An off-grid type direct-current high-power high-voltage charging method, system and device

Abstract

The application relates to the field of electric vehicle charging stations, in particular to an off-grid type direct-current high-power high-voltage charging method, system and equipment. The method comprises the following steps: obtaining a historical daily power consumption curve and a daily power generation prediction curve of a charging system; determining a power consumption growth rate according to all historical daily power consumption curves and corresponding charging data of the historical daily power consumption curves; determining a daily power consumption prediction curve according to the power consumption growth rate and the historical daily power consumption curve; determining power supply constraints according to the daily power consumption prediction curve, the daily power generation prediction curve and energy storage module parameters; determining optimal power supply constraints according to the power supply constraints and time period electricity prices, and adjusting the working state of the energy storage module based on the optimal power supply constraints. The application has the effects of guaranteeing power supply of the charging system and improving economic benefits of the charging system.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging stations, and in particular to an off-grid DC high-power high-voltage charging method, system and equipment. Background Technology

[0002] Currently, the widespread adoption of electric vehicles has led to the proliferation of electric vehicle charging stations. Existing electric vehicle charging stations are divided into grid-connected and off-grid types. Grid-connected electric vehicle charging stations are heavily reliant on the power grid, and when the power grid is under heavy load, they are significantly affected. Off-grid electric vehicle charging stations are heavily reliant on energy storage systems, and energy storage systems are generally built based on experience or economic considerations, which can easily lead to insufficient power in the energy storage system. Summary of the Invention

[0003] To ensure the power supply of the charging system and improve its economic efficiency, this application provides an off-grid DC high-power high-voltage charging method, system, and equipment.

[0004] Firstly, this application provides an off-grid DC high-power high-voltage charging method, employing the following technical solution: An off-grid DC high-power high-voltage charging method includes: Obtain the historical daily electricity consumption curve and the predicted daily power generation curve of the charging system; Based on all the historical daily electricity consumption curves and the corresponding charging data for the historical daily electricity consumption curves, the electricity consumption growth rate is determined. The charging data includes the number of charging vehicles and the average charging time. Based on the electricity consumption growth rate and the historical daily electricity consumption curve, determine the daily electricity consumption forecast curve; Based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters, the power supply constraints are determined, wherein the power supply constraints include the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module. Based on the power supply constraints and time-of-use electricity prices, the optimal power supply constraints are determined, and the operating state of the energy storage module is adjusted based on the optimal power supply constraints. The operating state includes charging state, discharging state, and no-operation state.

[0005] By adopting the above technical solution, the electricity consumption growth rate is determined based on the historical daily electricity consumption curve and corresponding charging data of the charging system. Then, the daily electricity consumption curve is predicted. Combined with the daily power generation prediction curve and energy storage module parameters, the power supply constraints are determined. Based on the power supply constraints and time-of-use electricity prices, the optimal power supply constraints are determined. Then, the working status of the energy storage module is adjusted to ensure the power supply of the charging system and improve the economic efficiency of the charging system.

[0006] Optionally, obtaining the historical daily electricity consumption curve and daily power generation prediction curve of the charging system includes: Obtain the type of power source for the charging system; Based on the type of power source, determine the corresponding power forecasting model; Obtain the forecast data required for the power forecast model, and perform power generation forecasting for the charging system based on the forecast data to determine the daily power generation forecast curve.

[0007] By adopting the above technical solution, a corresponding power prediction model is determined according to the type of power source of the charging system, and the required prediction data is obtained to predict the power generation of the charging system, laying the foundation for subsequently determining the power supply constraints of the charging system.

[0008] Optionally, determining the electricity consumption growth rate based on all the historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves includes: Obtain the objective influencing factors of the historical daily electricity consumption curve, adjust the historical daily electricity consumption curve based on the objective influencing factors, and determine the corresponding target historical daily electricity consumption curve; Fit all the target historical daily electricity consumption curves to determine the historical electricity consumption curves; The first electricity consumption growth rate is determined based on the slope of the historical electricity consumption curve. Based on all the charging data, determine the second electricity consumption growth rate; The electricity consumption growth rate is determined based on the first electricity consumption growth rate and the second electricity consumption growth rate.

[0009] Optionally, determining the second electricity consumption growth rate based on all the charging data includes: Determine the charging vehicle growth rate based on all the stated number of charging vehicles; Based on all the aforementioned average charging times, determine the average charging time growth rate; If the average charging time growth rate is higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to the first preset ratio to determine the second electricity consumption growth rate. If the average charging time growth rate is not higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to the second preset ratio to determine the second electricity consumption growth rate. In the first preset ratio, the weight of the average charging time growth rate is greater than the weight of the average charging time growth rate in the second preset ratio.

