Light storage and charging integrated control method and system based on vehicle network interaction

Abstract

The invention relates to the technical field of energy consumption and management, and discloses a light storage and charging integrated control method and system based on vehicle-network interaction, and the method comprises the steps: analyzing the current situation of charging facilities in a typical region, and recognizing the types, layout and power grid conditions of vehicles and the charging facilities; according to historical data and regional development factors, the charging load demand of the electric vehicle is predicted, and a charging behavior model is constructed; a light storage and charging integrated system is researched, and a capacity configuration model is constructed by taking maximization of comprehensive economic benefits as a target; according to the principle and technical requirements of power distribution network access, an ordered charging control strategy based on vehicle-network interaction is provided, and efficient utilization of photovoltaic output and an energy storage system is realized. Through systematic analysis and prediction, the management efficiency of the electric vehicle charging facility is improved, full application of renewable energy sources is promoted, and therefore effective technical support is provided for promoting wide application and sustainable development of electric vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption and management, and in particular to a photovoltaic storage and charging integrated control method and system based on vehicle-grid interaction. Background Art

[0002] In recent years, as the world's attention to clean energy and sustainable transportation has intensified, the development of electric vehicles (EVs) and charging infrastructure has made significant progress. Governments around the world have introduced policies to promote the popularization of electric vehicles, such as car purchase subsidies and charging facility construction subsidies, aiming to reduce urban air pollution and greenhouse gas emissions. In addition, with the increasing maturity and cost reduction of photovoltaic power generation technology, the integrated photovoltaic storage and charging system has gradually become an emerging charging method, combining photovoltaic power generation with electric vehicle charging and energy storage systems to achieve more efficient energy utilization. In this context, the concept of vehicle-grid interaction has gradually emerged, promoting two-way communication between electric vehicles and power grids through intelligent information technology, adjusting charging strategies in real time, and optimizing the allocation of power resources.

[0003] Although the existing electric vehicle charging infrastructure and photovoltaic storage and charging integrated technology have developed, they still face many shortcomings in the application process. First, the layout of existing charging stations often fails to fully consider the actual use and charging needs of electric vehicles in the region, resulting in insufficient supply of charging facilities in some areas, while other areas are full of idle resources. This unreasonable layout not only affects the user's charging experience, but also reduces the efficiency of the use of charging facilities. Secondly, the operation mode of most charging facilities is fixed and unchanged, lacks the ability to respond to real-time power demand and off-site environmental changes, and cannot flexibly adapt to load changes during peak and trough periods, resulting in increased load pressure on the power grid. Finally, the existing technology mostly relies on linear statistical models in the construction of charging behavior models, lacks a more flexible dynamic adjustment mechanism, and is unable to cope with changes in user habits and fluctuations in power demand, thereby limiting the maximization of the economic benefits of photovoltaic storage and charging integration. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an integrated control method of photovoltaic storage and charging based on vehicle-grid interaction, which can construct an integrated control method of photovoltaic storage and charging based on vehicle-grid interaction to effectively improve the utilization efficiency of charging facilities, while realizing optimized management of grid load, by targeted analysis of the current status of regional charging facilities, accurate prediction of charging load demand and flexible adjustment of charging strategies.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a photovoltaic storage and charging integrated control method based on vehicle-grid interaction, including: analyzing the current status of charging facilities in typical areas, identifying the types, layouts and grid conditions of vehicles and charging facilities; predicting the charging load demand of electric vehicles and building a charging behavior model based on historical data and regional development factors; studying the photovoltaic storage and charging integrated system, and building a capacity configuration model with the goal of maximizing comprehensive economic benefits; and proposing an orderly charging control strategy based on vehicle-grid interaction according to the principles and technical requirements of access to the distribution network, so as to achieve efficient utilization of photovoltaic output and energy storage systems.

[0007] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, wherein: the analysis of the current status of charging facilities in typical areas includes determining the areas of cities, communities and commercial areas, considering the number of electric vehicles in use, charging demand, and policy support, conducting preliminary literature research, and collecting information on charging facilities and electric vehicles in the survey area;

[0008] Clearly identify key elements, including the types and number of electric vehicles, the type and layout of charging facilities, and the conditions of the power supply network; obtain statistical data on the number and type of electric vehicles registered in the region, collect the type, number and location of charging piles through field surveys or GIS tools, use GIS software to analyze the location of charging facilities, generate heat maps of charging facilities, and identify high-density clusters and underserved areas.

