Light storage and charging integrated charging pile and shared charging model construction method

Abstract

The invention discloses a light storage and charging integrated charging pile and a shared charging model construction method, relates to the technical field of charging pile operation optimization, and aims to solve the problems of inaccurate data processing, one-sided priority evaluation, rigid resource constraint and the like of the existing shared charging technology. According to the method, on the basis of time-space associated data of an optical storage and charging system, a user and a power grid, a high-quality dynamic data set is output through processing of a double-exponential weighting algorithm; on the basis, user charging priorities are quantified through a three-dimensional coupling algorithm, and a priority sequence is formed; combining the data and the sequence to construct an energy storage capacity and power grid interaction power constraint model dynamically adjusted along with the priority; a multi-objective optimization function of a priority adaptive weight is constructed, a charging pile distribution and power scheduling scheme is solved by adopting an adaptive particle swarm algorithm, full-process cooperation of data processing, priority evaluation, resource constraint, optimization objective and dynamic response is realized, and high efficiency, fairness and stability of shared charging are improved.

Description

Technical Field

[0001] This invention relates to the field of charging pile operation optimization technology, specifically to a photovoltaic-storage-charging integrated charging pile and a method for constructing a shared charging model. Background Technology

[0002] Integrated photovoltaic-storage-charging piles, leveraging the combined advantages of "photovoltaic power generation - energy storage peak shaving - grid complementarity - charging services," effectively address the pain points of traditional charging piles, such as reliance on the grid and difficulties in renewable energy absorption, becoming a core development form of charging infrastructure. Meanwhile, the shared charging model, through coordinated resource allocation, significantly improves charging pile utilization and reduces user waiting costs, becoming a mainstream approach to alleviating the supply-demand imbalance in charging. Policy documents continue to be issued to promote the interconnection and shared operation of charging infrastructure, explicitly requiring improvements in the efficiency, fairness, and stability of shared charging.

[0003] However, existing photovoltaic-storage-charging shared charging technologies still face numerous bottlenecks, making it difficult to adapt to complex and ever-changing real-world application scenarios. Current technologies often employ simple preprocessing methods such as single-time filtering or spatial interpolation, failing to fully exploit the spatiotemporal correlations between photovoltaic-storage-charging systems, user needs, and grid operation data. They also neglect the inconsistent data quality caused by variations in sensor accuracy, resulting in significant data fluctuations and insufficient reliability for subsequent scheduling decisions. Regarding user priority assessment, traditional methods often focus on the urgency of charging time or the vehicle's remaining range, ignoring crucial factors such as users' historical sharing contributions and the spatiotemporal matching degree between charging piles and users. This can easily lead to problems such as "resource grabbing" and "unfair sharing," hindering the sustainability of the sharing model.

[0004] Existing technologies often use fixed parameters for constraints such as energy storage capacity and grid interconnection power, which cannot be dynamically adjusted according to the urgency of user needs. This makes it difficult to ensure targeted support for the emergency charging needs of high-priority users. In multi-objective optimization processes, the weights of objectives such as user satisfaction, system operating economy, and photovoltaic absorption rate are mostly statically set, failing to adapt to the core demand priorities under different scenarios, resulting in a disconnect between optimization results and actual needs. Furthermore, the dynamic response mechanism of existing models is lagging, with long data update cycles, making it difficult to quickly adapt to real-time situations such as random fluctuations in photovoltaic output and sudden changes in user demand. This easily leads to problems such as unstable power supply, low dispatch efficiency, and high photovoltaic curtailment rates.

