Dynamic load balancing control method and system based on real-time power collection

Abstract

The present application relates to the technical field of charging pile electric energy management and load control, in particular to a dynamic load balancing control method and system based on real-time power collection. In the method, a station-level controller establishes a charging session with each charging pile, acquires real-time power and user setting information, divides the remaining charging task into a core task segment, a target task segment and an opportunity task segment, and updates the completion progress of each task segment over time. The controller generates rigid, semi-flexible and high-flexible labels for each charging session according to the type of task segment and the remaining time to the estimated departure time, indicating the degree of power adjustment. When it is determined that the total power needs to be adjusted according to the power in the station and the power constraint condition, the power adjustment priority is determined according to the flexible label and combined with the charging mode and the charging pile adjustment fatigue, the target power distribution result is generated and issued to each charging pile, the dynamic load balancing is realized, and the power distribution safety margin and user charging experience are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile electric energy management and load control, in particular to a dynamic load balancing control method and system based on real-time power collection. BACKGROUND

[0002] With the large-scale promotion of new energy vehicles, the number of charging infrastructure such as public charging stations and park charging stations is rapidly increasing. The phenomenon of concentrated, high-power and superimposed access of electric vehicles to the distribution network is becoming increasingly prominent. The traditional "plug-and-charge" disordered charging method is easy to cause local distribution capacity tension and peak segment overload risk. Therefore, the industry gradually proposes orderly charging, intelligent power distribution, load management and other technical routes. By monitoring the total load in the station and limiting the power of a single pile, peak shaving and valley filling and distribution side investment optimization are realized. In recent years, schemes based on dynamic load management have begun to be applied. By real-time collection of station power and dynamic allocation of power among multiple charging piles according to algorithms, the power grid friendliness and charging efficiency are improved, and charging network load balancing and dynamic pricing research combining electricity price, demand prediction, reinforcement learning and other methods have appeared.

[0003] However, existing charging station load balancing control is mostly based on station-level total power and instantaneous power of each pile for macro adjustment. Usually, only fixed priority, simple power threshold or unified reduction ratio is used to control the output of each charging pile, lacking fine modeling of single vehicle charging task structure, difficult to distinguish "safety necessary power", "user target power" and "abandonable incremental power", and rarely considering the influence of user's estimated departure time on the rigidity of charging. When executing power limitation or power recovery, the existing scheme often ignores user charging mode preference, power grid side power constraint level and the influence of frequent adjustment of charging piles on service life, which is easy to cause poor user experience of part of users, long-term excessive adjustment of individual charging piles, insufficient utilization of redundant capacity in the station, and still has problems of local overload or insufficient response under peak or abnormal working conditions. Therefore, a dynamic load balancing control method and system based on real-time power collection are needed to solve the above problems. SUMMARY

[0004] (I) The technical problem solved: In view of the deficiencies of the prior art, the present application provides a dynamic load balancing control method and system based on real-time power collection, which solves the above problems.

[0005] (ii) Technical solution: To achieve the above object, the application provides the following technical solution: a dynamic load balancing control method based on real-time power collection, the method comprising the following steps: step S1, establishing a charging session with a charging pile and obtaining real-time power information and user setting information of the charging pile; the real-time power information at least includes current charging power and remaining charging power, and the user setting information at least includes target charging demand and estimated departure time; step S2, according to the real-time power information and the user setting information, dividing the remaining charging task of each charging session into at least three task segments with different priorities and adjustabilities; the at least three task segments include a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand, and an opportunity task segment executed when there is surplus power in the station; step S3, according to the progress of each task segment of each charging session and the remaining time to the estimated departure time, generating an elasticity label for each charging session to represent the charging power adjustability of the charging session, the elasticity label at least includes one of a rigid label for identifying a charging session with the smallest adjustment space, a semi-elastic label for identifying a charging session with limited adjustment space, and a high-elasticity label for identifying a charging session with priority adjustment space; step S4, when it is determined that the total power of the charging station needs to be adjusted according to the real-time power collection result and the power constraint condition, determining the power adjustment priority of each charging session according to the elasticity label of each charging session, and generating a target power distribution result of each charging pile; step S5, according to the target power distribution result, issuing power control instructions to each charging pile, so that each charging pile operates according to the target power distribution result, and repeating steps S3 and S4 based on the updated real-time power collection result to realize dynamic load balancing control based on real-time power collection.

[0006] Further, in step S1: the user setting information further includes a user-selected charging mode, the charging mode at least includes one of a time priority mode, a cost priority mode and a comprehensive balance mode, the charging mode is used to correct the charging power adjustability of each charging session when generating the elasticity label.

