Electric vehicle charging control method, device and computer equipment

Abstract

The application relates to an electric vehicle charging price optimization method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring environmental parameter information of a renewable energy source, and determining renewable energy output information based on the environmental parameter information; acquiring regional power load information in a target region, wherein the regional power load information comprises electric vehicle charging load, non-electric vehicle load and the number of electric vehicles; determining charging parameters of the target region based on a demand price elasticity model, the renewable energy output information and the regional power load information, wherein the charging parameters can be used to assist in guiding the distribution of electric vehicle charging load, and the demand price elasticity model is used to represent the relationship between charging demand and the charging parameters. By changing the charging parameters, the electric vehicle can be reasonably guided to orderly charge and discharge, the renewable energy output can be timely consumed, and the stability of the power distribution network can be enhanced.

Description

Technical Field

[0001] This application relates to the field of new energy energy management technology, and in particular to an electric vehicle charging control method, device, computer equipment, storage medium and computer program product. Background Technology

[0002] With the development of new energy technologies, my country is gradually achieving its "dual-carbon" goals, vigorously developing new energy vehicles and renewable energy. Among these, wind power and solar power have become representative renewable energy power generation methods due to their advantages of being clean, low-carbon, and sustainable. However, wind and solar power are characterized by fluctuations and intermittency, which can cause power imbalances and line overloads in the power distribution network. At the same time, with the gradual improvement of living standards in my country, the number of private cars has increased significantly, with electric vehicles experiencing explosive growth, posing a huge challenge to the stability of the power grid.

[0003] In traditional technologies, the flexible and adjustable load of electric vehicles is typically utilized. When renewable energy generation is volatile and intermittent, the load of electric vehicles is adjusted accordingly, thereby alleviating the pressure on the distribution network and realizing the absorption of renewable energy.

[0004] However, due to the uncertainty of renewable energy output and the difficulty in large-scale dispatching of electric vehicle loads, traditional methods cannot respond quickly to the needs of electric vehicles, resulting in untimely absorption of renewable energy. Summary of the Invention

[0005] Based on this, it is necessary to provide an electric vehicle charging control method, device, computer equipment, computer-readable storage medium, and computer program product that can reasonably guide the orderly charging and discharging of electric vehicles, timely absorb renewable energy output, and enhance the stability of the power distribution network, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for controlling the charging of an electric vehicle. The method includes:

[0007] Obtain environmental parameter information of renewable energy sources, and determine renewable energy output information based on the environmental parameter information;

[0008] Obtain regional electricity load information within the target area, including electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles;

[0009] Based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information, the charging parameters of the target area are determined. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load. The demand price elasticity model is used to characterize the relationship between charging demand and charging parameters.

[0010] In some embodiments, the method includes:

[0011] The renewable energy sources include wind power and solar power;

[0012] The environmental parameters include temperature, light intensity, and wind speed;

[0013] The renewable energy output information includes wind power output information and solar power output information.

[0014] In some embodiments, obtaining environmental parameter information of renewable energy and determining renewable energy output information based on the environmental parameter information includes:

[0015] The wind speed is obtained and combined with the relationship between wind energy output and wind speed to generate a definite value for wind energy output.

[0016] The light intensity and the ambient temperature are obtained, and a fixed value of light output is generated by combining the relationship between light output, light intensity, actual temperature and the relationship between actual temperature and ambient temperature.

[0017] The wind power output and solar power output are determined by processing them through a renewable energy output model to generate wind power output information and solar power output information; the renewable energy output model describes the renewable energy output under the influence of different external factors.

[0018] In some embodiments, determining the charging parameters of the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information includes:

[0019] By combining the regional electricity load information, the average charging cost of electric vehicles can be obtained;

[0020] By combining renewable energy output information with regional electricity load information, the difference between electricity supply and demand is obtained, and the fluctuation range and standard deviation of the difference between electricity supply and demand are generated.

[0021] The charging parameters for the target area are determined based on the average charging cost, the fluctuation range, and the standard deviation of the fluctuation.

[0022] In some embodiments, determining the charging parameters of the target area based on the average charging cost, the fluctuation amplitude, and the standard deviation of the fluctuation includes:

[0023] The optimization objective is normalized to generate a normalized optimization objective; the optimization objective includes the average charging cost, the fluctuation range, and the standard deviation of the fluctuation.

[0024] Assign weight coefficients to the normalized optimization objective based on the importance of the optimization objective;

[0025] The linear weighted average of the normalized optimization objectives is used as the overall optimization objective to generate the optimization function;

[0026] The charging parameters are determined based on the optimization function.

[0027] In some embodiments, determining the charging parameters based on the optimization function includes:

[0028] Based on the constraints of the optimization function, the minimum value of the overall optimization objective is calculated under the constraints.

[0029] The charging parameters corresponding to the minimum value are determined as the charging parameters for the target region.

[0030] Secondly, this application also provides an electric vehicle charging control device. The device includes:

[0031] A renewable energy output determination module is used to acquire environmental parameter information of renewable energy and determine renewable energy output information based on the environmental parameter information;

[0032] The regional power load determination module is used to obtain regional power load information within a target area. The regional power load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles.

[0033] The charging parameter determination module is used to determine the charging parameters of the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load, and the demand price elasticity model is used to characterize the relationship between charging demand and charging parameters.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0035] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described above.

[0036] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described above.

