Charging pile aggregation group control algorithm

Abstract

The invention discloses a charging pile aggregation group control algorithm, and relates to the technical field of charging pile control. According to the algorithm, centralized monitoring and cooperative control of a plurality of charging piles are realized by constructing a charging pile aggregation control model. The method comprises the following steps: firstly, collecting real-time operation data and user charging demand information of each charging pile; then, on the basis of a dynamic load balancing strategy and user demand priority ranking, power supply resources of the charging piles are optimally distributed; finally, a control instruction is adjusted in real time through a feedback adjusting mechanism, it is ensured that the charging pile group operates efficiently and stably, meanwhile, the charging requirement of a user is met to the maximum extent, and energy consumption is reduced. The overall operation efficiency of the charging pile group can be improved, the waiting time of a user is shortened, reasonable utilization of energy is achieved, and high practical value and popularization prospects are achieved.

Description

Technical Field

[0001] This invention relates to the field of charging pile control technology, and specifically to a charging pile aggregation and group control algorithm. Background Technology

[0002] With the rapid development of new energy vehicles, charging piles, as a key infrastructure for electric vehicle energy replenishment, are also seeing continuous advancements in their management and control technologies. Currently, charging pile systems mainly adopt a single-pile independent control mode, lacking an effective coordination mechanism between charging piles.

[0003] In existing technologies, the control methods for charging piles mainly include fixed power charging, time-limited controlled charging, and simple load balancing charging.

[0004] CN120080757B discloses a charging pile intelligent allocation system and charging pile based on dynamic load balancing. This system receives the operating characteristic groups of each charging pile, generates a characteristic weight set, and realizes dynamic load allocation of the charging piles according to the characteristic priorities. However, this system mainly focuses on load balancing within a single charging station and lacks the ability to coordinate and control multiple charging piles.

[0005] CN119428320A discloses a method and system for regulating the power of charging piles. This system collects data from multi-dimensional data sources, generates charging priority coefficients through a machine learning model, and divides charging piles into high-priority and low-priority categories based on a priority matrix, thereby dynamically adjusting the power allocation strategy. However, this method does not fully consider the diverse needs and dynamic changes of users, making it difficult to achieve accurate resource allocation.

[0006] Regarding the interaction between charging piles and the power grid, CN116353393B proposes a method and system for regulating the power of charging piles. This method controls the charging voltage and current of electric vehicle batteries through interaction between a vehicle-to-everything (V2X) platform, the vehicle's intelligent onboard terminal, and the battery management system, thereby achieving power regulation of the charging pile. While this method achieves control over charging power, it lacks a robust user demand priority assessment mechanism, making it unable to rationally allocate charging resources under limited conditions.

[0007] CN117944500A discloses a charging pile charging control system, including a central control system, a data processing module, a data acquisition module, and a scheduling management module, used for charging task arrangement. While this system possesses some scheduling capabilities, it lacks a dynamic feedback adjustment mechanism, making it difficult to cope with various changes during the charging process.

[0008] To address charging management during peak power supply periods, CN120049437A proposes a power supply optimization and control system for new energy vehicle charging stations during peak periods. This system includes a charging demand prediction module, a power load allocation module, a real-time control module, and a power performance monitoring module. The system can predict charging demand based on historical data and user behavior, and allocate power supply priorities and power limits according to the prediction results and grid conditions. However, its load balancing algorithm is relatively simple, making it difficult to achieve optimal coordinated control of charging pile groups.

[0009] In summary, the existing technologies suffer from the following problems: First, most charging piles adopt independent control modes, lacking effective coordination, which easily leads to some charging piles being overloaded while others are idle, resulting in low overall operating efficiency and excessively long waiting times for users. Second, existing load balancing algorithms are relatively simple and fail to fully consider the diversity and priority differences of user needs, making it difficult to achieve precise resource allocation. Third, there is a lack of effective feedback and adjustment mechanisms, making it impossible to adjust control strategies in a timely manner based on actual operating conditions. Fourth, during peak grid load periods, if multiple charging piles operate at high power simultaneously, it may affect grid stability, as existing technologies lack comprehensive consideration of grid load. These problems severely restrict the overall operating efficiency of charging pile clusters and the user experience. Summary of the Invention

