An electric vehicle orderly charging control method and device

Abstract

The application discloses an orderly charging control method and device for an electric vehicle, and comprises a user terminal, a concentrator and a smart meter; the smart meter is electrically connected with the concentrator; the control method is applied to the concentrator and comprises the following steps: receiving a charging request from the user terminal and sent by the smart meter; the charging request comprises charging demand information; obtaining a substation load data set of a target time period according to a resident load prediction power set of a target substation in the target time period and a historical charging power data set; calculating a charging plan power data set of the target time period based on a pre-determined optimization model according to the charging demand information and the substation load data set; and issuing the charging plan power data set of the target time period to the smart meter for execution. The application can effectively regulate and control the user charging behavior under the premise that the existing residential area distribution transformer capacity remains unchanged, and solves the problem that the existing residential area distribution transformer cannot meet the access of large-scale electric vehicle disorderly load.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to an orderly charging control method and device for electric vehicles. Background Technology

[0002] Faced with the gradual shortage or even depletion of fossil energy and the increasingly serious environmental problems, the use of new renewable energy to replace traditional fossil fuels will become a new trend in future development.

[0003] Compared to traditional gasoline-powered vehicles, electric vehicles are green, energy-saving, environmentally friendly, and highly efficient, making them a rapidly developing new energy vehicle industry. Electric vehicle users typically choose residential areas as their charging location. However, in reality, most existing residential communities were not designed with the charging needs of a large number of electric vehicles in mind. Furthermore, peak electricity consumption periods for residents and the peak periods of disorderly charging loads from electric vehicles in residential areas are highly coupled, meaning that existing residential power distribution transformers cannot meet the large-scale, disorderly charging loads from electric vehicles. Summary of the Invention

[0004] This invention provides an orderly charging control method and device for electric vehicles, which solves the problem that existing residential area distribution transformers cannot meet the needs of large-scale disordered electric vehicle load access.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides an orderly charging control method for electric vehicles, comprising a user terminal, a concentrator, and smart meters; wherein there are multiple smart meters, and they are electrically connected to the concentrator; the control method is applied to the concentrator, comprising:

[0007] The system receives a charging request from the user terminal sent by the smart meter; the charging request includes charging demand information.

[0008] The load data set of the target transformer area for the target time period is obtained by combining the predicted residential load power set of the target transformer area for the target time period with the historical charging power data set. The predicted residential load power set is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period.

[0009] Based on the charging demand information and the distribution area load data set, a charging plan power data set for the target time period is calculated using a pre-determined optimization model. The optimization model includes decision variables, an objective function, and constraints. The constraints are determined by the charging demand information and the distribution area load data set. The decision variable is the charging plan power data set. The objective function is the minimum sum of the accumulated user charging costs and the maximum load penalty term for the target time period. The user charging costs are determined based on the constraints and the decision variables, and the maximum load penalty term is determined based on the constraints and a preset penalty value.

[0010] The charging plan power data set for the target time period is sent to the smart meter for execution.

[0011] In one possible implementation, obtaining the load data set for the target transformer area during the target time period based on the predicted residential load power set and the historical charging power data set for the target transformer area during the target time period specifically involves:

[0012] The set of predicted residential load power and the set of historical charging power data are accumulated in chronological order using the following formula to obtain the set of transformer load data for the target time period.

[0013]

[0014] Among them, S j This represents the set of transformer load data for the target time period. S represents the set of historical charging power data; pre，j denoted as the set of predicted residential load power; J represents the number of sub-charging periods that are equally divided into the target time period.

[0015] In one possible implementation, before obtaining the load data set of the target transformer area for the target time period based on the predicted power set of residential load for the target transformer area during the target time period and the historical charging power data set, the method further includes:

[0016] Based on the set of historical residential load power data of the target transformer area before the target time period, the predicted set of residential load power for the target time period is predicted based on a neural network model.

[0017] Obtain the set of historical charging power data from the previous time period for the target time period of the target transformer area.

[0018] In one possible implementation, the objective function of the optimization model is specifically:

[0019]

[0020] Among them, P j U represents the planned charging power of the target electric vehicle at time j, i.e., the set of planned charging power data or decision variables for the target time period; j It is 0 or 1 when u j When u is 0, it indicates that the target electric vehicle is not charging during that time period. j When p is 1, it indicates that the target electric vehicle is charging during that time period; j This represents the time-of-use electricity price for each sub-charging period of the target electric vehicle within the target time period, where Δt represents the time interval between each sub-charging period.

[0021] M represents the preset penalty value; Z represents the maximum load of each sub-charging period.

[0022] In one possible implementation, the constraints of the optimization model include residential distribution transformer constraints, charging plan continuity constraints, user demand constraints, electric vehicle charging power constraints, and maximum load constraints during the control period with a penalty term added to the objective function.

