Photovoltaic storage direct flexible system configuration method for expressway electric vehicle charging scene

Abstract

The application relates to a highway electric vehicle charging scene-oriented photovoltaic storage direct flexible system configuration method, which comprises the following steps: classifying electric vehicle charging load data of each service area on a chain highway based on a clustering method to obtain electric vehicle charging load data under various scenes; taking the minimum energy supply cost of a photovoltaic storage direct flexible system and the most stable photovoltaic storage direct flexible system power purchase load stability as targets, establishing a photovoltaic storage direct flexible system capacity configuration model; taking electric vehicle charging load data under various scenes as input of the photovoltaic storage direct flexible system capacity configuration model, and solving the photovoltaic storage direct flexible system capacity configuration model to obtain photovoltaic storage direct flexible system configuration schemes under various scenes. The application realizes the economy and load stability of an energy supply system.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage-DC-flexible system configuration technology, and in particular to a method for configuring a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios. Background Technology

[0002] Photovoltaic-storage-DC-flexible power supply systems represent an important future development direction for highway energy supply systems. The coordinated power supply of photovoltaic power generation (photovoltaic), energy storage systems (storage), and the power grid (DC-flexible) can effectively address the volatility and uncertainty of electric vehicle charging loads, achieving a balance between economic efficiency and stability in energy supply.

[0003] The existing patent publication CN118944038A discloses a planning and evaluation method for a photovoltaic-storage-DC-flexible system that considers reliability and economy. This scheme only considers reliability and economy and lacks in-depth analysis of the charging load characteristics in highway scenarios. Therefore, applying this scheme to the electric vehicle charging scenario of a chain highway will lead to unreasonable system capacity configuration. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a configuration method for a photovoltaic-storage-DC-flexible system for electric vehicle charging scenarios on highways, so as to achieve the economy and load stability of the energy supply system.

[0005] The technical solution adopted by this invention to solve its technical problem is: to provide a method for configuring a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, including the following steps:

[0006] The electric vehicle charging load data of each service area on the chain highway is classified based on the clustering method to obtain electric vehicle charging load data under various scenarios.

[0007] With the goals of minimizing the energy supply cost of the photovoltaic-storage-direct-flexible system and maximizing the stability of the power purchase load of the photovoltaic-storage-direct-flexible system, a capacity configuration model for the photovoltaic-storage-direct-flexible system is established.

[0008] Electric vehicle charging load data under various scenarios are used as input to the capacity configuration model of the photovoltaic-storage-DC-flexible system, and the capacity configuration model of the photovoltaic-storage-DC-flexible system is solved to obtain the configuration scheme of the photovoltaic-storage-DC-flexible system under various scenarios.

[0009] The clustering method is used to classify the electric vehicle charging load data of each service area on the chain-like highway to obtain electric vehicle charging load data under various scenarios, specifically including:

[0010] The electric vehicle charging load data of each service area on the collected chain highway is processed by DTW to obtain the DTW distance matrix;

[0011] The optimal number of clusters K is determined using preset metrics;

[0012] Based on the optimal number of clusters K, cluster analysis is performed on the DTW distance matrix to obtain the results of K scene classifications of the DTW distance matrix;

[0013] Based on the classification labels, the electric vehicle charging load data of each service area on the chain highway is returned to obtain the electric vehicle charging load under different categories;

[0014] The average value of electric vehicle charging load under each category at the same time is taken to obtain electric vehicle charging load data under various scenarios.

[0015] The preset index is the profile coefficient index.

[0016] The objective function of the photovoltaic-storage direct-drive-flexible system capacity configuration model is: ,in, The objective function for the capacity configuration model of a photovoltaic-storage-direct-drive-flexible system. This indicates taking the minimum value. and These are the weighting coefficients. The cost of power supply for photovoltaic-storage-DC-flexible systems, This represents the maximum energy supply cost for a photovoltaic-storage-DC-flexible system. To ensure the stability of the power purchase load for the photovoltaic-storage-DC-flexible system, The maximum value for the stability of the power purchase load of the photovoltaic-storage-DC-flexible system.