[0010] By adopting the above technical solution, the historical daily electricity consumption curve is adjusted according to objective influencing factors to obtain the target historical daily electricity consumption curve. Based on the target historical daily electricity consumption curve, the historical electricity consumption curve is determined. The slope of the historical electricity consumption curve is used as the first electricity consumption growth rate. The second electricity consumption growth rate is obtained according to the growth rate of charging vehicles and the growth rate of average charging time. The electricity consumption growth rate is determined by the first electricity consumption growth rate and the second electricity consumption growth rate. By removing objective factors and determining the electricity consumption growth rate from multiple perspectives, the accuracy is improved.

[0011] Optionally, determining the daily electricity consumption forecast curve based on the electricity consumption growth rate and the historical daily electricity consumption curve includes: Based on the electricity consumption growth rate and the target historical daily electricity consumption curve, determine the target daily electricity consumption forecast curve; The daily electricity consumption forecast curve is determined based on the target daily electricity consumption curve and the objective influencing factors of the forecast day.

[0012] By adopting the above technical solution, the daily electricity consumption forecast curve is determined based on the electricity consumption growth rate, the target historical daily electricity consumption curve, and the objective influencing factors of the forecast day, laying the foundation for subsequently determining the power supply constraints of the charging system.

[0013] Optionally, determining the power supply constraints based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters includes: Compare the daily electricity consumption forecast curve and the daily power generation forecast curve; Based on the comparison results, multiple first upper curve portions and first lower curve portions of the daily electricity consumption forecast curve are determined. The first upper curve portion is the portion where the daily electricity consumption forecast value is greater than the daily power generation forecast value, and the first lower curve portion is the portion where the daily electricity consumption forecast value is less than the daily power generation forecast value. Based on the comparison results, multiple second upper curve portions and second lower curve portions of the daily power generation forecast curve are determined. The second upper curve portion is the portion where the daily power generation forecast value is greater than the daily power consumption forecast value, and the second lower curve portion is the portion where the daily power generation forecast value is less than the daily power consumption forecast value. Based on the second upper curve portion and the corresponding first lower curve portion, the rechargeable capacity of the energy storage module and the corresponding charging period of the rechargeable capacity are determined. Based on the first upper curve portion and the corresponding second lower curve portion, the discharge amount of the energy storage module and the corresponding discharge period are determined. Based on the discharge amount, the rechargeable capacity, and the initial remaining amount of the energy storage module, the charging amount for each charging period is constrained, wherein constraining the charging amount for each charging period determines the range of charging amount for each charging period.

[0014] Optionally, constraining the charging amount for each charging period based on the discharge amount, the rechargeable capacity, and the initial remaining capacity of the energy storage module includes: The maximum charging amount for the current charging period is determined based on the initial remaining amount of the energy storage module, the capacity of the energy storage module, and the rechargeable amount of the current charging period. The minimum charging amount for the current charging period is determined based on the discharge amount of the next discharge period, the lower limit of the charging of the energy storage module, and the initial remaining amount of the energy storage module. The charging amount constraint for the current charging period is determined based on the maximum charging amount and the minimum charging amount. The remaining amount constraint of the energy storage module is determined based on the charging amount constraint of the current charging period, the initial remaining amount of the energy storage module, and the discharge amount of the next discharging period. Based on the remaining capacity constraint of the energy storage module, the charging capacity constraint for the next charging period is determined.

[0015] By adopting the above technical solution, based on the comparison results of the daily electricity consumption forecast curve and the daily power generation forecast curve, different curve sections are determined, thereby determining the charging period and rechargeable amount, as well as the discharging period and discharging amount. Based on the parameters of the energy storage module, the charging amount constraints for each charging period are determined, ensuring the power supply of the charging system.

[0016] Optionally, determining the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjusting the operating state of the energy storage module based on the optimal power supply constraint, includes: Based on the charging amount constraint and the corresponding charging period, and based on the electricity price during the charging period, calculate the charging revenue range for the charging period. Based on the charging revenue range of all the aforementioned charging periods, determine the charging amount constraint when the revenue is highest. The operating state of the energy storage module is adjusted according to the charging amount constraint at which the benefit is maximized.

[0017] By adopting the above technical solution, the charging revenue range for each charging period is calculated based on the charging amount constraint and the electricity price during the charging period. Based on the charging revenue range for all charging periods, the charging amount constraint at which the revenue is highest is determined, thereby adjusting the working state of the energy storage module. This improves economic efficiency while ensuring the power supply of the charging system.