[0009] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, wherein: the identification of the types, layouts and grid conditions of vehicles and charging facilities includes collecting the rated power and actual load conditions of substations and distribution lines, and evaluating the power supply capacity of the overall grid;

[0010] Analyze load fluctuations in different periods through historical data and classify load types; obtain data from power companies on the number of power outages, average repair time, and average power outage frequency of customers to evaluate the power supply reliability of the power grid; confirm the access points of charging facilities and analyze the response capabilities of the power grid under high load conditions, especially the carrying capacity and stability of the power grid when charging facilities are used concurrently;

[0011] Investigate user charging behavior, including typical charging time, charging duration, and charging requirements of different vehicle models. Use linear regression analysis or time series analysis methods to establish a calculation model for charging load demand and predict charging demand in different time periods. By comparing the number of charging facilities with the number of electric vehicles in use, evaluate whether the current configuration of charging facilities meets the actual needs of the current region, identify mismatched areas, and determine whether there is a fixed time period with high demand and insufficient charging facilities.

[0012] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, the prediction of the charging load demand of electric vehicles includes collecting historical charging load data and performing detrending and seasonal adjustment, checking data stability, performing differential processing when necessary, and using a seasonal autoregressive integrated sliding average model to model the charging load of electric vehicles:

[0013] Y t =φ1Y t-1 +φ2Y t-2 +...+φ p Y t-p +θ1∈ t-1 +θ2∈ t-2 +...+θ q ∈ t-q +S t

[0014] Among them, Y t is the electric vehicle charging load at the current time t, φ is the autoregressive parameter, θ is the sliding average parameter, ∈ is the error term, S t is the seasonal effect, p is the order of the autoregressive term, and q is the order of the moving average term;

[0015] Statistical software is used to fit the model, obtain the parameters of the autoregressive and moving average terms, and use the fitted model to predict the charging load demand at future moments to generate young values.

[0016] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, the construction of the charging behavior model includes using the exponential smoothing method for short-term prediction, collecting charging load historical data, setting the season length, determining the initial value of the smoothing parameter, and using the error minimization method to optimize the parameters:

[0017] T t =β(L t -L t-1 )+(1-β)T t-1

[0018] L t =αY t +(1-α)(L t-1 +T t-1 )

[0019]

[0020] Among them, L t represents the level forecast value at time t, α represents the level smoothing constant, T trepresents the trend forecast value at time t, β represents the smoothing constant of the trend, γ represents the smoothing constant of the season, s is the length of the season, and h is the time delay of the future forecast point. It represents the predicted value after h time units from time t;

[0021] The next charging load value is predicted from the model and extended to the future to obtain a series of short-term prediction values.

[0022] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, wherein: the construction of the capacity configuration model includes collecting charging curves, charging efficiency, and aging characteristic data of different types of batteries, constructing corresponding mathematical equations based on the collected data, and conducting preliminary verification;

[0023] Construct the battery charging characteristic equation:

[0024] P bat (t) = η charge ·P pv (t)-E loss (t)

[0025] Among them, P bat The battery charging power, η charge is the battery charging efficiency, P pv is the photovoltaic power generation power, E loss is the energy loss during each hour;

[0026] Construct the system load matching equation:

[0027] D T =P bat (t)+P dis (t)

[0028] Among them, D T is the load demand, P dis The power discharged to the grid or vehicle;

[0029] The final economic benefit model is obtained:

[0030] R profit =P dis (t)·P market (t)-C cost

[0031] Among them, R profit is economic benefit, P market is the expected price in the electricity market, C cost for construction and operation costs;

[0032] Conduct multiple collaborative calculations to analyze the performance of different configuration schemes in terms of construction and operation costs, economic benefits, and grid constraints, maintain communication with all stakeholders, and adjust model parameters and configuration schemes based on real-time feedback.

