A charging pile intelligent charging processing method and device integrating photovoltaic and energy storage

Abstract

The application discloses a charging pile intelligent charging processing method and device integrating photovoltaic and energy storage, and relates to the technical field of intelligent charging. The method comprises the following steps: identifying historical charging interval data of a plurality of double-gun charging piles in a running process to obtain three-mode working state information of the double-gun charging piles; performing joint training optimization of charging efficiency and power grid carbon emission factors in an initial intelligent charging regulation model to obtain a double-gun charging regulation model; calculating scheduling correlation coefficients of all double-gun charging piles; determining a charging mode to be activated in the double-gun charging regulation model, generating a double-gun power distribution scheme of each double-gun charging pile in a current time period, forming a globally optimal charging strategy, and regulating output powers of a first gun and a second gun of each double-gun charging pile and an operating state of an energy storage unit in a target charging station. The application provides a new charging management mechanism which can balance charging service continuity and operation economy in a power grid capacity limited scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent charging, in particular to a charging pile intelligent charging processing method and device integrated with photovoltaic and energy storage. BACKGROUND

[0002] With the rapid increase of new energy vehicle penetration rate, urban public charging stations are facing increasingly severe power supply and demand contradictions. Especially in the charging hotspot areas formed by the transformation of old urban areas or industrial parks, the distribution network infrastructure is outdated, and the transformer capacity of the transformer area is generally insufficient (usually less than 500 kVA), and it is difficult to expand. In such scenarios, the centralized access of multiple high-power direct current fast charging piles can easily cause voltage sag, line overload, and even protection tripping, which seriously restricts the charging service capability.

[0003] Existing charging piles mostly use fixed power distribution or simple time-sharing control strategy, although some products integrate photovoltaic and energy storage modules, but their operation mode is fragmented: photovoltaic direct supply is given priority, and energy storage is only used as backup, lacking real-time stability sensing capability of the power grid. When the power grid voltage fluctuates or is in a peak high-carbon period, the system cannot dynamically switch to a power supply path dominated by energy storage, resulting in forced power reduction of fast charging service and decreased user experience; and in the low-carbon period, the low-cost green electricity is not fully utilized to economically charge the energy storage, causing waste of energy costs.

[0004] Therefore, there is an urgent need for a new charging management mechanism that can balance charging service continuity and operation economy in a power grid capacity limited scenario, to cope with the multiple challenges brought by frequent voltage fluctuations, electricity price period changes, and dynamic adjustment of carbon emission intensity, and to avoid the decline of fast charging capability or out-of-control energy costs due to rigid power supply path. SUMMARY

[0005] In order to solve the problems of the prior art, provide a new charging management mechanism that can balance charging service continuity and operation economy in a power grid capacity limited scenario, the present application provides a charging pile intelligent charging processing method and device integrated with photovoltaic and energy storage.

[0006] In the first aspect, the application aims to achieve the following technical solutions:

[0007] A charging pile intelligent charging processing method integrated with photovoltaic and energy storage, comprising:

[0008] According to the historical operation data set of the target charging station, historical charging interval data of a plurality of double-gun charging piles in the running process is identified, and three-mode working state information of each double-gun charging pile is obtained, the double-gun charging pile being associated with a pile body identifier;

[0009] Based on the pile body identification, historical regulation and control data sets of each double-gun charging pile are obtained, and joint training optimization based on charging efficiency and power grid carbon emission factor is performed in a preset initial intelligent charging regulation and control model to obtain an optimized double-gun charging regulation and control model;

[0010] Multi-dimensional coupling factors between each double-gun charging pile and photovoltaic output, shared energy storage system and transformer area load are calculated to obtain scheduling correlation coefficients of all double-gun charging piles;

[0011] In the optimized double-gun charging regulation and control model, according to the scheduling correlation coefficients, real-time power grid state, electricity price period label, current carbon emission factor and user interaction instruction, the current charging mode to be activated is dynamically determined, and a double-gun power distribution scheme, energy storage charge and discharge instruction and vehicle pile matching strategy of each double-gun charging pile in the current period are generated to form a globally optimal charging strategy;

[0012] According to the globally optimal charging strategy, 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 local and shared energy storage units are synchronously executed and regulated.

[0013] By adopting the above technical scheme, the three-mode working state information includes the fast charging mode, the peak-valley arbitrage mode and the manual mode, each mode corresponds to different power source strategies and double-gun output rules. The charging efficiency represents the ratio of effective charging power to total energy consumption per unit time, and the power grid carbon emission factor is the carbon dioxide emission intensity corresponding to unit grid power supply in the scheduling period. The present application provides an intelligent charging management method with photovoltaic access and energy storage function. Firstly, by introducing three-mode working state information (fast charging, peak-valley arbitrage, manual), and binding it with the pile body identification, the charging behavior in different operating scenarios is modeled in detail, avoiding service interruption or economic loss caused by traditional single scheduling strategy under grid fluctuation or electricity price change. Secondly, the double-objective mechanism of joint training optimization of charging efficiency and power grid carbon emission factor is adopted, so that the double-gun charging regulation and control model not only pursues the maximization of energy conversion efficiency, but also actively responds to the low-carbon operation demand of the power grid, significantly reducing the carbon footprint of unit charging power under the premise of guaranteeing the charging experience of users. Thirdly, by constructing multi-dimensional coupling factors and calculating scheduling correlation coefficients, the heterogeneous information such as photovoltaic output uncertainty, energy storage availability and transformer area load pressure is integrated into a unified decision basis. Finally, based on real-time power grid state, electricity price label, carbon emission factor and user instruction, a globally optimal charging strategy is dynamically generated, and the double-gun output power and local and shared energy storage units are synchronously regulated, which not only guarantees the fast charging demand of high-priority users, but also improves the operating income of the station through peak-valley arbitrage in non-emergency period, truly balancing service stability and low-carbon economy.