[0005] These technological shortcomings make it difficult for existing photovoltaic-storage-charging shared charging models to balance fairness, user charging experience, system operating efficiency, and renewable energy absorption efficiency in actual operation, severely limiting the large-scale promotion of integrated photovoltaic-storage-charging pile sharing models. Therefore, developing a shared charging model construction method with accurate data processing, comprehensive priority assessment, flexible resource constraints, optimized target adaptation, and timely dynamic response has become an urgent need to break through industry bottlenecks and promote the high-quality development of charging infrastructure. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing an integrated photovoltaic-storage-charging pile and a shared charging model. Based on high-quality spatiotemporal data, the method adapts user charging priority to the dynamic constraints of photovoltaic-storage-charging resources, and achieves efficient, fair and stable operation of shared charging through priority-adaptive multi-objective optimization and 8-minute closed-loop iteration.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for constructing a shared charging model for integrated photovoltaic, energy storage, and charging piles, characterized in that the method includes the following steps:

[0009] S1: Collect spatiotemporal correlation data of photovoltaic storage and charging system, users, and power grid. Process the data through a dual exponential weighted algorithm that integrates time decay, spatial distance, and data quality to output a high-quality dynamic dataset, which serves as the basic input for all subsequent steps.

[0010] S2: Based on the high-quality dynamic dataset output by S1, a three-dimensional coupled algorithm integrating the time urgency index, the range demand amplification term, the historical shared value, and the spatiotemporal matching correction term is used to calculate the user charging priority index and output the user priority sequence, which serves as the core input for S3 constraint construction and S4 optimization scheduling.

[0011] S3: Combining the high-quality dynamic dataset of S1 and the user priority sequence of S2, establish a resource capacity-priority sensitivity dynamic constraint model including dynamic energy storage capacity and dynamic power grid interaction, and output constraint boundary parameters as constraints for the objective function of S4.

[0012] S4: Based on the user priority sequence in S2 and the constraint boundary parameters in S3, construct a multi-objective coupled optimization objective function whose weights adaptively adjust with the average user priority, which serves as the core object for solving S5.

[0013] S5: Based on the optimization objective function of S4 and the constraint boundary parameters of S3, an adaptive particle swarm optimization algorithm with dynamic decay of inertia weights as the objective function value and user average priority is used to solve the problem, outputting a charging pile allocation and power scheduling scheme. The high-quality dynamic dataset of S1 is updated with real-time data every 8 minutes, and S2-S4 are iterated to achieve dynamic adjustment of the model.

[0014] Step S1 collects spatiotemporal correlation data of the photovoltaic-storage-charging system, users, and the power grid. The data is processed by a dual-exponential weighted algorithm that integrates time attenuation, spatial distance, and data quality to output a high-quality dynamic dataset. The formula for the dual-exponential weighted fusion algorithm is as follows:

[0015] ;

[0016] In the formula, This is a high-quality dataset after dynamic fusion, containing multi-dimensional data on photovoltaic output, energy storage SOC, user demand, and grid electricity prices. The current moment; The geographical coordinates of the target charging station; This is the historical time offset, with a value ranging from 0 to... ; The time window length, Hour; A set of geographic coverage grids for charging stations; For geographic coverage grid The coordinates of any grid node within; The time-dependent Gaussian decay weight is given by the formula: ; The spatially quadratic decay weight is given by the formula: , This is the distance attenuation coefficient; The weights are adjusted for data quality using a sinusoidal formula. , For grid exist The quality of the raw data at any given moment; for Time, Grid Raw, unprocessed data collected at the site; This is the data fluctuation suppression coefficient. ; for The standard deviation of the fluctuation of all raw data at time t is given by the formula: , For the number of grid cells, for Mean of data at any given time.

[0017] Step S2 is based on the high-quality dynamic dataset output by S1. A three-dimensional coupled model integrating time urgency index, range demand amplification term, historical sharing value, and spatiotemporal matching correction term is used to calculate user charging priority index. Output user priority sequence The three-dimensional coupling priority formula is as follows:

[0018] ;

[0019] In the formula, For the first Individual users The charging priority index at any given time; the higher the value, the higher the priority. The weighting coefficients of the three-dimensional evaluation indicators satisfy the following conditions: It is determined by the AHP entropy weight combination method; For the first Each user's desired charging completion time is input by the user's app. For the first The scheduled charging start time for each user; As a time urgency index, the shorter the charging reservation interval, the larger the index, thus amplifying the weight of time sensitivity; For the first The user's expected remaining battery power after charging; For the first Individual user vehicles The current remaining battery power at any given moment; For the amplification of battery life demand, the lower the current battery level, the greater the amplification factor. For the first The historical shared value of each user; For the first Total historical charging value for each user; For the spatiotemporal matching correction term, the formula is: , For the first The straight-line distance between each user and the charging station; Assign user numbers; classify all users according to their charging priority index. Sort the data to obtain the user priority sequence. .