[0033] As a preferred solution of the photovoltaic storage and charging integrated control method based on vehicle-grid interaction described in the present invention, the orderly charging control strategy includes: using historical data and real-time weather data to train the charging load demand model, predicting future charging load demand, and designing a dynamic adjustment strategy based on the prediction results and real-time data to set the charging power levels during peak and valley periods;

[0034] Through the intelligent control system, the charging power of the charging pile is automatically adjusted, photovoltaic power generation is used first to ensure the maximum use of renewable energy, the matching degree between the charging load and photovoltaic power generation is monitored in real time, a feedback loop is established, the output power is continuously adjusted, and the efficiency of charging strategy execution is optimized;

[0035] Collect real-time data of electric vehicles through in-vehicle intelligent terminals, promote real-time interaction between vehicles and the grid, analyze users' charging history and preferences, build user behavior models, identify charging habits, predict charging needs in different time periods, recommend the best charging time and location, and send charging reminders to users when the grid load is low; generate charging plans in real time based on user preferences and current power resources and push them through the App to improve user interactivity and satisfaction;

[0036] A two-way information exchange mechanism is established to share grid load information and demand for integrated photovoltaic storage and charging systems with the distribution network in real time. When the grid is at peak load, the system adjusts the charging strategy according to grid demand and uses energy storage equipment to balance the grid load. Based on the information of charging demand, photovoltaic power generation, energy storage system and grid, a comprehensive optimization model is constructed to achieve the optimal allocation of power resources, implement scenario analysis, predict the changes brought about by the popularization of electric vehicles in the future, and provide decision support for the long-term planning and configuration of charging facilities.

[0037] As a preferred solution of the integrated photovoltaic storage and charging control system based on vehicle-grid interaction described in the present invention, it includes: a data acquisition and analysis module, a charging load prediction module, a capacity configuration optimization module, a charging control and scheduling module, and a user interaction and feedback module;

[0038] The data collection and analysis module collects basic information about electric vehicles and charging facilities in the region, collects and analyzes grid load data, weather information, and historical charging demand in real time, and forms a complete data foundation;

[0039] The charging load prediction module uses historical charging load data and charging behavior models, adopts a seasonal autoregressive integrated sliding average model, predicts future charging loads, dynamically optimizes and adjusts the prediction results, and ensures the accuracy and real-time nature of the prediction;

[0040] The capacity configuration optimization module constructs a mathematical model of capacity configuration based on the charging curves and charging efficiency data of different types of batteries, evaluates economic benefits and costs, performs collaborative calculations, and analyzes the performance of different configuration schemes under construction and operating costs, economic benefits, and grid constraints, providing an optimization solution for the construction of a photovoltaic storage and charging integrated system;

[0041] The charging control and scheduling module intelligently adjusts the charging power of the charging pile according to real-time and historical data, realizes dynamic adjustment during peak and valley periods, gives priority to photovoltaic power generation, monitors the matching degree between charging load and photovoltaic power generation in real time, and makes adjustments through the intelligent control system, establishing a feedback mechanism to improve the execution efficiency of the charging strategy;

[0042] The user interaction and feedback module collects real-time data of electric vehicles, analyzes users' charging history and preferences, establishes a two-way information interaction mechanism, shares grid load information with the distribution network in real time intervals, adjusts charging strategies, and comprehensively optimizes power resource allocation.

[0043] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a photovoltaic storage and charging integrated control method based on vehicle-grid interaction.

[0044] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a photovoltaic storage and charging integrated control method based on vehicle-grid interaction are implemented.

[0045] Beneficial effects of the present invention: This method, by means of the establishment and application of a dynamic charging behavior model, combines real-time charging demand with photovoltaic power generation data, and proposes an orderly charging control strategy based on user habits and grid status, thereby improving the utilization efficiency of power resources and the economic benefits of charging facilities. In addition, the present invention also helps to strengthen the interaction between electric vehicles and power grids, achieve optimal allocation of power resources, and provide new solutions for the promotion of electric vehicles and the construction of charging facilities in the future, promoting the full utilization of renewable energy and the sustainable development of electric transportation. Therefore, the implementation of the present invention has significant application prospects and is expected to play an important role in promoting green transportation and intelligent energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0047] Figure 1 A schematic flow chart of a photovoltaic storage and charging integrated control method based on vehicle-grid interaction provided by an embodiment of the present invention.

