Electric vehicle charging control method and system

Abstract

The invention discloses an electric vehicle charging control method and system, and the method comprises the steps: dividing a day into a plurality of time periods; then, dynamic virtual electricity prices changing along with the load of the power distribution network are formulated for all time periods; wherein the power distribution network load and the dynamic virtual electricity price are in a positive correlation relationship; taking the lowest charging expense of the electric vehicle as a target, making a charging plan for the electric vehicle under a preset time constraint, and updating the load of the power distribution network according to the charging plan; wherein the charging cost expenditure and the dynamic virtual electricity price are in a positive correlation relationship, the time constraint is that the duration of a charging time period allowed by a user is longer than the duration required by charging of the electric vehicle, the task of peak load shifting is successfully converted into the task of reducing the charging cost expenditure of the electric vehicle, and finally a reasonable charging plan is obtained. The risk of insufficient power supply of a power grid is reduced, and electric energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to an electric vehicle charging control method and system. Background Art

[0002] Electric vehicles have become one of the main choices to replace traditional fuel vehicles due to their zero emissions. The number of electric vehicles is increasing, and the demand for electricity has also increased significantly. At present, electric vehicles are generally charged at the user's own discretion. This charging control method easily leads to a large difference in the peak and valley load of the power grid, which is prone to power shortage during peak power consumption periods, and a large amount of electricity is easily wasted during low power consumption periods. Therefore, it is necessary to provide an electric vehicle charging control method to reduce the peak load of the power grid and fill the valley load. Summary of the invention

[0003] Based on this, the present invention provides an electric vehicle charging control method and system to solve the problems of insufficient power supply from the power grid and large waste of electric energy caused by unreasonable electric vehicle charging plans.

[0004] To achieve the above object, an embodiment of the present invention provides an electric vehicle charging control method, comprising:

[0005] Divide the day into periods;

[0006] Formulate a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price;

[0007] With the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the time required for charging the electric vehicle.

[0008] To achieve the above purpose, an embodiment of the present invention further provides an electric vehicle charging control system, comprising:

[0009] The time period division module is used to divide a day into several time periods;

[0010] An electricity price setting module, used to set a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price;

[0011] A charging control module is used to formulate a charging plan for the electric vehicle with the goal of minimizing the charging cost expenditure of the electric vehicle under a preset time constraint, and update the distribution network load according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle.

[0012] Compared with the prior art, the electric vehicle charging control method and system disclosed in the embodiment of the present invention first divide a day into several time periods; then, a dynamic virtual electricity price that changes with the distribution network load is formulated for each time period; wherein the distribution network load is positively correlated with the dynamic virtual electricity price; and with the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle, thereby successfully converting the task of peak shaving and valley filling into the task of reducing the charging cost expenditure of the electric vehicle, and finally obtaining a reasonable charging plan, reducing the risk of insufficient power supply from the power grid, and reducing the waste of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a flow chart of a method for controlling charging of an electric vehicle provided by an embodiment of the present invention;

[0015] Figure 2 This is a charging plan formulation flow chart provided by an embodiment of the present invention;

[0016] Figure 3 It is a flow chart of a distributed controlled orderly charging strategy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 , is a flow chart of an electric vehicle charging control method provided by an embodiment of the present invention. Specifically, the electric vehicle charging control method includes steps S1 to S3:

[0019] S1. Divide a day into several time periods.

[0020] Specifically, a whole day is divided into time periods. Taking the length of a time period of half an hour as an example, a day is divided into 48 time periods, or taking the length of a time period of one hour as an example, a day is divided into 24 time periods, or taking the length of a time period of twenty minutes as an example, a day is divided into 72 time periods, etc. The total number of time periods in a day can be set according to actual conditions and is not limited to the above specific examples.

[0021] S2. Formulate a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price.

[0022] S3. With the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the time required to charge the electric vehicle.