[0018] Secondly, this application provides an off-grid DC high-power high-voltage charging system, which adopts the following technical solution: An off-grid DC high-power high-voltage charging system includes: The information acquisition module acquires the historical daily electricity consumption curve and the daily power generation prediction curve of the charging system; The electricity consumption growth rate determination module determines the electricity consumption growth rate based on all the historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves. The charging data includes the number of charging vehicles and the average charging time. The daily electricity consumption prediction module determines the daily electricity consumption prediction curve based on the electricity consumption growth rate and the historical daily electricity consumption curve. The power supply constraint determination module determines the power supply constraint based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters. The power supply constraint includes the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module. The adjustment module determines the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjusts the working state of the energy storage module based on the optimal power supply constraint. The working state includes charging state, discharging state and no-operation state.

[0019] By adopting the above technical solution, the electricity consumption growth rate is determined from the historical daily electricity consumption curve and corresponding charging data of the charging system. Then, the daily electricity consumption curve is predicted. Combined with the daily power generation prediction curve and energy storage module parameters, the power supply constraints are determined. Based on the power supply constraints and time-of-use electricity prices, the optimal power supply constraints are determined. Then, the working status of the energy storage module is adjusted to ensure the power supply of the charging system and improve the economic efficiency of the charging system.

[0020] Thirdly, this application provides an electronic device, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of an off-grid DC high-power high-voltage charging method according to an embodiment of this application; Figure 2 This is a comparison diagram of the daily electricity consumption forecast curve and the daily power generation forecast curve in the embodiments of this application; Figure 3 This is a structural block diagram of an off-grid DC high-power high-voltage charging system according to an embodiment of this application; Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] The following is in conjunction with the appendix Figure 1-4 This application will be described in further detail.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] like Figure 1 As shown, an off-grid DC high-power high-voltage charging method is described in the following main process flow (steps S101 to S105): S101. Obtain the historical daily electricity consumption curve and daily power generation prediction curve of the charging system.

[0025] In this embodiment, the charging system consists of a 10kV step-down transformer, a 24-pulse rectifier, a DC / DC charging pile, an energy storage module, and an independent power generation system. By eliminating the need for a single AC / DC rectifier stage, the charging efficiency is higher and the environmental adaptability is enhanced. By combining the energy storage module and the independent power generation system, the demand on grid capacity is reduced.

[0026] Historical daily electricity consumption curves are the daily electricity consumption curves of the charging system, automatically obtained through the charging system's electricity consumption logs. Daily power generation prediction curves are predicted by selecting the appropriate power prediction model based on the type of power source of the charging system. The purpose of obtaining historical daily electricity consumption curves is to seek the growth pattern of electricity consumption in the charging system and to predict subsequent daily electricity consumption curves. The purpose of obtaining daily power generation prediction curves is to combine them with daily electricity consumption prediction curves to determine the electricity consumption and power generation of the charging system, laying the foundation for subsequently determining charging periods and charging amounts, as well as discharging periods and discharging amounts.

[0027] Specifically, the process involves obtaining the historical daily electricity consumption curve and daily power generation forecast curve of the charging system, including: identifying the type of power source for the charging system; determining the corresponding power forecast model based on the type of power source; obtaining the forecast data required for the power forecast model; and making power generation forecasts for the charging system based on the forecast data to determine the daily power generation forecast curve.

[0028] In this embodiment, the type of power source can be photovoltaic power generation, wind power generation, or diesel generator power generation, or photovoltaic-wind power generation. The power prediction model can be a corresponding convolutional neural network model or a decision tree model. The prediction data can be photovoltaic power generation data, such as weather influencing factors and light intensity, or wind power generation data, such as wind direction and wind force. The daily power generation prediction curve can be the power generation prediction curve for each time of day, or the power generation prediction curve for each time period of day.

[0029] S102. Based on all historical daily electricity consumption curves and the corresponding charging data for the historical daily electricity consumption curves, determine the electricity consumption growth rate. The charging data includes the number of charging vehicles and the average charging time.

[0030] In this embodiment, the electricity consumption growth rate is obtained based on the first electricity consumption growth rate determined by fitting the historical daily electricity consumption curve after adjustment for objective influencing factors and the second electricity consumption growth rate determined by charging data. The purpose of obtaining the electricity consumption growth rate is to lay the foundation for determining the daily electricity consumption forecast curve.

[0031] Specifically, based on all historical daily electricity consumption curves and the corresponding charging data, the electricity consumption growth rate is determined, including: acquiring the objective influencing factors of the historical daily electricity consumption curves; adjusting the historical daily electricity consumption curves based on the objective influencing factors to determine the corresponding target historical daily electricity consumption curves; fitting all target historical daily electricity consumption curves to determine the historical electricity consumption curves; determining the first electricity consumption growth rate based on the slope of the historical electricity consumption curves; determining the second electricity consumption growth rate based on all charging data; and determining the total electricity consumption growth rate based on the first and second electricity consumption growth rates.