[0007] Further, in step S2: the core task segment corresponds to an electric quantity interval from the current electric quantity to a preset safe electric quantity threshold; the target task segment corresponds to an electric quantity interval from the preset safe electric quantity threshold to an electric quantity corresponding to the target charging demand; and the opportunity task segment corresponds to an electric quantity interval from the electric quantity corresponding to the target charging demand to a preset upper limit electric quantity threshold.

[0008] Further, the step S3 of generating the elasticity label comprises: when the charging session is in the core task segment and the distance from the estimated departure time is less than a first preset time threshold, marking the charging session as a rigid label; when the charging session is in the target task segment and the distance from the estimated departure time is greater than a second preset time threshold, marking the charging session as a semi-elastic label; and when the charging session is in the opportunity task segment or the user does not set the estimated departure time, marking the charging session as a high-elasticity label.

[0009] Further, the step S4 of determining the power adjustment priority of each charging session according to the elasticity label of each charging session comprises: preferentially performing the reduction or recovery of the charging power for the charging session with the high-elasticity label, and secondarily performing the charging power adjustment for the charging session with the semi-elastic label, and limiting or avoiding the charging power adjustment for the charging session with the rigid label.

[0010] Further, in the step S4, the power constraint condition comprises power constraint level information issued by the power grid side, and the step of determining the power adjustment priority of each charging session further comprises: correcting the power adjustment priority of the charging session with the high-elasticity label and the semi-elastic label according to the charging mode; when the power constraint level represents a severe constraint state, allowing the charging session with the rigid label to participate in the power adjustment; and when the power constraint level represents a non-severe constraint state, prohibiting or avoiding the charging session with the rigid label from participating in the power adjustment.

[0011] Further, the step of determining the power adjustment priority of each charging session further comprises: obtaining an adjustment fatigue parameter corresponding to each charging pile, the adjustment fatigue parameter being used to represent the number of times of performing the charging power adjustment of the charging pile within a preset time window; and preferentially selecting the charging session corresponding to the charging pile with a smaller adjustment fatigue parameter to participate in the power adjustment, and reducing the power adjustment priority of the charging session corresponding to the charging pile with a larger adjustment fatigue parameter, under the condition of the same elasticity label and the same charging mode.

[0012] Further, the application also provides a dynamic load balancing control system based on real-time power collection, which comprises: a session establishment and information acquisition module, which is used to establish a charging session with a charging pile and acquire real-time power information and user setting information of the charging pile; the real-time power information at least includes current charging power and remaining charging power, and the user setting information at least includes target charging demand and expected departure time; a residual task segmentation module, which is used to divide the residual charging task of each charging session into at least three task segments with different priorities and adjustabilities according to the real-time power information and user setting information; the at least three task segments include a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand, and an opportunity task segment executed when there is surplus power in the station; an elastic label generation module, which is used to generate an elastic label for each charging session to represent the adjustability of the charging power of the charging session according to the progress of each task segment of each charging session and the remaining time to the expected departure time, and the elastic label at least includes one of a rigid label for identifying a charging session with minimum adjustment space, a semi-elastic label for identifying a charging session with limited adjustment space, and a high-elastic label for identifying a charging session with priority adjustment space; a power allocation strategy generation module, which is used to determine the power adjustment priority of each charging session according to the elastic label of each charging session and generate a target power allocation result of each charging pile when it is determined that the total power of the charging station needs to be adjusted according to the real-time power collection result and the power constraint condition; and a power control execution module, which is used to issue a power control instruction to each charging pile according to the target power allocation result, so that each charging pile operates according to the target power allocation result, and repeatedly adjusts based on the updated real-time power collection result in cooperation with the elastic label generation module and the power allocation strategy generation module, so as to realize dynamic load balancing control based on real-time power collection.

[0013] (Three) beneficial effects: compared with the prior art, the application provides a dynamic load balancing control method and system based on real-time power collection, which has the following beneficial effects: 1. The dynamic load balancing control method and system based on real-time power collection divides the remaining charging task into core task segments, target task segments and opportunity task segments in the single vehicle dimension, and generates rigid, semi-elastic and high-elasticity elastic labels by combining the completion progress of each task segment and the remaining time from the estimated departure time, thereby realizing the fine differentiation and modeling of "safety required power", "user target power" and "abandonable incremental power". On this basis, when the controller performs power limitation or recovery, it no longer uses a simple unified reduction ratio or fixed priority for each charging pile, but preferentially adjusts high-elasticity sessions, cautiously adjusts semi-elastic sessions, and tries to avoid affecting rigid sessions, thereby prioritizing the charging needs of users close to departure and users with low power under the premise of meeting power distribution capacity constraints, reducing the risk of local overload, improving the utilization efficiency of redundant power in the station, and achieving comprehensive optimization of safety, user experience and overall energy utilization.