[0023] The charging demand information includes the target electric vehicle's battery capacity, the target electric vehicle's current battery charge, the target electric vehicle's expected battery charge, the target electric vehicle's start charging time, and the target electric vehicle's maximum charging power.

[0024] The specific constraints on the distribution transformers in the residential area are as follows:

[0025] P j +S j ≤η1S T j = 1, 2, ..., J,

[0026] Among them, P j S represents the planned charging power of the target electric vehicle at time j; j This represents the set of transformer load data for the target time period; η1 represents the percentage of safety constraints for the transformers in the target area; S T J represents the transformer capacity of the target area; J represents the number of sub-charging periods that are equally divided into the target time period.

[0027] The continuity constraint of the charging plan is specifically as follows:

[0028] -M j ≤u j+1 -u j ≤M j ,

[0029]

[0030] Among them, M j Indicates whether the charging plan for the target electric vehicle changes at time j; uj It is 0 or 1 when u j When u is 0, it indicates that the target electric vehicle is not charging during that time period. j A value of 1 indicates that the target electric vehicle is being charged during that time period;

[0031] The specific user requirement constraints are as follows:

[0032]

[0033] u j =0 j≥t end ,

[0034] Where C represents the battery capacity of the target electric vehicle; SOC1 represents the current battery charge of the target electric vehicle; SOC2 represents the expected battery charge of the target electric vehicle; Δt represents the time interval between each sub-charging period; t end Indicates the start time of charging for the target electric vehicle;

[0035] The electric vehicle charging power constraint is specifically as follows:

[0036] u j *P minn ≤P j ≤u j *P maxn j = 1, 2, ..., J,

[0037] Among them, P max P represents the maximum charging power of the target electric vehicle. min Indicates the preset minimum charging power;

[0038] The maximum load constraint for the control period with added penalty terms in the objective function is specifically as follows:

[0039]

[0040] Z≥0,

[0041] Where Z represents the maximum load of the target electric vehicle during the control period, and η2 represents the percentage of the maximum load constraint penalty limit of the target electric vehicle during the control period.

[0042] In one possible implementation, before calculating the charging plan power data set for the target time period based on a predetermined optimization model, the method further includes:

[0043] Construct the optimization model.

[0044] Secondly, the present invention provides an orderly charging control method for electric vehicles, including a user terminal, a concentrator, and smart meters; there are multiple smart meters, and they are electrically connected to the concentrator; the control method is applied to the smart meters, including:

[0045] The system receives a charging request from a user terminal and sends the charging request to the concentrator; the charging request includes charging demand information.

[0046] The concentrator receives a set of charging plan power data for a target time period from the concentrator, and charges the target electric vehicle according to the set of charging plan power data within the target time period. The set of charging plan power data is calculated by the concentrator based on a predetermined optimization model, according to the charging demand information and the set of transformer area load data.

[0047] Thirdly, the present invention provides an orderly charging control device for electric vehicles, comprising a user terminal, a concentrator, and smart meters; wherein there are multiple smart meters, and they are electrically connected to the concentrator; the concentrator includes:

[0048] A receiving unit is configured to receive a charging request sent by the smart meter from the user terminal; the charging request includes charging demand information.

[0049] The processing unit obtains the load data set of the target transformer area for the target time period based on the predicted power set of residential load for the target transformer area and the historical charging power data set. The predicted power set of residential load is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period.

[0050] The planning unit is used to calculate the planned charging power data set for a target time period based on the charging demand information and the distribution area load data set, using a pre-determined optimization model. The optimization model includes decision variables, an objective function, and constraints. The constraints are determined by the charging demand information and the distribution area load data set. The decision variables are the planned charging power data set. The objective function is the minimum value of the sum of the accumulated user charging costs and the maximum load penalty term for the target time period. The user charging costs are determined based on the constraints and the decision variables, and the maximum load penalty term is determined based on the constraints and a preset penalty value.

[0051] The sending unit is used to send the charging plan power data set for the target time period to the smart meter for execution.

[0052] Fourthly, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electric vehicle orderly charging control method as described in any of the preceding claims.

[0053] Fifthly, the present invention provides a computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the electric vehicle orderly charging control method as described in any of the preceding claims.

