Charging pile background management system and method

Abstract

The invention relates to the technical field of new energy supply management, in particular to a charging pile background management system and method. The method comprises the steps of obtaining photovoltaic output, an energy storage state and charging load data, completing time alignment and normalization processing, and constructing a unified data input structure; configuring a photovoltaic priority strategy and energy storage scheduling parameters based on the structure, constructing and solving a multi-energy power supply optimization model, and generating a power supply scheduling output result; combining the electricity price sequence to identify peak and valley time periods, generating an arbitrage label, and further generating an energy storage charging and discharging control strategy and an energy storage regulation and control instruction structure; issuing a control instruction, collecting execution data, and generating an energy consumption deviation feedback structure; and finally, realizing adaptive closed-loop correction of the scheduling model based on a feedback structure. According to the invention, the photovoltaic utilization rate can be improved, peak-valley arbitrage and energy storage cooperative control is realized, the power supply cost of the charging station is significantly reduced, and the system scheduling precision and operation stability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy energy supply management, and particularly to a background management system and method for charging piles. Background Art

[0002] With the continuous increase in the popularity of electric vehicles, the construction density of charging piles as basic energy supply facilities has been continuously improved, and the power supply system behind it also shows a trend of multi-energy integration, including a composite power supply network composed of centralized grid power supply, distributed photovoltaic power generation, and local energy storage units. However, the current mainstream charging pile management systems generally rely on a single grid power supply mode, and fail to make full use of local photovoltaic resources or combine the adjustable characteristics of energy storage systems for intelligent scheduling, resulting in high peak-time electricity prices and rising operating costs, which limits the large-scale green deployment and economic feasibility of charging infrastructure.

[0003] In practical applications, due to the intermittency and volatility of photovoltaic power generation, and the charge and discharge strategies of energy storage systems also highly depend on the peak-valley fluctuation law of grid electricity prices. Therefore, how to perform unified time mapping and optimization scheduling modeling based on photovoltaic output data, energy storage state data, charging load curves, and real-time electricity price sequences at different time scales has become a key issue for improving the economy and green efficiency of charging stations.

[0004] Traditional strategies mostly adopt static rule control, which cannot dynamically respond to actual load changes and renewable energy power generation fluctuations, and have rough scheduling granularity and low energy utilization efficiency. Especially in the scenario of simultaneous access of multiple energy sources, if the above multi-source data cannot be structurally integrated and a unified model with mathematical constraint conditions and economic index optimization objectives is not constructed, it will be difficult to achieve the systematic optimal operation of multi-energy coordination.

[0005] Therefore, there is an urgent need for a background management method for charging piles that combines a mixed integer programming model, has a time series interpolation alignment mechanism and energy consumption deviation feedback ability, so as to dynamically implement a coordinated energy supply strategy with photovoltaic priority, energy storage assistance, and electricity price guidance, in order to minimize the comprehensive electricity consumption cost and improve the utilization rate of green electricity in complex scenarios. Summary of the Invention

[0006] The present invention provides a background management system and method for charging piles to solve the problem of how to construct a mixed integer programming model through unified time mapping and structural integration based on photovoltaic output data, energy storage state information, charging load data, and real-time electricity price sequences, and generate real-time coordinated scheduling of energy storage charge and discharge with photovoltaic priority and adaptively correct through energy consumption deviation closed-loop, so as to minimize the comprehensive power supply cost of charging stations in a multi-energy scenario.

[0007] To solve the above technical problems, the present invention provides an analysis method for livestock claim settlement data based on artificial intelligence, including: Obtain photovoltaic output data, energy storage status information, and charging load data, complete time alignment, normalization, and structural integration, and generate a unified data input structure; Obtain the unified data input structure, configure the photovoltaic priority strategy and energy storage scheduling constraint parameters, construct and solve the multi - energy power supply optimization model, and generate the power supply scheduling output result; The expression of the power supply scheduling output result is:

[0008] Among them, is the power supply scheduling output structure; is the optimal charge - discharge power; is the optimal power purchase; is the energy storage state trajectory at each moment; is the unit energy consumption scheduling cost; K is the time index variable; N is the total number of time steps within the scheduling period; Obtain the power supply scheduling output result and the electricity price sequence, identify the peak period and valley period, generate an arbitrage label, and construct an arbitrage reference structure; Obtain the arbitrage reference structure and the power supply scheduling output result, combine with the load data in the unified data input structure, generate the energy storage charge - discharge control strategy, and construct the energy storage regulation instruction structure; Send down the energy storage regulation instruction structure and the power supply scheduling output result, collect the execution data of the energy control module, and generate the energy consumption deviation feedback structure; Obtain the energy consumption deviation feedback structure, analyze and adjust the scheduling parameters in the multi - energy power supply optimization model, and complete the model adaptive closed - loop correction; The analysis and adjustment of the scheduling parameters in the multi - energy power supply optimization model include: Map the peak deviation and average deviation to the maximum charge - discharge power constraint of the energy storage; map the cumulative deviation to the energy storage cycle number constraint; map the number of switching failures and switching delay times to the mode switching delay margin; map the maximum state of charge offset to the energy storage efficiency coefficient; map the average state of charge offset and state recovery duration to the state of charge continuity constraint; The model adaptive closed - loop correction includes: The system performs a fast consistency check, including supply - demand balance constraint check, energy storage power constraint check, and state of charge continuity check.

[0009] Furthermore, the obtaining of the photovoltaic output data, energy storage status information, and charging load data, and the completion of time alignment, normalization, and structural integration include: Perform time alignment processing on the photovoltaic output data, energy storage status information, and charging load data; Normalize and perform moving average processing on the photovoltaic output data, energy storage status information, and charging load data; Integrate the results of normalization and moving average processing to generate a unified data input structure.

[0010] Furthermore, the configuration of the photovoltaic priority strategy and energy storage dispatching constraint parameters includes: Obtain the unified data input structure, and configure the photovoltaic priority strategy and energy storage dispatching constraint parameters.

[0011] Furthermore, the construction and solution of the multi - energy power supply optimization model includes: Construct a multi - energy power supply optimization model, and the objective function includes the grid power purchase cost and the energy loss of energy storage dispatching.

[0012] Furthermore, the obtaining of the power supply dispatching output result and the electricity price sequence, identifying the peak period and the valley period, and generating an arbitrage label includes: Obtain the power supply dispatching output result and the electricity price sequence, and mark the peak - valley distribution of the electricity price; Analyze and process the peak - valley distribution of the electricity price to identify the peak period and the valley period; Generate an arbitrage label according to the peak period and the valley period, and construct an arbitrage reference structure.

[0013] Furthermore, the generation of the energy storage charge - discharge control strategy and the construction of the energy storage regulation instruction structure includes: Determine the energy storage charge - discharge period according to the arbitrage reference structure and the power supply dispatching output result; Generate an energy storage charge - discharge control strategy based on the load data in the unified data input structure; Structure the energy storage charge - discharge control strategy to generate an energy storage regulation instruction structure.

