A community-oriented flexible charging control method, system, device and medium

Abstract

The application relates to the technical field of community charging facility management, in particular to a flexible charging control method, system and device for a community and a medium, which comprises the following steps: collecting total load data of a community power distribution main line, branch load data of each power distribution branch, real-time charging power of each charging terminal and user charging request data; calculating a current dynamic power margin; based on the calculated dynamic power satisfaction degree, if the power satisfaction degree is less than or equal to a first threshold value, each charging terminal is allowed to operate according to the user request, if the power satisfaction degree is greater than or equal to a second threshold value, the power of part of the charging terminals is forced to be reduced, if the power satisfaction degree is between the two threshold values, a multi-objective optimization algorithm is called to optimize and calculate the distribution power of each charging terminal, an allowed power instruction is generated and sent to the corresponding charging terminal, the output voltage or current of the charging terminal is continuously adjusted, and the method is cyclically executed. Under the premise of guaranteeing the safe operation of the transformer, the method realizes fine dynamic regulation and control of the charging power.

Description

Technical Field

[0001] This application relates to the field of community charging facility management technology, specifically to a flexible charging control method, system, device, and medium for communities. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the continuous increase in the number of electric vehicles, the demand for charging facilities in residential communities, especially older neighborhoods, is becoming increasingly urgent. Communities with a certain history of construction generally suffer from weak power distribution infrastructure, insufficient transformer capacity, and aging power lines. Connecting electric vehicle charging equipment on a large scale to the existing power load can easily lead to power grid overload, voltage drops, and other safety risks. How to achieve safe, efficient, and large-scale deployment of charging facilities within the limited power distribution capacity of communities has become a crucial issue facing the current charging facility construction field.

[0003] To address the issue of limited power distribution capacity in the construction of community charging facilities, existing technologies offer several solutions. For example, one approach involves setting up a central controller to monitor the total current in the power distribution circuit. When the total current approaches a preset threshold, multiple charging piles are controlled for start / stop or power limitation using a polling or first-come-first-served method to ensure the total load does not exceed the safety limits of the power distribution equipment. These solutions typically involve installing a centralized controller on a power distribution circuit, which communicates with each charging pile. When the total load exceeds the limit, the charging power is uniformly reduced or the output of some charging piles is cut off, thus achieving basic overload protection. Furthermore, some solutions incorporate a power detection module within the charging terminal. When a voltage drop or current anomaly is detected, the output power is proactively reduced to adapt to the current power grid conditions.

[0004] However, the aforementioned community charging facility management solutions still have significant limitations in practical applications: existing solutions typically only monitor and control the total current of a single distribution circuit, lacking the ability to comprehensively perceive the total incoming load of the community, the load of each distribution branch, and the individual status of charging terminals. This makes it difficult to coordinate the power distribution of each branch and charging terminal at the level of the entire community power distribution network, resulting in uneven load distribution among different branches and low overall capacity utilization. Existing solutions mostly adopt simple polling or start-stop control strategies, which can only realize the on / off control of charging terminals or the power adjustment of fixed levels. They cannot make fine and continuous adjustments to the output power of each charging terminal according to the dynamically changing load conditions. When the load is tight, some charging terminals are often simply cut off, which not only affects the user's charging experience but also wastes power resources. The control logic of existing solutions is usually based on static thresholds and simple rules, lacking fine-grained division and differentiated processing for different operations. When the load changes rapidly, it is easy to have response lag or control oscillation, making it difficult to achieve stable and efficient regulation of charging power. Summary of the Invention

[0005] To address the limitations of existing community charging facility management solutions, which only allow for local monitoring of single power distribution circuits and employ simple polling or start-stop control, lacking the ability to perceive the overall load status of the community and to finely and continuously adjust the power of charging terminals, resulting in the inability to achieve differentiated and precise responses under different operating conditions, this application provides a flexible charging control method, system, device, and medium for communities. By collecting multi-level data from the main incoming line, each branch line, and charging terminals in real time, it executes differentiated control strategies based on dynamic power margin and multi-level state judgment. When capacity is tight, it uses the transformer's safe power threshold as a hard constraint to call an optimization algorithm for global power allocation. Finally, through continuous adjustment, it enables charging terminals to accurately track commands, forming a closed-loop control, thereby maximizing power distribution capacity utilization and charging efficiency while ensuring grid safety.

