A park charging station charging strategy calculation method and system for extreme weather

Abstract

The application discloses a charging strategy calculation method and system for a park charging station in extreme weather, and belongs to the technical field of power systems. n A cost function is calculated, and a gradient of the cost function on power consumption in each period is calculated; a current strategy is updated in a gradient direction of the negative gradient of the cost function, so that the charging cost is iterated in a descending direction; after each iteration, it is checked whether the convergence criterion is met; if the convergence criterion is met, the obtained load curve is an approximate solution of Nash equilibrium; if the convergence criterion is not met, the charging strategy is repeatedly updated until the convergence criterion is met. The application solves the load fluctuation problem caused by the concentrated charging of electric vehicles under the condition that the park contains photovoltaic power generation.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a method and system for calculating charging strategies for park charging stations in extreme weather conditions. Background Technology

[0002] The construction of smart grids has accelerated the development of smart industrial parks. Smart industrial park operators can use demand response mechanisms to comprehensively manage photovoltaic power generation systems and electric vehicle charging facilities, achieving peak-valley load regulation within the park, promoting intelligent two-way interaction between users and the grid, and significantly improving grid operating efficiency. In recent years, with the development of electric vehicles, the number of electric vehicles parked in smart industrial parks during the day has increased, and disorderly charging has exacerbated peak-hour demand. At the same time, the high costs of expanding and upgrading the park's power distribution facilities have placed greater pressure on the system's peak-shaving capacity. Furthermore, the low matching degree between photovoltaic power generation and disorderly electricity load in smart industrial parks exposes them to operational challenges posed by extreme weather. For example, extreme weather events such as rain, sandstorms, smog, and cold waves significantly weaken solar irradiance, leading to persistently low photovoltaic output, a resurgence of daytime load curves, and pressure on the system's supply-demand balance. Therefore, a well-organized electric vehicle charging strategy for these parks is particularly important.

[0003] Electric vehicle operators can design and publish electricity prices based on the load operation status of the park, further optimizing the adjustment of charging load. Based on this scenario, many researchers have focused on the design of time-of-use (TOU) pricing to shift the charging load of electric vehicles to off-peak periods. However, these traditional TOU pricing methods are usually static, changing only over time and not adjusting according to real-time load changes. This can lead to excessive concentration of charging load during low-price periods, triggering new load peaks. Summary of the Invention

[0004] This invention provides a method and system for calculating charging strategies for park charging stations in extreme weather conditions, addressing the load fluctuation problem caused by concentrated charging of electric vehicles within parks containing photovoltaic power generation. This invention allows each user to optimize their individual charging costs while smoothing the overall load curve of the park, reducing the pressure on the power grid. In the face of reduced photovoltaic output due to extreme weather such as rain and sandstorms, the proposed method guides electric vehicles to actively charge during periods of high photovoltaic output or low load, balancing the load curve. Users do not need to disclose their personal charging information to the park management, protecting privacy and reducing the central computing and communication burden, thereby lowering system operating costs and improving operational flexibility.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for calculating charging strategies for park charging stations in extreme weather conditions includes: Step 1: Constructing extreme weather scenarios and controllable electric vehicle load models; Step 2: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria; Step 3: Calculate EV n The cost function is defined, and the gradient of the cost function with respect to power consumption in each time period is calculated. Step 4: Update the current policy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction; Step 5: After each iteration, check whether the convergence criterion is met. If it is met, the obtained load curve is the approximate solution of Nash equilibrium. If it is not met, update the charging strategy repeatedly until the convergence criterion is met.

[0006] A further improvement of this invention is that step one: constructing an extreme weather scenario and a controllable electric vehicle load model, including: Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations. (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f It is the probability distribution function; Generate daily photovoltaic power output data and power output data under extreme weather conditions; (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening; it is represented using a standard Gaussian function. (3) in, Represents the peak time of photovoltaic output, the time when photovoltaic power is at its maximum within a day. For time variance; (4) in, For low photovoltaic power output data under extreme weather conditions, the coefficient q is usually between 0 and 1; The electric vehicle load model takes into account user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints. (5) in, N For electric vehicle collection ;No. n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in Formula (5), constitutes the EV. n Feasible range of charging power .

[0007] A further improvement of this invention lies in step two: based on extreme weather scenarios and a controllable electric vehicle load model, setting an initialization strategy and convergence criteria, as follows: (6) In the formula For EV n k+1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

[0008] A further improvement of this invention is that step three: calculating EV n Calculate the cost function and its gradient with respect to power consumption in different time periods. ,as follows: (7) (8) (9) In the formula, For EV n The charging cost function, for t Total load of the park during the time period For all electric vehicle loads, For the park's fixed load, Contribute to photovoltaic power To remove EV n Other electric vehicle charging power ; for t Dynamic electricity pricing for different time periods and This is the dynamic electricity price coefficient. For EV n The gradient vector of the cost function.

