Intelligent charging processing method and device for charging pile integrating photovoltaic and energy storage

Abstract

The invention discloses a photovoltaic and energy storage integrated charging pile intelligent charging processing method and device, and relates to the technical field of intelligent charging, and the method comprises the steps: recognizing the historical charging interval data of a plurality of double-gun charging piles in the operation process, and obtaining the three-mode working state information of the double-gun charging piles; performing combined training optimization of charging efficiency and power grid carbon emission factors in the initial intelligent charging regulation and control model to obtain a double-gun charging regulation and control model; scheduling correlation coefficients of all the double-gun charging piles are calculated; a charging mode needing to be activated is judged in the double-gun charging regulation and control model, a double-gun power distribution scheme of each double-gun charging pile in the current time period is generated, a global optimal charging strategy is formed, and the output power of the first gun and the second gun of each double-gun charging pile in the target charging station and the operation state of the energy storage unit are regulated and controlled. The invention provides a novel charging management mechanism which can give consideration to charging service continuity and operation economy in a power grid capacity limited scene.

Description

Technical Field

[0001] This application relates to the field of smart charging technology, and in particular to a smart charging processing method and device for charging piles that integrate photovoltaic and energy storage. Background Technology

[0002] With the rapid increase in the penetration rate of new energy vehicles, urban public charging stations are facing an increasingly severe power supply and demand imbalance. Especially in charging hotspots formed by the transformation of old urban areas or industrial parks, the power distribution network infrastructure is outdated, and the capacity of transformer substations is generally insufficient (often below 500kVA) and difficult to expand. In such scenarios, the concentrated connection of multiple high-power DC fast charging piles can easily cause voltage drops, line overloads, and even protection tripping, severely restricting charging service capacity.

[0003] Existing charging piles mostly adopt fixed power allocation or simple time-sharing control strategies. Although some products integrate photovoltaic and energy storage modules, their operation modes are fragmented: photovoltaic power is prioritized for direct supply, and energy storage is only used as a backup, lacking the ability to sense the real-time stability of the power grid. When the grid voltage fluctuates or during peak and high-carbon periods, the system cannot dynamically switch to a power supply path dominated by energy storage, causing fast charging services to be forced to reduce power, resulting in a decline in user experience. Conversely, during off-peak and low-carbon periods, the system fails to fully utilize cheap green electricity to economically supplement energy storage, resulting in wasted energy costs.

[0004] Therefore, there is an urgent need for a new charging management mechanism that can balance the continuity of charging services and the economic efficiency of operation in scenarios where grid capacity is limited, in order to cope with the multiple challenges brought about by frequent voltage fluctuations, changes in electricity prices during different periods and dynamic adjustments in carbon emission intensity, and to avoid the decline in fast charging capabilities or runaway energy costs due to rigid power supply paths. Summary of the Invention

[0005] To address the shortcomings of existing technologies and provide a novel charging management mechanism that balances charging service continuity and operational economy in scenarios with limited grid capacity, this application provides a smart charging processing method and device for charging piles that integrates photovoltaic and energy storage.

[0006] Firstly, the objective of this invention is achieved through the following technical solution: A smart charging method for charging piles integrating photovoltaic and energy storage includes: Based on the historical operation dataset of the target charging station, the historical charging interval data of multiple dual-gun charging piles during operation are identified, and the three-mode working status information of each dual-gun charging pile is obtained. The dual-gun charging pile is associated with the pile body identifier. Based on the pile identifier, the historical control dataset of each dual-gun charging pile is obtained. In the preset initial intelligent charging control model, joint training optimization based on charging efficiency and grid carbon emission factor is carried out to obtain the optimized dual-gun charging control model. Calculate the multidimensional coupling factor between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer area load, and obtain the scheduling correlation coefficient of all dual-gun charging piles; In the optimized dual-gun charging control model, based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor and user interaction instructions, the charging mode to be activated is dynamically determined, and the dual-gun power allocation scheme, energy storage charging and discharging instructions and vehicle-pile matching strategy for each dual-gun charging pile in the current time period are generated to form the global optimal charging strategy. Based on the global optimal charging strategy, the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local and shared energy storage units, are synchronously executed and controlled.

[0007] By adopting the above technical solution, the three-mode working status information includes fast charging mode, peak-valley arbitrage mode, and manual mode, each corresponding to different power source strategies and dual-gun output rules. Charging efficiency represents the ratio of effective charging power to total energy consumption per unit time, and the grid carbon emission factor is the carbon dioxide emission intensity corresponding to the unit grid power supply during the scheduling period. This invention provides an intelligent charging management method with photovoltaic access and energy storage functions. First, by introducing the three-mode working status information (fast charging, peak-valley arbitrage, and manual) and binding it with the charging pile identifier, it achieves refined modeling of charging behavior under different operating scenarios, avoiding service interruptions or economic losses caused by traditional single scheduling strategies when the grid fluctuates or electricity prices change. Second, by adopting a dual-objective mechanism of joint training and optimization of charging efficiency and grid carbon emission factor, the dual-gun charging control model not only pursues the maximization of energy conversion efficiency but also actively responds to the low-carbon operation requirements of the grid, significantly reducing the carbon footprint per unit of charging power while ensuring the user's charging experience. Third, by constructing multi-dimensional coupling factors and calculating scheduling correlation coefficients, heterogeneous information such as photovoltaic output uncertainty, energy storage availability, and transformer area load pressure are integrated into a unified decision-making basis. Finally, based on real-time grid status, electricity price tags, carbon emission factors, and user commands, a globally optimal charging strategy is dynamically generated, and the output power of the dual-gun system and local and shared energy storage units are simultaneously adjusted. This not only ensures the fast charging needs of high-priority users, but also improves the station's operating revenue through peak-valley arbitrage during non-emergency periods, truly balancing service stability and low-carbon economy.

[0008] In a preferred embodiment of this application: the three-mode operating status information includes fast charging mode, peak-valley arbitrage mode, and manual mode. Each mode corresponds to different power source strategies and dual-gun output rules, specifically including: The fast charging mode is activated when a high-priority user is detected to be connected to any dual-gun port or the grid voltage fluctuation rate exceeds a preset threshold. It prioritizes the dispatch of local energy storage units and shared energy storage systems to supply power to the corresponding charging gun, so that the output power of a single gun is not less than 60kW and the total output power of dual guns is not less than 90kW when running. The peak-valley arbitrage model initiates the energy storage replenishment process when the current electricity price tag is in the low-valley range. Direct grid power supply is prohibited during peak or peak electricity price ranges. The energy storage system discharges to electric vehicles, and the discharge revenue and carbon emission savings are included in the station operation optimization goals. The manual mode is actively selected and triggered by the user to temporarily override the automatic scheduling logic. It allows the user to specify whether to use the first or second charging gun, set the target charging amount, or lock the current power level. In manual mode, the automatic mode determination based on carbon emission factor and charging efficiency is paused until the user confirms exit or the charging task is completed.

[0009] By adopting the above technical solution and clearly defining the triggering conditions and power rules for the three modes of fast charging, peak-valley arbitrage, and manual operation, the system can prioritize the use of energy storage to ensure 60kW single-gun output when the power grid is abnormal, and automatically switch to energy storage arbitrage mode when electricity prices are favorable, while retaining the user intervention channel. This effectively avoids service degradation caused by power grid fluctuations or revenue loss caused by rigid strategies.

