Shared charging pile intelligent scheduling method and system based on multi-objective optimization

Abstract

The invention provides a shared charging pile intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that a power grid power supply strategy and an energy storage system optimization strategy are integrated, multi-source collaborative optimization is conducted on a power grid side, an energy storage side and a charging station side, and peak-valley electricity price response and global scheduling are achieved in combination with the power grid load, the charging demand emergency degree and the energy storage state; equipment state information reported by the energy storage equipment, the intelligent power distribution system and the charging pile is received and updated in real time, and vehicle behaviors are portrayed in combination with historical records; collecting vehicle charging queuing information of the charging pile, detecting idle state information of a charging gun, and generating a charging configuration strategy of the intelligent power distribution charging pile; and converting the charging configuration strategy into an action control instruction of a subordinate subsystem, and monitoring an execution result. According to the invention, the utilization rate of the charging pile is improved, the service life of equipment is prolonged, and more convenient charging service is provided for users.

Description

Technical Field

[0001] This application belongs to the field of charging pile system scheduling, and in particular relates to a method, system, storage medium and device for intelligent scheduling of shared charging piles based on multi-objective optimization. Background Technology

[0002] With the rapid growth of new energy vehicle ownership, charging piles, as a key infrastructure, are constantly expanding in terms of construction scale and coverage. However, the utilization rate of charging piles varies significantly across different times and regions. For example, there are instances where demand exceeds supply during peak hours, while utilization is insufficient at other times. Some areas have high idle rates for charging piles, while in some popular areas, demand exceeds supply, resulting in both resource waste and charging difficulties for users. Therefore, how to improve the utilization rate of charging piles has become an urgent problem to be solved by the industry.

[0003] To alleviate grid pressure and achieve rational allocation of electricity resources, peak-valley electricity pricing policies have been widely implemented across the country. Meanwhile, energy storage technology has matured, and the cost of energy storage equipment has continued to decrease. However, in practical applications, the coordination between charging piles, energy storage systems, and the power grid is low, failing to fully utilize peak-valley pricing policies to reduce operating costs and hindering effective global dispatching during grid emergencies. Simultaneously, users' demands for convenient and fast charging are increasing, leading to diversified charging needs. Beyond plug-and-charge, personalized needs such as scheduled charging are becoming more prevalent. However, existing charging pile systems suffer from insufficient intelligence, resulting in low utilization rates, long waiting times for users, inflexible scheduled charging implementation, and a lack of resource allocation based on user profiles. Further improvements are needed. Users are highly sensitive to waiting times for charging; therefore, during peak hours, differentiating between charging status and user waiting behavior after charging can optimize the order and accelerate turnaround. This means that, when necessary, adjusting power to release charging guns in advance can retain queuing users, significantly improving equipment utilization.

[0004] Traditional charging pile systems mostly operate independently, lacking an effective collaborative mechanism between charging piles and energy storage systems. The grid power supply strategy and energy storage system control are relatively separate, resulting in untimely responses to peak and off-peak electricity pricing and difficulty in global scheduling during emergencies. For example, some charging piles continue charging at conventional power during peak grid hours, increasing grid pressure and electricity costs. When charging piles are busy, they cannot optimize charging power allocation based on vehicles' historical charging habits, leading to long waiting times and users leaving. For scheduled charging management, existing technologies mostly rely on mechanical execution, failing to balance the needs of scheduled charging and on-demand charging. In emergencies, they cannot effectively and dynamically adjust the charging schedules of suspended users based on their impending expiration dates, easily leading to untimely charging or wasted charging pile resources.

[0005] In summary, the lack of coordination among the various parts of the existing charging pile system makes it impossible to optimize the power allocation between the energy storage subsystem and the charging pile subsystem, resulting in low charging pile utilization, high operating costs, and the inability of the reservation charging management mechanism to balance the demand for reservation charging and real-time charging, and to effectively and dynamically adjust the reservation charging plan. Summary of the Invention

[0006] This application addresses the shortcomings of existing technologies by providing a multi-objective optimization-based intelligent scheduling method, system, storage medium, and device for shared charging piles. It aims to optimize power allocation between the energy storage subsystem and the charging pile subsystem through multi-subsystem collaborative operation and multi-source heterogeneous data fusion analysis, thereby improving charging pile utilization and reducing operating costs. Based on real-time vehicle identification and charging demand prediction, it improves the charging experience for waiting users while simultaneously increasing charging pile utilization. Furthermore, by employing dynamic weight evaluation algorithms and dynamic priority scheduling, it balances reserved charging and real-time charging demands, periodically predicting and dynamically adjusting reserved charging plans to ensure smooth execution of reserved charging.

