A charging power distribution method, device, storage medium and electronic device

Abstract

The present application discloses a charging power allocation method, device, storage medium and electronic device. It relates to the field of smart grid technology, wherein the method includes: calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factors; using a preset optimization algorithm based on the regional carbon emission factors and preset constraints to solve the objective function with the goal of minimizing carbon emissions, and obtain the optimal charging power parameters corresponding to the different charging time periods of the object to be allocated power; based on each of the optimal charging power parameters, the charging power of each of the different charging time periods of the object to be allocated power is allocated. The method of the present application optimizes the power usage of charging piles, minimizes carbon emissions of the power grid, balances the load of the power grid, and improves the working efficiency of charging piles.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a charging power distribution method, device, storage medium and electronic equipment. Background Art

[0002] With the rapid growth of the number of electric vehicles (EVs), the demand for charging is also increasing, especially during peak hours. This disorderly charging phenomenon has brought many impacts on the power grid and social economy, mainly reflected in the following aspects: a large number of electric vehicles are charged in a short period of time, especially during periods of high power grid load (such as at night), which brings a huge burden to the power grid; although electric vehicles themselves do not produce tail gas emissions, the electricity used for charging still comes from the power grid, and the energy structure of the power grid still has a large proportion of fossil energy (such as coal-fired power and natural gas), which in turn increases carbon emissions; there is a time mismatch between the charging demand of electric vehicles and distributed photovoltaic power generation. Distributed photovoltaic power generation has a peak power generation during the day when there is sufficient sunlight, but the charging demand of electric vehicles is often concentrated at night or during non-peak hours; due to the restrictions on the number of charging piles and charging speed, disorderly charging often causes queuing at charging stations, and users may face long waits during peak hours, affecting user experience. Summary of the invention

[0003] In view of this, the present invention provides a charging power distribution method, device, storage medium and electronic device, the main purpose of which is to solve the current problems of timing mismatch between the charging demand of electric vehicles and distributed photovoltaic power generation, disordered charging causing queues at charging stations, and poor user experience.

[0004] In order to solve the above problems, the present application provides a charging power distribution method, comprising:

[0005] Calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0006] Based on the regional carbon emission factor and the preset constraint conditions, a preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, so as to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power;

[0007] The charging power of each of the different charging time periods of the object to be power allocated is allocated based on each of the optimal charging power parameters.

[0008] Optionally, the power generation parameters of different power generation objects based on the regional power grid and the carbon emission factors corresponding to each of the power generation objects are calculated to obtain the regional carbon emission factors, specifically including:

[0009] Calculation is performed based on the power generation of each of the different power generation objects and the total power generation of the region in the current period to obtain regional power supply ratio parameters corresponding to each of the different power generation objects;

[0010] Calculation is performed based on the power supply ratio parameters of each region, the carbon emission factor, and the predetermined power grid transmission loss factor to obtain the first power generation corresponding to each of the different power generation objects;

[0011] Calculate and process the carbon emissions of the region based on the first power generation, the second power generation of the distributed photovoltaic system, the discharge of the energy storage system, and the average carbon emission factor of the current period;

[0012] A calculation is performed based on the regional carbon emissions and the regional total power generation in the current period to obtain a regional carbon emission factor for the current period.

[0013] Optionally, the preset constraint conditions include: power constraint, energy constraint, time constraint and power grid constraint;

[0014] The power constraints include single pile charging power limit and energy storage device power limit;

[0015] The energy constraints include charging demand constraints and energy storage state constraints;

[0016] The time constraints include charging time window constraints;

[0017] The grid constraints include transformer capacity limitations and distributed photovoltaic consumption constraints.

[0018] Optionally, the method of solving the objective function with the goal of minimizing carbon emissions by using a preset optimization algorithm based on the regional carbon emission factor and preset constraints to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be power allocated specifically includes:

[0019] Initialize the parameters in the optimization algorithm;

[0020] Randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm and the preset constraint conditions;

[0021] Based on the initial position of each individual, calculating the initial fitness value of the corresponding individual, determining the initial global optimal individual position to determine the initial optimal parameter value corresponding to the initial global optimal individual;

[0022] Based on the initial global optimal individual position and each of the initial fitness values, a cyclic iteration method is adopted to update each individual in the population, so as to re-determine the current global optimal individual position based on the updated individuals until the preset iteration termination condition is met, and the target parameter value corresponding to the updated current global optimal individual position is obtained, so as to obtain the optimal charging power parameters corresponding to different charging time periods of the objects to be power allocated.