[0037] The aforementioned methods, devices, computer equipment, storage media, and computer programs for optimizing electric vehicle charging prices, by acquiring environmental parameter information of renewable energy, can determine renewable energy output information or predict renewable energy processing information for a certain period. Combined with electricity load information within the target area, including electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles, the electricity consumption situation within the target area can be understood. By analyzing renewable energy output information and electricity consumption, a preliminary better power allocation strategy can be derived. For example, when renewable energy output is high, it can guide users to increase charging demand. Based on the price elasticity model, which describes the relationship between charging parameters and charging demand, taking charging prices as an example, changing charging prices can guide the distribution of electric vehicle charging load, thereby guiding an increase or decrease in user charging demand. Therefore, by adopting the above method, the orderly charging and discharging of electric vehicles can be rationally guided by changing charging parameters, timely absorbing renewable energy output, and enhancing the stability of the power distribution network. Attached Figure Description

[0038] Figure 1 This is a diagram illustrating the application environment of an electric vehicle charging control method in one embodiment.

[0039] Figure 2 This is a flowchart illustrating an electric vehicle charging control method in one embodiment;

[0040] Figure 3 This is a schematic diagram of the process for determining renewable energy output information in one embodiment;

[0041] Figure 4 This is a flowchart illustrating the process of determining charging parameters for a target region in one embodiment;

[0042] Figure 5 This is a flowchart illustrating the process of determining charging parameters for a target region in one embodiment.

[0043] Figure 6 This is a flowchart illustrating the process of determining the charging parameters of the target region based on an optimization function in one embodiment.

[0044] Figure 7 This is a flowchart illustrating an electric vehicle charging control method in one embodiment;

[0045] Figure 8 This is a structural block diagram of an electric vehicle charging control device in one embodiment;

[0046] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] The electric vehicle charging control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the renewable energy output device 102, the charging parameter server 104, and the control terminal 106 communicate via a network. A data storage device can store the data that the renewable energy output device needs to process; this data storage device can be integrated into the renewable energy output device 102 or placed in the cloud or on another network server. Specifically, the environmental parameter information of renewable energy can be acquired by the renewable energy output device 102 and stored in the data storage device. After processing, renewable energy output information is generated and transmitted to the control terminal 106 via the network. The charging parameter server 104 can acquire electricity load information in the target area and can also transmit this information to the control terminal 106 via the network. The control terminal 106 can input the acquired information into a pre-established model or other processing methods to obtain optimal charging parameters, and then control the charging parameter server 104 based on these optimal charging parameters. The control terminal 106 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc., and the charging parameter server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0049] In one embodiment, such as Figure 2 As shown, an electric vehicle charging control method is provided, which is applied to... Figure 1 Taking control terminal 106 as an example, the explanation includes the following steps:

[0050] Step S202: Obtain environmental parameter information of renewable energy, and determine renewable energy output information based on the environmental parameter information.

[0051] Renewable energy refers to energy that is sustainably generated in nature and does not decrease or deplete due to use. It mainly includes solar energy, wind energy, hydropower, geothermal energy, and biomass energy. The renewable energy involved in this application includes solar and wind energy, manifested in the use of wind turbines and photovoltaic solar panels to achieve wind power generation and solar power generation. Environmental parameter information refers to information on various factors affecting renewable energy output. For solar power generation, environmental parameters mainly include sunlight and temperature; for wind power generation, environmental parameters mainly include wind speed. Renewable energy output information refers to the quantity or power of electrical or thermal energy generated by renewable energy equipment (such as wind turbines, solar photovoltaic panels, etc.) based on the operating status and environmental conditions. In this application, renewable energy output information can include wind power output information and solar power output information, with output information referring to electricity generation. The aforementioned renewable energy output information is of great significance for the stable operation of the power system and energy planning.

[0052] Specifically, environmental parameter information for renewable energy can be directly acquired by measurement systems such as sensors. These systems can be integrated into the renewable energy output device or set up independently, transmitting the measured environmental parameters to the control terminal via a network or other communication methods. For example, weather stations can be installed at wind and solar power generation sites to monitor environmental parameters such as wind speed, light intensity, and temperature in real time; alternatively, measurement devices such as wind speed meters, light intensity meters, and temperature sensors integrated into the renewable energy output device can monitor these environmental parameters in real time. After acquiring the environmental parameters, the control terminal 106 processes them, for example, by preprocessing the collected raw data, including data cleaning, noise reduction, and interpolation, to ensure the accuracy and continuity of the data. Further, based on the processed environmental parameter information and the relationship between renewable energy output and environmental parameters, renewable energy output information is generated. For example, wind speed is a major influencing factor on wind power output; higher wind speeds lead to increased wind turbine speeds, thereby increasing wind power output. Based on the acquired wind speed information, the wind power output situation can be inferred.

[0053] Environmental parameters such as wind speed, solar irradiance, and temperature are directly related to renewable energy output. Based on the obtained environmental parameter information, renewable energy output information can be effectively predicted, which is very important for optimizing the use of new energy resources and improving the reliability of the power grid.

[0054] Step S204: Obtain regional power load information within the target area. The regional power load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles.

[0055] Electricity load information refers to the electricity demand data of a certain area or equipment within a specific time period. In this application, the electricity load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles. Electricity load information can be presented in various forms, such as load curves, load peaks, and load fluctuations. These forms convey different information, and it is understood that the appropriate form of electricity load information can be selected as needed in practical applications. For example, a load curve visually describes the change in electricity load over time, showing the electricity demand over a period of time; a load peak visually describes the highest point in the load curve, showing the maximum electricity demand within that period; and load fluctuations visually describe the degree of fluctuation in the load curve, showing the volatility of the power system. Selecting appropriate electricity load information is beneficial for the operation and planning of the power system. This electricity load information can help power companies conduct load forecasting, optimize energy dispatch, and plan the expansion and upgrading of power equipment.