[0017] The energy supply cost of the photovoltaic-storage-DC-flexible system includes electricity purchase cost and investment cost, expressed as follows: ,in, for The power purchased from the power grid during the specified time period. For the power grid Electricity price during specific time periods The interest rate for investment profits For the lifespan of the system, The investment cost of photovoltaic systems, This refers to the investment cost of the energy storage system.

[0018] The stability of the power purchase load of the photovoltaic-storage-DC-flexible system is represented by the minimum standard deviation of the power purchase load from the power grid, as follows: ,in, for The power purchased from the power grid during the specified time period. This represents the average daily electricity load.

[0019] The constraints of the capacity configuration model for the photovoltaic-storage direct current-flexible system include:

[0020] The constraints of distributed photovoltaic power generation are expressed as: ;

[0021] The constraints of energy storage devices are expressed as follows: ;

[0022] Power grid purchase constraints are expressed as: ;

[0023] Power balance constraints are expressed as: ;

[0024] in, for Power generation of the photovoltaic array during a given time period For the installed capacity of photovoltaic arrays, for Light intensity during the time period The light intensity under STC standard testing conditions. For temperature coefficient, for The backsheet temperature of the photovoltaic modules in the photovoltaic array during a given time period. The backsheet temperature of the photovoltaic module in the photovoltaic array under STC standard test conditions; and These represent the energy storage devices in Charging and discharging power during the time period and These represent the maximum charging power and maximum discharging power of the energy storage device, respectively. and These represent the charging and discharging states of the energy storage device, respectively. and This indicates that the energy storage device is in a charging state. and This indicates that the energy storage device is in a discharging state; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. This indicates the actual output power of the energy storage device. Indicates that the energy storage device is in State of charge over a period of time; Indicates the capacity of the energy storage device. and This indicates the upper and lower limits of the state of charge of an energy storage device during operation; for The power purchased from the power grid during the specified time period. This is the upper limit for the amount of electricity that can be purchased from the power grid; for Charging power of electric vehicles during a given time period for The curtailment power of the photovoltaic-storage-DC-flexible system during specific time periods.

[0025] The technical solution adopted by this invention to solve its technical problem is: to provide a configuration device for a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, comprising:

[0026] The clustering module is used to classify the electric vehicle charging load data of each service area on the chain highway based on the DTW-K-means clustering method, so as to obtain electric vehicle charging load data under various scenarios.

[0027] A module is established to create a capacity configuration model for the photovoltaic-storage-direct-flexible system, with the goal of minimizing the energy supply cost of the photovoltaic-storage-direct-flexible system and maximizing the stability of the purchased load of the photovoltaic-storage-direct-flexible system.

[0028] The solution module is used to take electric vehicle charging load data under various scenarios as input to the capacity configuration model of the photovoltaic-storage-direct-flexible system, and solve the capacity configuration model of the photovoltaic-storage-direct-flexible system to obtain the configuration scheme of the photovoltaic-storage-direct-flexible system under various scenarios.

[0029] The technical solution adopted by the present invention to solve its technical problem is: to provide 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 computer program to implement the steps of the above-mentioned configuration method of the photovoltaic-storage-direct-flexible system for highway electric vehicle charging scenarios.

[0030] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-mentioned configuration method of the optical-storage-direct-flexible system for highway electric vehicle charging scenarios are implemented.

[0031] Beneficial effects

[0032] By adopting the above-mentioned technical solution, this invention has the following advantages and positive effects compared with the prior art: This invention classifies charging loads based on clustering methods to generate typical charging load scenarios, establishes a multi-objective photovoltaic-storage-direct-flexible system capacity configuration model based on the principles of economy and load stability, uses typical charging load scenarios as input to the multi-objective photovoltaic-storage-direct-flexible system capacity configuration model, and obtains the optimal photovoltaic-storage-direct-flexible system capacity configuration scheme under different scenarios by solving the multi-objective photovoltaic-storage-direct-flexible system capacity configuration model, thereby realizing the economy and load stability of the energy supply system. Attached Figure Description

