A new energy vehicle intelligent charging control system and method

Abstract

The application relates to the technical field of charging control, in particular to a new energy vehicle intelligent charging control system and method, which comprises a demand determination unit, a scheduling modeling unit and the like.The demand determination unit is used for acquiring charging information of all new energy vehicles in a target charging station, and determining a total charging demand of the target charging station according to the charging information; the scheduling modeling unit is used for acquiring power supply information of a power grid, determining a target function according to the total charging demand and the power supply information, and determining a constraint condition based on power balance, voltage, line capacity and distribution transformer capacity of the power grid.The demand determination unit is used for acquiring the charging information of all vehicles in the charging station, accurately calculating the total charging demand, ensuring reasonable allocation of power resources, avoiding waste or deficiency, and the scheduling modeling unit is used for considering factors such as power balance, voltage, line capacity and the like of the power grid, so that the charging load cannot cause excessive pressure on the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging control, and particularly relates to a new energy vehicle intelligent charging control system and method. BACKGROUND

[0002] The new energy vehicle refers to an automobile adopting unconventional vehicle fuel as a power source, and combining advanced technologies of vehicle power control and driving to form an automobile with advanced technical principles and new technologies and structures.

[0003] The traditional method lacks accurate calculation of charging demand, which easily leads to uneven power distribution and power waste or charging shortage. The traditional method does not fully consider factors such as power balance, voltage and line capacity of the power grid, and the charging load may cause excessive pressure on the power grid, leading to unstable power grid and even causing failure. Moreover, the traditional method lacks support of optimization algorithm, and cannot generate an optimal scheduling scheme, so that the charging station may not obtain sufficient power, leading to low charging efficiency and prolonged user waiting time. Furthermore, the traditional method cannot monitor the working state of the charging pile in real time, and cannot dynamically adjust the output and input load, leading to low operating efficiency of the charging pile under different loads, and the charging station cannot intelligently allocate power according to the load variation, which easily leads to local overload or power shortage, affecting the overall operating efficiency of the charging station. In addition, the traditional method lacks optimization scheduling and distribution mechanism, and the power loss is large, which increases the pressure on the power grid and operating cost, cannot ensure stable power supply of the charging station, prolongs the charging waiting time, and reduces the user experience, affecting the popularization of new energy vehicles. SUMMARY

[0004] The present application solves the technical problems of the prior art, and provides a new energy vehicle intelligent charging control system and method.

[0005] The technical scheme adopted to solve the above technical problems is as follows: a new energy vehicle intelligent charging control system, comprising:

[0006] A demand determination unit is configured to acquire charging information of all new energy vehicles in a target charging station, and determine a total charging demand of the target charging station according to the charging information.

[0007] A scheduling modeling unit is configured to acquire power supply information of a power grid, determine a target function according to the total charging demand and the power supply information, and determine a constraint condition based on power balance, voltage, line capacity and distribution transformer capacity of the power grid.

[0008] a power dispatching unit, configured to construct a power dispatching model of the power grid based on the target function and the constraint condition, solve the power dispatching model based on an optimization algorithm to obtain a power dispatching scheme, and dispatch power of the power grid to the target charging station based on the power dispatching scheme;

[0009] a load determination unit, configured to acquire working information of all charging piles in the target charging station, and determine output load and input load of new energy vehicles charging at the charging piles based on the working information;

[0010] a power distribution unit, configured to determine load variation of the charging piles according to the output load and the input load, and distribute power of the power grid to the corresponding charging piles according to the load variation.

[0011] Preferably, the charging information includes expected end charging time, charge amount before charging, expected charge amount at the end of charging, and maximum charging rate, the power supply information includes power generation amount of renewable energy, energy storage amount of energy storage equipment, and tradable power amount of the power grid, and the total charging demand amount is calculated according to the following formula:

[0012]

[0013] wherein Q represents the total charging demand amount, N represents the number of new energy vehicles in the target charging station, represents the expected charge amount of the new energy vehicle at the end of charging, f i represents the charge amount of the new energy vehicle before charging.