[0006] Firstly, this application provides a flexible charging control method for communities, comprising the following steps: S1. Real-time collection of total load data of the community's main power distribution line, branch load data of each power distribution branch, real-time charging power of each charging terminal, and user charging request data reported by each charging terminal. Each charging terminal belongs to a power distribution branch, and at least one charging terminal is connected to each power distribution branch. S2. Calculate the dynamic power margin currently available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. S3. Calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal, and user charging request data; If the power satisfaction is less than or equal to the preset first threshold, it is determined to be a state of sufficient capacity, and each charging terminal is allowed to operate according to the corresponding user charging request data. If the power satisfaction is greater than or equal to the preset second threshold, it is determined to be a capacity critical state, and the power of some charging terminals is forcibly reduced. If the power satisfaction is greater than the first threshold and less than the second threshold, it is determined to be a capacity shortage state, and steps S4 to S5 are executed. Wherein, the first threshold is less than the second threshold; S4. Call the multi-objective optimization algorithm, with the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the allowable power command for each charging terminal; S5. Send the power allowance command to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the power allowance command; Steps S1 to S5 are executed cyclically to achieve continuous closed-loop control of dynamic power distribution.

[0007] It should be further explained that step S1 also includes: calculating the basic load based on the collected total load data and the real-time charging power of each charging terminal, and performing cross-verification on the calculated value of the basic load based on the collected branch load data of each distribution branch and the real-time charging power of the charging terminal on the corresponding branch. If the cross-verification passes, the data is confirmed to be normal; if the cross-verification fails, the data is determined to be abnormal, and the data is collected again. The formula for calculating the basic load is as follows:

[0008] Indicates time The base load; Indicates time Total load data; Indicates time The sum of the real-time charging power of all charging terminals in all power distribution branches. For power distribution branch index, Indicates the first The sum of the real-time charging power of all charging terminals in each power distribution branch; The formula for cross-validation is:

[0009] in, Indicates time No. Branch load data for each power distribution branch; If the cross-validation formula is true, the cross-validation is considered to have passed; otherwise, the cross-validation is considered to have failed.

[0010] It should be further noted that the formula for calculating the dynamic power margin in step S2 is as follows:

[0011] in, Indicates time Dynamic power margin; This indicates the preset safe power threshold for the transformer; Indicates time The basic load is calculated using the following formula:

[0012] This represents the total load data at time t; It represents the sum of real-time charging power of charging terminals in all distribution branches at time t. For power distribution branch index, Indicates the first The sum of the real-time charging power of all charging terminals in each power distribution branch.

[0013] It should be further noted that the transformer safe power threshold Determined by the following formula:

[0014] in, Indicates the rated capacity of the transformer; This represents the safety factor, with a value ranging from 0.8 to 0.9.

[0015] In another specific embodiment of this application, in step S2, the formula for calculating the dynamic power margin is:

[0016] in, Indicates prediction Dynamic power margin available for charging at any given time; This indicates a forecast based on historical load data. The base load at any given time is calculated using the following formula:

[0017] in, Indicates the current Base load at any given time; This represents the average base load for the same historical period. This represents the smoothing coefficient, with a value range of 0-1.

[0018] It should be further noted that the formula for calculating the dynamic power satisfaction in step S3 is as follows:

[0019] in, express Dynamic power satisfaction at any given time; express The sum of the requested charging power of all charging terminals at any given time, obtained based on user charging request data. An index for the charging terminal; express Dynamic power margin at any given time; Indicates time The sum of real-time charging power of charging terminals in all power distribution branches.

