Charging and battery swapping station robust operation method and device considering battery characteristics of electric vehicle

Abstract

The invention provides a charging and swapping station robust operation method and device considering electric vehicle battery characteristics, and the method comprises the steps: building a dynamic power limit model based on the coupling relation between a charge state and a battery health state, and determining the nonlinear characteristics of the charge and discharge power along with the change of the charge state through piecewise linear constraint; constructing a multi-factor coupled battery life degradation model, and calculating battery life loss in real time; designing a data-driven multi-ellipsoid uncertain set, converting a robust problem into a deterministic second-order cone programming problem through dual conversion to optimize the multi-ellipsoid uncertain set, and considering the uncertainty and spatial-temporal correlation of wind and light output; on the basis of nondeterminacy and space-time correlation of nonlinear characteristics, battery life loss and wind and light output, an ordered charging mechanism guided by user intentions is formed by setting a minimum perceptible boundary and a stimulus boundary by utilizing a vehicle owner intention probability model, and the impact of electric vehicle charging on a power grid is effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging control, and particularly relates to a charging and battery swapping station robust operation method and device considering electric vehicle battery characteristics. BACKGROUND

[0002] The comprehensive charging and battery swapping station integrates photovoltaic / wind power generation systems, electric-hydrogen conversion systems and electric vehicle charging and battery swapping systems, provides charging services for electric vehicles, realizes benign interaction of loads, and has considerable development prospects and practical significance. In the charging and battery swapping system under the centralized charging and decentralized power distribution mode, the battery charging bin can be used as a charging device to charge the on-board battery and as an electric energy storage device to release power when the charging station needs power support, and has the advantages of fast charging speed and high equipment utilization rate. However, the charging and battery swapping station faces problems such as uncertain wind and light new energy output, difficult load peak shaving of large-scale disordered charging, and accelerated aging of batteries due to frequent charging and discharging, which affect its economy and sustainable development.

[0003] Currently, there have been many studies on the charging and battery swapping station in improving the reliability and economy of the power grid. Some studies focus on the influence of the battery exchange mode on the distribution network, proving that the charging station with charging and discharging capacity can enhance the resilience of the power grid and improve the power supply reliability through load transfer and power support. In addition, the optimization scheduling scheme of the charging, storage and battery integrated station combines the load prediction and site selection and capacity determination of the urban area, and takes into account the power grid support and economic benefits. However, these studies mostly do not consider the problem of life degradation caused by frequent charging and discharging of the battery, which affects the long-term economy of the charging station.

[0004] Therefore, how to reduce the impact of large-scale electric vehicle disordered charging on the power grid has become a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0005] The present application provides a charging and battery swapping station robust operation method and device considering electric vehicle battery characteristics to solve the defect of the large-scale electric vehicle disordered charging causing the impact on the power grid in the prior art.

[0006] In a first aspect, the present application provides a charging and battery swapping station robust operation method considering electric vehicle battery characteristics, comprising: establishing a dynamic power limit model based on the coupling relationship between the state of charge and the state of health of the battery, determining the nonlinear characteristics of the charging and discharging power changing with the state of charge through piecewise linear constraints; constructing a multi-factor coupled battery life degradation model, quantifying the synergistic effect of the charging and discharging rate and the depth of discharge on the life by using a polynomial function, linearizing the absolute value term by the big M method, and calculating the battery life loss in real time; Design a data-driven multi-ellipsoid uncertainty set, and convert the robust problem into a deterministic second-order cone programming problem by dual conversion to optimize the multi-ellipsoid uncertainty set, taking into account the uncertainty and space-time correlation of wind and light power output; Based on the nonlinear characteristics, the battery life loss and the uncertainty and space-time correlation of wind and light power output, a user willingness guided orderly charging mechanism is formed by setting a minimum perceptible boundary and a stimulus quantity boundary using a car owner willingness probability model.

[0007] According to the present application, a kind of charging and battery swapping station robust operation method considering electric vehicle battery characteristics is provided, the dynamic power limit model based on the coupling relationship of state of charge and battery health state is established, the nonlinear characteristics of charging and discharging power varying with state of charge are determined by piecewise linear constraint, including: According to the average battery health degree in the charging bin and the initial power limit switching threshold, the state of charge threshold for switching from the rated power limit of the charging bin to the variable power limit is determined; Based on the maximum state of charge of the charging bin, the minimum state of charge and the state of charge threshold, the piecewise constraint relationship of charging and discharging power and state of charge is determined; According to the piecewise constraint relationship, the battery protection constraint of power variation rate is determined.

[0008] According to the present application, a kind of charging and battery swapping station robust operation method considering electric vehicle battery characteristics is provided, the battery life degradation model coupled with multiple factors is constructed, the synergistic effect of charging and discharging rate and discharge depth on life is quantified by using a polynomial function, the absolute value term is linearized by using the big M method, and the battery life loss is calculated in real time, including: Based on the energy throughput of the battery and the influence coefficient of battery charging and discharging rate on life degradation, a battery life degradation model affected by multiple factors is constructed; The battery life degradation model is equivalent to a polynomial model; The power absolute value of total attenuation life of the battery in the scheduling process is linearized by using the big M method, and the battery life loss is determined in combination with the polynomial model.

[0009] According to the present application, a kind of charging and battery swapping station robust operation method considering electric vehicle battery characteristics is provided, the data-driven multi-ellipsoid uncertainty set is designed, including: Calculate the probability distance of the predicted scenario and the historical output to screen similar scenarios; Based on the similar scenarios, a minimum volume ellipsoid algorithm is used to construct a segmented low-dimensional ellipsoid set, and an ellipsoid model is established for each of the three time periods in the scheduling period; Introduce ellipsoid scaling coefficient to dynamically adjust the conservativeness of the set, and obtain a multi-ellipsoid uncertainty set.

[0010] The application provides a robust operation method of a charging and replacing station considering characteristics of an electric vehicle battery, and the method introduces an ellipsoid scaling coefficient dynamic adjustment set conservativeness, and comprises the following steps: When the ellipsoid scaling coefficient is smaller than a preset value, the high-dimensional ellipsoid is expanded to improve the conservativeness; When the ellipsoid scaling coefficient is greater than the preset value, the high-dimensional ellipsoid is reduced to reduce the conservativeness.