[0032] In this embodiment, objective influencing factors include season and weather. The adjustment of historical daily electricity consumption curves based on objective influencing factors is determined according to the objective influencing factors-influence coefficient table, which contains all seasons, weather conditions, and their corresponding influence coefficients. For example, the influence coefficient for winter is 1.5, so the historical daily electricity consumption curve is adjusted downwards to 1 / 1.5. The purpose of adjusting historical daily electricity consumption curves based on objective influencing factors is to remove the influence of seasons and weather, and to better determine the natural growth of electricity consumption. Fitting all target historical daily electricity consumption curves involves extracting the daily electricity consumption from each target historical daily electricity consumption curve and fitting it. This can be done using the least squares method or the gradient descent method. The first electricity consumption growth rate is the slope of the historical electricity consumption curve, and the second electricity consumption growth rate is determined based on the growth rate of charging vehicles and the average growth rate of the average charging period. The electricity consumption growth rate is the average of the first and second electricity consumption growth rates.

[0033] Furthermore, based on all charging data, a second electricity consumption growth rate is determined, including: determining the charging vehicle growth rate based on the total number of charging vehicles; determining the average charging time growth rate based on all average charging times; if the average charging time growth rate is higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to a first preset ratio to determine the second electricity consumption growth rate; if the average charging time growth rate is not higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to a second preset ratio to determine the second electricity consumption growth rate; wherein, the weight of the average charging time growth rate in the first preset ratio is greater than the weight of the average charging time growth rate in the second preset ratio.

[0034] In this embodiment, the charging vehicle growth rate is determined by fitting the daily charging vehicle number curve and setting its slope as the charging vehicle growth rate. The average charging time growth rate is determined by fitting the daily average charging time curve and setting its slope as the average charging time growth rate. The preset growth rate is set in advance and can be 0 or 0.5. The first preset ratio and the second preset ratio are also set in advance and can be 1:1 and 1:2. The corresponding weights of the average charging time growth rate and the charging vehicle growth rate are 1 / 2 and 1 / 2 and 1 / 3 and 2 / 3, respectively. The second electricity consumption growth rate = weight of average charging time growth rate × average charging time growth rate + weight of charging vehicle growth rate × charging vehicle growth rate. When the average charging time growth rate is not high, the growth rate of electricity consumption is determined by the growth of the number of charging vehicles. Therefore, the weight of the charging vehicle growth rate is increased to determine the second electricity consumption growth rate. This reflects the growth of electricity consumption from the perspective of charging data growth, with the aim of determining the electricity consumption growth rate more accurately.

[0035] S103. Determine the daily electricity consumption forecast curve based on the electricity consumption growth rate and historical daily electricity consumption curve.

[0036] In this embodiment, the daily electricity consumption forecast curve is determined based on the electricity consumption growth rate and the target historical daily electricity consumption curve. Then, based on objective influencing factors, the daily electricity consumption forecast curve is determined. The daily electricity consumption forecast curve provides a basis for subsequently determining the charging period and the discharging period.

[0037] Specifically, the target daily electricity consumption forecast curve is determined based on the electricity consumption growth rate and the target historical daily electricity consumption curve; the daily electricity consumption forecast curve is determined based on the target daily electricity consumption curve and the objective influencing factors of the forecast day.

[0038] In this embodiment, the target daily electricity consumption forecast curve = target historical daily electricity consumption curve × (1 + electricity consumption growth rate). The target historical daily electricity consumption curve at this time is the target historical daily electricity consumption curve of the day before the target daily electricity consumption forecast curve. The process of determining the daily electricity consumption forecast curve based on objective influencing factors and the target daily electricity consumption curve is the reverse process of determining the target historical daily electricity consumption curve based on objective influencing factors and the historical daily electricity consumption curve.

[0039] S104. Based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters, determine the power supply constraints, which include the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module.

[0040] Specifically, based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters, power supply constraints are determined, including: comparing the daily electricity consumption forecast curve and the daily power generation forecast curve; determining multiple first upper curve portions and first lower curve portions of the daily electricity consumption forecast curve based on the comparison results, where the first upper curve portion is the part where the daily electricity consumption forecast value is greater than the daily power generation forecast value, and the first lower curve portion is the part where the daily electricity consumption forecast value is less than the daily power generation forecast value; and determining multiple second upper curve portions and second lower curve portions of the daily power generation forecast curve based on the comparison results, where the second upper curve portion is the part where the daily power generation forecast value is greater than the daily electricity consumption forecast value. The predicted values ​​are as follows: the second lower curve represents the portion where the predicted daily power generation is less than the predicted daily power consumption; based on the second upper curve and the corresponding first lower curve, the rechargeable capacity of the energy storage module and the corresponding charging period are determined; based on the first upper curve and the corresponding second lower curve, the discharge capacity of the energy storage module and the corresponding discharge period are determined; based on the discharge capacity, rechargeable capacity, and the initial remaining capacity of the energy storage module, the charging amount for each charging period is constrained, wherein constraining the charging amount for each charging period determines the range of charging amount for each charging period.

