric vehicles under transformer m receive an average and consistent charging power.

[0025] Step 15: Transformer m obtains the total charging power value from the electric vehicle directly connected to it. And calculate the initial price p locally based on the uploaded charging power mτ (0);

[0026] Step 16: Define the electricity price trigger value of transformer m as And assign the variable an initial value of Set ∈3>∈ stop , k=0; define the state error as

[0027] Step 17: Assume that the individual electricity price trigger value of the mth transformer at the kth iteration is If the mth transformer meets the event triggering condition at the kth iteration, then let And individual m transmits the trigger value to other transformers through the information network. If the event trigger condition is not met, then remain unchanged;

[0028] Step 18: Update the state value p through the event-triggered consistency algorithm m,τ (k);

[0029] Step 19: Calculate individual electricity price error Update k=k+1, and judge whether the individual electricity price error ∈3 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 17; otherwise go to step 20;

[0030] Step 20: The transformer group obtains the average consistent electricity price

[0031] Step 21, the transformer is obtained according to the Local calculation updates the price ρ m (k+1);

[0032] Step 22: Calculate price error And update k=k+1. Determine whether the error ε is greater than the error threshold ε stop , if it is higher than the threshold, go to step 4, otherwise go to step 23;

[0033] Step 23: Get the optimal charging strategy for the system

[0034] Furthermore, the calculation formula for the initial value of the electricity price issued in step 3 is ρ mτ (0) = c τ′ (d τ )+r m ′ τ (d mτ ); quadratic function The charging time τ is based on the total demand The production cost of transformer is The total background requirement is c τ ′ (d τ ) is c τ (d τ ), a τ ,b τ and C τ is the power generation coefficient. The cost function is Where, φ m is the capacity of transformer m, x is the power passing through the transformer, β mτ is the penalty factor for exceeding the capacity of the transformer during the charging time τ. The transformer loss at a given flow rate is expressed as f mτ (x) (increasing, differentiable convex function) represents.

[0035] Furthermore, an event-triggered consistency electric vehicle charging scheduling method under a transformer hierarchy is characterized in that the event triggering condition in step 5 is: in

[0036]

[0037] in, represents the individual triggered electricity price of the j-th electric vehicle at the k-th iteration, represents the individual triggered electricity price of the kth iteration of the i-th electric vehicle, is a constant used to control the threshold for event triggering, and k represents the individual electricity price The number of update iterations.

[0038] Furthermore, the electric vehicle in step 6 updates the state value through the event-triggered consistency algorithm

[0039] Furthermore, the optimal charging strategy calculated locally in step 9 is in Under given charging conditions The system cost function under . The constraint (2) in the optimization problem is

[0040]

[0041] ‖u mn ‖1ΔT≤Ξ mn (2b)

[0042] Among them, the number of time periods in the time range is The length of a time period is ΔT.

[0043] And ‖u mn ‖1=∑ τ∈T u mn,τ , Ξ mn =Γ mn ΔT is the charging time of electric vehicles The maximum charging power and maximum energy storage capacity. mn,τ Under this condition, the benefit of electric vehicle is h mn (‖u mn ‖1)=-δ mn (‖i mn ‖1-Γ mn ) 2 , where δ mn Reflects the ability to obtain the maximum energy within the charging time range. The local cost of electric vehicle charging at time τ is given by a monotonically increasing, differentiable convex function g mn,τ (u mn,τ ) is given, reflecting the harmful impact of electric vehicle charging behavior on the distribution network and battery life.