[0009] A further improvement of this invention lies in step four: updating the current strategy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction, as follows: (10) in For EV n The gradient of the cost function with respect to the power consumption in different time periods. The iteration step size, For the first k EV in the next iteration n Charging strategy ， To project the new strategy onto the feasible region The operation.

[0010] A charging strategy calculation system for park charging stations designed for extreme weather conditions, comprising: Model building unit: Constructing extreme weather scenarios and controllable electric vehicle load models; Setting Unit: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria; Calculation unit: calculates EV n The cost function is defined, and the gradient of the cost function with respect to power consumption in each time period is calculated. Update iterative unit: Update the current policy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction; Convergence judgment unit: After each iteration, check whether the convergence criterion is met; if it is met, the obtained load curve is the approximate solution of Nash equilibrium; if it is not met, the charging strategy is repeatedly updated until the convergence criterion is met.

[0011] A further improvement of this invention is that, in the model building unit: constructing an extreme weather scenario and a controllable electric vehicle load model includes: Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations. (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f It is the probability distribution function; Generate daily photovoltaic power output data and power output data under extreme weather conditions; (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening; it is represented using a standard Gaussian function. (3) in, Represents the peak time of photovoltaic output, the time when photovoltaic power is at its maximum within a day. For time variance; (4) in, For low photovoltaic power output data under extreme weather conditions, the coefficient q is usually between 0 and 1; The electric vehicle load model takes into account user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints. (5) in, N For electric vehicle collection ;No. n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in Formula (5), constitutes the EV. n Feasible range of charging power .

[0012] A further improvement of this invention is that, in the setting unit, based on extreme weather scenarios and a controllable electric vehicle load model, an initialization strategy and convergence criteria are set as follows: (6) In the formula For EV n k+1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

[0013] An electronic device includes: a processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method for calculating charging strategies for park charging stations in extreme weather conditions.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for calculating charging strategies for park charging stations in extreme weather conditions.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a method and system for calculating charging strategies at park charging stations in extreme weather conditions. Firstly, the distributed approach protects user privacy. Users do not need to upload sensitive information such as their travel data to a central server, reducing the risk of data leakage. Secondly, the distributed approach supports large-scale electric vehicle calculations. Because each user's EV... n The charging strategy calculations are performed independently, without coupling between them, thus supporting parallel computation of the algorithm. This method can handle optimization problems with a large number of users, and the parallel algorithm can quickly calculate the equilibrium result. Third, this method maintains good operational performance under extreme weather conditions. When weather conditions such as rain, sandstorms, or cold waves cause significant changes in photovoltaic power generation output, the algorithm can dynamically adjust the charging strategy according to the photovoltaic output and load level, achieving a coordinated match between electric vehicle load and photovoltaic output, maintaining supply and demand balance in the park. Finally, compared to centralized optimization methods, distributed optimization methods allow the algorithm to be scaled to more users and more complex grid environments. Each user can adjust the optimization strategy according to their own needs and preferences, without relying on unified scheduling by a central authority. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A comparison chart of photovoltaic power output under normal sunny days and under extreme weather conditions; Figure 2 Comparison curves of electric vehicle charging load under extreme weather conditions in industrial parks; Figure 3 This is an overall flowchart of the method of the present invention; Figure 4 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0018] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0019] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0023] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] Example 1 This invention provides a method for calculating charging strategies for park charging stations in extreme weather conditions. The method includes: first, extracting the electricity consumption characteristics of electric vehicles based on historical operating data of the charging station, and combining this with historical photovoltaic power generation output information. The electric vehicle data includes the statistical distribution of vehicle arrival and departure times, initial power consumption, and target power consumption. Next, an electric vehicle charging model is constructed to characterize power consumption changes and power characteristics during the charging process. Then, a dynamic electricity price model is established, defining the basic elements of the electric vehicle group game. Finally, a gradient projection parallel algorithm is used to solve the problem, obtaining the optimized load curve of the system under Nash equilibrium conditions.

[0025] This invention provides a method for calculating charging strategies for park charging stations in extreme weather conditions, specifically including the following steps: Step 1: Construct extreme weather scenarios and controllable electric vehicle load models.

[0026] Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations.

[0027] (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f Let be the probability distribution function.