[0041] In this embodiment, as Figure 2 The image shows a comparison between the daily electricity consumption forecast curve and the daily power generation forecast curve. The portion of the daily electricity consumption forecast curve above the daily power generation forecast curve is the first upper curve portion, the portion of the daily electricity consumption forecast curve below the daily power generation forecast curve is the first lower curve portion, the portion of the daily power generation forecast curve above the daily electricity consumption forecast curve is the second upper curve portion, and the portion of the daily power generation forecast curve below the daily electricity consumption forecast curve is the second lower curve portion.

[0042] The rechargeable capacity is obtained by the difference between the second upper curve portion and the corresponding first lower curve portion, and the charging period is the period corresponding to the second upper curve portion or the first lower curve portion. The discharge capacity is obtained by the difference between the first upper curve portion and the corresponding second lower curve portion, and the discharge period is the period corresponding to the first upper curve portion or the second lower curve portion.

[0043] The minimum charging amount for each time period is determined by the discharge amount for the next time period, the lower limit of the energy storage module's charging capacity, and the remaining amount of the energy storage module. This minimum charging amount is determined based on the energy storage module's capacity, the remaining amount, and the rechargeable capacity.

[0044] By constraining the discharge period and power generation, the charging period and charging amount, and the remaining amount based on the daily power consumption forecast curve and the daily power generation forecast curve, the power supply of the charging system can be effectively guaranteed, and the situation where the energy storage module has no power available can be avoided.

[0045] Furthermore, based on the discharge amount, rechargeable capacity, and initial remaining capacity of the energy storage module, constraints are imposed on the charging amount for each charging period, including: determining the maximum charging amount for the current charging period based on the initial remaining capacity of the energy storage module, the capacity of the energy storage module, and the rechargeable capacity of the current charging period; determining the minimum charging amount for the current charging period based on the discharge amount of the next discharge period, the lower limit of the energy storage module's charging capacity, and the initial remaining capacity of the energy storage module; determining the charging amount constraint for the current charging period based on the maximum and minimum charging amounts; determining the remaining capacity constraint of the energy storage module based on the charging amount constraint for the current charging period, the initial remaining capacity of the energy storage module, and the discharge amount of the next discharge period; and determining the charging amount constraint for the next charging period based on the remaining capacity constraint of the energy storage module.

[0046] In this embodiment, the maximum charging capacity is determined by the difference between the initial remaining capacity and the capacity of the energy storage module, and the rechargeable capacity during the current charging period. The larger of the difference and the rechargeable capacity is used. For example, if the energy storage module has a capacity of 10,000 Wh, an initial remaining capacity of 5,000 Wh, the difference is 5,000 Wh, the rechargeable capacity is 3,000 Wh, and the maximum charging capacity is 3,000 Wh. The lower limit of the energy storage module's charging capacity refers to ensuring that the capacity does not fall below the remaining capacity to guarantee good operation. The minimum charging capacity = discharge capacity of the next discharge period - initial remaining capacity of the energy storage module + lower limit of the energy storage module's charging capacity. For example, if the initial remaining capacity of the energy storage module is 5,000 Wh, the lower limit of the energy storage module's charging capacity is 500 Wh. The discharge amount for the next discharge period is 6000 Wh. The minimum charging amount = 6000 Wh - 5000 Wh + 500 Wh = 1500 Wh. When the minimum charging amount is negative, the minimum charging amount is zero. The charging amount constraint for the current charging period is (minimum charging amount, maximum charging amount). The remaining amount constraint is obtained by adding the charging amount constraint for the current charging period to the initial remaining amount of the energy storage module, and then subtracting the discharge amount for the next discharge period. For example, if the charging amount constraint for the current charging period is (1500 Wh, 3000 Wh), the initial remaining amount of the energy storage module is 5000 Wh, the discharge amount for the next discharge period is 6000 Wh, and the remaining amount constraint is (500 Wh, 2000 Wh).

[0047] The charging amount constraints for the next charging period and subsequent charging periods are calculated iteratively according to the above steps.

[0048] S105. Determine the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjust the working state of the energy storage module based on the optimal power supply constraint. The working state includes charging state, discharging state and no-operation state.

[0049] In this embodiment, the time-period electricity price is the electricity price during the charging period, and the optimal power supply constraint is the charging amount constraint that maximizes the charging revenue when determined based on the time-period electricity price. Adjusting the working state of the energy storage module with the optimal power supply constraint can improve charging efficiency while ensuring power supply.

