A smart charging pile control method and system supporting light storage linkage

Abstract

The application relates to the field of information technology, in particular to a smart charging pile control method and system supporting photovoltaic storage linkage. The method comprises the following steps: acquiring real-time output data of a photovoltaic power generation system in a park, short-time predicted load of the park, charging state information of new energy vehicles, energy storage state data of fast charging and energy storage devices, and driving time of users; according to a preset scheduling period, the real-time output data, the short-time predicted load of the park and the energy storage state data are read, and whether there is light abandonment and power gap in the next scheduling period is judged; when there is light abandonment, new energy vehicles in a chargeable state are dispatched to consume abandoned light power for charging, and the consumed power is recorded; when there is a power gap, new energy vehicles meeting preset discharge conditions are controlled to reversely discharge to the park through charging piles, and the discharged power is recorded; the charging power, the consumed power and the discharged power of the new energy vehicles are respectively subjected to cost measurement, and the final charging cost is obtained.

Description

Technical Field

[0001] This application relates to the field of information technology, specifically to a smart charging pile control method and system that supports photovoltaic and energy storage linkage. Background Technology

[0002] In recent years, the penetration rate of photovoltaic (PV) power generation in industrial park microgrids has been continuously increasing. However, due to the intermittent and fluctuating nature of PV output, it can easily lead to overcapacity in certain periods or insufficient power supply during peak load periods, affecting the stability and economic efficiency of the park's power supply. Simultaneously, the number of new energy vehicles is rapidly increasing, and their power batteries possess mobile energy storage characteristics. Existing technologies already support bidirectional charging and discharging functions in some charging piles, enabling dynamic control of vehicle charging and discharging behavior. However, there is currently a lack of sophisticated scheduling that fully integrates user driving habits, vehicle status, and real-time operational data from the park. Therefore, there is an urgent need for a photovoltaic-energy storage linkage intelligent charging pile control method that can coordinate PV power generation, fast charging and discharging energy storage, and new energy vehicle resources to achieve multi-source synergy, intelligent response, and fair billing. Summary of the Invention

[0003] This specification describes a method and system for controlling smart charging piles that supports photovoltaic and energy storage linkage through several embodiments.

[0004] Firstly, embodiments of this specification provide a smart charging pile control method supporting photovoltaic-energy storage linkage. The method is executed by a dispatch system in a park where a photovoltaic power generation system is deployed, and the photovoltaic power generation system is connected to a fast-charging and discharging energy storage device. The method includes the following steps:

[0005] Acquire real-time output data of the park's photovoltaic power generation system, short-term predicted load of the park, charging status information of new energy vehicles, energy storage status data of fast charging and discharging energy storage devices, and users' vehicle usage time.

[0006] Based on the preset scheduling cycle, the real-time output data, short-term predicted load of the park and energy storage status data are read to determine whether there is curtailment of solar power and power shortage in the next scheduling cycle.

[0007] When there is curtailment of solar power, dispatch new energy vehicles that are in a rechargeable state to absorb the curtailed solar power for charging, and record the absorbed electricity.

[0008] When there is a power shortage, based on the charging status information of new energy vehicles, user vehicle usage time and preset discharge threshold, control new energy vehicles that meet the preset discharge conditions to discharge in reverse to the park through the charging pile, and record the discharge amount.

[0009] The charging power, consumption power, and discharge power of new energy vehicles are separately charged to obtain the final charging cost.

[0010] Among them, the preset discharge conditions include ensuring that the new energy vehicle has a preset minimum available power during the usage time.

[0011] Secondly, this specification provides an intelligent charging pile control system that supports photovoltaic-energy storage linkage, deployed in a park with a photovoltaic power generation system. The photovoltaic power generation system is connected to a fast-charging and discharging energy storage device. The system includes:

[0012] The acquisition module acquires real-time output data of the park's photovoltaic power generation system, short-term predicted load of the park, charging status information of new energy vehicles, energy storage status data of fast charging and discharging energy storage devices, and users' vehicle usage time.

[0013] The judgment module reads the real-time output data, short-term predicted load of the park and energy storage status data according to the preset scheduling cycle, and determines whether there is curtailment of solar power and power shortage in the next scheduling cycle.

[0014] The first scheduling module, when there is curtailment of solar power, schedules new energy vehicles that are in a rechargeable state to absorb the curtailed solar power for charging, and records the absorbed power.

[0015] The second scheduling module, when there is a power shortage, controls new energy vehicles that meet the preset discharge conditions to discharge in reverse to the park through the charging piles based on the charging status information of new energy vehicles, user vehicle usage time and preset discharge threshold, and records the discharge amount.

[0016] The calculation module measures the charging power, consumption power, and discharge power of new energy vehicles to obtain the final charging cost.

[0017] Among them, the preset discharge conditions include ensuring that the new energy vehicle has a preset minimum available power during the usage time.

[0018] Thirdly, embodiments of this specification provide an electronic device, including a processor and a memory;

[0019] The processor is connected to the memory;

[0020] The memory is used to store executable program code;

[0021] The processor runs a program corresponding to the executable program code stored in the memory to perform the method described in any of the above aspects.

[0022] Fourthly, embodiments of this specification provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described in any of the above aspects.