[0023] Optionally, based on the initial global optimal individual position and each of the initial fitness values, each individual in the population is updated in a cyclic iteration manner, so as to re-determine the current global optimal individual position based on each updated individual until a preset iteration termination condition is met, and obtain the target parameter value corresponding to the updated current global optimal individual position, specifically including:

[0024] Based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter, the initial speed of the same individual is updated to obtain the current speed;

[0025] The initial position is updated based on the current speed to obtain a current position corresponding to the same individual;

[0026] Based on the first parameter value corresponding to the current position, the objective function is used to perform calculation processing to obtain a current fitness value corresponding to the same individual;

[0027] Screening is performed based on each of the current fitness values ​​and each of the initial fitness values ​​to obtain the current global optimal individual position and the current optimal position corresponding to each of the individuals;

[0028] The current global optimal individual position and the current optimal position corresponding to each of the individuals are updated in a cyclic iterative manner until a preset iteration stop condition is reached, and the target parameter value corresponding to the updated current global optimal individual position is determined as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated.

[0029] Optionally, the updating of the initial speed of the same individual based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter to obtain the current speed specifically includes:

[0030] Performing a multiplication operation based on the preset inertia weight and the initial speed to obtain a first speed at which the individual moves forward;

[0031] Calculation is performed based on the initial global optimal individual position and the current individual position to obtain a first distance value;

[0032] Performing a multiplication operation based on the first distance value, the first learning factor, and the first random parameter to obtain a second speed;

[0033] Performing calculation based on the current optimal position and the current position of the same individual to obtain a second distance value;

[0034] Performing a multiplication operation based on the second distance value, the second learning factor and the second random parameter to obtain a third speed;

[0035] The current speed is obtained by performing an addition operation based on the first speed, the second speed and the third speed.

[0036] Optionally, the preset iteration termination condition is that the current iteration round is greater than or equal to a preset iteration round threshold or,

[0037] When the absolute value of the difference between the fitness values ​​corresponding to the global optimal individual positions obtained in two adjacent iteration rounds is less than or equal to the preset threshold, the iteration is terminated.

[0038] In order to solve the above problems, the present application provides a charging power distribution device, comprising:

[0039] A calculation module, used to calculate and process the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0040] An optimization module, configured to optimize an objective function with the goal of minimizing carbon emissions by using a preset optimization algorithm based on the regional carbon emission factor and preset constraints, and obtain optimal charging power parameters corresponding to different charging time periods of the object to be allocated power;

[0041] The allocation module is used to allocate the charging power of each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

[0042] In order to solve the above problem, the present application provides a storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned charging power allocation method are implemented.

[0043] In order to solve the above problem, the present application provides an electronic device, which at least includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned charging power allocation method when executing the computer program in the memory.

[0044] Beneficial effects of the present application: The present application obtains a regional carbon emission factor by calculating and processing the power generation parameters of different power generation objects based on the regional power grid and the carbon emission factors corresponding to each of the power generation objects; the potential of distributed photovoltaics in reducing regional carbon emissions is brought into play, and an optimization model for light-charging timing matching is constructed based on the calculation method of the regional carbon emission factor, with carbon emissions as the optimization target, to optimize the power use of charging piles, minimize grid carbon emissions, balance grid loads, and improve the working efficiency and environmental friendliness of charging piles. Based on the regional carbon emission factor and the preset constraints, a preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to different charging periods of the object to be allocated power are obtained; based on each of the optimal charging power parameters, the charging power of each of the different charging periods of the object to be allocated power is allocated. By regulating the charging power and time allocation of the charging pile, carbon emissions are reduced, and an optimization method for green charging is realized. The present application fully considers the impact of distributed photovoltaic power generation in the region, gives priority to the use of green electricity during the peak period of photovoltaic power generation, and significantly reduces charging-related carbon emissions. Improving photovoltaic utilization by optimizing scheduling also helps alleviate the phenomenon of abandoned light.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 A schematic diagram of a charging power distribution method provided in an embodiment of the present application is shown;

[0048] Figure 2 A schematic diagram of a charging power distribution method provided in another embodiment of the present application is shown;

[0049] Figure 3 A structural block diagram of a charging power distribution device provided in another embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0051] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0052] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0053] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0054] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0055] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0056] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0057] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0058] The present application embodiment provides a charging power distribution method, such as Figure 1 As shown, including:

[0059] Step S101: Calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0060] In the specific implementation process of this step, calculation and processing are performed based on the power generation of each of the different power generation objects in the current time period and the total power generation of the region to obtain the regional power supply ratio parameters corresponding to each of the different power generation objects; calculation and processing are performed based on each of the regional power supply ratio parameters, the carbon emission factor and the predetermined power grid transmission loss factor to obtain the first power generation corresponding to each of the different power generation objects; calculation and processing are performed based on each of the first power generation, the second power generation of distributed photovoltaics, the discharge of the energy storage system and the average carbon emission factor of the current time period to obtain the regional carbon emissions; calculation and processing are performed based on the regional carbon emissions and the total power generation of the region in the current time period to obtain the regional carbon emission factor of the current time period.