[0014] Preferably, power supply information of the power grid is acquired, and a target function is determined according to the total charging demand amount and the power supply information, including:

[0015] a first supply-demand difference is determined according to the total charging demand amount and the power supply information;

[0016] when the first supply-demand difference is positive, a second supply-demand difference is determined according to the first supply-demand difference and the power supply information;

[0017] a revenue amount of the target charging station is determined based on the first supply-demand difference and the second supply-demand difference, wherein the revenue amount is calculated according to the following formula:

[0018]

[0019] wherein, represents the revenue amount of the target charging station, T maxcontrol period of the target charging station, Q(t), H(t), g(t) and b(t) represent total charging demand, power generation of renewable energy, energy storage capacity of energy storage device and tradable power of power grid at t moment, P H , P g and P b represent electricity price of renewable energy, energy storage device and power grid transaction at t moment, QH dif represents the first supply-demand difference, and QH dif = Q(t)-H(t), QHg dif represents the second supply-demand difference, and QHg dif = Q(t)-H(t)-g(t);

[0020] determine the objective function based on the income amount of the target charging station, wherein the objective function is to maximize the income amount of the target charging station.

[0021] Preferably, the constraint conditions include power balance constraint condition, voltage constraint condition, line capacity constraint condition and distribution transformer capacity constraint condition, wherein the expression of the power balance constraint condition is as follows:

[0022]

[0023] wherein j represents a node connected to the branch of node i in the power grid, P i , Q i and U i represent injected active power, reactive power and voltage amplitude of node i in the power grid, G ij and B ij represent real part and imaginary part of mutual admittance, θ ij represents the phase angle difference of the first and last end voltage of the branch;

[0024] The expression of the voltage constraint condition is as follows:

[0025] U min ≤ U i ≤ U max ;

[0026] wherein U min and U max represent lower and upper limits of node voltage;

[0027] The expression of the line capacity constraint condition is as follows:

[0028] |S ij | < S ijmax ;

[0029] wherein S ijmax represents the upper limit of branch capacity S ij connecting node i and j.

[0030] The expression of the power distribution transformer capacity constraint condition is as follows:

[0031]

[0032] Wherein, P Ti,t , P Li,t , P Ei，t and represent the total power of the power distribution transformer, the regular load power, the charging power and the upper limit of the charging power at the t period, S i , p i and cos i represent the rated capacity, the efficiency and the power factor of the power distribution transformer.

[0033] Preferably, the power dispatching model is solved based on an optimization algorithm to obtain a power dispatching scheme, comprising:

[0034] A group of individuals is randomly generated, and the position of each individual represents a combination of decision variables of the objective function;

[0035] For the combination of decision variables of the objective function represented by each individual, a fitness value is calculated, wherein the fitness value is the value of the objective function;

[0036] The light intensity and the attraction degree of the individual are updated, and the position of the individual is updated based on the light intensity and the attraction degree of the individual;

[0037] It is judged whether the training error reaches a convergence value or the number of iterations reaches a maximum value, if the training error reaches the convergence value and the number of iterations reaches the maximum value, the iteration is ended and the combination of decision variables of the objective function is output, otherwise the number of iterations is increased by 1.

[0038] Preferably, the update formula of the light intensity of the individual is as follows:

[0039]

[0040] Wherein, and represent the light intensity of the i-th individual at the t-th iteration and the t-1-th iteration, p represents a preset proportion, and t represents a preset proportion coefficient, X t represents the position of the individual at the t-th iteration, f(X t ) represents the fitness value corresponding to the position of the individual at the t-th iteration;

[0041] The update formula of the attraction degree of the individual is as follows:

[0042]

[0043] wherein β(r) represents the attraction degree of the individual, β0represents the maximum attraction degree, r ij represents the distance between the ith individual and the jth individual, and r ij = ||X i -X j ||, X i and X j represent the positions of the ith individual and the jth individual;

[0044] The update formula of the position of the individual is as follows:

[0045]

[0046] wherein, and represent the position of the ith individual at the t+1th iteration and the tth iteration, represents the position of the jth individual at the tth iteration, ε i represents the random factor of the ith individual, and a represents the step scaling factor.