[0014] In a preferred example of the present application: the three-mode working state information includes a fast-charging mode, a peak-valley arbitrage mode, and a manual mode, each mode corresponding to different power source strategies and double-gun output rules, specifically including:

[0015] The fast-charging mode is activated when any double-gun port accesses a high-priority user or the grid voltage fluctuation rate exceeds a preset threshold, preferentially scheduling the local energy storage unit and the shared energy storage system to supply power to the corresponding charging gun, so that the single-gun output power is not less than 60kW, and the total output power when the double guns are running is not less than 90kW;

[0016] The peak-valley arbitrage mode starts the energy storage power supply process when the current period electricity price label belongs to the low valley interval, prohibits direct grid power supply in the high peak or sharp peak electricity price interval, discharges the electric vehicle through the energy storage system, and includes the discharge benefit and the carbon emission saving amount into the station operation optimization target;

[0017] The manual mode is triggered by user active selection, used to temporarily override the automatic scheduling logic, allowing the user to specify the use of the first gun or the second gun, set the target charging capacity, or lock the current power level. The automatic mode judgment based on the carbon emission factor and the charging efficiency is suspended in the manual mode until the user confirms to exit or the charging task is completed.

[0018] By adopting the above technical solution, the trigger conditions and power rules of the three modes of fast-charging, peak-valley arbitrage, and manual are clearly defined. The system can prioritize the use of energy storage to ensure 60kW single-gun output during grid anomalies, automatically switch to the energy storage arbitrage mode when the electricity price is favorable, and retain a user intervention channel. This effectively avoids service degradation caused by grid fluctuations or revenue loss caused by rigid strategies.

[0019] In a preferred example of the present application: the multi-dimensional coupling factor includes a photovoltaic accommodation capacity factor, an energy storage availability factor, a substation load sensitivity factor, and a carbon efficiency coordination factor; the multi-dimensional coupling factor between each double-gun charging pile and the photovoltaic output, the shared energy storage system, and the substation load is calculated to obtain the scheduling correlation coefficient of all double-gun charging piles, including:

[0020] The actual photovoltaic feed-in power, the energy storage state of charge (SOC), the normalized load value of the substation, the grid carbon emission factor, and the double-gun port load rate of each scheduling period are extracted from the historical operation data set;

[0021] Based on the photovoltaic prediction deviation and the actual accommodation ratio, the photovoltaic accommodation capacity factor is calculated;

[0022] According to the remaining dischargeable capacity, the maximum charge and discharge power, and the cycle life loss model of the energy storage unit, the energy storage availability factor is quantified;

[0023] A load sensitivity factor of the transformer area is constructed according to whether the transformer area load is in a peak interval and a fluctuation slope in the current period;

[0024] A carbon efficiency coordination factor is constructed based on the integrated carbon emission intensity corresponding to the unit charging capacity;

[0025] The photovoltaic consumption capacity factor, the energy storage availability factor, the transformer area load sensitivity factor and the carbon efficiency coordination factor are normalized and weighted to generate a scheduling correlation coefficient of each double-gun charging pile.

[0026] By adopting the above technical solution, the four-dimensional coupling factors of photovoltaic consumption capacity, energy storage availability, transformer area load sensitivity and carbon efficiency coordination are introduced, and the scheduling correlation coefficient is generated by normalization and weighting, so that the system can comprehensively evaluate the dispatchability of photovoltaic storage resources, the load bearing pressure of distribution network and the carbon emission cost, which is beneficial to overcome the problems of fragmentation of multi-source information and strong blindness of scheduling in the prior art.

[0027] In a preferred example of the present application: in the optimized double-gun charging regulation model, according to the scheduling correlation coefficient, the real-time power grid state, the electricity price period label, the current carbon emission factor and the user interaction instruction, the current charging mode to be activated is dynamically determined, including:

[0028] 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 is only effective when the user explicitly triggers and there is no high-priority event;

[0029] When any double-gun port accesses an electric vehicle with a charging capacity lower than 20% and the user marks it as emergency charging, or the absolute value of the transformer area voltage deviation exceeds 8% of the rated value, the fast charging mode is forcibly activated and locked for at least 10 minutes;

[0030] When the fast charging condition is not triggered and the current electricity price label is in the low valley or peak interval, the peak-valley arbitrage profit index is calculated based on the carbon efficiency coordination factor and the energy storage availability factor, and if the peak-valley arbitrage profit index is greater than a preset threshold, the peak-valley arbitrage mode is activated;

[0031] When the user selects the manual mode through the human-machine interface and there is no power grid safety risk, the manual mode is allowed to be entered;

[0032] A smooth transition window within a preset time period is performed before each mode switching, and the proportion of energy storage output power and grid supply power is gradually adjusted in the window.

[0033] By adopting the above technical solution, a three-mode priority matrix is constructed and a forced activation condition is set to ensure that the fast charging service is not disturbed by economic strategies in high-priority scenarios; at the same time, a smooth transition window is introduced to avoid power mutation caused by mode switching and improve safety performance.

[0034] The application in a preferred example: the generation of each double-gun charging pile in the current period of double-gun power allocation scheme, comprising:

[0035] Obtain the remaining battery capacity, maximum allowable charging power and user-set expected departure time of the electric vehicle currently accessing the first gun and the second gun;

[0036] Based on the total available power of the double guns, a dynamic proportional allocation algorithm is used to calculate the target power of each gun, and the dynamic proportional allocation algorithm aims to minimize the weighted charging completion time, and the weight is determined by the user type label and the carbon efficiency coordination factor;

[0037] If the target power of any gun exceeds the corresponding vehicle allowable upper limit, the excess power is redistributed to another gun or temporarily stored in the local energy storage unit;

[0038] In the fast charging mode, if only a single gun is used, all available power is supplied until 60kW is reached; if both guns are used simultaneously, the power is distributed in proportion to the power gap of the two vehicles, but the power of each gun is not less than the preset low power threshold.

[0039] By using the above technical scheme, the dynamic proportional allocation algorithm enables the double-gun power to be intelligently allocated according to actual demand and low-carbon weight, and to be flexibly readjusted when the limit is exceeded or a single gun is used, thereby shortening the charging waiting time of high-priority users and avoiding power waste.

[0040] The application in a preferred example further comprises:

[0041] When it is detected that the temperature of the local energy storage unit exceeds 65℃ or the SOC is lower than 10%, the fast charging mode is exited and switched to the limited grid supply mode, and a prompt is pushed to the user that the fast charging capability is temporarily limited;

[0042] When the shared energy storage system communication is interrupted or the normalized load value of the station area is continuously higher than 0.95 for more than 3 minutes, the discharging operation in the peak-valley arbitrage mode is suspended and the hybrid mode of only photovoltaic direct supply plus grid supplement is switched to;

[0043] If a power module fault occurs in the first gun or the second gun, the vehicle to be served is guided to the other normal gun, and the fault information is marked on the human-machine interface, and the identification of the faulty pile is added to the maintenance queue.