[0054] When the electric vehicle orderly charging control method provided in this embodiment of the invention is in use, if a user needs to charge a target electric vehicle, the user sends a charging request to the smart meter to be used through a user terminal. The charging request includes the charging demand information of the target electric vehicle. After receiving the charging request, the concentrator first calculates the load data set of the target transformer area for the target time period based on the predicted power set of residential load in the target transformer area during the target time period and the historical charging power data set. Then, based on the charging demand information and the load data set of the transformer area, it calculates the planned charging power data set for the target time period based on a pre-determined optimization model. Finally, the planned charging power data set is sent to the target smart meter. The target smart meter charges the target electric vehicle within the target time period according to the received planned charging power data set. This solution processes user-uploaded charging demand information, the predicted power set of residential load in the target area during the target time period, and the historical charging power data set through a concentrator. It comprehensively considers the maximum load of the target area during the control period and the minimum overall charging cost for users to optimize the solution. The resulting charging plan power data set for the target time period is used as an orderly charging control strategy for electric vehicles. This strategy transfers disordered electric vehicle charging loads that highly overlap with the peak load of residential areas to the off-peak electricity hours in residential areas, thereby reducing the overall charging cost for users. Compared with traditional charging methods, this solution has a smaller peak-to-valley load difference, higher grid operation safety, and can meet the charging needs of more users. Attached Figure Description

[0055] Figure 1 A flowchart illustrating the steps of an orderly charging control method for electric vehicles provided in an embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram of the connection structure of the user terminal, concentrator, and smart meter in an electric vehicle orderly charging control method provided in an embodiment of the present invention.

[0057] Figure 3 A schematic diagram comparing the electric vehicle load curve of an orderly charging control method for electric vehicles provided in an embodiment of the present invention with the electric vehicle load curve of a traditional disordered charging control method.

[0058] Figure 4 This is a structural block diagram of an electric vehicle orderly charging control device provided in an embodiment of the present invention. Detailed Implementation

[0059] 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.

[0060] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0061] To address the issue that existing residential area distribution transformers cannot meet the needs of large-scale, disordered electric vehicle load access, this invention provides an electric vehicle orderly charging control method and apparatus.

[0062] In a first aspect, embodiments of the present invention provide a method for orderly charging control of electric vehicles.

[0063] like Figure 1 and Figure 2 As shown, an orderly charging control method for electric vehicles includes a user terminal, a concentrator, and smart meters; there are multiple smart meters, and they are electrically connected to the concentrator.

[0064] The concentrator is the central management and control device of the remote centralized meter reading system, responsible for functions such as timed reading of terminal data, system command transmission, network management, event logging, and horizontal data transmission.

[0065] A concentrator is a central connection point device that connects terminals, computers, or communication equipment. It becomes the central point for cable convergence. In areas with a high density of terminals, to reduce communication lines, the terminals are typically connected to the concentrator first, and then the concentrator is connected to the computer's communication controller via high-speed lines. Therefore, a concentrator is also an effective device for sharing lines and improving line utilization.

[0066] Smart meters are one of the basic devices for data acquisition in smart grids. They are responsible for the collection, measurement, and transmission of raw electrical energy data and are the foundation for information integration, analysis and optimization, and information presentation.

[0067] The user terminal can receive charging demand information input by the user and generate a charging request based on the charging demand information. In this embodiment, the user terminal can be a mobile phone or an information collection module of a smart meter.

[0068] like Figure 2 As shown, in this embodiment, there are five smart meters 12 connected to the same concentrator 11 via cables. After the target electric vehicle 13 is connected, the smart meters 12 can receive charging requests from the user terminal and send these requests to the concentrator 11. The concentrator 11 can then upload the charging requests to the concentrator 11. Based on the uploaded charging requests, the predicted residential load power of the area where the concentrator 11 is located, and the historical charging power strategies pre-stored in the concentrator 11, the concentrator 11 generates a predicted charging power strategy and distributes this strategy to the corresponding smart meters 12 for execution. Within a 24-hour period, the day is divided into 96 sub-time periods, each 15 minutes long. The predicted charging power includes the preset charging power for each of these 96 sub-time periods.

[0069] like Figure 1 As shown, the control method applied to the concentrator includes:

[0070] S101, Receive a charging request from the user terminal sent by the smart meter.

[0071] The charging request includes charging demand information.

[0072] Specifically, charging demand information includes the electric vehicle's battery capacity, current battery charge, expected battery charge, start charging time, and maximum charging power.

[0073] Electric vehicle batteries include traditional batteries such as lead-acid batteries, lithium iron phosphate batteries, lithium batteries, sodium-sulfur batteries, and nickel-cadmium batteries, as well as fuel cells and novel liquid batteries representing future technologies. Due to differences in performance, the battery capacity of electric vehicles also varies. Battery capacity represents the amount of electricity discharged under necessary conditions, such as discharge rate, temperature, and stop voltage.

[0074] The current charge of an electric vehicle's battery refers to the amount of charge remaining in the battery before it is recharged.

[0075] The expected charge of an electric vehicle's battery refers to the amount of charge that the electric vehicle is expected to achieve.

[0076] The start time of charging an electric vehicle refers to the time when the power grid begins to charge the electric vehicle. In actual calculations, the time when the electric vehicle is connected to the power grid for charging can be used as the standard.

[0077] The maximum charging power of an electric vehicle refers to the maximum charging power that the charging equipment can provide when the electric vehicle is being charged, that is, the charging power that the charging pile and the vehicle battery can withstand.