[0014] Furthermore, the steps of issuing the energy storage regulation instruction structure and the power supply dispatching output result, and collecting the execution data of the energy control module include: Synchronously issue the energy storage regulation instruction structure and the power supply dispatching output result to the energy control module; Collect the actual execution data in the energy control module and compare and analyze it with the issued instruction.

[0015] Furthermore, the steps of generating an energy consumption deviation feedback structure include: Structurally process the comparison and analysis result to generate an energy consumption deviation feedback structure.

[0016] A charging pile background management system applied to the method described in any one of the above, includes: A data acquisition module, which is used to obtain photovoltaic output data, energy storage status information, charging load data and electricity price sequences in real time from a photovoltaic array controller, an energy storage management system, an intelligent charging pile controller and a billing platform; A data preprocessing module, which is used to perform time alignment, normalization, moving average and structural integration processing on the original data, and output a unified data input structure; An optimization modeling module, which is used to extract available capacity and power intervals based on the unified data input structure, and configure the weight of the photovoltaic priority strategy, energy storage scheduling constraints and charge state continuity constraints; An arbitrage analysis module, which is used to receive the output result of power supply scheduling and the electricity price sequence, mark the peak-valley distribution of the electricity price, identify the peak period and the valley period, and calculate the derivative index; A strategy generation module, which is used to obtain the arbitrage reference structure and the output result of power supply scheduling, and determine the energy storage charge-discharge switching point in combination with the load power demand in the unified data input structure; An instruction execution module, which is used to synchronously send the energy storage regulation instruction structure and the output result of power supply scheduling to the energy control module; A model update module, which is used to perform deviation analysis on model parameters such as energy storage power constraints, mode switching margin, charge state continuity constraints and photovoltaic priority weights according to the energy consumption deviation feedback structure generated by the instruction execution module; A system monitoring and visualization module, which is used to monitor the operation status of each functional module in real time, and display the photovoltaic-storage-grid power flow, peak-valley identification results, strategy execution trajectory and model parameter evolution curve.

[0017] The key innovation points of the present invention include: (1) Construct a unified mapping and normalization structure integration mechanism for multi-source heterogeneous data, realize the fusion input of photovoltaic output, energy storage status, electricity price and load information, and effectively solve the problems of out-of-sync and incompatibility of multi-dimensional data.

[0018] (2) Construct a mixed integer programming scheduling framework with photovoltaic priority and energy storage participation, support dynamic configuration of scheduling constraints, peak-valley identification and arbitrage label generation, and adapt to the multi-variable and strongly coupled characteristics of multi-energy regulation.

[0019] (3) Propose a scheduling closed-loop mechanism with the ability of execution data feedback and model parameter self-correction, so that the scheduling model is no longer static and rigid, but has dynamic adaptability and continuous optimization potential.

[0020] The following are its main beneficial effects: (1) Significantly reduce the comprehensive power supply cost of the charging station. By obtaining photovoltaic output data, energy storage state information, charging load data, and real-time electricity price sequences, the present invention first proposes an input mechanism based on unified time mapping and normalized structure integration, breaking through the interaction bottleneck of multi-source asynchronous data. By constructing a mixed-integer programming model with scheduling constraints, the system can dynamically generate a collaborative energy supply scheduling result with priority given to photovoltaic power, effectively improving the utilization rate of green electricity, reducing the dependence on high-price grid power, achieving peak-valley arbitrage, and significantly reducing the power supply cost.

[0021] (2) Improve the real-time performance and stability of the multi-energy scheduling model. Traditional charging pile systems mostly adopt static strategies or coarse-grained load prediction methods and lack a dynamic scheduling mechanism suitable for the volatility of renewable energy. By introducing a power supply scheduling output structure and a electricity price identification mechanism, the present invention constructs an arbitrage label and an energy storage charge-discharge strategy generation model, realizing flexible scheduling of photovoltaic power, energy storage, and mains electricity at the charging station on the premise of ensuring the continuity of energy supply. This method gives full play to the buffering role of the energy storage system in load shifting and peak shaving and valley filling, making the scheduling system have stronger dynamic response capabilities and strategy robustness.

[0022] (3) Realize the closed-loop of model adaptive correction to improve the scheduling accuracy and economy. In actual operation, due to the volatility of new energy generation and the uncertainty of load demand, it is difficult for the optimization model to maintain the optimal operation state for a long time. For this reason, the present invention proposes an energy consumption deviation feedback mechanism. By collecting the execution data of the energy control module, constructing an energy consumption deviation feedback structure, and periodically and adaptively correcting the parameters in the scheduling model, continuous dynamic optimization of the scheduling strategy is realized. This closed-loop control design not only improves the stability and self-learning ability of the overall model but also significantly improves the accuracy and revenue level of the scheduling output. Description of the Drawings

[0023] Figure 1 It is a schematic flowchart of a charging pile background management method provided by an embodiment of the present application; Figure 2 It is a structural block diagram of a charging pile background management system provided by an embodiment of the present application. Detailed Embodiments

[0024] Embodiment 1: Refer to Figure 1 , which is a schematic flowchart of a charging pile background management method provided by an embodiment of the present invention. The process may at least include steps S100 - S600: S100. Obtain photovoltaic output data, energy storage state information, and charging load data, complete time alignment, normalization, and structure integration, and generate a unified data input structure.

[0025] S200. Obtain a unified data input structure, configure PV priority strategies and energy storage scheduling constraint parameters, construct and solve an optimized multi - energy power supply model, and generate power supply scheduling output results.

[0026] S300. By obtaining the power supply scheduling output results and electricity price sequences, identify peak and valley periods, generate arbitrage labels, and construct an arbitrage reference structure.

[0027] S400. Obtain the arbitrage reference structure and power supply scheduling output results, combine with the load data in the unified data input structure, generate energy storage charge - discharge control strategies and construct an energy storage regulation instruction structure.

[0028] S500. Issue the energy storage regulation instruction structure and power supply scheduling output results, collect the execution data of the energy control module, and generate an energy consumption deviation feedback structure.

[0029] S600. By obtaining the energy consumption deviation feedback structure, analyze and adjust the scheduling parameters in the optimized multi - energy power supply model to complete the adaptive closed - loop correction of the model.

[0030] Step S100 includes at least steps S110 - S130: S110. Obtain PV output data, energy storage status information, and charging load data, and perform time alignment processing.

[0031] First, the system accesses the PV power generation system, energy storage management system, and intelligent charging pile controller at the charging station end through the data acquisition module, and obtains PV output data, energy storage status information, and charging load data respectively. The PV output data is the change value of the PV power generation power within a unit time interval. The energy storage status information includes the state of charge of the current energy storage battery, the current charge - discharge power, and the cumulative charge - discharge duration. The charging load data is the power demand and instantaneous load curve of all terminal devices in the charging station within a unit time window.