[0023] Fifthly, embodiments of this specification provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the above aspects.

[0024] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0025] In several embodiments of this specification, the intelligent charging pile control method and system supporting photovoltaic-energy storage linkage are provided. These methods can dynamically schedule electric vehicles to participate in energy regulation based on real-time photovoltaic output and load demand in the industrial park. During periods of high photovoltaic power generation, excess electricity is absorbed, effectively reducing curtailment and improving renewable energy utilization. Through the joint peak-shaving of fast-charging and discharging energy storage units and vehicle batteries, electrical energy is released during peak load periods or when photovoltaic output is insufficient, smoothing power fluctuations, reducing dependence on the main grid, and optimizing electricity costs by utilizing peak-valley electricity price differences, thereby improving the overall energy economy of the industrial park. A dynamic billing mechanism based on contribution is introduced, providing electricity fee reductions or revenue returns to users who participate in peak shaving and absorb curtailed photovoltaic power, thus stimulating user participation. By integrating multi-dimensional information such as user appointment travel time, battery state of charge, and historical usage habits, charging and discharging strategies are intelligently planned while ensuring normal user usage, achieving coordination between user convenience and grid control objectives.

[0026] Other features and advantages of various embodiments of this specification will be further revealed in the following detailed description and accompanying drawings. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the intelligent charging pile control method provided in this manual.

[0029] Figure 2 This is a schematic diagram of the intelligent charging pile control method provided in this manual.

[0030] Figure 3 This is a schematic diagram illustrating the method and process for obtaining a user's vehicle usage time as provided in this manual.

[0031] Figure 4 This is a schematic diagram of the process for determining whether there is light abandonment in the next scheduling cycle, as provided in this manual.

[0032] Figure 5 This is a schematic diagram of the method for scheduling new energy vehicles to absorb the abandoned solar power provided in this manual.

[0033] Figure 6 This is a schematic diagram of the method for controlling reverse discharge of new energy vehicles provided in this manual.

[0034] Figure 7 This is a schematic diagram of the intelligent charging pile control system provided in this manual.

[0035] Figure 8 This is a schematic diagram of the electronic device provided in this manual. Detailed Implementation

[0036] The technical solutions of the embodiments of this specification will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of this specification and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of this specification.

[0037] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0038] In the following description, terms such as “inner,” “outer,” “upper,” “lower,” “left,” and “right” are used only to facilitate the description of the embodiments and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this specification.

[0039] All data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0040] Before introducing the technical solutions described in this manual, the application scenarios and related technologies of the technical solutions will be introduced.

[0041] Photovoltaic (PV) power generation is characterized by intermittency, volatility, and uncontrollability. Mainstream PV dispatching methods emphasize multi-timescale coordination and multi-resource coupling optimization. In terms of time, a combination of day-ahead dispatching and intraday rolling adjustments is typically employed: day-ahead dispatching is based on medium- to long-term weather forecasts to formulate a basic operational plan; intraday dispatching utilizes high-precision ultra-short-term PV forecasts, such as 15-minute forecasts, to dynamically adjust energy storage charging and discharging strategies or adjustable load operation. Regarding resource coordination, PV systems are often operated in conjunction with battery energy storage systems to smooth out power output fluctuations through peak shaving and valley filling.

[0042] New energy vehicles with bidirectional charging and discharging capabilities (i.e., vehicles supporting V2G, Vehicle-to-Grid technology) have become an important resource for flexibility. Unlike traditional unidirectional charging vehicles that can only draw power from the grid, V2G vehicles can use dedicated bidirectional charging stations to feed energy from their onboard batteries back to the grid or local loads when needed. With large-scale deployment, a large number of V2G vehicles can aggregate to form considerable regulation capacity, offering not only fast response times and suitability for frequency regulation, backup power, and other auxiliary power services, but also effectively supporting the consumption of a high proportion of renewable energy.

[0043] Park 11 typically refers to a comprehensive area within a certain geographical range that concentrates industrial, commercial, office, research, or residential functions, such as industrial parks, science parks, university campuses, business complexes, or low-carbon demonstration communities. It possesses relatively independent energy load characteristics, infrastructure systems, and management mechanisms. Internal electrical equipment is dense, and energy consumption patterns are diverse. Some Park 11s also have their own distributed energy systems, such as rooftop photovoltaics, energy storage devices, small gas turbines, or ground source heat pumps. Considering Park 11 as an independent dispatch unit has significant technical and economic implications. In the distribution network, Park 11 can serve as an energy management entity with clearly defined boundaries, coordinateable internal resources, and controllable external interactions. Internally, Park 11 can be collaboratively optimized through a unified dispatch system 21. However, currently, there is a lack of systems 12 specifically designed for Park 11 to coordinate new energy vehicles and photovoltaic power generation systems.