[0061] Step S102: Based on the regional carbon emission factor and preset constraints, a preset optimization algorithm is used to solve an objective function with the goal of minimizing carbon emissions, and optimal charging power parameters corresponding to different charging time periods of the object to be allocated power are obtained;

[0062] In the specific implementation of this step, the preset constraints include: power constraints, energy constraints, time constraints and grid constraints; the power constraints include single-pile charging power limits and energy storage equipment power limits; the energy constraints include charging demand constraints and energy storage state constraints; the time constraints include charging time window constraints; the grid constraints include transformer capacity limits and distributed photovoltaic consumption constraints. Based on the regional carbon emission factors and preset constraints, the particle swarm optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to different charging time periods of the object to be power allocated are obtained. Optimization methods such as genetic algorithms, ant colony algorithms, differential evolution algorithms, and Ye Beisi optimization algorithms can also be used to solve the objective function of this application to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be power allocated.

[0063] Step S103: allocating charging power for each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

[0064] In the specific implementation process of this step, charging power is allocated to the object to be allocated power based on the optimal charging power parameters corresponding to the different charging time periods, which solves the problem that the existing charging optimization strategy usually ignores the timing mismatch between the charging demand of electric vehicles and distributed photovoltaic power generation. Considering the peak period of photovoltaic power generation, green electricity is given priority for charging, and electric vehicles are charged during the peak period of photovoltaic power generation, thereby avoiding the grid's dependence on fossil energy, reducing carbon emissions and improving the utilization rate of green electricity.

[0065] The present application calculates and processes the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factor; gives full play to the potential of distributed photovoltaics in reducing regional carbon emissions, and constructs an optimization model for light-charging timing matching based on the calculation method of the regional carbon emission factor, taking carbon emissions as the optimization target, optimizing the power use of charging piles, minimizing the carbon emissions of the power grid, balancing the load of the power grid, and improving the working efficiency and environmental friendliness of the charging piles. Based on the regional carbon emission factor and the preset constraints, the preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to the different charging periods of the object to be allocated power are obtained; based on the optimal charging power parameters, the charging power of each of the different charging periods of the object to be allocated power is allocated. By regulating the charging power and time allocation of the charging pile, carbon emissions are reduced and an optimization method for green charging is realized. The present application fully considers the impact of distributed photovoltaic power generation in the region, gives priority to the use of green electricity during the peak period of photovoltaic power generation, and significantly reduces the carbon emissions related to charging. Improving photovoltaic utilization by optimizing scheduling also helps to alleviate the phenomenon of abandoned light.

[0066] Another embodiment of the present application provides another charging power distribution method, such as Figure 2 As shown, including:

[0067] Step S201: Calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0068] In the specific implementation process of this step, the power generation of each of the different power generation objects and the total power generation of the region in the current period are calculated and processed to obtain the regional power supply ratio parameters corresponding to each of the different power generation objects. ; Each of the different power generation objects includes: coal power generation objects, gas power generation objects, photovoltaic power generation objects, wind power generation objects and other power generation objects corresponding to the power generation categories; regional power supply ratio parameters The mathematical expression of can be expressed as follows:

[0069] (1)

[0070] in, For electricity source i of power generation; is the total power generation in the region. Based on the power supply ratio parameters of each region, the carbon emission factor and the predetermined power grid transmission loss factor, a calculation is performed to obtain the first power generation corresponding to each of the different power generation objects; the mathematical expression of the first power generation can be expressed as the following formula (2):

[0071] (2)

[0072] in, For electricity source i Carbon emission factor; is the transmission loss factor of the power grid, corresponding to the source of the power generation object i The transmission loss rate of the distributed photovoltaic system is calculated based on the first power generation, the second power generation of the distributed photovoltaic system, the discharge of the energy storage system and the average carbon emission factor of the current period to obtain the regional carbon emissions. The mathematical expression of the regional carbon emissions can be expressed as follows:

[0073] (3)

[0074] in, is the second power generation of distributed photovoltaics, which represents the contribution of distributed photovoltaic power supply, that is, the effective part of distributed photovoltaic power supply, which is limited by load demand; is the discharge capacity of the energy storage system, in units of kWh ; The average carbon emission factor of the current power grid power supply. The regional carbon emission factor of the current period is obtained by calculating and processing based on the regional carbon emissions and the total power generation of the region in the current period. The mathematical expression of can be shown as the following formula (4).

[0075] (4)

[0076] in, is the regional carbon emission factor.