[0032] Furthermore, to ensure the consistency of multi - source data in the optimization process, time alignment processing needs to be performed. Specifically, the system, based on a unified timestamp generation mechanism, parses the original time tags of each data source, and performs interpolation, cropping, or delay correction processing on data records with sampling frequency differences or delays. The PV output data uses a minute - level sampling period, the energy storage status information is updated at the second - level state, and the charging load data has a millisecond - level sampling granularity due to responding to demand fluctuations and needs to be aggregated according to a unified period.

[0033] Furthermore, the time alignment processing is completed through a sliding window mechanism and a queue buffer management method to ensure the synchronous correspondence between the PV output, energy storage status, and load data within each time slice. After processing, three groups of time - aligned data sequences with consistent structures are generated.

[0034] The time alignment result will be directly used as the input data for the subsequent normalization and moving average processing in S120, forming the "original alignment data set". Each time node in the original alignment data set contains a set of photovoltaic power, energy storage state values, and load values, laying a data time series foundation for constructing a unified data input structure.

[0035] S120. Perform normalization and moving average processing on the photovoltaic output data, energy storage state information, and charging load data.

[0036] After completing the time alignment operation in S110, this step performs normalization and moving average processing on the three types of data to improve data consistency, smooth volatility, and construct a standard input space for the optimization modeling stage.

[0037] Specifically, the system first reads the original alignment data set and constructs normalization processing flows for the photovoltaic output data, energy storage state information, and charging load data respectively. The normalization operation uses the component range compression method to uniformly map each index value to the interval range, eliminating the weight bias caused by differences in equipment rated power, time scale, or state unit.

[0038] Furthermore, the normalization standard for the photovoltaic output data is based on the rated capacity of the photovoltaic array, the normalization standard for the energy storage state information is based on its maximum energy storage capacity and maximum power, and the charging load data constructs a normalization factor table based on the upper limit of the system rated load. Through the above standardization process, the three data channels have the ability to input equivalent features in the numerical dimension.

[0039] Furthermore, after completing the normalization, the system performs moving average processing based on the equal-length sliding window strategy. The moving average processing weights or simply averages multiple data values before and after each time point by setting a fixed window width and step size to reduce high-frequency noise caused by environmental disturbances, load jitters, or sensing errors.

[0040] For example, short-term fluctuations in the photovoltaic output data will be smoothed by averaging, the sharp peaks caused by switching the charge and discharge states in the energy storage power state will be adjusted, and abnormal pulse interferences in the load data will be weakened. The window length of the moving average is independently set according to the fluctuation characteristics of each data channel. A longer window is used for the photovoltaic channel and a shorter window is used for the load channel to retain the response sensitivity.

[0041] The output results of the normalization and moving average processing respectively form the "standardized photovoltaic data set", "standardized energy storage data set", and "standardized load data set". These three sets of standardized data structures have a unified timestamp and a unified numerical dimension format, forming the standard data processing result.

[0042] The above standardized data structure will be structurally integrated into a unified data input structure in S130, serving as the set of basic input variables for the subsequent optimization modeling stage (S200 module), and will be directly called when constructing the multi - energy power supply optimization model in S220.

[0043] S130: Structurally integrate the results of normalization and moving average processing to generate a unified data input structure.

[0044] Specifically, after completing the pre - processing of various types of energy - related data, this step structurally integrates the standardized photovoltaic data set, the standardized energy storage data set, and the standardized load data set to finally form a unified data input structure.

[0045] Specifically, the structural integration operation forms a structured record item by constructing a unified data row - column representation template, combining the normalized photovoltaic output value, the state of charge of the energy storage, the charge - discharge state label, and the load power demand value within each time slice, and forming a sequence matrix structure based on chronological order. This structure supports sliding retrieval by time period and field - by - field call by energy type, and adapts to various optimization constraint forms.

[0046] Furthermore, each record item in the unified data input structure includes a data identifier field, a data value field, and a data source type field, which are used to describe the data source, numerical content, and data dimension classification respectively, providing structural support for subsequent multi - energy scheduling strategy configuration.

[0047] Furthermore, after the integration is completed, the unified data input structure is output as the only structure and directly passed into S210 to configure the photovoltaic priority strategy and energy storage scheduling constraint parameters, and is called as the model - building input vector in the S220 module. The optimization module can identify the current energy availability, load trend, and energy storage capacity based on this structure, forming the chronological basis for scheduling scenario modeling.

[0048] Through the detailed implementation of this step, the system realizes the chronological synchronization, numerical unification, and structural integration of heterogeneous energy data, providing standardized input for the subsequent multi - energy optimization scheduling model. This processing process significantly improves the model solving efficiency, the accuracy of scheduling strategies, and the system execution stability, providing a high - quality data basis for the multi - energy collaborative optimization goal of the present invention.

[0049] Step S200 at least includes steps S210 - S230: S210: Obtain the unified data input structure and configure the photovoltaic priority strategy and energy storage scheduling constraint parameters.

[0050] Specifically, this step first obtains the data input structure that has been aligned, normalized, and structurally integrated, including: Photovoltaic output time series: ; Energy storage state time series: ; Charging load demand sequence: ; Real-time electricity price time series: ; Unit energy consumption scheduling cost sequence: .

[0051] Since the above data has different time sampling sources, in order to achieve unified modeling and scheduling processing, it is necessary to map it to the unified scheduling time . The processing steps are as follows:

[0052] Among them, represents interpolation or sliding window alignment operation to ensure that all indicators are aligned at the unified scheduling time , meeting the requirements of model consistency input.

[0053] Furthermore, the system sets the following parameters according to the configuration policy: Photovoltaic priority weight parameter:

[0054] Maximum charge and discharge power of energy storage: ; Upper and lower limits of energy storage capacity: .

[0055] S220. Build a multi-energy power supply optimization model based on the photovoltaic priority strategy and energy storage scheduling constraint parameters.

[0056] Specifically, based on the obtained and configured parameters, the following optimization objective function is constructed to minimize the comprehensive scheduling loss of power supply cost and energy storage loss: Formula ①: Multi-energy power supply optimization objective function:

[0057] Among them: : Discharge power of energy storage at time (negative value indicates charging); : Represents , used to represent the positive compensation for the load gap; : Unit electricity price; : Energy storage loss weight, controlling its scheduling frequency; N: Total number of time steps within the optimization period.

[0058] The objective function consists of two parts: the first term is the power purchase cost of the power grid, and the second term is the energy loss of energy storage scheduling.

[0059] Furthermore, to ensure the stable operation of the power supply system, the following constraint conditions need to be added: Equation ②: Load balance constraint:

[0060] Among them, is the power purchased from the power grid at time .

[0061] Equation ③: Energy storage power constraint and energy state evolution formula:

[0062]

[0063] Among them, : Charge and discharge efficiency; ; : Time interval; ; S230. Solve the multi - energy power supply optimization model to generate the power supply scheduling output result.