[0033] Figure 1 This is a flowchart of the configuration method of the photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios according to the first embodiment of the present invention;

[0034] Figure 2 This is a flowchart of obtaining typical scenario loads using DTW-K-means clustering in the first embodiment of the present invention. Detailed Implementation

[0035] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0036] The first embodiment of the present invention relates to a configuration method for a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, such as... Figure 1 As shown, it includes the following steps:

[0037] Step 1: Based on clustering methods, classify the electric vehicle charging load data of each service area on the chain-like highway to obtain electric vehicle charging load data under various scenarios. In this embodiment, the chain-like highway refers to an electrified highway that conforms to a chain-like energy supply network. The chain-like energy supply network is characterized by its narrow and long shape and segmented interconnection. Its energy system has functions such as segmented access, transient balance control, redundancy switching, and efficient transformation, which can realize the aggregation, networking, and clean utilization of highway resources.

[0038] The specific process for this step is as follows: Figure 2 As shown. Assuming the collected electric vehicle charging load data from each service area on the chain-like highway is a 365×24 matrix E, dynamic time warming (DTW) is performed on matrix E as follows:

[0039] ;

[0040] This yields the DTW distance matrix D, a 365×365 matrix. The elements of the DTW distance matrix D are... DTW distance represents the distance between the i-th and j-th rows of matrix E. Compared to traditional K-means clustering, DTW-K-means clustering can flexibly align the time axis, grouping time series with similar shapes into one class, while traditional K-means clustering has a fixed time axis and focuses on absolute differences.

[0041] Subsequently, the optimal number of clusters K is determined using a pre-defined evaluation metric (such as the silhouette coefficient), calculated as follows:

[0042] ;

[0043] Where a(i) is the intra-cluster distance, which is the average distance between data point i and all other data points in its cluster; b(i) is the nearest cluster distance, which is the average distance between data point i and all points in the nearest cluster to which it does not belong.

[0044] After obtaining the optimal number of clusters K, DTW-K-means clustering analysis is performed to obtain the results of K typical scenario classifications of the DTW distance matrix. Then, based on the classification labels, the electric vehicle charging load data (i.e., matrix E) of each service area on the chain highway is returned to obtain the EV charging load under different categories. Finally, the average value of the data of each category at the same time is taken to obtain the electric vehicle charging load data under various typical scenarios.

[0045] Step 2: To achieve the goals of minimizing the energy supply cost of the photovoltaic-storage-direct current-flexible system and maximizing the stability of the purchased load, a capacity configuration model for the photovoltaic-storage-direct current-flexible system is established.

[0046] This step aims to minimize the energy supply cost of the photovoltaic-storage-DC-flexible system (PV-SGC-Flexible System) in a chain-like highway scenario, and to achieve the most stable power purchase load for the PV-SGC-Flexible System. The energy supply cost of the PV-SGC-Flexible System needs to consider the cost of electricity purchased from the grid and the daily investment cost of the system, while the power purchase load stability needs to consider the standard deviation of the power purchase load from the grid. Based on this, the energy supply cost and power purchase load stability of the PV-SGC-Flexible System can be expressed as follows:

[0047] ;

[0048] ;

[0049] in, The cost of power supply for photovoltaic-storage-DC-flexible systems, for The power purchased from the power grid during the specified time period. For the power grid Electricity price during specific time periods The interest rate for investment profits For the lifespan of the system, The investment cost of photovoltaic systems, The investment cost of the energy storage system; To ensure the stability of the power purchase load for the photovoltaic-storage-DC-flexible system, This represents the average daily electricity load.

[0050] The objective function of the photovoltaic-storage-direct-flexible system capacity configuration model established in this embodiment is:

[0051] ;

[0052] in, The objective function for the capacity configuration model of a photovoltaic-storage-direct-drive-flexible system. This represents the maximum energy supply cost for a photovoltaic-storage-DC-flexible system. The maximum value for the stability of the power purchase load of the photovoltaic-storage-DC-flexible system. and The weighting coefficient is determined by the user's preference for economy and stability.