[0078] S102. Based on the predicted power set of residential load in the target area during the target time period and the historical charging power data set, obtain the load data set of the target area during the target time period.

[0079] Among them, the residential load forecast power set is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period.

[0080] In this embodiment, the target time period refers to the next 24 hours, and the charging strategy for electric vehicles is a 96-point, 15-minute-level charging strategy for the next 24 hours. That is, the 24 hours are divided into 96 sub-time periods with 15-minute intervals.

[0081] The historical charging power data set refers to the charging power of 96 sub-time periods within 24 hours stored in the concentrator; the residential load prediction power set refers to the historical residential load power of the target transformer area obtained by the concentrator, that is, the residential load power of 96 sub-time periods within the next 24 hours predicted from the historical residential load power.

[0082] The set of transformer load data for the target time period can be the sum of the corresponding point data of the historical charging power data set and the residential load prediction power set, or the average value of the corresponding point data can be taken.

[0083] S103. Based on the charging demand information and the set of transformer area load data, and using a pre-determined optimization model, calculate the set of charging plan power data for the target time period.

[0084] The optimization model includes decision variables, objective function, and constraints. The constraints are determined by charging demand information and the set of transformer area load data. The decision variables are the set of charging plan power data. The objective function is the minimum value of the sum of user charging costs accumulated over the target time period and the maximum load penalty term. The user charging costs are determined based on the constraints and decision variables, and the maximum load penalty term is determined based on the constraints and preset penalty value.

[0085] Specifically, an optimization model refers to a model representing the optimal solution, determined using linear programming, nonlinear programming, dynamic programming, integer programming, and systems science methods in economic management. It reflects the conditional extremum problem in economic activities, that is, how to most effectively utilize various resources under a given objective, or how to achieve the best results under limited resources.

[0086] The three essential elements of an optimization model are decision variables, the objective function, and constraints. Decision variables are the quantities to be determined in an optimization problem, related to the constraints and the objective function. Generally, they have certain limitations (constraints) closely related to the objective function. In optimization problems, the function whose extreme value (or maximum / minimum value) is to be found related to the variables is called the objective function. The restrictions that variables must satisfy when finding the extreme value of the objective function are called constraints. For example, many practical problems require variables to be non-negative, which is a constraint; in studying circuit optimization design problems, variables must obey the basic laws of circuits, which is also a constraint, and so on. When studying a problem, these constraints must be accurately described using mathematical expressions.

[0087] In this embodiment, the decision variable is the charging plan power data set, specifically including the planned charging power for 96 time periods within the next 24 hours; the objective function is the minimum value of the sum of user charging costs accumulated during the target time period and the maximum load penalty term; the constraints are determined by the charging demand information and the distribution area load data set.

[0088] S104. Send the charging plan power data set for the target time period to the smart meter for execution.

[0089] Specifically, the concentrator distributes the calculated planned charging power for 96 sub-time periods within the next 24 hours to the smart meters corresponding to the target electric vehicles. The smart meters then charge the target electric vehicles according to the planned charging power for the corresponding sub-time periods within the next 24 hours.

[0090] like Figure 1 , Figure 2As shown, further, based on the predicted residential load power set and historical charging power data set of the target transformer area during the target time period, the transformer area load data set for the target time period is obtained, specifically:

[0091] The following formula is used to accumulate the residential load forecast power set and the historical charging power data set in chronological order to obtain the transformer load data set for the target time period;

[0092]

[0093] Among them, S j This represents the set of transformer load data for the target time period. S represents the set of historical charging power data; pre，j denoted as the set of predicted residential load power; J represents the number of sub-charging periods that are equally divided into the target time period.

[0094] Specifically, after receiving a charging request, the concentrator activates its electric vehicle orderly charging strategy control and solution mechanism through an event trigger. That is, after receiving the charging request, the concentrator calculates the set of transformer load data for the target time period using formula (1).

[0095] In this embodiment, J is 96.

[0096] Furthermore, before obtaining the load data set of the target transformer area for the target time period based on the predicted residential load power set and the historical charging power data set of the target transformer area for the target time period, the method also includes:

[0097] Based on the collection of multiple sets of historical residential load power data for the target transformer area before the target time period, a set of predicted residential load power for the target time period is obtained using a neural network model.

[0098] In this embodiment, a pre-trained neural network model is used to predict the residential load power set for the next 24 hours by using a set of historical residential load power data from multiple sets of data for the target transformer area.

[0099] Obtain the set of historical charging power data from the previous time period for the target time period of the target transformer area.

[0100] In this embodiment, the historical charging power data set of the previous time period is stored in the concentrator, and the data can be read directly from the corresponding area of ​​the concentrator.