[0011] The application provides a robust operation method of a charging and replacing station considering characteristics of an electric vehicle battery, and the method converts a robust problem into a deterministic second-order cone programming problem by dual conversion to optimize the multi-ellipsoid uncertainty set, and comprises the following steps: A multi-objective comprehensive income function is constructed; The electric-hydrogen equipment collaborative optimization constraint, the hydrogen storage tank safe operation constraint, the power balance constraint and the wind-solar output constraint are determined; Based on the multi-objective comprehensive income function, the electric-hydrogen equipment collaborative optimization constraint, the hydrogen storage tank safe operation constraint, the power balance constraint and the wind-solar output constraint are processed by dual conversion, and the multi-ellipsoid uncertainty set problem is converted into a second-order cone programming problem.

[0012] The application provides a robust operation method of a charging and replacing station considering characteristics of an electric vehicle battery, and the method utilizes a car owner willingness probability model, sets a minimum perceptible boundary and a stimulus quantity boundary, and forms an orderly charging mechanism guided by user willingness, and comprises the following steps: An electric vehicle cluster charging model is constructed; A car owner willingness probability model based on the W-F law is constructed; Based on the electric vehicle cluster charging model, the car owner willingness probability model and the flexible scheduling constraint of the orderly charging load, an orderly charging mechanism guided by user willingness is formed.

[0013] The application provides a robust operation method of a charging and replacing station considering characteristics of an electric vehicle battery, and the car owner willingness probability model is as follows: ; In the formula, is a probability that an electric vehicle owner selects a non-scheduling mode under a charging subsidy φ is a probability that an electric vehicle owner selects a non-scheduling mode under a charging subsidy I M is a stimulus quantity boundary; p 0 is a stimulus response limit; I N is a minimum perceptible boundary, k 0 is a Weber coefficient; s 0 is a stimulus constant.

[0014] According to the present invention, a robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries is provided, wherein the flexible scheduling constraint of the ordered charging load is: ; ; In the formula, for t The actual dispatch power of the time-based charging station for the orderly charging load of electric vehicles; The total electricity demand of the orderly charging load during the scheduling cycle; and For orderly charging load in t Minimum and maximum electricity demand for a given time period. For scheduling intervals, N T This is the total scheduling duration.

[0015] Secondly, the present invention also provides a robust operating device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries, comprising: The modeling module is used to establish a dynamic power limit model based on the coupling relationship between the state of charge and the battery health state, and to determine the nonlinear characteristics of the charge and discharge power as a function of the state of charge through piecewise linear constraints. The calculation module is used to construct a multi-factor coupled battery life degradation model. It uses a polynomial function to quantify the synergistic effect of charge and discharge rate and depth of discharge on life, and linearizes the absolute value term using the Big M method to calculate battery life loss in real time. The optimization module is used to design a data-driven multi-ellipsoidal uncertainty set and to transform the robust problem into a deterministic second-order cone programming problem through dual transformation to optimize the multi-ellipsoidal uncertainty set, taking into account the uncertainty of wind and solar power output and its spatiotemporal correlation. The charging module is used to form an orderly charging mechanism guided by user will by setting a minimum perceptible boundary and a stimulus quantity boundary, based on the nonlinear characteristics, battery life loss and the uncertainty and spatiotemporal correlation of wind and solar power output, and utilizing a vehicle owner's willingness probability model.

[0016] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the robust operation method of a charging and swapping station taking into account the characteristics of electric vehicle batteries as described above.

[0017] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a robust operation method for a charging and swapping station taking into account the characteristics of electric vehicle batteries as described above.

[0018] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the method for robust operation of a charging and swapping station taking into account the characteristics of an electric vehicle battery according to any one of the preceding aspects.

[0019] The present application provides a method and device for robust operation of a charging and swapping station taking into account the characteristics of an electric vehicle battery, which comprises establishing a dynamic power limit model based on the coupling relationship between state of charge and state of health, determining the nonlinear characteristics of charging and discharging power with respect to state of charge through piecewise linear constraints; constructing a multi-factor coupled battery life degradation model, quantifying the synergistic effect of charging and discharging rate and depth of discharge on life by using a polynomial function, linearizing the absolute value term by the big M method, and calculating the battery life loss in real time; designing a data-driven multi-ellipsoid uncertainty set, and converting the robust problem into a deterministic second-order cone programming problem to optimize the multi-ellipsoid uncertainty set by dual conversion, taking into account the uncertainty and spatiotemporal correlation of wind and light output; based on the nonlinear characteristics, battery life loss, and uncertainty and spatiotemporal correlation of wind and light output, using a car owner willingness probability model, setting a minimum perceptible boundary and a stimulus boundary to form an orderly charging mechanism guided by user willingness, and through battery characteristic modeling and life loss prediction, the orderly charging mechanism guided by user willingness is formed by means of certainty transformation of uncertainty and multi-objective collaborative optimization, effectively reducing the impact of electric vehicle charging on the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flowchart of the method for robust operation of a charging and swapping station taking into account the characteristics of an electric vehicle battery provided by the present embodiment; Figure 2 is a schematic diagram of the relationship between the dynamic power limit of the battery and the SoC constraint provided by the present embodiment; Figure 3 is a schematic diagram of the similar scene calculation based on time sliding window provided by the present embodiment; Figure 4 is a schematic diagram of the ellipsoid uncertainty set before and after correction provided by the present embodiment; Figure 5 is a schematic diagram of the orderly charging load comparison provided by the present embodiment; Figure 6 is a schematic diagram of the power balance of the charging and swapping station provided by the present embodiment; Figure 7 is a power schematic diagram of an electrolytic cell / fuel cell provided by the embodiment; Figure 8 is a hydrogen load and hydrogen storage tank pressure schematic diagram provided by the embodiment; Figure 9 is a charging bin state of charge schematic diagram provided by the embodiment; Figure 10 is a dynamic limit power model schematic diagram provided by the embodiment; Figure 11 is a charging bin discharge depth schematic diagram provided by the embodiment; Figure 12 is a structure schematic diagram of a charging and battery swapping station robust operation device considering electric vehicle battery characteristics provided by the embodiment; Figure 13 is a structure schematic diagram of an electronic device provided by the embodiment. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] Figure 1 is a flow schematic diagram of a charging and battery swapping station robust operation method considering electric vehicle battery characteristics provided by the embodiment.