[0014] 2. The dynamic load balancing control method and system based on real-time power collection introduces user-selected charging modes, power grid side power constraint level information and charging pile adjustment fatigue degree parameters in the power distribution strategy generation process, cooperatively corrects the power adjustment priority based on the elastic label, and constructs a user side, power grid side and device side cooperative load adjustment mechanism. Specifically, in the normal or mild constraint state, the system distinguishes the order of high-elasticity and semi-elastic sessions according to the user's time priority or cost priority preference, improves the matching degree of user's individualized demand; in the severe constraint state, the rigid session is allowed to participate in adjustment within a limited range, improving the response accuracy of the system in extreme working conditions; at the same time, according to the adjustment fatigue degree parameter of each charging pile, the charging pile with less long-term adjustment frequency is preferentially selected to participate in power adjustment, avoiding the decrease of service life and the increase of fault risk caused by frequent power adjustment of individual devices. Therefore, the overall charging experience of the user side is improved, the single pile over-adjustment problem is reduced, and the reliability and maintainability of the system level operation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The overall flowchart of the dynamic load balancing control method based on real-time power collection provided by the application.

[0016] Figure 2 The framework diagram of the dynamic load balancing control method based on real-time power collection provided by the application.

[0017] Figure 3 The system structure diagram of the dynamic load balancing control method based on real-time power collection provided by the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0019] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0020] Please refer to Figures 1-2 , Figure 1 The overall flowchart of the dynamic load balancing control method based on real-time power collection provided by the present application is shown in the figure. Figure 2 The framework diagram of the dynamic load balancing control method based on real-time power collection provided by the present application is shown in the figure. One embodiment provided by the present application is a dynamic load balancing control method based on real-time power collection, which comprises the following steps: step S1, establishing a charging session with a charging pile and obtaining real-time power information and user setting information of the charging pile; the real-time power information at least includes current charging power and remaining charging power, and the user setting information at least includes target charging demand and estimated departure time; step S2, according to the real-time power information and the user setting information, dividing the remaining charging task of each charging session into at least three task segments with different priorities and adjustabilities; the at least three task segments include a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand, and an opportunity task segment executed when there is surplus power in the station; step S3, according to the progress of each task segment of each charging session and the remaining time to the estimated departure time, generating an elasticity label for each charging session to represent the adjustability of its charging power, the elasticity label at least includes one of a rigid label for identifying the charging session with the smallest adjustment space, a semi-elastic label for identifying the charging session with limited adjustment space, and a high-elasticity label for identifying the charging session with priority adjustment space; step S4, when it is determined that the total power of the charging station needs to be adjusted according to the real-time power collection result and the power constraint condition, determining the power adjustment priority of each charging session according to the elasticity label of each charging session, and generating the target power distribution result of each charging pile; step S5, according to the target power distribution result, issuing power control instructions to each charging pile, so that each charging pile operates according to the target power distribution result, and repeating steps S3 and S4 based on the updated real-time power collection result, to realize dynamic load balancing control based on real-time power collection.

[0021] In this embodiment, the dynamic load balancing control method based on real-time power collection is executed by the station-level controller in the charging station. When an electric vehicle accesses any charging pile, the controller first establishes a charging session with the charging pile, collects the current charging power of the charging pile in real time, estimates the remaining charging capacity, and obtains the target charging demand and estimated departure time set by the user through the human-machine interface or vehicle-network communication. On this basis, the remaining charging task of the session is logically divided into three task segments with different priorities, i.e., a core task segment that must be completed before the user departs to ensure basic travel safety, a target task segment for trying to meet the user's expected power, and an opportunity task segment that is only executed when there is a surplus of power in the station, which can be reduced or canceled at any time, thereby converting the original continuous charging demand into a structured segmented task model. Subsequently, the controller collects real-time power and updates the task progress of all charging sessions at a preset scheduling period during operation. According to the completed power, remaining power, and remaining time to the estimated departure time of each task segment, it is determined whether the current session is in the core task stage, the target task stage, or the opportunity task stage, and accordingly, an elastic label representing the power adjustable space is assigned to each charging session. For example, a rigid label is assigned to sessions with low power and approaching departure, still in the core task segment, a semi-elastic label is assigned to sessions with completed core tasks and certain time margin, and a high-elasticity label is assigned to sessions that only execute opportunity tasks or have no departure time limit. When the station-level detects that the total power is close to the upper limit of the power distribution capacity or receives a power constraint instruction from the grid side, the controller sorts the power adjustment priorities of all charging sessions according to the elastic labels based on the real-time power collection results and power constraint conditions, prioritizes the adjustment of sessions with large deliverable space, generates the target power distribution results of each charging pile in the next scheduling period, such as reducing the power of some high-elasticity sessions by one level, and keeping or moderately increasing the power of some rigid sessions, thereby meeting the charging demands of key users as much as possible without exceeding the station-level capacity constraints. Finally, through the power control instructions, each charging pile operates according to the target power, and the task progress evaluation and power distribution strategy generation are repeated based on the new real-time power collection results, realizing a continuous dynamic load balancing control loop. This implementation is suitable for public charging stations, campus charging stations, bus or logistics vehicle centralized charging stations, etc., and can significantly reduce the risk of power distribution overload when multiple users access simultaneously, improve the utilization rate of redundant capacity and the overall charging service quality.