[0077] Step S202: constructing an objective function;

[0078] In the specific implementation process of this step, the objective function of the dynamic control strategy is constructed with the goal of minimizing regional carbon emissions and meeting the vehicle charging needs and the safe operation of the power grid. The mathematical expression of the objective function can be expressed as the following formula (5):

[0079] (5)

[0080] in, For this area t Carbon emission factor at the time; For this area t The net power drawn from the grid at any given moment; The mathematical expression of can be shown as follows:

[0081] (6)

[0082] in, for t Total charging power of electric vehicles at all times; for t Discharge power of energy storage device at any moment; For this area t Total photovoltaic power generation at the moment, in units of kw .

[0083] Step S203: constructing preset constraints;

[0084] In the specific implementation process of this step, power constraints, energy constraints, time constraints and grid constraints are constructed. The power constraints include single-pile charging power constraints and energy storage device power constraints; the mathematical expression of the single-pile charging power constraint can be expressed as the following formula (7):

[0085] (7)

[0086] Indicates that each charging pile is at any time t The charging power must meet its rated power range; For the i Charging piles at time t Charging power; For the i The maximum power of a charging pile. The mathematical expression of the power limit constraint of the energy storage device can be expressed as follows:

[0087] (8)

[0088] in, represents the charging power of the energy storage device at time t; represents the discharge power of the energy storage device at time t; Represents the rated charging and discharging power of the energy storage device. The energy constraint includes the charging demand constraint and the energy storage state constraint; each charging object needs to complete sufficient charging before the deadline to meet its travel needs: the charging object can be a charging vehicle that needs to be charged; the mathematical expression of the charging demand constraint can be shown as follows:

[0089] (9)

[0090] in, is the charging demand of the i-th vehicle; the mathematical expression of the energy storage state constraint can be expressed as follows:

[0091] (10)

[0092] in, is the charging state of the energy storage device at time t; , are the minimum and maximum values ​​of the energy storage device. SOC The change of the energy storage device state can be expressed as follows:

[0093] (11)

[0094] in, For energy storage devices The state of the moment, and are the charging and discharging efficiency of energy storage, is the total capacity of the energy storage device; is the time step. The time constraint includes a charging time window constraint; each charging object and each electric vehicle is only allowed to charge within its reserved time window: the mathematical expression of the charging time window constraint can be expressed as the following formula (12):

[0095] (12)

[0096] , are the start time and end time of the reservation time of the i-th electric vehicle respectively. The grid constraints include transformer capacity limitation and distributed photovoltaic consumption constraint. The total charging power of the entire area cannot exceed the capacity limitation of the grid transformer: The mathematical expression of the transformer capacity limitation can be expressed as follows:

[0097] (13)

[0098] in, is the rated capacity of the transformer; this application gives priority to photovoltaic power generation to avoid waste of photovoltaic power generation. The mathematical expression of the distributed photovoltaic consumption constraint can be expressed as follows:

[0099] (14)

[0100] in, The application considers the peak period of photovoltaic power generation, gives priority to using green electricity for charging, and charges electric vehicles during the peak period of photovoltaic power generation, thereby avoiding the grid's reliance on fossil energy, reducing carbon emissions and improving the utilization rate of green electricity.

[0101] Step S204: Initialize various parameters in the optimization algorithm;

[0102] In the specific implementation process of this step, the parameters in the optimization algorithm include algorithm parameters such as population size, maximum number of iterations, inertia weight, first learning factor, second learning factor, first random number, and second random number.

[0103] Step S205: randomly initializing the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm and the preset constraint condition;

[0104] In the specific implementation process of this step, based on the value range of each parameter in the optimization algorithm and the limit range of each preset constraint condition, the initial position of each individual in the population is randomly initialized, and the population is randomly generated within the value range of each parameter. The initial position of each individual in the population corresponds to the charging power parameter of each individual in different charging time periods. Each individual represents a possible charging power allocation scheme, that is, the charging power allocation of the charging pile in different time periods. The dimension of each individual is N × T . N Represents the number of individuals; T represents the number of different charging periods; the position of the initialized particle is expressed by the following formula (15):

[0105] (15)

[0106] in, k Represents the number of the individual. The initial velocity can be randomly generated and represents the magnitude of the change in the charging power distribution. The dimension of the velocity is the same as the position of the particle. The mathematical expression of the initial velocity can be expressed as follows:

[0107] (16)

[0108] Step S206: Based on the initial position of each individual, calculate the initial fitness value of the corresponding individual, determine the initial global optimal individual position to determine the initial optimal parameter value corresponding to the initial global optimal individual;

[0109] In the specific implementation process of this step, the initial fitness value of the corresponding individual is calculated based on the initial position of each individual. Specifically, the initial charging power parameters of each individual in different time periods and the carbon emission factors of each individual in different time periods are substituted into the objective function for calculation and processing. The calculated objective function value of each individual is the initial fitness value corresponding to it. Among them, the position of the individual with the smallest initial fitness value is the initial global optimal individual position, and the charging power parameters of different charging time periods corresponding to the individual with the smallest initial fitness are the initial optimal parameter values ​​corresponding to the initial global optimal individual. Each individual calculates the value of the objective function according to the current position, that is, the carbon emissions. Carbon emissions The mathematical expression of can be shown as follows:

[0110] (17)

[0111] Step S207: updating the initial speed of the same individual based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter to obtain the current speed;

[0112] In the specific implementation of this step, the mathematical expression of the individual speed update can be expressed as follows:

[0113] (18)

[0114] in, To preset the inertia weight, control the inertia of the particles moving forward; , are learning factors, which control the degree of learning of particles towards individual optimality and global optimality respectively; , are two random numbers in the range of [0,1], used to increase the randomness of the algorithm. To initialize the initial global optimal position of the particle swarm, is the optimal position for an individual.

[0115] Step S208: updating the initial position based on the current speed to obtain a current position corresponding to the same individual;

[0116] In the specific implementation process of this step, the position of the individual is updated according to the update speed. The mathematical expression for updating the initial position can be expressed as follows:

[0117] (19)

[0118] in, represents the initial position; is the updated current position; Represents the update speed.

[0119] Step S209: performing calculation processing using the objective function based on the first parameter value corresponding to the current position to obtain a current fitness value corresponding to the same individual;

[0120] In the specific implementation process of this step, the first parameter value of the charging power at different time periods corresponding to the current position and the carbon emission factor at different time periods are substituted into the objective function for calculation and processing, and the current fitness value corresponding to the same individual is calculated and obtained.

[0121] Step S210: Screening is performed based on each of the current fitness values ​​and each of the initial fitness values ​​to obtain the current global optimal individual position and the current optimal position corresponding to each of the individuals;

[0122] In the specific implementation process of this step, if the current fitness of the individual is better than its historical optimal fitness, the individual optimal position is updated: the mathematical expression of the individual optimal update can be expressed as follows:

[0123] (20)

[0124] If the current fitness of an individual is better than the historical optimal fitness of all individuals, the global optimal position is updated: The mathematical expression of the global optimal update can be expressed as follows:

[0125] (twenty one)

[0126] Specifically, screening is performed based on each of the current fitness values, and the individual position corresponding to the smallest current target fitness value among the current fitness values ​​is the target optimal position of the current iteration round; the current target fitness value is compared with the initial target fitness value corresponding to the initial global optimal position. When the current target fitness value is less than the initial target fitness value, the initial global optimal position corresponding to the initial target fitness value is updated with the target optimal position corresponding to the current target fitness value.

[0127] Step S211: Iteratively update the current global optimal individual position and the current optimal position corresponding to each individual until a preset iteration stop condition is reached, and determine the target parameter value corresponding to the updated current global optimal individual position as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated.

[0128] In the specific implementation process of this step, the current global optimal individual position and the current optimal position corresponding to each individual are updated in a loop iteration until the current iteration round is greater than or equal to the preset iteration round threshold, and the target parameter value corresponding to the updated current global optimal individual position is determined as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated; or the current global optimal individual position and the current optimal position corresponding to each individual are updated in a loop iteration until the absolute value of the difference between the fitness values ​​corresponding to the global optimal individual positions obtained in two adjacent iteration rounds is less than or equal to the preset threshold, and the iteration is terminated. The target parameter value corresponding to the updated current global optimal individual position is determined as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated.

[0129] The present application obtains a regional carbon emission factor by calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects; the carbon emission factors of the present application can be dynamically adjusted hour by hour based on the load forecast and scheduling data in the power grid; construct an objective function; by constructing an objective function of a dynamic control strategy with the goal of minimizing regional carbon emissions and under the premise of meeting vehicle charging needs and safe operation of the power grid, the minimum carbon emissions can be achieved and the environment can be protected. Construct preset constraints; initialize each parameter in the optimization algorithm; randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm and the preset constraints; based on the initial position of each individual, calculate the initial fitness value of the corresponding individual, determine the initial global optimal individual position to determine the initial optimal parameter value corresponding to the initial global optimal individual; based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter, update the initial speed of the same individual to obtain the current speed; update the initial position based on the current speed New, obtain the current position corresponding to the same individual; based on the first parameter value corresponding to the current position, use the objective function to calculate and process, and obtain the current fitness value corresponding to the same individual; based on each of the current fitness values ​​and each of the initial fitness values, screen to obtain the current global optimal individual position and the current optimal position corresponding to each of the individuals; iteratively update the current global optimal individual position and the current optimal position corresponding to each of the individuals until the preset iteration stop condition is reached, and determine the target parameter value corresponding to the updated current global optimal individual position as the optimal charging power parameter corresponding to different charging periods of the object to be allocated power. Considering the high-generation period of photovoltaic power generation, green electricity is used for charging first, and electric vehicles are charged during the peak period of photovoltaic power generation, so as to avoid the grid from relying on fossil energy, reduce carbon emissions and improve the utilization rate of green electricity. Using the particle swarm optimization algorithm, combined with carbon emission factors, grid load, electric vehicle charging demand and other factors for scheduling optimization, comprehensively consider low-carbon goals, grid load balance and charging demand, so as to achieve green charging and energy saving.