[0044] This manual provides a control method and system for smart charging piles that supports photovoltaic and energy storage linkage. Please refer to the appendix. Figure 1By deeply integrating distributed photovoltaic power generation, fixed energy storage systems, and new energy vehicle resources with bidirectional charging and discharging capabilities, the system achieves coordinated and optimized operation of multiple elements including power generation, grid, load, and storage. Relying on sensing layer devices deployed within the park, such as smart meters, photovoltaic inverter monitoring modules, charging pile communication terminals, and battery management systems, the system collects real-time data on photovoltaic power generation, load demand at different times, the state of charge and charging / discharging capacity of energy storage devices, as well as key information such as the number of new energy vehicles connected to charging piles, the current battery level of each vehicle, the user-set travel time, and the available scheduling time. Combining high-precision short-term photovoltaic output forecasts with park load forecast data, a rolling optimization scheduling model is constructed, with economic efficiency, photovoltaic absorption rate, and power supply reliability as comprehensive objectives.

[0045] For example, rolling decisions are made on a 15-minute or shorter timescale throughout the day. When the photovoltaic output within Park 11 exceeds the local load and there is a risk of curtailment, idle electric vehicles that are allowed to discharge are prioritized to enter charging mode, while simultaneously charging stationary energy storage, maximizing the local consumption of renewable energy. During periods when photovoltaic power generation is suspended in the evening or at night, when grid electricity prices are at their peak, or when the park's load experiences a spike, the system intelligently determines whether to activate discharge mode based on whether the remaining battery power of vehicles meets the user's subsequent travel needs. This involves coordinating with the energy storage system to supply power to the loads within Park 11, thereby reducing reliance on grid electricity purchases, lowering electricity costs, and alleviating distribution transformer overload pressure. This achieves refined and intelligent management of energy flow within Park 11, significantly improving photovoltaic utilization and energy economy. More importantly, it empowers the park as an independent market entity to participate in interactions with the higher-level power grid.

[0046] This specification first provides a smart charging pile control method supporting photovoltaic-energy storage linkage. This method is executed by a dispatch system 21 in a park where a photovoltaic power generation system 12 is deployed. The photovoltaic power generation system 12 is connected to a fast-charging and discharging energy storage device. Please refer to the appendix. Figure 2 This includes the following steps:

[0047] Step S1) Obtain real-time output data of the photovoltaic power generation system 12 in the park, short-term predicted load of the park, charging status information of new energy vehicles 30, energy storage status data of fast charging and discharging energy storage devices, and user vehicle usage time.

[0048] The real-time output data of the photovoltaic power generation system 12 in the park is typically collected through the communication module built into the photovoltaic inverter or a dedicated power sensor, and uploaded to the park's dispatch system 21 at a frequency of seconds or minutes. For example, in a 500,000-square-meter science park 11, an 8-megawatt distributed photovoltaic system is installed on the rooftops and carports. The dispatch system 21 receives the power generation data of each subarray every 5 minutes to determine if there is a risk of curtailment. If the measured output reaches 7.2 MW at 12:30 pm on a certain day, while the park's load at that time is only 4.5 MW, a potential surplus of 2.7 MW of electricity is initially identified, requiring the activation of the power consumption mechanism. The park's short-term predicted load refers to the predicted electricity demand value for the next 15 minutes to 4 hours, typically generated through a machine learning model based on historical load curves, weather data, weekday types, and the operating schedules of major equipment within the park 11. For example, if at 16:00 on a summer weekday afternoon, it is predicted that the total load of the park will increase from 5 MW to 6.8 MW in the next hour due to the increase in air conditioning load, which will directly affect whether to schedule energy storage or electric vehicles to release electricity before the load ramps up.

[0049] The charging status information for new energy vehicles 30 includes the current State of Charge (SOC) of each vehicle connected to the V2G charging pile, the rated battery capacity, the maximum charging and discharging power, whether it is in a dispatchable state (i.e., the user has authorized participation in dispatching), and the current connection status (plugged in but not charging, charging, waiting to discharge, etc.). Assuming there are 200 V2G charging piles in the underground parking lot of the park, and 35 vehicles have a current SOC higher than 60% and the user has set a departure time of 8:00 AM the next day, or the user's historical charging data predicts a high probability of departure at 8:00 AM the next day, then these vehicles are considered dispatchable resources between 8:00 PM that evening and 7:00 AM the next day, and have the ability to feed power back to the park. The energy storage status data of the fast-charging and discharging energy storage device helps to smooth out short-term, small-amplitude energy imbalances. This includes imbalances caused by the output imbalance of the photovoltaic power generation system 12 and short-term, small-amplitude fluctuations in the park's electricity load. Simultaneously, the fast-charging and discharging energy storage device can also assist in determining whether there is curtailment of solar power or a power supply gap during the energy balancing process. When there is a possibility of solar power curtailment, the fast-charging and discharging energy storage device will be rapidly charged during the balancing process, thus providing further evidence of a significant fluctuation in photovoltaic output. Conversely, when there is a potential power shortage, the limited energy stored in the fast-charging and discharging energy storage device will be rapidly consumed, similarly indicating an impending power shortage. This is used to assist in the charging and discharging scheduling of smart charging piles.

[0050] Please see the appendix Figure 3 Methods for obtaining a user's car usage time include:

[0051] Step S11) Obtain the user's historical charging data, cluster it according to the charging start time, and group the historical charging data according to the clustering results.