[0024] As shown in Figure 1 The charging and battery swapping station robust operation method considering electric vehicle battery characteristics provided by the embodiment mainly includes the following steps: 101. Establish a dynamic power limit model based on the coupling relationship between state of charge and battery health state, and determine the nonlinear characteristics of charging and discharging power with state of charge by piecewise linear constraint.

[0025] First, the centralized battery swapping bin charging and storage characteristic model is constructed: The charging stations utilize charging piles and fast-swapping batteries to charge vehicles. The fast-swapping model involves a contract between the charging station and the vehicle company, where batteries are leased to the company. The vehicle company only pays a fixed fee and doesn't need to worry about battery maintenance. Compared to traditional charging piles, buses, taxis, and other profit-driven special vehicles, where operating time is revenue time, benefit more from the fast-swapping model for rapid charging, reducing charging costs and time. The charging stations leverage the energy storage characteristics of the vehicle's batteries to shift load and smooth peak demand, improving renewable energy utilization and achieving a win-win situation. The fast-swapping model is suitable for large-scale special economic vehicles such as buses and taxis; therefore, the battery models are consistent, and charging bays replace the charging of all batteries. (1) (2) In the formula, N represents the number of replaceable batteries; , Indicates the rated power and capacity of a single battery; , This represents the rated power and capacity of the charging case.

[0026] To ensure accurate power scheduling of the charging case and extend battery life, the power and degradation characteristics of the battery will be modeled below. First, a dynamic power limit model based on the state of charge (SoC) is established, which accurately describes the nonlinear characteristics of the upper limit of charging power decreasing as SoC increases and the lower limit of discharging power increasing as SoC decreases through piecewise linear constraints.

[0027] The relationship between the power limit of electric vehicles and the state-of-charge (SoC) constraints is shown in the appendix. Figure 2 As shown in the figure. Battery charging typically consists of two stages: First, the battery is charged with a constant current, during which the voltage increases slowly; when the voltage reaches its maximum value, the second stage begins, where the voltage remains constant at the maximum value and the charging current decreases slowly. However, for battery energy storage systems, charging stations are primarily concerned with describing the power flow, not the voltage characteristics. Based on simulation results from the battery kinetic model, the battery power variation characteristics during constant voltage and constant current charging and discharging are shown in the attached figure. Figure 2 As shown.

[0028] From the appendix Figure 2It can be seen that when the SoC exceeds / is lower than a certain threshold, the maximum power absorbed / released by the battery decreases nonlinearly with the increase / decrease of the SoC. However, the traditional battery model usually considers that the battery is charged / discharged at the rated power, ignoring the constraint relationship between the battery power limit and the SoC, which will lead to the inconsistency between the actual output and the expected output of the charging warehouse. Therefore, a set of linear constraints is proposed to approximate the dynamic power characteristics of the battery (3): (3) where, represents the charging of the charging warehouse, represents the discharging of the charging warehouse; and are the SoC thresholds that represent the switch from the rated power limit to the variable power limit of the charging warehouse; and represent the maximum state of charge and the minimum state of charge of the charging warehouse, respectively; represents the charging / discharging power at time t; represents the upper limit of the rated charging / discharging power.

[0029] When the SoC exceeds , the upper limit of the charging power decreases linearly with the increase of the SoC; when the SoC is lower than , the lower limit of the discharging power increases linearly with the decrease of the SoC; otherwise, the charging warehouse is charged / discharged at the rated power. Among them, the charging and discharging power conversion threshold is related to the battery health (SOH), as shown in (4) (5): (3) (4) where, and are the initial power limit switching thresholds, SOH represents the average battery health in the charging warehouse, which is in the range of 0 to 1, and is obtained by the charging warehouse collecting and detecting the batch of batteries.

[0030] The charging efficiency of the charging warehouse is high, so the energy loss is not considered, and the SoC change of the charging warehouse is shown as follows (6): (6) where, is the scheduling interval, represents the state of charge at time t, represents the state of charge at time t-1, represents the battery capacity.

[0031] In addition, in order to avoid permanent damage to the battery, the battery power change between two schedules cannot exceed (7): (7)

[0032] 102、Constructing a multi-factor coupled battery life degradation model, using a polynomial function to quantify the synergistic effect of charge / discharge rate and discharge depth on life, linearizing the absolute value term by large M method, and calculating the battery life loss in real time.

[0033] Battery life is mainly affected by charge / discharge rate, discharge depth, working temperature and other factors. Deep discharge can accelerate the aging of energy storage materials. Long-term high-power charge / discharge of the battery may cause the battery to heat up too quickly, resulting in electrode aging, battery bulging and other problems. For the above reasons, the discharge depth and charge / discharge rate are considered as the main factors affecting the battery life, and a multi-factor battery life degradation model (8) is formed: (8) q

[0034] The calculation formula of is as shown in (9) and the calculation formula of is as shown in (10): (9) (10) (11)

[0035] Therefore, the multi-factor battery life degradation model (8) is equivalent to the following polynomial form (12): (12) ​​​​​​​​​​​​​​​​​​​, , , All of these are fitting parameters.

[0036] The total degradation of the battery during the entire scheduling process is composed of the linear accumulation of the degradation of the battery in a single cycle, as shown in (13): (13) In the formula, This represents the total battery degradation lifetime during the scheduling process. For For absolute power values ​​that are difficult to solve, a large... The method linearizes it, letting The following auxiliary constraints exist (14): (14) In the formula, Variables are 0 and 1. It is a positive number.

[0037] This embodiment employs a multi-factor coupled battery life degradation model. It quantifies the effects of key influencing factors using polynomial functions and utilizes piecewise linearization, significantly reducing computational complexity while maintaining accuracy. Actual operational data demonstrates that this model is more than 10 times more computationally efficient than the physical model, while keeping the prediction error within 5%, perfectly balancing the requirements of accuracy and practicality.

[0038] 103. Design a data-driven multi-ellipsoidal uncertainty set, and transform the robust problem into a deterministic second-order cone programming problem to optimize the multi-ellipsoidal uncertainty set through dual transformation, taking into account the uncertainty of wind and solar power output and its spatiotemporal correlation.