[0023] Specifically, in order to make the distribution network load more balanced in each period of the day, a dynamic virtual electricity price that changes with the distribution network load is formulated for each period, and the distribution network load is presented in the form of a dynamic virtual electricity price. Then, with the lowest charging cost of the electric vehicle as the goal, the charging plan of the electric vehicle is continuously adjusted and the distribution network load is updated on the premise of ensuring that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle. Finally, a charging plan that minimizes the charging cost of the electric vehicle is found, and the charging of the electric vehicle is controlled according to the charging plan finally found. Specifically, the charging plan includes the charging time, and optionally, the charging plan also includes the charging power. It can be understood that the charging power can be planned according to the electric vehicle charging control method, or a preset charging power can be used, which is not limited here.

[0024] Compared with the existing technology, this implementation method converts the task of peak shaving and valley filling into the task of reducing the charging cost of electric vehicles by formulating dynamic virtual electricity prices for each time period that changes with the load of the distribution network, and finds a charging plan that minimizes the charging cost of electric vehicles to control the charging of electric vehicles, thereby reducing the peak-to-valley difference of the power grid load, reducing the risk of insufficient power supply in the power grid, and reducing the waste of electricity.

[0025] In a preferred embodiment, the time required for charging the electric vehicle is determined by:

[0026] Acquire the initial power information of the electric vehicle when charging starts, the expected power information required to be reached by the electric vehicle when charging ends, and the charging power of the electric vehicle;

[0027] The time required for charging the electric vehicle is calculated according to the initial power information, the expected power information and the charging power.

[0028] Exemplarily, the initial power information is the remaining battery capacity when the electric vehicle starts to connect to the power grid for charging, and the expected power information is the remaining battery capacity that the electric vehicle expects to reach when charging ends. The amount of power required for charging is calculated based on the remaining battery capacity at these two time points, and then the amount of power required for charging is divided by the charging power to obtain the time required for charging the electric vehicle.

[0029] It is worth noting that the specific forms of the initial power information and the expected power information are not limited to the remaining battery capacity, and can be set according to actual conditions. For example, the specific forms of the initial power information and the expected power information are the state of charge (SOC) of the remaining battery power. At this time, it is necessary to combine the total capacity of the battery, the SOC when the electric vehicle starts charging, the expected SOC when the electric vehicle ends charging, and the charging power to calculate the time required for the electric vehicle to charge.

[0030] In a preferred embodiment, the charging plan for the electric vehicle is formulated with the goal of minimizing the charging cost of the electric vehicle under a preset time constraint, including:

[0031] Obtain the current distribution network load in each period and formulate the current dynamic virtual electricity price for each period;

[0032] Get the time period allowed for charging by the user;

[0033] Set a time constraint that the duration of the time period allowed for charging by the user is longer than the time required for charging an electric vehicle:

[0034] T i ≥H i ;

[0035]

[0036]

[0037] Among them, T i H represents the number of charging time periods allowed by the user for electric vehicle i; i S represents the number of time periods required for charging electric vehicle i;end,i represents the expected power information of electric vehicle i, S soc,i represents the initial power information of electric vehicle i, V represents the total battery capacity of the electric vehicle, P r represents the charging power of the electric vehicle, T end,i represents the departure time of electric vehicle i, T st,i represents the access time of electric vehicle i, Indicates rounding up. It means rounding down, Δt means the duration of a period;

[0038] Taking the lowest charging cost of the electric vehicle as the goal, the objective function is set as follows:

[0039]

[0040] Where j represents time period j, T represents the total number of time periods in a day, and N j represents the number of electric vehicles connected to the distribution network in time period j, i represents electric vehicle i, P r Indicates the charging power of the electric vehicle, C j represents the dynamic virtual electricity price in period j, S i,j represents the charging status of electric vehicle i in time period j. When electric vehicle i is not charged in time period j, S i,j = 0, when electric vehicle i is charging in time period j, S i,j =1;

[0041] The objective function is solved to obtain a charging plan for the electric vehicle.