[0047] Preferably, the output load and the input load of the new energy vehicle charging at the charging pile are determined based on the working information, comprising:

[0048] The output load of the new energy vehicle charging at the charging pile is determined based on the working information, wherein the calculation formula of the output load is as follows:

[0049]

[0050] wherein, represents the output load of the new energy vehicle charging at the charging pile within a preset control period, β knt represents whether the new energy vehicle is connected to the charging pile at time t, if connected to the charging pile, 1 is taken, otherwise, 0 is taken, represents the energy of the new energy vehicle arriving at the charging pile to start charging at time t, and V represents the set of new energy vehicles charging at the charging pile.

[0051] Preferably, the output load and the input load of the new energy vehicle charging at the charging pile are determined based on the working information, further comprising:

[0052] The input load of the new energy vehicle charging at the charging pile is determined based on the working information, wherein the calculation formula of the input load is as follows:

[0053]

[0054] wherein, represents the input load of the new energy vehicle charging at the charging pile within a preset control period.

[0055] Preferably, the load change amount is calculated according to the following formula:

[0056]

[0057] wherein E nt，V represents the load change amount of the charging pile at time t, P nt，V represents the charging power in a preset control period, η nt，V represents the charging efficiency in the preset control period, and Δt represents the preset control period.

[0058] The technical solution adopted to solve the above technical problems is: a new energy vehicle intelligent charging control method, which is applicable to the new energy vehicle intelligent charging control system, and comprises the following steps:

[0059] Obtaining charging information of all new energy vehicles in a target charging station, and determining a total charging demand of the target charging station according to the charging information;

[0060] Obtaining power supply information of a power grid, determining a target function according to the total charging demand and the power supply information, and determining a constraint condition based on power balance, voltage, line capacity and distribution transformer capacity of the power grid;

[0061] Constructing a power dispatching model of the power grid based on the target function and the constraint condition, solving the power dispatching model based on an optimization algorithm to obtain a power dispatching scheme, and dispatching power of the power grid to the target charging station based on the power dispatching scheme;

[0062] Obtaining working information of all charging piles in the target charging station, and determining output load and input load of new energy vehicles charging at the charging piles based on the working information;

[0063] Determining a load change amount of the charging piles according to the output load and the input load, and distributing power of the power grid to corresponding charging piles according to the load change amount.

[0064] The beneficial effects of the present invention are as follows: (1) The present invention obtains the charging information of all vehicles in the charging station through the demand determination unit, accurately calculates the total charging demand, ensures the reasonable allocation of power resources, avoids waste or shortage, and considers the power balance, voltage, line capacity and other factors of the power grid through the scheduling modeling unit, ensuring that the charging load will not put too much pressure on the power grid and maintain the stable operation of the power grid; (2) The present invention solves the power scheduling model through the power scheduling unit by the optimization algorithm to generate the optimal scheduling scheme, ensuring that the charging station obtains sufficient power, improves charging efficiency, and reduces waiting time, and monitors the working status of the charging pile in real time through the load determination unit, dynamically adjusts the output and input load, and ensures that the charging pile operates efficiently under different loads; (3) The present invention allocates power according to the load change through the power allocation unit, ensuring that each charging pile obtains the required power, avoids local overload or power shortage, improves the overall operating efficiency of the charging station, and reduces power loss and power grid pressure by optimizing power scheduling and allocation, helping the charging station reduce operating costs and improve economic benefits. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the overall system architecture in one embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the overall method steps in one embodiment of the present invention.