[0048] Figure 2 A schematic diagram of the working modules of a photovoltaic storage and charging integrated control system based on vehicle-grid interaction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0053] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0054] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a photovoltaic storage and charging integrated control method based on vehicle-grid interaction, including:

[0056] S1: Analyze the current status of charging facilities in typical areas and identify the types, layout and grid conditions of vehicles and charging facilities.

[0057] Furthermore, we identified areas of cities, communities, and commercial districts, considered the number of electric vehicles, charging needs, and policy support, conducted a preliminary literature survey, and collected information on charging facilities and electric vehicles in the survey area;

[0058] Clearly identify key elements, including the types and number of electric vehicles, the type and layout of charging facilities, and the conditions of the power supply network; obtain statistical data on the number and type of electric vehicles registered in the region, collect the type, number and location of charging piles through field surveys or GIS tools, use GIS software to analyze the location of charging facilities, generate heat maps of charging facilities, and identify high-density clusters and underserved areas.

[0059] It should be noted that the rated power and actual load of substations and distribution lines are collected to evaluate the power supply capacity of the overall power grid;

[0060] Through historical data, analyze load fluctuations in different time periods and classify load types; obtain data from power companies on the number of power outages, average repair time, and average power outage frequency of customers to evaluate the power supply reliability of the power grid; confirm the access points of charging facilities, and analyze the response capabilities of the power grid under high load conditions, especially the carrying capacity and stability of the power grid when charging facilities are used concurrently.

[0061] Investigate user charging behavior, including typical charging time, charging duration, and charging requirements of different vehicle models. Use linear regression analysis or time series analysis methods to establish a calculation model for charging load demand and predict charging demand in different time periods. By comparing the number of charging facilities with the number of electric vehicles in use, evaluate whether the current configuration of charging facilities meets the actual needs of the current region, identify mismatched areas, and determine whether there is a fixed time period with high demand and insufficient charging facilities.

[0062] S2: Based on historical data and regional development factors, predict the charging load demand of electric vehicles and build a charging behavior model.

[0063] Furthermore, historical charging load data is collected and detrended and seasonally adjusted, the data is checked for stationarity, and difference processing is performed if necessary. The seasonal autoregressive integrated moving average model is used to model the charging load of electric vehicles:

[0064] Y t =φ1Y t-1 +φ2Y t-2 +...+φ p Y t-p +θ1∈ t-1 +θ2∈ t-2 +...+θ q ∈ t-q +S t

[0065] Among them, Y t is the electric vehicle charging load at the current time t, φ is the autoregressive parameter, θ is the sliding average parameter, ∈ is the error term, S t is the seasonal effect, p is the order of the autoregressive term, and q is the order of the moving average term;

[0066] Statistical software is used to fit the model, obtain the parameters of the autoregressive and moving average terms, and use the fitted model to predict the charging load demand at future moments to generate young values.

[0067] Furthermore, exponential smoothing is used for short-term forecasting. The historical data of charging load is collected, the length of the season is set, the initial value of the smoothing parameter is determined, and the error minimization method is used to optimize the parameter:

[0068] T t =β(L t -L t-1 )+(1-β)T t-1

[0069] L t =αY t +(1-α)(L t-1 +T t-1)

[0070]

[0071] Among them, L t represents the level forecast value at time t, α represents the level smoothing constant, T t represents the trend forecast value at time t, β represents the smoothing constant of the trend, γ represents the smoothing constant of the season, s is the length of the season, and h is the time delay of the future forecast point. It represents the predicted value after h time units from time t;

[0072] The next charging load value is predicted from the model and extended to the future to obtain a series of short-term prediction values.

[0073] S3: Research the integrated photovoltaic storage and charging system, and build a capacity configuration model with the goal of maximizing comprehensive economic benefits.