[0010] In a preferred embodiment of this application: the multidimensional coupling factors include a photovoltaic absorption capacity factor, an energy storage availability factor, a transformer area load sensitivity factor, and a carbon efficiency synergy factor; the calculation of the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, the shared energy storage system, and the transformer area load, to obtain the scheduling correlation coefficients of all dual-gun charging piles, includes: Extract the actual photovoltaic feed-in power, energy storage state of charge (SOC), normalized load value of the distribution area, grid carbon emission factor, and dual-gun port load rate for each scheduling period from the historical operation dataset. Calculate the photovoltaic absorption capacity factor based on the photovoltaic prediction deviation and the actual absorption ratio; Based on the remaining dischargeable capacity, maximum charge and discharge power, and cycle life loss model of the energy storage unit, the energy storage availability factor is quantified. By combining whether the load of the transformer area is in the peak range and the fluctuation slope during the current period, a load sensitivity factor for the transformer area is constructed. Based on the comprehensive carbon emission intensity corresponding to a unit of charging power, a carbon efficiency synergy factor is constructed. The photovoltaic absorption capacity factor, energy storage availability factor, transformer area load sensitivity factor, and carbon efficiency synergy factor are normalized and then weighted and fused to generate the scheduling correlation coefficient of each dual-gun charging pile.

[0011] By adopting the above technical solutions, a four-dimensional coupling factor is introduced, which includes photovoltaic absorption capacity, energy storage availability, distribution area load sensitivity, and carbon efficiency synergy. By generating scheduling correlation coefficients through normalized weighting, the system can comprehensively evaluate the dispatchability of photovoltaic and energy storage resources, distribution network carrying capacity pressure, and carbon emission costs. This helps to overcome the problems of fragmented multi-source information and strong scheduling blindness in existing technologies.

[0012] In a preferred embodiment of this application, the optimized dual-gun charging control model dynamically determines the charging mode to be activated based on the scheduling correlation coefficient, real-time grid status, electricity price period label, current carbon emission factor, and user interaction instructions, including: A three-mode activation priority matrix is ​​constructed, in which the fast charging mode has the highest priority, the peak-valley arbitrage mode is second, and the manual mode only takes effect when the user explicitly triggers it and there are no high-priority events. When an electric vehicle with less than 20% battery power is detected at any dual-gun port and the user marks it as emergency charging, or when the absolute value of the voltage deviation in the distribution area exceeds 8% of the rated value, the fast charging mode will be forcibly activated and locked for at least 10 minutes. When the fast charging guarantee condition is not triggered and the current electricity price tag is in the low or high range, the peak-valley arbitrage profit index is calculated by combining the carbon efficiency synergy factor and the energy storage availability factor. If the peak-valley arbitrage profit index is greater than the preset threshold, the peak-valley arbitrage mode is activated. When a user selects manual mode through the human-machine interface and there is no risk to power grid safety, the user is allowed to enter manual mode. Before each mode switch, a smooth transition window with a preset duration is executed, during which the ratio of energy storage output power to grid-supplied power is gradually adjusted.

[0013] By adopting the above technical solutions, a three-mode priority matrix is ​​constructed and mandatory activation conditions are set to ensure that fast charging services are not interfered with by economic strategies in high-priority scenarios; at the same time, a smooth transition window is introduced to avoid power surges caused by mode switching and improve safety performance.

[0014] In a preferred embodiment of this application, the generation of the dual-gun power allocation scheme for each dual-gun charging station during the current time period includes: Obtain the remaining battery power, maximum allowable charging power, and user-set expected departure time of the electric vehicles currently connected to the first and second charging stations; Based on the total available power of the dual guns, a dynamic proportional allocation algorithm is used to calculate the target power of each gun. The dynamic proportional allocation algorithm aims to minimize the weighted charging completion time, and the weights are determined by the user type label and the carbon efficiency synergy factor. If the target power of any gun exceeds the corresponding vehicle's allowable limit, the excess power will be redistributed to another gun or temporarily stored in the local energy storage unit. In fast charging mode, if only one gun is used, all available power will be supplied until it reaches 60kW; if both guns are used at the same time, the power will be allocated according to the power gap between the two vehicles, but the power of each gun will not be lower than the preset low power threshold.

[0015] By adopting the above technical solution and dynamic proportional allocation algorithm, the power of the dual guns is intelligently allocated according to actual needs and low-carbon weight, and flexibly readjusted when exceeding limits or when using a single gun. This can shorten the charging waiting time for high-priority users and avoid power waste.

[0016] In a preferred embodiment, this application also includes: When the temperature of the local energy storage unit exceeds 65°C or the SOC is below 10%, the fast charging mode will be exited and switched to the restricted grid supply mode. At the same time, a notification will be sent to the user that the fast charging capability is temporarily limited. When the shared energy storage system communication is interrupted or the normalized value of the transformer area load remains above 0.95 for more than 3 minutes, the discharge operation in the peak-valley arbitrage mode is suspended and switched to a hybrid mode of direct photovoltaic power supply plus grid compensation only. If the power module fails in the first or second charging station, the vehicle waiting for service will be guided to another working charging station, the fault information will be marked on the human-machine interface, and the faulty charging station will be added to the maintenance queue.

[0017] By adopting the above technical solutions, setting temperature thresholds and SOC thresholds for local energy storage units, and continuous over-limit judgment criteria and gun-end fault detection for shared energy storage systems, the charging pile system is degraded to improve the availability and operation and maintenance efficiency of the system under extreme conditions, avoids the shutdown of the entire charging pile due to local faults, and improves the reliability of public charging facilities.

[0018] In a preferred embodiment of this application, the energy storage charging and discharging control of the peak-valley arbitrage mode includes: Based on formula (1), the dispatchable power of the shared energy storage system in the peak-valley arbitrage mode is constrained and calculated: in This represents the net dispatch power of the energy storage system within time period t, with positive values ​​indicating charging and negative values ​​indicating discharging. To improve energy storage charging efficiency; For energy storage discharge efficiency; This refers to the maximum grid-supplied supplementary power capacity allowed during off-peak electricity pricing periods; The total electric vehicle charging power demand of the dual-gun charging piles during the current time period; These are the indicator functions for off-peak and peak electricity price periods, respectively, with values ​​of 0 or 1; The energy storage state of charge (SOC(t)) must satisfy: ,in , This is for a safety buffer margin.

[0019] By adopting the above technical solutions, the net dispatch power of energy storage and the SOC safety boundary are constrained, ensuring that peak-valley arbitrage operations are always carried out within the equipment safety margin and preventing overcharging and over-discharging; at the same time, the off-peak power replenishment and peak discharge power are dynamically bound to the actual charging demand to avoid ineffective cycles.

[0020] Secondly, the objective of this invention is achieved through the following technical solution: A smart charging system for charging piles integrating photovoltaic and energy storage is provided for executing the smart charging method for charging piles integrating photovoltaic and energy storage as described above. The system includes: The data recognition module is used to identify the historical charging interval data of multiple dual-gun charging piles during operation based on the historical operation dataset of the target charging station, and to obtain the three-mode working status information of each dual-gun charging pile. The model building and optimization module is used to obtain the historical control dataset of each dual-gun charging pile by the pile identification, and to perform joint training and optimization based on charging efficiency and grid carbon emission factor in the preset initial intelligent charging control model to obtain the optimized dual-gun charging control model. The coupling relationship calculation module is used to calculate the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer area load, and obtain the scheduling correlation coefficient of all dual-gun charging piles. The strategy generation module is used to dynamically determine the charging mode to be activated in the optimized dual-gun charging control model based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor and user interaction instructions, and generate a dual-gun power allocation scheme for each dual-gun charging pile in the current time period to form a globally optimal charging strategy. The collaborative execution module is used to synchronously execute and close-loop control the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local energy storage unit and the shared energy storage unit, according to the global optimal charging strategy.