[0039] First, a multi-ellipsoidal uncertainty set description is performed. Wind and solar power output exhibits significant uncertainty; a high-dimensional ellipsoid can effectively account for the correlations between uncertain variables, forming an uncertainty set with lower conservatism. Therefore, a data-driven multi-ellipsoidal uncertainty set is proposed: 1) Based on the predicted values, similar scenarios are selected from historical data. The time sliding window method is used to calculate the probabilistic distance between the predicted values ​​and historical output values ​​one by one. The calculation process is illustrated in the attached diagram. Figure 3 As shown. The calculation formulas are as follows (15) and (16): (15) (16) In the formula, This represents the scene to be predicted under the i-th time sliding window. With historical scenes The probability distance; The L2 norm is used to measure the probabilistic distance of a sample distribution; the smaller the distance, the closer it is to the true output. represents the historical similar scenarios under the i-th time sliding window.

[0040] 2) Based on the historical similar samples, a set of high-dimensional ellipsoids is constructed. With the help of the minimum volume ellipsoid algorithm, a high-dimensional closed ellipsoid is constructed, which contains the selected historical error scenarios, and the following optimization (17) is solved: (17) In the formula, represents the volume of the high-dimensional ellipsoid; represents the first selected historical scenario; represents the second selected historical scenario; represents the s-th selected historical scenario; is the to-be-solved quantity, representing the center of the high-dimensional ellipsoid; wherein is a positive definite matrix representing the deviation direction of the symmetry axis of the high-dimensional ellipsoid relative to the coordinate axis.

[0041] The final obtained high-dimensional ellipsoid uncertainty set is as shown in (18): (18) In the formula, is a high-dimensional ellipsoid uncertainty set, represents a random error vector.

[0042] 3) The farther the interval of new energy output is, the less obvious the correlation is. Using a single ellipsoid to wrap the entire error scenario will lead to a larger coverage of invalid space in the uncertainty set and a higher degree of conservatism. A set of multiple low-dimensional ellipsoids is used to replace a single high-dimensional ellipsoid. The historical error scenarios are divided into three segments according to the time sequence (24-hour scheduling period), and the following three ellipsoid uncertainty sets are formed as shown in (19): (19) In the formula, , , is an error vector segment, , , is a corresponding ellipsoid uncertainty set, , , represents a corresponding to-be-solved quantity, , , represents a corresponding positive definite matrix representing the deviation direction of the symmetry axis of the high-dimensional ellipsoid relative to the coordinate axis; is a set of multiple ellipsoid uncertainty sets, and a random error vector is contained in the union of the three low-dimensional ellipsoids.

[0043] Then, robust conservatism correction is performed based on scene coverage. The more historical error scenes the ellipsoid includes, the greater the likelihood that it includes real error scenes, and thus the more conservative it is. An ellipsoid scaling factor is introduced. The conservatism of the model is corrected by changing the size of the high-dimensional ellipsoid volume. Schematic diagrams of the ellipsoid before and after correction are attached. Figure 4 As shown. For example, the ellipsoid correction formula is as follows (20): (20) In the formula, The corrected ellipsoid positive definite matrix. It is a real number greater than 0. Because the volume of the ellipsoid and the positive definite matrix... Inversely proportional, so when As the higher-dimensional ellipsoid expands, it can cover more historical scenarios, thus increasing its conservatism. As the high-dimensional ellipsoid shrinks, the covered scene decreases, and the conservatism decreases.

[0044] by For example, the corrected high-dimensional ellipsoidal uncertainty set is shown in (21): (twenty one) By introducing an ellipsoid scaling factor γ to dynamically adjust the conservatism of the set, the ellipsoid is expanded to cover more extreme scenarios when γ < 1, and the ellipsoid is shrunk to improve economic efficiency when γ > 1, thus achieving a balance between risk preference and operational efficiency.

[0045] To improve the economic efficiency of charging station operation, the following comprehensive objective optimization function is established. Coordinate and optimize the various devices, such as (22): (twenty two) In the formula, This represents the total revenue of the system; Represents an uncertain set; This represents the uncertain power output of wind and solar power generation; This represents the system's direct revenue, which consists of revenue from battery replacement, revenue from hydrogen sales, and revenue from electric vehicle charging. Indicates the total scheduling period; This indicates the electricity price for swapping batteries at charging and battery swapping stations; This indicates the charging state of the charging compartment at the end of the scheduling process; Indicates time-of-use electricity pricing; This indicates the price of hydrogen sold. Indicates the charging power of electric vehicles; This indicates the amount of hydrogen sold. The system's penalty cost consists of electricity purchase cost, wind and solar curtailment penalty cost, and electric vehicle orderly charging subsidy cost. , These represent the time-of-use electricity price and the penalty coefficient for wind and solar power curtailment, respectively. Indicates the power of the tie line; This represents the uncertain power output of wind and solar power generation; This indicates the predicted power output of wind and solar power generation; This represents the subsidy cost required for orderly charging. This indicates the penalty cost for battery degradation in the charging case. The amount of battery life degradation is represented by a piecewise linearization method. This represents the battery degradation weighting coefficient, used to weigh the long-term and short-term benefits of charging stations.

[0046] Determine the constraints for the coordinated optimization of electric-hydrogen equipment. Specifically, equipment such as electrolyzers and fuel cells can effectively track and absorb renewable energy output, playing a role in peak shaving and valley filling for charging and swapping stations. An electrolyzer operation model is shown in (23): (twenty three) In the formula, express Hydrogen produced by the time-phase electrolyzer; express Electrolytic cell power during specific time periods; Operating voltage; Faraday constant; Compressor efficiency.