[0042] Specifically, first, a time constraint is set that the duration of the time period allowed by the user to charge is greater than the time required for the electric vehicle to charge. If the duration of the time period allowed by the user to charge is greater than the time required for the electric vehicle to charge, the user can execute the orderly charging strategy, otherwise cancel and re-acquire relevant information until the time constraint is met. The duration of the time period allowed by the user to charge and the time required for the electric vehicle to charge are specifically calculated in the following way: Taking the example of allowing the electric vehicle to charge when the user is at home, assuming that the number of time periods required for the electric vehicle i to charge to the expected value is H i , the number of time periods from when an electric car user returns home to when he leaves home is T i, the specific type of the expected power information and the initial power information is SOC. The SOC expected to be reached at the end of charging is subtracted from the SOC at the beginning of charging, and then multiplied by the total capacity of the battery to obtain the power required to be charged, and the power required to be charged is divided by the charging power to obtain the total charging time. Finally, the total charging time is divided by the length of a period and rounded to obtain the number of time periods required for electric vehicle i to charge to the expected value. The time when electric vehicle i leaves the charging pile is subtracted from the time when electric vehicle i is connected to the charging pile, and then divided by the length of a period and rounded to obtain the number of time periods from the time when the electric vehicle user returns home to the time when he leaves home. Then, under the above-mentioned time constraint, an objective function with the lowest charging cost expenditure of the electric vehicle is set as the goal. The objective function covers the charging costs of all electric vehicles. In a period, the charging cost of an electric vehicle is obtained by multiplying the charging power, the dynamic virtual electricity price and the charging time of a period. The charging costs of all electric vehicles in all periods are added together to obtain the charging costs of all electric vehicles. With the lowest charging cost of all electric vehicles as the goal, under the time constraint, the charging plan of each electric vehicle is solved.

[0043] It is worth noting that, in the process of solving the objective function, the charging time in the charging plan obtained is the charging time allowed by the user.

[0044] Further, solving the objective function to obtain the charging plan of the electric vehicle includes:

[0045] Introducing Lagrange multipliers Convert the time constraint and the objective function into an uncoupled constraint model:

[0046]

[0047] Among them, P i,j represents the charging plan of electric vehicle i in time period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, and V represents the transformer capacity;

[0048] Decouple the first term of the above equation into non-coupled sub-functions related to i, and obtain the corresponding Lagrangian dual function:

[0049]

[0050] max D(λ);

[0051] stj∈[1,H i ],λ j ≥0;

[0052] Where P represents the charging plan to be solved;

[0053] Substituting the optimal Lagrange multiplier into the Lagrange dual function, we get the distributed model:

[0054]

[0055] st0≤P i,j ≤S i,j P r ;

[0056]

[0057] S 0 ≤S k ≤S E ;

[0058]

[0059] Among them, S k Indicates the current state of charge, S 0 Indicates the preset state of charge when charging starts, S E Indicates the preset state of charge when charging ends, and J indicates the total charging time;

[0060] For the distributed model, initialize the Lagrange multiplier λ j (0) = 0, initializing the number of iterations m = 0, initializing the maximum number of iterations M and the number of electric vehicles N that implement the orderly charging strategy in the community;

[0061] Perform multiple rounds of iterations on the initialized distributed model until the degree of change of the Lagrange multiplier in adjacent iteration rounds is less than a set threshold or the total number of iterations of the distributed model reaches the maximum number of iterations, thereby obtaining a final charging plan for the electric vehicle;

[0062] In each iteration of the distributed model, the dynamic virtual electricity price is formulated according to the current round of distribution network load, and the cost signal C of the current round is converted into j Δt+λ j (m) sending to a battery management system of the electric vehicle so that the battery management system formulates a charging plan for the electric vehicle in the current round according to the cost signal; after updating the distribution network load according to the charging plan for the electric vehicle in the current round, the Lagrange multiplier is updated.