[0044] Furthermore, an event-triggered consistency electric vehicle charging scheduling method under a transformer hierarchy is characterized in that the event triggering condition in step 11 is in

[0045] Furthermore, the specific formula for updating the state value according to the event-triggered consistency algorithm in step 12 is:

[0046] Furthermore, the initial value of the transformer electricity price p in step 15 is mτ (0)

[0047] Furthermore, the event triggering condition in step 17 is in

[0048] Furthermore, the specific formula for updating the state value according to the event-triggered consistency algorithm in step 18 is:

[0049] Furthermore, the electric vehicle electricity price update value in step 21 is

[0050] The beneficial effects of the present invention are:

[0051] 1. The present invention realizes the system's global optimal charging strategy by comprehensively considering the trade-offs among the power generation cost, transformer load cost, electric vehicle battery loss, and user preferences generated by electric vehicle charging;

[0052] 2. On the basis of ensuring the effective completion of charging scheduling tasks, the present invention adopts an event triggering mechanism when the transformer and electric vehicle in the system exchange information. Compared with the traditional periodic sampling mechanism, it can enable individuals to transmit on demand, avoid wasting communication resources, alleviate communication pressure and ensure the safe and stable operation of the charging scheduling system;

[0053] 3. The present invention uses a distributed consensus algorithm to update electricity prices for electric vehicles and transformers. Compared to traditional centralized information collection, individuals in this algorithm only need to exchange electricity price information with their neighbors to obtain the final average electricity price. This protects user privacy and avoids high maintenance costs caused by single points of failure. Distributed information exchange has higher robustness and stability, and can greatly reduce communication pressure. Therefore, it is particularly suitable for the future large-scale and widely distributed plug-in electric vehicle charging environment.

[0054] 4. The event triggering conditions proposed by the present invention introduce parameters By setting different value, you can control the number of triggers, when increasing The value will increase the trigger threshold of the trigger condition. By adjusting the parameters appropriately It can effectively reduce the number of triggers to reduce communication pressure;

[0055] 5. The present invention can solve the problem of large-scale distributed electric vehicle charging scheduling under the condition of limited communication network bandwidth. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 2 is a diagram of a two-layer structure of a transformer and an electric vehicle according to an embodiment of the present invention.

[0057] Figure 2 This is a flow chart of how electric vehicles obtain individual electricity prices in a distributed event-triggered manner in an embodiment of the present invention.

[0058] Figure 3 This is a flow chart of how an electric vehicle obtains an average consistent charging power in a distributed event-triggered manner in an embodiment of the present invention.

[0059] Figure 4 This is a flow chart of how transformers obtain an average consistent electricity price in a distributed event-triggered manner in an embodiment of the present invention.

[0060] Figure 5 4 is a diagram of charging requirements of electric vehicles at different times according to an embodiment of the present invention.

[0061] Figure 6 1 is an electricity price diagram of each transformer at different times according to an embodiment of the present invention.

[0062] Figure 7 1 is a diagram of basic demand and total demand of each transformer at different times according to an embodiment of the present invention.

[0063] Figure 8 This is a diagram of triggering moments of individuals in the electric vehicle group 3 during the charging process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following diagrams illustrate embodiments of the present invention. For clarity, many practical details are included in the following description. However, it should be understood that these practical details are not intended to limit the present invention. In other words, in some embodiments of the present invention, these practical details are not essential.

[0065] The present invention is a distributed event-triggered charging scheduling method for electric vehicles under a transformer hierarchy, the method comprising the following steps:

[0066] Step 1: Set system parameters, including transformer group Where M represents the number of transformers, for any transformer Its electric vehicle group where N m Indicates the number of electric vehicles in the group and the charging time range right The maximum average charging power of electric vehicles Γ mn , parameter β, parameter Parameter δ, iteration step θ. Among them, the value range of β is 0<β<1. Given the inelastic demand dq of the system in the charging time range, the individual electricity price threshold ∈ stop , system electricity price threshold ε stop , m represents the transformer number, m=1,2,…,M, represents the transformer number, τ represents the charging time,

[0067] Step 2: Set the connection coefficient a according to the communication topology between each transformer mn , n=1,2,…,M, where Similarly, the connection coefficients between each group of electric vehicles j=1,2,…,N m , and use the coefficient b io,m To represent the case where the transformer m is directly connected to the electric vehicle below it, And for each group of electric vehicles there is at least one b io,m =1;

[0068] Step 3: Calculate the power value d of each transformer partition at different times according to the given inelastic demand dq, and thus the total background demand of each transformer at time τ can be obtained. For the initial time τ0, that is, τ=τ0, calculate the initial value of the electricity price of each group of electric vehicles Set k = 0, ε > ε stop ;