[0020] The spatiotemporal matching correction term of Call S1 Real-time user location Location of charging station Calculations show that the closer the distance, The larger the value, the higher the user priority.

[0021] Step S3 combines the high-quality dynamic dataset from S1 User priority sequence with S2 Multiple resource constraint models for photovoltaic, energy storage, and charging are established that dynamically change with user priority. These multiple constraint models together constitute the output constraint boundary parameters. This serves as a constraint on the objective function S4.

[0022] The multiple photovoltaic-storage-charging resource constraint models that dynamically change with user priority include dynamic capacity constraints for energy storage and power grid interaction constraints, specifically including:

[0023] Dynamic capacity constraints for energy storage:

[0024] ;

[0025] In the formula, This refers to the minimum allowable remaining capacity of the energy storage system. This represents the maximum permissible remaining capacity of the energy storage system. This is the priority sensitivity coefficient. ; for The average priority of all users waiting to charge at any given time; For energy storage systems in The current remaining capacity at any given moment; To constrain the calculation time interval; For integration variables; for The charging power of the instantaneous energy storage system; for The discharge power of the energy storage system at any given time;

[0026] Power grid interaction constraints:

[0027] ;

[0028] In the formula, This represents the minimum interaction power between the power grid and the photovoltaic-storage-charging system. This represents the maximum interaction power between the power grid and the photovoltaic-storage-charging system. Prioritize users on average. for The actual interaction power between the time system and the power grid.

[0029] Step S4 is based on and the constraint boundary parameters output by S3 Construct a multi-objective coupled optimization objective function where weights adaptively adjust according to the average user priority. The formula is as follows:

[0030] ;

[0031] In the formula, for The multi-objective optimization objective function at each time step; For priority adaptive weights, satisfying The formulas are respectively , , The higher the priority, the higher the weight of user satisfaction. The larger; Prioritize users on average. The total number of users waiting to charge; For the first The actual charging completion time for each user; For the first The priority index of each user; for The total operating cost of the time-based system is given by the formula: This includes the cost of purchasing electricity from the grid and the cost of energy storage degradation. The maximum allowable operating cost of the system; for The actual photovoltaic power consumed at any given time; for The available output of the photovoltaic array at any given time is calculated based on S1 fused data, using the following formula: .

[0032] Step S5 is based on the optimization objective function of S4. Constraint boundary parameters with S3 An adaptive particle swarm optimization algorithm that integrates the objective function value and user priority is used to solve the problem, outputting a charging pile allocation and power scheduling scheme. The inertia weight formula is as follows:

[0033] ;

[0034] In the formula, For the first The target value for the next iteration; This represents the number of iterations. The maximum number of iterations, This is the highest target value in history; This is a priority-driven term; the higher the priority, the faster the inertia weight decays, accelerating convergence to a solution that meets the needs of high-priority users.

[0035] Step S5 outputs the charging pile allocation and power scheduling scheme, at each interval Minutes, updating S1 with real-time data. Iterate through S2-S4 to achieve dynamic adjustment.

[0036] In step S5, the learning factor of the adaptive particle swarm optimization algorithm is: and ,in , The higher the priority, the higher the individual learning factor. The larger the value, the faster the particles move toward the optimal solution for high-priority user needs.

[0037] A photovoltaic-energy storage-charging integrated charging pile is characterized by comprising a multi-port charging pile, wherein several flexible expansion ports are provided on both sides of the multi-port charging pile, one of which is connected to a photovoltaic-energy storage interface; a rectifier is provided inside the multi-port charging pile, the rectifier is connected to the AC mains power of the power grid and distributed to each port through a power line; a communication line is synchronously provided on the power line, the communication line collects data from each port and connects to a host computer, the host computer transmits the data to the cloud, the cloud stores the charging model, and the cloud communicates synchronously with the user.