[0022] Further, the present application provides an embodiment: in step S1, the user setting information further includes a user-selected charging mode, the charging mode including at least one of a time priority mode, a cost priority mode, and a comprehensive balance mode, the charging mode being used to correct the adjustable degree of charging power of each charging session when generating the elastic label.

[0023] In the embodiment, the controller further receives user-selected charging mode information when establishing the charging session with the charging pile and obtaining the user setting information, the charging mode including at least one of a time priority mode, a cost priority mode and a comprehensive balance mode, wherein the time priority mode corresponds to that the user pays more attention to completing the target charging demand as much as possible before the estimated departure time, in the mode, the controller tends to reduce the adjustable degree of the charging session when generating the flexible label subsequently, for example, down-regulating from semi-flexible to rigid under the condition that the task segment can only be marked as semi-flexible, so as to reduce the probability of power reduction; the cost priority mode corresponds to that the user pays more attention to the overall charging cost or is willing to obtain a discount for peak shaving, in the mode, the controller appropriately increases the adjustable degree of the charging session when generating the flexible label, for example, increasing from semi-flexible to high flexibility or allowing a larger power fluctuation under the condition that the task segment can only be marked as semi-flexible, so as to preferentially excavate dispensable space from such users when the station-level power is tight; the comprehensive balance mode is between the two modes, the controller keeps the default flexibility determination strategy when correcting the label, and only moderately increases the adjustable degree when there is sufficient time margin, so that the subsequent power allocation strategy based on the flexible label not only reflects the objective task structure and the departure constraint, but also reflects the user's trade-off between charging timeliness and cost sensitivity in the form of simple mode options, so as to improve the user experience and the demand response participation while achieving station-level load balancing.

[0024] Further, the application provides an embodiment: in step S2: the core task segment corresponds to an electric quantity interval from the current electric quantity to a preset safe electric quantity threshold; the target task segment corresponds to an electric quantity interval from the preset safe electric quantity threshold to an electric quantity corresponding to the target charging demand; and the opportunity task segment corresponds to an electric quantity interval from the electric quantity corresponding to the target charging demand to a preset upper limit electric quantity threshold.

[0025] In this embodiment, the segmentation manner of the remaining charging task in step S2 is further limited, that is, after obtaining the current power and the user's target charging demand, the controller first determines a preset safety power threshold for guaranteeing the basic travel and safe return of the vehicle, and defines the power interval from the current power to the power corresponding to the preset safety power threshold as a core task segment. The power corresponding to the core task segment is usually used to meet the minimum endurance requirement of the user after leaving the station to complete the daily commute or return to the fixed location. For example, when the user's current power is low, even if the user's target is set to "as full as possible", the system still prioritizes guaranteeing the completion of this interval before the estimated departure time. Then, the controller calculates the power corresponding to the target charging demand (such as the expected SoC percentage or the expected endurance mileage) according to the user's set target charging demand, and defines the power interval from the preset safety power threshold to the target power as a target task segment. This segment corresponds to the ideal power level that the user hopes to achieve, and is completed as much as possible under the condition that the power distribution in the station allows, but can be moderately compressed without breaking the core task segment when the grid or station-level power constraint is tight. Further, the controller sets an upper power threshold based on the battery health and charging strategy, such as a certain percentage higher than the user's target power or close to the upper limit of the battery health charging, and defines the power interval from the power corresponding to the target charging demand to the preset upper power threshold as an opportunity task segment. This opportunity task segment is only arranged to be executed when there is surplus power in the station, and can be used as an incremental part of "charging a little more to improve the convenience of subsequent travel". It can be preferentially reduced or directly cancelled when the load is high or the grid issues a peak shaving instruction. Through the above segmentation, the originally continuous remaining charging demand is divided into three power intervals with clear semantics, decreasing priority and increasing adjustability. The controller can adopt differentiated strategies for different task segments when generating the flexible label and executing power allocation in the subsequent, such as forcibly guaranteeing the core task segment, considering the target task segment, and actively sacrificing the opportunity task segment, thereby realizing the structured management of "rigid demand, expected demand and disposable demand" of the single vehicle charging task without the need for accurate and complex algorithms. This enables the skilled person in the art to easily embed the segmentation model into the existing ordered charging or load management system.