[0067] Attached reference numerals: 1. Demand determination unit; 2. Scheduling modeling unit; 3. Power dispatching unit; 4. Load determination unit; 5. Power allocation unit. Detailed Implementation

[0068] Example 1, as Figure 1 As shown, the present invention proposes an intelligent charging control system for new energy vehicles, comprising:

[0069] Demand Determination Unit 1 is used to obtain the charging information of all new energy vehicles in the target charging station and determine the total charging demand of the target charging station based on the charging information.

[0070] Scheduling modeling unit 2 is used to obtain power supply information of the power grid, determine the objective function based on the total charging demand and power supply information, and determine the constraints based on the power balance, voltage, line capacity and distribution transformer capacity of the power grid.

[0071] Power dispatching unit 3 is used to construct a power dispatching model of the power grid based on the objective function and constraints, solve the power dispatching model based on the optimization algorithm to obtain the power dispatching scheme, and dispatch the power of the power grid to the target charging station based on the power dispatching scheme.

[0072] The load determination unit 4 is configured to acquire working information of all charging piles in the target charging station, and determine output load and input load of the new energy vehicles charging at the charging piles based on the working information.

[0073] The power distribution unit 5 is configured to determine load variation of the charging piles according to the output load and the input load, and distribute power of the power grid to the corresponding charging piles according to the load variation.

[0074] In the present application, the load refers to the power consumed by the equipment or system, the output load can refer to the power provided by the charging pile to the new energy vehicle, and the input load can refer to the power obtained by the charging pile from the power grid; the load variation refers to the change of the load of the charging pile during the charging process, which can be caused by the change of the charging demand of the new energy vehicle or the change of the power supply condition of the power grid; the power dispatching model is a mathematical model for describing the power distribution in the power grid, which is constructed based on the objective function and the constraint condition, and is used to determine how to optimally dispatch the power to meet the demand of the charging station.

[0075] In the second embodiment, the new energy vehicle intelligent charging control system is further provided with: the charging information includes the expected end charging time, the charge amount before charging, the expected charge amount at the end of charging and the maximum charging rate; the power supply information includes the power generation amount of the renewable energy, the energy storage amount of the energy storage device and the tradable power amount of the power grid; and the calculation formula of the total charging demand amount is as follows:

[0076]

[0077] Wherein, Q represents the total charging demand amount, N represents the number of new energy vehicles in the target charging station, represents the expected charge amount of the new energy vehicle at the end of charging, f i represents the charge amount of the new energy vehicle before charging.

[0078] In an optional embodiment, the power supply information of the power grid is acquired, and the objective function is determined according to the total charging demand amount and the power supply information, including:

[0079] The first supply-demand difference is determined according to the total charging demand amount and the power supply information;

[0080] When the first supply-demand difference is positive, the second supply-demand difference is determined according to the first supply-demand difference and the power supply information;

[0081] The benefit amount of the target charging station is determined based on the first supply-demand difference and the second supply-demand difference, wherein the calculation formula of the benefit amount is as follows:

[0082]

[0083] Wherein, T represents a revenue amount of the target charging station max Q(t), H(t), g(t), and b(t) represent a total charging demand amount, a power generation amount of a renewable energy source, a storage power amount of a storage device, and a power tradable amount of a power grid at time t, P H , P g , and P b represent power prices of the renewable energy source, the storage device, and the power grid at time t, QH dif represents a first supply-demand difference, and OH dif = Q(t) - H(t), QHg dif represents a second supply-demand difference, and QHg dif = Q(t) - H(t) - g(t).