[0056] Specifically, obtaining electricity load information typically involves collecting and analyzing actual electricity demand data. There are many methods for obtaining electricity load data, and it's understandable that in practical applications, any one or more methods can be selected as needed. For example, installing power measuring instruments, such as electricity meters and smart meters, at key nodes in the power system can measure electricity consumption and record electricity load data in real time. This data can also be obtained directly from power companies or electricity market operators, as they typically collect and maintain data on user electricity consumption, including historical electricity load data. Furthermore, future electricity load can be predicted based on historical data, trend analysis, and machine learning methods, thereby obtaining relevant electricity load data.

[0057] Step S206: Based on the demand price elasticity model, renewable energy output information, and regional electricity load information, determine the charging parameters for the target area. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load, and the demand price elasticity model is used to characterize the relationship between charging demand and charging parameters.

[0058] The price elasticity of demand model is an economic model used to describe the sensitivity of consumers to price changes in a market, that is, the elasticity of consumer demand for goods or services. In the power sector, the price elasticity of demand model describes the relationship between charging demand and charging parameters, such as consumers' sensitivity to changes in electricity prices. This model can explain how consumers' electricity consumption changes when electricity prices rise or fall. The price elasticity of demand model can help analyze the impact of price changes on consumer purchasing behavior, thereby better understanding market mechanisms and predicting market changes. Based on this model, power system officials can influence users' charging demand by adjusting electricity prices.

[0059] For example, when the charging parameter is the charging electricity price, the relationship between electric vehicle charging demand and the charging electricity price is analyzed using a demand price elasticity model, thereby determining the user's charging demand under different charging electricity prices. Combining renewable energy output information, the output of renewable energy within a certain time period can be understood. Referring to multiple sets of historical renewable energy output data, the output of renewable energy within a future time period can be further predicted. Utilizing regional electricity load information, the load situation of the entire power grid can be understood. The charging station control center updates the charging electricity price in real time based on the received renewable energy output information, non-electric vehicle charging load information, and electric vehicle charging load information, guiding users to charge in an orderly manner and adjusting the distribution of electric vehicle charging load to reduce fluctuations in renewable energy output and charging costs.

[0060] In summary, based on the aforementioned demand price elasticity model, renewable energy output information, and regional electricity load information, reasonable charging strategies and parameters can be formulated to maximize user demand, reduce charging costs, and minimize the impact on the power grid. Charging demand and renewable energy output are interrelated. For example, renewable energy output (such as solar and wind power) may vary significantly across different time periods. Solar power generates more electricity during the day when there is ample sunlight, while wind power generates more electricity when winds are strong. Therefore, periods with higher renewable energy output may be the optimal times for electric vehicle charging. Utilizing the characteristics of renewable energy output, charging demand can be matched with renewable energy output. For instance, if renewable energy output is high during a certain period, charging demand can be increased by lowering charging prices to ensure timely absorption of renewable energy output; conversely, if renewable energy output is low during a certain period, charging demand can be reduced by raising charging prices to alleviate grid pressure. By considering these factors, a reasonable charging strategy can be developed to better align electric vehicle charging demand with renewable energy output, thereby maximizing user demand, reducing charging costs, and minimizing the impact on the power grid.

[0061] In this embodiment, the above method involves multiple steps and aims to optimize the energy utilization and charging load distribution of electric vehicles (EVs) within a target area. By acquiring environmental parameter information of renewable energy sources, the impact of these parameters on the output of renewable energy (such as solar and wind power) is analyzed. Based on this environmental parameter information, the expected output of renewable energy is estimated through models or measurements, such as predicting solar and wind power output over a specific time period. Regional electricity load information within the target area, including EV charging load, non-EV load, and the number of EVs, is obtained through grid monitoring and charging facility data to analyze the electricity load situation within the target area. Combining renewable energy output information with regional electricity load information allows for a more accurate determination of when charging demand is relatively high and when renewable energy can be utilized to the maximum extent for charging. A demand price elasticity model is used to describe the relationship between charging demand and charging parameters, such as the relationship between charging price and charging load. Based on this model, a reasonable charging strategy can be formulated to guide EV charging behavior at different times. This embodiment achieves the matching of renewable energy output with EV charging demand, thereby timely absorbing renewable energy output and enhancing grid stability.

[0062] In some embodiments, renewable energy includes wind energy and solar energy; environmental parameter information includes temperature, irradiance, and wind speed; renewable energy output information includes wind energy output information and solar energy output information.

[0063] Renewable energy refers to energy sources that can continuously supply energy without being depleted, including wind energy (generated by wind power) and solar energy (generated by photovoltaics). Environmental parameter information refers to the environmental conditions that affect the output of the above-mentioned renewable energy sources, including temperature, light intensity, and wind speed. Environmental parameters directly affect the generation of wind and solar energy. Renewable energy output information refers to the actual output data of wind and solar energy within a certain period of time, usually expressed in power (such as kilowatts or megawatts), and can be calculated or measured based on environmental parameters and performance models of renewable energy devices.

[0064] In some embodiments, such as Figure 3 As shown, obtaining environmental parameter information of renewable energy sources and determining renewable energy output information based on the environmental parameter information includes the following steps:

[0065] Step S302: Obtain the wind speed and combine it with the relationship between wind power output and wind speed to generate a definite value for wind power output.