[0044] By using the above technical scheme, the temperature threshold and the SOC threshold of the local energy storage unit, and the continuous over-limit criterion of the shared energy storage system and the gun-end fault detection are set, the charging pile system is degraded to run, the availability and operation efficiency of the system under extreme conditions are improved, the entire pile body is avoided from being shut down due to local faults, and the reliability of public charging facilities is improved.

[0045] In a preferred example, the energy storage charge and discharge control of the peak-valley arbitrage mode includes:

[0046] Based on formula (1), the dispatchable power of the shared energy storage system in the peak-valley arbitrage mode is calculated:

[0047] Wherein is the net dispatchable power of the energy storage system in period t, a positive value indicates charging, and a negative value indicates discharging; is the energy storage charging efficiency; is the energy storage discharging efficiency; is the maximum grid-supplied power allowed during the low-valley power period; is the total electric vehicle charging demand power of the double-gun charging pile in the current period; are the indicator functions of the low-valley and peak power periods, respectively, taking values of 0 or 1;

[0048] The state of charge SOC(t) of the energy storage needs to satisfy: Wherein , is the safety buffer margin.

[0049] By adopting the above technical solution, the net dispatchable power of the energy storage and the SOC safety boundary are constrained, ensuring that the peak-valley arbitrage operation is always within the safety margin of the equipment, preventing overcharging and overdischarging; at the same time, the low-valley power supply and the peak power discharging power are dynamically bound with the actual charging demand, avoiding invalid cycles.

[0050] In a second aspect, the application aims to achieve the following technical solutions:

[0051] An integrated photovoltaic and energy storage charging pile intelligent charging processing system for executing an integrated photovoltaic and energy storage charging pile intelligent charging processing method as described above, the system comprising:

[0052] A data recognition module for recognizing historical charging interval data of multiple double-gun charging piles in the running process according to historical operation data sets of a target charging station, and obtaining three-mode working state information of each double-gun charging pile;

[0053] A model construction optimization module for obtaining historical control data sets of each double-gun charging pile based on the pile body identification, and performing joint training and optimization based on charging efficiency and grid carbon emission factors in a preset initial intelligent charging control model to obtain an optimized double-gun charging control model;

[0054] A coupling relationship calculation module for calculating multi-dimensional coupling factors between each double-gun charging pile and photovoltaic output, shared energy storage system, and substation load, and obtaining dispatching correlation coefficients of all double-gun charging piles;

[0055] The strategy generation module is configured to determine a current charging mode to be activated in the optimized double-gun charging regulation model according to the scheduling correlation coefficient, a real-time power grid state, a power price period label, a current carbon emission factor, and a user interaction instruction, and generate a double-gun power distribution scheme of each double-gun charging pile in a current period to form a globally optimal charging strategy.

[0056] The cooperative execution module is configured to synchronize execution and closed-loop regulation of output powers of the first gun and the second gun of each double-gun charging pile in the target charging station, and operation states of the local energy storage unit and the shared energy storage unit according to the globally optimal charging strategy.

[0057] In a third aspect, the application achieves the above purposes by adopting the following technical scheme:

[0058] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned integrated photovoltaic and energy storage charging pile intelligent charging processing method when executing the computer program.

[0059] In a fourth aspect, the application achieves the above purposes by adopting the following technical scheme:

[0060] A computer-readable storage medium stores a computer program, and the computer program implements the steps of the above-mentioned integrated photovoltaic and energy storage charging pile intelligent charging processing method when executed by a processor.

[0061] In summary, the present application includes at least one of the following beneficial technical effects:

[0062] 1. The application fuses historical operation data and multi-source real-time information, constructs a double-gun charging regulation model with charging efficiency and power grid carbon emission factor as double targets, and introduces a scheduling correlation coefficient to generate a globally optimal charging strategy, thereby realizing cooperative regulation of double charging gun power and energy storage resources. Compared with the traditional fixed strategy system, the application can guarantee high-power fast charging service while actively responding to power price and carbon emission signals, significantly improving energy utilization economy and low-carbon level of the station, and solving the technical contradiction that fast charging guarantee and green operation cannot be considered under a weak power grid.

[0063] 2. By optimizing the power price period indication function, the system can accurately identify the low-valley and high-peak power price periods, thereby effectively formulating the energy storage charging and discharging strategy; by utilizing the time-of-use price difference, the application realizes charging in the low-power price period and discharging in the high-power price period, thereby significantly improving the economic benefit of energy use. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1is a flowchart of an integrated photovoltaic and energy storage charging pile intelligent charging processing method in an embodiment of the present application;

[0065] Figure 2 is another flowchart of an integrated photovoltaic and energy storage charging pile intelligent charging processing method in an embodiment of the present application;

[0066] Figure 3 is a device schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The present application will be further described in detail below with reference to the accompanying drawings.

[0068] In an embodiment, as shown in Figure 1 The present application discloses an integrated photovoltaic and energy storage charging pile intelligent charging processing method, which specifically comprises the following steps:

[0069] S1: According to the historical operation data set of the target charging station, the historical charging interval data of the multiple double-gun charging piles in the running process is identified, the three-mode working state information of each double-gun charging pile is obtained, and the double-gun charging pile is associated with the pile body identifier.

[0070] In this embodiment, the historical operation data set refers to the structured operation log recorded by all double-gun charging piles in the target charging station in the past at least 30 days, including timestamp, pile body identifier, first gun, second gun port state, real-time output power, cumulative charging power, grid voltage, local energy storage SOC, photovoltaic feed-in power, time-of-use electricity price label and user operation event fields, wherein the port state includes: idle, charging, fault. The historical charging interval data refers to the time-power sequence segment cut out from the continuous operation log, which is a single charging task unit, and the start and end points are determined by the charging gun plug-in signal or power mutation threshold. The three-mode working state information refers to the running mode label to which each charging interval belongs. The three-mode working state information includes fast charging mode, peak-valley arbitrage mode and manual mode, each mode corresponds to different power source strategy and double-gun output rule.