[0084] In an optional embodiment, the constraint conditions include a power balance constraint condition, a voltage constraint condition, a line capacity constraint condition, and a distribution transformer capacity constraint condition, wherein an expression of the power balance constraint condition is as follows:

[0085]

[0086] where j represents a node connected to a branch in which node i exists in a power grid, P i , Q i , and U i represent injected active power, reactive power, and voltage amplitude of node i in the power grid, G ij and B ij represent a real part and an imaginary part of mutual admittance, θ ij represents a phase angle difference of the first and last ends of the branch;

[0087] An expression of the voltage constraint condition is as follows:

[0088] U min ≤ U i ≤ U max ;

[0089] where U min and U max represent lower and upper limits of the node voltage;

[0090] An expression of the line capacity constraint condition is as follows:

[0091] |S ij | < S ijmax ;

[0092] where S ijmax represents an upper limit of the branch capacity S ij connecting node i and j;

[0093] An expression of the distribution transformer capacity constraint condition is as follows:

[0094]

[0095] wherein P Ti，t , P Li，t , P Ei，t and denote the total power of the distribution transformer, the regular load power, the charging power and the upper limit of the charging power for the time period t, S i , p i and cos y i denote the rated capacity, the efficiency and the power factor of the distribution transformer.

[0096] In an alternative embodiment, the power dispatch model is solved based on an optimization algorithm to obtain a power dispatch scheme, comprising:

[0097] a set of individuals is randomly generated, and each individual represents a combination of decision variables of the objective function;

[0098] for each combination of decision variables of the objective function represented by an individual, a fitness value is calculated, wherein the fitness value is the value of the objective function;

[0099] the light intensity and the attraction degree of the individual are updated, and the position of the individual is updated based on the light intensity and the attraction degree of the individual;

[0100] it is determined whether the training error reaches a convergence value or the number of iterations reaches a maximum value, if the training error reaches the convergence value and the number of iterations reaches the maximum value, the iteration is ended and the combination of decision variables of the objective function is output, otherwise the number of iterations is increased by 1.

[0101] It should be noted that in the optimization algorithm, an individual usually represents a potential solution or a candidate solution, each individual represents a combination of a set of decision variables of the objective function, i.e. a possible scheme of power dispatch of the power grid; the decision variables are unknown quantities that need to be determined in the optimization problem, which together determine the solution of the problem, in the power dispatch model, the decision variables can include the power allocated to each charging station, the power distribution of each line in the power grid, etc.; in some optimization algorithms inspired by nature, an individual has properties such as light intensity and attraction degree, the light intensity can represent the fitness value (i.e. the degree of excellence) of the individual, and the attraction degree determines the possibility of other individuals moving to it.

[0102] In an alternative embodiment, the update formula of the light intensity of the individual is as follows:

[0103]

[0104] wherein, and represents the light intensity of the i-th individual at the t-th iteration and the (t-1)-th iteration, p represents a preset proportion, and τ represents a preset proportion coefficient, X t represents the position of the individual at the t-th iteration, f(X t ) represents the fitness value corresponding to the position of the individual at the t-th iteration.

[0105] The update formula of the attraction degree of the individual is as follows:

[0106]

[0107] wherein β(r) represents the attraction degree of the individual, β0 represents the maximum attraction degree, r ij represents the distance between the i-th individual and the j-th individual, and r ij =||X i -X j ||, X i and X j represent the positions of the i-th individual and the j-th individual.

[0108] The update formula of the position of the individual is as follows:

[0109]

[0110] wherein, and represent the position of the i-th individual at the (t+1)-th iteration and the t-th iteration, represents the position of the j-th individual at the t-th iteration, ε i represents a random factor of the i-th individual, and a represents a step scaling factor.

[0111] In an optional embodiment, the output load and the input load of the new energy vehicle charging at the charging pile are determined based on the working information, comprising:

[0112] The output load of the new energy vehicle charging at the charging pile is determined based on the working information, wherein the calculation formula of the output load is as follows:

[0113]

[0114] wherein, represents the output load of the new energy vehicle charging at the charging pile within a preset control period, β knt represents whether the new energy vehicle is connected to the charging pile at time t, if connected to the charging pile, 1 is taken, otherwise, 0 is taken, represents the energy of the new energy vehicle arriving at the charging pile to start charging at time t, and V represents a set of new energy vehicles charging at the charging pile.