[0066] Taking wind turbines (hereinafter referred to as wind turbines) as an example, a wind turbine is a device used to convert wind energy into electrical energy. The relationship between wind power output and wind speed can be expressed by the following formula:

[0067]

[0068] Where P0 is a definite value of wind energy output, physically meaning power, and can be considered as a definite value of the wind turbine's output power at time t, v t Let v be the wind speed at time t. in To input wind speed, v out To output wind speed, v r For the rated wind speed, P Wtr The rated power output of wind energy.

[0069] Obtaining wind speed typically requires wind speed measurement equipment, including anemometers and wind speed meters. Wind speed can be monitored in real time using these devices, or it can be directly received from weather stations. Among these, v in Also known as cut-in wind speed, it is the minimum wind speed at which the wind turbine begins to spin and generate electricity; v out Also known as cut-out velocity, it is the velocity at which a fan stops operating because the wind speed is too high; that is, it only stops operating when the wind speed is at v. in and v out The wind turbine will only operate normally and convert wind energy into electrical energy during the interval between these two points. The wind speed v at time t... t Reaching rated wind speed v r And it did not exceed the cut-out wind speed v out At time t, the wind turbine's power output is a constant value, i.e., the rated power of wind energy output; when the wind speed v at time t... t Not exceeding the rated wind speed v r And not less than the cut-in wind speed v in At time t, the output power P0 changes with the wind speed v. t The increase is linear, and the rate of increase is determined by... Decide.

[0070] In addition, the wind-electric characteristic curves provided by the wind turbine manufacturer can be used to understand the output performance of the wind turbine at different wind speeds.

[0071] Step S304: Obtain the light intensity and the ambient temperature, and combine the relationship between light output, light intensity, actual temperature, and the relationship between actual temperature and ambient temperature to generate a determined value for light output.

[0072] Taking photovoltaic modules as an example, a photovoltaic module is a device that uses sunlight to convert light energy into electrical energy. The relationship between light output, light intensity, and actual temperature can be expressed by the following formula:

[0073]

[0074] Where P1 is a definite value of light energy output, which physically means power, and can be considered as a definite value of the output power of the photovoltaic module at time t. pvr R is the rated power of the photovoltaic module. at Let R be the light intensity at time t, χ be a correction factor representing environmental factors affecting light intensity, and R be the light intensity at time t. N The light intensity under standard test conditions is typically a known constant related to the inherent properties of photovoltaic modules. The power temperature coefficient (T) of a photovoltaic (PV) module represents its performance under different temperature conditions. t Let T be the actual temperature of the photovoltaic module at time t. N It is the battery temperature under standard test conditions, usually a known constant related to the inherent properties of photovoltaic modules.

[0075] The above formula describes the output power of a photovoltaic module at a specific moment, taking into account irradiance, ambient temperature, and the characteristics of the photovoltaic module itself. The correction factor χ considers the impact of environmental factors on irradiance, correcting for actual irradiance to a certain extent, while the power temperature coefficient... The effect of temperature on photovoltaic module performance was taken into account, aiming to help determine the actual output of photovoltaic modules under different environments.

[0076] Due to the actual temperature T of the photovoltaic module t It is usually not equal to the ambient temperature, but it can be determined using the ambient temperature. The formula for determination is as follows:

[0077]

[0078]

[0079] α=(T max -T min ) / 2

[0080] β=(T max +T min ) / 2

[0081] Among them, T t Let T be the actual temperature of the photovoltaic module at time t. at Let T be the ambient temperature at time t, which can be represented by a sine function, where α and β are the parametric models of the sine function, respectively. max The highest temperature of the day, T min The lowest temperature of the day, T max and T minIt can be inferred based on historical data. The above formula simulates how the actual temperature of the photovoltaic module changes with the ambient temperature. The sine function simulates the diurnal periodic change of the temperature, and the parameters α and β control the amplitude and longitudinal offset, which can be determined according to the actual situation.

[0082] By combining the relationship between solar energy output and irradiance, actual temperature, and the relationship between actual temperature and ambient temperature, the information on solar energy output is determined, taking into account not only irradiance and ambient temperature, but also the characteristics of photovoltaic modules.

[0083] Step S306: The determined values ​​of wind power output and solar power output are processed through the renewable energy output model to generate wind power output information and solar power output information; the renewable energy output model describes the renewable energy output under the influence of different external factors.

[0084] The renewable energy output model is a mathematical model used to describe how the power generation capacity of renewable energy sources (such as wind and solar power) changes with time and environmental conditions. Since renewable energy output often involves many uncertainties, to better handle these uncertainties, the uncertainties and deterministic aspects of renewable energy output can be separated by introducing a correlation coefficient model, expressed as follows:

[0085] U = A + Bi

[0086] In the formula, U is the connection number itself, A is the relatively definite part of the connection number, B is the uncertain part of the connection number, and i∈[-1,1] is the uncertainty coefficient, which is usually taken as an extreme value.

[0087] For solar and wind power systems, the main uncertainties affecting their output include solar irradiance, temperature, and wind speed, which have been considered as much as possible in the process of determining their values. Using the central form of the uncertainty level, the renewable energy output range is divided into three sub-ranges, and the analytic hierarchy process (AHP) is used to assign weights to each sub-range. The central form of the uncertainty level is a method for handling fuzzy and uncertain information, used to calculate expected values ​​to quantify the impact of uncertainty; the AHP is a decision-making method that decomposes complex problems into hierarchical structures and considers subjective and objective factors to determine the relative weights of each element. Finally, the following formula for expressing renewable energy output can be derived:

[0088]

[0089] P1, P2, and P3 represent the expected output values ​​of renewable energy within different intervals. The expected output value of renewable energy refers to the average estimated value calculated based on probability distribution or other uncertain factors, which helps to understand the average results that may occur under different circumstances.