[0101] Furthermore, the objective function for optimizing the model is specifically as follows:

[0102]

[0103] Among them, Pj U represents the planned charging power of the target electric vehicle at time j, i.e., the set of planned charging power data or decision variables for the target time period; j It is 0 or 1 when u j When u is 0, it indicates that the target electric vehicle is not charging during that time period. j When p is 1, it indicates that the target electric vehicle is charging during that time period; j This represents the time-of-use electricity price for each sub-charging period of the target electric vehicle within the target time period, where Δt represents the time interval between each sub-charging period.

[0104] M represents the preset penalty value; Z represents the maximum load of each sub-charging period.

[0105] State Grid charging stations offer three pricing levels: peak, off-peak, and normal. Peak hours are generally 8:00-11:00 and 18:00-23:00; off-peak hours are generally 23:00-7:00; and normal hours are generally 7:00-8:00 and 11:00-18:00. The fees differ for each of these three periods. For example: Peak hours: 1.6-1.8 yuan / kWh (including service fee); Off-peak hours: 0.9-1.2 yuan / kWh (including service fee); Normal hours: 1.3-1.5 yuan / kWh (including service fee).

[0106] In this embodiment, when u during a certain sub-charging period j When the value is 0, the planned charging power P corresponding to this sub-charging period is... j The value is 0; the time interval between each sub-charging period is 15 minutes, which is represented by 0.25 in the calculation; M is a relatively large preset value, which can be set to 1000 in the calculation; the time-of-use price of each sub-charging period is generally stored in the concentrator in advance.

[0107] Furthermore, the constraints of the optimization model include residential area distribution transformer constraints, charging plan continuity constraints, user demand constraints, electric vehicle charging power constraints, and maximum load constraints during the control period with added penalty terms in the objective function.

[0108] The charging demand information includes the target electric vehicle's battery capacity, the target electric vehicle's current battery charge, the target electric vehicle's expected battery charge, the target electric vehicle's start charging time, and the target electric vehicle's maximum charging power.

[0109] The specific constraints on transformer distribution in residential areas are as follows:

[0110] P j +S j ≤η1S T j = 1, 2, ..., J(3),

[0111] Among them, Pj S represents the planned charging power of the target electric vehicle at time j; j This represents the set of transformer load data for the target time period; η1 represents the percentage of safety constraints for the transformers in the target area; S T J represents the transformer capacity of the target area; J represents the number of sub-charging periods that are equally divided into the target time period.

[0112] In the process of implementing the orderly charging control strategy for electric vehicles, in order to avoid the intermittent nature of charging behavior, that is, to avoid the charging pile frequently switching between charging mode and non-charging mode, and to avoid the damage to the battery life of electric vehicles caused by this behavior, the continuity constraint of orderly charging control needs to be added to the optimization model.

[0113] The continuity constraints of the charging plan are as follows:

[0114] -M j ≤u j+1 -u j ≤M j (4),

[0115]

[0116] Among them, M j Indicates whether the charging plan for the target electric vehicle changes at time j; u j It is 0 or 1 when u j When u is 0, it indicates that the target electric vehicle is not charging during that time period. j A value of 1 indicates that the target electric vehicle is being charged during that time period.

[0117] Specifically, M j This indicates whether the charging plan for the target electric vehicle changes at time j, and when u at time j+1... j+1 With time j u j When the variables are different, it is assumed that the charging behavior of electric vehicles changes, M j The value is 1; when u is at time j+1 j+1 With time j u j When the variables are the same, it is assumed that the charging behavior of electric vehicles does not change, M j The value is 0.

[0118] When users upload charging demand information through smart meters, and the optimization model is used to solve the corresponding orderly charging strategy for electric vehicles, the objective function needs to be optimized based on meeting the user's basic charging needs and the actual charging capacity of the electric vehicles. Therefore, user demand constraints need to be added to the optimization model.

[0119] The specific user requirement constraints are as follows:

[0120]

[0121] u j =0 j≥t end (7),

[0122] Where C represents the battery capacity of the target electric vehicle; SOC1 represents the current battery charge of the target electric vehicle; SOC2 represents the expected battery charge of the target electric vehicle; Δt represents the time interval between each sub-charging period; t end This indicates the start time of charging for the target electric vehicle.

[0123] Specifically, the starting charging time of the target electric vehicle can be the time when the electric vehicle connects to the power grid through the charging pile; the time interval between each sub-charging period is 15 minutes, which is taken as 0.25 hours in the calculation; the number of sub-charging periods that are equally divided into the target time period is 96.

[0124] The specific constraints on electric vehicle charging power are as follows:

[0125] u j *P minn ≤P j ≤u j *P maxn j = 1, 2, ..., J (8),

[0126] Among them, P max P represents the maximum charging power of the target electric vehicle. min This indicates the preset minimum charging power.

[0127] Specifically, the maximum charging power is the maximum charging power supported by the electric vehicle or charging station; to ensure a short charging time for the electric vehicle, the preset minimum charging power is the minimum charging power set by the charging station.