[0028] Generate daily photovoltaic power output data and power output data under extreme weather conditions.

[0029] (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening. It is represented using a standard Gaussian function.

[0030] (3) in, Represents the peak time of photovoltaic output, the time when photovoltaic power is at its maximum within a day. For time variance.

[0031] (4) in, Data on low photovoltaic output under extreme weather conditions, coefficient q It is usually between 0 and 1.

[0032] The load model for electric vehicles needs to consider user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints.

[0033] (5) in, N For electric vehicle collection ; For the first n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in Formula (5), constitutes the EV. n Feasible range of charging power .

[0034] Since arrival and departure times both follow a normal distribution, it is possible that an EV may not be able to charge at maximum power to reach the required level within the dwell time, thus leading to an unsolvable problem. Consider the following method to iteratively generate example data to ensure the feasibility of the problem: (1) Set the number of electric vehicles to be generated and the normal distribution parameters. (2) For each electric vehicle EV... n Generate the arrival and departure times and battery level data of the electric vehicle according to formula (1). (3) Determine the EV n Do the arrival and departure times meet the requirements? If the conditions are met, continue generating data for the next electric vehicle; otherwise, return to step (2) and regenerate the EV. n data.

[0035] Step 2: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria.

[0036] (6) In the formula For EV n k +1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

[0037] Step 3: Calculate EV according to formula (7) n Calculate the cost function and its gradient with respect to power consumption in different time periods. .

[0038] (7) (8) (9) In the formula, For EV n The charging cost function, for t Total load of the park during the time period For all electric vehicle loads, For the park's fixed load, Contribute to photovoltaic power To remove EV n Other electric vehicle charging power ; for t Dynamic electricity pricing for different time periods and This is the dynamic electricity price coefficient. For EV n The gradient vector of the cost function.

[0039] Step 4: Update the current strategy using the negative gradient of the cost function, ensuring the charging cost iterates in a decreasing direction. This is to prevent the new strategy from failing to meet EV requirements. n Given the charging constraints, we need to find the vector closest to the new policy within the feasible region as the policy result for this iteration.

[0040] (10) in For EVn The gradient of the cost function with respect to the power consumption in different time periods. The iteration step size, For the first k EV in the next iteration n Charging strategy ， To project the new strategy onto the feasible region The operation.

[0041] Step 5: After each iteration, check if the convergence criterion is met. If it is, the algorithm terminates, and the resulting load curve is the approximate solution to the Nash equilibrium. If it is not, return to Step 4 to repeatedly update the charging strategy until the convergence criterion is met.

[0042] Example 2 First, data on the arrival and departure times, required charging capacity, and extreme weather scenarios for all electric vehicles are generated. Both arrival and departure times follow a normal distribution, with the standard deviation of the arrival time being [data missing]. ,expect standard deviation of departure time ,expect The expected arrival time is 9:00 AM and the expected departure time is 6:00 PM. The battery charge of the electric vehicle upon arrival at the industrial park also approximately follows a normal distribution, with the standard deviation of the charge being used. Expected value. The expected charge level is 51.37%, and the probability density function formulas for the above variables are shown in (11)-(13). Since the arrival and departure times both follow a normal distribution, it is possible that the EV cannot charge at maximum power to reach the required charge level within the dwell time, thus leading to an unsolvable problem. Consider the following method to generate example data in a loop to ensure the feasibility of the problem. (1) Set the number of electric vehicles to be generated and the normal distribution parameters. (2) For each electric vehicle EV n Generate the arrival and departure times and battery level data of the electric vehicle according to formula (1). (3) Determine whether the arrival and departure times of EVn satisfy the following conditions. If the conditions are met, continue generating data for the next electric vehicle; otherwise, return to step (2) and regenerate the EV. n data.

[0043] The parameters in the aforementioned formula (5) are set to the upper and lower limits of the charging power. ,Pick ,in The maximum battery capacity of a single electric vehicle is taken as the charging efficiency. In formula (8), the dynamic electricity price coefficient is taken as... .

[0044] (11) (12) (13) Photovoltaic (PV) output data is generated based on typical operating characteristics of industrial parks. Under normal weather conditions, PV power generation exhibits a diurnal single-peak distribution, gradually increasing from early morning, reaching its peak between 11:00 AM and 2:00 PM, and then gradually decreasing until reaching zero in the evening, forming a solar radiation curve consistent with irradiance. Under extreme weather scenarios (such as overcast skies, rain, sandstorms, or cold waves), PV power generation weakens overall, with the daily output level reduced proportionally to a normal day, and a significantly low output is set between 11:00 AM and 2:00 PM to simulate the midday power generation reduction caused by thick cloud cover or attenuated sunlight. By adjusting the ratio of solar radiation periods to peak values, corresponding PV output curves can be obtained under different weather conditions, enabling comparative analysis between normal and extreme weather conditions.