[0071] Specifically, step S1 comprises:

[0072] S11: The fast charging mode is activated when any double-gun port accesses a high-priority user or the grid voltage fluctuation rate exceeds a preset threshold, and the local energy storage unit and the shared energy storage system are preferentially scheduled to supply power to the corresponding charging gun, so that the single-gun output power is not less than 60kW, and the total output power of the double-gun is not less than 90kW when the double-gun is running.

[0073] Specifically, high-priority users refer to electric vehicle users marked by identity authentication as high service levels; identity authentication such as network car platform API docking, VIP membership card recognition, or users actively checking "emergency charging" in the APP. The power grid voltage fluctuation rate is defined as the ratio of the standard deviation of the substation bus voltage in the past 5 seconds to the rated voltage, which is used to represent the stability of power quality, and the rated voltage is, for example, 220V. The preset threshold is set to 8% by default and can be dynamically adjusted according to historical data of the site. Local energy storage units refer to modular battery packs integrated into single piles (capacity ≥20kWh), and shared energy storage systems refer to centralized energy storage at the site level (total capacity ≥200kWh), both of which are connected in parallel through a DC bus and support millisecond-level switching of power supply paths.

[0074] For example, the first gun of the pile body "DCZ-08" is inserted into a special car, and the vehicle-mounted T-Box sends the VIN code to the site server through 4G. The system queries that the vehicle belongs to the operator on the white list, and automatically marks it as "high-priority user". At the same time, EMS detects that the substation voltage drops from 225V to 208V and then rises again between 14:19:50-14:20:00, and the fluctuation rate is calculated as std([225, 210, 208, 215, 222]) / 220≈6.8%, which does not exceed the threshold, but the user priority triggers the fast charging mode. The system immediately disconnects the network supply contactor, instructs the local energy storage to supply power to the first gun at 55kW, and the shared energy storage at 10kW, with an actual output of 60.3kW. If the second gun is also connected to a normal user at this time, the remaining 30kW will be allocated according to the proportion of the power gap (e.g. the first gun needs 60kW and is full, and the second gun gets 30kW), ensuring that the total power is ≥90kW. If the total available power of the energy storage is less than 90kW (e.g. SOC<15%), it will be downgraded to "limited fast charging" and a prompt will be pushed to the user.

[0075] S12: The peak-valley arbitrage mode starts the energy storage power supply process when the current period price tag belongs to the low valley interval, and prohibits direct network supply power in the peak or sharp peak price interval. The energy storage system discharges to the electric vehicle, and the discharge income and carbon emission saving amount are jointly included in the site operation optimization target.

[0076] In this embodiment, the electricity price tag is parsed from the next day's 96-point (15 minutes per point) time-of-use electricity price file pushed by the provincial power trading center at 0 o'clock every day, divided into low valley, flat section, peak, and peak. The low valley period is 00:00-08:00, the flat section period is 08:00-12:00, 17:00-20:00, the peak period is 12:00-17:00, and the peak period is 20:00-24:00. The energy storage power supply process refers to charging the shared energy storage at the maximum allowed power (such as 80kW) during the low valley period until the SOC reaches 85%, and the maximum allowed power is 80kW. Direct grid power supply is prohibited, which means physically disconnecting the grid supply relay, and only allowing photovoltaic direct supply + energy storage discharge combination power supply. The peak-valley arbitrage mode includes peak-valley arbitrage charging mode and peak-valley arbitrage discharging mode. Discharge income wherein represents the actual discharging power of the energy storage system to the electric vehicle within a certain dispatching period, in kW; Δt is the time length of the discharging behavior; represents the peak (or peak) electricity price of the current period, in yuan / kWh; represents the equivalent use cost of the energy storage system, in yuan / kWh, including: low valley period charging electricity fee (such as 0.3 yuan / kWh) and energy storage cycle loss depreciation (such as 0.07 yuan / kWh), and the typical value is about 0.4 yuan / kWh. Carbon emission saving amount= wherein c is the electricity price and e is the carbon emission factor; represents the carbon emission factor of the current period, in kgCO2 / kWh; represents the life cycle carbon emission factor of photovoltaic power generation, in kgCO2 / kWh. Carbon emission saving amount represents the amount of carbon dioxide emission reduced by replacing 1 kWh of coal power with 1 kWh of energy storage discharging (from green electricity).

[0077] Specifically, at 02:00 on July 6, 2024, when the low valley electricity price is 0.3 yuan / kWh, the system detects that all the vehicles are charging, and automatically starts the peak-valley arbitrage charging mode: closes the grid supply switch, charges the shared energy storage at 80kW, and the SOC rises from 50% to 85% in 2.2 hours. At 19:30 on the same day, when the peak electricity price is 1.5 yuan / kWh, a private car is connected to the pile "DCZ-03" at this time, and the system activates the peak-valley arbitrage discharging mode: disconnects the grid supply, and preferentially uses photovoltaic (30kW at that time) direct supply, and the insufficient part (such as 60kW) is supplemented by 30kW of energy storage. This time, 30kWh is discharged, and the discharge income is 30×(1.5−0.4)=33 yuan ( including depreciation 0.4 yuan / kWh); carbon emission saving amount=30×(0.75−0.05)=21kgCO2 ( =0.75, = 0.05).

[0078] S13: The manual mode is triggered by the user's active selection, used to temporarily override the automatic scheduling logic, allowing the user to specify the use of the first gun or the second gun, set the target charging capacity or lock the current power level, and suspend the automatic mode judgment based on the carbon emission factor and charging efficiency in the manual mode until the user confirms to exit or the charging task is completed.

[0079] In this embodiment, the user's active selection can be achieved by the "mode switching" button of the charging pile touch screen or the remote instruction of the mobile phone APP. Specifying the use of the first gun or the second gun is suitable for the same pile with double guns, but the user prefers one side, such as the right side for easy getting off the car. Setting the target charging capacity means inputting the desired charging kWh number, such as "charging to 40 kWh", and the system adjusts the power curve dynamically accordingly. Locking the current power level means fixing the output to the current gear (such as 60 kW, 90 kW, 120 kW). Suspending the automatic mode judgment means that even if the power grid suddenly changes or enters the peak period, the system will not switch back to the fast charging or peak valley arbitrage, unless the user manually exits or the charging is completed.

[0080] S2: Obtain the historical control data set of each double-gun charging pile based on the pile body identification, 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 double-gun charging control model.