[0021] Thirdly, the objective of this invention is achieved through the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described intelligent charging processing method for an integrated photovoltaic and energy storage charging pile.

[0022] Fourthly, the objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described intelligent charging processing method for an integrated photovoltaic and energy storage charging pile.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention integrates historical operational data with multi-source real-time information to construct a dual-gun charging control model with charging efficiency and grid carbon emission factors as dual objectives. It also introduces a scheduling correlation coefficient to generate a globally optimal charging strategy, achieving coordinated control of dual charging gun power and energy storage resources. Compared to traditional fixed-strategy systems, this invention can proactively respond to electricity price and carbon emission signals while ensuring high-power fast charging services, significantly improving the energy efficiency and low-carbon level of power stations, and resolving the technical contradiction of balancing "fast charging guarantee" and "green operation" under weak power grid conditions. 2. By using an optimized electricity price period indication function, the system can accurately identify off-peak and peak electricity price periods, thereby effectively formulating energy storage charging and discharging strategies. By utilizing time-of-use electricity price differences, this invention enables charging during low-price periods and discharging during high-price periods, significantly improving the economic efficiency of energy use. Attached Figure Description

[0024] Figure 1 This is a flowchart of a smart charging processing method for a charging pile integrating photovoltaic and energy storage according to an embodiment of this application; Figure 2 This is another flowchart of a smart charging processing method for a charging pile integrating photovoltaic and energy storage in one embodiment of this application; Figure 3 This is a schematic diagram of a device according to one embodiment of this application. Detailed Implementation

[0025] The present application will be further described in detail below with reference to the accompanying drawings.

[0026] In one embodiment, such as Figure 1 As shown, this application discloses a smart charging method for charging piles that integrates photovoltaic and energy storage, specifically including the following steps: S1: Based on the historical operation dataset of the target charging station, identify the historical charging interval data of multiple dual-gun charging piles during operation, obtain the three-mode working status information of each dual-gun charging pile, and associate the dual-gun charging pile with the pile body identifier.

[0027] In this embodiment, the historical operation dataset refers to the structured operation logs recorded by all dual-gun charging piles within the target charging station over the past 30 days. This includes fields such as timestamps, pile identification, first and second gun port status, real-time output power, cumulative charging power, grid voltage, local energy storage SOC, photovoltaic feed-in power, time-of-use tariff tags, and user operation events. Port status includes: idle, charging, and fault. Historical charging interval data refers to time-power sequence segments extracted from continuous operation logs, with each charging task as a unit. The start and end points are determined by charging gun insertion / removal signals or power surge thresholds. The three-mode operating status information refers to the operating mode label assigned to each charging interval. The three-mode operating status information includes fast charging mode, peak-valley arbitrage mode, and manual mode, each corresponding to different power source strategies and dual-gun output rules.

[0028] Specifically, step S1 includes: S11: The fast charging mode is activated when a high-priority user is detected at any dual-gun port or the grid voltage fluctuation rate exceeds a preset threshold. It prioritizes the dispatch of local energy storage units and shared energy storage systems to supply power to the corresponding charging gun, so that the output power of a single gun is not less than 60kW and the total output power of dual guns is not less than 90kW when running.

[0029] Specifically, high-priority users refer to electric vehicle users marked with a high service level through identity authentication; identity authentication can be achieved through API integration with ride-hailing platforms, VIP membership card recognition, or users actively selecting "emergency charging" in the app. Grid voltage fluctuation rate is defined as the ratio of the standard deviation of the transformer substation bus voltage over the past 5 seconds to the rated voltage, used to characterize power quality stability; the rated voltage is, for example, 220V. The default preset threshold is 8%, which can be dynamically adjusted based on historical data from the site. Local energy storage units refer to modular battery packs (capacity ≥ 20kWh) integrated within a single pile, while shared energy storage systems refer to centralized energy storage at the site level (total capacity ≥ 200kWh). Both are connected in parallel via a DC bus, supporting millisecond-level switching of power supply paths.

[0030] For example, when the first charging station of the "DCZ-08" pile is connected to a dedicated vehicle, its onboard T-Box sends the VIN code to the station server via 4G. The system checks that the vehicle's operator is on the cooperative whitelist and automatically marks it as a "high-priority user." Simultaneously, the EMS monitors that the voltage in the distribution area drops sharply from 225V to 208V between 14:19:50 and 14:20:00 before rising again. The calculated fluctuation rate is std([225, 210, 208, 215, 222]) / 220≈6.8%, which is within the threshold. However, due to the user's priority trigger, the fast charging mode is still activated. The system immediately disconnects the grid supply contactor and instructs the local energy storage to supply power to the first charging station at 55kW and the shared energy storage to supply power at 10kW, with a measured output of 60.3kW. If the second charging station is also connected to a regular user at this time, the remaining 30kW is allocated according to the power shortage ratio (e.g., if the first charging station requires 60kW and is already full, the second charging station receives 30kW), ensuring a total power ≥ 90kW. If the total available energy storage power is less than 90kW (e.g., SOC < 15%), it will be downgraded to "limited fast charging" and a notification will be sent to the user.

[0031] S12: The peak-valley arbitrage model initiates the energy storage replenishment process when the current electricity price tag is in the off-peak range. Direct grid power supply is prohibited during peak or high-peak electricity price ranges. The energy storage system discharges electricity to electric vehicles, and the discharge revenue and carbon emission savings are included in the station operation optimization goals.

[0032] In this embodiment, the electricity price tag is derived from the time-of-use electricity price document (15 minutes / point) pushed daily at 0:00 by the provincial power trading center at 9:00 AM the following day, and is divided into four categories: off-peak, flat, peak, and super-peak. Off-peak periods include 00:00–08:00, flat periods include 08:00–12:00 and 17:00–20:00, peak periods include 12:00–17:00, and super-peak periods include 20:00–24:00. The energy storage replenishment process refers to charging the shared energy storage at the maximum permissible power (e.g., 80kW) during off-peak periods until the SOC reaches 85%, with the maximum permissible power being 80kW. Prohibiting direct grid power supply means physically disconnecting the grid supply relay; only a combination of direct photovoltaic power supply and energy storage discharge is allowed. Peak-valley arbitrage models include peak-valley arbitrage charging mode and peak-valley arbitrage discharging mode. Discharge revenue = ,in This represents the actual power discharged by the energy storage system to the electric vehicle during a certain scheduling period, expressed in kW; Δt is the duration of this discharge behavior. This indicates the peak (or super-peak) electricity price of the power grid for the current time period, in yuan / kWh; This represents the equivalent operating cost of an energy storage system, expressed in yuan / kWh. It includes: off-peak charging costs (e.g., 0.3 yuan / kWh) and energy storage cycle losses / depreciation (e.g., 0.07 yuan / kWh), with a typical value of approximately 0.4 yuan / kWh. Carbon emission savings = Where c is the electricity price and e is the carbon emission factor; This represents the grid carbon emission factor for the current period, expressed in kgCO2 / kWh. This represents the carbon emission factor over the entire life cycle of photovoltaic power generation, expressed in kgCO2 / kWh. Carbon emission savings represent the reduction in carbon dioxide emissions resulting from replacing 1 kWh of coal-fired power with 1 kWh of energy storage discharge (derived from green electricity).