[0063] Specifically, a distributed control method is used to formulate a charging plan: under distributed control, the coordination center is connected to the distribution network control center to transmit the load information of the distribution network in the community in real time; the coordination center formulates dynamic virtual electricity prices for each time period based on the load information; when an electric vehicle is connected to a charging pile, the battery management system on the vehicle analyzes the current vehicle battery status and user needs based on the virtual electricity price published by the coordination center, formulates a charging plan and feeds it back to the coordination center. In this embodiment, the Lagrange multiplier method is used to solve the charging plan, see Figure 2 The charging plan formulation flow chart shown in the figure inputs the load information of the distribution network in the community, user needs (such as the charging time period allowed by the user, the battery capacity expected by the electric vehicle, etc.), formulates a dynamic virtual electricity price, initializes the Lagrange multiplier, sets the maximum number of iterations M, initializes the current iteration round m to 0, and converts the cost signal C j Δt+λ j (m) is transmitted to the electric vehicle, which formulates a charging plan based on the cost signal and feeds it back to the coordination center. The coordination center updates the Lagrange multiplier until the difference θ between the Lagrange multipliers of adjacent iteration rounds is less than the set threshold ε or the iteration round m has reached the maximum number of iterations M, and then outputs the final charging plan.

[0064] Specifically, in this embodiment, the Lagrange multiplier method is introduced to construct the model to obtain a distributed model, and the charging plan is solved for the distributed model. Figure 3 The distributed control orderly charging strategy flow chart is shown in the figure. The specific operation steps of the distributed model are as follows:

[0065] (1) The coordination center sets a virtual electricity price C based on the community load conditions in time period j j , j∈[1,H i ]. Initialize the Lagrange multiplier λ j (0) = 0; initialize the number of iterations m = 0; initialize the maximum number of iterations M and the number of electric vehicles N that implement the orderly charging strategy in the community.

[0066] (2) At the mth iteration, the coordination center sends a cost signal C to the electric vehicle. j Δt+λ j (m),j∈

[0067] [1,H i ], after receiving the signal, the battery management system of the electric vehicle formulates a charging plan based on the vehicle information (such as vehicle stay time, battery power expectation, vehicle access time, user departure time and cost signal, etc.) Among them, the vehicle residence time is the time that the user allows the electric vehicle to charge, which is calculated based on the vehicle access time and the user departure time. The expected battery power is the power that the electric vehicle can reach at the end of charging as expected by the user. The vehicle access time is the time when the vehicle accesses the charging device.

[0068] (3) The battery management system sends the charging plan to the coordination center, and the coordination center updates the Lagrange multiplier according to the charging plan formulated for each electric vehicle during this period and the distribution network load situation during the current period.

[0069] (4) The coordination center updates C j Δt + λ j (m), j ∈ [1, H i , and calculates the adjacent maximum absolute difference θ = max|λ(m + 1) - λ(m)|. ε is a set value greater than zero. If θ ≤ ε, the charging plan is output; otherwise, let m = m + 1.

[0070] (5) If the number of iterations m > M, the charging plan is output; if the number of iterations m < M, return to step (2).

[0071] In an implementation manner, the relationship between the distribution network load and the dynamic virtual electricity price is as follows:

[0072] j ∈ [1, T];

[0073]

[0074] Among them, C j represents the dynamic virtual electricity price of period j, represents the basic electricity price, represents the electricity price adjustment base, γ j represents the load rate of the distribution transformer in period j, L 0,j represents the conventional load in period j, L j represents the load connected to the charging pile in period j, α represents the transformer power factor, V represents the transformer capacity, and T represents the total number of periods in a day.