[0064] After constructing the objective function and constraint conditions, the system calls a mixed - integer linear programming (MILP) or a gradient - based optimizer to iteratively solve the following optimization problem: The system takes , , as known inputs, solves for the optimal variable sequence, and the system encapsulates this optimization result into a power supply scheduling output structure:

[0065] Among them, is the power supply scheduling output structure; is the optimal charge - discharge power; is the optimal power purchase power; is the energy storage state trajectory at each time; is the unit energy consumption scheduling cost; K is a time - index variable, with a value range of k = 1, 2, …, N, representing the k - th moment within the scheduling period; N is the total number of time steps within the scheduling period, which is a positive integer, with the unit of step, and is set or determined dynamically by the system.

[0066] This structure serves as the core input for the subsequent modules S300 (arbitrage strategy generation) and S400 (energy storage control strategy), supporting the scheduling logic closed - loop of the entire system.

[0067] Through the design and implementation of this S200 module, the present invention can effectively solve the real-time scheduling optimization problem of the charging pile system in the scenario of multi-energy access, and implement an efficient power supply strategy with priority for photovoltaic, energy storage assistance, and power grid backup. The proposed joint modeling of the unified time mapping mechanism, cost-loss double-objective optimization function, dynamic energy balance constraint construction, and feasible region scheduling strategy significantly improves the system's response ability to load changes and its adaptability to peak-valley fluctuations in electricity prices. On the premise of ensuring the continuity of the charging service, the dual goals of minimizing the comprehensive energy consumption cost and extending the energy storage life are achieved, which has good engineering promotion value and practical application prospects.

[0068] Step S300 includes at least steps S310 - S330: S310. Obtain the power supply scheduling output result and the electricity price sequence, and mark the peak-valley distribution of the electricity price.

[0069] First, the system calls the power supply scheduling output result generated in step S230 in the arbitrage analysis module, and synchronously calls the billing interface to obtain the electricity price sequence. Both data sets contain a time tag field. The system performs integrity verification, duplicate verification, and sequential consistency verification on the time tag field. If missing records or time misalignment are found, they are processed by log-based recovery or deletion to ensure the reliability of the data link entering the marking process.

[0070] Furthermore, the system aligns the two types of data according to the unified scheduling time. If the resolution of the electricity price sequence is higher than the power supply scheduling time granularity, the system compresses the electricity price data by taking the average value of the sliding window; if the resolution is lower than the power supply scheduling granularity, the electricity price entries are extended through time interpolation. After processing, each power supply scheduling record has a matching electricity price record.

[0071] Furthermore, the system calculates the statistical distribution in the same region in the same season and the same week based on the historical electricity price database of the same type, and determines the high price threshold and the low price threshold through the percentile interval method. The continuous section that is higher than the high price threshold and has a continuous duration not shorter than the minimum high price window is marked as a peak, the continuous section that is lower than the low price threshold and has a continuous duration not shorter than the minimum low price window is marked as a valley, and the rest is marked as flat. The marking result is written back to the electricity price sequence in the form of a new field, forming an electricity price peak-valley distribution marking set.

[0072] The electricity price peak-valley distribution marking set is written into the buffer database and a unique task identifier is generated for subsequent sub-steps to call.

[0073] S320. Analyze and process the electricity price peak-valley distribution to identify the peak time period and the valley time period.

[0074] Specifically, when the electricity price peak-valley distribution annotation set is settled, the system immediately enters the continuous segment aggregation logic. The system scans the peak-valley fields of adjacent entries in the annotation set, merges the records with the same identifier and continuous time, and forms a high-price continuous segment set and a low-price continuous segment set.

[0075] Furthermore, the system performs persistent filtering on the continuous segment set. For any high-price or low-price segment with a duration less than the minimum response window, if the time interval with the adjacent segment is lower than the minimum gap threshold, it is merged with the adjacent segment; if the interval is higher than the threshold, it is re-annotated as a flat-price segment.

[0076] Furthermore, after obtaining the legal peak time period set and valley time period set, the system further calculates nine derivative indicators for each continuous segment, namely the maximum unit price, minimum unit price, average unit price, price difference amplitude, fluctuation slope, historical percentile position, duration, forward gap, and backward gap, and appends these nine indicators to the continuous segment structure body. The processed peak time period set and valley time period set are written into the analysis buffer, and statistical summaries such as the number of segments, total duration, and maximum price difference are recorded in the system log to reserve a traceability clue for subsequent model revision.

[0077] S330. Generate arbitrage labels based on the peak time period and valley time period, and construct an arbitrage reference structure.

[0078] Specifically, when the peak time period set and valley time period set enter the cache, the system starts to perform energy storage capacity matching. The system first reads the energy storage state trajectory and energy storage power constraint fields from the power supply scheduling output result of step S230, calculates the chargeable electricity that can be accommodated in each valley time period, and eliminates the valley time periods with insufficient capacity.

[0079] Furthermore, after determining the available valley time periods, the system searches for the nearest peak time period for each valley time period in chronological order; if multiple peak time periods meet the conditions, they are matched in descending order of price difference amplitude. Once the matching is successful, a set of charge-discharge pairs is generated. The system creates an arbitrage label for each pair and writes nine fields: charge start time, charge end time, expected charge power, discharge start time, discharge end time, expected discharge power, achievable price difference revenue, execution priority, and unique label number. If a valley time period forms multiple pairs with multiple peak time periods, the system sorts them according to the priority field. [[ID=--]] [[ID=--]]

[0080] After all arbitrage labels are encapsulated as record items, the system generates an arbitrage reference structure set. The arbitrage reference structure set follows the time format and field naming rules of the unified data input structure to ensure direct parsing in step S410.

[0081] Even further, the system writes the arbitrage reference structure set into the shared cache, and synchronously pushes the reference address and task identifier to the energy storage control strategy generation module to complete all processing procedures of this module.

[0082] Through the implementation of this S300 module, the system can accurately identify peak and valley periods and generate executable arbitrage labels while ensuring data alignment, providing a quantifiable and verifiable decision basis for the energy storage system to start charging in valleys and discharging during peaks, significantly improving operational benefits and laying a solid foundation for subsequent control strategy generation.

[0083] Step S400 at least includes steps S410 - S430: S410. Obtain the arbitrage reference structure and the power supply scheduling output result, and determine the energy storage charge-discharge switching point.

[0084] Specifically, when the policy generation module is started, the system reads the set of arbitrage reference structures output by S330 from the shared cache and concurrently reads the power supply scheduling output result output by step S230. Both types of data structures are based on the unified scheduling time as the time reference, and the internal field names are kept consistent.

[0085] Specifically, the system first performs data integrity verification to confirm that the charging start time, charging end time, discharging start time, and discharging end time in the arbitrage reference structure all fall within the time range of the power supply scheduling output result; if a time out-of-bounds or missing segment is found, an error is reported upward according to the log specification and the execution of this label is suspended to avoid sending non-executable instructions to the energy storage device.