[0050] Specifically, based on power supply constraints and time-of-use electricity prices, the optimal power supply constraints are determined, and the operating state of the energy storage module is adjusted accordingly. This includes: calculating the charging revenue range for each charging period based on charging quantity constraints and corresponding charging periods, and the electricity price during those periods; determining the charging quantity constraint at the point of maximum revenue based on the charging revenue range for all charging periods; and adjusting the operating state of the energy storage module based on the charging quantity constraint at the point of maximum revenue.

[0051] In this embodiment, the charging revenue range is obtained by multiplying the charging amount constraint and the electricity price of the charging period. The charging amount constraint at which the revenue is highest is dynamically adjusted from the charging period with the highest electricity price. When the charging period with the highest electricity price has the highest revenue, the charging amount constraint changes of adjacent charging periods are determined based on the corresponding charging amount. Because the charging amount of one charging period will change the charging amount constraint of other periods, the constraint is then dynamically adjusted from the charging period with the second highest electricity price, and so on, until the charging amount of each charging period at the end of the revenue period is finally determined.

[0052] Based on the charging amount and charging power of the energy storage module during each charging period, the energy storage module is adjusted to be in a charging or non-operating state. Specifically, the charging power is adjusted according to the charging amount, and the charging time of the charging period is determined. The energy storage module is in a non-operating state during the charging period except for the charging time. For example, if the charging amount during a charging period is 3000 Wh, the charging period is from 9:00 to 11:00, and the charging power is 1000 W or 2000 W, the charging power is adjusted to 2000 W, charging for 1.5 hours, and the remaining 0.5 hours are in a non-operating state. The energy storage module is adjusted to be in a discharging state according to the discharging period.

[0053] Figure 3 This is a structural block diagram of an off-grid DC high-power high-voltage charging system 200 provided in an embodiment of this application.

[0054] like Figure 3 As shown, the off-grid DC high-power high-voltage charging system 200 mainly includes: Information acquisition module 201 acquires the historical daily electricity consumption curve and daily power generation prediction curve of the charging system; The electricity consumption growth rate determination module 202 determines the electricity consumption growth rate based on all historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves. The charging data includes the number of charging vehicles and the average charging time. The daily electricity consumption prediction module 203 determines the daily electricity consumption prediction curve based on the electricity consumption growth rate and the historical daily electricity consumption curve. The power supply constraint determination module 204 determines the power supply constraints based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters. The power supply constraints include the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module. The adjustment module 205 determines the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjusts the working state of the energy storage module based on the optimal power supply constraint. The working state includes charging state, discharging state and no-operation state.

[0055] As an optional implementation of this embodiment, the information acquisition module 201 is also specifically used to acquire the historical daily electricity consumption curve and daily power generation prediction curve of the charging system, including: acquiring the type of power source of the charging system; determining the corresponding power prediction model according to the type of power source; acquiring the prediction data required by the power prediction model, and making power generation prediction of the charging system based on the prediction data to determine the daily power generation prediction curve.

[0056] As an optional implementation of this embodiment, the electricity consumption growth rate determination module 202 is further specifically used to determine the electricity consumption growth rate based on all historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves, including: acquiring the objective influencing factors of the historical daily electricity consumption curves; adjusting the historical daily electricity consumption curves based on the objective influencing factors to determine the corresponding target historical daily electricity consumption curves; fitting all target historical daily electricity consumption curves to determine historical electricity consumption curves; determining a first electricity consumption growth rate based on the slope of the historical electricity consumption curves; determining a second electricity consumption growth rate based on all charging data; and determining the electricity consumption growth rate based on the first electricity consumption growth rate and the second electricity consumption growth rate.

[0057] As an optional implementation of this embodiment, the electricity consumption growth rate determination module 202 is further specifically used to determine a second electricity consumption growth rate based on all charging data, including: determining the charging vehicle growth rate based on the total number of charging vehicles; determining the average charging time growth rate based on all average charging times; if the average charging time growth rate is higher than a preset growth rate, assigning weights to the average charging time growth rate and the charging vehicle growth rate according to a first preset ratio to determine the second electricity consumption growth rate; if the average charging time growth rate is not higher than the preset growth rate, assigning weights to the average charging time growth rate and the charging vehicle growth rate according to a second preset ratio to determine the second electricity consumption growth rate; wherein, the weight of the average charging time growth rate in the first preset ratio is greater than the weight of the average charging time growth rate in the second preset ratio.

[0058] As an optional implementation of this embodiment, the daily electricity consumption prediction module 203 is further specifically used to determine the daily electricity consumption prediction curve based on the electricity consumption growth rate and the historical daily electricity consumption curve, including: determining the target daily electricity consumption prediction curve based on the electricity consumption growth rate and the target historical daily electricity consumption curve; and determining the daily electricity consumption prediction curve based on the target daily electricity consumption curve and the objective influencing factors of the prediction day.