[0052] The system extracts a user's long-term historical charging records from the park's charging pile management platform or a user-authorized vehicle-to-everything (V2X) platform. Each record includes at least the charging start time, charging end time, charging duration, charged amount, and the corresponding date, such as weekday or weekend. Using the charging start time as the primary feature, a time-series clustering algorithm is employed for grouping. For example, if a park employee has 48 charging records in the past 6 months, system analysis reveals that 32 of these occurred between 7:00 PM and 9:00 PM on weekdays, 12 occurred between 9:00 AM and 11:00 AM on weekends, and 4 were temporary charging incidents on holidays. Clustering categorizes these into two main behavioral pattern groups: "Weekday Evening Charging Group" and "Weekend Morning Charging Group." This grouping effectively captures the differences in users' charging habits across different life scenarios.

[0053] Step S12) Establish a vehicle usage time prediction model based on the historical charging data of each group, and train it using the historical charging data of each group.

[0054] Independent vehicle usage time prediction models were built for each cluster group. Since user behavior differs across scenarios, separate modeling improves prediction accuracy. For example, for the "weekday evening charging group," users typically drive to work between 7:00 and 8:00 AM the following morning after charging, resulting in a strong time interval between usage time and charging completion time. In contrast, for the "weekend morning charging group," users usually depart for short trips within 1-2 hours of charging completion. Lightweight regression models, such as linear regression, random forest, or gradient boosting trees, were trained for each group. Input features included charging start time, day type (weekday or weekend), season, and charged amount; the output was the target variable. Vehicle usage time is defined as the time the vehicle is unplugged or started. The models underwent cross-validation and parameter tuning on historical data for each group to ensure generalization ability.

[0055] Step S13) Obtain the user's current usage time based on the usage time prediction model corresponding to the current charging start time. When the user initiates a new charging request on a certain day, first record the current charging start time, such as 19:25 on Tuesday, January 6, 2025, and determine which cluster group it belongs to, taking "Weekday Evening Charging Group" as an example. Then, automatically call the pre-trained prediction model corresponding to the group, taking the context information of this charging, such as January being winter, Tuesday being a weekday, and the current SOC being low and requiring charging of about 40 kWh, as input, the model then outputs the prediction result: the estimated usage time is 7:40 am the next day. This prediction value will serve as a key constraint for subsequent scheduling optimization. Between 23:00 at night and 6:00 am the next day, if the power grid needs peak shaving and the user's vehicle SOC is sufficient, it can be arranged to participate in V2G discharge, but it must be ensured that the recharging is completed before 7:40 am, so that the SOC is not lower than the minimum travel threshold set by the user (for example, 40%).

[0056] Step S2) Based on the preset scheduling cycle, read the real-time output data, the short-term predicted load of the park and the energy storage status data, and determine whether there is curtailment of solar power and power shortage in the next scheduling cycle.

[0057] Please see the appendix Figure 4 The method for determining whether there will be curtailment of solar power in the next scheduling cycle by reading the real-time output data, short-term predicted load of the park, and energy storage status data according to the preset scheduling cycle includes:

[0058] Step S21) Calculate the net power of the park in the next scheduling cycle based on the short-term predicted photovoltaic output of the photovoltaic power generation system 12 for the next scheduling cycle and the short-term predicted load of the park. Call the photovoltaic power prediction module and the load prediction module to obtain the short-term predicted output value of the photovoltaic power generation system 12 in the next scheduling cycle, such as the average power over the next 15 minutes, and the short-term predicted load value of the park during the same period. The net power is the predicted photovoltaic output value minus the short-term predicted load of the park. That is, net power = predicted photovoltaic output value − predicted short-term load of the park. A positive net power indicates that the photovoltaic output exceeds the load demand, posing a risk of backfeeding to the main grid or curtailment of photovoltaic power. A negative net power indicates insufficient local power supply, potentially requiring the purchase of electricity from the main grid or activation of energy storage discharge.

[0059] Step S22) Obtain the SOC change rate and SOC change trend of the fast-charging and discharging energy storage device over a preset period of time.

[0060] A net power output greater than zero is insufficient to definitively determine curtailment of solar power, as this portion of electricity can be absorbed by adjusting energy storage or flexible loads. Therefore, a dynamic analysis of the recent operating status of fast-charge / discharge energy storage devices is necessary. This involves reviewing past periods, such as the recent 5 minutes, to analyze the SOC changes borne by the energy storage, calculating the SOC change rate—the rate of SOC change per unit time for the fast-charge / discharge energy storage device. SOC change trends include rising, falling, or remaining stable.

[0061] Step S23) When the net power is greater than zero, or the SOC change trend is increasing and the SOC change rate is greater than the preset reference value, it is determined that there is light curtailment in the next scheduling cycle.

[0062] When the State of Charge (SOC) change rate of the fast-charging and discharging energy storage device exceeds the threshold, it indicates that the positive and negative attributes of the net power of the entire park remain unchanged. This is because if the positive and negative attributes of the net power were to change, it would not cause the SOC change rate to exceed the threshold, but rather result in slight fluctuations in the load of the fast-charging and discharging energy storage device. When the positive and negative attributes of the net power remain unchanged, it indicates that there will be curtailment of solar power in the next scheduling cycle.