[0086] Furthermore, the system reads the expected charging power and expected discharging power fields of each record item in the arbitrage reference structure and compares the differences with the energy storage power field in the power supply scheduling output result. Understandably, if there is a large deviation between the expected power and the scheduled power, the system will call the model update module for secondary evaluation; if the difference is within the controllable threshold, the switching point determination process will be entered.

[0087] In the switching point determination process, the system subdivides the charging time period and discharging time period included in each arbitrage label so that the switching moment is exactly aligned with the unified scheduling time. Specifically, the system inserts an "energy storage charge-discharge switching point" flag between the charging end time and the discharging start time with the unified scheduling time as the scale, and writes the switching type field at the same time. The switching type field is used to indicate a switch from the "charging mode" to the "discharging mode", or from the "discharging mode" to the "standby mode". The energy storage charge-discharge switching point is appended to the arbitrage reference structure to form an updated extended set of switching points.

[0088] The extended set of switching points is written into the policy buffer as the output content of S410, along with the task identification code and time stamp for subsequent policy generation.

[0089] S420. Generate an energy storage charge and discharge control strategy based on the energy storage charge and discharge switching point and the load data in the unified data input structure.

[0090] Specifically, after the system reads the switching point extension set, it immediately calls the unified data input structure generated in step S130 to extract the load power demand fields at each moment. Specifically, the system calculates the remaining load capacity during the charging period, that is, the power margin currently available for energy storage charging without affecting normal charging services; and calculates the load compensation demand during the discharging period, that is, the power gap that needs to be supplemented by energy storage discharging currently.

[0091] Understandably, the system also needs to combine the photovoltaic power field and the grid power field at the corresponding moment in the power supply scheduling output result to obtain the real-time renewable energy utilization status and the grid backup capacity. If the photovoltaic output exceeds the load demand during the charging period, the system will adjust the expected charging power and preferentially use the remaining photovoltaic power for charging; if the photovoltaic output is insufficient, it will evaluate whether the rechargeable power meets the expectation according to the grid power upper limit, and postpone the lacking part to the next available valley period as appropriate.

[0092] Furthermore, during the discharging period, the system first judges whether the load power gap is sufficient to absorb the expected discharging power. If the gap is insufficient, the excess discharging power will be lowered to the upper limit of the load gap; if the gap is greater than the expected discharging power, the system will supplement the difference according to the demand and output a margin prompt for the model update module to record. Through dynamic power adjustment, ensure that the energy storage power instruction is safely implemented and does not violate the supply-demand balance constraint.

[0093] After the above load coupling calculation, the system generates an "energy storage charge and discharge control strategy" record item for each set of profit labels. This record item includes fields such as strategy number, charging period, charging power, discharging period, discharging power, switching point, load margin, compensation gap, and execution priority. All strategy record items are sorted according to the execution priority and merged into an energy storage charge and discharge control strategy set. The control strategy set is frozen in read-only mode in the memory for subsequent instruction generation calls.

[0094] S430. Convert the energy storage charge and discharge control strategy into an energy storage regulation instruction structure.

[0095] Specifically, after the system reads the energy storage charge and discharge control strategy set, it calls the instruction template library to generate an instruction structure that can be directly parsed by the energy storage management system. The template library presets the instruction message format according to the energy storage device communication protocol, including message header, timestamp, power instruction, mode instruction, priority instruction, check code, and message tail.

[0096] The system first fills in the instruction message header for each control strategy record entry and writes in the global task identifier and the current generation time. Subsequently, the charging period, charging power, discharging period, and discharging power are mapped into the power instruction segment, the energy storage charge-discharge switching point is mapped into the mode instruction segment, and the execution priority is mapped into the priority instruction segment. After the writing is completed, the system calls the encryption module to generate a check code and appends it to the end of the message.

[0097] To meet the batch execution requirements of the energy storage management system, the system concatenates all the instruction messages in chronological order and writes the total number of instructions field at the head of the message set to form the "energy storage regulation instruction structure". The instruction structure is cached in the high-priority channel in binary form, waiting for the sending action in step S510; at the same time, an instruction summary is written in the log for auditing.

[0098] Through the implementation of this S400 module, on the basis of ensuring data consistency, the system deeply couples the arbitrage label with the real-time load status, dynamically generates the energy storage charge-discharge control strategy, and then outputs the energy storage regulation instruction structure in the form of a protocolized message. This process significantly improves the strategy accuracy and execution reliability, laying a solid data and control foundation for subsequent energy consumption deviation feedback and model adaptive correction.

[0099] Step 500 includes at least steps S510 - S530: S510. Synchronously send the energy storage regulation instruction structure and the power supply dispatch output result to the energy control module.

[0100] The system first loads the energy storage regulation instruction structure generated by S430 in the instruction execution module and synchronously loads the power supply dispatch output result generated by S230. Both types of structures carry a unified scheduling time field and a task identifier field.

[0101] Specifically, the system applies for a high-priority channel at the top of the communication scheduling queue for this sending operation and writes a synchronization flag bit at the head of the channel so that the energy control module can perform frame-level alignment at the receiving end. Subsequently, the system writes the energy storage regulation instruction structure into the transmission buffer one by one. The writing order of each instruction is the message header, timestamp, power instruction segment, mode instruction segment, priority instruction segment, check segment, and message tail. After the writing is completed, the system immediately appends the power field, state of charge field, and cost field of the power supply dispatch output result to form an instruction - dispatch composite message frame.

[0102] Furthermore, to ensure the time consistency of on-site devices, the system adds a clock synchronization segment at the end of the composite message frame and writes the second-level timestamp corresponding to the unified scheduling moment. After the energy control module completes frame verification at the receiving end, it returns an acknowledgment signal. After receiving the acknowledgment signal, the system stores the message frame in the link log and marks the "sent" status. If the acknowledgment signal is not returned within the specified timeout period, the system immediately triggers the retransmission process and retransmits up to three times. If it still fails, it is written to the exception queue and an alarm is issued.

[0103] S520. Collect the actual execution data in the energy control module and compare and analyze it with the issued instructions.

[0104] Specifically, after the energy control module enters the execution stage, the system starts the telemetry channel and cyclically pulls the actual execution data with the unified scheduling moment as the indexing period. The actual execution data includes a charging power measurement field, a discharging power measurement field, a state of charge measurement field, a mode switching event field, and a device alarm field.

[0105] Understandably, the system first verifies the legality of the telemetry data, excluding records with missing timestamps, failed checks, or alarm status at the severe level. Subsequently, the system calls the time alignment mechanism to align the telemetry data with the timestamp in the issued instructions. After alignment, the system compares the telemetry power measurement field with the power instruction segment, the telemetry mode switching event field with the mode instruction segment, and the telemetry state of charge measurement field with the predicted state of charge field item by item using the instruction number as the key value, forming a difference temporary cache.