[0090] ω1, ω2, and ω3 represent the weighting coefficients for different intervals, determined by the analytic hierarchy process (AHP); P is the output value of renewable energy; P A To determine the output value of renewable energy, and taking wind and solar power output as examples above, P A It can represent a definite value of wind power output or solar power output; γ is the uncertainty of renewable energy output, defined as:

[0091] γ=P B / P A

[0092] Where P B The uncertainty in the output of renewable energy can come from a variety of factors, such as measurement errors and randomness.

[0093] Substituting the determined wind and solar power output values ​​obtained above into the renewable energy output model, we obtain the wind and solar power output information. The wind power output information can be expressed by the following formula:

[0094]

[0095] In the formula, P wt The wind energy output is represented by the physical meaning of power, which can be considered as the output power of the wind turbine. P0(1), P0(2), and P0(3) are the expected values ​​of wind energy output in different intervals.

[0096] Solar energy output information can be expressed by the following formula:

[0097]

[0098] In the formula, P pv P1(1), P1(2), and P1(3) represent the expected values ​​of light energy output in different intervals.

[0099] In this embodiment, by collecting environmental parameter data, including wind speed, solar irradiance, and ambient temperature, and using this data in conjunction with a corresponding relational model, the deterministic values ​​for wind power output and solar power output can be calculated. These deterministic values ​​are then input into a renewable energy output model. This model considers the impact of various external factors (such as weather conditions and seasonal variations) on renewable energy output and generates the final wind power output and solar power output information by separating the deterministic and uncertain values. The acquisition of renewable energy output information considers the influence of multiple factors simultaneously to enhance the accuracy of renewable energy output prediction.

[0100] In some embodiments, such as Figure 4 As shown, based on the demand price elasticity model, renewable energy output information, and regional electricity load information, the charging parameters for the target area are determined, including the following steps:

[0101] Step S402: Combine regional electricity load information to obtain the average charging cost of electric vehicles.

[0102] The regional electricity load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles. Electric vehicle charging cost is an important indicator for evaluating electric vehicle user satisfaction, and can be expressed by the following formula:

[0103]

[0104] In the formula, Cost ev N represents the average charging cost of electric vehicles. EV The number of electric vehicles can be obtained through surveys or population estimates, P rev (t) and P wev (t) represents the charging load of electric vehicles at residential charging stations and the charging load at workplace charging stations, respectively. The sum of these two loads represents the total charging load of electric vehicles within the target area. c (t) represents the charging price at time t. The above expression yields the average charging cost for electric vehicle users by multiplying the charging price at each time point by the charging load of the electric vehicle and then averaging the results over time.

[0105] Furthermore, the charging load of electric vehicles can also be expressed as a function of electricity prices. Since electric vehicles are significantly affected by price, users typically adjust their charging behavior based on electricity prices to reduce charging costs. Therefore, a demand price elasticity model can be used to represent the relationship between charging demand and electricity prices. This demand price model can be expressed by the following formula:

[0106]

[0107] In the formula, ε represents the price elasticity of demand, which measures the impact of price changes on the quantity demanded, ΔQ ev Let Q represent the change in electric vehicle charging demand, Δprice represent the change in electricity price, and price represent the initial value of the electricity price. ev0 This represents the initial value of electric vehicle charging demand. The relationship between electric vehicle charging load and electricity prices can help formulate charging strategies, adjust electricity prices according to demand, and thus guide electric vehicle charging behavior.

[0108] Step S404: Combine renewable energy output information with regional electricity load information to obtain the difference between electricity supply and demand, and generate the fluctuation range and standard deviation of the difference between electricity supply and demand.

[0109] The difference between electricity supply and demand refers to the discrepancy or imbalance between electricity supply and demand at a specific point in time or within a specific time period, which can be expressed by the following formula:

[0110] P D (t)=P wt (t)+P pv (t)-P ev (t)

[0111] In the formula, P D (t) represents the sum of renewable energy output and electric vehicle charging load at time t, i.e., the difference between electricity supply and demand. wt (t) represents the wind power output at time t, P pv (t) represents the light energy output at time t, P ev (t) represents the electric vehicle charging load at time t. The difference between power supply and demand is a key factor in power system management and operation. Power system personnel need to meet power demand as much as possible at any given time, while maintaining the stability and reliability of the power system.

[0112] Fluctuation amplitude describes the degree of change in the amplitude of a variable over a certain period of time. The fluctuation amplitude of the electricity supply-demand gap describes the degree of change between power supply capacity and electricity demand over a period of time, and the expression is as follows:

[0113]

[0114] In the formula, R e P represents the fluctuation range. D Let (t) represent the difference between electricity supply and demand at time t, and Δt represent the time interval. The fluctuation range of the electricity supply and demand difference is obtained by calculating the relative rate of change between adjacent times t and (t-Δt), and averaging all relative rates of change. Calculating the fluctuation range of the electricity supply and demand difference is crucial in power systems. For example, a smaller fluctuation range indicates higher stability of the power system, making it easier for power companies to maintain a balanced power supply; a larger fluctuation range may lead to power system instability, requiring measures to stabilize power supply.