[0081] In this embodiment, the historical control data set refers to the control parameter set corresponding to the historical charging interval, including the mode activated at that time, the double-gun power distribution ratio, the energy storage charging and discharging instruction, the photovoltaic direct supply ratio, and the grid supply power. The initial intelligent charging control model uses a lightweight neural network. The charging efficiency is defined as the ratio of the actual received electric quantity of the vehicle battery to the total input energy consumption of the system; the grid carbon emission factor uses the hourly carbon emission intensity data published by the provincial grid, with the unit of kgCO2 / kWh.

[0082] The joint training target is to minimize the loss function:

[0083]

[0084] wherein is the charging efficiency of the whole station in the scheduling period t, and a and β are weight coefficients; is the total electric energy consumed by the grid supply in the scheduling period t, with the unit of kWh; j is the jth power sampling time in the scheduling period t; is the instantaneous grid supply power at the jth power sampling time; is the duration of the jth sampling interval; n is the total number of samplings within the scheduling period t. As an example, a dedicated control model is constructed for the pile "DCZ-07": the first 150 of the 182 charging interval data are taken as the training set, and the last 32 are taken as the validation set. The input features of each sample include: normalized load of the transformer area (such as 0.62), current carbon emission factor (such as 0.58 kgCO2 / kWh), photovoltaic availability (such as 70%), user type (such as ordinary / urgent), etc.; the output label is the actual executed mode and power ratio, such as [fast charging, 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 deployed to the station edge controller as the optimization double-gun charging control model of the pile.

[0085] S3: Calculate the multi-dimensional coupling factors between each double-gun charging pile and photovoltaic output, shared energy storage system, and transformer area load to obtain the scheduling correlation coefficients of all double-gun charging piles.

[0086] In this embodiment, the multi-dimensional coupling factor is a dimensionless index for quantifying the interaction intensity of the charging pile with external energy systems, reflecting its dependence or influence on light, storage, and grid resources in a specific period. The multi-dimensional coupling factor includes a photovoltaic accommodation capacity factor, an energy storage availability factor, a transformer area load sensitivity factor, and a carbon efficiency coordination factor. The scheduling correlation coefficient is a comprehensive score obtained by weighting and fusing multiple coupling factors, which is used to sort the priority or response sensitivity of each 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 rolling.

[0087] Specifically, step S3 includes:

[0088] S31: Extract the actual photovoltaic feed-in power, energy storage state of charge SOC, normalized load value of the transformer area, grid carbon emission factor, and double-gun port load rate of each scheduling period from the historical operation data set.

[0089] Specifically, the actual photovoltaic feed-in power is the measured power output from the photovoltaic inverter to the DC bus, with a unit of kW; the energy storage state of charge SOC is the remaining percentage of electricity reported by the shared energy storage system BMS; the normalized load value of the transformer area is defined as the ratio of the current total load of the transformer area to the rated capacity of the transformer (such as 500 kW), with a range of [0, 1]; the grid carbon emission factor uses the hourly data published by the provincial ecological environment office, with a unit of kgCO2 / kWh; the double-gun port load rate refers to the ratio of the current total output power of the double guns to their maximum rated power.

[0090] For example, taking the period of 11:00-11:15 on a certain day as an example, the actual photovoltaic feed-in power = 78 kW, the set charging station roof photovoltaic installation 100 kW; shared energy storage SOC = 63%; the total load of the transformer district = 435 kW, then the normalized load value = 435 / 500 = 0.87; the grid carbon emission factor = 0.71 kgCO2 / kWh, indicating that the proportion of coal power is high during the day; the total output of the pile "DCZ-12" double gun is 92 kW, then the load rate = 92 / 120 ≈ 0.77.

[0091] S32: Calculate the photovoltaic consumption capacity factor based on the photovoltaic prediction deviation and the actual consumption ratio.

[0092] Specifically, the photovoltaic prediction deviation refers to the relative error between the photovoltaic output predicted by the NWP (numerical weather prediction) model on the previous day and the actual feed-in power; the actual consumption ratio is defined as the ratio of the actual feed-in power to the theoretical maximum available power; wherein the theoretical maximum available power is calculated from the irradiance and the component efficiency. The photovoltaic consumption capacity factor reflects the on-site consumption potential of the charging pile for photovoltaic power generation under the current working condition, with a value range of [0, 1], and the higher the value, the more suitable it is to use photovoltaic power first.

[0093] For example, the photovoltaic prediction value at 11:00 on the previous day is 85 kW, and the actual value is 78 kW, the prediction deviation = |85-78| / 85 ≈ 8.2%. At the same time, according to the meteorological station data, the theoretical maximum power generation is 90 kW, so the actual consumption ratio = 78 / 90 ≈ 86.7%, and the photovoltaic consumption factor is calculated using the weighted formula = 0.4 × (1-0.082) + 0.6 × 0.867 ≈ 0.367 + 0.520 = 0.887, in the alternative, if the prediction deviation > 20%, then directly set = consumption ratio.

[0094] S33: Quantify the energy storage availability factor according to the remaining dischargeable capacity, the maximum charge-discharge power, and the cycle life loss model.

[0095] In this embodiment, the remaining dischargeable capacity = SOC-discharge safety lower limit, wherein the discharge safety lower limit is 10%; the maximum charge-discharge power is determined by the PCS (energy storage converter) rated value; the cycle life loss model estimates the current cycle equivalent number of times using the rain flow counting method, and maps it to the health degree SOH; the energy storage availability factor comprehensively reflects the energy availability and power availability of the energy storage, and is attenuated and corrected by SOH.

[0096] ​​​For example, the current shared energy SOC is 63%, so the remaining dischargeable capacity is 63%-10%=53%. The PCS maximum discharge power is 100kW, the current temperature is 25℃, and there is no derating. The SOH is evaluated by the BMS as 92%, and the following formula is used to quantify the energy storage availability factor :

[0097] where 80% is the effective SOC window (10%-90%), =100kW; is the maximum rated charge and discharge power of the energy storage system; is the actual available charge and discharge power of the energy storage system at present. Substituting gives =(53% / 80%)×1.0×0.92≈0.6625×0.92≈0.609. If SOC<15%, then =0.

[0098] S34: In combination with whether the area load is in the peak interval and the fluctuation slope in the current period, an area load sensitivity factor is constructed.