[0020] It should be further noted that in step S3, the value range of the first threshold is 0.7-0.9; The second threshold ranges from 0.9 to 1.1.

[0021] It should be further noted that in step S3, the first threshold and the second threshold are dynamically adjusted according to the time period: During nighttime hours, the first threshold is set to 0.9, and the second threshold is set to 1.1. During daytime hours, the first threshold is set to 0.8, and the second threshold is set to 1.0. During the preset evening peak hours, the first threshold is set to 0.7, and the second threshold is set to 0.9. The nighttime, daytime, and evening peak hours do not overlap.

[0022] It should be further noted that step S3 also includes: Calculate the rate of change of power satisfaction: ; If the power satisfaction is less than the first threshold and the rate of change of the power satisfaction is greater than the preset first rate of change threshold, then the control strategy for the capacity shortage state will be forcibly executed. If the power satisfaction is less than the second threshold and the rate of change of the power satisfaction is greater than the preset second rate of change threshold, then the control strategy for the capacity critical state will be forcibly executed.

[0023] It should be further noted that the first rate of change threshold ranges from 0.05 / min to 0.12 / min; The second rate of change threshold ranges from 0.15 / min to 0.25 / min.

[0024] It should be further explained that, in step S3, when the capacity is determined to be at a critical state, the specific rules for forcibly reducing the power of some charging terminals are as follows: The charging terminals are sorted according to their battery state of charge. The power of charging terminals with higher state of charge is reduced or their charging is suspended first, while charging terminals with state of charge below the preset charge threshold are kept at the basic charging power.

[0025] It should be further noted that the preset charge threshold is 25%-35%.

[0026] It should be further noted that in step S4, the multi-objective optimization algorithm constructs a multi-objective function with the optimization objectives of maximizing user satisfaction, maximizing operator revenue, and flattening the power grid load curve. User satisfaction is quantified by a combination of charging completion rate, waiting time, and power satisfaction. The expression for user satisfaction is:

[0027] in, Indicates the first The overall user weight of each charging terminal ; For the first User priority coefficient for each charging terminal; For the first The battery charging urgency coefficient of each charging terminal; Indicates the first Each charging terminal corresponds to the vehicle's state of charge at the end of charging. Indicates the first Each charging terminal corresponds to the target state of charge of the vehicle set by the user; Indicates the first Total grid connection time for each charging terminal; Indicates the first The actual charging time corresponding to each charging terminal; Indicates the first The actual average charging power of each charging terminal; Indicates the first The user requests power for each charging terminal; For the target weight of the state of charge, As a target weight for waiting time, As the target weight for charging power, satisfy , , All are greater than 0 and ; The expression for operator revenue is:

[0028] in, For time period indexes within the scheduling period, Indicates the total number of time periods within the scheduling period; Indicates the first The charging terminal is in the first Charging power during each time period; Indicates the first The duration of each time period; Indicates the first The charging price charged to users for each time period; Indicates the first Electricity purchase price for operators in different time periods; Represents the equipment loss cost function; The expression for the flattest power grid load curve is:

[0029] in, Indicates the first The total community load for each time period, including the base load and the sum of the charging power of all charging terminals; This represents the average load within the scheduling period.

[0030] It should be further explained that the user priority coefficient The value range is from 0.6 to 2.0; Battery charging urgency level coefficient The value range is from 0.2 to 2.0.

[0031] It should be further explained that the user priority coefficient Determined based on at least one of user type, appointment time, or historical response behavior; Battery charging urgency level coefficient It decreases as the state of charge increases.