[0026] Further, the present application provides an embodiment: the step S3 of generating the flexible label comprises: when the charging session is in the core task segment and the distance from the estimated departure time is less than a first preset time threshold, the charging session is marked as a rigid label; when the charging session is in the target task segment and the distance from the estimated departure time is greater than a second preset time threshold, the charging session is marked as a semi-flexible label; when the charging session is in the opportunity task segment or the user does not set the estimated departure time, the charging session is marked as a high-flexible label.

[0027] In this embodiment, the generation rule of the elastic label in step S3 is further limited, that is, the controller not only judges whether the charging session is currently in the core task segment, the target task segment or the opportunity task segment in each scheduling period, but also combines the remaining time length from the user's estimated departure time to superimpose the "task segment position" and the "time urgency" to give the charging session three types of labels of rigidity, semi-elasticity or high elasticity: when a certain charging session is still in the core task segment and the estimated departure time is less than the first preset time threshold, it is indicated that the user's power has not reached the basic travel safety level and the departure time is approaching, so the session belongs to a typical "rigid demand", and the system marks it as a rigid label, which does not participate or as little as possible participates in power reduction in subsequent load adjustment; when the charging session is in the target task segment and the estimated departure time is greater than the second preset time threshold, it is indicated that the basic safety power has been guaranteed and there is a certain time margin, so the session has a target demand but has a moderate compression space, and the system marks it as a semi-elastic label, which can limit the charging power or delay the completion of the target task segment for a short time when the power is tight; when the charging session is in the opportunity task segment, it is indicated that the core and target demands have been basically met, and subsequent charging is more "a little more" to add a finishing touch, so the system marks it as a high-elasticity label as a priority adjustment object of power reduction or recovery; for the case where the user does not set the estimated departure time, since the time constraint is uncertain, it is also treated as a high-elasticity label to release the adjustment space by using this part of the "time and power are relatively loose" session to give priority to the power reduction or recovery when the station load is controlled. Through the above label generation logic, the physical task segment and the time urgency are directly mapped to three adjustable degrees, so that the technical personnel in the art can directly control according to the label category in the engineering implementation without relying on a complex model, and the user experience and the power grid constraint are considered.

[0028] Further, the application provides an embodiment: the step S4 of determining the power adjustment priority of each charging session according to the elastic label of each charging session comprises: preferentially reducing or recovering the charging power of the charging session with the high-elasticity label, secondly adjusting the charging power of the charging session with the semi-elasticity label, and limiting or avoiding adjusting the charging power of the charging session with the rigidity label.

[0029] In this embodiment, the specific strategy for determining the power adjustment priority according to the elastic label in step S4 is further limited, that is, when the station-level controller determines that the total power of the charging station needs to be reduced or increased according to the real-time power collection results and the power constraint condition, instead of uniformly adjusting all charging sessions in proportion or simply sorting them according to the order of access or fixed priority, the aforementioned rigid, semi-elastic and high-elastic three types of labels are directly used as the main sorting basis for power regulation: in the scenario of needing to reduce the total power, the controller first selects objects from the charging sessions with high-elastic labels, releases the adjustable capacity in the station by reducing the charging power level, shortening the execution time of the opportunity task segment or suspending the execution of the opportunity task segment for a short time, and so on, for example, the vehicles that have completed the core task segment and the target task segment and are executing the opportunity task segment are preferentially compressed in power; when the adjustable space of the high-elastic session is insufficient to meet the current reduction demand, part of the sessions with semi-elastic labels are selected according to certain rules (such as distance from the departure time, current progress, etc.), and the charging power is appropriately reduced or the completion of the target task segment is delayed, so as to supplement the adjustment capacity without affecting the timely completion of the core task segment; during the entire adjustment process, the controller sets the charging sessions with rigid labels as prohibited or strongly restricted adjustment objects, and only in extreme working conditions and after confirming that other strategies cannot meet the safety operation requirements, the minimum intervention is considered, so that through the hierarchical control logic of “prioritizing high-elastic, then semi-elastic, and avoiding rigid”, the power reduction is concentrated as much as possible on the sessions with higher user acceptance and greater task flexibility; in the scenario of needing to restore or increase the total power in the station, the priority sorting can also be used in reverse, for example, the power is preferentially restored or increased for the high-elastic label sessions, and then the power is restored for the semi-elastic label sessions, while the rigid label sessions are not required to be compensated greatly as they have been kept with sufficient supply in the early stage. Through this label-based power adjustment priority control mechanism, the person skilled in the art can significantly simplify the scheduling logic implementation in actual engineering, while improving the fine degree of peak shaving and valley filling and the user experience.