[0115] The standard deviation of fluctuation describes the dispersion of data points relative to the data mean. The standard deviation of fluctuation in the electricity supply and demand difference describes the magnitude of change in electricity supply and demand over a period of time. The expression is as follows:

[0116]

[0117] In the formula, S d For P D The standard deviation of the fluctuation of (t), P D1 (t) represents the difference between electricity supply and demand at time t, P. D The average value of (t). The larger the standard deviation of fluctuation, the higher the volatility of the difference between electricity supply and demand, and the more unstable the system.

[0118] Step S406: Determine the charging parameters for the target area based on the average charging cost, fluctuation range, and standard deviation of fluctuation.

[0119] Determining charging parameters for a target area based on average charging cost, fluctuation range, and standard deviation of fluctuation is a comprehensive approach that considers charging strategies. Charging costs can be adjusted by controlling factors such as charging time periods and electricity pricing strategies, with the goal of minimizing costs during charging to meet users' economic needs. Fluctuation range reflects the volatility of electricity supply; large fluctuations can lead to grid instability, so they can be reduced by adjusting charging time periods and charging rates. Standard deviation of fluctuation is a statistical measure of electricity supply stability; reducing it can improve the reliability of the power system. However, there may be trade-offs among these objectives; for example, reducing average charging cost may increase volatility. Therefore, the most suitable charging parameters need to be determined based on actual needs.

[0120] This embodiment describes how to integrate different information to determine electric vehicle charging parameters for a target area to meet system requirements. Charging parameters are determined by calculating the average charging cost of electric vehicles, the fluctuation range and standard deviation of the power supply-demand gap, while also considering economic indicators, power system stability, and reliability. This allows for the development of an optimal electric vehicle charging strategy to achieve efficient power system operation and improve user satisfaction.

[0121] In some embodiments, such as Figure 5 As shown, the charging parameters for the target area are determined based on the average charging cost, fluctuation range, and standard deviation of fluctuation, including the following steps:

[0122] Step S502: Normalize the optimization objectives to generate normalized optimization objectives; the optimization objectives include average charging cost, fluctuation range, and fluctuation standard deviation.

[0123] Normalization refers to the process of converting data of different scales or ranges into a unified standard scale. It typically maps data to a specific range or standard distribution for comparison, analysis, or processing. Different optimization objectives often have different scales and magnitudes, which may cause some objectives to dominate others during the optimization process. Normalization can unify the scale of all objectives, which is crucial for decision-making and analysis in multi-objective optimization problems because it allows for easier weighting of different objectives.

[0124] In mathematics, there are many ways to normalize, and we will not limit ourselves to any particular method here. The normalized results for average charging cost, fluctuation range, and standard deviation of fluctuation are denoted as: as well as

[0125] Step S504: Assign weight coefficients to the normalized optimization objective based on the importance of the optimization objective.

[0126] In practical problems, it is necessary to first determine the importance of each optimization objective relative to other objectives. There are various methods for this; taking the Analytic Hierarchy Process (AHP) as an example, the process of assigning weight coefficients in AHP includes: creating a judgment matrix to assess the relative importance of different factors, typically involving a series of pairwise comparisons. Decision-makers use scales (such as numbers from 1 to 9) to represent the relative importance between two factors. The judgment matrix is ​​then used to calculate the weight of each factor, which represents the importance of each factor relative to the objective. Assigning weights to each optimization objective allows decision-makers more flexible control over the relative influence of different optimization objectives to meet the needs of a specific problem, weighing the weights of different objectives in multi-objective optimization, and ensuring that the influence of different optimization objectives in the final objective function is weighted according to their relative importance.

[0127] Step S506: Use the linear weighted average of the normalized optimization objective as the overall optimization objective to generate the optimization function.

[0128] The linear weighted average of the normalized optimization objectives is used as the overall optimization objective to generate the optimization function, as shown in the following formula:

[0129]

[0130] In the formula, minF represents the minimum objective value. These refer to the normalized fluctuation amplitude, fluctuation standard deviation, and average charging cost, respectively, as three sub-objective functions, ω a ω b ω cThe weight coefficients for the three sub-objectives can be assigned using the analytic hierarchy process (AHP) according to the importance of each sub-objective, and the linear weighted sum of the functions of each sub-objective can be used as the overall optimization objective. This overall optimization objective can help power system administrators balance multiple optimization objectives when determining charging parameters, enabling them to make more reasonable and comprehensive decisions.

[0131] Step S508: Determine the charging parameters based on the optimization function.

[0132] The optimization function represents the selection of charging parameters corresponding to minimizing the overall objective value. Although the minimized objective value is just a numerical value, it is also a guiding tool. The decision strategy obtained in the process of determining the minimized objective value can be transformed into specific charging operations, that is, to determine the specific charging parameters.

[0133] This embodiment describes how to perform multi-objective optimization to determine the optimal electric vehicle charging strategy. Different indicators (average charging cost, fluctuation range, and standard deviation of fluctuation) are normalized, and a weight coefficient is assigned to each objective. The normalized optimization objectives are then linearly weighted to form an overall optimization objective function to determine the charging parameters. This method allows for the comprehensive consideration of multiple objectives and their relative importance to formulate an optimal charging strategy that minimizes charging costs and improves power system stability while meeting system requirements.

[0134] In some embodiments, such as Figure 6 As shown, determining charging parameters based on an optimization function includes the following steps:

[0135] Step S602: Based on the constraints of the optimization function, calculate the minimum value of the overall optimization objective under the constraints.