[0045] The algorithm was solved using the Yalmip+Cplex solver in the MATLAB R2020a environment.

[0046] The steps for performing parallel computation of electric vehicle balancing at charging stations based on gradient projection are as follows: First, set the initial iteration strategy. The convergence criterion is determined based on the number of electric vehicles and the target accuracy. The following steps are then repeated until the convergence condition is met: 1) Calculate the gradient direction of the electric vehicle's cost function and update the charging power plan for each user along the negative gradient direction. 2) To ensure that the updated charging strategy is within the feasible region, project it onto the corresponding constraint set. 3) Determine whether the current result meets the convergence criterion. If Nash equilibrium is reached, output the final load curve; otherwise, return to step 1) to continue iterating.

[0047] Figure 2 The graph shows a comparison of electric vehicle charging load under extreme weather conditions in the industrial park. As can be seen, when photovoltaic output is generally low due to the impact of cloudy and rainy weather, the charging load of electric vehicles is mainly concentrated between 9:00 AM and 6:00 PM, with the period from 10:00 AM to 3:00 PM being the peak charging time. This charging distribution avoids the park's peak electricity consumption periods in the morning and evening, and avoids concentrated charging during the system's peak load period, thus effectively smoothing the overall load curve throughout the day. Compared to the load considering only photovoltaic output, the total load curve including electric vehicles shows an increase during off-peak hours and a more gradual change during peak periods, indicating that electric vehicles achieve adaptive load adjustment through optimized charging behavior, playing a positive role in balancing output.

[0048] Example 3 like Figure 4As shown, the present invention provides a charging strategy calculation system for park charging stations in extreme weather conditions, comprising: Model building unit: Constructing extreme weather scenarios and controllable electric vehicle load models; Setting Unit: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria; Calculation unit: calculates EV n The cost function is defined, and the gradient of the cost function with respect to power consumption in each time period is calculated. Update iterative unit: Update the current policy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction; Convergence judgment unit: After each iteration, check whether the convergence criterion is met; if it is met, the obtained load curve is the approximate solution of Nash equilibrium; if it is not met, the charging strategy is repeatedly updated until the convergence criterion is met.

[0049] In the model building unit of this embodiment: the model building of extreme weather scenarios and controllable electric vehicle load includes: Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations. (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f It is the probability distribution function; Generate daily photovoltaic power output data and power output data under extreme weather conditions; (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening; it is represented using a standard Gaussian function. (3) in, Represents the peak time of photovoltaic output, the time when photovoltaic power is at its maximum within a day. For time variance; (4) in, For low photovoltaic power output data under extreme weather conditions, the coefficient q is usually between 0 and 1; The electric vehicle load model takes into account user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints. (5) in, N For electric vehicle collection ;No. n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in Formula (5), constitutes the EV. n Feasible range of charging power .

[0050] In the setting unit of this embodiment: based on extreme weather scenarios and a controllable electric vehicle load model, the initialization strategy and convergence criteria are set as follows: (6) In the formula For EV n k+1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

[0051] Example 4 The present invention provides an electronic device comprising: a processor and a memory coupled to the processor, the memory storing a computer program, wherein when the computer program is executed by the processor, the steps of the above-described method for calculating charging strategies for park charging stations in extreme weather conditions are implemented.

[0052] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0053] The processor controls the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory stores various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method operating on the electronic device, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia components may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio components are used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0054] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing a storage medium sharing method.

[0055] Example 5 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for calculating charging strategies for park charging stations in extreme weather conditions.

[0056] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0060] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0061] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for calculating charging strategies for park charging stations in extreme weather conditions, characterized in that, include: Step 1: Constructing extreme weather scenarios and controllable electric vehicle load models; Step 2: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria; Step 3: Calculate EV n The cost function is defined, and the gradient of the cost function with respect to power consumption in each time period is calculated. Step 4: Update the current policy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction; Step 5: After each iteration, check whether the convergence criterion is met. If it is met, the obtained load curve is the approximate solution of Nash equilibrium. If it is not met, update the charging strategy repeatedly until the convergence criterion is met.