[0075] Specifically, the dynamic virtual electricity price is dynamically updated according to the actual operating state of the power grid to reflect the real-time distribution network load state. Assuming that a day is evenly divided into T periods, each period is of length Δt. At the end of each period, the coordination center counts the number of electric vehicles connected to charge during this period and formulates the dynamic virtual electricity price according to the distribution network load situation during this period. The dynamic virtual electricity price is not limited to specific numerical values and can be quickly set as long as it conforms to the price order relationship. The price order relationship means that it satisfies the positive correlation between the dynamic virtual electricity price and the distribution network load of the community, that is, a higher load level corresponds to a higher virtual price set.

[0076] Compared with the prior art, the method disclosed in the embodiment of the present invention first divides a day into several time periods; then, a dynamic virtual electricity price that changes with the distribution network load is formulated for each time period; wherein the distribution network load is positively correlated with the dynamic virtual electricity price; and with the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle, thereby successfully converting the task of peak shaving and valley filling into the task of reducing the charging cost expenditure of the electric vehicle, and finally obtaining a reasonable charging plan, reducing the risk of insufficient power supply from the power grid, and reducing the waste of electric energy.

[0077] The embodiment of the present invention further provides an electric vehicle charging control system, the electric vehicle charging control system comprising:

[0078] The time period division module is used to divide a day into several time periods;

[0079] An electricity price setting module, used to set a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price;

[0080] A charging control module is used to formulate a charging plan for the electric vehicle with the goal of minimizing the charging cost expenditure of the electric vehicle under a preset time constraint, and update the distribution network load according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle.

[0081] In one embodiment, the time required for charging the electric vehicle is determined by:

[0082] Acquire the initial power information of the electric vehicle when charging starts, the expected power information required to be reached by the electric vehicle when charging ends, and the charging power of the electric vehicle;

[0083] The time required for charging the electric vehicle is calculated according to the initial power information, the expected power information and the charging power.

[0084] In one embodiment, the charging control module is used to formulate a charging plan for the electric vehicle with the goal of minimizing the charging cost of the electric vehicle under a preset time constraint, specifically including:

[0085] Obtain the current distribution network load in each period and formulate the current dynamic virtual electricity price for each period;

[0086] Get the time period allowed for charging by the user;

[0087] Set a time constraint that the duration of the time period allowed for charging by the user is longer than the time required for charging an electric vehicle:

[0088] T i ≥H i ;

[0089]

[0090]

[0091] Among them, T i H represents the number of charging time periods allowed by the user for electric vehicle i; i S represents the number of time periods required for charging electric vehicle i; end,i represents the expected power information of electric vehicle i, S soc,i represents the initial power information of electric vehicle i, V represents the total battery capacity of the electric vehicle, P r represents the charging power of the electric vehicle, T end,i represents the departure time of electric vehicle i, T st,i represents the access time of electric vehicle i, Indicates rounding up. It means rounding down, Δt means the duration of a period;

[0092] Taking the lowest charging cost of the electric vehicle as the goal, the objective function is set as follows:

[0093]

[0094] Where j represents time period j, T represents the total number of time periods in a day, and N j represents the number of electric vehicles connected to the distribution network in time period j, i represents electric vehicle i, P r Indicates the charging power of the electric vehicle, C j represents the dynamic virtual electricity price in period j, S i,j represents the charging status of electric vehicle i in time period j. When electric vehicle i is not charged in time period j, S i,j = 0, when electric vehicle i is charging in time period j, S i,j =1;

[0095] Solving the objective function, a charging plan for the electric vehicle is obtained.