[0128] The maximum load constraint for the control period with added penalty terms in the objective function is as follows:

[0129]

[0130] Z≥0 (10),

[0131] Where Z represents the maximum load of the target electric vehicle during the control period, and η2 represents the percentage of the maximum load constraint penalty limit of the target electric vehicle during the control period.

[0132] In this embodiment, the transformer capacity of the target distribution area is 550KVA, the safety constraint percentage of the transformer in the target distribution area is 100%, and the maximum load constraint penalty limit percentage of the target electric vehicle during the control period is 80%.

[0133] A total of 32 electric vehicles are connected to the charging system for charging. The current battery charge of the target electric vehicle follows the formula N(0.3, 0.05). 2 The expected battery charge of the target electric vehicle follows the formula N(0.85, 0.05). 2 The target electric vehicle has a battery capacity of 65kWh, and the charging start time follows N(19,3). 2 The charging end time distribution follows N(8,3). 2 The target electric vehicles have three maximum charging power options: 11kW, 7kW, and 3.5kW. Among them, vehicles with a maximum charging power of 7kW account for 50%, while vehicles with maximum charging power of 11kW and 3.5kW each account for 25%.

[0134] The optimization model is invoked to calculate the decision variables in the objective function, thereby obtaining the charging plan power data set for the target time period.

[0135] Figure 3 This is a schematic diagram comparing the electric vehicle load curve obtained by the ordered charging control method with the electric vehicle load curve obtained by the traditional disordered charging control method for the target time period charging plan power data set calculated in this embodiment.

[0136] like Figure 3 It can be seen that the vehicle load curve with orderly charging control strategy reduces the peak load by 29.8% compared with the original disordered electric vehicle load curve. The load that exceeds the transformer capacity limit in the original disordered electric vehicle load curve is transferred to the low-end area, and the charging cost of electric vehicle users is saved.

[0137] Furthermore, before calculating the charging plan power data set for the target time period based on a predetermined optimization model, the method also includes:

[0138] Build an optimization model.

[0139] Before applying the optimization model to calculate the charging plan power data set for the target time period, it is necessary to determine the objective function of the optimization model based on the minimum value of the sum of the accumulated user charging costs and the maximum load penalty term for the target time period; and to determine the constraints of the optimization model based on the charging demand information and the distribution area load data set.

[0140] When the electric vehicle orderly charging control method provided in this embodiment of the invention is in use, if a user needs to charge a target electric vehicle, the user sends a charging request to the smart meter to be used through a user terminal. The charging request includes the charging demand information of the target electric vehicle. After receiving the charging request, the concentrator first calculates the load data set of the target transformer area for the target time period based on the predicted power set of residential load in the target transformer area during the target time period and the historical charging power data set. Then, based on the charging demand information and the load data set of the transformer area, it calculates the planned charging power data set for the target time period based on a pre-determined optimization model. Finally, the planned charging power data set is sent to the target smart meter. The target smart meter charges the target electric vehicle within the target time period according to the received planned charging power data set.

[0141] This solution processes user-uploaded charging demand information, the predicted power set of residential load in the target area during the target time period, and the historical charging power data set through a concentrator. It comprehensively considers the maximum load of the target area during the control period and the minimum overall charging cost for users to optimize the solution. The resulting charging plan power data set for the target time period is used as an orderly charging control strategy for electric vehicles. This strategy transfers disordered electric vehicle charging loads that highly overlap with the peak load of residential areas to the off-peak electricity hours in residential areas, thereby effectively controlling user charging behavior and reducing overall charging costs for users. Compared with traditional charging methods, this solution has a smaller peak-to-valley load difference, higher grid operation safety, and can meet the charging needs of more users.

[0142] Secondly, the present invention also provides an orderly charging control method for electric vehicles, including a user terminal, a concentrator, and smart meters; multiple smart meters are present and electrically connected to the concentrator; the control method is applied to the smart meters and includes:

[0143] It receives charging requests from user terminals and forwards them to the concentrator.

[0144] The charging request includes charging demand information. After the user terminal sends a charging request, the smart meter receives the request and forwards it to the concentrator. Within the concentrator, the charging request can be triggered by an event to activate the concentrator's electric vehicle orderly charging strategy control and solution mechanism.

[0145] The system receives a set of charging plan power data for a target time period from the concentrator and charges the target electric vehicle according to the charging plan power data set within the target time period. The charging plan power data set is calculated by the concentrator based on the charging demand information and the distribution area load data set, using a pre-determined optimization model.

[0146] Specifically, after the concentrator starts the optimization model to calculate the charging plan power data set for the target time period, the concentrator sends the charging plan power data set to the smart meter. The smart meter then adjusts the charging power of the charging pile in each sub-time period based on the charging plan power data set.