[0007] This application is achieved through the following technical solution: A shared charging pile intelligent scheduling method based on multi-objective optimization includes: S1. Integrate grid power supply strategy and energy storage system optimization strategy, and carry out multi-source collaborative optimization of grid side, energy storage side and charging station side. Combine grid load, charging demand urgency and energy storage status to realize peak-valley electricity price response and global scheduling. S2. Establish device communication through the device IoT interface, receive and update device status information reported by energy storage devices, intelligent power distribution systems and charging piles in real time, and create a profile of vehicle behavior based on historical records. S3. Collect vehicle charging queue information of charging piles and detect the idle status information of charging guns. Based on the charging queue information and the idle status information, generate a charging configuration strategy for intelligent power distribution charging piles. S4. Convert the charging configuration strategy into action control instructions for the subordinate subsystems and monitor the execution results.

[0008] In some embodiments, the multi-source collaborative optimization includes: On the grid side, it responds to frequency deviation and voltage fluctuation events; on the energy storage side, it monitors transformer temperature and performs coordinated control of the energy storage station cluster; on the charging station side, when the real-time load rate is >80% and the number of waiting vehicles is >1, or the SOC balance is >30%, it initiates power rebalancing within the station.

[0009] In some embodiments, the real-time receiving and updating of device status information reported by energy storage devices, intelligent power distribution systems, and charging piles, combined with historical records to profile vehicle behavior, further includes: Record status and report information from energy storage systems, smart power distribution systems, and charging piles; acquire AI recognition information from camera images, extract license plate information and perform hash processing, and combine historical records to create a profile of vehicle behavior, including the hashed license plate number, whether it is a new energy vehicle, charging status, whether someone is in the vehicle, parking space, charging pile ID, charging gun ID, shooting time, temporary charging status, average dwell time, and number of visits; receive vehicle data parsed by IoT infrastructure, in the form of images or recognized text, with text content including the hashed license plate, parking space, charging pile ID, charging gun ID, whether someone is in the vehicle, and shooting time; if images are received, while recognizing the image, bind the charging pile ID and charging gun ID in conjunction with the charging pile's control and status messages; and optimize the utilization rate of charging pile resources using a dynamic weighted scoring algorithm.

[0010] The charging configuration strategy is executed by the scheduled charging power supply strategy service and the plug-and-charge power supply strategy service, respectively. The scheduled charging power supply strategy service evaluates whether all scheduled charging plans can be completed as planned, and triggers dynamic adjustments when deviations occur; when an emergency causes scheduled charging to be suspended, it automatically evaluates recoverable solutions; the condition for triggering adjustment is that the evaluation algorithm detects that the customer's scheduled charging time cannot be completed and sets a configurable time advance. The plug-and-charge power supply strategy service returns the target charging gun, adjusts the scheme and the completion time of the plan, configures intelligent power distribution equipment, and adjusts the charging pile power configuration without changing the power input; configures fast charging piles, and controls the power of the two charging guns through commands while keeping the quota input power unchanged, so as to achieve priority charging of the target vehicle; when using Pareto optimal solution selection, the score of each adjustment method adopts a dynamic weighted scoring algorithm to calculate: The user's habitual delayed departure time D is normalized to D_norm = 1 / (D+ε), where ε is a local minimum. The time W required for the planned charging to complete is normalized to W_norm = 1 / (W+ε), where ε is the minimum value; The user's charging time T is normalized to T_norm = T / T_max, where T_max is the maximum charging time allowed by the system. Calculate dynamic weighted scores: PriorityScore = α D_norm+β W_norm+γ T_norm Where α, β, and γ are the scenario-based weighting coefficients.