[0096] In one implementation, solving the objective function to obtain the charging plan for the electric vehicle includes:

[0097] Introducing Lagrange multipliers Convert the time constraint and the objective function into an uncoupled constraint model:

[0098]

[0099] Among them, P i,j represents the charging plan of electric vehicle i in time period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, and V represents the transformer capacity;

[0100] Decouple the first term of the above equation into non-coupled sub-functions related to i, and obtain the corresponding Lagrangian dual function:

[0101]

[0102] max D(λ);

[0103] stj∈[1,H i ],λ j ≥0;

[0104] Where P represents the charging plan to be solved;

[0105] Substituting the optimal Lagrange multiplier into the Lagrange dual function, we get the distributed model:

[0106]

[0107] st0≤P i,j ≤S i,j P r ;

[0108]

[0109] S 0 ≤S k ≤S E ;

[0110]

[0111] Among them, S k Indicates the current state of charge, S 0 Indicates the preset state of charge when charging starts, S E Indicates the preset state of charge when charging ends, and J indicates the total charging time;

[0112] For the distributed model, initialize the Lagrange multiplier λ j(0) = 0, initializing the number of iterations m = 0, initializing the maximum number of iterations M and the number of electric vehicles N that implement the orderly charging strategy in the community;

[0113] Perform multiple rounds of iterations on the initialized distributed model until the degree of change of the Lagrange multiplier in adjacent iteration rounds is less than a set threshold or the total number of iterations of the distributed model reaches the maximum number of iterations, thereby obtaining a final charging plan for the electric vehicle;

[0114] In each iteration of the distributed model, the dynamic virtual electricity price is formulated according to the current round of distribution network load, and the cost signal C of the current round is converted into j Δt+λ j (m) sending to a battery management system of the electric vehicle so that the battery management system formulates a charging plan for the electric vehicle in the current round according to the cost signal; after updating the distribution network load according to the charging plan for the electric vehicle in the current round, the Lagrange multiplier is updated.

[0115] In one implementation, the relationship between the distribution network load and the dynamic virtual electricity price is as follows:

[0116] j∈[1,T];

[0117]

[0118] Among them, C j represents the dynamic virtual electricity price in time period j, represents the basic electricity price, represents the electricity price adjustment base, γ j represents the load rate of the distribution transformer in period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, V represents the transformer capacity, and T represents the total number of time periods in a day.

[0119] It is worth noting that the specific working process of the electric vehicle charging control system can refer to the working process of the electric vehicle charging control method described in the above embodiment, which will not be repeated here.

[0120] Compared with the prior art, the electric vehicle charging control system disclosed in the embodiment of the present invention first divides a day into several time periods; then, a dynamic virtual electricity price that changes with the distribution network load is formulated for each time period; wherein the distribution network load is positively correlated with the dynamic virtual electricity price; and with the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle, thereby successfully converting the task of peak shaving and valley filling into the task of reducing the charging cost expenditure of the electric vehicle, and finally obtaining a reasonable charging plan, reducing the risk of insufficient power supply from the power grid, and reducing the waste of electric energy.

[0121] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling charging of an electric vehicle, characterized in that: include: Divide the day into periods; Formulate a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price; With the goal of minimizing the charging cost expenditure of the electric vehicle, a charging plan is formulated for the electric vehicle under a preset time constraint, and the distribution network load is updated according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the time required for charging the electric vehicle.

2. The electric vehicle charging control method according to claim 1, characterized in that: The time required for charging the electric vehicle is determined by the following method: Acquire the initial power information of the electric vehicle when charging starts, the expected power information required to be reached by the electric vehicle when charging ends, and the charging power of the electric vehicle; The time required for charging the electric vehicle is calculated according to the initial power information, the expected power information and the charging power.