[0063] The method for determining whether there is a power shortage in the next scheduling cycle by reading the real-time output data, the short-term predicted load of the park, and the energy storage status data according to the preset scheduling cycle includes:

[0064] When the net power is less than zero, or when the SOC changes in a decreasing trend and the SOC change rate is greater than a preset reference value, it is determined that there is a power gap in the next scheduling cycle.

[0065] Based on a preset scheduling cycle, such as 15 minutes, a comprehensive assessment is conducted by reading the real-time output data of the photovoltaic power generation system 12, the short-term predicted load of the park, and the current status of the fast-charging and discharging energy storage device, especially the dynamic trend of its SOC change. When the net power is less than zero, or when the SOC change trend of the fast-charging and discharging energy storage device is continuously decreasing and its rate of change exceeds the preset reference value, it is determined that there is a power gap in the next scheduling cycle. Net power is still defined as the photovoltaic output forecast value minus the short-term predicted load of the park. When the net power is less than zero, it means that even if the photovoltaic system is fully powered, it cannot cover the park's electricity demand, resulting in a basic power shortage.

[0066] Relying solely on net power may lag behind actual risks, especially during transitional periods of rapid photovoltaic power decline or sudden load surges. Therefore, introducing dynamic monitoring of the State of Charge (SOC) changes of fast-charging and discharging energy storage devices as a supplementary criterion has significant early warning value. A decreasing SOC trend indicates that the energy storage is in a discharging state, while a SOC change rate greater than a preset reference value indicates a rapid discharge rate, reflecting significant power pressure on the industrial park. If this trend continues, even if the current net power is not yet significantly negative, it may foreshadow a supply shortage in future dispatch cycles. This dual-criteria mechanism significantly improves the system's forward-looking perception of power gaps.

[0067] Step S3) When there is curtailment of solar power, dispatch 30 new energy vehicles in a rechargeable state to absorb the curtailed solar power for charging, and record the absorbed power.

[0068] Please see the appendix Figure 5 The methods for scheduling 30 new energy vehicles in a rechargeable state to consume the abandoned solar power for charging, and recording the consumed power, include:

[0069] Step S31) Select 30 new energy vehicles currently connected to charging piles, not charging, and with batteries not fully charged as eligible charging vehicles. Obtain vehicle connection status in real time from all V2G charging piles in the park. Only vehicles meeting three conditions simultaneously will be included as eligible charging vehicles: plugged in and having completed a communication handshake, not currently performing a charging or discharging task, and having a battery SOC below a set upper limit, with remaining charging capacity. For example, at 12:10 PM in an industrial park, the system detects 28 new energy vehicles connected to charging piles, of which 12 are charging, 5 are fully charged (SOC ≥ 95%), and the remaining 11 are plugged in but have not yet started charging, with an SOC between 30% and 85%. These 11 vehicles are marked as "eligible charging vehicles" and become the targets for subsequent scheduling.

[0070] Step S32) Based on the predicted curtailment of solar power in the next scheduling cycle and the rechargeable capacity of vehicles capable of absorbing it, the charging task power is allocated according to priority. The priority is generated based on the vehicle's rechargeable capacity and the user's historical response records. If a curtailment risk is confirmed, such as a prediction of 1.8 MWh of curtailed solar power in the next 15 minutes, this portion of green electricity needs to be allocated reasonably. The rechargeable capacity of each vehicle capable of absorbing it = battery rated capacity × (1 - current SOC). For example, a 70 kWh vehicle with a current SOC of 60% can only handle a maximum of 28 kWh of charging power. Priority ranking considers two factors: First, the size of the rechargeable capacity; the larger the capacity, the stronger the single-use capacity, and the higher the priority. Second, the user's historical response records, such as the number of times they participated in scheduling in the past week, whether they violated regulations and had their authorization revoked, etc. A response credit score is maintained for each user; users with high scores enjoy higher scheduling priority when resources are scarce, forming a positive incentive.

[0071] Step S33) Control the charging pile to charge the vehicle to the required charge level, and record the actual charge amount. After the dispatch instruction is issued, the park's dispatch system 21 sends the charging power curve and target charge level to the corresponding charging pile via the communication protocol. The charging pile starts charging and monitors the current, voltage, and cumulative charge amount in real time. If the vehicle disconnects the charging gun prematurely, the battery management system limits the current, or grid fluctuations cause an interruption, the actual charge amount will be recorded.

[0072] Step S34) Obtain and record the consumed electricity based on the actual charging amount. Record the actual charging amount of all vehicles participating in this dispatch.

[0073] Step S4) When a power shortage exists, based on the charging status information of the new energy vehicle 30, the user's vehicle usage time, and the preset discharge threshold, control the new energy vehicle 30 that meets the preset discharge conditions to discharge in reverse to the park through the charging pile, and record the discharge amount. The preset discharge conditions include ensuring that the new energy vehicle 30 has a preset minimum available power during the usage time.

[0074] Please see the appendix Figure 6 The method for controlling 30 new energy vehicles that meet preset discharge conditions to discharge in reverse to the park through charging piles and recording the discharge amount includes:

[0075] Step S41) From the new energy vehicles 30 currently connected to the charging pile and supporting bidirectional charging and discharging functions, select new energy vehicles 30 whose battery charge state is higher than the preset discharge start threshold, whose time away from the user's vehicle use is greater than the preset reference time, and whose users have authorized to participate in discharge scheduling as dispatchable vehicles.