[0047] Fuel cell operation models, such as (24): (twenty four) In the formula, express Hydrogen consumed by fuel cells during a given period; for Power generated by fuel cells during a given time period; Fuel cell operating efficiency Inverter efficiency. Operating power limits for electrolyzers and fuel cells, as well as operating state constraints, are as follows: (25)(26)(27): (25) (26) (27) In the formula, , It is a binary variable; Indicates the minimum power of the electrolytic cell; Indicates the maximum power of the electrolytic cell; Indicates the minimum power of the fuel cell; Pmax represents the maximum power of fuel; constraint (27) represents the prohibition of electrolyzer / fuel cell running simultaneously. To prolong the service life of the equipment, ensure the stability of hydrogen production and power generation, the start-stop time and power change of the electrolyzer and fuel cell are constrained. Start-stop constraints are (28) and (29): (28) (29) wherein, represents the start-stop state of the electrolyzer at t-1 time; represents the start-stop state of the electrolyzer in the future p period ； represents the start-stop state of the fuel cell at t-1 time; represents the start-stop state of the fuel cell in the future p period; , respectively represent the minimum start time and the minimum stop time of the equipment, both of which are one hour.

[0048] The power ramping constraints of the electrolyzer / fuel cell are (30) and (31): (30) (31) wherein, represents the power of the electrolyzer at t-1 time; represents the power of the fuel cell at t-1 time; , respectively represent the rising power limit and the falling power limit of the equipment, both of which are 40% of the maximum power of each equipment.

[0049] Determine the safety operation constraints of the hydrogen storage tank. The mass change of hydrogen stored in the hydrogen storage tank is shown in (32): (32) wherein, represents t the mass of hydrogen stored in the hydrogen storage tank at t time; represents t-1 the mass of hydrogen stored in the hydrogen storage tank at t+1 time; is the inflow / outflow of hydrogen in the p period, in mol; is the amount of substance of hydrogen; is the hydrogen load supplied in the p period. High-pressure gaseous hydrogen storage is one of the most mature hydrogen storage methods at present, and the state of the hydrogen storage tank is measured by the pressure of the gas. The change of the pressure in the tank is shown in (33) and (34): (33) (34) wherein, , is a fitting parameter, denotes hydrogen storage tank pressure at time period t; denotes hydrogen storage mass at time period t; hydrogen storage tank volume; ideal gas constant; hydrogen storage tank temperature. hydrogen storage tank maximum pressure.

[0050] In addition to the above constraints, the following power balance constraints and new energy station output constraints are included, such as (35) (36) (37): (35) (36) (37) wherein, is the wind and light output absorbed by the charging station at time t; denotes a multi-ellipsoid robust uncertainty set.

[0051] The multi-ellipsoid uncertainty set used in this embodiment is constructed by a data-driven method, which can accurately depict the space-time correlation of wind and light output, and realize flexible adjustment of conservatism through ellipsoid scaling coefficients, achieving the best balance between modeling accuracy and computational efficiency.

[0052] 104、Based on the nonlinear characteristics, battery life loss, and uncertainty and space-time correlation of wind and light output, a user willingness guide ordered charging mechanism is formed by setting the minimum perceptible boundary and stimulus boundary using the car owner willingness probability model.

[0053] Specifically, first, an electric vehicle charging model is constructed. For an electric vehicle charging at a charging pile, it is assumed that the vehicle charges at a constant charging power during charging. The charging model of a single electric vehicle can be represented as (38): (38) wherein, is the charging demand power of the i-th electric vehicle; is the rated charging power of the charging pile; is the expected charging time of the i-th electric vehicle; is the charging start time of the i-th electric vehicle; is the time when the vehicle completes charging and leaves; is the charging power of the i-th electric vehicle at time period t, which will not exceed the rated power of the charging pile.

[0054] The charging demand of a single period is summed to obtain the charging power required by the charging station in any period, as shown in equation (39): (39) In the formula, is the total charging demand in period t; is the number of electric vehicles that need to be charged in the corresponding period.

[0055] A probability function model of the willingness of the vehicle owner based on the charging discount is then constructed. The vehicle owner has absolute autonomy over the electric vehicle, and the charging station cannot arbitrarily schedule the charging and discharging of the electric vehicle. A multi-mode self-selected charging mechanism is adopted, which considers that the basic charging price is unchanged, but provides the following two charging modes for the vehicle owner to self-select: (1) Non-scheduling mode: the charging station charges at the maximum charging power until it is fully charged, and the vehicle owner pays the charging fee at the basic price; (2) Scheduling mode: a charging discount is given, and the charging station can schedule the charging power to ensure that the electric vehicle is fully charged within the optimized period.

[0056] This mechanism clearly defines the charging completion time of the vehicle owner, reduces the uncertainty of the charging load, and the vehicle owner selects whether to participate in the orderly charging scheduling according to his own willingness. Under the above charging mechanism, the charging station needs to estimate the willingness of the vehicle owner to select different modes, and therefore introduces the W-F law to accurately express the functional relationship between the human response quantity s and the objective stimulus quantity I The W-F law is shown in equation (40): (40) In the formula, s is the human response quantity; I is the objective stimulus quantity; k 0 is the Weber coefficient; s 0 is the stimulus constant. It is considered that the willingness of the vehicle owner to accept the scheduling mode is mainly affected by the size of the charging discount, so the charging subsidy φ is taken as the objective stimulus quantity, and the human response quantity is the probability of the vehicle owner selecting the non-scheduling mode. The following probability model of the willingness of the vehicle owner is then constructed (41): (41) In the formula, is the probability of the vehicle owner of the electric vehicle selecting the non-scheduling mode under the charging subsidy φ I M is the boundary of the stimulus quantity; p 0 is the stimulus response limit, generally taken as p 0>0 represents that only the subsidy factor cannot completely motivate the vehicle owner to select the scheduling mode; I N ​The minimum perceptible boundary represents the boundary at which the vehicle owner will perceive and react when the subsidy reaches this boundary.

[0057] The benefit-oriented orderly charging constraint, i.e., the flexible scheduling constraint of the orderly charging load, is (42) (43): (42) (43) In the formula, is the actual scheduling power of the orderly charging load of the electric vehicle at the charging station in the time period; t is the total power demand of the orderly charging load in the scheduling period; and are the minimum power demand and the maximum power demand of the orderly charging load in the time period, which are related to the subsidy given to the user; is the scheduling interval; t T is the scheduling duration. N The subsidy cost paid for the orderly charging can be expressed as (44):

[0058] (44) In the formula, is the expected power demand of the electric vehicle in the time period. t

[0059] By innovatively applying the W-F (Weber-Fechner) law to construct a vehicle owner willingness probability model, the model can more accurately describe the nonlinear response characteristics of human beings to economic stimulation based on the principles of psychophysics. Practical application shows that the W-F law model can reflect the actual selection probability of the user more than the traditional behavioral economics model, so that the implementation effect of the orderly charging strategy is significantly improved.