[0115] In an optional embodiment, the method further comprises:

[0116] The input load of the new energy vehicle charging at the charging pile is determined based on the working information, and a calculation formula of the input load is as follows:

[0117]

[0118] wherein, represents the input load of the new energy vehicle charging at the charging pile in a preset control period.

[0119] In an optional embodiment, the calculation formula of the load variation is as follows:

[0120]

[0121] wherein, E nt，V represents the load variation of the charging pile at time t, P nt，V represents the charging power in the preset control period, η nt，V represents the charging efficiency in the preset control period, and Δt represents the preset control period.

[0122] Embodiment three, as shown in Figure 2 The present application provides a new energy vehicle intelligent charging control method, which is applicable to the new energy vehicle intelligent charging control system, and comprises the following steps:

[0123] S1, obtaining the charging information of all new energy vehicles in the target charging station, and determining the total charging demand of the target charging station according to the charging information;

[0124] S2, obtaining the power supply information of the power grid, determining the objective function according to the total charging demand and the power supply information, and determining the constraint condition based on the power balance, voltage, line capacity and distribution transformer capacity of the power grid;

[0125] S3, constructing the power dispatching model of the power grid based on the objective function and the constraint condition, solving the power dispatching model based on an optimization algorithm to obtain a power dispatching scheme, and dispatching the power of the power grid to the target charging station based on the power dispatching scheme;

[0126] S4, obtaining the working information of all charging piles in the target charging station, and determining the output load and the input load of the new energy vehicle charging at the charging pile based on the working information;

[0127] S5, determining the load variation of the charging pile according to the output load and the input load, and distributing the power of the power grid to the corresponding charging pile according to the load variation.

[0128] The embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the embodiments, and various changes can be made by those skilled in the art within the scope of knowledge acquired from the present disclosure, without departing from the spirit of the present application.

Claims

1. A smart charging control system for new energy vehicles, characterized in that, include: Demand determination unit (1), the demand determination unit (1) is used to obtain the charging information of all new energy vehicles in the target charging station, and determine the total charging demand of the target charging station based on the charging information; The scheduling modeling unit (2) is used to obtain the power supply information of the power grid, determine the objective function based on the total charging demand and the power supply information, and determine the constraint conditions based on the power balance, voltage, line capacity and distribution transformer capacity of the power grid. Power dispatching unit (3), the power dispatching unit (3) is used to construct the power dispatching model of the power grid based on the objective function and the constraint conditions, solve the power dispatching model based on the optimization algorithm to obtain the power dispatching scheme, and dispatch the power of the power grid to the target charging station based on the power dispatching scheme; The load determination unit (4) is used to obtain the working information of all charging piles in the target charging station and determine the output load and input load of the new energy vehicle charging at the charging pile based on the working information. Power distribution unit (5), the power distribution unit (5) is used to determine the load change of the charging pile according to the output load and the input load, and to distribute the power of the power grid to the corresponding charging pile according to the load change; The power dispatching model is solved using an optimization algorithm to obtain a power dispatching scheme, including: A set of individuals is randomly generated, and the position of each individual represents a combination of decision variables of the objective function; For each individual representing a combination of decision variables of the objective function, a fitness value is calculated, wherein the fitness value is the value of the objective function; Update the light intensity and attractiveness of the individual, and update the position of the individual based on the light intensity and attractiveness of the individual; Determine whether the training error has reached the convergence value or the number of iterations has reached the maximum value. If the training error has reached the convergence value and the number of iterations has reached the maximum value, the iteration ends and the combination of decision variables of the objective function is output; otherwise, the number of iterations is incremented by 1. The formula for updating the light intensity of the individual is as follows: in, and Let X represent the light intensity of the i-th individual at the t-th iteration and the (t-1)-th iteration, ρ represent the preset proportion, τ represent the preset proportionality coefficient, and X represent the light intensity of the i-th individual. t f(X) represents the position of an individual in the t-th iteration. t ) represents the fitness value corresponding to the position of an individual at the t-th iteration; The formula for updating the attractiveness of the individual is as follows: Where β(r) represents the individual's attractiveness, β0 represents the maximum attractiveness, and r ij Let r represent the distance between the i-th individual and the j-th individual, and r ij =||X i -X j ||,X i and X j Indicates the positions of the i-th individual and the j-th individual; The formula for updating the position of the individual is as follows: in, and This represents the position of the i-th individual at the (t+1)-th iteration and the t-th iteration. ε represents the position of the j-th individual in the t-th iteration. i Let α represent the random factor for the i-th individual, and let α represent the step size scaling factor.