[0032] It should be further noted that in step S4, the multi-objective optimization algorithm uses the linear weighted sum method to transform the multi-objective function into a single-objective function for solution. The transformed single-objective function is:

[0033] in, The objective function is user satisfaction. This represents the theoretical maximum value of the user satisfaction objective function. This is a weighting coefficient for user satisfaction. Let be the objective function for operator revenue. This represents the theoretical maximum value of the operator's revenue objective function. This is the operator's revenue weighting coefficient; Let the objective function be the power grid load curve. This represents the theoretical maximum value of the objective function for the power grid load curve. The weighting coefficient for the power grid load curve; satisfy , , All are greater than 0 and .

[0034] It should be further noted that in step S4, the hard constraints of the multi-objective optimization algorithm include the transformer safe power threshold constraint, as shown in the formula:

[0035] in, For time period indexes within the scheduling period; Indicates the first Basic load for each time period; Indicates the first The charging terminal is in the first Charging power during each time period; This indicates the safe power threshold for the transformer.

[0036] It should be further noted that, in step S4, the hard constraints of the multi-objective optimization algorithm also include: The power constraint of the charging terminal is given by the following formula:

[0037] in, For the first The rated maximum power of each charging terminal; Battery charging constraints, the formula is:

[0038]

[0039] in, Indicates the first The vehicle corresponding to the charging terminal is in the first State of charge at the end of each time period; Indicates the first The vehicle corresponding to the charging terminal is in the first State of charge at the end of each time period; For charging efficiency; For the first The duration of each time period; For the first The vehicle battery capacity corresponding to each charging terminal; This represents the minimum permissible state of charge for the battery. This represents the maximum permissible state of charge of the battery. The continuous charging constraint is given by the following formula:

[0040] in, The maximum allowable power change rate for the charging terminal.

[0041] It should be further noted that in step S4, the multi-objective optimization algorithm is solved using an improved particle swarm optimization algorithm, specifically including: Initialize the particle swarm, with each particle representing a power allocation scheme; Adaptive inertial weights are used to update particle velocity and position, with the inertial weights decreasing with the number of iterations. The penalty function method is used to handle the transformer safety power threshold constraint; Iterative optimization continues until the maximum number of iterations or the convergence condition is reached, outputting the optimal power allocation scheme.

[0042] It should be further noted that the improved particle swarm optimization algorithm uses a model predictive control framework to achieve rolling optimization, specifically including: At the current moment, collect the current total load data, the real-time charging power of each charging terminal, and the charge status of each vehicle; Predict base load for multiple future time periods based on historical data; Using the current state as the initial condition, solve for the power allocation scheme for multiple future time periods; Only the power adjustment command for the next time period is executed; the commands for the remaining time periods are recalculated in the next round of optimization.

[0043] It should be further noted that in step S5, the output voltage or current of the charging terminal is continuously adjusted, specifically including: After receiving the allowed power command, the charging terminal adjusts the output voltage or current steplessly by adjusting the conduction angle or duty cycle of the power semiconductor switching device, so that the actual output power of the charging terminal continuously tracks the allowed power command.

[0044] It should be further noted that the charging terminal continuously adjusts its output power within the range of 0 to the rated maximum power, and the rated maximum power is determined according to the type of charging terminal.

[0045] Secondly, this application provides a community-oriented flexible charging control system for implementing the aforementioned flexible charging control method, comprising: The data acquisition module is used to collect in real time the total load data of the community's main power distribution line, the branch load data of each power distribution branch, the real-time charging power of each charging terminal, and the user charging request data reported by each charging terminal. The dynamic power margin calculation module is used to calculate the current dynamic power margin available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. The dynamic power satisfaction calculation and condition judgment module is used to calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal and the user charging request data, and to judge the comparison relationship between the power satisfaction and the preset first threshold and second threshold. The permissible power command generation module is used to call a multi-objective optimization algorithm, with the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the permissible power command for each charging terminal. The instruction execution module is used to send the allowable power instruction to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the allowable power instruction.

[0046] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described flexible charging control method.

[0047] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described flexible charging control method.