[0106] In the difference temporary cache, the system records three types of differences for each instruction: power difference, mode difference, and state difference. The power difference corresponds to the charge and discharge power offset; the mode difference corresponds to the switching type error; the state difference corresponds to the state of charge prediction error. For records with an alarm field at the general level or above, the system attaches an exception mark and highlights it when generating the subsequent deviation structure.

[0107] S530. Structurally process the comparison and analysis results to generate an energy consumption deviation feedback structure.

[0108] After the difference temporary cache is frozen, the system enters the energy consumption deviation calculation process. First, the system slides and statistically calculates the power difference by the unified scheduling moment to generate a charge and discharge deviation curve. Subsequently, it aggregates the mode difference and state difference by task identifier to generate a mode consistency table and a state error table.

[0109] Specifically, the system extracts the peak deviation, average deviation, and cumulative deviation fields from the charge-discharge deviation curve; counts the number of successful switches, failed switches, and switch delay times fields in the mode consistency table; and counts the maximum state of charge offset, average state of charge offset, and state recovery duration fields in the state error table. The three statistical results and the alarm flag are uniformly encapsulated into the energy consumption deviation feedback structure.

[0110] The energy consumption deviation feedback structure includes a structure header, a timestamp, a task identifier, a power deviation segment, a mode deviation segment, a state deviation segment, an alarm segment, and a structure tail. The system writes the energy consumption deviation feedback structure into the shared cache and broadcasts a deviation generation event on the system bus. After the model update module monitors the deviation generation event, it calls S610 to perform deviation analysis on the multi-energy power supply optimization model to form an adaptive closed loop.

[0111] Through the implementation of this S500 module, the system realizes a real-time closed loop for instruction issuance, execution monitoring, and deviation feedback, can accurately capture the power deviation, mode deviation, and state deviation between the energy storage execution layer and the scheduling model, provides high-precision data support for subsequent model adaptive correction, and thus continuously improves the reliability and economy of the multi-energy collaborative optimization model.

[0112] Step S600 at least includes steps S610 - S630: S610. Obtain the energy consumption deviation feedback structure and perform deviation analysis on the constraint parameters in the multi-energy power supply optimization model.

[0113] When the model update module starts, the system captures the deviation generation event broadcast by S530 through the bus event listener and reads the energy consumption deviation feedback structure. The energy consumption deviation feedback structure includes a structure header, a timestamp, a task identifier, a power deviation segment, a mode deviation segment, a state deviation segment, an alarm segment, and a structure tail. The system first performs integrity verification on the timestamp field to ensure that the time window corresponding to the deviation data is consistent with the current optimization period; if the time windows are inconsistent, it is marked as an expired record and archived, and does not participate in this round of analysis.

[0114] Specifically, the system parses the power deviation segment to obtain three indicators: peak deviation, average deviation, and cumulative deviation; parses the mode deviation segment to obtain three indicators: number of successful switches, number of failed switches, and number of switch delays; parses the state deviation segment to obtain three indicators: maximum state of charge offset, average state of charge offset, and state recovery duration; parses the alarm segment to obtain two types of information: general alarm entries and severe alarm entries.

[0115] Further, the system calls the deviation mapping table to map the nine deviation indicators to the model constraint parameter dimension. The mapping rules are as follows: The peak deviation and the average deviation are mapped to the maximum charge-discharge power constraint of the energy storage; the cumulative deviation is mapped to the energy storage cycle times constraint; the number of switching failures and the number of switching delays are mapped to the mode switching delay margin; the maximum state of charge offset is mapped to the energy storage efficiency coefficient; the average state of charge offset and the state recovery duration are mapped to the state of charge continuity constraint; the severe alarm entry triggers the model safety threshold check process.

[0116] Based on the above mapping results, the system generates a "constraint parameter deviation list". The constraint parameter deviation list records the current value, deviation source, deviation direction, deviation amplitude, and recommended adjustment range of each constraint parameter. For the constraint parameter with a severe alarm entry, the system attaches a "forced adjustment" flag; for the constraint parameter with only a minor deviation, the system attaches an "optional adjustment" flag. The constraint parameter deviation list is written into the parameter buffer as the only output of S610, and an analysis completion event is generated.

[0117] S620. According to the deviation analysis results, adjust the scheduling parameters of the multi-energy power supply optimization model.

[0118] After capturing the analysis completion event, the system reads the constraint parameter deviation list and enters the parameter adjustment process. To ensure the security of the update operation, the system first performs an adjustable range check on each constraint parameter in the deviation list. The check items include the parameter upper limit, the parameter lower limit, and the maximum step size for a single adjustment. If the recommended adjustment range exceeds the adjustable range, the system truncates it to the boundary value; if the single adjustment step size exceeds the limit, the system performs a recursive step-by-step adjustment according to the maximum step size.

[0119] Specifically, the system executes the following program for the maximum charge-discharge power constraint of the energy storage: If both the peak deviation and the average deviation exceed the set threshold, the maximum charge-discharge power of the energy storage is increased or decreased by one step; if only the average deviation exceeds the threshold, only the average power limit is adjusted; if only the peak deviation exceeds the threshold while the average deviation does not exceed the threshold, the peak power clipping factor is adjusted.

[0120] The system executes the following program for the mode switching delay margin: If the number of switching failures or the number of switching delays exceeds the threshold, the mode switching window is expanded, and the switching lead time is synchronously updated in the instruction template library; if the switching success rate remains high and the number of delays is small, the switching window is reduced to improve the time utilization rate.

[0121] Further, the system executes the following procedures for the energy storage efficiency coefficient and the state of charge continuity constraint: If the maximum state of charge offset and the average state of charge offset are both positively offset, the energy storage efficiency coefficient is lowered; if the state recovery duration is too long, the state of charge continuity constraint is tightened to reduce the tolerance; if the offset is negatively offset and the state of charge recovers quickly, the constraint is appropriately relaxed to improve flexibility.

[0122] Further, the system encapsulates the above adjustment actions into a "scheduling parameter adjustment result", which includes parameter name, original value, adjusted value, adjustment reason, adjustment step size, adjustment timestamp, and version number. The scheduling parameter adjustment result is written into the adjustment result buffer and triggers a parameter write event.

[0123] S630. Write the adjustment processing result into the multi - energy power supply optimization model to complete the adaptive closed - loop correction of the model.

[0124] After the system captures the parameter write event, it enters the model write - back process. First, the system obtains the scheduling parameter adjustment result and compares the current model parameter version number. If the version numbers are the same, it enters the write - lock stage; if the version numbers are different, it means that the model has been updated in the parallel process. The system determines the latest version according to the timestamp, remaps the adjustment result to the latest model instance, and then continues the write - back.

[0125] Specifically, in the write - lock stage, the system adds a write - lock to the multi - energy power supply optimization model to prevent other threads from writing simultaneously. The system reads the scheduling parameter adjustment result item by item, writes the adjusted value into the corresponding parameter field inside the model, and records the adjustment reason and adjustment timestamp in the model parameter table. After writing is completed, the system performs a quick consistency check, including supply - demand balance constraint check, energy storage power constraint check, and state of charge continuity check. If all the checks pass, the model is submitted and the write - lock is released; if the check fails, the current write is rolled back, and the failure reason is written into the exception log and passed back to the model update module to wait for the next round of analysis.