[0053] Furthermore, the photovoltaic-storage-DC-flexible system for chain-type highways in this embodiment needs to consider constraints on photovoltaic power generation, energy storage devices, grid power purchase, and power balance. Therefore, the constraints of the capacity configuration model for the photovoltaic-storage-DC-flexible system established in this embodiment include:

[0054] Constraints of distributed photovoltaic power generation:

[0055] Since the main factors affecting photovoltaic power generation are irradiance and backsheet temperature, the unlimited output of the photovoltaic array in the maximum output point tracking mode is used as a constraint for distributed photovoltaic power generation, which is expressed as:

[0056] ;

[0057] in, for Power generation of the photovoltaic array during a given time period For the installed capacity of photovoltaic arrays, for Light intensity during the time period The light intensity under STC standard testing conditions. For temperature coefficient, for The backsheet temperature of the photovoltaic modules in the photovoltaic array during a given time period. This refers to the backsheet temperature of the photovoltaic modules in the photovoltaic array under STC standard test conditions.

[0058] Constraints of energy storage devices:

[0059] Energy storage devices exist in three states: charging, discharging, and resting. Therefore, the constraints of energy storage devices are expressed as follows:

[0060] ;

[0061] in, and These represent the energy storage devices in Charging and discharging power during the time period and These represent the maximum charging power and maximum discharging power of the energy storage device, respectively. and These represent the charging and discharging states of the energy storage device, respectively. and This indicates that the energy storage device is in a charging state. and This indicates that the energy storage device is in a discharging state; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. This indicates the actual output power of the energy storage device. Indicates that the energy storage device is in State of charge over a period of time; Indicates the capacity of the energy storage device. and This indicates the upper and lower limits of the state of charge of an energy storage device during operation.

[0062] Power grid purchase constraints:

[0063] The photovoltaic-storage-DC-flexible system is a grid-connected system. When the power supply is insufficient, it purchases electricity from the main grid, and any surplus electricity must be discarded and is not allowed to be fed into the grid. Therefore, the grid power purchase constraint can be expressed as:

[0064] ;

[0065] in, This represents the upper limit of the power that can be purchased from the power grid.

[0066] The power balance constraints are as follows:

[0067] ;

[0068] in, for Charging power of electric vehicles during a given time period for The curtailment power of the photovoltaic-storage-DC-flexible system during specific time periods.

[0069] Step 3: Use electric vehicle charging load data under various scenarios as input to the capacity configuration model of the photovoltaic-storage-direct-flexible system, and solve the capacity configuration model of the photovoltaic-storage-direct-flexible system to obtain the configuration scheme of the photovoltaic-storage-direct-flexible system under various scenarios.

[0070] The capacity configuration of a photovoltaic-storage-DC-flexible system for EV charging scenarios on highways needs to consider the optimized matching of power generation, grid, load, and storage. Here, "power generation" refers to photovoltaic power generation, and the photovoltaic capacity is a parameter to be optimized. "Grid" refers to the upstream power grid, and the maximum power output of the grid needs to be set beforehand; the purchased load is a parameter to be optimized. "Load" refers to the EV charging load, and typical daily charging load data for highway EVs is obtained using simulation or actual measurement methods; this is a deterministic parameter. "Storage" refers to the energy storage system, and the constrained parameters of the state of charge need to be set beforehand; the energy storage system capacity, charging power, and discharging power are parameters to be optimized.

[0071] After determining the above parameters, the electric vehicle charging load data under various scenarios are used as input. The photovoltaic-storage-DC-flexible system needs to meet the electric vehicle charging load while adjusting the installed capacity of the photovoltaic arrays in each service area. and the capacity of energy storage devices The optimal capacity is optimized by using an optimization solver to solve the capacity configuration model of the optical-storage-direct-flexible system, and finally obtaining the configuration schemes and optimal operating strategies of the optical-storage-direct-flexible system under various scenarios.