[0048] As can be seen from the above technical solutions, this application has the following advantages: 1. This application achieves refined and dynamic closed-loop control of the overall charging power of the community by real-time collection of multi-level load data from the community's main power distribution line, each distribution branch, and charging terminals. This is combined with dynamic power margin calculation and multi-level power satisfaction status determination. Different control strategies are implemented under different conditions, such as sufficient capacity, tight capacity, and critical capacity, enabling precise and dynamic closed-loop control of the community's overall charging power. Compared to the extensive management of existing solutions that rely solely on single-circuit monitoring and simple start-stop control, this method can coordinate charging resources from a community-wide perspective. It maximizes the utilization of existing power distribution capacity while ensuring the safe operation of transformers, providing reliable technical support for the large-scale deployment of charging facilities in communities without requiring grid expansion or upgrades.

[0049] 2. This application allows charging terminals to operate at the power requested by the user when capacity is sufficient. When capacity is critical, it achieves rapid response by forcibly reducing the power of some charging terminals. When capacity is tight, it invokes a multi-objective optimization algorithm to optimize the power allocation for each charging terminal. This multi-level control mechanism based on power satisfaction, compared to the simple control relying on a single threshold trigger in existing schemes, can automatically match appropriate control strategies under different load scenarios. It avoids excessively restricting user charging demand when capacity is sufficient, and achieves optimal allocation of power resources through optimization algorithms when capacity is tight, effectively balancing the relationship between grid security, user demand, and charging efficiency.

[0050] 3. This application uses the transformer's safe power threshold as a hard constraint under capacity constraints, and employs a multi-objective optimization algorithm to optimize the power allocation for each charging terminal, generating an allowable power command for each terminal. Compared to existing solutions that use simple rules such as polling or start / stop for power allocation, this method comprehensively considers multiple factors, including transformer capacity limitations, the real-time status of each charging terminal, and user charging requests. Through optimization calculations, it obtains a globally optimal power allocation scheme, maximizing user charging satisfaction and the overall utilization rate of charging facilities while meeting grid safety constraints.

[0051] 4. This application, after issuing the allowable power command to each charging terminal, continuously adjusts the output voltage or current of the charging terminal to ensure that the charging terminal operates according to the allowable power command. Compared to existing solutions that can only achieve on / off control or fixed-level adjustment, this method, through continuous adjustment of output power, enables the actual output power of the charging terminal to accurately track the allowable power command issued by the optimization algorithm, achieving refined execution of power allocation. This avoids power fluctuations or capacity waste caused by excessive adjustment steps, further improving the system's control accuracy and operational stability.

[0052] 5. This application establishes a continuous closed-loop control for dynamic power allocation by iteratively executing data acquisition, margin calculation, state determination, optimized allocation, and instruction execution. Compared to existing open-loop or semi-open-loop control methods, this method can respond in real-time to changes in community load and dynamic adjustments in user charging demand. In each control cycle, it recalculates the optimal allocation scheme based on the latest operating state, achieving continuous tracking and dynamic optimization of charging power, effectively improving the system's adaptability and robustness under load fluctuation scenarios. Attached Figure Description

[0053] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of a community-oriented flexible charging control method in one embodiment of this application.

[0055] Figure 2 This is a schematic block diagram of a community-oriented flexible charging control system in one embodiment of this application.

[0056] Figure 3 This is a schematic diagram of the hardware structure of an electronic device in one embodiment of this application. Detailed Implementation

[0057] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] The flexible charging control method of this application will be described in detail below. Specific details such as particular system structures and technologies are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0059] In the flexible charging control method disclosed in this application, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0060] To facilitate a clear description of the technical solutions of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0061] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0062] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0063] The flexible charging control method provided in this application embodiment is executed by a computer device, and correspondingly, the flexible charging control system for the community runs in the computer device.