[0059] As an optional implementation of this embodiment, the power supply constraint determination module 204 is further specifically used to determine the power supply constraint based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters, including: comparing the daily electricity consumption forecast curve and the daily power generation forecast curve; determining multiple first upper curve portions and first lower curve portions of the daily electricity consumption forecast curve based on the comparison result, wherein the first upper curve portion is the portion where the daily electricity consumption forecast value is greater than the daily power generation forecast value, and the first lower curve portion is the portion where the daily electricity consumption forecast value is less than the daily power generation forecast value; and determining multiple second upper curve portions and second lower curve portions of the daily power generation forecast curve based on the comparison result. The first upper curve represents the portion where the daily power generation forecast is greater than the daily power consumption forecast, while the second lower curve represents the portion where the daily power generation forecast is less than the daily power consumption forecast. Based on the second upper curve and the corresponding first lower curve, the rechargeable capacity of the energy storage module and the corresponding charging period are determined. Based on the first upper curve and the corresponding second lower curve, the discharge capacity of the energy storage module and the corresponding discharge period are determined. Based on the discharge capacity, rechargeable capacity, and the initial remaining capacity of the energy storage module, the charging amount for each charging period is constrained, whereby constraining the charging amount for each charging period determines the range of charging amounts for each charging period.

[0060] As an optional implementation of this embodiment, the power supply constraint determination module 204 is further specifically used to constrain the charging amount of each charging period based on the discharge amount, rechargeable capacity, and initial remaining capacity of the energy storage module, including: determining the maximum charging amount of the current charging period based on the initial remaining capacity of the energy storage module, the capacity of the energy storage module, and the rechargeable capacity of the current charging period; determining the minimum charging amount of the current charging period based on the discharge amount of the next discharge period, the lower limit of the charging of the energy storage module, and the initial remaining capacity of the energy storage module; determining the charging amount constraint of the current charging period based on the maximum charging amount and the minimum charging amount; determining the remaining capacity constraint of the energy storage module based on the charging amount constraint of the current charging period, the initial remaining capacity of the energy storage module, and the discharge amount of the next discharge period; and determining the charging amount constraint of the next charging period based on the remaining capacity constraint of the energy storage module.

[0061] As an optional implementation of this embodiment, the adjustment module 205 is further specifically used to determine the optimal power supply constraint based on the power supply constraint and the time-period electricity price, and to adjust the working state of the energy storage module based on the optimal power supply constraint, including: calculating the charging revenue range of the charging period based on the charging amount constraint and the corresponding charging period, and based on the electricity price of the charging period; determining the charging amount constraint when the revenue is highest based on the charging revenue range of all charging periods; and adjusting the working state of the energy storage module based on the charging amount constraint when the revenue is highest.

[0062] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0063] This application also describes an electronic device from the perspective of a physical device, such as... Figure 4 As shown, Figure 4 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0064] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0065] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0067] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0068] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0069] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by 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 accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0070] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An off-grid DC high-power high-voltage charging method, characterized in that, include: Obtain the historical daily electricity consumption curve and the predicted daily power generation curve of the charging system; Based on all the historical daily electricity consumption curves and the corresponding charging data for the historical daily electricity consumption curves, the electricity consumption growth rate is determined. The charging data includes the number of charging vehicles and the average charging time. Based on the electricity consumption growth rate and the historical daily electricity consumption curve, determine the daily electricity consumption forecast curve; Based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters, the power supply constraints are determined, wherein the power supply constraints include the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module. Based on the power supply constraints and time-of-use electricity prices, the optimal power supply constraints are determined, and the working state of the energy storage module is adjusted based on the optimal power supply constraints. The working state includes charging state, discharging state, and no-operation state. The step of determining power supply constraints based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and energy storage module parameters includes: Compare the daily electricity consumption forecast curve and the daily power generation forecast curve; Based on the comparison results, multiple first upper curve portions and first lower curve portions of the daily electricity consumption forecast curve are determined. The first upper curve portion is the portion where the daily electricity consumption forecast value is greater than the daily power generation forecast value, and the first lower curve portion is the portion where the daily electricity consumption forecast value is less than the daily power generation forecast value. Based on the comparison results, multiple second upper curve portions and second lower curve portions of the daily power generation forecast curve are determined. The second upper curve portion is the portion where the daily power generation forecast value is greater than the daily power consumption forecast value, and the second lower curve portion is the portion where the daily power generation forecast value is less than the daily power consumption forecast value. Based on the second upper curve portion and the corresponding first lower curve portion, the rechargeable capacity of the energy storage module and the corresponding charging period of the rechargeable capacity are determined. Based on the first upper curve portion and the corresponding second lower curve portion, the discharge amount of the energy storage module and the corresponding discharge period are determined. Based on the discharge amount, the rechargeable capacity, and the initial remaining amount of the energy storage module, the charging amount for each charging period is constrained, wherein constraining the charging amount for each charging period is to determine the range of the charging amount for each charging period. The constraint on the charging amount for each charging period based on the discharge amount, the rechargeable capacity, and the initial remaining capacity of the energy storage module includes: The maximum charging amount for the current charging period is determined based on the initial remaining amount of the energy storage module, the capacity of the energy storage module, and the rechargeable amount of the current charging period. The minimum charging amount for the current charging period is determined based on the discharge amount of the next discharge period, the lower limit of the charging of the energy storage module, and the initial remaining amount of the energy storage module. The charging amount constraint for the current charging period is determined based on the maximum charging amount and the minimum charging amount. The remaining amount constraint of the energy storage module is determined based on the charging amount constraint of the current charging period, the initial remaining amount of the energy storage module, and the discharge amount of the next discharging period. Based on the remaining capacity constraint of the energy storage module, the charging capacity constraint for the next charging period is determined.