[0060] The technical scheme of the embodiment not only improves the operation efficiency and economic benefit of the charging and swapping station, but also enhances the reliability and sustainability of the system, and provides a complete solution for the intelligent operation of the charging and swapping station under the background of the new power system. By carefully selecting the technical route, the best combination of theoretical rigor and engineering practicability is achieved at each link. Compared with the traditional scheme, the embodiment not only has better technical performance indicators, but also has obvious advantages in implementation cost, calculation efficiency, operability and the like, and provides a feasible optimization solution for the actual operation of the charging and swapping station. This systematic optimization design concept makes the application have outstanding competitive advantage and broad application prospect in the same kind of technology.

[0061] In order to verify the performance of the technical scheme of the application, the following simulation test is carried out: ​​​1. Electric-hydrogen charging service stations and parameters Taking a wind-solar-hydrogen storage-charging-battery swapping station as an example, the charging bay within the station is connected to a 0.35kV grid, supporting simultaneous charging of 200 vehicle batteries. Each battery has a capacity of 70kWh and a charging power of 8kW, with an initial SoC of 0.3. Charging piles are provided in an incentive-driven, orderly charging system. The high-pressure hydrogen storage tank has a hydrogen storage density of 28.77kg / m³ and a rated pressure of 45MPa. The hydrogen mass in the tank remains consistent before and after operation, representing 50% of its maximum capacity. Historical wind and solar power generation data are sourced from the open-source database of Elia, a Belgian company. Wind and solar power generation data from January 2021 to October 2024 were selected. A robust optimization model was established using the Yalmip language in the MATLAB environment. The scheduling cycle is 24 hours, with scheduling occurring every hour.

[0062] 2. Analysis of Optimization Results of Charging and Swapping Stations As shown in Table 1, the economic viability of charging stations was tested by setting different scenarios. The unit capacity cost of vehicle batteries is approximately between 1300-2000 CNY / kWh. The higher the battery degradation weight, the more the charging station prioritizes battery life decay; the greater the subsidy, the more electric vehicles can be dispatched. The economic operation results of the charging stations are shown in Table 2.

[0063] Table 1 Parameters for Each Scene

[0064] Table 2 Economic Operation Results of Charging Stations

[0065] This analysis will examine the charging station from both energy storage characteristics and economic aspects. Regarding battery degradation characteristics, it can be seen that as the battery degradation weight increases, the degradation amount of the on-board battery within the charging station steadily decreases, from 0.3254 kWh to 0.2659 kWh, and finally to 0.1560 kWh. Example 4 extends battery life by 52.1% compared to Example 1, while the total revenue only slightly decreases. Further analysis of Examples 3 and 4 shows that the orderly charging of electric vehicle loads within the charging station can effectively achieve peak shaving and valley filling of the electrical load. The electric vehicle charging load is shown in the attached figure. Figure 5 As shown, charging stations relinquish a portion of their profits, effectively reducing penalties for wind and solar curtailment and lowering grid power purchase costs, indirectly increasing their net revenue. This improves the utilization rate of renewable energy while lowering charging costs for electric vehicle owners, achieving a win-win situation. However, as subsidies increase, marginal benefits diminish. This is because while the available charging load increases, subsidy costs also rise, while the revenue from orderly charging increases slowly. Therefore, setting reasonable charging discount subsidies based on future wind and solar forecasts and charging load is key to improving the profitability of charging stations.

[0066] Appendix Figure 6 The power allocation scheme of the charging station shown in the diagram utilizes fuel cells to provide energy to electric vehicles and charging bays during the low-load period from 00:00 to 05:00. This strategy effectively alleviates the pressure surge problem in the hydrogen storage tank caused by hydrogen accumulation. Subsequently, during the peak photovoltaic output period from 10:30 to 15:30, the electrolyzer enters full-load hydrogen production mode, and the produced hydrogen is centrally stored in the hydrogen storage tank, causing a significant increase in the tank pressure. From 16:00 to 19:00 and from 22:00 to 00:00, the system ensures power supply to the station's equipment through fuel cell power generation and external grid power purchase. At the same time, the charging bay implements charging regulation based on the characteristics of renewable energy output: from 00:00 to 2:30, gradual charging is initiated; from 03:00 to 07:00, charging is suspended due to insufficient wind and solar output; from 07:30 to 14:00, high-power continuous charging is performed during the peak photovoltaic period; and from 14:00 to 00:00, power is adaptively reduced based on the SoC status, maximizing the efficiency of new energy consumption. The output and gas load of the electrolyzer / fuel cell and the status of the hydrogen storage tank are shown in the attached figure. Figure 7 and Figure 8 As shown.

[0067] The operating curves and energy storage characteristics of the charging station's charging compartment are shown in the attached figure. Figure 9 As shown in the attached figure, renewable energy output is strong from 07:30 to 14:00, and the battery's State of Charge (SoC) continuously increases. However, due to the battery's dynamic power limit, the rechargeable power limit decreases as the SoC increases, and the rate of increase of the SoC curve slows down, eventually approaching full load at a slower pace. Compared to the traditional rated power model, the dynamic power limit model considers the battery's dynamic power limit characteristics, and its charging curve more closely matches the actual operating curve, as shown in the attached figure. Figure 10 As shown. The flexible load of the charging compartment can improve the energy storage capacity of the charging station, but frequent charging and discharging will accelerate battery aging.

[0068] As attached Figure 11 As shown, with the increase of the battery degradation weight, both the total depth of discharge and the average depth of discharge of the charging compartment steadily decrease. Example 1 has the longest discharge time and an average depth of discharge of 16.83%. Examples 1 and 2 have two reverse discharges and two cycle counts. Example 3 has a slight discharge process, while Example 4 has almost no reverse discharge stage. Therefore, under the constraint of the degradation weight, the charging station will reduce the discharge time and cycle count of the batteries in the charging compartment.