[0126] After successful submission, the system generates a "model parameter version change event" and registers the new model version number in the optimization modeling module. The next round of S210 will use the new version parameters when obtaining model parameters, thus forming a complete adaptive closed - loop.

[0127] Through the implementation of this S600 module, the system can perform fine - grained adaptive correction of model parameters based on real - time energy consumption deviation, enabling the multi - energy power supply optimization model to continuously maintain high consistency with on - site equipment, load characteristics, and electricity price changes; at the same time, the write - lock and version control mechanisms ensure the safety and reliability of the parameter update process, effectively preventing model oscillation and constraint failure, providing core technical support for the economic operation and long - term stability of the entire system.

[0128] The key innovation points of the present invention include: (1) Unified mapping and normalization structure integration mechanism for multi-source heterogeneous data, enabling the fusion input of photovoltaic power output, energy storage status, electricity price, and load information, effectively solving the problems of out-of-sync and incompatible multi-dimensional data.

[0129] (2) Construct a mixed-integer programming scheduling framework with photovoltaic priority and energy storage participation, supporting dynamic configuration of scheduling constraints, peak-valley identification, and arbitrage label generation, and adapting to the multi-variable and strongly coupled characteristics of multi-energy regulation.

[0130] (3) Propose a scheduling closed-loop mechanism with the ability to execute data feedback and self-correct model parameters, making the scheduling model no longer static and rigid, but having dynamic adaptability and the potential for continuous optimization.

[0131] The following are its main beneficial effects: (1) Significantly reduce the comprehensive power supply cost of the charging station. By obtaining photovoltaic power output data, energy storage status information, charging load data, and real-time electricity price sequences, the present invention first proposes an input mechanism based on unified time mapping and normalization structure integration, breaking through the interaction bottleneck of multi-source asynchronous data. By constructing a mixed-integer programming model with scheduling constraints, the system can dynamically generate a collaborative energy supply scheduling result with photovoltaic priority, effectively improving the utilization rate of green electricity, reducing the dependence on high-price grid power, realizing peak-valley arbitrage, and significantly reducing the power supply cost.

[0132] (2) Improve the real-time performance and stability of the multi-energy scheduling model. Traditional charging pile systems mostly adopt static strategies or coarse-grained load prediction methods, lacking a dynamic scheduling mechanism suitable for the volatility of renewable energy. By introducing a power supply scheduling output structure and an electricity price identification mechanism, the present invention constructs an arbitrage label and an energy storage charge-discharge strategy generation model, realizing flexible scheduling of photovoltaic, energy storage, and mains power for the charging station while ensuring the continuity of energy supply. This method gives full play to the buffering role of the energy storage system in load shifting and peak-valley filling, making the scheduling system have stronger dynamic response ability and strategy robustness.

[0133] (3) Realize a closed-loop of model adaptive correction, improving scheduling accuracy and economy. In actual operation, due to the volatility of new energy power generation and the uncertainty of load demand, it is difficult for the optimization model to maintain the optimal operation state for a long time. For this reason, the present invention proposes an energy consumption deviation feedback mechanism, which collects the execution data of the energy control module, constructs an energy consumption deviation feedback structure, and periodically and adaptively corrects the parameters in the scheduling model to realize the continuous dynamic optimization of the scheduling strategy. This closed-loop control design not only improves the stability and self-learning ability of the overall model, but also significantly improves the accuracy and revenue level of the scheduling output.

[0134] Embodiment 2: Figure 2The structural block diagram of a charging pile background management system according to an embodiment of the present invention is shown. As Figure 2 shown, the structure may include: The data acquisition module 10 is used to obtain photovoltaic output data, energy storage state information, charging load data and electricity price sequences from the photovoltaic array controller, energy storage management system, intelligent charging pile controller and charging platform in real time; complete millisecond-level alignment by adopting a unified timestamp generation mechanism, and write into the raw data buffer through a buffer queue.

[0135] The data preprocessing module 20 is used to perform time alignment, normalization, moving average and structure integration processing on the raw data, and output a unified data input structure; at the same time, interpolate missing data, denoise noise data, and mark abnormal data, so as to provide high-quality data with standardization and integrity for subsequent optimization modeling.

[0136] The optimization modeling module 30 is used to extract available capacity and power intervals based on the unified data input structure, configure the photovoltaic priority strategy weight, energy storage scheduling constraints and state of charge continuity constraints; construct a multi-energy power supply optimization model, and the objective function comprehensively considers the grid power purchase cost, energy storage use cost and photovoltaic priority weight; call the mixed integer programming solver to output the power supply scheduling output result.

[0137] The arbitrage analysis module 40 is used to receive the power supply scheduling output result and the electricity price sequence, mark the peak-valley distribution of the electricity price, identify the peak period and the valley period and calculate the derivative index; on the premise of considering the energy storage capacity and power limit, generate charge-discharge pairs and form arbitrage labels, and output the arbitrage reference structure.

[0138] The strategy generation module 50 is used to obtain the arbitrage reference structure and the power supply scheduling output result, and combine the load power demand in the unified data input structure to determine the energy storage charge-discharge switching point; calculate the charge-discharge power value and generate the energy storage charge-discharge control strategy; encapsulate the message according to the energy storage device communication protocol and construct the energy storage regulation instruction structure.

[0139] The instruction execution module 60 is used to synchronously send the energy storage regulation instruction structure and the power supply scheduling output result to the energy control module; establish a high-priority communication channel to complete clock synchronization and message verification; collect on-site charge-discharge execution data, mode switching logs and device alarm information in real time, and provide basic data for deviation analysis.

[0140] The model update module 70 is used to perform deviation analysis on model parameters such as energy storage power constraints, mode switching margin, state of charge continuity constraints and photovoltaic priority weight according to the energy consumption deviation feedback structure generated by the instruction execution module; complete parameter adjustment according to the recursive step-by-step principle; write the adjustment result into the multi-energy power supply optimization model after adding a write lock and trigger a version update event to realize the adaptive closed-loop correction of the model.

[0141] The system monitoring and visualization module 80 is used to monitor the operating status of each functional module in real time, and display the photovoltaic-storage-grid power flow, peak-valley identification results, strategy execution trajectory, and model parameter evolution curve; it supports the operation and maintenance personnel to view historical data online, download message logs, and manually trigger rollback or retraining operations to ensure the long-term stable operation of the system under complex working conditions.

[0142] Beneficial effects of the embodiment: (1) Cost reduction and efficiency improvement. By optimizing the modeling module to solve the multi-energy power supply optimization model in real time and superimposing the peak-valley arbitrage strategy of the arbitrage analysis module, the comprehensive operating cost of the charging station is reduced by about 25% compared with the power supply mode that only relies on the power grid.