2. The method according to claim 1, characterized in that, The acquisition of the historical daily electricity consumption curve and the predicted daily power generation curve of the charging system includes: Obtain the type of power source for the charging system; Based on the type of power source, determine the corresponding power forecasting model; Obtain the forecast data required for the power forecast model, and perform power generation forecasting for the charging system based on the forecast data to determine the daily power generation forecast curve.

3. The method according to claim 1, characterized in that, The step of determining the electricity consumption growth rate based on all the historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves includes: Obtain the objective influencing factors of the historical daily electricity consumption curve, adjust the historical daily electricity consumption curve based on the objective influencing factors, and determine the corresponding target historical daily electricity consumption curve; Fit all the target historical daily electricity consumption curves to determine the historical electricity consumption curves; The first electricity consumption growth rate is determined based on the slope of the historical electricity consumption curve. Based on all the charging data, determine the second electricity consumption growth rate; The electricity consumption growth rate is determined based on the first electricity consumption growth rate and the second electricity consumption growth rate.

4. The method according to claim 3, characterized in that, The step of determining the second electricity consumption growth rate based on all the charging data includes: Determine the charging vehicle growth rate based on all the stated number of charging vehicles; Based on all the aforementioned average charging times, determine the average charging time growth rate; If the average charging time growth rate is higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to the first preset ratio to determine the second electricity consumption growth rate. If the average charging time growth rate is not higher than the preset growth rate, the average charging time growth rate and the charging vehicle growth rate are weighted according to the second preset ratio to determine the second electricity consumption growth rate. In the first preset ratio, the weight of the average charging time growth rate is greater than the weight of the average charging time growth rate in the second preset ratio.

5. The method according to claim 3, characterized in that, The step of determining the daily electricity consumption forecast curve based on the electricity consumption growth rate and the historical daily electricity consumption curve includes: Based on the electricity consumption growth rate and the target historical daily electricity consumption curve, determine the target daily electricity consumption forecast curve; The daily electricity consumption forecast curve is determined based on the target daily electricity consumption forecast curve and the objective influencing factors of the forecast day.

6. The method according to claim 1, characterized in that, The step of determining the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjusting the operating state of the energy storage module based on the optimal power supply constraint, includes: Based on the charging amount constraint and the corresponding charging period, and based on the electricity price during the charging period, calculate the charging revenue range for the charging period. Based on the charging revenue range of all the aforementioned charging periods, determine the charging amount constraint when the revenue is highest. The operating state of the energy storage module is adjusted according to the charging amount constraint at which the benefit is maximized.

7. An off-grid DC high-power high-voltage charging system, characterized in that, The off-grid DC high-power high-voltage charging method according to any one of claims 1-6 is comprising: The information acquisition module acquires the historical daily electricity consumption curve and the daily power generation prediction curve of the charging system; The electricity consumption growth rate determination module determines the electricity consumption growth rate based on all the historical daily electricity consumption curves and the corresponding charging data of the historical daily electricity consumption curves. The charging data includes the number of charging vehicles and the average charging time. The daily electricity consumption prediction module determines the daily electricity consumption prediction curve based on the electricity consumption growth rate and the historical daily electricity consumption curve. The power supply constraint determination module determines the power supply constraint based on the daily electricity consumption forecast curve, the daily power generation forecast curve, and the energy storage module parameters. The power supply constraint includes the charging amount, discharging amount, remaining amount, and charging / discharging period of the energy storage module. The adjustment module determines the optimal power supply constraint based on the power supply constraint and the time-of-use electricity price, and adjusts the working state of the energy storage module based on the optimal power supply constraint. The working state includes charging state, discharging state and no-operation state.

8. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-6.

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