[0038] First, the S1 dual-exponential weighted fusion algorithm integrates the photovoltaic-storage-charging system, including photovoltaic output, energy storage SOC, and users, as well as spatiotemporal correlation data of location, charging demand, and the power grid. Through triple weighting of time Gaussian decay, spatial secondary decay, and data quality sine correction, data fluctuation interference and outliers are eliminated, and a high-quality dynamic dataset is output, providing accurate data support for subsequent priority assessment, constraint construction, and optimized scheduling.

[0039] Based on this dataset, S2 constructs a user charging priority system through a three-dimensional coupling algorithm. It takes into account factors such as time urgency, the weight of shorter reservation intervals, range requirements, historical sharing value, and spatiotemporal matching degree, calculates and sorts the priority index, forms a user charging sequence, and clarifies the core priority basis for resource allocation.

[0040] S3 then deeply binds user priority with photovoltaic, energy storage and charging resource constraints to build a dynamic constraint model: the energy storage capacity constraint is adaptively adjusted according to the average user priority. The higher the priority, the more relaxed the minimum allowable capacity of energy storage and the more tightened the maximum allowable capacity, ensuring power supply for high-priority users. The power grid interaction power constraint is adapted synchronously. The higher the priority, the higher the maximum input power of the power grid and the more relaxed the minimum power sales power, enhancing power supply stability and forming constraint boundary parameters that are precisely matched with user needs.

[0041] Based on this, S4 constructs a multi-objective optimization objective function with priority adaptive weights. By dynamically adjusting the weight ratios of user satisfaction, system economy, and photovoltaic grid integration rate, it achieves multi-objective collaborative optimization, ensuring both the needs of high-priority users and taking into account system operating efficiency and renewable energy grid integration.

[0042] Finally, S5 employs an adaptive particle swarm optimization algorithm that integrates objective function values ​​and user priorities to solve for the optimal charging pile allocation and power scheduling scheme within the constraint boundary. Simultaneously, it updates a high-quality dynamic dataset with real-time data every 8 minutes, iteratively performing priority evaluation, constraint updates, and optimization solutions to form a closed-loop iterative mechanism. This dynamically adapts to real-time scenarios such as fluctuations in photovoltaic power output and changes in user demand, ensuring the fairness, efficiency, and stability of shared charging.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. More accurate data processing: Compared with the single-dimensional data preprocessing methods of existing technologies, this invention effectively reduces data fluctuation interference by integrating time, space and data quality into a dual exponential weighting algorithm, outputting high-quality dynamic datasets and providing more reliable data support for subsequent scheduling decisions;

[0045] 2. More comprehensive priority assessment: Breaking through the existing technology's priority judgment logic that only focuses on time or battery life, it achieves accurate quantification and fair ranking of users' charging needs through a three-dimensional coupling algorithm that considers time urgency, battery life requirements, historical sharing value, and spatiotemporal matching, taking into account both sharing fairness and the urgency of needs.

[0046] 3. More flexible resource constraints: Existing technologies mostly adopt a fixed resource constraint mode. This invention constructs an energy storage capacity and grid interaction power constraint model that dynamically adjusts according to user priority. The higher the priority, the more targeted power supply guarantee can be obtained, improving the charging experience for users with high urgent needs.

[0047] 4. More Adaptive Optimization Objectives: Compared with the static weight multi-objective optimization of existing technologies, this invention dynamically balances user satisfaction, system economy and photovoltaic absorption rate through priority adaptive weight adjustment, thereby improving the overall operating efficiency of the system while ensuring core needs are met.

[0048] 5. More timely dynamic response: Through an 8-minute closed-loop iteration mechanism, data is updated in real time and the scheduling scheme is iteratively optimized. Compared with the problem of delayed response of existing technologies, it can quickly adapt to scenarios such as photovoltaic power output fluctuations and changes in user demand, ensuring the continuous, stable and efficient operation of shared charging. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for constructing a shared charging model for an integrated photovoltaic, energy storage, and charging pile according to the present invention.