[0011] This application also provides a shared charging pile intelligent scheduling system based on multi-objective optimization for implementing the aforementioned method, the system comprising: The power supply strategy module for new energy power supply and energy storage system is used to integrate grid power supply strategy and energy storage system optimization strategy. It performs multi-source collaborative optimization of grid side, energy storage side and charging station side, and realizes peak-valley electricity price response and global scheduling by combining grid load, charging demand urgency and energy storage status. The device status and user profile module is used to establish device communication through the device IoT interface, receive and update device status information reported by energy storage devices, intelligent power distribution systems and charging piles in real time, and profile vehicle behavior in combination with historical records. The charging pile power supply strategy module is used to collect vehicle charging queuing information of the charging pile and detect the idle status information of the charging gun. Based on the charging queuing information and the idle status information, it generates a charging configuration strategy for the charging pile with intelligent power distribution. The device control service module is used to convert the charging configuration strategy into action control instructions for subordinate subsystems and monitor the execution results.

[0012] This application also provides a computer-readable storage medium storing one or more programs, which, when executed, can realize the aforementioned intelligent scheduling method for shared charging piles based on multi-objective optimization.

[0013] This application also provides a device, including a processor, a communication interface, a computer-readable storage medium, and a communication bus; wherein the processor, the communication interface, and the computer-readable storage medium communicate with each other through the communication bus; The processor is used to execute programs stored in a computer-readable storage medium.

[0014] Compared with the prior art, this application has the following advantages: By implementing an intelligent power allocation system, the utilization rate of charging piles can be increased by 18-25%. For example, in a practical application at a charging station in a certain industrial park, by dynamically adjusting power output and charging task allocation, the average daily charging frequency of charging piles has increased from 50 to 60 times. Optimizing the coordinated control of charging strategies and energy storage systems reduces electricity costs during peak grid periods, while extending the lifespan of charging piles and energy storage equipment, resulting in a 12-16% reduction in operating costs. Prioritizing charging pile scheduling allows for targeted power allocation during peak charging times, avoiding long queues and improving the user charging experience. Furthermore, dynamically adjusting charging strategies based on grid load and energy prices optimizes system costs and provides users with convenient charging services. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the intelligent power scheduling system for shared charging piles of this application is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This application covers multiple modules, including new energy power supply and energy storage synergy, and plug-and-play rapid response. Through multi-subsystem collaboration and data fusion, it dynamically adjusts charging strategies based on parameters such as grid load and charging demand to optimize power allocation between the energy storage and charging pile subsystems, improving equipment utilization, reducing costs, and enhancing user experience. It also leverages vehicle recognition and user profiling services to further improve resource utilization while ensuring secure communication of the device's IoT interface. This application aims to address the problems of low charging pile utilization and high operating costs caused by the independent operation of various parts in traditional charging pile systems and the lack of collaboration between the energy storage and charging pile subsystems. It optimizes power allocation between the two, achieving cost reduction and efficiency improvement. It overcomes the mechanical plug-and-charge mode of existing charging pile systems, enabling rapid charging resource scheduling based on real-time information, reducing queuing time, increasing charging pile utilization, providing users with timely charging services, and significantly improving the charging experience. It also addresses the shortcomings of existing scheduled charging management mechanisms, balancing scheduled charging and real-time charging demands, and enabling intelligent dynamic adjustment of scheduled charging plans, improving the system's adaptability to complex charging scenarios. The installation of cameras in the charging area can effectively identify queuing vehicles. By adjusting the queuing strategy and optimizing charging priority based on user behavior, timely responses can be provided to customers, reducing waiting time and improving the utilization rate of charging resources.

[0019] The intelligent power method for shared charging piles in this application includes: S1. Integrate grid power supply strategy and energy storage system optimization strategy, and carry out multi-source collaborative optimization of grid side, energy storage side and charging station side. Combine grid load, charging demand urgency and energy storage status to realize peak-valley electricity price response and global scheduling.

[0020] See Figure 1 This application's intelligent power allocation system for park charging piles integrates power supply strategies from the power grid and optimization strategies from the energy storage system, enabling peak-valley electricity price response and global scheduling in emergency situations. The charging pile power supply strategy service adjusts the power configuration of different charging piles in the intelligent power distribution system or the power configuration of the charging guns on the charging piles based on the charging queue and charging gun idle status, achieving optimal resource allocation. The device status and user profile service receives and updates the device status, allocation values, charging parameters, etc., of energy storage devices, the intelligent power distribution system, and the charging pile system in real time, updates the number of queued vehicles and parking space occupancy, and stores and summarizes user charging records from the past two weeks. The device control service converts the configuration strategies of the superior strategy service into action control commands for subordinate subsystems and monitors the execution results. The IoT interface service is used for communication adaptation of IoT devices, completing communication between the device status and control services and the devices, supporting industrial protocols such as Modbus / TCP and MQTT.