3. The electric vehicle charging control method according to claim 1, characterized in that: The method aims to minimize the charging cost of the electric vehicle and formulates a charging plan for the electric vehicle under a preset time constraint, including: Obtain the current distribution network load in each period and formulate the current dynamic virtual electricity price for each period; Get the time period allowed for charging by the user; Set a time constraint that the duration of the charging time period allowed by the user is longer than the time required to charge the electric vehicle: T i ≥H i ; Among them, T i H represents the number of charging time periods allowed by the user for electric vehicle i; i S represents the number of time periods required for charging electric vehicle i; end,i represents the expected power information of electric vehicle i, S soc,i represents the initial power information of electric vehicle i, V represents the total battery capacity of the electric vehicle, P r represents the charging power of the electric vehicle, T end,i represents the departure time of electric vehicle i, T st,i represents the access time of electric vehicle i, Indicates rounding up. It means rounding down, Δt means the duration of a period; Taking the lowest charging cost of the electric vehicle as the goal, the objective function is set as follows: Where j represents time period j, T represents the total number of time periods in a day, and N j represents the number of electric vehicles connected to the distribution network in time period j, i represents electric vehicle i, P r Indicates the charging power of electric vehicles, C j represents the dynamic virtual electricity price in period j, S i,j represents the charging status of electric vehicle i in time period j. When electric vehicle i is not charged in time period j, S i,j = 0, when electric vehicle i is charging in time period j, S i,j =1; Solving the objective function, a charging plan for the electric vehicle is obtained.

4. The electric vehicle charging control method according to claim 3, characterized in that: The step of solving the objective function to obtain a charging plan for the electric vehicle includes: Introducing Lagrange multipliers Convert the time constraint and the objective function into an uncoupled constraint model: Among them, P i,j represents the charging plan of electric vehicle i in time period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, and V represents the transformer capacity; Decouple the first term of the above equation into non-coupled sub-functions related to i, and obtain the corresponding Lagrangian dual function: max D(λ); s.t.j∈[1,H i ],λ j ≥0; Where P represents the charging plan to be solved; Substituting the optimal Lagrange multiplier into the Lagrange dual function, we get the distributed model: s.t.0≤P i,j ≤S i,j P r ; S0≤S k ≤S E ; Among them, S k Indicates the current state of charge, S0 indicates the preset state of charge when charging starts, S E Indicates the preset state of charge when charging ends, and J indicates the total charging time; For the distributed model, initialize the Lagrange multiplier λ j (0) = 0, initializing the number of iterations m = 0, initializing the maximum number of iterations M and the number of electric vehicles N that implement the orderly charging strategy in the community; Perform multiple rounds of iterations on the initialized distributed model until the degree of change of the Lagrange multiplier in adjacent iteration rounds is less than a set threshold or the total number of iterations of the distributed model reaches the maximum number of iterations, thereby obtaining a final charging plan for the electric vehicle; In each iteration of the distributed model, the dynamic virtual electricity price is formulated according to the current round of distribution network load, and the cost signal C of the current round is converted into j Δt+λ j (m) sending to a battery management system of the electric vehicle so that the battery management system formulates a charging plan for the electric vehicle in the current round according to the cost signal; after updating the distribution network load according to the charging plan for the electric vehicle in the current round, the Lagrange multiplier is updated.

5. The electric vehicle charging control method according to claim 1, characterized in that: The relationship between the distribution network load and the dynamic virtual electricity price is as follows: Among them, C j represents the dynamic virtual electricity price in time period j, represents the basic electricity price, represents the electricity price adjustment base, γ j represents the load rate of the distribution transformer in period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, V represents the transformer capacity, and T represents the total number of time periods in a day.

6. An electric vehicle charging control system, characterized in that: include: The time period division module is used to divide a day into several time periods; An electricity price setting module, used to set a dynamic virtual electricity price for each time period that changes with the load of the distribution network; wherein the load of the distribution network is positively correlated with the dynamic virtual electricity price; A charging control module is used to formulate a charging plan for the electric vehicle with the goal of minimizing the charging cost expenditure of the electric vehicle under a preset time constraint, and update the distribution network load according to the charging plan; wherein the charging cost expenditure is positively correlated with the dynamic virtual electricity price, and the time constraint is that the duration of the charging time period allowed by the user is greater than the duration required for charging the electric vehicle.

7. The electric vehicle charging control system according to claim 6, characterized in that: The time required for charging the electric vehicle is determined by: Acquire the initial power information of the electric vehicle when charging starts, the expected power information required to be reached by the electric vehicle when charging ends, and the charging power of the electric vehicle; The time required for charging the electric vehicle is calculated according to the initial power information, the expected power information and the charging power.