[0076] From the 30 new energy vehicles currently connected to charging piles and supporting bidirectional charging and discharging, those with a battery state of charge higher than a preset discharge initiation threshold, a time remaining before user usage time greater than a preset reference duration, and user authorization to participate in discharge scheduling are selected as dispatchable vehicles. Specifically, this includes technical capabilities: the vehicle must support V2G functionality and be successfully connected to a bidirectional charging pile; sufficient battery power, meaning the current SOC must be higher than the discharge initiation threshold, such as 30%, to avoid deep discharge damaging the battery; ample time window, meaning the current time remaining before the user-set usage time must be greater than a preset reference duration, such as 1 hour, to allow for subsequent recharging; and user authorization, meaning the user has enabled participation in scheduling in the App.

[0077] For example, at 18:20 on a winter evening in a business park, a power shortage of approximately 1.2 MW was detected within the next 30 minutes. At this time, 15 V2G vehicles were connected to the parking lot. Among them, 8 vehicles had not been authorized for dispatch, 3 vehicles had a SOC of only 25% (below the 30% threshold), and 2 vehicles had a reservation for use at 18:45 (only 25 minutes away, less than 1 hour). Ultimately, only 2 vehicles met all the conditions: Vehicle A had an SOC of 65% and was predicted to be used at 7:30 the next day; Vehicle B had an SOC of 58% and was predicted to be used at 20:00. These two vehicles were listed as dispatchable vehicles.

[0078] Step S42) Based on the predicted power deficit for the next scheduling cycle and the available discharge capacity of each schedulable vehicle, allocate the discharge task power and duration according to the discharge priority. The discharge priority is determined based on the vehicle's remaining battery power, maximum discharge power, and the number of historical discharge responses from the user. Calculate the available discharge capacity for each vehicle. Under the premise of ensuring a minimum battery level during vehicle use, such as 30% SOC, determine the amount of battery power allowed to be released. For example, vehicle A has a battery capacity of 80 kWh, a current SOC of 65% (52 kWh), and a minimum reserved battery level of 24 kWh (30%), so it can discharge a maximum of 28 kWh. If its maximum discharge power is 11 kW, it can discharge for a maximum of approximately 2.5 hours. The discharge priority is based on three indicators: the higher the remaining battery power, the greater the adjustment margin, and the higher the priority; the greater the maximum discharge power, the faster the response speed, and the higher the priority; users with more historical responses and better performance have higher credit ratings and are prioritized for scheduling as an incentive.

[0079] Step S43) Ensuring that each dispatchable vehicle has a battery level no lower than the preset minimum during the user's usage time, control the charging pile to initiate reverse discharge operation, and monitor the discharge power and duration in real time. Before issuing the discharge command, dynamically verify that even after completing the discharge task, the vehicle still has sufficient time to reach the minimum battery level requirement through low-power recharging before use. For example, if vehicle B starts discharging at 18:20 and is used at 20:00, the SOC after discharge will drop to 45%, still far above the 30% minimum limit, and no recharging is needed. However, if the SOC drops to 32% after discharge, it needs to be recharged to 35% in slow charging mode between 19:30 and 19:50 to ensure a safety margin.

[0080] Step S44) Calculate the discharged power based on the real-time monitored discharge power and discharge duration. After the discharge is completed, the system integrates the discharge curve of each vehicle to obtain the actual discharged power.

[0081] Step S5) The charging power, consumption power and discharge power of the new energy vehicle 30 are respectively charged and the final charging fee 31 is obtained.

[0082] The methods for calculating the final charging cost 31 by separately measuring the charging volume, consumption volume, and discharge volume of new energy vehicles 30 include:

[0083] Based on the preset billing prices corresponding to the charging capacity, the consumption capacity, and the discharge capacity, the final charging cost is calculated as follows: 31 = Charging capacity × Regular electricity price + Consumption capacity × Preset low price - Discharge capacity × Preset high price.

[0084] Charging power refers to the total electrical energy a user obtains from the grid or the park's power source, and is charged according to the regular electricity price in the local time-of-use pricing system. Consumed power refers to the portion of charging used to absorb predicted curtailment of solar power. Because it helps the park reduce curtailment and improve green energy utilization, it is subject to a preset low price (e.g., 0.3 yuan / kWh) to form a green incentive. Discharge power refers to the electrical energy that users feed back to the park via V2G. Because it provides peak-shaving services and alleviates power shortages, it is repurchased at a preset high price (e.g., 1.5 yuan / kWh) as compensation for the user's resource contribution. Increased consumption and discharge significantly reduce energy costs, and in some scenarios (e.g., large discharge volume and high consumption ratio), negative electricity costs (i.e., net profit) may even occur. Low prices encourage users to charge during peak solar power generation periods, while high prices incentivize them to discharge during peak load periods, naturally forming a response pattern aligned with grid demand.