[0074] Furthermore, we collected data on charging curves, charging efficiency, and aging characteristics of different types of batteries, constructed corresponding mathematical equations based on the collected data, and conducted preliminary verification;

[0075] Construct the battery charging characteristic equation:

[0076] P bat (t) = η charge ·P pv (t)-E loss (t)

[0077] Among them, P bat The battery charging power, η charge is the battery charging efficiency, P pv is the photovoltaic power generation power, E loss is the energy loss during each hour;

[0078] Construct the system load matching equation:

[0079] D T =P bat (t)+P dis (t)

[0080] Among them, D T is the load demand, P dis The power discharged to the grid or vehicle;

[0081] The final economic benefit model is obtained:

[0082] R profit =P dis (t)·P market (t)-C cost

[0083] Among them, R profit is economic benefit, P market is the expected price in the electricity market, C cost for construction and operation costs;

[0084] Conduct multiple collaborative calculations to analyze the performance of different configuration schemes in terms of construction and operation costs, economic benefits, and grid constraints, maintain communication with all stakeholders, and adjust model parameters and configuration schemes based on real-time feedback.

[0085] It should be noted that the optimal configuration scheme for calculating the photovoltaic storage capacity includes setting constraints and optimization goals; the constraints include grid load restrictions and battery discharge depth restrictions, and the optimization goal is: MaximizeR profit , by adjusting P pv 、Battery capacity C bat and other system parameters to maximize economic benefits;

[0086] The comprehensive cost function is obtained:

[0087] C cost =C inv +C op (P bat ,T)

[0088] Among them, C inv is the initial investment cost, C op The costs incurred during the operation process include maintenance, management and other expenses.

[0089] S4: According to the principles and technical requirements of access to the distribution network, an orderly charging control strategy based on vehicle-grid interaction is proposed to achieve efficient utilization of photovoltaic output and energy storage systems.

[0090] Furthermore, the charging load demand model is trained using historical data and real-time weather data to predict future charging load demand. Based on the prediction results and real-time data, a dynamic adjustment strategy is designed to set the charging power level during peak and valley periods.

[0091] Through the intelligent control system, the charging power of the charging pile is automatically adjusted, photovoltaic power generation is used first to ensure the maximum use of renewable energy, the matching degree between the charging load and photovoltaic power generation is monitored in real time, a feedback loop is established, the output power is continuously adjusted, and the efficiency of charging strategy execution is optimized;

[0092] Collect real-time data of electric vehicles through in-vehicle intelligent terminals, promote real-time interaction between vehicles and the grid, analyze users' charging history and preferences, build user behavior models, identify charging habits, predict charging needs in different time periods, recommend the best charging time and location, and send charging reminders to users when the grid load is low; generate charging plans in real time based on user preferences and current power resources and push them through the App to improve user interactivity and satisfaction;

[0093] A two-way information exchange mechanism is established to share grid load information and demand for integrated photovoltaic storage and charging systems with the distribution network in real time. When the grid is at peak load, the system adjusts the charging strategy according to grid demand and uses energy storage equipment to balance the grid load. Based on the information of charging demand, photovoltaic power generation, energy storage system and grid, a comprehensive optimization model is constructed to achieve the optimal allocation of power resources, implement scenario analysis, predict the changes brought about by the popularization of electric vehicles in the future, and provide decision support for the long-term planning and configuration of charging facilities.

[0094] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0097] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a photovoltaic storage and charging integrated control system based on vehicle-grid interaction, characterized in that it includes a data acquisition and analysis module 1, a charging load prediction module 2, a capacity configuration optimization module 3, a charging control and scheduling module 4, and a user interaction and feedback module 5;

[0100] Data collection and analysis module 1, collects basic information about electric vehicles and charging facilities in the region, collects and analyzes grid load data, weather information, and historical charging demand in real time to form a complete data foundation;

[0101] Charging load forecasting module 2 uses historical charging load data and charging behavior models, and adopts a seasonal autoregressive integrated sliding average model to forecast future charging loads, dynamically optimize and adjust forecast results, and ensure the accuracy and real-time nature of the forecast;

[0102] Capacity configuration optimization module 3, based on the charging curves and charging efficiency data of different types of batteries, builds a mathematical model for capacity configuration, evaluates economic benefits and costs, performs collaborative calculations, and analyzes the performance of different configuration schemes under construction and operating costs, economic benefits, and grid constraints, providing optimization solutions for the construction of integrated photovoltaic storage and charging systems;

[0103] Charging control and scheduling module 4, based on real-time and historical data, intelligently adjusts the charging power of the charging pile, realizes dynamic adjustment during peak and valley periods, gives priority to photovoltaic power generation, monitors the matching degree between charging load and photovoltaic power generation in real time, and adjusts through the intelligent control system, establishes a feedback mechanism, so as to improve the execution efficiency of the charging strategy;