[0099] In the present embodiment, the peak interval of the area load is determined by the electricity price tag or the load history 95% quantile. The fluctuation slope refers to the standard deviation of the load change rate of the past 5 15-minute points; the area load sensitivity factor is used to represent the degree of influence of the newly added charging load on the safety of the area, and the higher the value, the more the grid supply should be limited or the energy storage should be enabled.

[0100] For example, since the current area load normalized value 0.87 is less than 0.9 at this time, but is in the electricity price peak period, it is marked as “quasi-peak”. The load sequence of the past 5 periods is [410, 420, 435, 430, 435]kW, and the slope standard deviation is calculated as approximately 9.2kW / 15min. At this time, the area load sensitivity factor is defined as : where represents the standard deviation of the area load in the recent 5 time windows, with a value such as 0.92; where k=0.02(1 / kW). At this time, the calculation is approximately equal to 0.984, and it is set that when greater than 0.95, the system will suppress the grid supply of new energy, and preferentially call the light storage.

[0101] S35: Based on the comprehensive carbon emission intensity corresponding to the unit charging electricity, a carbon efficiency coordination factor is constructed.

[0102] In the present embodiment, the comprehensive carbon emission intensity refers to the equivalent CO2 emissions generated per 1kWh of charging electricity, and when the power supply composition is considered, the comprehensive carbon emission intensity of the charging pile in a charging task is calculated based on the following formula : wherein is the discharging energy from the energy storage system; is the energy storage full life cycle carbon emission factor, with a typical value of 0.25 kgCO2 / kWh; is the direct supply energy from the photovoltaic power generation; is the photovoltaic power generation carbon emission factor, with a value of 0.05. Carbon efficiency synergy factor is defined as: wherein normalizes the carbon efficiency synergy factor to [0, 1], with a higher value indicating lower carbon.

[0103] S36: The photovoltaic consumption capacity factor, the energy storage availability factor, the substation load sensitivity factor, and the carbon efficiency synergy factor are normalized and weighted to generate a scheduling correlation coefficient of each double-gun charging pile.

[0104] In this embodiment, since the dimensions and distributions of each factor are different, the value of the substation load sensitivity factor may be greater than 1, and the rest are ∈ [0, 1], which need to be normalized to the [0, 1] interval by Min-Max. The weight coefficients are set according to the station operation target: if the economy is emphasized, the weights of and are increased; if the grid safety is emphasized, the weight of is increased. The default weight is [0.3, 0.25, 0.25, 0.2].

[0105] For example, for the 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, normalized to ≈ 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 is used to sort the response priority of the pile in the global scheduling, and the higher the value, the more suitable it is to undertake high power or participate in arbitrage.

[0106] For example, take the period of 10:00-10:15 on July 1, 2024: the system collects the total photovoltaic output of the whole station as 85kW, the shared energy storage SOC as 65%, and the district load as 420kW (rated 500kW, normalized value 0.84). For pile "DCZ-07", the dual-gun load rate is 78%, and the historical same-period photovoltaic consumption ratio is 92%. According to this, the calculation is as follows: photovoltaic consumption capacity factor = 0.92 x (85 / 120) = 0.65, where 120kW is the installed capacity; energy storage availability factor = (65%-10%) / (90%-10%) = 0.69; district load sensitivity factor = 0.84 x 1.2 (since it is on the rising slope) = 1.01, at which time the normalized value is 1; carbon efficiency coordination factor = 0.58 x (1-0.65) = 0.20, where it is assumed that 65% of the electricity comes from photovoltaic. After normalization, the scheduling correlation coefficient is 0.61 according to the weight [0.3, 0.25, 0.25, 0.2] in turn. The target charging station calculates all the charging piles according to this process to generate a scheduling priority list.

[0107] S4: In the optimized dual-gun charging regulation model, according to the scheduling correlation coefficient, the real-time grid state, the electricity price period label, the current carbon emission factor and the user interaction instruction, the current charging mode to be activated is dynamically determined, and the dual-gun power distribution scheme of each dual-gun charging pile in the current period is generated to form a globally optimal charging strategy.

[0108] In this embodiment, the real-time grid state includes the current voltage, frequency and district load rate; the electricity price period label is pushed by the power trading platform API connected by the station in real time, and the electricity price period label includes peak, flat and valley; the user interaction instruction comes from the operation event of the charging pile touch screen or mobile phone APP. The scheduling correlation coefficient, the real-time grid state, the electricity price period label, the current carbon emission factor and the user interaction instruction are spliced into a feature vector and input into the trained regulation model in S2 to output mode decision and power allocation suggestion. The globally optimal strategy coordinates the output of all piles to ensure that the total power does not exceed the district capacity and the carbon emission is minimized.

[0109] For example, on July 1, 2024, at 18:30, set the evening peak electricity price as 1.2 yuan / kWh, the carbon emission factor as 0.72, the first gun of pile "DCZ-07" connected to a network car with 15% of the electricity, the user type marked as "emergency", and the second gun idle. The system reads its real-time features: scheduling correlation coefficient 0.61, district load rate 0.88, and energy storage SOC 62%. The optimized dual-gun charging regulation model determines that the "fast charging protection mode" should be activated, and outputs the power scheme: the first gun 60kW all from the energy storage, and the second gun 0kW. At the same time, the dispatching center checks that the total demand of the whole station does not exceed the limit, confirms that the strategy is feasible, and forms a global strategy to issue.

[0110] Specifically, in the optimized double-gun charging regulation model in step S4, according to the dispatch correlation coefficient, the real-time power grid state, the electricity price period label, the current carbon emission factor and the user interaction instruction, the currently activated charging mode is dynamically determined, including:

[0111] S41: A three-mode activation priority matrix is constructed, in which the safety-critical mode has the highest priority, the peak-valley arbitrage mode is second, and the manual mode is only effective when the user explicitly triggers and there is no high-priority event.

[0112] In this embodiment, the three-mode activation priority matrix is a preset Boolean decision table used to solve the mode conflict problem when multiple conditions occur. The core rule is: safety-critical mode (Safety-Critical) > peak-valley arbitrage mode (Economic) > manual mode (User-Override). The system performs mode determination once every dispatching period, such as every 15 seconds. First, it checks whether the safety-critical trigger condition is met; if not, it evaluates the peak-valley arbitrage feasibility; only when both conditions are not met and the user actively requests, the manual mode is allowed.