[0033] Specifically, at 02:00 on July 6, 2024, when the off-peak electricity price was 0.3 yuan / kWh, the system detected no vehicles charging and automatically activated the peak-valley arbitrage charging mode: closing the grid power switch and charging the shared energy storage at 80kW, with the SOC rising from 50% to 85% in 2.2 hours. At 19:30 on the same day, when the peak electricity price was 1.5 yuan / kWh, if a private car connected to the charging pile "DCZ-03" at this time, the system activated the peak-valley arbitrage discharge mode: disconnecting the grid power supply and prioritizing direct supply from the photovoltaic system (output of 30kW at the time), with the remaining 30kW supplemented by the energy storage if 60kW was needed. This discharge was 30kWh, and the discharge revenue = 30 × (1.5 − 0.4) = 33 yuan ( (Including depreciation of 0.4 yuan / kWh); Carbon emission savings = 30 × (0.75 − 0.05) = 21 kg CO2 ( =0.75, =0.05).

[0034] S13: Manual mode is triggered by user selection and is used to temporarily override the automatic scheduling logic. It allows users to specify whether to use the first or second charging gun, set the target charging capacity, or lock the current power level. In manual mode, the automatic mode determination based on carbon emission factor and charging efficiency is paused until the user confirms exit or the charging task is completed.

[0035] In this embodiment, users can actively select the charging mode via the "Mode Switching" button on the charging pile's touchscreen or through remote commands via a mobile app. Specifying the first or second charging gun is suitable for users who prefer one side of the charging pile (e.g., the right side is easier to exit the vehicle on). Setting the target charging capacity refers to inputting the desired kWh, such as "charge to 40kWh," and the system dynamically adjusts the power curve accordingly. Locking the current power level means fixing the output at the current level (e.g., 60kW, 90kW, 120kW). Pausing automatic mode determination means that even with sudden grid changes or peak hours, the system will not switch back to fast charging or peak-valley arbitrage unless the user manually exits or charging is complete.

[0036] S2: Based on the pile identification, obtain the historical control dataset of each dual-gun charging pile, and perform joint training and optimization based on charging efficiency and grid carbon emission factor in the preset initial intelligent charging control model to obtain the optimized dual-gun charging control model.

[0037] In this embodiment, the historical control dataset refers to a set of control parameters corresponding one-to-one with historical charging intervals, including the activated mode at that time, the dual-gun power allocation ratio, energy storage charging and discharging commands, photovoltaic direct supply ratio, grid-supplied power, etc. The initial intelligent charging control model adopts a lightweight neural network. Charging efficiency is defined as the ratio of the actual amount of electricity received by the vehicle battery to the total input energy consumption of the system; the grid carbon emission factor adopts the hourly carbon emission intensity data published by the provincial power grid, with the unit being kgCO2 / kWh.

[0038] The joint training objective is to minimize the loss function: in The charging efficiency of all stations during the scheduling period t is α and β, which are weighting coefficients. The total electrical energy consumed by the internal power grid during the scheduling period t, expressed in kWh. j represents the j-th power sampling time within the scheduling period t; Provide power to the instantaneous network at the j-th power sampling time; is the duration of the j-th sampling interval; n is the total number of samples within the scheduling period t. For example, a dedicated control model is constructed for the charging pile "DCZ-07": the first 150 data points from 182 charging intervals are used as the training set, and the last 32 as the validation set. The input features of each sample include: normalized load value of the transformer area (e.g., 0.62), current carbon emission factor (e.g., 0.58 kgCO2 / kWh), photovoltaic availability (e.g., 70%), user type (e.g., normal / emergency), etc.; the output label is the actual executed mode and power ratio, such as [fast charging guaranteed, 1.0:0]. The initial intelligent charging control model is trained using the PyTorch framework, α=0.6, β=0.4, and after 200 iterations, the validation set accuracy reaches 92%. Finally, the ONNX format model is exported and deployed to the site edge controller as the optimized dual-gun charging control model for this charging pile.

[0039] S3: Calculate the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer load, and obtain the scheduling correlation coefficient of all dual-gun charging piles.

[0040] In this embodiment, the multidimensional coupling factor is a dimensionless indicator used to quantify the interaction intensity between charging piles and external energy systems, reflecting their dependence on or influence on photovoltaic, energy storage, and grid resources during a specific time period. The multidimensional coupling factor includes a photovoltaic absorption capacity factor, an energy storage availability factor, a transformer area load sensitivity factor, and a carbon efficiency synergy factor. The scheduling correlation coefficient is a comprehensive score obtained by weighted fusion of multiple coupling factors, used to rank the priority or response sensitivity of each charging pile in global scheduling. The calculation process is based on a sliding window mechanism, with a 15-minute scheduling period, and the values ​​of each factor are updated continuously.

[0041] Specifically, step S3 includes: S31: Extract the actual photovoltaic feed-in power, energy storage state of charge (SOC), normalized load value of the distribution area, grid carbon emission factor, and dual-gun port load rate for each scheduling period from the historical operation dataset.

[0042] Specifically, the actual photovoltaic feed-in power is the measured power output from the photovoltaic inverter to the DC bus, in kW; the state of charge (SOC) of the energy storage system is the percentage of remaining power reported by the shared energy storage system BMS; the normalized load value of the distribution area is defined as the ratio of the current total load of the distribution area to the rated capacity of the transformer (e.g., 500kW), in the range [0, 1]; the grid carbon emission factor adopts hourly data released by the provincial ecological and environmental departments, in kgCO2 / kWh; the dual-gun port load rate refers to the ratio of the current total output power of the dual guns of the pile to its maximum rated power.

[0043] For example, taking the period from 11:00 to 11:15 on a certain day as an example, the actual photovoltaic feed-in power is 78 kW, and the photovoltaic installation capacity on the roof of the charging station is set at 100 kW; the shared energy storage SOC is 63%; the total load of the distribution area is 435 kW, then the load factor is 435 / 500 = 0.87; the grid carbon emission factor is 0.71 kgCO2 / kWh, indicating that the proportion of coal power is high at noon on that day; the total output of the "DCZ-12" dual guns is 92 kW, then the load factor is 92 / 120 ≈ 0.77.

[0044] S32: Calculate the photovoltaic absorption capacity factor based on the photovoltaic prediction deviation and the actual absorption ratio.

[0045] Specifically, photovoltaic (PV) prediction bias refers to the relative error between the PV output predicted by the NWP (numerical weather forecast) model the previous day and the actual power fed into the grid; the actual grid integration ratio is defined as the ratio of the actual PV power fed into the grid to the theoretical maximum power generation; where the theoretical maximum power generation is calculated from irradiance and module efficiency. The PV grid integration capacity factor reflects the potential of charging piles to locally integrate PV power generation under current operating conditions, with a value range of [0, 1]. A higher value indicates that PV is more suitable for priority use.

[0046] For example, the photovoltaic (PV) forecast at 11:00 AM the previous day was 85 kW, while the actual power generation was 78 kW, resulting in a forecast deviation of approximately 8.2% (|85−78| / 85). Meanwhile, according to meteorological station data, the theoretical maximum power generation is 90 kW, so the actual grid integration ratio is approximately 86.7% (78 / 90). The PV grid integration factor is calculated using a weighted formula. : ,in ,have to =0.4×(1−0.082)+0.6×0.867≈0.367+0.520=0.887. In the alternative plan, if the prediction deviation is >20%, then directly set... =Consumption ratio.