[0021] Among them, the power supply strategy service for new energy power supply and energy storage systems is used to achieve multi-source collaborative optimization. On the grid side, it responds to frequency deviation (>±0.5Hz for 30 seconds) and voltage fluctuation (>±10% for 30 seconds) events. On the energy storage side, it monitors transformer temperature (>70℃ triggers an alarm) and executes collaborative control of the energy storage station cluster. On the charging station side, when the real-time load rate is >80% and the number of waiting vehicles is >1, or the SOC balance is >30%, it initiates power rebalancing within the station.

[0022] In dynamic priority scheduling, a three-level decision matrix is ​​constructed, comprehensively considering parameters such as grid load, charging demand urgency, and energy storage status. During peak grid periods, the system ensures the execution rate of scheduled charging and forces the energy storage system to discharge. When only grid power is available, plug-and-charge current is limited to 30%. During off-peak grid periods, full-power charging is enabled; priority is given to charging scheduled vehicles; and the energy storage system stores energy. In emergency situations, supercharging mode is activated for plug-and-charge vehicles; non-emergency scheduling is suspended as needed; and the energy storage system discharges as required.

[0023] S2. Establish device communication through the device IoT interface, receive and update device status information reported by energy storage devices, intelligent power distribution systems and charging piles in real time, and create a profile of vehicle behavior based on historical records.

[0024] The IoT interface services of the park's intelligent power distribution system for charging piles include: In the status monitoring channel, the status information of the underlying devices is obtained in real time, usually using a publish-subscribe model.

[0025] In the control command channel, command responses are implemented based on the RPC framework, or by publishing messages to the message queue, the system configuration adapts to the device's power settings, charging pause, emergency stop, and other operations.

[0026] For security, AES-256 encryption is used for transmission, and two-factor authentication (device ID + dynamic token) is configured to ensure the integrity and non-repudiation of control commands.

[0027] The equipment status and user profile services of the park's intelligent power allocation system for charging piles include the following functions: Record device reported status information: Record status and report information from energy storage system, smart power distribution system and charging pile.

[0028] Vehicle profiling: AI recognition information is obtained from camera images, license plate information is extracted and hashed, and vehicle behavior is profiled by combining historical records, including the hashed license plate number, whether it is a new energy vehicle, charging status, whether there are people in the vehicle, parking space, charging pile ID, charging gun ID, shooting time, temporary charging status, average dwell time, number of visits, etc.

[0029] Receive vehicle data parsed by the IoT infrastructure: the default data frequency is twice per minute, and the data format is either an image or recognized text. The text content includes the hashed license plate, parking space, charging pile ID, charging gun ID, vehicle occupancy status, and shooting time. If the received data is an image, the charging pile ID and charging gun ID are bound together with the charging pile's control and status messages while recognizing the image.

[0030] Data Usage: Utilizing a dynamic weighted scoring algorithm to optimize charging pile resource utilization, prioritizing vehicles with high turnover rates, providing charging spaces for new users, and retaining new or waiting users. Furthermore, during peak electricity consumption periods, charging power and time slots can be coordinated based on user occupancy habits to optimize the allocation of charging spaces occupied by office workers in corporate parks.

[0031] S3. Collect vehicle charging queue information of charging piles and detect the idle status information of charging guns. Based on the charging queue information and the idle status information, generate a charging configuration strategy for intelligent power distribution charging piles.

[0032] The charging pile power supply strategy service of the park's intelligent power allocation system adopts a rolling optimization algorithm based on a time window, with a default cycle of 1 minute. The execution process is as follows: Status polling: Through device status and user profile services, the charging gun voltage, current, SOC and planned charging amount are collected in real time to obtain user profiles, which are used to identify users who have a long waiting time after charging is completed. These users are usually corporate employees or shopping users, and the charging queue length is obtained.

[0033] Status detection: The idle rate is calculated by recognizing the occupancy ratio of charging guns, parking space occupancy and queue length through image recognition. Since there are non-electric vehicles occupying the space or temporary parking, the charging capacity is the result of a combination of factors of parking space and charging gun. The idle rate is the ratio of the available charging space to the available charging capacity. When the idle rate is <10%, it is a tense state, and when the queue number is >1, it is an emergency state.

[0034] Decision execution: In idle state (idle rate ≥ 10%): standard charging protocol is executed.