[0136] In the actual optimization process, constraints need to be considered, which can be expressed by the following relationship:

[0137] Q ev_tatal ≥0.9Q ev-limit (1)

[0138] Q ev (t)≥0 (2)

[0139]

[0140]

[0141] U min ≤U i，t ≤U max (5)

[0142] P l ≤Pl max (6)

[0143] In the formula, Q ev_tatal Let Q be the initial value of the total charging demand for electric vehicles. ev-limit To optimize the total charging demand for electric vehicles, G ij B ij δ ij P represents the conductance, susceptance, and phase angle difference between nodes i and j, respectively. Gi P Di P represents the active and reactive power outputs of node i, respectively. Di Q Di These represent the active and reactive power of the load at node i, respectively, U min and U max U represents the minimum and maximum values ​​of the node voltage, respectively. i，t P represents the voltage at node i at time t. l P represents the load of line l. l max This indicates the line's safe load.

[0144] The above constraints mean the following: (1) The total charging demand of electric vehicles after optimization should be greater than or equal to the total charging demand of electric vehicles before optimization; (2) After optimization, the charging demand of electric vehicles at each moment must be greater than or equal to 0; (3) The active power balance equation is that the active power injected into the grid by the generator minus the active power consumed by the node equals the active power transmitted through the line; (4) The reactive power balance equation is that the reactive power injected into the grid by the generator minus the reactive power consumed by the node equals the reactive power transmitted through the line; (5) Regarding the constraint on node voltage, the node voltage should be between the maximum and minimum values ​​to keep the voltage within a stable range; (6) Regarding the constraint on line load, the line load should be less than or equal to the maximum line load to ensure that the line load does not exceed its carrying capacity. Among them, the active power balance equation and the reactive power balance equation are used to maintain the power balance of the power system and ensure the power matching between power generation and load.

[0145] Under these constraints, the minimum value of the overall optimization objective can be calculated using mathematical algorithms, such as linear programming, global programming, and nonlinear programming. These algorithms will find the optimal solution while satisfying all constraints.

[0146] Step S604: Determine the charging parameters corresponding to the minimum value as the charging parameters for the target area.

[0147] This step determines the optimal charging parameters to meet the objectives of the optimization problem and achieve best performance in the power system. Specifically, by calculating the minimum values, the parameters of the charging strategy, including the charging price, can be determined to meet the system's needs.

[0148] This embodiment describes how to determine the optimal charging parameters by calculating the minimum value of the optimization function under given constraints. By applying all constraints to the optimization problem, such as the safe operation of the power system and the minimum charging demand of the electric vehicle, the search space for charging parameters is limited. Then, mathematical optimization methods (such as linear programming and nonlinear programming) are used to calculate the minimum value of the overall optimization objective. This minimum value corresponds to the optimal charging parameters, thereby optimizing the electric vehicle charging strategy and enabling the electric vehicle to meet various optimization objectives to the greatest extent during the charging process, such as reducing charging costs, reducing the difference between power supply and demand, and controlling the fluctuation range.

[0149] In some embodiments, such as Figure 7 As shown, an electric vehicle charging control method is provided, including the following steps:

[0150] Step S702: Obtain the wind speed and combine it with the relationship between wind power output and wind speed to generate a wind power output determination value.

[0151] Step S704: Obtain the light intensity and the ambient temperature, and combine the relationship between light output and light intensity, actual temperature and the relationship between actual temperature and ambient temperature to generate a determined value for light output.

[0152] Step S706: The determined values ​​of wind power output and solar power output are processed through the renewable energy output model to generate wind power output information and solar power output information; the renewable energy output model describes the renewable energy output under the influence of different external factors.

[0153] Step S708: Obtain regional power load information within the target area. The regional power load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles.

[0154] Step S710: Combine regional electricity load information to obtain the average charging cost of electric vehicles.

[0155] Step S712: Combine renewable energy output information with regional electricity load information to obtain the difference between electricity supply and demand, and generate the fluctuation range and standard deviation of the difference between electricity supply and demand.

[0156] Step S714: Normalize the optimization objectives to generate normalized optimization objectives; the optimization objectives include average charging cost, fluctuation range, and fluctuation standard deviation.

[0157] Step S716: Assign weight coefficients to the normalized optimization objective based on the importance of the optimization objective.

[0158] Step S718: Use the linear weighted average of the normalized optimization objective as the overall optimization objective to generate the optimization function.

[0159] Step S720: Based on the constraints of the optimization function, calculate the minimum value of the overall optimization objective under the constraints.

[0160] Step S722: Determine the charging parameters corresponding to the minimum value as the charging parameters for the target area.

[0161] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0162] Based on the same inventive concept, this application also provides an electric vehicle charging control device for implementing the electric vehicle charging control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the electric vehicle charging control device provided below can be found in the limitations of the electric vehicle charging control device described above, and will not be repeated here.

[0163] In one embodiment, such as Figure 8 As shown, an electric vehicle charging control device is provided, including: a renewable energy output determination module 802, a regional electricity load determination module 804, and a charging parameter determination module 806, wherein:

[0164] The renewable energy output determination module 802 is used to acquire environmental parameter information of renewable energy and determine renewable energy output information based on the environmental parameter information.

[0165] The regional power load determination module 804 is used to obtain regional power load information within a target area. The regional power load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles.

[0166] The charging parameter determination module 806 is used to determine the charging parameters of the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load. The demand price elasticity model is used to characterize the relationship between charging demand and charging parameters.

[0167] In some embodiments, the renewable energy sources in the renewable energy output determination module include wind energy and solar energy, the environmental parameters include temperature, irradiance, and wind speed, and the renewable energy output information includes wind energy output information and solar energy output information.