[0010] The purpose of this invention is to provide a charging pile aggregation and group control algorithm to solve the problems of low control efficiency and unreasonable resource allocation of charging piles in the prior art.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a charging pile aggregation and group control algorithm, comprising the following steps:

[0012] Step S1: Data Acquisition: Real-time acquisition of the operating parameters of each charging pile, including current charging power, remaining capacity, fault status, and user charging demand information, including expected charging amount and estimated charging time;

[0013] Step S2: Prioritize demand: Determine the priority of each user's charging demand based on the user's expected charging amount, estimated charging time, and user type;

[0014] Step S3: Dynamic load balancing calculation: Calculate the load balancing coefficient of each charging pile based on the real-time operating parameters of each charging pile and the user demand priority. The load balancing coefficient is negatively correlated with the current load rate and remaining capacity of the charging pile, and positively correlated with the user demand priority.

[0015] Step S4: Power supply resource allocation: Based on the load balancing coefficient, the user's charging demand is allocated to the corresponding charging pile, and an initial power supply control command is generated;

[0016] Step S5: Feedback Adjustment: Monitor the operating status of the charging piles after executing control commands and the changes in user charging needs in real time, calculate the deviation between the actual operating status and the expected status, and adjust the power supply control commands according to the deviation until the charging pile group operates stably and the user charging needs are met.

[0017] Preferably, in step S2, the user types include emergency users, regular users, and appointment users, with emergency users having a higher priority than regular users, and regular users having a higher priority than appointment users.

[0018] Preferably, the algorithm is characterized in that, in step S3, the formula for calculating the load balancing coefficient is: ( ) in, For the first Load balancing coefficient of each charging station For the first The current load rate of each charging station. For the first The percentage of remaining capacity of each charging station To be allocated to the first The first charging pile The priority coefficient of each user's needs , , These are the weighting coefficients, and .

[0019] Preferably, in step S5, the deviation value is calculated as follows: | - Among them, This is the deviation value. These are the actual operating parameters of the charging pile. These are the expected operating parameters obtained based on the initial power supply control command.

[0020] Preferably, when the deviation value If the deviation exceeds the preset threshold, repeat steps S3-S5 until the deviation value is reached. Less than or equal to the preset threshold.

[0021] Preferably, step S1 further includes collecting grid load information of the area where the charging piles are located, and in step S4, when allocating power supply resources, the grid load information is combined to avoid the total power of the charging pile group exceeding the grid load threshold.

[0022] Beneficial effects

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] This invention optimizes the allocation of power supply resources for charging piles by collecting charging pile operation data and user demand information, combined with dynamic load balancing strategies and user demand priority ranking, thereby improving the overall operating efficiency of the charging pile group and reducing user waiting time.

[0025] By taking grid load information into account during the allocation of power supply resources, the impact of charging pile clusters on grid stability is avoided, thus achieving the rational use of energy.

[0026] By collecting charging pile operation data and user demand information, and combining dynamic load balancing strategies and user demand priority ranking, the power supply resources of charging piles are optimized, improving the overall operating efficiency of the charging pile group and reducing user waiting time.

[0027] Compared with the independent control mode of charging piles in the prior art, the charging pile aggregation group control algorithm of the present invention can effectively solve the problems of some charging piles being overloaded and some charging piles being idle, so that charging resources can be allocated more rationally.

[0028] Furthermore, this invention introduces a feedback adjustment mechanism that can adjust control commands in real time, ensuring the stability of the charging pile group during operation and improving system reliability. By calculating the deviation between the actual operating state and the expected state, and making dynamic adjustments based on the deviation, the system can quickly respond to various changes and maintain optimal operating conditions.

[0029] Meanwhile, by considering grid load information during the allocation of power supply resources, the invention avoids the impact of charging pile clusters on grid stability and achieves rational energy utilization. Especially during peak grid load periods, this invention can intelligently regulate the total power of the charging pile clusters to ensure that it does not exceed the grid load threshold, thereby guaranteeing the safe and stable operation of the grid.

[0030] In summary, the charging pile aggregation and group control algorithm of the present invention can effectively improve the operating efficiency and stability of charging pile groups, and has high practical value and promotion prospects. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the charging pile aggregation and group control algorithm of the present invention.