[0064] Figure 1 This is a flowchart of a community-oriented flexible charging control method according to an embodiment of this application. Figure 1 The executing entity can be a flexible charging control system. Depending on different requirements, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0065] like Figure 1 As shown, this community-oriented flexible charging control method includes: Step S1: Collect in real time the total load data of the community's main power distribution line, the branch load data of each power distribution branch, the real-time charging power of each charging terminal, and the user charging request data reported by each charging terminal. Each charging terminal belongs to a power distribution branch, and at least one charging terminal is connected to each power distribution branch.

[0066] By collecting real-time data on the total load of the community's main power distribution line, the branch load data of each power distribution branch, the real-time charging power of each charging terminal, and the user charging request data reported by each charging terminal, and clarifying the structural relationship that each charging terminal belongs to a power distribution branch and that each power distribution branch is connected to at least one charging terminal, a multi-level, multi-dimensional global data perception system is constructed from the community's main power distribution line layer, the power distribution branch layer, to the charging terminal layer. This provides complete and accurate data support for dynamic power margin calculation, power satisfaction determination, and optimized allocation, ensuring that the information on which control decisions are based covers both the overall power consumption status of the community and is refined to the individual needs of each charging terminal.

[0067] In some specific embodiments, step S1 further includes: calculating the basic load based on the collected total load data and the real-time charging power of each charging terminal, and performing cross-verification on the calculated value of the basic load based on the collected branch load data of each distribution branch and the real-time charging power of the charging terminal on the corresponding branch. If the cross-verification passes, the data is confirmed to be normal; if the cross-verification fails, the data is determined to be abnormal, and the data is collected again. The formula for calculating the basic load is as follows:

[0068] Indicates time The base load; Indicates time Total load data; Indicates time The sum of the real-time charging power of all charging terminals in all power distribution branches. For power distribution branch index, Indicates the first The sum of the real-time charging power of all charging terminals in each power distribution branch; The formula for cross-validation is:

[0069] in, Indicates time No. Branch load data for each power distribution branch; If the cross-validation formula is true, the cross-validation is considered to have passed; otherwise, the cross-validation is considered to have failed.

[0070] The base load is calculated based on the total load data collected and the real-time charging power of each charging terminal. The calculated base load is then cross-validated based on the branch load data of each distribution branch and the real-time charging power of the charging terminals on the corresponding branch. If the cross-validation passes, the data is confirmed to be normal. If the cross-validation fails, the data is determined to be abnormal and the data is re-collected. This dual verification mechanism, which uses the base load calculated at the main incoming line level and the base load calculated and summarized at each branch level to verify each other, effectively identifies and eliminates data distortion problems caused by collection errors or communication anomalies.

[0071] Step S2: Calculate the dynamic power margin currently available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold.

[0072] The dynamic power margin available for charging is calculated based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. By using the base load obtained after deducting the real-time power of all charging terminals from the total load as the benchmark for calculating the dynamic power margin, the remaining available capacity of the transformer is accurately quantified, so that the dynamic power margin can truly reflect the actual power surplus available for charging facilities in the community's power distribution system at the current moment.

[0073] In some specific embodiments, the formula for calculating the dynamic power margin is as follows:

[0074] in, Indicates time Dynamic power margin; This indicates the preset safe power threshold for the transformer; Indicates time The basic load is calculated using the following formula:

[0075] This represents the total load data at time t; It represents the sum of real-time charging power of charging terminals in all distribution branches at time t. For power distribution branch index, Indicates the first The sum of the real-time charging power of all charging terminals in each power distribution branch.

[0076] By clarifying the calculation relationship between dynamic power margin and base load, the difference between the transformer's safe power threshold and the current actual base load is used as the dynamic power margin available for charging. This calculation method fully considers the occupation of available capacity by the non-adjustable base load in the community, so that the dynamic power margin can truly reflect the actual power reserve that the charging facilities can call upon under the current power consumption state.

[0077] In some specific embodiments, the transformer safe power threshold Determined by the following formula:

[0078] in, Indicates the rated capacity of the transformer; This represents the safety factor, with a value ranging from 0.8 to 0.9.