[0072] It is easy to see that this invention classifies charging loads based on clustering methods to generate typical charging load scenarios. Based on the principles of economy and load stability, a multi-objective photovoltaic-storage-direct-drive-flexible system capacity configuration model is established. The typical charging load scenarios are used as inputs to the multi-objective photovoltaic-storage-direct-drive-flexible system capacity configuration model. By solving the multi-objective photovoltaic-storage-direct-drive-flexible system capacity configuration model, the optimal photovoltaic-storage-direct-drive-flexible system capacity configuration scheme under different scenarios is obtained, thereby achieving the economy and load stability of the energy supply system.

[0073] The second embodiment of the present invention relates to a configuration device for a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, comprising:

[0074] The clustering module is used to classify the electric vehicle charging load data of each service area on the chain highway based on the clustering method, so as to obtain electric vehicle charging load data under various scenarios.

[0075] A module is established to create a capacity configuration model for the photovoltaic-storage-direct-flexible system, with the goal of minimizing the energy supply cost of the photovoltaic-storage-direct-flexible system and maximizing the stability of the purchased load of the photovoltaic-storage-direct-flexible system.

[0076] The solution module is used to take electric vehicle charging load data under various scenarios as input to the capacity configuration model of the photovoltaic-storage-direct-flexible system, and solve the capacity configuration model of the photovoltaic-storage-direct-flexible system to obtain the configuration scheme of the photovoltaic-storage-direct-flexible system under various scenarios.

[0077] The clustering module includes:

[0078] The DTW conversion processing unit is used to perform DTW conversion processing on the electric vehicle charging load data of each service area on the collected chain highway to obtain the DTW distance matrix.

[0079] The cluster number determination unit is used to determine the optimal number of clusters K using preset indicators;

[0080] The clustering analysis unit is used to perform clustering analysis on the DTW distance matrix based on the optimal number of clusters K, and obtain the results of K scene classifications of the DTW distance matrix;

[0081] The return unit is used to return electric vehicle charging load data for each service area on the chain highway according to the classification label, so as to obtain the electric vehicle charging load under different categories;

[0082] The averaging unit is used to take the average value of the electric vehicle charging load under each category at the same time to obtain electric vehicle charging load data under various scenarios.

[0083] The preset index is the profile coefficient index.

[0084] The objective function of the capacity configuration model for the photovoltaic-storage-direct-flex system established by the module is: ,in, The objective function for the capacity configuration model of a photovoltaic-storage-direct-drive-flexible system. This indicates taking the minimum value. and These are the weighting coefficients. The cost of power supply for photovoltaic-storage-DC-flexible systems, This represents the maximum energy supply cost for a photovoltaic-storage-DC-flexible system. To ensure the stability of the power purchase load for the photovoltaic-storage-DC-flexible system, The maximum value for the stability of the power purchase load of the photovoltaic-storage-DC-flexible system.

[0085] The energy supply cost of the photovoltaic-storage-DC-flexible system includes electricity purchase cost and investment cost, expressed as follows: ,in, for The power purchased from the power grid during the specified time period. For the power grid Electricity price during specific time periods The interest rate for investment profits For the lifespan of the system, The investment cost of photovoltaic systems, This refers to the investment cost of the energy storage system.

[0086] The stability of the power purchase load of the photovoltaic-storage-DC-flexible system is represented by the minimum standard deviation of the power purchase load from the power grid, as follows: ,in, for The power purchased from the power grid during the specified time period. This represents the average daily electricity load.

[0087] The constraints of the capacity configuration model for the photovoltaic-storage-direct-drive-flexible system established by the module include:

[0088] The constraints of distributed photovoltaic power generation are expressed as: ;

[0089] The constraints of energy storage devices are expressed as follows: ;

[0090] Power grid purchase constraints are expressed as: ;

[0091] Power balance constraints are expressed as: ;