[0050] Figure 2 This is a schematic diagram of a photovoltaic, energy storage and charging integrated charging pile structure according to the present invention;

[0051] Figure 3 This is a schematic diagram of the internal connection structure of an integrated photovoltaic, energy storage, and charging pile according to the present invention. Detailed Implementation

[0052] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0053] like Figure 1 As shown, step S1 collects spatiotemporal correlation data of the photovoltaic-storage-charging system, users, and the power grid. The data is processed by a dual-exponential weighted algorithm that integrates time decay, spatial distance, and data quality to output a high-quality dynamic dataset. The formula for the dual-exponential weighted fusion algorithm is as follows:

[0054] ;

[0055] In the formula, This is a high-quality dataset after dynamic fusion, containing multi-dimensional data on photovoltaic output, energy storage SOC, user demand, and grid electricity prices. The current moment; The geographical coordinates of the target charging station; This is the historical time offset, with a value ranging from 0 to... ; The time window length, Hour; A set of geographic coverage grids for charging stations; For geographic coverage grid The coordinates of any grid node within; The time-dependent Gaussian decay weight is given by the formula: ; The spatially quadratic decay weight is given by the formula: , This is the distance attenuation coefficient; The weights are adjusted for data quality using a sinusoidal formula. , For grid exist The quality of the raw data at any given moment; for Time, Grid Raw, unprocessed data collected at the site; This is the data fluctuation suppression coefficient. ; for The standard deviation of the fluctuation of all raw data at time t is given by the formula: , For the number of grid cells, for Mean of data at any given time.

[0056] Step S2 is based on the high-quality dynamic dataset output by S1. A three-dimensional coupled model integrating time urgency index, range demand amplification term, historical sharing value, and spatiotemporal matching correction term is used to calculate user charging priority index. Output user priority sequence The three-dimensional coupling priority formula is as follows:

[0057] ;

[0058] In the formula, For the first Individual users The charging priority index at any given time; the higher the value, the higher the priority. The weighting coefficients of the three-dimensional evaluation indicators satisfy the following conditions: It is determined by the AHP entropy weight combination method; For the first Each user's desired charging completion time is input by the user's app. For the first The scheduled charging start time for each user; As a time urgency index, the shorter the charging reservation interval, the larger the index, thus amplifying the weight of time sensitivity; For the first The user's expected remaining battery power after charging; For the first Individual user vehicles The current remaining battery power at any given moment; For the amplification of battery life demand, the lower the current battery level, the greater the amplification factor. For the first The historical shared value of each user; For the first Total historical charging value for each user; For the spatiotemporal matching correction term, the formula is: , For the first The straight-line distance between each user and the charging station; Assign user numbers; classify all users according to their charging priority index. Sort the data to obtain the user priority sequence. .

[0059] The spatiotemporal matching correction term of Call S1 Real-time user location Location of charging station Calculations show that the closer the distance, The larger the value, the higher the user priority.

[0060] Step S3 combines the high-quality dynamic dataset from S1 User priority sequence with S2 Multiple resource constraint models for photovoltaic, energy storage, and charging are established that dynamically change with user priority. These multiple constraint models together constitute the output constraint boundary parameters. This serves as a constraint on the objective function S4.

[0061] The multiple photovoltaic-storage-charging resource constraint models that dynamically change with user priority include dynamic capacity constraints for energy storage and power grid interaction constraints, specifically including:

[0062] Dynamic capacity constraints for energy storage:

[0063] ;

[0064] In the formula, This refers to the minimum allowable remaining capacity of the energy storage system. This represents the maximum permissible remaining capacity of the energy storage system. This is the priority sensitivity coefficient. ; for The average priority of all users waiting to charge at any given time; For energy storage systems in The current remaining capacity at any given moment; To constrain the calculation time interval; For integration variables; for The charging power of the instantaneous energy storage system; for The discharge power of the energy storage system at any given time;

[0065] Power grid interaction constraints:

[0066] ;

[0067] In the formula, This represents the minimum interaction power between the power grid and the photovoltaic-storage-charging system. This represents the maximum interaction power between the power grid and the photovoltaic-storage-charging system. Prioritize users on average. for The actual interaction power between the time system and the power grid.