[0030] Further, the present application provides an embodiment: in step S4, the power constraint condition includes power constraint level information issued by the power grid side, and the determination of the power adjustment priority of each charging session further includes: correcting the power adjustment priority of the charging sessions with high-elastic labels and semi-elastic labels according to the charging mode; when the power constraint level represents a severe constraint state, the charging sessions with rigid labels are allowed to participate in power adjustment; when the power constraint level represents a non-severe constraint state, the charging sessions with rigid labels are prohibited or avoided from participating in power adjustment.

[0031] In this embodiment, the power adjustment priority is further corrected in coordination with the power grid side power constraint level information and the user charging mode. Specifically, the station-level controller periodically receives the power constraint level information corresponding to the current period through the communication interface with the upper dispatch system or the power distribution monitoring system, for example, the power grid operating state is divided into normal, mild constraint, severe constraint, and the like. When the power constraint level represents a normal or mild constraint state, the controller only selects between the charging sessions with high flexibility labels and semi-flexibility labels when determining the power adjustment priority, and further refines the priority through the aforementioned charging mode: for the sessions with cost priority mode and high flexibility or semi-flexibility labels, the power adjustment priority of the sessions can be appropriately adjusted upwards, so that the sessions are more easily selected to obtain the electricity price or integral incentive during peak shaving; for the sessions with time priority mode, the probability of being adjusted is relatively reduced under the same flexibility label, so as to reduce the influence on the time-sensitive users; when the power constraint level represents a severe constraint state, after the adjustment space of the high flexibility and semi-flexibility sessions is used up, the controller can limit the charging sessions with rigidity labels to be included in the candidate set, and select part of the rigidity sessions to participate in small-scale power adjustment according to the preset rules, so as to ensure that the power grid can still meet the safe operation requirement under extreme working conditions. When the power constraint level is in a non-severe constraint state, the rigidity sessions are prohibited or avoided from being selected to participate in power adjustment, and the high flexibility and semi-flexibility sessions are always used to release the adjustment capacity; through the above mechanism, the priority of the flexibility label is dynamically corrected based on the power grid constraint level and the user charging mode, so that the user experience and willingness are more focused when the power grid pressure is not high, and part of the rigidity demand is appropriately sacrificed to obtain the overall system safety when the power grid pressure is high, so that the method of the present application can be flexibly adapted to different power grid operating scenarios.

[0032] Further, the present application provides an embodiment: the method further comprises: obtaining an adjustment fatigue parameter corresponding to each charging pile, the adjustment fatigue parameter being used to represent the number of times that the charging pile is executed for charging power adjustment within a preset time window; under the condition of the same flexibility label and the same charging mode, the charging session corresponding to the charging pile with a smaller adjustment fatigue parameter is preferentially selected to participate in power adjustment, and the power adjustment priority of the charging session corresponding to the charging pile with a larger adjustment fatigue parameter is reduced.

[0033] In this embodiment, in order to avoid the individual charging pile being frequently adjusted in power during long-time operation, which may cause the increase of thermal stress of the device, the shortening of the service life of the relay or the power module, the controller further introduces an adjustment fatigue parameter as a device-side constraint factor, that is, the number of times of the charging pile in each charging station being executed power adjustment in a preset time window is recorded (optionally, the adjustment range or duration can also be integrated), and the adjustment fatigue parameter used to represent the "accumulative burden" of the charging pile in recent participation adjustment is calculated; when multiple charging sessions have the same or similar elasticity label level and the same charging mode (for example, both are high elasticity and cost priority), the controller no longer simply selects the charging session according to the session access order or randomly selects the charging session, but preferentially selects the charging session corresponding to the charging pile with a smaller adjustment fatigue parameter to participate in the power reduction or recovery in this round, and the priority of the charging session corresponding to the charging pile with a larger adjustment fatigue parameter is downgraded or temporarily excluded from the candidate set, so that the charging pile participating in frequent adjustment obtains a certain "rest time" in the subsequent period under the premise of ensuring the overall load balancing and the execution of the user strategy, the adjustment task is horizontally shared among multiple charging piles, which is beneficial to reduce the thermal shock and mechanical shock generated by excessive frequent adjustment of a single device, prolong the service life of the device, reduce the maintenance cost, and improve the long-term reliability and stability of the operation of the whole station.