[0092] in, for Power generation of the photovoltaic array during a given time period For the installed capacity of photovoltaic arrays, for Light intensity during the time period The light intensity under STC standard testing conditions. For temperature coefficient, for The backsheet temperature of the photovoltaic modules in the photovoltaic array during a given time period. The backsheet temperature of the photovoltaic module in the photovoltaic array under STC standard test conditions; and These represent the energy storage devices in Charging and discharging power during the time period and These represent the maximum charging power and maximum discharging power of the energy storage device, respectively. and These represent the charging and discharging states of the energy storage device, respectively. and This indicates that the energy storage device is in a charging state. and This indicates that the energy storage device is in a discharging state; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. This indicates the actual output power of the energy storage device. Indicates that the energy storage device is in State of charge over a period of time; Indicates the capacity of the energy storage device. and This indicates the upper and lower limits of the state of charge of an energy storage device during operation; for The power purchased from the power grid during the specified time period. This is the upper limit for the amount of electricity that can be purchased from the power grid; for The charging power of electric vehicles during a given time period for The curtailment power of the photovoltaic-storage-DC-flexible system during specific time periods.

[0093] The third embodiment of the present invention relates to 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 computer program to implement the steps of the first embodiment of the method for configuring a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios.

[0094] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the first embodiment of the method for configuring a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios.

[0095] 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 implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0096] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (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 device that provides the functions specified in one or more boxes.

[0097] 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 operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] 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 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, characterized in that, Includes the following steps: The electric vehicle charging load data collected from various service areas along the chain-like highway were classified using clustering methods to obtain electric vehicle charging load data under various scenarios, specifically including: The electric vehicle charging load data of each service area on the collected chain highway is processed by DTW to obtain the DTW distance matrix; The optimal number of clusters K is determined using a preset metric; wherein the preset metric is the silhouette coefficient, and the formula for calculating the silhouette coefficient is: a(i) is the intra-cluster distance; b(i) is the nearest cluster distance. The profile coefficient index; Based on the optimal number of clusters K, cluster analysis is performed on the DTW distance matrix to obtain the results of K scene classifications of the DTW distance matrix; Based on the classification labels, the electric vehicle charging load data of each service area on the chain highway is returned to obtain the electric vehicle charging load under different categories; The average value of electric vehicle charging load under each category at the same time is taken to obtain electric vehicle charging load data under various scenarios; A capacity configuration model for a photovoltaic-storage-direct-current-flexible system is established with the objectives of minimizing the energy supply cost and maximizing the stability of the power purchase load. The objective function of the capacity configuration model is: ,in, The objective function for the capacity configuration model of a photovoltaic-storage-direct-drive-flexible system. This indicates taking the minimum value. and These are the weighting coefficients. The cost of power supply for photovoltaic-storage-DC-flexible systems, This represents the maximum energy supply cost for a photovoltaic-storage-DC-flexible system. To ensure the stability of the power purchase load for the photovoltaic-storage-DC-flexible system, This represents the maximum stability of the power purchase load for the photovoltaic-storage-DC-flexible system; the energy supply cost of the photovoltaic-storage-DC-flexible system includes power purchase cost and investment cost, expressed as: ,in, for The power purchased from the power grid during the specified time period. For the power grid Electricity price during specific time periods The interest rate for investment profits For the lifespan of the system, The investment cost of photovoltaic systems, The investment cost of the energy storage system; the stability of the power purchase load of the photovoltaic-storage-DC-flexible system is represented by the minimum standard deviation of the power purchase load from the grid, expressed as: ,in, for The power purchased from the power grid during the specified time period. This represents the average daily electricity load. Electric vehicle charging load data under various scenarios are used as input to the capacity configuration model of the photovoltaic-storage-DC-flexible system, and the capacity configuration model of the photovoltaic-storage-DC-flexible system is solved to obtain the configuration scheme of the photovoltaic-storage-DC-flexible system under various scenarios.