[0032] Figure 2 This is a schematic diagram of the formula for dynamic load balancing calculation in this invention.

[0033] Figure 3 System architecture diagram for application scenarios of the algorithm of this invention Detailed Implementation

[0034] Example 1

[0035] See Figures 1-3 The above describes a charging pile aggregation and group control algorithm, which includes the following steps:

[0036] Step S1: Data Acquisition: Real-time operating parameters of five charging piles are collected using sensors and communication modules. Charging pile 1 currently has a charging power of 30kW, a remaining capacity of 60%, and is fault-free; Charging pile 2 currently has a charging power of 20kW, a remaining capacity of 70%, and is fault-free; Charging pile 3 currently has a charging power of 40kW, a remaining capacity of 40%, and is fault-free; Charging pile 4 currently has a charging power of 0kW, a remaining capacity of 90%, and is fault-free; Charging pile 5 currently has a charging power of 25kW, a remaining capacity of 50%, and is fault-free. Simultaneously, charging demand information from three users is collected: User A is an emergency user with a desired charging capacity of 50kWh and an estimated charging time of 1 hour; User B is a regular user with a desired charging capacity of 30kWh and an estimated charging time of 1.5 hours; User C is a user with a reservation with a desired charging capacity of 40kWh and an estimated charging time of 2 hours. The grid load threshold for the area where the charging piles are located is 150kW.

[0037] Step S2: Prioritize requests: Set the priority coefficient for urgent users to 0.8, for regular users to 0.5, and for scheduled users to 0.3. Therefore, user A has a higher priority than user B, and user B has a higher priority than user C.

[0038] Step S3: Dynamic Load Balancing Calculation: Settings , , Calculate the load rate of each charging pile. Assuming the rated power of each charging pile is 50kW, the load rate of charging pile 1 is... The load rate of charging pile 2 The load rate of charging pile 3 The load rate of charging pile 4 The load rate of charging pile 5 The percentage of remaining capacity for each charging station. The values ​​are 0.6, 0.7, 0.4, 0.9, and 0.5, respectively.

[0039] Assuming user A is assigned to charging station 1, user B to charging station 2, and user C to charging station 4, the load balancing coefficient for each charging station is:

[0040]

[0041]

[0042]

[0043] Step S4: Power Supply Resource Allocation: Based on the load balancing coefficient, user A is assigned to charging pile 4, user B to charging pile 2, and user C to charging pile 1. At this time, the total power of the charging pile group is user A's charging power (50kW) + user B's charging power (20kW) + user C's charging power (20kW) + the power of the original charging piles (30 + 25) = 50 + 20 + 20 + 30 + 25 = 145kW, which does not exceed the grid load threshold of 150kW. An initial power supply control command is then generated.

[0044] Step S5: Feedback Adjustment: Real-time monitoring revealed that the actual charging power of charging pile 4 was 48kW, while the expected power was 50kW. The deviation value was... 2kW, which is less than the preset threshold of 5kW, requires no adjustment and the system operates stably.

[0045] The algorithm in this embodiment enables the rational allocation of charging pile resources, improves operational efficiency, and ensures grid stability.

[0046] Example 2

[0047] This embodiment also provides a charging pile aggregation and group control algorithm. This algorithm can further optimize the allocation of resources through intelligent scheduling and resource optimization of the charging pile group, thereby achieving efficient utilization of charging resources and precise satisfaction of user needs. The algorithm includes the following steps:

[0048] Step S1: Data Acquisition: Real-time acquisition of operating parameters for each charging pile, including current charging power, remaining capacity, fault status, and user charging demand information, including expected charging amount and estimated charging time. In addition, grid load information for the area where the charging piles are located is also collected to provide grid load constraints for subsequent power supply resource allocation.

[0049] Step S2: Demand Prioritization: Based on the user's expected charging amount, estimated charging time, and user type, the priority of each user's charging demand is determined. User types include emergency users, regular users, and scheduled users. Emergency users have higher priority than regular users, and regular users have higher priority than scheduled users. The system will assign a corresponding priority coefficient to each user according to this priority order for subsequent load balancing calculations.