[0047] S33: Based on the remaining dischargeable capacity, maximum charge / discharge power, and cycle life loss model of the energy storage unit, quantify the energy storage availability factor.

[0048] In this embodiment, the remaining dischargeable capacity = SOC - discharge safety lower limit, where the discharge safety lower limit is 10%; the maximum charge and discharge power is determined by the rated value of the PCS (energy storage converter); the cycle life loss model uses the rainflow counting method to estimate the current equivalent number of cycles and maps it to the health status SOH; the energy storage availability factor comprehensively reflects the energy availability and power availability of energy storage, and is attenuated and corrected by SOH.

[0049] For example, the current shared energy storage SOC is 63%, therefore the remaining dischargeable capacity is 63% − 10% = 53%. The PCS's maximum discharge power is 100kW, the current temperature is 25℃, and there is no derating. The SOH is assessed as 92% by the BMS. The energy storage availability factor is quantified using the following formula. : Of these, 80% are effective SOC windows (10%~90%). =100kW; This refers to the maximum rated charge and discharge power of the energy storage system. Let be the currently available charging and discharging power of the energy storage system. Substituting, we get: =(53% / 80%)×1.0×0.92≈0.6625×0.92≈0.609. If SOC<15%, then mandatory =0.

[0050] S34: Construct a load sensitivity factor for the transformer area by combining whether the load of the transformer area is in the peak range and the fluctuation slope during the current period.

[0051] In this embodiment, the peak load range of the distribution area is determined by the electricity price tag or the 95th percentile of the historical load. The fluctuation slope refers to the standard deviation of the load change rate over the past 5 15-minute points; the distribution area load sensitivity factor is used to characterize the impact of new charging load on the safety of the distribution area, and the higher the value, the more the grid supply should be restricted or energy storage should be activated.

[0052] For example, since the normalized load value of the current transformer area is 0.87, which is less than 0.9, but it is during the peak electricity price period, it is marked as "quasi-peak". The load sequence for the past 5 periods is [410, 420, 435, 430, 435] kW, and the calculated slope standard deviation is approximately 9.2 kW / 15min. At this point, the transformer area load sensitivity factor is defined. : ,in This represents the standard deviation of the load in the transformer area over the recent five time windows, with a value of 0.92; where k = 0.02 (1 / kW). The calculation is then performed. Approximately equal to 0.984, set When the value is greater than 0.95, the system will suppress new grid power supply and prioritize the use of photovoltaic and energy storage.

[0053] S35: Based on the comprehensive carbon emission intensity corresponding to a unit of charging power, construct a carbon efficiency synergy factor.

[0054] In this implementation, the comprehensive carbon emission intensity refers to the equivalent CO2 emissions generated per 1 kWh of charging power provided. Considering the power source composition, the comprehensive carbon emission intensity of the charging pile consumed in a single charging task is calculated based on the following formula. : ,in This refers to the discharge energy from the energy storage system; The carbon emission factor for the entire life cycle of energy storage is typically 0.25 kgCO2 / kWh. Energy is directly supplied from photovoltaic power generation; This represents the carbon emission factor for photovoltaic power generation, with a value such as 0.05. Carbon efficiency synergy factor. Defined as: ,in This normalizes the carbon efficiency synergy factor to [0, 1], where a higher value indicates lower carbon emissions.

[0055] S36: Normalize and weight the photovoltaic absorption capacity factor, energy storage availability factor, transformer area load sensitivity factor and carbon efficiency synergy factor to generate the scheduling correlation coefficient of each dual-gun charging pile.

[0056] In this embodiment, due to the different dimensions and distributions of each factor, such as the transformer area load sensitivity factor... The value may be greater than 1, and the rest are ∈ [0, 1]. Min-Max normalization to the [0, 1] interval is required first. The weighting coefficient is set according to the station's operational objectives: if economic efficiency is emphasized, then increase... and Weighting; if the focus is on power grid security, then increase the weighting. Weights. The default weights are [0.3, 0.25, 0.25, 0.2].

[0057] For example, regarding pile "DCZ-12": =0.887, normalized to 0.887; =0.609, normalized to 0.609; =0.984, since its original value is close to the upper limit, after normalization it is ≈0.95 (assuming the historical maximum is 1.05). =0.673, normalized to 0.673. The weighted calculation of the scheduling correlation coefficient K is K=0.3×0.887+0.25×0.609+0.25×0.95+0.2×0.673=0.2661+0.1523+0.2375+0.1346≈0.7905. The final scheduling correlation coefficient K=0.79, which is used to rank the response priority of this stub in the global scheduling. The higher the value, the more suitable it is for undertaking high power or participating in arbitrage.

[0058] For example, taking the period from 10:00 to 10:15 on July 1, 2024 as an example: the system collected data showing that the total photovoltaic output of the entire station was 85kW, the shared energy storage SOC was 65%, and the load of the distribution area was 420kW (rated 500kW, normalized value 0.84). For the "DCZ-07" pile, its dual-gun load rate was 78%, and the historical photovoltaic absorption ratio for the same period was 92%. Based on this, the following calculations were made: Photovoltaic absorption capacity factor = 0.92 × (85 / 120) = 0.65, where 120kW is the installed capacity; Energy storage availability factor = (65% − 10%) / (90% − 10%) = 0.69; Distribution area load sensitivity factor = 0.84 × 1.2 (due to the rising slope) = 1.01, with a normalized value of 1; Carbon efficiency synergy factor = 0.58 × (1 − 0.65) = 0.20, assuming that 65% of the electricity comes from photovoltaics. After normalization, the parameters are weighted sequentially according to weights [0.3, 0.25, 0.25, 0.2], resulting in a scheduling correlation coefficient of 0.61. This process is applied to all charging piles at the target charging station to generate a scheduling priority list.

[0059] S4: In the optimized dual-gun charging control model, based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor and user interaction instructions, the charging mode that should be activated at the moment is dynamically determined, and a dual-gun power allocation scheme for each dual-gun charging pile in the current time period is generated to form a globally optimal charging strategy.

[0060] In this embodiment, the real-time grid status includes current voltage, frequency, and transformer load rate; the electricity price time period label is pushed in real-time by the power trading platform API connected to the power station, and the electricity price time period label includes peak, flat, and off-peak periods; user interaction commands come from operation events of the charging pile touch screen or mobile APP. The scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor, and user interaction commands are concatenated into a feature vector, which is input into the pre-trained control model in S2, and outputs mode decision and power allocation suggestions. The global optimal strategy coordinates the output of all piles to ensure that the total power does not exceed the transformer capacity and carbon emissions are minimized.

[0061] For example, at 18:30 on July 1, 2024, with the peak evening electricity price set at 1.2 yuan / kWh and a carbon emission factor of 0.72, the first charging station "DCZ-07" connects to a ride-hailing vehicle with 15% battery charge, and the user type is marked as "emergency." The second charging station is idle. The system reads its real-time characteristics: scheduling correlation coefficient 0.61, transformer load rate 0.88, and energy storage SOC 62%. The optimized dual-charging control model determines that the "fast charging mode" should be activated and outputs the following power scheme: the first charging station's 60kW comes entirely from energy storage, and the second charging station's 0kW. At the same time, the dispatch center checks that the total demand for the entire site is not exceeded, confirms the feasibility of the strategy, and issues a global strategy.