[0069] 3. Analysis of Optimization Results under Uncertain Conditions The deterministic optimization method does not consider the prediction error of photovoltaic and wind turbine, and the solution result is the highest revenue but sacrifices the system operation reliability. In order to explore the influence of wind and light uncertainty on the operation of the electric-hydrogen charging station, the operation scenario is designed to verify the operation characteristics of the equipment in the station under uncertainty.

[0070] Table 3 Economic operation results of the charging station under uncertainty

[0071] The operation results of the charging and battery swapping station under uncertainty are shown in Table 3. According to Table 3, it can be found that under the deterministic condition, the electric-hydrogen charging station has good economy, the battery degradation is 0.1641 kWh, the electricity purchase cost is 1494 CNY, and the operation revenue is 75409 CNY. However, when considering uncertainty, the operation revenue of the box uncertainty set decreases to 62323 CNY, the electricity purchase cost increases from 1494 CNY to 6988 CNY, and the renewable energy is almost not consumed, and the operation scheme is too conservative. The ellipsoid uncertainty set considers the time correlation of new energy output, compared with the box uncertainty set, the conservative degree decreases, the battery degradation is 0.1794 kWh, the operation income increases to 69832 yuan, and the overall operation effect is between the deterministic optimization and the box uncertainty set. At the same time, in order to cope with uncertainty, the battery swapping system of the charging station is frequently used, compared with the box uncertainty set, the battery degradation increases to 0.2899 kWh, and the increase of battery life degradation is large. The conservative operation strategy not only reduces the economy of the charging station, but also affects the service life of the battery of the battery swapping system.

[0072] Through simulation test, it is found that the scheme realizes significant technical progress and economic benefits, which are embodied in the following aspects: 1. Improve the economic benefit and new energy utilization rate of the charging and battery swapping station. By establishing a dynamic power limit model and a multi-factor coupled battery life degradation model, the charging and discharging strategy is optimized, which makes the total revenue of the charging and battery swapping station increase by 7.2%. At the same time, the data-driven multi-ellipsoid uncertainty set modeling method is adopted, which effectively improves the consumption rate of wind and light and other renewable energy by 35%, significantly reduces the penalty cost of abandoned wind and light by 44%, and realizes the win-win of economic benefit and environmental benefit.

[0073] 2. Effectively prolong the service life of the battery and reduce the maintenance and replacement cost. The multi-factor coupled battery life degradation model considers the influence of factors such as charging and discharging rate and discharge depth on the service life of the battery, and through optimization of the dispatching strategy, the battery degradation is reduced from 0.3254 kWh to 0.1560 kWh, which prolongs the service life by 52.1%. This not only greatly reduces the battery replacement frequency and maintenance cost, but also improves the sustainable utilization efficiency of battery resources.

[0074] 3. Smoothing grid load fluctuations and improving grid operation safety. By designing a vehicle owner willingness probability model based on the WF law and a charging subsidy mechanism, users are guided to participate in orderly charging, effectively adjusting the spatiotemporal distribution of charging load. Simulation results show that this method can significantly reduce the peak-to-valley difference of the grid, improve the smoothness of the load curve, and thus enhance the stability and safety of grid operation.

[0075] 4. Enhance user participation and achieve shared benefits. The dual-mode charging mechanism, employing both a no-schedule and scheduled charging modes, gives users ample choice, while reasonable subsidy policies incentivize user participation in orderly charging. This flexible guidance strategy not only improves user satisfaction but also fosters a positive interaction mechanism between charging and battery swapping stations and users, achieving shared benefits for all parties.

[0076] 5. Robust optimization enhances the system's adaptability to uncertainties in new energy sources, reducing operational risks. A data-driven multi-ellipsoidal uncertainty set modeling method is adopted, and an adjustable ellipsoidal scaling factor γ is introduced, enabling the system to flexibly cope with uncertainties in wind and solar power output. By adjusting the γ value (range 0.8-1.2), operators can choose conservative or aggressive operating strategies based on their risk preferences, maximizing economic benefits while ensuring system reliability, and significantly reducing operational risks caused by fluctuations in new energy sources.

[0077] Based on the same general inventive concept, this invention also protects a robust operating device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries. The robust operating device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries described below can be referred to in correspondence with the robust operating method for a charging and swapping station that takes into account the characteristics of electric vehicle batteries described above.

[0078] Figure 12 This is a schematic diagram of the robust operation device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries, provided in this embodiment.

[0079] like Figure 12 As shown, this embodiment provides a robust operation device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries, including: Modeling module 1201 is used to establish a dynamic power limit model based on the coupling relationship between state of charge and battery health state, and to determine the nonlinear characteristics of charge and discharge power as a function of state of charge through piecewise linear constraints. The calculation module 1202 is used to construct a multi-factor coupled battery life degradation model. It uses a polynomial function to quantify the synergistic effect of charge and discharge rate and discharge depth on life, and linearizes the absolute value term through the big M method to calculate battery life loss in real time. Optimization module 1203 is used to design data-driven multi-ellipsoidal uncertainty sets and transform the robust problem into a deterministic second-order cone programming problem to optimize the multi-ellipsoidal uncertainty sets through dual transformation, taking into account the uncertainty and spatiotemporal correlation of wind and solar power output. The charging module 1204 is used to form an orderly charging mechanism guided by user will by setting a minimum perceptible boundary and a stimulus quantity boundary, based on the nonlinear characteristics, battery life loss and the uncertainty and spatiotemporal correlation of wind and solar power output.

[0080] Figure 13 This is a schematic diagram of the structure of the electronic device provided in this embodiment.

[0081] like Figure 13 As shown, the electronic device may include a processor 1310, a communications interface 1320, a memory 1330, and a communication bus 1340. The processor 1310, communications interface 1320, and memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logic instructions from the memory 1330 to execute a robust operation method for charging and swapping stations that takes into account the characteristics of electric vehicle batteries.

[0082] Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the robust operation method for charging and swapping stations that takes into account the characteristics of electric vehicle batteries provided by the above methods.

[0084] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the method for robust operation of a battery swap station considering characteristics of an electric vehicle battery.