[0130] Another embodiment of the present application provides a charging power distribution device, such as Figure 3 As shown, including:

[0131] A calculation module 1 is used to calculate and process the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0132] Optimization module 2, used to optimize the objective function with the goal of minimizing carbon emissions by using a preset optimization algorithm based on the regional carbon emission factor and preset constraints, and obtain the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power;

[0133] The allocation module 3 is used to allocate the charging power of each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

[0134] In the specific implementation process, the calculation module 1 is specifically used for: performing calculations based on the power generation of each of the different power generation objects in the current time period and the total regional power generation to obtain the regional power supply ratio parameters corresponding to each of the different power generation objects; performing calculations based on the regional power supply ratio parameters, the carbon emission factor and the predetermined power grid transmission loss factor to obtain the first power generation corresponding to each of the different power generation objects; performing calculations based on the first power generation, the second power generation of distributed photovoltaics, the discharge of the energy storage system and the average carbon emission factor of the current time period to obtain the regional carbon emissions; performing calculations based on the regional carbon emissions and the total regional power generation of the current time period to obtain the regional carbon emission factor of the current time period.

[0135] During the specific implementation process, the optimization module 2 is specifically used for: the preset constraints include: power constraints, energy constraints, time constraints and grid constraints; the power constraints include single-pile charging power limitations and energy storage equipment power limitations; the energy constraints include charging demand constraints and energy storage status constraints; the time constraints include charging time window constraints; the grid constraints include transformer capacity limitations and distributed photovoltaic absorption constraints.

[0136] During the specific implementation process, the optimization module 2 is also used to: initialize each parameter in the optimization algorithm; randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm and the preset constraints; based on the initial position of each individual, calculate the initial fitness value of the corresponding individual, determine the initial global optimal individual position to determine the initial optimal parameter value corresponding to the initial global optimal individual; based on the initial global optimal individual position and each initial fitness value, use a cyclic iteration method to update each individual in the population, so as to re-determine the current global optimal individual position based on the updated individuals, until the preset iteration termination condition is met, and obtain the target parameter value corresponding to the updated current global optimal individual position, so as to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be power allocated.

[0137] In the specific implementation process, the optimization module 2 is also used to: update the initial speed of the same individual based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter to obtain the current speed; update the initial position based on the current speed to obtain the current position corresponding to the same individual; use the objective function to calculate and process based on the first parameter value corresponding to the current position to obtain the current fitness value corresponding to the same individual; screen based on each of the current fitness values ​​and each of the initial fitness values ​​to obtain the current global optimal individual position and the current optimal position corresponding to each of the individuals; iteratively update the current global optimal individual position and the current optimal position corresponding to each of the individuals until the preset iteration stop condition is reached, and determine the target parameter value corresponding to the updated current global optimal individual position as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated.

[0138] During the specific implementation process, the optimization module 2 is also used to: perform multiplication processing based on the preset inertia weight and the initial speed to obtain a first speed of the individual's forward movement; perform calculation processing based on the initial global optimal individual position and the current individual position to obtain a first distance value; perform multiplication processing based on the first distance value, the first learning factor and the first random parameter to obtain a second speed; perform calculation processing based on the current optimal position and the current position of the same individual to obtain a second distance value; perform multiplication processing based on the second distance value, the second learning factor and the second random parameter to obtain a third speed; perform addition processing based on the first speed, the second speed and the third speed to obtain the current speed.

[0139] During the specific implementation process, the optimization module 2 is also used for: the preset iteration termination condition is that the current iteration round is greater than or equal to the preset iteration round threshold or the absolute value of the difference between the fitness values ​​corresponding to the global optimal individual positions obtained in two adjacent iteration rounds is less than or equal to the preset threshold, the iteration is terminated.

[0140] The present application calculates and processes the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factor; gives full play to the potential of distributed photovoltaics in reducing regional carbon emissions, and constructs an optimization model for light-charging timing matching based on the calculation method of the regional carbon emission factor, taking carbon emissions as the optimization target, optimizing the power use of charging piles, minimizing the carbon emissions of the power grid, balancing the load of the power grid, and improving the working efficiency and environmental friendliness of the charging piles. Based on the regional carbon emission factor and the preset constraints, the preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to the different charging periods of the object to be allocated power are obtained; based on the optimal charging power parameters, the charging power of each of the different charging periods of the object to be allocated power is allocated. By regulating the charging power and time allocation of the charging pile, carbon emissions are reduced and an optimization method for green charging is realized. The present application fully considers the impact of distributed photovoltaic power generation in the region, gives priority to the use of green electricity during the peak period of photovoltaic power generation, and significantly reduces the carbon emissions related to charging. Improving photovoltaic utilization by optimizing scheduling also helps to alleviate the phenomenon of abandoned light.