[0068] Step S4 is based on and the constraint boundary parameters output by S3 Construct a multi-objective coupled optimization objective function where weights adaptively adjust according to the average user priority. The formula is as follows:

[0069] ;

[0070] In the formula, for The multi-objective optimization objective function at each time step; For priority adaptive weights, satisfying The formulas are respectively , , The higher the priority, the higher the weight of user satisfaction. The larger; Prioritize users on average. The total number of users waiting to charge; For the first The actual charging completion time for each user; For the first The priority index of each user; for The total operating cost of the time-based system is given by the formula: This includes the cost of purchasing electricity from the grid and the cost of energy storage degradation. The maximum allowable operating cost of the system; for The actual photovoltaic power consumed at any given time; for The available output of the photovoltaic array at any given time is calculated based on S1 fused data, using the following formula: .

[0071] Step S5 is based on the optimization objective function of S4. Constraint boundary parameters with S3 An adaptive particle swarm optimization algorithm that integrates the objective function value and user priority is used to solve the problem, outputting a charging pile allocation and power scheduling scheme. The inertia weight formula is as follows:

[0072] ;

[0073] In the formula, For the first The target value for the next iteration; This represents the number of iterations. The maximum number of iterations, This is the highest target value in history; This is a priority-driven term; the higher the priority, the faster the inertia weight decays, accelerating convergence to a solution that meets the needs of high-priority users.

[0074] Step S5 outputs the charging pile allocation and power scheduling scheme, at each interval Minutes, updating S1 with real-time data. Iterate through S2-S4 to achieve dynamic adjustment.

[0075] In step S5, the learning factor of the adaptive particle swarm optimization algorithm is: and ,in , The higher the priority, the higher the individual learning factor. The larger the value, the faster the particles move toward the optimal solution for high-priority user needs.

[0076] A photovoltaic-energy storage-charging integrated charging pile is characterized by comprising a multi-port charging pile, wherein several flexible expansion ports are provided on both sides of the multi-port charging pile, one of which is connected to a photovoltaic-energy storage interface; a rectifier is provided inside the multi-port charging pile, the rectifier is connected to the AC mains power of the power grid and distributed to each port through a power line; a communication line is synchronously provided on the power line, the communication line collects data from each port and connects to a host computer, the host computer transmits the data to the cloud, the cloud stores the charging model, and the cloud communicates synchronously with the user.

[0077] In practical implementation, taking the daily operation of a shared charging station in a core urban business district as an example, the system first collects multi-source spatiotemporal data, including real-time output of the photovoltaic array, remaining power of the energy storage system, user reservation charging information, vehicle current range data, and peak-valley electricity prices. Through weighted processing that integrates the quality of spatiotemporal data, outliers in photovoltaic output are removed, and missing user location information is supplemented, ultimately outputting a high-quality dataset containing valid data from all dimensions. Based on this dataset, the charging priority of 12 users who made reservations that day is evaluated. Considering the time interval between reservation completion and start, the remaining range, the number of historical sharing attempts, and the straight-line distance to the charging station, the user with only 18% remaining range and 5 historical sharing attempts has the highest priority, while the user with a reservation interval of 3 hours and a distance of 2 kilometers has the lowest priority, forming a clear user charging sequence. Resource constraints are dynamically adjusted according to this sequence. At this point, high-priority users account for 40%, the minimum allowable remaining capacity of the energy storage system is relaxed to 15%, the maximum allowable capacity is tightened to 82%, the maximum interaction power between the grid and the charging station is increased to 110 kW, and the minimum power sales power is relaxed to -30 kW. Subsequently, a multi-objective optimization approach was constructed. At this point, the average user priority was high, and the weight of user satisfaction was correspondingly increased. The core objective was to prioritize the charging needs of high-priority users while ensuring the daily operating cost of the system did not exceed 600 yuan and the photovoltaic absorption rate was not less than 88%. An adaptive particle swarm optimization algorithm was used to derive a specific allocation scheme. Charging piles No. 1 and No. 3, with the highest power output, were allocated to the first two high-priority users, with charging power set to 65 kW and 58 kW respectively. The remaining users were allocated the remaining charging piles according to their priority and matched with appropriate power. Real-time data was collected from the charging station's sensors every 8 minutes to update the dataset, re-evaluate user priority adjustment constraints, and refine the optimization scheme. This ensured that the charging station maintained efficient and stable operation even under real-world scenarios such as fluctuations in photovoltaic output, peak charging times at midday, and sudden user additions, guaranteeing both user charging experience and rational resource utilization.