8. The electric vehicle charging control system according to claim 6, characterized in that: The charging control module is used to formulate a charging plan for the electric vehicle with the goal of minimizing the charging cost of the electric vehicle under a preset time constraint, specifically including: Obtain the current distribution network load in each period and formulate the current dynamic virtual electricity price for each period; Get the time period allowed for charging by the user; Set a time constraint that the duration of the time period allowed for charging by the user is longer than the time required for charging an electric vehicle: T i ≥H i ; Among them, T i H represents the number of charging time periods allowed by the user for electric vehicle i; i S represents the number of time periods required for charging electric vehicle i; end,i represents the expected power information of electric vehicle i, S soc,i represents the initial power information of electric vehicle i, V represents the total battery capacity of the electric vehicle, P r represents the charging power of the electric vehicle, T end,i represents the departure time of electric vehicle i, T st,i represents the access time of electric vehicle i, Indicates rounding up. It means rounding down, Δt means the duration of a period; Taking the lowest charging cost of the electric vehicle as the goal, the objective function is set as follows: Where j represents time period j, T represents the total number of time periods in a day, and N j represents the number of electric vehicles connected to the distribution network in time period j, i represents electric vehicle i, P r Indicates the charging power of electric vehicles, C j represents the dynamic virtual electricity price in period j, S i,j represents the charging status of electric vehicle i in time period j. When electric vehicle i is not charged in time period j, S i,j = 0, when electric vehicle i is charging in time period j, S i,j =1; Solving the objective function, a charging plan for the electric vehicle is obtained.

9. The electric vehicle charging control system according to claim 8, characterized in that: The step of solving the objective function to obtain a charging plan for the electric vehicle includes: Introducing Lagrange multipliers Convert the time constraint and the objective function into an uncoupled constraint model: Among them, P i,j represents the charging plan of electric vehicle i in time period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, and V represents the transformer capacity; Decouple the first term of the above equation into non-coupled sub-functions related to i, and obtain the corresponding Lagrangian dual function: max D(λ); s.t.j∈[1,H i ],λ j ≥0; Where P represents the charging plan to be solved; Substituting the optimal Lagrange multiplier into the Lagrange dual function, we get the distributed model: s.t.0≤P i,j ≤S i,j P r ; S0≤S k ≤S E ; Among them, S k Indicates the current state of charge, S0 indicates the preset state of charge when charging starts, S E Indicates the preset state of charge when charging ends, and J indicates the total charging time; For the distributed model, initialize the Lagrange multiplier λ j (0) = 0, initializing the number of iterations m = 0, initializing the maximum number of iterations M and the number of electric vehicles N that implement the orderly charging strategy in the community; Perform multiple rounds of iterations on the initialized distributed model until the degree of change of the Lagrange multiplier in adjacent iteration rounds is less than a set threshold or the total number of iterations of the distributed model reaches the maximum number of iterations, thereby obtaining a final charging plan for the electric vehicle; In each iteration of the distributed model, the dynamic virtual electricity price is formulated according to the current round of distribution network load, and the cost signal C of the current round is converted into j Δt+λ j (m) sending to a battery management system of the electric vehicle so that the battery management system formulates a charging plan for the electric vehicle in the current round according to the cost signal; after updating the distribution network load according to the charging plan for the electric vehicle in the current round, the Lagrange multiplier is updated.

10. The electric vehicle charging control system according to claim 6, characterized in that: The relationship between the distribution network load and the dynamic virtual electricity price is as follows: Among them, C j represents the dynamic virtual electricity price in time period j, represents the basic electricity price, represents the electricity price adjustment base, γ j represents the load rate of the distribution transformer in period j, L 0,j represents the normal load of period j, L j represents the load connected to the charging pile in time period j, α represents the transformer power factor, V represents the transformer capacity, and T represents the total number of time periods in a day.

Images