[0141] Another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following method steps are implemented:

[0142] Step 1: Calculate and process the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0143] Step 2: Based on the regional carbon emission factor and preset constraints, a preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power are obtained;

[0144] Step three: allocating charging power for each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

[0145] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0146] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0147] The specific implementation process of the above method steps can refer to the embodiments of any of the above charging power allocation methods, and this embodiment will not be repeated here.

[0148] The present application calculates and processes the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factor; gives full play to the potential of distributed photovoltaics in reducing regional carbon emissions, and constructs an optimization model for light-charging timing matching based on the calculation method of the regional carbon emission factor, taking carbon emissions as the optimization target, optimizing the power use of charging piles, minimizing the carbon emissions of the power grid, balancing the load of the power grid, and improving the working efficiency and environmental friendliness of the charging piles. Based on the regional carbon emission factor and the preset constraints, the preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to the different charging periods of the object to be allocated power are obtained; based on the optimal charging power parameters, the charging power of each of the different charging periods of the object to be allocated power is allocated. By regulating the charging power and time allocation of the charging pile, carbon emissions are reduced and an optimization method for green charging is realized. The present application fully considers the impact of distributed photovoltaic power generation in the region, gives priority to the use of green electricity during the peak period of photovoltaic power generation, and significantly reduces the carbon emissions related to charging. Improving photovoltaic utilization by optimizing scheduling also helps to alleviate the phenomenon of abandoned light.

[0149] Another embodiment of the present application provides an electronic device, which may be a server, and the electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client through a network connection. When the electronic device program is executed by the processor, it implements the functions or steps of a charging power distribution method server side.

[0150] In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server via a network connection. When the electronic device program is executed by the processor, it implements the functions or steps of a client side of a charging power distribution method.

[0151] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0152] Step 1: Calculate and process the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor;

[0153] Step 2: Based on the regional carbon emission factor and preset constraints, a preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power are obtained;

[0154] Step three: allocating charging power for each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

[0155] The specific implementation process of the above method steps can refer to the embodiments of any of the above charging power allocation methods, and this embodiment will not be repeated here.

[0156] The present application calculates and processes the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factor; gives full play to the potential of distributed photovoltaics in reducing regional carbon emissions, and constructs an optimization model for light-charging timing matching based on the calculation method of the regional carbon emission factor, taking carbon emissions as the optimization target, optimizing the power use of charging piles, minimizing the carbon emissions of the power grid, balancing the load of the power grid, and improving the working efficiency and environmental friendliness of the charging piles. Based on the regional carbon emission factor and the preset constraints, the preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, and the optimal charging power parameters corresponding to the different charging periods of the object to be allocated power are obtained; based on the optimal charging power parameters, the charging power of each of the different charging periods of the object to be allocated power is allocated. By regulating the charging power and time allocation of the charging pile, carbon emissions are reduced and an optimization method for green charging is realized. The present application fully considers the impact of distributed photovoltaic power generation in the region, gives priority to the use of green electricity during the peak period of photovoltaic power generation, and significantly reduces the carbon emissions related to charging. Improving photovoltaic utilization by optimizing scheduling also helps to alleviate the phenomenon of abandoned light.

[0157] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A charging power distribution method, characterized in that: include: Calculating and processing the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain a regional carbon emission factor; Based on the regional carbon emission factor and the preset constraint conditions, a preset optimization algorithm is used to solve the objective function with the goal of minimizing carbon emissions, so as to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power; Allocating the charging power of each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters; The calculation and processing of power generation parameters of different power generation objects based on the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factors specifically includes: Calculation is performed based on the power generation of each of the different power generation objects and the total power generation of the region in the current period to obtain regional power supply ratio parameters corresponding to each of the different power generation objects; Calculation is performed based on the power supply ratio parameters of each region, the carbon emission factor, and the predetermined power grid transmission loss factor to obtain the first power generation corresponding to each of the different power generation objects; Calculate and process the carbon emissions of the region based on the first power generation, the second power generation of the distributed photovoltaic system, the discharge of the energy storage system, and the average carbon emission factor of the current period; A calculation is performed based on the regional carbon emissions and the regional total power generation in the current period to obtain a regional carbon emission factor for the current period.