[0147] Thirdly, the present invention also provides an orderly charging control device for electric vehicles.

[0148] like Figure 2 and Figure 4 As shown, it includes a user terminal, a concentrator 11, and a smart meter 12; there are multiple smart meters, and they are electrically connected to the concentrator 12; the concentrator 12 includes:

[0149] The receiving unit 301 is used to receive a charging request sent by the smart meter from the user terminal; the charging request includes charging demand information.

[0150] Processing unit 302 obtains the load data set of the target transformer area during the target time period based on the predicted power set of residential loads and the historical charging power data set of the target transformer area during the target time period. The predicted power set of residential loads is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period.

[0151] Planning unit 303 is used to calculate the charging plan power data set for the target time period based on the charging demand information and the distribution area load data set, according to a pre-determined optimization model. The optimization model includes decision variables, objective function, and constraints. The constraints are determined by the charging demand information and the distribution area load data set. The decision variable is the charging plan power data set. The objective function is the minimum value of the sum of the user charging cost and the maximum load penalty term accumulated during the target time period. The user charging cost is determined based on the constraints and decision variables, and the maximum load penalty term is determined based on the constraints and a preset penalty value.

[0152] The sending unit 304 is used to send the charging plan power data set for the target time period to the smart meter for execution.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0154] Fourthly, embodiments of the present invention also provide an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the electric vehicle orderly charging control method in the embodiments of the present invention.

[0155] Fifthly, embodiments of the present invention also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the electric vehicle orderly charging control method of the present invention.

[0156] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)). The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for orderly charging control of electric vehicles, characterized in that, It includes a user terminal, a concentrator, and smart meters; there are multiple smart meters, and they are electrically connected to the concentrator. The control method is applied to the concentrator and includes: The system receives a charging request from the user terminal sent by the smart meter; the charging request includes charging demand information. The load data set of the target transformer area for the target time period is obtained by combining the predicted residential load power set of the target transformer area for the target time period with the historical charging power data set. The predicted residential load power set is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period. Based on the charging demand information and the distribution area load data set, a charging plan power data set for the target time period is calculated using a pre-determined optimization model. The optimization model includes decision variables, an objective function, and constraints. The constraints are determined by the charging demand information and the distribution area load data set. The decision variable is the charging plan power data set. The objective function is the minimum sum of the accumulated user charging costs and the maximum load penalty term for the target time period. The user charging costs are determined based on the constraints and the decision variables, and the maximum load penalty term is determined based on the constraints and a preset penalty value. The set of charging plan power data for the target time period is sent to the smart meter for execution; The constraints of the optimization model include residential area distribution transformer constraints, charging plan continuity constraints, user demand constraints, electric vehicle charging power constraints, and maximum load constraints during the control period with a penalty term added to the objective function. The charging demand information includes the target electric vehicle's battery capacity, the target electric vehicle's current battery charge, the target electric vehicle's expected battery charge, the target electric vehicle's end time for charging, and the target electric vehicle's maximum charging power. The specific constraints on the distribution transformers in the residential area are as follows: ， in, express The target charging power of electric vehicles at all times; This represents the set of transformer load data for the target time period; Indicates the percentage of safety constraints for the target transformer area; Indicates the transformer capacity of the target distribution area; This indicates the number of sub-charging periods that are equally divided into the target time period; The continuity constraint of the charging plan is specifically as follows: ， ， in, express Whether the charging plan for the target electric vehicle has changed; It is 0 or 1 when When the value is 0, it indicates that the target electric vehicle is not charging during that time period. A value of 1 indicates that the target electric vehicle is being charged during that time period; The specific user requirement constraints are as follows: ， ， in, Indicates the battery capacity of the target electric vehicle; This indicates the current charge level of the target electric vehicle's battery. This indicates the expected charge amount of the target electric vehicle's battery. This indicates the time interval between each sub-charging period; Indicates the end of charging time for the target electric vehicle; The electric vehicle charging power constraint is specifically as follows: ， in, This indicates the maximum charging power of the target electric vehicle. Indicates the preset minimum charging power; The maximum load constraint for the control period with added penalty terms in the objective function is specifically as follows: ， ， in, This indicates the maximum load of the target electric vehicle during the control period. This indicates the percentage of the maximum load constraint penalty limit for the target electric vehicle during the control period.

2. The control method according to claim 1, characterized in that, The process of obtaining the load data set for the target transformer area during the target time period based on the predicted residential load power set and the historical charging power data set for the target transformer area during the target time period is as follows: The set of predicted residential load power and the set of historical charging power data are accumulated in chronological order using the following formula to obtain the set of transformer load data for the target time period. ， in, This represents the set of transformer load data for the target time period. This represents a set of historical charging power data. Represents the set of predicted residential load power; This indicates the number of sub-charging periods that are equally divided into the target time period.