[0079] By defining the transformer's safe power threshold as the product of the transformer's rated capacity and the safety factor, and setting the safety factor within a reasonable range, a safety boundary is established between the transformer's rated capacity and the dynamic power margin calculation. This fully utilizes the transformer's rated capacity while reserving sufficient safety margin for load fluctuations and measurement errors.

[0080] In some specific embodiments, the formula for calculating the dynamic power margin is as follows:

[0081] in, Indicates prediction Dynamic power margin available for charging at any given time; This indicates a forecast based on historical load data. The base load at any given time is calculated using the following formula:

[0082] in, Indicates the current Base load at any given time; This represents the average base load for the same historical period. This represents the smoothing coefficient, with a value range of 0-1.

[0083] By employing a predictive dynamic power margin calculation method, and performing weighted prediction based on the current base load and the average of the historical base loads for the same period, the dynamic power margin available for charging in the future can be estimated in advance. This enables control decisions to be planned based on forward-looking capacity information, effectively addressing the impact of base load change trends on charging capacity availability and improving the adaptability of the control strategy in load fluctuation scenarios.

[0084] Step S3: Calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal, and the user charging request data. If the power satisfaction is less than or equal to the preset first threshold, it is determined to be a state of sufficient capacity, and each charging terminal is allowed to operate according to the corresponding user charging request data. If the power satisfaction is greater than or equal to the preset second threshold, it is determined to be a capacity critical state, and the power of some charging terminals is forcibly reduced. If the power satisfaction is greater than the first threshold and less than the second threshold, it is determined to be a capacity shortage state, and steps S4 to S5 are executed.

[0085] Based on dynamic power margin, real-time charging power of each charging terminal, and user charging request data, dynamic power satisfaction is calculated. The system state is divided into three states: sufficient capacity, tight capacity, and critical capacity, based on the comparison between the power satisfaction and the first and second thresholds. When the capacity is sufficient, each charging terminal is allowed to operate according to the corresponding user charging request data to fully meet user needs. When the capacity is critical, the power of some charging terminals is forcibly reduced to achieve rapid overload protection response. When the capacity is tight, the subsequent optimization allocation process is initiated. Through a differentiated control strategy with multi-level thresholds, adaptive matching of control objectives and control methods under different load scenarios is achieved, avoiding the defects of overly aggressive control strategies or delayed responses under a single threshold triggering mechanism.

[0086] In some specific embodiments, the formula for calculating dynamic power satisfaction is as follows:

[0087] in, express Dynamic power satisfaction at any given time; express The sum of the requested charging power of all charging terminals at any given time, obtained based on user charging request data. An index for the charging terminal; express Dynamic power margin at any given time; time The sum of real-time charging power of charging terminals in all power distribution branches.

[0088] The dynamic power satisfaction level is calculated by comparing the sum of the requested charging power of all charging terminals with the sum of the dynamic power margin and the sum of the real-time charging power of all charging terminals. This quantifies the supply and demand relationship between the actual charging power and the requested charging power at the current moment into a power satisfaction index, which intuitively reflects the tension of charging demand relative to the total available charging capacity of the system, and provides a quantitative basis for the accurate determination of the subsequent operating status.

[0089] In some specific embodiments, the value of the first threshold ranges from 0.7 to 0.9; The second threshold ranges from 0.9 to 1.1.

[0090] By setting the first and second thresholds within a reasonable range, a quantitative boundary with clear physical meaning is established between power satisfaction and operating status determination. A first threshold value below 1 represents the critical point of entering a capacity shortage state, and a second threshold value around 1 represents the critical point of entering a capacity critical state. This makes the division of the three states of sufficient capacity, capacity shortage, and capacity criticality clear mathematical boundaries and reasonable engineering margins.

[0091] In some specific embodiments, the first threshold and the second threshold are dynamically adjusted according to the time period: During nighttime hours, the first threshold is set to 0.9, and the second threshold is set to 1.1. During daytime hours, the first threshold is set to 0.8, and the second threshold is set to 1.0. During the preset evening peak hours, the first threshold is set to 0.7, and the second threshold is set to 0.9. The nighttime, daytime, and evening peak hours do not overlap.