[0085] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0086] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0087] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A robust operation method for a charging and battery swapping station considering the characteristics of electric vehicle batteries, characterized in that, include: A dynamic power limit model based on the coupling relationship between state of charge and battery health state is established, and the nonlinear characteristics of charge and discharge power as a function of state of charge are determined by piecewise linear constraints. A multi-factor coupled battery life degradation model is constructed. The synergistic effect of charge / discharge rate and depth of discharge on life is quantified by polynomial function. The absolute value term is linearized by the big M method, and battery life loss is calculated in real time. Design a data-driven multi-ellipsoidal uncertainty set, and transform the robust problem into a deterministic second-order cone programming problem through dual transformation to optimize the multi-ellipsoidal uncertainty set, taking into account the uncertainty and spatiotemporal correlation of wind and solar power output; Based on the aforementioned nonlinear characteristics, battery life loss, and the uncertainty and spatiotemporal correlation of wind and solar power output, an orderly charging mechanism guided by user intention is formed by using a vehicle owner's willingness probability model and setting minimum perceptible boundaries and stimulus quantity boundaries.

2. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 1, characterized in that, The establishment of a dynamic power limit model based on the coupling relationship between state of charge and battery health state, and the determination of the nonlinear characteristics of charge and discharge power as a function of state of charge through piecewise linear constraints, includes: Based on the average battery health level in the charging compartment and the initial power limit switching threshold, determine the state of charge threshold for switching from the rated power limit of the charging compartment to the variable power limit; Based on the maximum state of charge, minimum state of charge, and the state of charge threshold of the charging compartment, a piecewise constraint relationship between charging and discharging power and state of charge is determined. Based on the segmented constraint relationship, the battery protection constraint for the power change rate is determined.

3. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 1, characterized in that, The proposed multi-factor coupled battery life degradation model employs polynomial functions to quantify the synergistic effects of charge / discharge rate and depth of discharge on battery life. The absolute value term is linearized using the Big M method, and battery life loss is calculated in real time, including: Based on the influence coefficients of battery energy throughput and battery charge / discharge rate on battery life degradation, a battery life degradation model with multiple factors is constructed. The battery life degradation model is equivalently transformed into a polynomial model; The Big M method is used to linearize the absolute power value of the total battery degradation lifetime during the scheduling process, and the battery lifetime loss is determined by combining it with the polynomial model.

4. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 1, characterized in that, The design data-driven multi-ellipsoidal uncertainty set includes: Calculate the probability distance between the predicted scenario and the historical output to filter similar scenarios; Based on the aforementioned similar scenarios, a set of segmented low-dimensional ellipsoids is constructed using the minimum volume ellipsoid algorithm, and the scheduling period is divided into three time periods to establish ellipsoid models for each period. By introducing an ellipsoid scaling factor to dynamically adjust the conservatism of the set, a multi-ellipsoidal uncertain set is obtained.

5. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 4, characterized in that, The introduction of an ellipsoid scaling factor to dynamically adjust the set conservatism includes: When the ellipsoid scaling factor is less than the preset value, the higher-dimensional ellipsoid is enlarged to improve conservatism. When the ellipsoid scaling factor is greater than the preset value, the high-dimensional ellipsoid is reduced to decrease the conservatism.

6. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 1, characterized in that, The process of transforming the robust problem into a deterministic second-order cone programming problem through dual transformation to optimize the multi-ellipsoidal uncertain set includes: Construct a multi-objective comprehensive benefit function; Determine the constraints for collaborative optimization of electric-hydrogen equipment, safe operation of hydrogen storage tanks, power balance constraints, and wind and solar power output constraints. Based on the multi-objective comprehensive benefit function, dual transformation is used to handle the constraints of collaborative optimization of the electric-hydrogen equipment, safe operation of the hydrogen storage tank, power balance and wind and solar power output, transforming the multi-ellipsoidal uncertainty set problem into a second-order cone programming problem.

7. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 1, characterized in that, The aforementioned mechanism, which utilizes a probabilistic model of car owner willingness and establishes a user-intention-guided orderly charging mechanism by setting minimum perceptible boundaries and stimulus quantity boundaries, includes: Constructing an electric vehicle cluster charging model; Construct a probabilistic model of car owner intentions based on the WF law; Based on the electric vehicle cluster charging model, the vehicle owner willingness probability model, and the flexible scheduling constraints of the orderly charging load, an orderly charging mechanism guided by user willingness is formed.

8. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 7, characterized in that, The probability model for the car owner's willingness is as follows: ； In the formula, Electric vehicle owners receive charging subsidies φ The probability of selecting the no-scheduling mode; I M This is the boundary of the stimulus amount; p 0 represents the limit of the stimulus response; I N For the minimum perceptible boundary, k 0 represents the Weber coefficient; s 0 represents the stimulus constant.

9. The robust operation method for charging and swapping stations considering the characteristics of electric vehicle batteries according to claim 7, characterized in that, The flexible scheduling constraint for the ordered charging load is: ； ； In the formula, for t The actual dispatch power of the time-based charging station for the orderly charging load of electric vehicles; The total electricity demand of the orderly charging load during the scheduling cycle; and For orderly charging load in t Minimum and maximum electricity demand for a given time period. For scheduling intervals, N T This is the total scheduling duration.

10. A robust operating device for a charging and swapping station that takes into account the characteristics of electric vehicle batteries, characterized in that, include: The modeling module is used to establish a dynamic power limit model based on the coupling relationship between the state of charge and the battery health state, and to determine the nonlinear characteristics of the charge and discharge power as a function of the state of charge through piecewise linear constraints. The calculation module is used to construct a multi-factor coupled battery life degradation model. It uses a polynomial function to quantify the synergistic effect of charge and discharge rate and depth of discharge on life, and linearizes the absolute value term using the Big M method to calculate battery life loss in real time. The optimization module is used to design a data-driven multi-ellipsoidal uncertainty set and to transform the robust problem into a deterministic second-order cone programming problem through dual transformation to optimize the multi-ellipsoidal uncertainty set, taking into account the uncertainty of wind and solar power output and its spatiotemporal correlation. The charging module is used to form an orderly charging mechanism guided by user willingness by using a vehicle owner willingness probability model based on the nonlinear characteristics, battery life loss and uncertainty and spatiotemporal correlation of wind and solar power output, and by setting a minimum perceptible boundary and a stimulus quantity boundary.

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