[0143] (2) Green electricity priority. The weight of the photovoltaic priority strategy is explicitly reflected in the model, maximizing the utilization rate of local renewable energy, reducing the electricity purchase volume during peak periods, and reducing carbon emissions.

[0144] (3) Fast closed-loop. The energy consumption deviation feedback structure drives the model update module to adaptively adjust the constraints and weights within a minute-level cycle, realizing a high-speed closed-loop of strategy-execution-feedback-correction, and significantly improving the scheduling accuracy and energy storage life.

[0145] (4) Safe and reliable. The instruction execution module adopts a clock synchronization, write lock, version control, and alarm linkage mechanism to ensure the reliable implementation of the control instructions and avoid the risks of overcharging, over-discharging, or incorrect mode switching of the energy storage.

[0146] (5) Easy to expand. The system monitoring and visualization module provides open interfaces, which can be connected to new energy devices such as wind power and hydrogen energy storage as needed, and has good horizontal expansion and industry migration capabilities.

[0147] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The drawings give preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields shall be within the scope of the patent protection of the present application by the same token.

Claims

1. A background management method for a charging pile, characterized in that, It includes the following steps: Obtain photovoltaic output data, energy storage status information, and charging load data, complete time alignment, normalization, and structural integration, and generate a unified data input structure; Obtain the unified data input structure, configure the photovoltaic priority strategy and energy storage scheduling constraint parameters, construct and solve the multi-energy power supply optimization model, and generate the power supply scheduling output result; The expression of the power supply scheduling output result is: Among them, is the power supply dispatching output structure; is the optimal charge and discharge power; is the optimal power purchase power; is the energy storage state trajectory at each moment; is the unit energy consumption dispatching cost; K is the time index variable; N is the total number of time steps within the dispatching period; Obtain the power supply scheduling output result and the electricity price sequence, identify peak periods and valley periods, generate arbitrage labels, and construct an arbitrage reference structure; Obtain the arbitrage reference structure and the power supply scheduling output result, combine with the load data in the unified data input structure, generate the energy storage charge and discharge control strategy, and construct the energy storage regulation instruction structure; Send down the energy storage regulation instruction structure and the power supply scheduling output result, collect the execution data of the energy control module, and generate the energy consumption deviation feedback structure; Obtain the energy consumption deviation feedback structure, analyze and adjust the scheduling parameters in the multi-energy power supply optimization model, and complete the model adaptive closed-loop correction; The analysis and adjustment of the scheduling parameters in the multi-energy power supply optimization model include: Mapping the peak deviation and average deviation to the maximum power constraint of energy storage charge and discharge; mapping the cumulative deviation to the energy storage cycle number constraint; mapping the number of switching failures and switching delay times to the mode switching delay margin; mapping the maximum state of charge offset to the energy storage efficiency coefficient; mapping the average state of charge offset and state recovery duration to the state of charge continuity constraint; The model adaptive closed-loop correction includes: The system performs a fast consistency check, including supply-demand balance constraint check, energy storage power constraint check, and state of charge continuity check.

2. The charging pile background management method according to claim 1, characterized in that, The obtaining of photovoltaic output data, energy storage status information, and charging load data, and the completion of time alignment, normalization, and structural integration include: Perform time alignment processing on the photovoltaic output data, energy storage status information, and charging load data; Perform normalization and moving average processing on the photovoltaic output data, energy storage status information, and charging load data; Integrate the results of normalization and moving average processing to generate a unified data input structure.

3. The charging pile background management method according to claim 1, characterized in that, The configuration of the photovoltaic priority strategy and energy storage scheduling constraint parameters includes: Obtain the unified data input structure, and configure the photovoltaic priority strategy and energy storage scheduling constraint parameters.

4. The charging pile background management method according to claim 1, characterized in that The construction and solution of the multi-energy power supply optimization model include: Construct a multi-energy power supply optimization model, and the objective function includes the grid power purchase cost and the energy loss of energy storage scheduling.

5. The charging pile background management method according to claim 1, characterized in that The obtaining of the power supply scheduling output result and the electricity price sequence, the identification of peak periods and valley periods, and the generation of arbitrage labels include: Obtain the power supply scheduling output result and the electricity price sequence, and mark the peak and valley distribution of electricity prices; Analyze and process the peak and valley distribution of electricity prices to identify peak periods and valley periods; Generate arbitrage labels according to the peak periods and valley periods, and construct an arbitrage reference structure.

6. The charging pile background management method according to claim 1, characterized in that The generation of the energy storage charge and discharge control strategy and the construction of the energy storage regulation instruction structure include: Determine the energy storage charge and discharge periods according to the arbitrage reference structure and the power supply scheduling output result; Generate the energy storage charge and discharge control strategy based on the load data in the unified data input structure; Structure the energy storage charge and discharge control strategy to generate the energy storage regulation instruction structure.

7. The charging pile background management method according to claim 1, wherein The steps of issuing the energy storage regulation instruction structure and the power supply dispatching output result, and collecting the execution data of the energy control module include: Synchronously issue the energy storage regulation instruction structure and the power supply dispatching output result to the energy control module; Collect the actual execution data in the energy control module and compare and analyze it with the issued instructions.

8. The charging pile background management method according to claim 1, characterized in that, The steps of generating the energy consumption deviation feedback structure include: Structurally process the comparison and analysis results to generate an energy consumption deviation feedback structure.

9. A background management system for a charging pile, which is applied to the method described in any one of claims 1-8, and is characterized in that, Include: A data acquisition module for real-time obtaining photovoltaic output data, energy storage state information, charging load data, and electricity price sequences from a photovoltaic array controller, an energy storage management system, an intelligent charging pile controller, and a billing platform; A data preprocessing module for performing time alignment, normalization, moving average, and structure integration processing on the original data and outputting a unified data input structure; An optimization modeling module for extracting available capacity and power intervals based on the unified data input structure, and configuring photovoltaic priority strategy weights, energy storage dispatching constraints, and state of charge continuity constraints; An arbitrage analysis module for receiving the power supply dispatching output result and the electricity price sequence, marking the peak and valley distribution of the electricity price, identifying peak and valley periods, and calculating derivative indicators; A strategy generation module for obtaining the arbitrage reference structure and the power supply dispatching output result, and determining the energy storage charge and discharge switching point in combination with the load power demand in the unified data input structure; An instruction execution module for synchronously issuing the energy storage regulation instruction structure and the power supply dispatching output result to the energy control module; A model update module for performing deviation analysis on model parameters such as energy storage power constraints, mode switching margins, state of charge continuity constraints, and photovoltaic priority weights according to the energy consumption deviation feedback structure generated by the instruction execution module; A system monitoring and visualization module for real-time monitoring the operating states of each functional module and displaying the photovoltaic-storage-grid power flow, peak and valley identification results, strategy execution trajectories, and model parameter evolution curves.

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