2. The configuration method of a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios according to claim 1, characterized in that, The constraints of the capacity configuration model for the photovoltaic-storage direct current-flexible system include: The constraints of distributed photovoltaic power generation are expressed as: ; The constraints of energy storage devices are expressed as follows: ; Power grid purchase constraints are expressed as: ; Power balance constraints are expressed as: ; in, for Power generation of the photovoltaic array during a given time period For the installed capacity of photovoltaic arrays, for Light intensity during the time period The light intensity under STC standard testing conditions. For temperature coefficient, for The backsheet temperature of the photovoltaic modules in the photovoltaic array during a given time period. The backsheet temperature of the photovoltaic module in the photovoltaic array under STC standard test conditions; and These represent the energy storage devices in Charging and discharging power during the time period and These represent the maximum charging power and maximum discharging power of the energy storage device, respectively. and These represent the charging and discharging states of the energy storage device, respectively. and This indicates that the energy storage device is in a charging state. and This indicates that the energy storage device is in a discharging state; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. This indicates the actual output power of the energy storage device. Indicates that the energy storage device is in State of charge over a period of time; Indicates the capacity of the energy storage device. and This indicates the upper and lower limits of the state of charge of an energy storage device during operation; for The power purchased from the power grid during the specified time period. The upper limit for the amount of electricity that can be purchased from the grid; for Charging power of electric vehicles during a given time period for The curtailment power of the photovoltaic-storage-DC-flexible system during specific time periods.

3. A configuration device for a photovoltaic-storage-DC-flexible system for highway electric vehicle charging scenarios, characterized in that, include: The clustering module is used to classify the electric vehicle charging load data of each service area on the chain highway based on the clustering method, so as to obtain electric vehicle charging load data under various scenarios. The clustering module includes: The DTW conversion processing unit is used to perform DTW conversion processing on the electric vehicle charging load data of each service area on the collected chain highway to obtain the DTW distance matrix. A cluster number determination unit is used to determine the optimal number of clusters K using a preset index; wherein, the preset index is the silhouette coefficient index, and the formula for calculating the silhouette coefficient index is: a(i) is the intra-cluster distance; b(i) is the nearest cluster distance. The profile coefficient index; The clustering analysis unit is used to perform clustering analysis on the DTW distance matrix based on the optimal number of clusters K, and obtain the results of K scene classifications of the DTW distance matrix; The return unit is used to return electric vehicle charging load data for each service area on the chain highway according to the classification label, so as to obtain the electric vehicle charging load under different categories; The average value unit is used to take the average value of the electric vehicle charging load under each category at the same time to obtain electric vehicle charging load data under various scenarios; A module is established to create a capacity configuration model for a photovoltaic-storage-direct-current-flexible system (PV-SESV-Flex) system, aiming to minimize the energy supply cost and maximize the stability of the purchased load. The objective function of the capacity configuration model established by this module is: ,in, The objective function for the capacity configuration model of a photovoltaic-storage-direct-drive-flexible system. This indicates taking the minimum value. and These are the weighting coefficients. The cost of power supply for photovoltaic-storage-DC-flexible systems, This represents the maximum energy supply cost for a photovoltaic-storage-DC-flexible system. To ensure the stability of the power purchase load for the photovoltaic-storage-DC-flexible system, This represents the maximum stability of the power purchase load for the photovoltaic-storage-DC-flexible system; the energy supply cost of the photovoltaic-storage-DC-flexible system includes power purchase cost and investment cost, expressed as: ,in, for The power purchased from the power grid during the specified time period. For the power grid Electricity price during specific time periods The interest rate for investment profits For the lifespan of the system, The investment cost of photovoltaic systems, The investment cost of the energy storage system; the stability of the power purchase load of the photovoltaic-storage-DC-flexible system is represented by the minimum standard deviation of the power purchase load from the grid, expressed as: ,in, for The power purchased from the power grid during the specified time period. This represents the average daily electricity load. The solution module is used to take electric vehicle charging load data under various scenarios as input to the capacity configuration model of the photovoltaic-storage-direct-flexible system, and solve the capacity configuration model of the photovoltaic-storage-direct-flexible system to obtain the configuration scheme of the photovoltaic-storage-direct-flexible system under various scenarios.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic-storage-direct-flexible system configuration method for highway electric vehicle charging scenarios as described in any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the configuration method for a photovoltaic-storage-direct-flexible system for highway electric vehicle charging scenarios as described in any of claims 1-2.

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