3. The control method according to claim 2, characterized in that, Before obtaining the load data set of the target transformer area for the target time period based on the predicted power set of residential load for the target transformer area during the target time period and the historical charging power data set, the method further includes: Based on the set of historical residential load power data of the target transformer area before the target time period, the predicted set of residential load power for the target time period is predicted based on a neural network model. Obtain the set of historical charging power data from the previous time period for the target time period of the target transformer area.

4. The control method according to any one of claims 1-3, characterized in that, The objective function of the optimization model is specifically: ， in, express The planned charging power of the target electric vehicle at any given time, i.e., the set of planned charging power data or decision variables for the target time period; It is 0 or 1 when When the value is 0, it indicates that the target electric vehicle is not charging during that time period. A value of 1 indicates that the target electric vehicle is being charged during that time period; p j This indicates the time-of-use electricity price for the target electric vehicle during each sub-charging period within the target time period. This indicates the time interval between each sub-charging period; Indicates the preset penalty value; This indicates the maximum load during each sub-charging period.

5. The control method according to claim 1, characterized in that, Before calculating the charging plan power data set for the target time period based on a predetermined optimization model, the method further includes: Construct the optimization model.

6. A method for orderly charging control of electric vehicles, characterized in that, It includes a user terminal, a concentrator, and smart meters; there are multiple smart meters, and they are electrically connected to the concentrator. The control method is applied to the smart meter and includes: The system receives a charging request from a user terminal and sends the charging request to the concentrator; the charging request includes charging demand information. The concentrator receives a set of charging plan power data for a target time period from the concentrator, and charges the target electric vehicle according to the set of charging plan power data within the target time period. The set of charging plan power data is calculated by the concentrator based on a predetermined optimization model, according to the charging demand information and the set of transformer area load data.

7. An orderly charging control device for electric vehicles, characterized in that, It includes a user terminal, a concentrator, and smart meters; there are multiple smart meters, and they are electrically connected to the concentrator; the concentrator includes: A receiving unit is configured to receive a charging request sent by the smart meter from the user terminal; the charging request includes charging demand information. The processing unit obtains the load data set of the target transformer area for the target time period based on the predicted power set of residential load for the target transformer area and the historical charging power data set. The predicted power set of residential load is determined based on the historical residential load power data of the target transformer area, and the historical charging power data set is the charging power data set of the previous time period of the target time period. The planning unit is used to calculate the planned charging power data set for a target time period based on the charging demand information and the distribution area load data set, using a pre-determined optimization model. The optimization model includes decision variables, an objective function, and constraints. The constraints are determined by the charging demand information and the distribution area load data set. The decision variables are the planned charging power data set. The objective function is the minimum value of the sum of the accumulated user charging costs and the maximum load penalty term for the target time period. The user charging costs are determined based on the constraints and the decision variables, and the maximum load penalty term is determined based on the constraints and a preset penalty value. The sending unit is used to send the charging plan power data set for the target time period to the smart meter for execution; The constraints of the optimization model include residential area distribution transformer constraints, charging plan continuity constraints, user demand constraints, electric vehicle charging power constraints, and maximum load constraints during the control period with a penalty term added to the objective function. The charging demand information includes the target electric vehicle's battery capacity, the target electric vehicle's current battery charge, the target electric vehicle's expected battery charge, the target electric vehicle's end time for charging, and the target electric vehicle's maximum charging power. The specific constraints on the distribution transformers in the residential area are as follows: ， in, express The target charging power of electric vehicles at all times; This represents the set of transformer load data for the target time period; Indicates the percentage of safety constraints for the target transformer area; Indicates the transformer capacity of the target distribution area; This indicates the number of sub-charging periods that are equally divided into the target time period; The continuity constraint of the charging plan is specifically as follows: ， ， in, express Whether the charging plan for the target electric vehicle has changed; It is 0 or 1 when When the value is 0, it indicates that the target electric vehicle is not charging during that time period. A value of 1 indicates that the target electric vehicle is being charged during that time period; The specific user requirement constraints are as follows: ， ， in, Indicates the battery capacity of the target electric vehicle; This indicates the current charge level of the target electric vehicle's battery. This indicates the expected charge amount of the target electric vehicle's battery. This indicates the time interval between each sub-charging period; Indicates the end of charging time for the target electric vehicle; The electric vehicle charging power constraint is specifically as follows: ， in, This indicates the maximum charging power of the target electric vehicle. Indicates the preset minimum charging power; The maximum load constraint for the control period with added penalty terms in the objective function is specifically as follows: ， ， in, This indicates the maximum load of the target electric vehicle during the control period. This indicates the percentage of the maximum load constraint penalty limit for the target electric vehicle during the control period.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electric vehicle orderly charging control method as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or instruction set is loaded and executed by a processor to implement the electric vehicle orderly charging control method as described in any one of claims 1-5.

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