[0104] The user interaction and feedback module 5 collects real-time data of electric vehicles, analyzes the user's charging history and preferences, establishes a two-way information interaction mechanism, shares grid load information with the distribution network in real time intervals, adjusts charging strategies, and comprehensively optimizes power resource allocation.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic storage and charging integrated control method based on vehicle-grid interaction, characterized by: include, Analyze the current status of charging facilities in typical areas, identify vehicle and charging facility types, layout, and grid conditions; Based on historical data and regional development factors, predict the charging load demand of electric vehicles and build a charging behavior model; Study the integrated photovoltaic storage and charging system and build a capacity configuration model with the goal of maximizing comprehensive economic benefits; According to the principles and technical requirements of access to the distribution network, an orderly charging control strategy based on vehicle-grid interaction is proposed to achieve efficient utilization of photovoltaic output and energy storage systems.

2. The photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 1, characterized in that: The analysis of the current status of charging facilities in typical areas includes determining the areas of cities, communities and commercial areas, considering the number of electric vehicles in use, charging demand, and policy support, conducting preliminary literature research, and collecting information on charging facilities and electric vehicles in the survey area; Clearly identify key elements, including the types and number of electric vehicles, the type and layout of charging facilities, and the conditions of the power supply network; obtain statistical data on the number and type of electric vehicles registered in the region, collect the type, number and location of charging piles through field surveys or GIS tools, use GIS software to analyze the location of charging facilities, generate heat maps of charging facilities, and identify high-density clusters and underserved areas.

3. The photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 2, characterized in that: The identification of vehicle and charging facility types, layouts and grid conditions includes collecting the rated power and actual load conditions of substations and distribution lines to evaluate the power supply capacity of the overall grid; Analyze load fluctuations in different periods through historical data and classify load types; obtain data from power companies on the number of power outages, average repair time, and average power outage frequency of customers to evaluate the power supply reliability of the power grid; Confirm the access points of charging facilities and analyze the grid’s response capabilities under high load conditions, especially the grid’s carrying capacity and stability when charging facilities are used concurrently; Investigate user charging behavior, including typical charging time, charging duration, and charging requirements of different vehicle models. Use linear regression analysis or time series analysis methods to establish a calculation model for charging load demand and predict charging demand at different times. By comparing the number of charging facilities with the number of electric vehicles in use, we can evaluate whether the current configuration of charging facilities meets the actual needs of the current region, identify mismatched areas, and determine whether there are fixed time periods with high demand and insufficient charging facilities.

4. The photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 3 is characterized by: The prediction of the charging load demand of electric vehicles includes collecting historical charging load data and performing detrending and seasonal adjustment, checking data stability, performing differential processing when necessary, and using a seasonal autoregressive integrated moving average model to model the charging load of electric vehicles: Y t =φ1Y t-1 +φ2Y t-2 +...+φ p Y t-p +θ1∈ t-1 +θ2∈ t-2 +...+θ q ∈ t-q +S t Among them, Y t is the electric vehicle charging load at the current time t, φ is the autoregressive parameter, θ is the sliding average parameter, ∈ is the error term, S t is the seasonal effect, p is the order of the autoregressive term, and q is the order of the moving average term; Statistical software is used to fit the model, obtain the parameters of the autoregressive and moving average terms, and use the fitted model to predict the charging load demand at future moments to generate young values.

5. The photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 4, characterized in that: The construction of the charging behavior model includes using the exponential smoothing method for short-term prediction, collecting historical data of charging load, setting the season length, determining the initial value of the smoothing parameter, and using the error minimization method to optimize the parameter: T t =β(L t -L t-1 )+(1-β)T t-1 L t =αY t +(1-α)(L t-1 +T t-1 ) Among them, L t represents the level forecast value at time t, α represents the level smoothing constant, T t represents the trend forecast value at time t, β represents the smoothing constant of the trend, γ represents the smoothing constant of the season, s is the length of the season, and h is the time delay of the future forecast point. It represents the predicted value after h time units from time t; The next charging load value is predicted from the model and extended to the future to obtain a series of short-term prediction values.