[0168] In some embodiments, the renewable energy output determination module is specifically used for:

[0169] Wind speed is acquired and combined with the relationship between wind power output and wind speed to generate a definite wind power output value; solar irradiance and ambient temperature are acquired and combined with the relationship between solar power output and solar irradiance, actual temperature, and actual temperature and ambient temperature to generate a definite solar power output value; then the definite wind power output value and the definite solar power output value are processed through a renewable energy power output model to generate wind power output information and solar power output information; wherein the renewable energy power output model describes the renewable energy power output under the influence of different external factors.

[0170] In some embodiments, the area power load determination module is specifically used for:

[0171] The system obtains regional electricity load information within the target area, including electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles. Based on the regional electricity load information, it obtains the average charging cost of electric vehicles. Furthermore, by combining renewable energy output information with regional electricity load information, it obtains the difference between electricity supply and demand, and generates the fluctuation range and standard deviation of the difference between electricity supply and demand.

[0172] In some embodiments, the charging parameter determination module is specifically used for:

[0173] The optimization objectives are normalized to generate normalized optimization objectives, which include average charging cost, fluctuation range, and standard deviation of fluctuation. Weight coefficients are assigned to the normalized optimization objectives based on their importance. Further, the linear weighted average of the normalized optimization objectives is used as the overall optimization objective to generate an optimization function. Based on the constraints of the optimization function, the minimum value of the overall optimization objective is calculated under these constraints. The charging parameters corresponding to the minimum value are then determined as the charging parameters for the target region.

[0174] Each module in the aforementioned electric vehicle charging control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0175] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data related to renewable energy output, regional electricity load, and charging parameters. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for optimizing electric vehicle charging prices.

[0176] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0177] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0178] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0179] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0180] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling the charging of an electric vehicle, characterized in that, The method includes: Obtain environmental parameter information of renewable energy sources, and determine renewable energy output information based on the environmental parameter information; Obtain regional electricity load information within the target area, including electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles; Based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information, the charging parameters of the target area are determined. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load. The demand price elasticity model is used to characterize the relationship between charging demand and charging parameters. The process of determining the charging parameters for the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information includes: By combining the regional electricity load information, the average charging cost of electric vehicles can be obtained; By combining renewable energy output information with regional electricity load information, the difference between electricity supply and demand is obtained, and the fluctuation range and standard deviation of the difference between electricity supply and demand are generated. The charging parameters for the target area are determined based on the average charging cost, the fluctuation range, and the standard deviation of the fluctuation. Determining the charging parameters for the target area based on the average charging cost, the fluctuation range, and the standard deviation of the fluctuation includes: The optimization objective is normalized to generate a normalized optimization objective; the optimization objective includes the average charging cost, the fluctuation range, and the standard deviation of the fluctuation. Assign weight coefficients to the normalized optimization objective based on the importance of the optimization objective; The linear weighted average of the normalized optimization objective is used as the overall optimization objective to generate the optimization function; The charging parameters are determined based on the optimization function.

2. The method according to claim 1, characterized in that, The method includes: The renewable energy sources include wind power and solar power; The environmental parameters include temperature, light intensity, and wind speed; The renewable energy output information includes wind power output information and solar power output information.

3. The method according to claim 2, characterized in that, The process of acquiring environmental parameter information of renewable energy and determining renewable energy output information based on the environmental parameter information includes: The wind speed is obtained and combined with the relationship between wind energy output and wind speed to generate a definite value for wind energy output. Obtain light intensity and ambient temperature, and combine the relationship between light output and light intensity, actual temperature and the relationship between actual temperature and ambient temperature to generate a definite value for light output. The wind power output and solar power output are determined by processing them through a renewable energy output model to generate wind power output information and solar power output information; the renewable energy output model describes the renewable energy output under the influence of different external factors.

4. The method according to claim 1, characterized in that, Determining the charging parameters based on the optimization function includes: Based on the constraints of the optimization function, the minimum value of the overall optimization objective is calculated under the constraints. The charging parameters corresponding to the minimum value are determined as the charging parameters for the target region.

5. An electric vehicle charging control device, characterized in that, The device includes: A renewable energy output determination module is used to acquire environmental parameter information of renewable energy and determine renewable energy output information based on the environmental parameter information; The regional power load determination module is used to obtain regional power load information within a target area. The regional power load information includes electric vehicle charging load, non-electric vehicle load, and the number of electric vehicles. The charging parameter determination module is used to determine the charging parameters of the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information. The charging parameters can be used to assist in guiding the distribution of electric vehicle charging load. The demand price elasticity model is used to characterize the relationship between charging demand and charging parameters. The process of determining the charging parameters for the target area based on the demand price elasticity model, the renewable energy output information, and the regional electricity load information includes: By combining the regional electricity load information, the average charging cost of electric vehicles can be obtained; By combining renewable energy output information with regional electricity load information, the difference between electricity supply and demand is obtained, and the fluctuation range and standard deviation of the difference between electricity supply and demand are generated. The charging parameters for the target area are determined based on the average charging cost, the fluctuation range, and the standard deviation of the fluctuation. Determining the charging parameters for the target area based on the average charging cost, the fluctuation range, and the standard deviation of the fluctuation includes: The optimization objective is normalized to generate a normalized optimization objective; the optimization objective includes the average charging cost, the fluctuation range, and the standard deviation of the fluctuation. Assign weight coefficients to the normalized optimization objective based on the importance of the optimization objective; The linear weighted average of the normalized optimization objective is used as the overall optimization objective to generate the optimization function; The charging parameters are determined based on the optimization function.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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