[0113] S42: When any double-gun port accesses an electric vehicle with less than 20% of the battery level and the user marks it for emergency charging, or the absolute value of the substation voltage deviation exceeds 8% of the rated value, the safety-critical mode is forcibly activated and locked for at least 10 minutes.

[0114] In this embodiment, the battery level of less than 20% is reported by the vehicle BMS through the CC / CP signal or ISO 15118 protocol; the user marking for emergency charging can be achieved by checking the APP, swiping the VIP card or voice command recognition. The substation voltage deviation is defined as |measured substation voltage - rated voltage|, the substation voltage fluctuation rate = |measured substation voltage - rated voltage| / rated voltage, and the rated voltage = 220V (single-phase) or 380V (three-phase). The 10-minute lock is to avoid frequent mode switching due to transient disturbances.

[0115] S43: When the safety-critical condition is not triggered and the current electricity price label is in the low valley or peak interval, the peak-valley arbitrage profit index is calculated by combining the carbon efficiency coordination 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.

[0116] In this embodiment, the peak-valley arbitrage profit index is a dimensionless score that combines economic benefits and low-carbon benefits, defined as: where, = 1.0 yuan / kWh is the reference electricity price; is the unit electricity price difference of peak-valley arbitrage, is the peak (or peak) period electricity price, Equivalent use cost of energy storage system; γ = 0.7 is economic weight; carbon efficiency synergy factor The preset threshold is 0.65 by default.

[0117] S44: When the user selects the manual mode through the man-machine interface and there is no power grid safety risk, the manual mode is allowed to be entered.

[0118] Specifically, the man-machine interface includes a charging pile touch screen, a matching mobile phone APP or a voice interaction terminal. The condition that there is no power grid safety risk means that the following conditions are simultaneously met: the normalized value of the transformer area load is less than 0.9, the voltage deviation is less than 8%, the shared energy storage communication is normal, and there is no protection alarm such as over-temperature, over-voltage and insulation fault.

[0119] S45: A smooth transition window in a preset time period is performed before each mode switching, and the ratio of the energy storage output power to the grid supply power is gradually adjusted in the window.

[0120] 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 supply according to a linear or S-shaped curve. For example, when switching from “grid supply as the main power supply” to “energy storage as the main power supply”, the grid supply power is reduced by 2kW per second, and the energy storage is simultaneously increased by 2kW per second, and the total output power remains constant.

[0121] S5: According to the global optimal charging strategy, the output power of the first gun and the second gun of each double-gun charging pile in the target charging station, and the running state of the local and shared energy storage units are synchronously executed and controlled.

[0122] In this embodiment, synchronous execution means that the strategy instruction is broadcast to all related devices, including the double-gun power module, the local DC / DC converter, the shared energy storage PCS (energy storage converter) and the photovoltaic inverter, within 1 second through the station energy management system (EMS). The running state control includes setting the target power, the start-stop instruction, the SOC protection threshold and the like, and the closed-loop control is realized through the CAN or ModbusTCP protocol.

[0123] For example, the EMS sends the instruction “Mode = fast charging protection, Gun1_Power = 60kW, Gun2_Power = 0kW” to the pile “DCZ-07” at 18:30:02; at the same time, it sends “Discharge_Power = 62kW (including 2kW line loss), Min_SOC = 15%” to the shared energy storage PCS. After the pile controller parses the instruction, the grid supply contactor is closed, the energy storage power supply path is opened, and the DC / DC output is adjusted to 60kW. The energy storage PCS synchronously adjusts the discharge current, and the SOC slowly decreases from 62%. The whole process is completed within 500ms, and the monitoring is continued until the charging is completed.

[0124] Further, in step S4, a double-gun power distribution scheme for each double-gun charging pile in the current period is generated, including:

[0125] S401: Obtain the remaining battery capacity, maximum allowed charging power and user-set expected departure time of the electric vehicle accessing the first gun and the second gun.

[0126] In the embodiment, the expected departure time is set by the user when inputting on the charging pile touch screen, when reserving through the APP, or predicted through a historical behavior model, such as the 30 minutes often set by a ride-hailing driver. These parameters are refreshed at the start of each charging start or dispatching period.

[0127] S402: Based on the total available power of the double guns, a dynamic proportional allocation algorithm is used to calculate the target power of each gun, which aims to minimize the weighted charging completion time, and the weight is determined by the user type label and the carbon efficiency coordination factor.

[0128] In the embodiment, the total available power of the double guns refers to the maximum output power that can be called by the pile in the current period, which is issued by the global optimal strategy.

[0129] Specifically, the dynamic proportional allocation algorithm constructs the following optimization problem:

[0130] Wherein is the estimated charging time of the i-th gun, is the required power, which is calculated based on the SOC and the departure time; is the allocated power; the weight is the user type label ∈{1.0 (high priority), 0.6 (ordinary)}; is the service priority weight coefficient.

[0131] S403: If the target power of any gun exceeds the corresponding vehicle allowed upper limit, the excess power is redistributed to another gun or temporarily stored in the local energy storage unit.

[0132] In the embodiment, the system performs vehicle constraint verification after calculating the preliminary allocation: if the target power of any gun exceeds the corresponding vehicle allowed upper limit, it is clamped to the power value of the vehicle allowed upper limit. The remaining available power , ΔP>0: indicates that there is surplus power that can be reused, if ΔP=0: the power has been fully allocated, ΔP<0: indicates that the total demand exceeds the available power, and needs to be re-optimized; wherein is the total available output power upper limit of the double-gun charging pile in the current period; is the actual allocated power of the i-th gun after the vehicle constraint limit, which is the clamped power.

[0133] Specifically, the priority processing order is: if the other gun has not reached the upper limit and its estimated charging time can be further shortened, additional allocation is made; if both guns are full or have no benefit, ΔP is converted to charging the local energy storage unit (if SOC < 85%) to achieve energy storage and avoid waste.

[0134] S404: In the fast-charging mode, if only one gun is used, all available power is concentrated to supply until 60kW is reached; if both guns are used simultaneously, the power is allocated in proportion to the power gap of the two vehicles, but the power of each gun is not less than the preset low power threshold.