[0062] Specifically, in step S4, in the optimized dual-gun charging control model, based on the scheduling correlation coefficient, real-time grid status, electricity price period label, current carbon emission factor, and user interaction commands, the current charging mode to be activated is dynamically determined, including: S41: Construct a three-mode activation priority matrix, in which the fast charging mode has the highest priority, the peak-valley arbitrage mode is second, and the manual mode only takes effect when the user explicitly triggers it and there are no high-priority events.

[0063] In this embodiment, the three-mode activation priority matrix is ​​a preset Boolean decision table used to resolve mode conflicts under multiple concurrent conditions. Its core rule is: Safety-Critical mode > Economic mode > User-Override mode. The system performs a mode determination every scheduling cycle, such as every 15 seconds. First, it checks whether the safety-critical mode trigger condition is met; if not, it evaluates the feasibility of peak-valley arbitrage; only when neither of the first two conditions is met and the user actively requests it is the system allowed to enter manual mode.

[0064] S42: When an electric vehicle with less than 20% battery power is detected at any dual-gun port and the user marks it as emergency charging, or when the absolute value of the voltage deviation in the distribution area exceeds 8% of the rated value, the fast charging mode will be forcibly activated and locked for at least 10 minutes.

[0065] In this embodiment, when the battery level drops below 20%, the vehicle's BMS reports this via CC / CP signal or ISO 15118 protocol. Users can mark the battery as needing emergency charging via an app checkbox, VIP card swipe, or voice command recognition. The transformer substation voltage deviation is defined as |Measured Substation Voltage − Rated Voltage|, and the transformer substation voltage fluctuation rate = |Measured Substation Voltage − Rated Voltage| / Rated Voltage. The rated voltage is 220V (single-phase) or 380V (three-phase). Locking for 10 minutes is to avoid frequent mode switching due to momentary disturbances.

[0066] S43: When the fast charging guarantee condition is not triggered and the current electricity price tag is in the off-peak or peak range, the peak-valley arbitrage profit index is calculated by combining the carbon efficiency synergy factor and the energy storage availability factor. If the peak-valley arbitrage profit index is greater than the preset threshold, the peak-valley arbitrage mode is activated.

[0067] In this embodiment, the peak-valley arbitrage profit index It is a dimensionless score that combines economic benefits and low-carbon benefits, and is defined as follows: ,in, =1.0 yuan / kWh is the reference electricity price; The unit electricity price difference for peak-valley arbitrage, Electricity price during peak (or peak) hours. The equivalent operating cost of the energy storage system; γ=0.7 is the economic weight; carbon efficiency synergy factor. It must be greater than 0.5. The default threshold is 0.65. S44: When the user selects manual mode through the human-machine interface and there is no risk to power grid safety, manual mode is allowed.

[0068] Specifically, the human-machine interface includes a charging pile touchscreen and a supporting mobile APP or voice interaction terminal. "No grid safety risk" means that the following conditions are met simultaneously: normalized load value of the distribution area <0.9, voltage deviation <8%, normal communication of shared energy storage, and no protection alarms such as over-temperature, over-voltage, or insulation faults.

[0069] S45: Before each mode switch, a smooth transition window with a preset duration is executed, and the ratio of energy storage output power to grid-supplied power is gradually adjusted within this window.

[0070] Specifically, the smooth transition window is set to 30 seconds by default. During the transition period, the system gradually changes the output ratio of each power source according to a linear or S-shaped curve. For example, when switching from "grid-based" to "energy storage-based", the grid power decreases by 2kW per second, while the energy storage power increases by 2kW simultaneously, keeping the total output power constant.

[0071] S5: Based on the global optimal charging strategy, the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local and shared energy storage units, are synchronously executed and controlled.

[0072] In this embodiment, synchronous execution refers to broadcasting strategy commands to all relevant devices within 1 second through the site energy management system (EMS), including dual-gun power modules, local DC / DC converters, shared energy storage PCS (energy storage converter), and photovoltaic inverters. Operational status control includes setting target power, start / stop commands, and SOC protection thresholds, achieving closed-loop control via CAN or Modbus TCP protocols.

[0073] For example, at 18:30:02, the EMS sends the following command to charging pile "DCZ-07": "Mode=Fast Charging Guarantee, Gun1_Power=60kW, Gun2_Power=0kW"; simultaneously, it sends the following command to the shared energy storage PCS: "Discharge_Power=62kW (including 2kW line loss), Min_SOC=15%". After parsing the command, the on-pile controller shuts off the grid power contactor, activates the energy storage power supply path, and adjusts the DC / DC output to 60kW. The energy storage PCS synchronously adjusts the discharge current, and the SOC slowly decreases from 62%. The entire process is completed within 500ms, and monitoring continues until charging is finished.

[0074] Further, in step S4, a dual-gun power allocation scheme for each dual-gun charging station in the current time period is generated, including: S401: Obtain the remaining battery power, maximum allowable charging power, and user-set expected departure time of the electric vehicles currently connected to the first and second charging stations.

[0075] In this embodiment, the expected departure time is set by the user through input on the charging pile touchscreen, during APP reservation, or predicted by historical behavior models, such as a 30-minute timeframe typically set by ride-hailing drivers. These parameters are refreshed each time charging begins or a scheduling cycle starts.

[0076] S402: Based on the total available power of the two guns, a dynamic proportional allocation algorithm is used to calculate the target power of each gun. The dynamic proportional allocation algorithm aims to minimize the weighted charging completion time, and the weights are determined by the user type label and the carbon efficiency synergy factor.

[0077] In this embodiment, the total available power of the dual guns refers to the maximum output power that the pile can call upon during the current time period, which is issued by the global optimal strategy.

[0078] Specifically, the dynamic proportional allocation algorithm constructs the following optimization problem: ,in Estimate the charging time for the i-th gun. The required power is estimated based on SOC and departure time; For power allocation; weighting User type tags ∈{1.0 (high priority), 0.6 (normal)}; This is the service priority weighting coefficient.

[0079] S403: If the target power of any gun exceeds the corresponding vehicle's allowable limit, the excess power will be redistributed to another gun or temporarily stored in the local energy storage unit.

[0080] In this embodiment, after calculating the initial allocation, the system performs a vehicle constraint check: if the target power of any gun exceeds the corresponding vehicle's allowable upper limit, it is clamped to the vehicle's allowable upper limit power value. Remaining available power. ΔP>0: indicates there is surplus power that can be reused; ΔP=0: power has been fully allocated; ΔP<0: indicates total demand exceeds available power and needs to be re-optimized. This represents the maximum total available output power of the dual-gun charging station during the current time period. The actual power allocated to the i-th gun after being constrained by the vehicle is the power after clamping.

[0081] Specifically, the priority processing order is as follows: if the other gun has not reached its limit and its estimated charging time can be further shortened, then additional allocation is made; if both guns are fully loaded or have no benefit, then ΔP is converted to charge the local energy storage unit (if SOC < 85%) to achieve energy storage and avoid waste.

[0082] S404: In fast charging mode, if only one gun is used, all available power will be supplied until 60kW is reached; if both guns are used at the same time, the power will be allocated according to the power gap between the two vehicles, but the power of each gun will not be lower than the preset low power threshold.