Claims

1. A method for constructing a light storage and charging integrated charging pile sharing type charging model, characterized in that, The method comprises the following steps: S1: Collecting the space-time correlation data of the light storage charging system, users and power grids, processing the data through a double exponential weighting algorithm integrating time decay, spatial distance and data quality, and outputting a high-quality dynamic data set as the basic input for all subsequent steps; S2: Based on the high-quality dynamic data set output by S1, a three-dimensional coupling algorithm integrating time urgency index, endurance demand amplification item, historical sharing value and space-time matching correction item is used to calculate the user charging priority index, and the user priority sequence is output as the core input for S3 constraint construction and S4 optimization scheduling; S3: Based on the high-quality dynamic data set of S1 and the user priority sequence of S2, a resource capacity-priority sensitivity dynamic constraint model containing dynamic capacity of energy storage and dynamic interactive power of power grid is established, and the constraint boundary parameters are output as the constraint conditions of the objective function of S4; S4: According to the user priority sequence of S2 and the constraint boundary parameters of S3, a multi-objective coupling optimization objective function with the weight self-adaptively adjusted according to the average priority of users is constructed as the core object for S5 solution; S5: Based on the optimization objective function of S4 and the constraint boundary parameters of S3, an adaptive particle swarm algorithm with inertia weight dynamically decaying with the target function value and the average priority of users is used for solution, and the charging pile allocation and power scheduling scheme is output, and the high-quality dynamic data set of S1 is updated every 8 minutes, and S2-S4 are iterated to realize dynamic adjustment of the model.

2. The method according to claim 1, wherein, Step S1 collects the space-time correlation data of the light storage and charging system, users and power grids, processes the data through a double exponential weighted algorithm that fuses time attenuation, spatial distance and data quality, and outputs a high-quality dynamic data set The formula of the double exponential weighted fusion algorithm is: ； In the formula, The high-quality data set after dynamic fusion contains multi-dimensional data such as photovoltaic output, energy storage SOC, user demand and power grid price The current time; The geographic coordinates of the target charging pile; The historical time offset is 0 to ; The time window length is hours; The geographic coverage grid set of the charging pile; Any grid node coordinates in the geographic coverage grid ; The time Gaussian attenuation weight; The spatial quadratic attenuation weight; The data quality sine correction weight; The instantaneous, grid collected raw data; The data fluctuation suppression coefficient is ; The fluctuation standard deviation of all raw data at the time.

3. The method according to claim 1, wherein, Step S2 is based on the high-quality dynamic data set output by S1 , a three-dimensional coupling model integrating time urgency index, endurance demand amplification item, historical shared value and space-time matching correction item is used to calculate the user charging priority index , output the user priority sequence , the three-dimensional coupling priority formula is: ； In the formula, For the first Individual users The charging priority index at any given time; the higher the value, the higher the priority. The weighting coefficients of the three-dimensional evaluation indicators satisfy the following conditions: It is determined by the AHP entropy weight combination method; For the first Each user's desired charging completion time is input by the user's app. For the first The scheduled charging start time for each user; As a time urgency index, the shorter the charging reservation interval, the larger the index, thus amplifying the weight of time sensitivity; For the first The user's expected remaining battery power after charging; For the first Individual user vehicles The current remaining battery power at any given moment; For the amplification of battery life demand, the lower the current battery level, the greater the amplification factor. For the first The historical shared value of each user; For the first Total historical charging value for each user; For spatiotemporal matching correction; Assign user numbers; classify all users according to their charging priority index. Sort the data to obtain the user priority sequence. .