[0035] In busy conditions (idle rate <10%): trigger the Pareto optimal algorithm, complete the enumeration of power allocation schemes within 15 seconds, and select the adjustment scheme that minimizes the total waiting time of the waiting queue.

[0036] The real-time queuing level (divided into 4 levels: 0, 1, 2, 3, corresponding to the length of the charging queue, with 3 being the maximum value, representing 3 or above) is transmitted to the new energy power supply and energy storage collaborative service module, triggering the power allocation optimization of the previous level.

[0037] The charging configuration strategy is executed by the scheduled charging power supply strategy service and the plug-and-charge power supply strategy service, respectively.

[0038] The scheduled charging power supply strategy service executes periodically every 5 minutes: it assesses whether all scheduled charging plans can be completed as planned, and triggers dynamic adjustments when deviations occur; when an emergency causes scheduled charging to be suspended, it automatically assesses recoverable solutions: when charging is suspended, it releases low-priority suspension instructions; otherwise, it initiates power adjustment. The condition for triggering adjustment is that the evaluation algorithm detects that the customer's scheduled charging time cannot be completed and sets a configurable time advance.

[0039] The plug-and-charge power supply strategy service seeks optimization solutions from multiple perspectives, returns the target charging gun, and adjusts the solution and planning completion time. Methods for optimizing charging speed include: configuring intelligent power distribution equipment to adjust the charging pile power configuration without changing the power input; configuring fast charging piles, taking a one-to-two fast charging pile as an example, which allows control of the power of two charging guns through commands while keeping the quota input power unchanged, thus prioritizing charging of the target vehicle. When using Pareto optimal solution selection, the score for each adjustment method is calculated using a dynamic weighted scoring algorithm. User's habitual delayed departure time (D): reflects the potential risk of a user occupying a parking space (the larger D is, the lower the priority), normalized to D_norm = 1 / (D+ε). ε is a minimum value to avoid division by zero.

[0040] The waiting time (W) before the planned charging is completed reflects the urgency of the vehicle freeing up the parking space (the smaller W is, the higher the priority), and is normalized to W_norm = 1 / (W+ε). ε is a minimum value to avoid division by zero.

[0041] User charging time (T): helps determine the charging progress (the longer T is, the closer it is to completion, but it needs to be combined with W), normalized to T_norm = T / T_max (T_max is the maximum charging time allowed by the system).

[0042] Calculate dynamic weighted scores: PriorityScore = α D_norm+β W_norm+γ T_norm Where α, β, and γ are scenario-based weighting coefficients, which are set differently based on peak and off-peak electricity consumption.

[0043] S4. Convert the charging configuration strategy into action control instructions for the subordinate subsystems and monitor the execution results.

[0044] The equipment control services of the park's intelligent power allocation system for charging piles include: Receive and store configuration policy parameters from the upper-level module, queue them for execution; parse the configuration policy to form a set of device control action instructions; send the device control action instructions to the IoT interface service and monitor the execution status.

[0045] The technical solution of this application will be illustrated below with a specific implementation case.

[0046] (I) System Startup and Initialization The software service programs for the new energy power supply and energy storage collaborative module, the plug-and-play fast response module, the scheduled charging management module, and the device IoT interface system are activated. Self-test commands are sent through the device IoT interface system to check the voltage and current output capabilities of the charging pile, the charging and discharging efficiency of the energy storage system, and the stability of the communication link.

[0047] (II) Data Acquisition and Processing The device status and user profile service module collects and obtains device-related data in real time through the device IoT interface system. 1) The charging pile has built-in sensor data to collect the voltage, current, SOC (state of charge) of the charging terminals and the planned charging amount data reported by the vehicle in real time. 2) Raw video from cameras, or parsed data. In a certain park management system, we use the video network platform to parse the data. The data includes two categories: A) The number of vehicles in the queuing area, identified as new energy vehicles and non-new energy vehicles; B) The parking status of vehicles in the charging area: parking space, available parking space, hashed license plate number, new energy vehicle, and people waiting in the vehicle.

[0048] Available parking spaces in charging areas are often occupied by non-charging vehicles, preventing normal charging services and affecting the actual availability of charging guns. Therefore, occupancy and vehicle type data within the camera's electronic fence are used to adjust the number of available charging guns. Additionally, when prioritizing charging, the average dwell time after charging in the user profile is a crucial reference indicator; if no profile information is available for a vehicle, a default usage time of half an hour is used. User profile data does not need to be stored for extended periods; data within the last two weeks is retained by default. For some charging piles, the device information is scrambled IDs to protect privacy. Therefore, hashed license plate information is used as auxiliary identification information. Furthermore, because the data is hashed and stored for only two weeks, effective privacy protection is achieved.