2. The intelligent charging control system for new energy vehicles according to claim 1, characterized in that, The charging information includes the expected charging end time, the amount of charge before charging, the expected amount of charge at the end of charging, and the maximum charging rate. The power supply information includes the power generation of renewable energy, the energy storage capacity of energy storage devices, and the tradable power supply of the grid. The formula for calculating the total charging demand is as follows: Where Q represents the total charging demand, and N represents the number of new energy vehicles in the target charging station. f represents the expected charge level at the end of charging for a new energy vehicle. i This indicates the amount of charge a new energy vehicle has before it is charged.

3. The intelligent charging control system for new energy vehicles according to claim 2, characterized in that, Obtain power supply information from the power grid, and determine an objective function based on the total charging demand and the power supply information, including: The first supply-demand gap is determined based on the total charging demand and the power supply information; When the first supply-demand difference is positive, the second supply-demand difference is determined based on the first supply-demand difference and the power supply information; The revenue of the target charging station is determined based on the first supply-demand difference and the second supply-demand difference, wherein the revenue is calculated using the following formula: in, T represents the revenue of the target charging station. max Let P represent the control period of the target charging station, Q(t), H(t), g(t), and b(t) represent the total charging demand, renewable energy generation, energy storage capacity of the energy storage device, and tradable electricity supply at time t, respectively. H P g and P b QH represents the electricity price at time t for renewable energy, energy storage devices, and grid transactions. dif This indicates the first supply-demand gap, and QH dif =Q(t)-H(t), QHg dif This indicates the second supply-demand gap, and QHg dif =Q(t) - H(t) - g(t); The objective function is determined based on the revenue of the target charging station, wherein the objective function is to maximize the revenue of the target charging station.

4. The intelligent charging control system for new energy vehicles according to claim 3, characterized in that, The constraints include power balance constraints, voltage constraints, line capacity constraints, and distribution transformer capacity constraints. The expression for the power balance constraint is as follows: Where j represents a node in the power grid with a connected branch to node i, P i Q i and U i G represents the injected active power, reactive power, and voltage amplitude at node i in the power grid. ij and B ij Let θ represent the real and imaginary parts of the mutual admittance. ij This indicates the phase angle difference between the voltages at the beginning and end of a branch. The expression for the voltage constraint condition is as follows: IN min ≤U i ≤U max ; Among them, U min and U max Indicates the lower and upper limits of the node voltage; The expression for the line capacity constraint is as follows: |S ij |<S ijmax ; Among them, S ijmax S represents the branch capacity connecting nodes i and j. ij The upper limit; The expression for the capacity constraint condition of the distribution transformer is as follows: Among them, P Ti，t P Li，t P Ei，t and S represents the total power of the distribution transformer, the power of the conventional load, the charging power, and the upper limit of the charging power during time period t. i ρ i and cosψ i This indicates the rated capacity, efficiency, and power factor of the distribution transformer.