[0135] In this embodiment, the power gap ratio = the required charging power of the first gun / (the required charging power of the first gun + the required charging power of the second gun); the preset low power threshold is 30kW. In the fast-charging mode, the system prioritizes power lower limit and then considers proportional fairness. For example, in the scenario of two guns accessing the charging pile, the first gun (SOC = 10%, requiring 50kWh), the second gun (SOC = 30%, requiring 30kWh), and the total power gap is 80kWh. The power gap ratio is 50:30 = 5:3.

[0136] In one embodiment, as shown in Figure 3 The intelligent charging processing method of the charging pile integrated with photovoltaic and energy storage further includes:

[0137] S10: When it is detected that the temperature of the local energy storage unit exceeds 65℃ or the SOC is lower than 10%, the fast-charging mode is exited and switched to the limited grid supply mode, and a prompt of temporary limitation of fast-charging capacity is pushed to the user.

[0138] In this embodiment, the local energy storage unit refers to an auxiliary battery pack integrated in the single pile, with a typical capacity of 10-20kWh, used to support the instantaneous high-power output in the fast-charging mode. The limited grid supply mode refers to limiting the maximum output power (e.g. ≤45kW) and prohibiting simultaneous fast-charging of both guns to reduce the impact on the power grid under the premise of guaranteeing basic charging service. When the temperature of the local energy storage is continuously less than 60℃ for 5 minutes and the SOC is greater than 15%, the fast-charging mode is automatically restored. The prompt is achieved through the charging pile screen, the supporting APP or SMS, and the content includes the reason and the estimated recovery time.

[0139] S20: When the communication of the shared energy storage system is interrupted or the normalized value of the area load is continuously higher than 0.95 for more than 3 minutes, the discharging operation in the peak-valley arbitrage mode is suspended and the hybrid mode of photovoltaic direct supply plus grid supplement is converted.

[0140] In the embodiment, the shared energy storage system communication interruption refers to that the station EMS does not receive the Modbus TCP or CAN message from the energy storage PCS in 3 consecutive heartbeat periods; the substation load normalization value = current total load / transformer rated capacity. The photovoltaic direct supply plus grid supplement hybrid mode refers to preferentially using photovoltaic output, and supplementing the insufficient part by the power grid. If the shared energy storage communication is restored within 10 minutes, the peak-valley arbitrage condition is re-evaluated; otherwise, the long-term hybrid mode is switched to.

[0141] S30: If a power module fault occurs in the first gun or the second gun, the vehicle to be served is guided to another normal gun, and the fault information is marked on the human-machine interface, and the identification of the faulty pile body is added to the maintenance queue.

[0142] In the embodiment, the power module fault includes IGBT overcurrent, DC bus under-voltage, insulation resistance <500 Ω / V, or temperature sensor failure, which is diagnosed in real time by the internal self-checking circuit of the charging pile and reported through the CAN bus. Guiding to another normal gun refers to achieving through voice prompts, screen arrow animations, and APP navigation; the maintenance queue is a priority task list of the station operation and maintenance platform, including fault type, occurrence time, and pile body location.

[0143] In an embodiment, the energy storage charge and discharge control in the peak-valley arbitrage mode includes:

[0144] Based on formula (1), the dispatchable power of the shared energy storage system in the peak-valley arbitrage mode is calculated:

[0145] Wherein is the net dispatchable power of the energy storage system in period t, a positive value indicates charging, and a negative value indicates discharging; is the energy storage charging efficiency; is the energy storage discharging efficiency; is the maximum grid-supplement power allowed during the low-valley electricity price period; is the total electric vehicle charging demand power of the double-gun charging pile in the current period; are the indicator functions of the low-valley and peak electricity price periods, respectively, and take values of 0 or 1; that is, the value is 1 when the current period belongs to the electricity price type, and the value is 0 when it does not belong. The energy storage state of charge SOC(t) needs to satisfy: Wherein , is the safety buffer margin.

[0146] It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0147] In an embodiment, an integrated photovoltaic and energy storage charging pile intelligent charging processing system is provided, which corresponds to the integrated photovoltaic and energy storage charging pile intelligent charging processing method in the above embodiment.

[0148] An integrated photovoltaic and energy storage charging pile intelligent charging processing system includes a data recognition module, a model construction optimization module, a coupling relationship calculation module, a strategy generation module, and a collaborative execution module. The detailed description of each functional module is as follows:

[0149] The data recognition module is used to identify historical charging interval data of multiple double-gun charging piles in the running process according to the historical operation data set of the target charging station, and obtain the three-mode working state information of each double-gun charging pile.

[0150] The model construction optimization module is used to obtain the historical control data set of each double-gun charging pile through pile body identification, and perform joint training and optimization based on charging efficiency and grid carbon emission factors in the preset initial intelligent charging control model to obtain an optimized double-gun charging control model.

[0151] The coupling relationship calculation module is used to calculate the multi-dimensional coupling factors between each double-gun charging pile and photovoltaic output, shared energy storage system, and substation load, and obtain the scheduling correlation coefficients of all double-gun charging piles.

[0152] The strategy generation module is used to dynamically determine the current charging mode to be activated in the optimized double-gun charging control model according to the scheduling correlation coefficients, real-time grid state, electricity price period label, current carbon emission factor, and user interaction instructions, and generate a double-gun power distribution scheme for each double-gun charging pile in the current period to form a globally optimal charging strategy.

[0153] The collaborative execution module is used to synchronize the output power of the first gun and the second gun of each double-gun charging pile in the target charging station, the operation state of the local energy storage unit and the shared energy storage unit according to the globally optimal charging strategy, and perform closed-loop control.

[0154] For specific limitations of an integrated photovoltaic and energy storage charging pile intelligent charging processing system, refer to the limitations of an integrated photovoltaic and energy storage charging pile intelligent charging processing method in the above, which will not be repeated here; each module in the above integrated photovoltaic and energy storage charging pile intelligent charging processing system can be realized by software, hardware, and their combinations; each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional units or modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0160] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A smart charging method for charging piles integrating photovoltaic and energy storage, 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. According to 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. 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 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. 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.

2. The intelligent charging method for charging piles 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.

3. The intelligent charging method for charging piles 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.

4. The intelligent charging method for charging piles 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.

5. The intelligent charging method for charging piles integrating photovoltaic and energy storage according to claim 1, 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 provides a safety buffer margin.

6. 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 5, 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.

7. 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 5.

8. 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 5.

Images