[0049] Since smart technology is already relatively widespread in parks, fully utilizing the image and video data processing capabilities of the park's smart video network platform can reduce the system's ownership cost and the hidden costs of the original system, such as vehicle recognition, license plate recognition, and facial recognition within electronic fences (parking spaces or charging waiting areas).

[0050] (III) Implementation of power allocation strategy In idle state (idle rate of available charging guns ≥ 10%): The charging station executes the standard charging protocol and outputs power according to the planned charging amount reported by the vehicle. The real-time status of the charging station (idle, charging) is uploaded to the backend management system via the device IoT interface system.

[0051] During busy periods (idleness rate of available charging guns <10%): Triggering the Pareto optimal algorithm: Enumerate power allocation schemes and select the adjustment scheme that prioritizes ending the charging of a certain vehicle. For example, when ten electric vehicles are charging simultaneously, two of them will finish charging after 20 minutes. However, one user is a park employee who usually occupies the maximum charging parking discount time of 2 hours before moving out of the parking space. The other user is not recorded. According to the dynamic weight scoring algorithm, the vehicle without a record is preferred because its average dwell time is calculated using the default half hour.

[0052] Dynamic priority scheduling: A three-level decision matrix is ​​constructed based on parameters such as grid load, charging demand urgency, and energy storage status. During peak grid periods, current to plug-and-charge vehicles is limited to 30% to ensure the execution rate of scheduled charging and force the energy storage system to discharge. During off-peak periods, full-power charging is enabled, prioritizing charging scheduled vehicles, and the energy storage system stores energy. In emergency situations, supercharging mode is activated for plug-and-charge vehicles, non-urgent scheduling is suspended as needed, and the energy storage system discharges as needed to support the grid. Charging demand urgency is determined by a queue of vehicles; when there are more than one queued vehicle, it is considered an emergency.

[0053] The equipment status and user profile services provide unified charging gun status information. The park uses public screens to display the usage and queuing status of charging stations, and displays more usage details through the park's APP, such as: the number of people queuing in the waiting area, the charging area is about to be completed, and the availability reminder.

[0054] These expansions have further improved parking order in the charging area, increased the utilization rate of charging piles, and enhanced user recognition of the park's technological level and service satisfaction. The implementation of this system in pilot parks is expected to increase charging pile utilization by 18-25%, reduce operating costs by 12-16%, significantly improve the user charging experience, and reduce waiting times.

[0055] Another aspect of this application provides a shared charging pile intelligent scheduling system based on multi-objective optimization, comprising: The power supply strategy module for new energy power supply and energy storage system is used to integrate grid power supply strategy and energy storage system optimization strategy. It performs multi-source collaborative optimization of grid side, energy storage side and charging station side, and realizes peak-valley electricity price response and global scheduling by combining grid load, charging demand urgency and energy storage status. The device status and user profile module is used to establish device communication through the device IoT interface, receive and update device status information reported by energy storage devices, intelligent power distribution systems and charging piles in real time, and profile vehicle behavior in combination with historical records. The charging pile power supply strategy module is used to collect vehicle charging queuing information of the charging pile and detect the idle status information of the charging gun. Based on the charging queuing information and the idle status information, it generates a charging configuration strategy for the charging pile with intelligent power distribution. The device control service module is used to convert the charging configuration strategy into action control instructions for subordinate subsystems and monitor the execution results.

[0056] It is evident that the intelligent scheduling method for shared charging piles based on multi-objective optimization proposed in this application has the following advantages compared to related technologies: The utilization rate of charging piles can be increased by 18-25% through intelligent power allocation systems. For example, in the actual application of a charging station in a certain park, by dynamically adjusting the power output and charging task allocation, the average number of daily charging times of the charging piles has increased from 50 to 60. By optimizing charging strategies and coordinating the control of energy storage systems, electricity costs during peak grid hours were reduced, while the lifespan of charging piles and energy storage equipment was extended, resulting in a 12-16% reduction in operating costs. By prioritizing charging stations and adjusting power allocation during peak charging times, long queues are avoided, improving the user charging experience. Simultaneously, the charging strategy is dynamically adjusted based on grid load and energy prices, optimizing system costs and providing convenient charging services for users.