6. A photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 5, characterized in that: The construction of the capacity configuration model includes collecting charging curves, charging efficiency, and aging characteristic data of different types of batteries, constructing corresponding mathematical equations based on the collected data, and performing preliminary verification; Construct the battery charging characteristic equation: P bat ( t ) =η charge ·P pv ( t ) -E loss ( t) Among them, P bat The battery charging power, η charge is the battery charging efficiency, P pv is the photovoltaic power generation power, E loss is the energy loss during each hour; Construct the system load matching equation: D T =P bat (t)+P dis (t) Among them, D T is the load demand, P dis The power discharged to the grid or vehicle; The final economic benefit model is obtained: R profit =P dis (t)·P market (t)-C cost Among them, R profit is economic benefit, P market is the expected price in the electricity market, C cost for construction and operating costs; Conduct multiple collaborative calculations to analyze the performance of different configuration schemes in terms of construction and operation costs, economic benefits, and grid constraints, maintain communication with all stakeholders, and adjust model parameters and configuration schemes based on real-time feedback.

7. A photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in claim 6, characterized in that: The orderly charging control strategy includes using historical data and real-time weather data to train a charging load demand model, predicting future charging load demand, and designing a dynamic adjustment strategy based on the prediction results and real-time data to set the charging power levels during peak and valley periods; Through the intelligent control system, the charging power of the charging pile is automatically adjusted, photovoltaic power generation is used first to ensure the maximum use of renewable energy, the matching degree between the charging load and photovoltaic power generation is monitored in real time, a feedback loop is established, the output power is continuously adjusted, and the efficiency of charging strategy execution is optimized; Collect real-time data of electric vehicles through in-vehicle intelligent terminals, promote real-time interaction between vehicles and the grid, analyze users' charging history and preferences, build user behavior models, identify charging habits, predict charging needs in different time periods, recommend the best charging time and location, and send charging reminders to users when the grid load is low; generate charging plans in real time based on user preferences and current power resources and push them through the App to improve user interactivity and satisfaction; A two-way information exchange mechanism is established to share grid load information and demand for integrated photovoltaic storage and charging systems with the distribution network in real time. When the grid is at peak load, the system adjusts the charging strategy according to grid demand and uses energy storage equipment to balance the grid load. Based on the information of charging demand, photovoltaic power generation, energy storage system and grid, a comprehensive optimization model is constructed to achieve the optimal allocation of power resources, implement scenario analysis, predict the changes brought about by the popularization of electric vehicles in the future, and provide decision support for the long-term planning and configuration of charging facilities.

8. A system using the photovoltaic storage and charging integrated control method based on vehicle-grid interaction as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and analysis module, charging load prediction module, capacity configuration optimization module, charging control and scheduling module, and user interaction and feedback module; The data collection and analysis module collects basic information about electric vehicles and charging facilities in the region, collects and analyzes grid load data, weather information, and historical charging demand in real time, and forms a complete data foundation; The charging load prediction module uses historical charging load data and charging behavior models, adopts a seasonal autoregressive integrated sliding average model, predicts future charging loads, dynamically optimizes and adjusts the prediction results, and ensures the accuracy and real-time nature of the prediction; The capacity configuration optimization module constructs a mathematical model of capacity configuration based on the charging curves and charging efficiency data of different types of batteries, evaluates economic benefits and costs, performs collaborative calculations, and analyzes the performance of different configuration schemes under construction and operating costs, economic benefits, and grid constraints, providing an optimization solution for the construction of a photovoltaic storage and charging integrated system; The charging control and scheduling module intelligently adjusts the charging power of the charging pile according to real-time and historical data, realizes dynamic adjustment during peak and valley periods, gives priority to photovoltaic power generation, monitors the matching degree between charging load and photovoltaic power generation in real time, and makes adjustments through the intelligent control system, establishing a feedback mechanism to improve the execution efficiency of the charging strategy; The user interaction and feedback module collects real-time data of electric vehicles, analyzes users' charging history and preferences, establishes a two-way information interaction mechanism, shares grid load information with the distribution network in real time intervals, adjusts charging strategies, and comprehensively optimizes power resource allocation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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