[0069] Step 4: Transformer m converts the price ρ m (k) Transmit to the electric vehicles directly connected to it, and define the individual electricity price ρ for the electric vehicles under each group of transformers mi,τ (k), Each electric vehicle sends its own trigger electricity price through the communication network To the adjacent electric vehicle, and set the initial value of the trigger electricity price to Set v1>v stop , k=0; define the state error as

[0070] Step 5: Assume that the individual trigger price of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0071] Step 6: Update the state value ρ through the event-triggered consistency algorithm mi,τ (k);

[0072] Step 7: Calculate individual electricity price error Update k=k+1, and judge whether the individual electricity price error ∈1 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 5; otherwise go to step 8;

[0073] Step 8, Transformer The average electricity value of the electric vehicle group under m,τ ;

[0074] Step 9: Each group of electric vehicles calculates the optimal charging strategy locally based on the obtained price information.

[0075] Step 10: Define a dummy variable X for each set of electric vehicles under the transformer. mi,τ (t), Each electric vehicle sends variable trigger values ​​through the communication network To the adjacent electric car, and assign the initial value Set ∈2>∈ stop , k=0; define the state error as

[0076] Step 11: Assume that the individual power trigger value of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0077] Step 12: Update the state value X through the event-triggered consistency algorithm mi,τ (k);

[0078] Step 13: Calculate individual state error Update k=k+1, determine whether the state error∈2 is higher than the threshold, if it is higher than the threshold v stop , go to step 11; otherwise go to step 14;

[0079] Step 14: The electric vehicles under transformer m receive an average and consistent charging power.

[0080] Step 15: Transformer m obtains the total charging power value from the electric vehicle directly connected to it. And calculate the initial price p locally based on the uploaded charging power mτ (0);

[0081] Step 16: Define the transformer m electricity price trigger value as And assign the initial value of the trigger value to Set ∈3>∈ stop , k=0; define the state error as

[0082] Step 17: Assume that the individual electricity price trigger value of the mth transformer at the kth iteration is If the mth transformer meets the event triggering condition at the kth iteration, then let And individual m transmits the trigger value to other transformers through the information network. If the event trigger condition is not met, then remain unchanged;

[0083] Step 18: Update the state value p through the event-triggered consistency algorithm m,τ (k);

[0084] Step 19, calculate individual state error Update k=k+1, and judge whether the individual state error ∈3 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 17; otherwise go to step 20;

[0085] Step 20: All transformers get the average consistent electricity price

[0086] Step 21, the transformer is obtained according to step 20 Local calculation updates the price ρ m (k+1);

[0087] Step 22: Calculate price error And update k=k+1. Determine whether the error price ε is greater than the error threshold ε stop , if it is higher than the threshold, go to step 4, otherwise go to step 23;

[0088] Step 23: Get the optimal charging strategy for the system

[0089] Example 1

[0090] like Figure 1 As shown, a transformer group and its three groups of electric vehicles Taking the double-layer charging scheduling system as an example, the specific distributed time-departure charging scheduling method for electric vehicles includes the following steps:

[0091] Step (1) Set system parameters and related functions: β τ =[3,4,2]×10 -5 , φ m ={600 6001000}, ∈ stop =0.0001,ε stop =0.0001,δ 1n =[0.5;0.2;0.3;0.1;0.3];δ 2n =[0.3;0.5;0.1;0.2;0.2];δ 3n =[0.2;0.3;0.1;0.3;0.4;0.5];

[0092] Γ 1n =[70,74,55,140,100];Γ 2n =[48,50,80,120,200];

[0093] Γ 3n =[100,40,70,70,100,140];

[0094] Given the inelastic demand dq for each group of electric vehicles, calculate the demand d under different time zones τ τ =500*dq

[0095] Step (2) describes the communication network topology of the charging scheduling system: transformer communication topology L0 = [2, -1, -1; -1, 1, 0; -1, 0, 1]; the communication topologies of each group of electric vehicles are:

[0096] L1=[3,-1,-1,-1,0;-1,3,-1,-1,0;-1,-1,4,-1,-1;-1,-1,-1,3,0;0,0,-1,0,1];

[0097] L2=[2,0,-1,-1,0;0,2,-1,0,-1;-1,-1,4,-1,-1;-1,0,-1,3,-1;0,-1,-1,-1,3];

[0098] L3=[4,0,-1,-1,-1,-1;0,2,0,-1,-1,0;-1,0,3,-1,0,-1;-1,-1,-1,3,0,0;-1,-1,0,0,2,0;-1,0,-1,0,0,2];

[0099] The connection coefficients between the transformer and the electric vehicle group are:

[0100] B 1o =[1 0 0 0 0]; B 2o =[1 0 0 0 0]; B 3o =[1 0 0 0 0 0];

[0101] Step (3) calculates the power value d of each transformer partition at different times according to the given inelastic demand dq, thereby obtaining the total background demand of each transformer at time τ. For the initial time τ0, that is, τ=τ0, calculate the initial value of the electricity price of each group of electric vehicles where ρ mτ (0) = 0.00006*M*d mτ+0.05; set k=0,ε>ε stop ;

[0102] Step (4) Transformer m sets the price ρ m (k) Transmit to the electric vehicles directly connected to it, and define a virtual variable ρ for the electric vehicles under each group of transformers mi,τ (k), Each electric vehicle sends its own trigger electricity price through the communication network To the adjacent electric vehicle, and set the initial value of the trigger electricity price to Define the state error as

[0103] Step (5) Assume that the individual trigger price of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration: in

[0104]

[0105] Then order And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0106] Step (6) Update the state value through the event-triggered consistency algorithm

[0107] Step (7) Calculate individual electricity price error Update k=k+1, and judge whether the individual electricity price error ∈1 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step (5), otherwise go to step (8);

[0108] Step (8) Transformer The average electricity value of the electric vehicle group under m,τ ;

[0109] Step (9) Each group of electric vehicles calculates the optimal charging strategy locally based on the obtained price information.

[0110] Step (10) defines a virtual variable X for each set of electric vehicles under the transformer mi,τ (t), Each electric vehicle sends variable trigger values ​​through the communication network To the adjacent electric car, and define the trigger value of the variable as the initial value Set ∈2>∈ stop , k=0; define the state error as

[0111] Step (11) The individual power trigger value of the i-th electric vehicle at the k-th iteration is If the i-th electric car meets the event triggering condition at the k-th iteration in Then order And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged;

[0112] Step (12) Update the state value through the event-triggered consistency algorithm

[0113] Step (13) calculates the individual state error Update k=k+1, and judge whether the individual state error ∈2 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step (11); otherwise go to step (14);

[0114] Step (14) The electric vehicle under transformer m obtains an average and consistent charging power

[0115] Step (15) Transformer m obtains the total charging power value from the electric vehicle directly connected to it And calculate the initial price locally based on the uploaded charging power

[0116] Step (16) defines the transformer m electricity price trigger value as And assign the variable an initial value of Set ∈3>∈ stop , k=0; define the state error as

[0117] Step (17) Let the individual electricity price trigger value of the mth transformer at the kth iteration be If your mth transformer meets the event triggering condition at the kth iteration in Then order And individual m transmits the trigger value to other transformers through the information network. If the event trigger condition is not met, then remain unchanged;

[0118] Step (18) updates the state value by triggering the consistency algorithm through events

[0119] Step (19) Calculate individual electricity price error Update k=k+1, and judge whether the individual electricity price error ∈3 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step (17); otherwise go to step (20);

[0120] Step (20) All transformers get the average consistent electricity price

[0121] Step (21) The transformer is obtained according to step (20) Local calculation updates and releases prices

[0122] Step (22) Calculate the price error And update k=k+1. Determine whether the error ε is greater than the error threshold ε stop , if it is higher than the threshold, go to step (4), otherwise go to step (23);

[0123] Step (23) obtains the optimal charging strategy of the system

[0124] In order to verify the effectiveness of the present invention, a simulation experiment was carried out.