[0083] In this embodiment, the power shortage ratio = the charging power required for the first charging gun / (the charging power required for the first charging gun + the charging power required for the second charging gun); the preset low power threshold is 30kW. In fast charging mode, the system prioritizes ensuring the lower power limit, and then considers proportional fairness. For example, in a scenario where both charging guns are connected to a charging pile, the first charging gun (SOC=10%, requires 50kWh), the second charging gun (SOC=30%, requires 30kWh), and the total power shortage is 80kWh. The power shortage ratio = 50:30 = 5:3.

[0084] In one embodiment, such as Figure 3 As shown, a smart charging method for charging piles integrating photovoltaics and energy storage also includes: S10: When the temperature of the local energy storage unit exceeds 65°C or the SOC is below 10%, exit the fast charging mode and switch to the limited grid supply mode, while pushing a temporary limited fast charging capability prompt to the user.

[0085] In this embodiment, the local energy storage unit refers to the auxiliary battery pack integrated inside the charging pile, with a typical capacity of 10–20 kWh, used to support instantaneous high-power output in the fast-charging mode. The grid-restricted mode refers to limiting the maximum output power (e.g., ≤45 kW) and prohibiting simultaneous dual-gun fast charging while ensuring basic charging services, in order to reduce the impact on the power grid. When the local energy storage temperature is <60℃ for 5 consecutive minutes and the SOC >15%, the fast-charging mode is automatically restored. Push notifications are sent via the charging pile screen, the accompanying app, or SMS, including the reason and the estimated recovery time.

[0086] S20: When the communication of the shared energy storage system is interrupted or the normalized value of the transformer area load is higher than 0.95 for more than 3 minutes, the discharge operation in the peak-valley arbitrage mode is suspended and switched to a hybrid mode of direct photovoltaic supply plus grid compensation only.

[0087] In this embodiment, a shared energy storage system communication interruption refers to the site EMS not receiving Modbus TCP or CAN messages from the energy storage PCS within three consecutive heartbeat cycles; the normalized value of the transformer area load = current total load / transformer rated capacity. The hybrid mode of direct photovoltaic power supply plus grid supplementation means prioritizing photovoltaic output, with any shortfall supplemented by the grid. If shared energy storage communication is restored within 10 minutes, peak-valley arbitrage conditions are reassessed; otherwise, it switches to a long-term hybrid mode.

[0088] S30: If the power module fails in the first or second gun, the vehicle to be serviced will be guided to another working gun, the fault information will be marked on the human-machine interface, and the faulty pile will be added to the maintenance queue.

[0089] In this embodiment, power module faults include: IGBT overcurrent, DC bus undervoltage, insulation resistance <500 Ω / V, or temperature sensor failure, which are diagnosed in real time by the charging pile's internal self-test circuit and reported via the CAN bus. Guiding to another working charging station is achieved through voice prompts, on-screen arrow animations, and APP navigation; the maintenance queue is a priority task list from the site's operation and maintenance platform, including fault type, occurrence time, and charging pile location.

[0090] In one embodiment, the energy storage charging and discharging control in the peak-valley arbitrage mode includes: Based on formula (1), the dispatchable power of the shared energy storage system in the peak-valley arbitrage mode is constrained and calculated: in This represents the net dispatch power of the energy storage system within time period t, with positive values ​​indicating charging and negative values ​​indicating discharging. To improve energy storage charging efficiency; For energy storage discharge efficiency; This refers to the maximum grid-supplied supplementary power capacity allowed during off-peak electricity pricing periods; The total electric vehicle charging power demand of the dual-gun charging piles during the current time period; These are indicator functions for off-peak and peak electricity price periods, respectively, taking values ​​of 0 or 1; that is, a value of 1 is taken if the current period belongs to that electricity price type, and a value of 0 is taken otherwise. The energy storage state of charge (SOC(t)) must satisfy: ,in , This is for a safety buffer margin.

[0091] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0092] In one embodiment, a smart charging processing system for charging piles integrating photovoltaic and energy storage is provided, which corresponds to a smart charging processing method for charging piles integrating photovoltaic and energy storage in the above embodiment.

[0093] A smart charging processing system for charging piles integrating photovoltaic and energy storage includes a data identification module, a model building and optimization module, a coupling relationship calculation module, a strategy generation module, and a collaborative execution module. Detailed descriptions of each functional module are as follows: The data recognition module is used to identify the historical charging interval data of multiple dual-gun charging piles during operation based on the historical operation dataset of the target charging station, and to obtain the three-mode working status information of each dual-gun charging pile. The model building and optimization module is used to obtain the historical control dataset of each dual-gun charging pile by identifying the pile body, and to perform joint training and optimization based on charging efficiency and grid carbon emission factor in the preset initial intelligent charging control model to obtain the optimized dual-gun charging control model. The coupling relationship calculation module is used to calculate the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer area load, and obtain the scheduling correlation coefficient of all dual-gun charging piles. The strategy generation module is used to dynamically determine the charging mode to be activated in the optimized dual-gun charging control model based on the scheduling correlation coefficient, real-time grid status, electricity price period label, current carbon emission factor and user interaction instructions, and generate the dual-gun power allocation scheme for each dual-gun charging pile in the current period to form the global optimal charging strategy. The collaborative execution module is used to synchronously execute and close-loop control the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local energy storage unit and the shared energy storage unit, according to the global optimal charging strategy.

[0094] For specific limitations regarding the intelligent charging processing system for integrated photovoltaic and energy storage charging piles, please refer to the limitations of the intelligent charging processing method for integrated photovoltaic and energy storage charging piles mentioned above, which will not be repeated here. Each module in the aforementioned intelligent charging processing system for integrated photovoltaic and energy storage charging piles can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0095] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores historical operating datasets, dual-gun charging control models, etc. The network interface communicates with external terminals via a network. When the computer program is executed by the processor, it implements a smart charging processing method for charging piles integrating photovoltaic and energy storage.

[0096] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a smart charging processing method for a charging pile integrating photovoltaic and energy storage.

[0097] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a smart charging processing method for a charging pile integrating photovoltaic and energy storage.