[0057] The estimated charging time for each charging gun in the device status service can provide users with valuable information: The waiting area displays: Gun #3 will be fully charged in 5 minutes; The park's public screen dynamically displays: N charging guns are available; or the charging piles are full, and an available one is expected in 5 minutes.

[0058] This application will not only improve the company's asset operation efficiency and save on electricity and equipment maintenance costs, but also attract more users and enhance the company's competitiveness in the charging pile operation market.

[0059] Based on the same inventive concept, this application also provides a computer-readable storage medium storing one or more programs, which, when executed, can realize the aforementioned intelligent scheduling method for shared charging piles based on multi-objective optimization.

[0060] This application also provides a device including a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory is a computer-readable storage medium used to store one or more programs. The processor is used to execute the programs stored in the computer-readable storage medium. The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; alternatively, it may exist independently and not assembled into the device / apparatus.

[0061] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent scheduling of shared charging piles based on multi-objective optimization, characterized in that, The method comprises the following steps: S1, integrate power grid power supply strategy and energy storage system optimization strategy, and perform multi-source collaborative optimization on the grid side, the energy storage side and the charging station side, and realize peak-valley electricity price response and global scheduling in combination with grid load, charging demand urgency and energy storage state; S2, establish device communication through device internet of things interface, receive and update device state information reported by energy storage devices, intelligent power distribution system and charging piles in real time, and portrait vehicle behavior in combination with historical records; S3, collect vehicle charging queue information of the charging pile and detect idle state information of the charging gun, and generate a charging configuration strategy of the intelligent power distribution charging pile according to the charging queue information and the idle state information; S4, convert the charging configuration strategy into action control instructions of subordinate subsystems, and monitor the execution results.

2. The method of claim 1, wherein, The multi-source collaborative optimization comprises: on the grid side, responding to frequency deviation and voltage fluctuation events; on the energy storage side, monitoring transformer temperature and performing energy storage station cluster collaborative control; on the charging station side, when real-time load rate > 80% and waiting vehicles > 1, or SOC balance degree > 30%, starting station power rebalancing.

3. The method of claim 1, wherein, The real-time receiving and updating of the device state information reported by the energy storage devices, the intelligent power distribution system and the charging piles, and the portrait of the vehicle behavior in combination with the historical records further comprise: recording state and report information from the energy storage system, the intelligent power distribution system and the charging pile; obtaining AI recognition information of the camera image, extracting license plate information and performing hash processing, and portrait of the vehicle behavior in combination with the historical records, including the hashed license plate number, whether it is a new energy vehicle, the charging state, the in-vehicle state, the parking space, the charging pile ID, the charging gun ID, the shooting time, the temporary charging condition, the average stay time, the access frequency; receiving vehicle data parsed by the internet of things infrastructure, the data form being a picture or recognized text, and the text content including the hashed license plate, the parking space, the charging pile ID, the charging gun ID, the in-vehicle state and the shooting time; if a picture is received, the charging pile ID and the charging gun ID are bound by combining the control and state messages of the charging pile while identifying the image; and optimizing the charging pile resource utilization rate by using a dynamic weight scoring algorithm.

4. The method of claim 1, wherein, The charging configuration strategy is executed by a reservation charging power supply strategy service and a plug-and-charge power supply strategy service respectively; The reservation charging power supply strategy service evaluates whether all reservation charging plans can be completed as planned, and triggers dynamic adjustment when deviation occurs; When the emergency state causes the reservation charging to be suspended, an automatic evaluation of a recoverable solution is performed; the condition for triggering adjustment is that the evaluation algorithm detects that the customer reservation period cannot complete charging, and a configurable time advance is set; The plug-and-charge power supply strategy service returns the target charging gun, the adjustment scheme and the planned completion time, configures the intelligent power distribution device, adjusts the charging pile power configuration without changing the power distribution input; configures a fast charging pile, controls the power of two charging guns to realize the priority charging of the target vehicle under the condition that the input power is unchanged; when the Pareto optimal solution is selected, the score of each adjustment method is calculated by using a dynamic weight scoring algorithm. User habit delay departure time D, normalized as D_norm = 1 / (D+ε), ε is a minimum value; The waiting time W required for the completion of the planned charging is normalized as W_norm = 1 / (W+ε), ε is a minimum value; The user charging time T is normalized as T_norm = T / T_max, T_max is the maximum charging time allowed by the system; The dynamic weight score is calculated: PriorityScore = α*D_norm+β*W_norm+γ*T_norm Wherein α, β, γ are scenario weight coefficients.