[0125] Figure 5 The figure shows the changes in charging demand of the three groups of electric vehicles within the charging time interval. It can be seen that all electric vehicles can achieve their optimal charging strategy through the distributed event triggering method proposed in the present invention, achieving a trade-off between benefits and consumption costs.

[0126] Figure 6 Indicates that each transformer at different times Taking into account the system power generation cost and transformer loss, a distributed event triggering method is used to calculate the convergent and consistent electricity value at each moment.

[0127] Figure 7 It represents the changes in the basic demand and total demand under each transformer in the hierarchical structure at different times, reflecting the trade-off between the power generation cost, transformer load cost, and electric vehicle battery loss in the system under the hierarchical structure.

[0128] Figure 8 To select the event triggering moment of individuals in electric vehicle group 3 in the process of price information transmission using the distributed event triggering method, combined with Figure 5 , the electric vehicle group can achieve the optimal charging strategy, which shows the feasibility of the event triggering algorithm proposed in this invention.

[0129] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A distributed event-triggered charging scheduling method for electric vehicles under a transformer hierarchy, characterized by: The electric vehicle distributed event-triggered charging scheduling method comprises the following steps: Step 1: Set system parameters, including transformer group Where M represents the number of transformers, for any transformer Its electric vehicle group where N m Indicates the number of electric vehicles in the group and the charging time range right The maximum average charging power of electric vehicles Γ mn , parameter β, parameter Iteration step θ, is a constant used to control the threshold for event triggering, where the value range of β is 0<β<1. Given the inelastic demand dq of the system in the charging time range, the individual electricity price threshold ε stop , system electricity price threshold ε stop , m represents the transformer number, m=1,2,…,M, represents the transformer number, τ represents the charging time, Step 2: Set the connection coefficient a according to the communication topology between each transformer mn , n=1,2,…,M, where Similarly, the connection coefficients between each group of electric vehicles j=1,2,…,N m , and use the coefficient b io,m To represent the case where the transformer m is directly connected to the electric vehicle below it, And for each group of electric vehicles there is at least one b io,m =1; Step 3: Calculate the power value d of each transformer partition at different times according to the given inelastic demand dq, and thus the total background demand of each transformer at time τ can be obtained. For the initial time τ0, that is, τ=τ0, calculate the initial value of the electricity price of each group of electric vehicles Set k = 0, ε > ε stop ; Step 4: Transformer m converts price ρ m (k) Transmit to the electric vehicles directly connected to it, and define the individual electricity price ρ for the electric vehicles under each group of transformers mi,τ (k), Each electric vehicle sends its own trigger electricity price through the communication network To the adjacent electric vehicle, and set the initial value of the trigger electricity price to Set ∈1>ε stop , k=0, define the state error as Step 5: Let the individual trigger price of the i-th electric vehicle at the k-th iteration be If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, then remain unchanged; Step 6: Update the state value ρ through the event-triggered consensus algorithm mi,τ (k); Step 7: Calculate individual electricity price errors Update k=k+1, and judge whether the individual electricity price error ∈1 is higher than the threshold. If it is higher than the threshold ε stop , go to step 5; otherwise go to step 8; Step 8: Transformer The average electricity value of the electric vehicle group under m,τ ; Step 9: Electric vehicles in each group calculate the optimal charging strategy locally based on the obtained price information Step 10: Define a dummy variable X for each set of electric vehicles under the transformer mi,τ (t), Each electric vehicle sends variable trigger values ​​through the communication network To the adjacent electric car, and assign the initial value Set ε2>ε stop , k=0, define the state error as Step 11: Let the individual power trigger value of the i-th electric vehicle at the k-th iteration be If the i-th electric car meets the event triggering condition at the k-th iteration, then let And individual i transmits the trigger value to other electric vehicles through the information network. If the event trigger condition is not met, remain unchanged; Step 12: Update the state value X through the event-triggered consensus algorithm mi,τ (k); Step 13: Calculate individual state errors Update k=k+1, and determine whether the state error ε2 is higher than the threshold. If it is higher than the threshold ε stop , go to step 11; otherwise go to step 14; Step 14: The electric vehicles under transformer m receive an average and consistent charging power Step 15: Transformer M obtains the total charging power value from the electric vehicles directly connected to it And calculate the initial price p locally based on the uploaded charging power mτ (0); Step 16: Define the transformer m electricity price trigger value as And assign the initial value of the trigger value to Set ∈3>∈ stop , k=0, define the state error as Step 17: Let the individual electricity price trigger value of the mth transformer at the kth iteration be If the mth transformer meets the event triggering condition at the kth iteration, then let And individual m transmits the trigger value to other transformers through the information network. If the event trigger condition is not met, then remain unchanged; Step 18: Update the state value p through the event-triggered consensus algorithm m,τ (k); Step 19: Calculate individual state errors Update k=k+1, and judge whether the individual state error ∈3 is higher than the threshold. If it is higher than the threshold ∈ stop , go to step 17; otherwise go to step 20; Step 20: All transformers get an average consistent electricity price Step 21: The transformer is obtained according to step 20 Local calculation updates the price ρ m (k+1); Step 22: Calculate Price Error And update k=k+1, and judge whether the error price ε is greater than the error threshold ε stop , if it is higher than the threshold, go to step 4, otherwise go to step 23; Step 23: Obtain the optimal charging strategy for the system 2. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The calculation formula for the initial value of the electricity price issued in step 3 is ρ mτ (0) = c′ τ (d τ )+r′ mτ (d mτ ), quadratic function The charging time τ is based on the total demand The production cost of transformer is The total background requirement is c′ τ (d τ ) is c τ (d τ ), a τ ,b τ and C τ is the power generation coefficient, where the transformer is charged during the charging time The cost function is Where, φ m is the capacity of transformer m, x is the power passing through the transformer, β mτ is the penalty factor for exceeding the capacity of the transformer during the charging time τ, the transformer The transformer loss at a given flow rate is expressed as f mτ (x) indicates.

3. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The event triggering conditions in step 5 are: in in: represents the individual triggered electricity price of the j-th electric vehicle at the k-th iteration, represents the individual triggered electricity price of the i-th electric vehicle at the k-th iteration at time t, is a constant used to control the threshold for event triggering, and k represents the individual electricity price The number of update iterations.

4. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The electric vehicles in step 6 update their individual electricity prices ρ through the event-triggered consensus algorithm mi,τ (k+1)=ρ mi,τ (k)+ 5. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The optimal charging strategy calculated locally in step 9 is: in Under given charging conditions System cost function under ; The constraint (2) in the optimization problem is ‖u mn ‖1ΔT <h2 style=";text-align:left;direction:ltr">≤Ξ<h2 style=";text-align:left;direction:ltr"> mn <h2 style=";text-align:left;direction:ltr"> (2b) Among them, the number of time periods in the time range is The length of a time period is ΔT, g mn,τ (u mn,τ ) is a differentiable convex function, the benefit of electric vehicles h mn (‖u mn ‖1)=-δ mn (‖u mn ‖1-Γ mn ) 2 ; ‖u mn ‖1=∑ τ∈T u mn,τ , The charging time of electric vehicles The maximum charging power and maximum energy storage capacity.

6. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The event triggering condition in step 11 is: in The event triggering condition in step 17 is: in 7. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The specific formula for updating the state value according to the event-triggered consistency algorithm in step 12 is:

8. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The initial value of the transformer electricity price in step 15 is p mτ (0) Among them, the quadratic function The charging time τ is based on the total demand The production cost of transformer is The total background requirement is c′ τ (d τ ) is c τ (d τ ), a τ ,b τ and C τ is the power generation coefficient.

9. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The specific formula for updating the state value according to the event-triggered consistency algorithm in step 18 is:

10. The method for distributed event-triggered charging scheduling of electric vehicles under a transformer hierarchy according to claim 1, characterized in that: The electric vehicle electricity price update value in step 21

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