[0034] Further, please refer to Figure 3The application also provides a dynamic load balancing control system based on real-time power collection, which comprises: a session establishment and information acquisition module, which is configured to establish a charging session with a charging pile and acquire real-time power information and user setting information of the charging pile; the real-time power information at least includes current charging power and residual charging power, and the user setting information at least includes target charging demand and estimated departure time; a residual task segmentation module, which is configured to divide the residual charging task of each charging session into at least three task segments with different priorities and adjustabilities according to the real-time power information and user setting information; the at least three task segments include a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand, and an opportunity task segment executed when there is surplus power in the station; an elastic label generation module, which is configured to generate an elastic label for each charging session to represent the adjustability of the charging power of the charging session according to the progress of each task segment of each charging session and the remaining time to the estimated departure time, and the elastic label at least includes one of a rigid label for identifying a charging session with minimum adjustment space, a semi-elastic label for identifying a charging session with limited adjustment space, and a high-elastic label for identifying a charging session with priority adjustment space; a power allocation strategy generation module, which is configured to determine the power adjustment priority of each charging session according to the elastic label of each charging session and generate a target power allocation result of each charging pile when it is determined that the total power of the charging station needs to be adjusted according to the real-time power collection result and the power constraint condition; and a power control execution module, which is configured to issue a power control instruction to each charging pile according to the target power allocation result, so that each charging pile operates according to the target power allocation result, and repeatedly adjusts based on the updated real-time power collection result in cooperation with the elastic label generation module and the power allocation strategy generation module, so as to realize dynamic load balancing control based on real-time power collection.

[0035] The working principle of the application is: the application realizes dynamic load balancing by synchronously modeling and hierarchically deciding the "in-station power" and "single vehicle charging task" on the side of the station-level controller. Specifically, when an electric vehicle accesses a charging pile, the controller first establishes a charging session and collects information such as current charging power, remaining charge to be charged, target charging demand and estimated time to leave, divides the originally continuous remaining charging demand into three task intervals of different priorities and adjustabilities, namely core task segment, target task segment and opportunity task segment; in the running process, the controller continuously updates the completion progress of each task segment based on real-time power collection results, generates rigid, semi-flexible or high-flexible elastic labels for each charging session in combination with the remaining stay time, and compresses "power demand structure + time urgency" into an adjustable degree identifier that can be directly used for sorting; when detecting that the total power in the station is close to the limit of the power distribution capacity or receiving a power constraint level instruction from the grid side, the controller no longer proportionally limits the power of all charging piles, but instead, according to the elastic label as the main line, in combination with the charging mode selected by the user and information such as the grid constraint level, the charging pile adjustment fatigue, etc., it preferentially excavates the deliverable power space from the charging sessions that are high in flexibility, insensitive to time and have fewer recent adjustment times, and if necessary, it also calls semi-flexible or even a small number of rigid sessions, generates target power distribution results for each charging pile and issues power control instructions, so that the output power of each charging pile is dynamically adjusted in each scheduling period; with the feedback of new round of real-time power data and task progress back to the controller, the system re-evaluates the task segment and the elastic label and updates the power distribution strategy, thereby realizing the dynamic load balancing control process, which takes into account user experience, redundant capacity utilization and equipment life under the premise of ensuring power distribution safety.

[0036] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. In this document, the terms "comprises", "comprising", or any other variation thereof, will cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0037] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are only by way of example and that changes can be made to the embodiments without departing from the spirit and scope of the application, which is defined in the appended claims and their equivalents.

Claims

1. A dynamic load balancing control method based on real-time power collection, characterized in that, The method comprises the following steps: step S1, establishing a charging session with a charging pile and acquiring real-time power information and user setting information of the charging pile; the real-time power information at least comprises current charging power and remaining charging power, and the user setting information at least comprises target charging demand, estimated departure time and user-selected charging mode; step S2, according to the real-time power information and the user setting information, dividing the remaining charging task of each charging session into at least three task segments with different priorities and adjustabilities; the at least three task segments comprise a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand and an opportunity task segment executed when there is surplus power in the station; step S3, generating an elasticity label for each charging session for representing the charging power adjustability of the charging session according to the progress of each task segment of each charging session and the remaining time length from the estimated departure time, the elasticity label at least comprising one of a rigid label for identifying a charging session with minimum adjustment space, a semi-elastic label for identifying a charging session with limited adjustment space and a high-elastic label for identifying a charging session with priority adjustment space; step S4, when it is determined that the total power of the charging station needs to be adjusted according to real-time power acquisition results and power constraint conditions, determining the power adjustment priority of each charging session according to the elasticity label of each charging session, and generating a target power distribution result of each charging pile; wherein the power constraint conditions comprise power constraint level information issued by the power grid side, and the determination of the power adjustment priority of each charging session further comprises: according to the charging mode, correcting the power adjustment priority of the charging session with the high-elastic label and the semi-elastic label; when the power constraint level represents a severe constraint state, allowing the charging session with the rigid label to participate in power adjustment, and when the power constraint level represents a non-severe constraint state, prohibiting or avoiding the charging session with the rigid label from participating in power adjustment; acquiring an adjustment fatigue parameter corresponding to each charging pile, the adjustment fatigue parameter being used to represent the number of times that the charging pile is executed for charging power adjustment within a preset time window; under the condition of the same elasticity label and the same charging mode, preferentially selecting the charging session corresponding to the charging pile with a smaller adjustment fatigue parameter to participate in power adjustment, and reducing the power adjustment priority of the charging session corresponding to the charging pile with a larger adjustment fatigue parameter; step S5, issuing a power control instruction to each charging pile according to the target power distribution result, so that each charging pile operates according to the target power distribution result, and repeatedly executing steps S3 and S4 based on updated real-time power acquisition results to realize dynamic load balancing control based on real-time power acquisition.