[0050] Step S3: Dynamic Load Balancing Calculation: Based on the real-time operating parameters of each charging station and the priority of user demand, calculate the load balancing coefficient for each charging station. The load balancing coefficient is negatively correlated with the current load rate and remaining capacity of the charging station, and positively correlated with the priority of user demand. The formula for calculating the load balancing coefficient is: ,in, For the first Load balancing coefficient of each charging station For the first The current load rate of each charging station. For the first The percentage of remaining capacity of each charging station To be allocated to the first The first charging pile The priority coefficient of each user's needs , , These are the weighting coefficients, and + + =1.

[0051] Step S4: Power Supply Resource Allocation: Based on the load balancing coefficient, user charging demands are allocated to corresponding charging piles, and initial power supply control commands are generated. During power supply resource allocation, grid load information is considered to prevent the total power of the charging pile group from exceeding the grid load threshold, ensuring the safe and stable operation of the grid. The algorithm prioritizes allocating charging demands to charging piles with higher load balancing coefficients while considering grid load constraints to achieve reasonable resource allocation.

[0052] Step S5: Feedback Adjustment: Real-time monitoring of the charging pile's operational status after executing control commands and changes in user charging demand; calculation of the deviation between the actual and expected operational status; and adjustment of power supply control commands based on the deviation until the charging pile group operates stably and user charging needs are met. The deviation is calculated as follows: = |Preal - Pexpect|, where, Here, Preal represents the actual operating parameters of the charging pile, and Pexpect represents the expected operating parameters obtained based on the initial power supply control command. When the deviation value... If the deviation exceeds the preset threshold, repeat steps S3-S5 until the deviation value is reached. The charging pile aggregation and control algorithm can achieve dynamic optimization allocation of charging resources through the above steps, meeting the charging needs of different users and ensuring the stable operation of the power grid. This algorithm has adaptive adjustment capabilities, enabling real-time adjustments based on actual operating conditions and changes in user demand, thus improving the operating efficiency of the charging pile group and user satisfaction.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A charging pile aggregation and group control algorithm, characterized in that, Includes the following steps: Step S1: Data Acquisition: Real-time acquisition of the operating parameters of each charging pile, including current charging power, remaining capacity, fault status, and user charging demand information, including expected charging amount and estimated charging time; Step S2: Prioritize demand: Determine the priority of each user's charging demand based on the user's expected charging amount, estimated charging time, and user type; Step S3: Dynamic load balancing calculation: Calculate the load balancing coefficient of each charging pile based on the real-time operating parameters of each charging pile and the user demand priority. The load balancing coefficient is negatively correlated with the current load rate and remaining capacity of the charging pile, and positively correlated with the user demand priority. Step S4: Power supply resource allocation: Based on the load balancing coefficient, the user's charging demand is allocated to the corresponding charging pile, and an initial power supply control command is generated; Step S5: Feedback Adjustment: Monitor the operating status of the charging piles after executing control commands and the changes in user charging needs in real time, calculate the deviation between the actual operating status and the expected status, and adjust the power supply control commands according to the deviation until the charging pile group operates stably and the user charging needs are met.

2. The algorithm according to claim 1, characterized in that, In step S2, the user types include emergency users, regular users, and scheduled users. Emergency users have a higher priority than regular users, and regular users have a higher priority than scheduled users.

3. The algorithm according to claim 1, characterized in that, In step S3, the formula for calculating the load balancing coefficient is: ( ) + in, For the first Load balancing coefficient of each charging station For the first The current load rate of each charging station. For the first The percentage of remaining capacity of each charging station To be allocated to the first The first charging pile The priority coefficient of each user's needs , , These are the weighting coefficients, and .

4. The algorithm according to claim 1, characterized in that, In step S5, the deviation value is calculated as follows: | - Among them, This is the deviation value. These are the actual operating parameters of the charging pile. These are the expected operating parameters obtained based on the initial power supply control command.

5. The algorithm according to claim 4, characterized in that, When deviation value If the deviation exceeds the preset threshold, repeat steps S3-S5 until the deviation value is reached. Less than or equal to the preset threshold.

6. The algorithm according to claim 1, characterized in that, Step S1 also includes collecting grid load information in the area where the charging piles are located. In step S4, when allocating power supply resources, the grid load information is combined to prevent the total power of the charging pile group from exceeding the grid load threshold.

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