5. The intelligent charging control system for new energy vehicles according to claim 1, characterized in that, Based on the aforementioned working information, the output load and input load of the new energy vehicle charging at the charging pile are determined, including: Based on the aforementioned working information, the output load of the new energy vehicle charging at the charging pile is determined, wherein the calculation formula for the output load is as follows: in, β represents the output load of new energy vehicles charging at charging piles within a preset control period. knt This indicates whether the new energy vehicle is connected to a charging station at time t. If it is connected, the value is 1; otherwise, the value is 0. V represents the energy of a new energy vehicle when it arrives at a charging station at time t and begins charging, while V represents the set of new energy vehicles charging at the charging station.

6. The intelligent charging control system for new energy vehicles according to claim 5, characterized in that, Based on the aforementioned working information, determining the output load and input load of the new energy vehicle charging at the charging pile also includes: Based on the aforementioned working information, the input load of the new energy vehicle charging at the charging pile is determined, wherein the calculation formula for the input load is as follows: in, This represents the input load of new energy vehicles charging at charging stations within a preset control period.

7. A new energy vehicle intelligent charging control system according to claim 6, characterized in that, The formula for calculating the load change is as follows: Among them, E nt,V P represents the change in load on the charging station at time t. nt,V η represents the charging power within a preset control cycle. nt,V This represents the charging efficiency within a preset control period, where Δt represents the preset control period.

8. A method for intelligent charging control of new energy vehicles, applicable to the intelligent charging control system for new energy vehicles as described in any one of claims 1-7, characterized in that, include: Obtain the charging information of all new energy vehicles in the target charging station, and determine the total charging demand of the target charging station based on the charging information; Obtain power supply information from the power grid, determine the objective function based on the total charging demand and the power supply information, and determine the constraints based on the power balance, voltage, line capacity and distribution transformer capacity of the power grid. A power dispatch model for the power grid is constructed based on the objective function and the constraints. The power dispatch model is solved using an optimization algorithm to obtain a power dispatch scheme. Based on the power dispatch scheme, the power of the power grid is dispatched to the target charging station. Obtain the working information of all charging piles in the target charging station, and determine the output load and input load of the new energy vehicles charging at the charging piles based on the working information; The load change of the charging pile is determined based on the output load and the input load, and the power of the power grid is allocated to the corresponding charging pile based on the load change. The power dispatching model is solved using an optimization algorithm to obtain a power dispatching scheme, including: A set of individuals is randomly generated, and the position of each individual represents a combination of decision variables of the objective function; For each individual representing a combination of decision variables of the objective function, a fitness value is calculated, wherein the fitness value is the value of the objective function; Update the light intensity and attractiveness of the individual, and update the position of the individual based on the light intensity and attractiveness of the individual; Determine whether the training error has reached the convergence value or the number of iterations has reached the maximum value. If the training error has reached the convergence value and the number of iterations has reached the maximum value, the iteration ends and the combination of decision variables of the objective function is output; otherwise, the number of iterations is incremented by 1. The formula for updating the light intensity of the individual is as follows: in, and Let X represent the light intensity of the i-th individual at the t-th iteration and the (t-1)-th iteration, ρ represent the preset proportion, τ represent the preset proportionality coefficient, and X represent the light intensity of the i-th individual. t f(X) represents the position of an individual in the t-th iteration. t ) represents the fitness value corresponding to the position of an individual at the t-th iteration; The formula for updating the attractiveness of the individual is as follows: Where β(r) represents the individual's attractiveness, β0 represents the maximum attractiveness, and r ij Let r represent the distance between the i-th individual and the j-th individual, and r ij =||X i -X j ||,X i and X j Indicates the positions of the i-th individual and the j-th individual; The formula for updating the position of the individual is as follows: in, and This represents the position of the i-th individual at the (t+1)-th iteration and the t-th iteration. ε represents the position of the j-th individual in the t-th iteration. i Let α represent the random factor for the i-th individual, and let α represent the step size scaling factor.

Images