2. The method according to claim 1, characterized in that The preset constraints include: power constraints, energy constraints, time constraints and power grid constraints; The power constraints include single pile charging power limit and energy storage device power limit; The energy constraints include charging demand constraints and energy storage state constraints; The time constraints include charging time window constraints; The grid constraints include transformer capacity limitations and distributed photovoltaic consumption constraints.

3. The method according to claim 1, characterized in that The method of solving the objective function with the goal of minimizing carbon emissions by using a preset optimization algorithm based on the regional carbon emission factor and the preset constraint condition to obtain the optimal charging power parameters corresponding to different charging time periods of the object to be allocated power specifically includes: Initialize the parameters in the optimization algorithm; Randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm and the preset constraint conditions; Based on the initial position of each individual, calculating the initial fitness value of the corresponding individual, determining the initial global optimal individual position to determine the initial optimal parameter value corresponding to the initial global optimal individual; Based on the initial global optimal individual position and each of the initial fitness values, a cyclic iteration method is adopted to update each individual in the population, so as to re-determine the current global optimal individual position based on the updated individuals until the preset iteration termination condition is met, and the target parameter value corresponding to the updated current global optimal individual position is obtained, so as to obtain the optimal charging power parameters corresponding to different charging time periods of the objects to be power allocated.

4. The method according to claim 3, characterized in that The method of updating each individual in the population based on the initial global optimal individual position and each initial fitness value in a cyclic iteration manner, so as to re-determine the current global optimal individual position based on each updated individual until a preset iteration termination condition is met, and obtains the target parameter value corresponding to the updated current global optimal individual position, specifically includes: Based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter, the initial speed of the same individual is updated to obtain the current speed; The initial position is updated based on the current speed to obtain a current position corresponding to the same individual; Based on the first parameter value corresponding to the current position, the objective function is used to perform calculation processing to obtain a current fitness value corresponding to the same individual; Screening is performed based on each of the current fitness values ​​and each of the initial fitness values ​​to obtain the current global optimal individual position and the current optimal position corresponding to each of the individuals; The current global optimal individual position and the current optimal position corresponding to each of the individuals are updated in a cyclic iterative manner until a preset iteration stop condition is reached, and the target parameter value corresponding to the updated current global optimal individual position is determined as the optimal charging power parameter corresponding to different charging time periods of the object to be power allocated.

5. The method according to claim 4, characterized in that The updating of the initial speed of the same individual based on the initial fitness value of the same individual, the position of the initial global optimal individual, the preset inertia weight, the first learning factor, the second learning factor, the first random parameter, and the second random parameter to obtain the current speed specifically includes: A multiplication operation is performed based on the preset inertia weight and the initial speed to obtain a first speed at which the individual moves forward; Calculation is performed based on the initial global optimal individual position and the current individual position to obtain a first distance value; Performing a multiplication operation based on the first distance value, the first learning factor, and the first random parameter to obtain a second speed; Performing calculation based on the current optimal position and the current position of the same individual to obtain a second distance value; Performing a multiplication operation based on the second distance value, the second learning factor and the second random parameter to obtain a third speed; The current speed is obtained by performing an addition operation based on the first speed, the second speed and the third speed.

6. The method according to claim 4, characterized in that The preset iteration termination condition is that the current iteration round is greater than or equal to the preset iteration round threshold or, When the absolute value of the difference between the fitness values ​​corresponding to the global optimal individual positions obtained in two adjacent iteration rounds is less than or equal to the preset threshold, the iteration is terminated.

7. A charging power distribution device, characterized in that: include: A calculation module is used to perform calculations based on the power generation parameters of different power generation objects in the regional power grid and the carbon emission factors corresponding to each of the power generation objects to obtain the regional carbon emission factors; specifically used to: perform calculations based on the power generation of each of the different power generation objects in the current period and the total regional power generation to obtain the regional power supply ratio parameters corresponding to each of the different power generation objects; perform calculations based on the regional power supply ratio parameters, the carbon emission factors and the predetermined power grid transmission loss factors to obtain the first power generation corresponding to each of the different power generation objects; perform calculations based on the first power generation, the second power generation of distributed photovoltaics, the discharge of the energy storage system and the average carbon emission factor of the current period to obtain the regional carbon emissions; perform calculations based on the regional carbon emissions and the total regional power generation of the current period to obtain the regional carbon emission factor of the current period; An optimization module, configured to optimize an objective function with the goal of minimizing carbon emissions by using a preset optimization algorithm based on the regional carbon emission factor and preset constraints, and obtain optimal charging power parameters corresponding to different charging time periods of the object to be power allocated; The allocation module is used to allocate the charging power of each of the different charging time periods of the object to be power allocated based on each of the optimal charging power parameters.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the charging power allocation method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: The device at least comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the charging power allocation method according to any one of claims 1 to 6 when executing the computer program on the memory.

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