[0085] On the other hand, this manual provides a smart charging pile control system that supports photovoltaic and energy storage linkage, which can be deployed in parks with photovoltaic power generation systems 12. Please refer to the appendix. Figure 7 The photovoltaic power generation system 12 is connected to a fast-charging and discharging energy storage device, and the system includes:

[0086] The module 100 acquires real-time output data of the park's photovoltaic power generation system 12, short-term predicted load of the park, charging status information of new energy vehicles 30, energy storage status data of fast charging and discharging energy storage devices, and user vehicle usage time.

[0087] The judgment module 200 reads the real-time output data, the short-term predicted load of the park and the energy storage status data according to the preset scheduling cycle, and determines whether there is curtailment of solar power and power shortage in the next scheduling cycle.

[0088] The first scheduling module 300, when there is curtailment of solar power, schedules new energy vehicles 30 that are in a rechargeable state to absorb the curtailed solar power for charging, and records the absorbed power.

[0089] The second scheduling module 400, when there is a power shortage, controls the new energy vehicle 30 that meets the preset discharge conditions to discharge in reverse to the park through the charging pile based on the charging status information of the new energy vehicle 30, the user's vehicle usage time and the preset discharge threshold, and records the discharge amount.

[0090] The calculation module 500 calculates the charging power, consumption power and discharge power of the new energy vehicle 30 respectively to obtain the final charging fee 31.

[0091] The preset discharge conditions include ensuring that the new energy vehicle 30 has a preset minimum available power during the vehicle usage time.

[0092] Please see Figure 8 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this specification.

[0093] like Figure 8As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, and at least one communication bus 1102. The communication bus 1102 can be used to connect and communicate with the various components mentioned above. The user interface 1103 may include buttons, and optionally may include standard wired or wireless interfaces. The network interface 1104 may include, but is not limited to, a Bluetooth module, an NFC module, or a Wi-Fi module. The processor 1101 may include one or more processing cores. The processor 1101 connects to various parts within the electronic device 1100 using various interfaces and lines, and performs various functions of the routing device and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1105, and by calling data stored in the memory 1105. Optionally, the processor 1101 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 1101 may integrate one or more combinations of CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content that the display screen needs to show; and the modem is used for wireless communication.

[0094] It is understandable that the aforementioned modem may not be integrated into the processor 1101, but may be implemented using a separate chip.

[0095] The memory 1105 may include RAM or ROM. Optionally, the memory 1105 may include a non-transitory computer-readable medium. The memory 1105 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1105 may also be at least one storage device located remotely from the aforementioned processor 1101. As a computer storage medium, the memory 1105 may include an operating system, a network communication module, a user interface module, and application programs. The processor 1101 may be used to call the application programs stored in the memory 1105 and execute the methods in the above-described embodiments.

[0096] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform multiple steps as described in the above embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0097] This specification also provides a computer program product, including a computer program that, when executed by a processor, implements the multiple steps described in the above embodiments.

[0098] Where there is no conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0099] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes multiple computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center integrating multiple available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0100] When implemented through hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and achieve the corresponding function. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit, whose logic function is determined by the user programming the device. Designers can program a digital system onto a PLD themselves, eliminating the need for chip manufacturers to design and fabricate dedicated integrated circuit chips. Furthermore, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, similar to the software compiler used in program development. The original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There is not just one HDL, but many. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of the aforementioned hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logic method flow can be easily obtained.

[0101] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A control method for intelligent charging piles supporting photovoltaic-energy storage linkage, wherein the method is executed by a dispatching system of a park where photovoltaic power generation systems are deployed, characterized in that... The photovoltaic power generation system is connected to a fast-charging and discharging energy storage device, and includes the following steps: Acquire real-time output data of the park's photovoltaic power generation system, short-term predicted load of the park, charging status information of new energy vehicles, energy storage status data of fast charging and discharging energy storage devices, and users' vehicle usage time. Based on the preset scheduling cycle, the real-time output data, short-term predicted load of the park and energy storage status data are read to determine whether there is curtailment of solar power and power shortage in the next scheduling cycle. When there is curtailment of solar power, dispatch new energy vehicles that are in a rechargeable state to absorb the curtailed solar power for charging, and record the absorbed electricity. When there is a power shortage, based on the charging status information of new energy vehicles, user vehicle usage time and preset discharge threshold, control new energy vehicles that meet the preset discharge conditions to discharge in reverse to the park through the charging pile, and record the discharge amount. The charging power, consumption power, and discharge power of new energy vehicles are separately charged to obtain the final charging cost. Among them, the preset discharge conditions include ensuring that the new energy vehicle has a preset minimum available power during the vehicle use period; The method for determining whether there will be curtailment of solar power in the next scheduling cycle by reading the real-time output data, short-term predicted load of the park, and energy storage status data according to the preset scheduling cycle includes: Based on the short-term forecast of photovoltaic output of the photovoltaic power generation system for the next scheduling cycle and the short-term forecast of the park's load, calculate the net power of the park in the next scheduling cycle. Obtain the SOC change rate and SOC change trend of the fast-charge and discharge energy storage device over a preset time period; When the net power is greater than zero, and the SOC changes with an increasing trend and the SOC change rate is greater than a preset reference value, it is determined that there will be light curtailment in the next scheduling cycle. The net power is the predicted photovoltaic output value minus the predicted short-term load of the park.