4. The method according to claim 3, characterized in that, The spatiotemporal matching correction term The Call S1 User real-time location And charging pile location Calculate, the closer the distance, The higher the user priority.

5. The method according to claim 1, wherein, Step S3 combines the high-quality dynamic data set of S1 The user priority sequence of S2 , a plurality of light storage and charging resource constraint models dynamically changing with user priority are established, and the plurality of constraint models jointly constitute the output constraint boundary parameter , as a constraint condition of the target function of S4.

6. The method according to claim 5, wherein, The multiple light storage charging resource constraint models dynamically changing with the priority of users include dynamic capacity constraint of energy storage and interactive power constraint of power grid, specifically including: Dynamic capacity constraint of energy storage: ； In the formula, This refers to the minimum allowable remaining capacity of the energy storage system. This represents the maximum permissible remaining capacity of the energy storage system. This is the priority sensitivity coefficient. ; for The average priority of all users waiting to charge at any given time; For energy storage systems in The current remaining capacity at any given moment; To constrain the calculation time interval; For integration variables; for The charging power of the instantaneous energy storage system; for Discharge power of the energy storage system at any time; Interactive power constraint of power grid: ； wherein, is the minimum interaction power of the grid with the light storage charging system; is the maximum interaction power of the grid with the light storage charging system; is the average priority of the user; is the is the actual interaction power of the system with the grid at the moment.

7. The method according to claim 1, wherein, Step S4 is based on and the constraint boundary parameters output by S3 , a multi-objective coupling optimization objective function with adaptive adjustment of weights according to the average priority of the user is constructed , the formula is as follows: ； In the formula, for The multi-objective optimization objective function at each time step; For priority adaptive weights, satisfying ; Prioritize users on average. The total number of users waiting to charge; For the first The actual charging completion time for each user; For the first The priority index of each user; for Total operating cost of the time tracking system; The maximum allowable operating cost of the system; for The actual photovoltaic power consumed at any given time; for The available output of the photovoltaic array at any given time. 8.The method according to claim 1, characterized in that, Step S5 is based on the optimization objective function of S4 Constraint boundary parameters of S3 An adaptive particle swarm algorithm is used to solve the fusion objective function value and the user priority, and an output charging pile allocation and power scheduling scheme is obtained, and the inertia weight formula is: ； wherein, is the target value for the is the iteration number; is the maximum number of iterations, is the historical maximum target value; is the priority driver; the higher the priority, the faster the inertia weight decay, accelerating convergence to a solution that satisfies the high-priority user demand.

9. The method according to claim 8, characterized in that, Step S5 outputs the charging pile allocation and power scheduling scheme, at each interval Minutes, updating S1 with real-time data. Iterate through S2-S4 to achieve dynamic adjustment.

10. The method for constructing a shared charging model for an integrated photovoltaic, energy storage, and charging pile according to claim 1, characterized in that, In step S5, the learning factor of the adaptive particle swarm algorithm is With wherein , The higher the priority, the greater the individual learning factor of the individual, and the acceleration of the particle to the optimal solution of the high-priority user demand.

11. A light storage and charging integrated charging pile, characterized in that, The multi-port charging pile is provided with a plurality of flexible expansion ports on both sides, one of which is connected to a photovoltaic-energy storage interface, a rectifier is arranged in the multi-port charging pile, the rectifier is connected to the alternating current power supply of the power grid and is distributed to each port through a power line, the power line is provided with a communication line at the same time, the communication line collects data of each port and is connected to an upper computer, the upper computer transmits data to the cloud, the cloud stores the charging model of claims 1-10, and the cloud is in synchronous communication with the user.

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