[0098] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0100] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An integrated photovoltaic and energy storage charging pile intelligent charging processing method, characterized in that, include: Based on the historical operation dataset of the target charging station, the historical charging interval data of multiple dual-gun charging piles during operation are identified, and the three-mode working status information of each dual-gun charging pile is obtained. The dual-gun charging pile is associated with the pile body identifier. Based on the pile identifier, the historical control dataset of each dual-gun charging pile is obtained. In the preset initial intelligent charging control model, joint training optimization based on charging efficiency and grid carbon emission factor is carried out to obtain the optimized dual-gun charging control model. Calculate the multidimensional coupling factor between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer area load, and obtain the scheduling correlation coefficient of all dual-gun charging piles; In the optimized dual-gun charging control model, based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor and user interaction instructions, the charging mode to be activated is dynamically determined, and a dual-gun power allocation scheme for each dual-gun charging pile in the current time period is generated to form a globally optimal charging strategy. Based on the global optimal charging strategy, the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local and shared energy storage units, are synchronously executed and controlled. 2.The integrated photovoltaic and energy storage charging pile intelligent charging processing method according to claim 1, characterized in that, The three-mode operating status information includes fast charging mode, peak-valley arbitrage mode, and manual mode. Each mode corresponds to different power source strategies and dual-gun output rules, specifically including: The fast charging mode is activated when a high-priority user is detected to be connected to any dual-gun port or the grid voltage fluctuation rate exceeds a preset threshold. It prioritizes the dispatch of local energy storage units and shared energy storage systems to supply power to the corresponding charging gun, so that the output power of a single gun is not less than 60kW and the total output power of dual guns is not less than 90kW when running. The peak-valley arbitrage model initiates the energy storage replenishment process when the current electricity price tag is in the low-valley range. Direct grid power supply is prohibited during peak or peak electricity price ranges. The energy storage system discharges to electric vehicles, and the discharge revenue and carbon emission savings are included in the station operation optimization goals. The manual mode is actively selected and triggered by the user to temporarily override the automatic scheduling logic. It allows the user to specify whether to use the first or second charging gun, set the target charging amount, or lock the current power level. In manual mode, the automatic mode determination based on carbon emission factor and charging efficiency is paused until the user confirms exit or the charging task is completed. 3.The charging pile intelligent charging processing method integrating photovoltaic and energy storage according to claim 1, characterized in that, The multidimensional coupling factors include photovoltaic absorption capacity factor, energy storage availability factor, transformer area load sensitivity factor, and carbon efficiency synergy factor; the calculation of the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, shared energy storage system, and transformer area load yields the scheduling correlation coefficients of all dual-gun charging piles, including: Extract the actual photovoltaic feed-in power, energy storage state of charge (SOC), normalized load value of the distribution area, grid carbon emission factor, and dual-gun port load rate for each scheduling period from the historical operation dataset. Calculate the photovoltaic absorption capacity factor based on the photovoltaic prediction deviation and the actual absorption ratio; Based on the remaining dischargeable capacity, maximum charge and discharge power, and cycle life loss model of the energy storage unit, the energy storage availability factor is quantified. By combining whether the load of the transformer area is in the peak range and the fluctuation slope during the current period, a load sensitivity factor for the transformer area is constructed. Based on the comprehensive carbon emission intensity corresponding to a unit of charging power, a carbon efficiency synergy factor is constructed. The photovoltaic absorption capacity factor, energy storage availability factor, transformer area load sensitivity factor, and carbon efficiency synergy factor are normalized and then weighted and fused to generate the scheduling correlation coefficient of each dual-gun charging pile. 4.The charging pile intelligent charging processing method integrating photovoltaic and energy storage according to claim 1, characterized in that, In the optimized dual-gun charging control model, based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor, and user interaction commands, the current charging mode to be activated is dynamically determined, including: A three-mode activation priority matrix is ​​constructed, in which the fast charging mode has the highest priority, the peak-valley arbitrage mode is second, and the manual mode only takes effect when the user explicitly triggers it and there are no high-priority events. When an electric vehicle with less than 20% battery power is detected at any dual-gun port and the user marks it as emergency charging, or when the absolute value of the voltage deviation in the distribution area exceeds 8% of the rated value, the fast charging mode will be forcibly activated and locked for at least 10 minutes. When the fast charging guarantee condition is not triggered and the current electricity price tag is in the low or high range, the peak-valley arbitrage profit index is calculated by combining the carbon efficiency synergy factor and the energy storage availability factor. If the peak-valley arbitrage profit index is greater than the preset threshold, the peak-valley arbitrage mode is activated. When a user selects manual mode through the human-machine interface and there is no risk to power grid safety, the user is allowed to enter manual mode. Before each mode switch, a smooth transition window with a preset duration is executed, during which the ratio of energy storage output power to grid-supplied power is gradually adjusted. 5.The charging pile intelligent charging processing method integrating photovoltaic and energy storage according to claim 1, characterized in that, The generation of the dual-gun power allocation scheme for each dual-gun charging pile in the current time period includes: Obtain the remaining battery power, maximum allowable charging power, and user-set expected departure time of the electric vehicles currently connected to the first and second charging stations; Based on the total available power of the dual guns, a dynamic proportional allocation algorithm is used to calculate the target power of each gun. The dynamic proportional allocation algorithm aims to minimize the weighted charging completion time, and the weights are determined by the user type label and the carbon efficiency synergy factor. If the target power of any gun exceeds the corresponding vehicle's allowable limit, the excess power will be redistributed to another gun or temporarily stored in the local energy storage unit. In fast charging mode, if only one gun is used, all available power will be supplied until it reaches 60kW; if both guns are used at the same time, the power will be allocated according to the power gap between the two vehicles, but the power of each gun will not be lower than the preset low power threshold. 6.The charging pile intelligent charging processing method integrating photovoltaic and energy storage according to claim 1, characterized in that, Also includes: When the temperature of the local energy storage unit exceeds 65°C or the SOC is below 10%, the fast charging mode will be exited and switched to the restricted grid supply mode. At the same time, a notification will be sent to the user that the fast charging capability is temporarily limited. When the shared energy storage system communication is interrupted or the normalized value of the transformer area load remains above 0.95 for more than 3 minutes, the discharge operation in the peak-valley arbitrage mode is suspended and switched to a hybrid mode of direct photovoltaic power supply plus grid compensation only. If the power module fails in the first or second charging station, the vehicle waiting for service will be guided to another working charging station, the fault information will be marked on the human-machine interface, and the faulty charging station will be added to the maintenance queue.

7. The intelligent charging method for charging piles integrating photovoltaic and energy storage according to claim 2, characterized in that, The energy storage charging and discharging control of the peak-valley arbitrage mode includes: Based on formula (1), the dispatchable power of the shared energy storage system in the peak-valley arbitrage mode is constrained and calculated: in This represents the net dispatch power of the energy storage system within time period t, with positive values ​​indicating charging and negative values ​​indicating discharging. To improve energy storage charging efficiency; For energy storage discharge efficiency; This refers to the maximum grid-supplied supplementary power capacity allowed during off-peak electricity pricing periods; The total electric vehicle charging power demand of the dual-gun charging piles during the current time period; These are the indicator functions for off-peak and peak electricity price periods, respectively, with values ​​of 0 or 1; The energy storage state of charge (SOC(t)) must satisfy: ,in , This is for a safety buffer margin.

8. A smart charging processing system for charging piles integrating photovoltaic and energy storage, characterized in that, The system is used to execute the intelligent charging processing method for an integrated photovoltaic and energy storage charging pile as described in any one of claims 1 to 7, the system comprising: The data recognition module is used to identify the historical charging interval data of multiple dual-gun charging piles during operation based on the historical operation dataset of the target charging station, and to obtain the three-mode working status information of each dual-gun charging pile. The model building and optimization module is used to obtain the historical control dataset of each dual-gun charging pile by the pile identification, and to perform joint training and optimization based on charging efficiency and grid carbon emission factor in the preset initial intelligent charging control model to obtain the optimized dual-gun charging control model. The coupling relationship calculation module is used to calculate the multidimensional coupling factors between each dual-gun charging pile and photovoltaic output, shared energy storage system and transformer area load, and obtain the scheduling correlation coefficient of all dual-gun charging piles. The strategy generation module is used to dynamically determine the charging mode to be activated in the optimized dual-gun charging control model based on the scheduling correlation coefficient, real-time grid status, electricity price time period label, current carbon emission factor and user interaction instructions, and generate a dual-gun power allocation scheme for each dual-gun charging pile in the current time period to form a globally optimal charging strategy. The collaborative execution module is used to synchronously execute and close-loop control the output power of the first and second guns of each dual-gun charging pile in the target charging station, as well as the operating status of the local energy storage unit and the shared energy storage unit, according to the global optimal charging strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent charging processing method for an integrated photovoltaic and energy storage charging pile as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent charging processing method for an integrated photovoltaic and energy storage charging pile as described in any one of claims 1 to 7.

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