2. The intelligent charging pile control method supporting photovoltaic-energy storage linkage according to claim 1, characterized in that, Methods for obtaining a user's car usage time include: Obtain users' historical charging data, cluster it according to the charging start time, and group the historical charging data according to the clustering results; A vehicle usage time prediction model is built based on the historical charging data of each group, and the model is trained using the historical charging data of each group. Based on the vehicle usage time prediction model corresponding to the start time of this charging, the user's vehicle usage time for this period is obtained.

3. The intelligent charging pile control method supporting photovoltaic-energy storage linkage according to claim 1, characterized in that, The method for determining whether there is a power shortage in the next scheduling cycle by reading the real-time output data, the short-term predicted load of the park, and the energy storage status data according to the preset scheduling cycle includes: When the net power is less than zero, and the SOC changes in a decreasing trend and the SOC change rate is greater than a preset reference value, it is determined that there is a power gap in the next scheduling cycle.

4. The intelligent charging pile control method supporting photovoltaic-energy storage linkage according to claim 1, characterized in that, Methods for scheduling new energy vehicles in a rechargeable state to consume surplus solar power for charging, and recording the consumed power, include: Select new energy vehicles that are currently connected to charging piles, are not charging, and have not fully charged batteries as eligible vehicles; Based on the predicted curtailment of solar power in the next scheduling cycle and the rechargeable capacity of vehicles that can absorb it, the charging task power is allocated according to priority, and the priority is generated based on the vehicle's rechargeable capacity and the user's historical response records. Control the charging pile to charge the vehicle that can absorb the charge, so that the charging amount reaches the charging task amount, and record the actual charging amount. The amount of electricity consumed is obtained and recorded based on the actual charging amount.

5. The intelligent charging pile control method supporting photovoltaic-energy storage linkage according to claim 1, characterized in that, Methods for controlling new energy vehicles that meet preset discharge conditions to discharge in reverse into the park via charging piles and recording the discharge amount include: From the current new energy vehicles connected to charging piles and supporting bidirectional charging and discharging functions, select new energy vehicles whose battery state of charge is higher than the preset discharge start threshold, whose time away from user use is greater than the preset reference time, and whose user has authorized participation in discharge scheduling as dispatchable vehicles. Based on the predicted power deficit for the next scheduling cycle and the available discharge capacity of each schedulable vehicle, the power and duration of the discharge task are allocated according to the discharge priority. The discharge priority is determined based on the vehicle's remaining power, maximum discharge power, and the number of historical discharge responses from the user. Under the premise of ensuring that each dispatchable vehicle has a battery level not lower than the preset minimum during the user's usage time, the charging pile is controlled to start reverse discharge operation, and the discharge power and discharge duration are monitored in real time. The discharge capacity is calculated based on the real-time monitored discharge power and discharge duration.

6. The intelligent charging pile control method supporting photovoltaic-energy storage linkage according to claim 1, characterized in that, Methods for calculating the final charging cost by separately measuring the charging volume, consumption volume, and discharge volume of new energy vehicles include: Based on the preset billing prices corresponding to the charging capacity, the consumption capacity, and the discharge capacity, the final charging cost is calculated as follows: Charging capacity × Regular electricity price + Consumption capacity × Preset low price − Discharge capacity × Preset high price.

7. A smart charging pile control system supporting photovoltaic-energy storage linkage, deployed in a park with a photovoltaic power generation system, characterized in that, The photovoltaic power generation system is connected to a fast-charging and discharging energy storage device, and the system includes: The acquisition module acquires real-time output data of the park's photovoltaic power generation system, short-term predicted load of the park, charging status information of new energy vehicles, energy storage status data of fast charging and discharging energy storage devices, and users' vehicle usage time. The judgment module reads the real-time output data, short-term predicted load of the park and energy storage status data according to the preset scheduling cycle, and determines whether there is curtailment of solar power and power shortage in the next scheduling cycle. The first scheduling module, when there is curtailment of solar power, schedules new energy vehicles that are in a rechargeable state to absorb the curtailed solar power for charging, and records the absorbed power. The second scheduling module, when there is a power shortage, controls new energy vehicles that meet the preset discharge conditions to discharge in reverse to the park through the charging piles based on the charging status information of new energy vehicles, user vehicle usage time and preset discharge threshold, and records the discharge amount. The calculation module measures the charging power, consumption power, and discharge power of new energy vehicles to obtain the final charging cost. Among them, the preset discharge conditions include ensuring that the new energy vehicle has a preset minimum available power during the vehicle use period; The method for determining whether there will be curtailment of solar power in the next scheduling cycle by reading the real-time output data, short-term predicted load of the park, and energy storage status data according to the preset scheduling cycle includes: Based on the short-term forecast of photovoltaic output of the photovoltaic power generation system for the next scheduling cycle and the short-term forecast of the park's load, calculate the net power of the park in the next scheduling cycle. Obtain the SOC change rate and SOC change trend of the fast-charge and discharge energy storage device over a preset time period; When the net power is greater than zero, and the SOC changes with an increasing trend and the SOC change rate is greater than a preset reference value, it is determined that there will be light curtailment in the next scheduling cycle. The net power is the predicted photovoltaic output value minus the predicted short-term load of the park.

8. An electronic device, characterized in that, Including the processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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