5. A multi-objective optimization-based shared charging pile intelligent scheduling system, characterized in that, The system comprises: A new energy power supply and energy storage system power supply strategy module for integrating power grid power supply strategy and energy storage system optimization strategy, performing multi-source collaborative optimization of the power grid side, the energy storage side and the charging station side, combining power grid load, charging demand urgency and energy storage state to realize peak-valley electricity price response and global scheduling; A device state and user portrait module for establishing device communication through a device internet of things interface, receiving and updating device state information reported by energy storage devices, intelligent power distribution systems and charging piles in real time, and profiling vehicle behavior in combination with historical records; A charging pile power supply strategy module for collecting vehicle charging queue information of charging piles and detecting idle state information of charging guns, generating a charging configuration strategy of intelligent power distribution charging piles according to the charging queue information and the idle state information; A device control service module for converting the charging configuration strategy into action control instructions of subordinate subsystems and monitoring execution results.

6. The system of claim 5, wherein, The multi-source collaborative optimization comprises: On the power grid side, responding to frequency deviation and voltage fluctuation events; on the energy storage side, monitoring transformer temperature and performing energy storage station cluster collaborative control; on the charging station side, when the real-time load rate > 80% and the waiting vehicles > 1, or the SOC balance degree > 30%, starting the in-station power rebalancing.

7. The system of claim 5, wherein, The device state and user portrait module is further used for: Recording state and report information from energy storage systems, intelligent power distribution systems and charging piles; obtaining AI recognition information of camera images, extracting license plate information and performing hash processing, profiling vehicle behavior in combination with historical records, including hashed license plate number, whether it is a new energy vehicle, charging state, whether there is someone in the vehicle, parking space, charging pile ID, charging gun ID, shooting time, temporary charging condition, average stay time, access frequency; Receiving vehicle data parsed by the internet of things infrastructure, in the form of pictures or recognized text, the text content including hashed license plate, parking space, charging pile ID, charging gun ID, whether there is someone in the vehicle, shooting time; if a picture is received, the charging pile ID and charging gun ID are bound in combination with the control and state messages of the charging pile while the image is identified; the dynamic weight score algorithm is used to optimize the charging pile resource utilization rate.

8. The system of claim 5, wherein, The charging configuration strategy is executed by a reservation charging power supply strategy service and a plug-and-charge power supply strategy service respectively; The reservation charging power supply strategy service evaluates whether all reservation charging plans can be completed as planned, and triggers dynamic adjustment when deviation occurs; When the emergency state causes the appointment charging to be suspended, an automatic assessment of a recoverable solution is performed; the condition for triggering the adjustment is that the assessment algorithm detects that the charging cannot be completed during the customer's appointment period, and a configurable time advance is set; The plug-and-charge power supply strategy service returns the target charging gun, the adjustment solution and the planned completion time, configures the intelligent power distribution equipment, adjusts the charging pile power configuration without changing the power distribution input, configures the fast charging pile, controls the power of two charging guns to realize the priority charging of the target vehicle under the condition that the input power is unchanged, and calculates the score of each adjustment method using a dynamic weight scoring algorithm when the Pareto optimal solution is selected: The user habit delay departure time D is normalized as D_norm = 1 / (D+ε), and ε is a minimum value; The waiting time W for the planned charging to be completed is normalized as W_norm = 1 / (W+ε), and ε is a minimum value; The user charging time T is normalized as T_norm = T / T_max, and T_max is the maximum charging time allowed by the system; The dynamic weight score is calculated as follows: PriorityScore = α*D_norm+β*W_norm+γ*T_norm Wherein α, β, γ are scenario-based weight coefficients. 9.A computer readable storage medium storing one or more programs, wherein when the one or more programs are executed, the method of claim 1-4 based on multi-objective optimization of shared charging pile intelligent scheduling can be implemented. The processor, the communication interface, and the computer readable storage medium communicate with each other through the communication bus; and the processor is configured to execute the program stored in the computer readable storage medium.

10. An apparatus comprising a processor, a communication interface, the computer- readable storage medium of claim 9, and a communication bus; wherein, ​

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