2. The method for calculating charging strategies for park charging stations in extreme weather conditions according to claim 1, characterized in that, Step 1: Construct a model of extreme weather scenarios and controllable electric vehicle loads, including: Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations. (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f It is the probability distribution function; Generate daily photovoltaic power output data and power output data under extreme weather conditions; (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening; it is represented using a standard Gaussian function. (3) in, Represents the peak time of photovoltaic output, the point in time when photovoltaic power is at its maximum within a day. For time variance; (4) in, For data on low photovoltaic output under extreme weather conditions, the coefficient q is typically between 0 and 1; The electric vehicle load model takes into account user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints. (5) in, N For electric vehicle collection ;No. n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in formula (5), constitutes the EV. n Feasible range of charging power .

3. The method for calculating charging strategies for park charging stations in extreme weather conditions according to claim 2, characterized in that, Step 2: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria as follows: (6) In the formula For EV n k+1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

4. The method for calculating charging strategies for park charging stations in extreme weather conditions according to claim 3, characterized in that, Step 3: Calculate EV n Calculate the cost function and its gradient with respect to power consumption in different time periods. ,as follows: (7) (8) (9) In the formula, For EV n The charging cost function, for t Total load of the park during the time period For all electric vehicle loads, For the park's fixed load, Contribute to photovoltaic power To remove EV n Other electric vehicle charging power ; for t Dynamic electricity pricing for different time periods and This is the dynamic electricity price coefficient. For EV n The gradient vector of the cost function.

5. The method for calculating charging strategies for park charging stations in extreme weather conditions according to claim 4, characterized in that, Step 4: Update the current policy using the negative gradient of the cost function, causing the charging cost to iterate in the decreasing direction, as follows: (10) in For EV n The gradient of the cost function with respect to the power consumption in different time periods. The iteration step size, For the first k EV in the next iteration n Charging strategy ， To project the new strategy onto the feasible region The operation.

6. A charging strategy calculation system for park charging stations in extreme weather conditions, characterized in that, include: Model building unit: Constructing extreme weather scenarios and controllable electric vehicle load models; Setting Unit: Based on extreme weather scenarios and a controllable electric vehicle load model, set the initialization strategy and convergence criteria; Calculation unit: calculates EV n The cost function is defined, and the gradient of the cost function with respect to power consumption in each time period is calculated. Update iterative unit: Update the current policy using the negative gradient direction of the cost function, so that the charging cost iterates in the decreasing direction; Convergence judgment unit: After each iteration, check whether the convergence criterion is met; if it is met, the obtained load curve is the approximate solution of Nash equilibrium; if it is not met, the charging strategy is repeatedly updated until the convergence criterion is met.

7. A charging strategy calculation system for park charging stations in extreme weather conditions according to claim 6, characterized in that, The model building unit constructs extreme weather scenarios and controllable electric vehicle load models, including: Photovoltaic data and electric vehicle charging data are generated based on normal distribution simulations. (1) in, Represents a normal distribution, where and EV n Arrival and departure times For EV n Total charging power required These are the expected variance and the expected variance, respectively. f It is the probability distribution function; Generate daily photovoltaic power output data and power output data under extreme weather conditions; (2) in, For photovoltaic power output under non-extreme conditions, To represent the output power of the photovoltaic system under maximum irradiance conditions, It is a time function, exhibiting a unimodal curve, meaning it gradually rises from morning to noon and gradually declines from noon to evening; it is represented using a standard Gaussian function. (3) in, Represents the peak time of photovoltaic output, the point in time when photovoltaic power is at its maximum within a day. For time variance; (4) in, For data on low photovoltaic output under extreme weather conditions, the coefficient q is typically between 0 and 1; The electric vehicle load model takes into account user arrival and departure times, upper and lower limits of charging power, and electricity demand constraints. (5) in, N For electric vehicle collection ;No. n electric vehicles EV n Charging time period collection ,in and EV n Arrival and departure times; outside this range, the charging power of electric vehicles is 0. For EV n During the period t charging power, and These represent the minimum and maximum charging power, respectively. For charging efficiency, For EV n The total amount of electricity required for charging, as shown in formula (5), constitutes the EV. n Feasible range of charging power .

8. A charging strategy calculation system for park charging stations in extreme weather conditions according to claim 7, characterized in that, In the configuration unit: based on extreme weather scenarios and a controllable electric vehicle load model, the initialization strategy and convergence criteria are set as follows: (6) In the formula For EV n k+1 and k The L2 norm of the difference in the total strategy across rounds of iterations, i.e., the computational error. The value depends on the required convergence accuracy and the number of electric vehicles.

9. An electronic device, characterized in that, include: A processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method for calculating charging strategies for park charging stations in extreme weather conditions, as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for calculating charging strategies for park charging stations in extreme weather conditions, as described in any one of claims 1-5.

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