2. The dynamic load balancing control method based on real-time power collection according to claim 1, characterized in that, In step S1: the charging mode at least comprises one of a time priority mode, a cost priority mode and a comprehensive balance mode, and the charging mode is used to correct the charging power adjustability of each charging session when the elasticity label is generated.

3. The dynamic load balancing control method based on real-time power collection according to claim 1, characterized in that, In step S2, the core task section corresponds to a power interval from the current power to a preset safe power threshold; the target task section corresponds to a power interval from the preset safe power threshold to a power corresponding to the target charging demand; The opportunity task section corresponds to a power interval from the power corresponding to the target charging demand to a preset upper limit power threshold.

4. The dynamic load balancing control method based on real-time power collection according to claim 1, characterized in that, In step S3, generating the elasticity labels comprises: when the charging session is in the core task section and the distance to the estimated departure time is less than a first preset time threshold, marking the charging session as a rigid label; when the charging session is in the target task section and the distance to the estimated departure time is greater than a second preset time threshold, marking the charging session as a semi-elastic label; when the charging session is in the opportunity task section or the user does not set the estimated departure time, marking the charging session as a high-elasticity label.

5. The method of claim 1, wherein, In step S4, determining the power adjustment priorities of the charging sessions according to the elasticity labels of the charging sessions comprises: preferentially reducing or restoring the charging power of the charging sessions with high-elasticity labels, secondarily adjusting the charging power of the charging sessions with semi-elasticity labels, and limiting or avoiding adjusting the charging power of the charging sessions with rigid labels.

6. A dynamic load balancing control system based on real-time power acquisition, characterized in that, The system comprises: a session establishment and information acquisition module, configured to establish a charging session with a charging pile and acquire real-time power information and user setting information of the charging pile; the real-time power information at least comprises current charging power and remaining charging power, and the user setting information at least comprises target charging demand, estimated departure time and user-selected charging mode; a residual task segmentation module, configured to segment a residual charging task of each charging session into at least three task segments with different priorities and adjustabilities according to the real-time power information and the user setting information; the at least three task segments comprise a core task segment for meeting basic travel demand, a target task segment for meeting user target charging demand and an opportunity task segment executed when there is surplus power in the station; an elastic label generation module, configured to generate an elastic label for each charging session according to progress of each task segment of each charging session and remaining time to the estimated departure time, the elastic label at least comprises one of a rigid label for identifying a charging session with minimum adjustment space, a semi-elastic label for identifying a charging session with limited adjustment space and a high-elastic label for identifying a charging session with priority adjustment space; a power allocation strategy generation module, configured to determine power adjustment priorities of the charging sessions according to the elastic labels of the charging sessions and generate target power allocation results of the charging piles when it is determined that the total power of the charging station needs to be adjusted according to real-time power acquisition results and power constraint conditions; wherein the power constraint conditions comprise power constraint level information issued by a power grid side, the determination of the power adjustment priorities of the charging sessions further comprises: correcting the power adjustment priorities of the charging sessions with the high-elastic label and the semi-elastic label according to the charging mode; when the power constraint level represents a severe constraint state, allowing the charging session with the rigid label to participate in power adjustment, and when the power constraint level represents a non-severe constraint state, prohibiting or avoiding the charging session with the rigid label from participating in power adjustment; acquiring an adjustment fatigue parameter corresponding to each charging pile, the adjustment fatigue parameter being used to represent a number of times that the charging pile is executed for charging power adjustment within a preset time window; under the condition of the same elastic label and the same charging mode, preferentially selecting the charging session corresponding to the charging pile with a smaller adjustment fatigue parameter to participate in power adjustment, and reducing the power adjustment priority of the charging session corresponding to the charging pile with a larger adjustment fatigue parameter; and a power control execution module, configured to issue power control instructions to each charging pile according to the target power allocation results, so that each charging pile operates according to the target power allocation results, and repeatedly adjusts based on updated real-time power acquisition results, the elastic label generation module and the power allocation strategy generation module, to realize dynamic load balancing control based on real-time power acquisition.

Images