[0092] By dynamically adjusting the values ​​of the first and second thresholds according to the time period, a relatively lenient threshold is used during nighttime, a moderate threshold during daytime, and a relatively strict threshold during evening peak hours. This allows the load characteristics and electricity consumption patterns of different time periods to be integrated into the boundary conditions of the state determination. During periods of relatively relaxed load, off-peak power resources are fully utilized, and control is intervened in advance during periods of high load, thus achieving dynamic adaptation between the state determination logic and time-domain characteristics.

[0093] In some specific embodiments, step S3 further includes: Calculate the rate of change of power satisfaction: ; If the power satisfaction is less than the first threshold and the rate of change of the power satisfaction is greater than the preset first rate of change threshold, then the control strategy for the capacity shortage state will be forcibly executed. If the power satisfaction is less than the second threshold and the rate of change of the power satisfaction is greater than the preset second rate of change threshold, then the control strategy for the capacity critical state will be forcibly executed.

[0094] By calculating the rate of change of power satisfaction, and forcibly implementing the control strategy for a capacity shortage state when the power satisfaction is less than a first threshold and the rate of change of power satisfaction exceeds the first rate of change threshold, and forcibly implementing the control strategy for a capacity critical state when the power satisfaction is less than a second threshold and the rate of change of power satisfaction exceeds the second rate of change threshold, the dynamic trend of power satisfaction is used as a supplementary basis for state determination. This allows the system to trigger corresponding levels of control measures in advance when the power satisfaction has not yet reached the traditional threshold boundary but has already shown a rapid deterioration trend, effectively improving the system's response speed to load changes.

[0095] In some specific embodiments, the first rate of change threshold ranges from 0.05 / min to 0.12 / min; The second rate of change threshold ranges from 0.15 / min to 0.25 / min.

[0096] By setting the first and second rate of change thresholds within a reasonable range, a clear quantitative trigger boundary is established between the rate of change of power satisfaction and the mandatory control strategy. The first rate of change threshold is set in a lower range for early intervention in capacity stress, and the second rate of change threshold is set in a higher range for emergency intervention in capacity criticality, thus giving the rate of change criterion reasonable engineering sensitivity.

[0097] In some specific embodiments, the specific rules for forcibly reducing the power of some charging terminals when the capacity is determined to be at a critical state are as follows: The charging terminals are sorted according to their battery state of charge. The power of charging terminals with higher state of charge is reduced or their charging is suspended first, while charging terminals with state of charge below the preset charge threshold are kept at the basic charging power.

[0098] By sorting the charging terminals according to their battery state of charge when the capacity critical state is determined, the power of charging terminals with higher state of charge is reduced or their charging is suspended first, while charging terminals with a state of charge below the preset charge threshold are given priority to maintain basic charging power. By linking the forced power reduction rule in the capacity critical state with the battery state of charge, the limited power resources are prioritized to serve vehicles with low battery power to ensure their basic charging needs in emergency situations, while restrictive measures are taken for vehicles with high battery power to quickly release capacity reserves, thus realizing differentiated power allocation in emergency situations.

[0099] In some specific embodiments, the preset charge threshold is 25%-35%.

[0100] By setting the preset charge threshold within a reasonable range, a clear quantitative boundary is established for the protection rules of low-battery vehicles under capacity critical conditions. This range corresponds to the lower charge range of the power battery. Prioritizing charging within this range can effectively prevent the vehicle from being affected by low battery levels, thus providing a reasonable engineering basis for the power allocation rules under capacity critical conditions.

[0101] Step S4: Invoke the multi-objective optimization algorithm, using the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the allowable power command for each charging terminal.

[0102] When a capacity shortage is detected, a multi-objective optimization algorithm is invoked. The power allocation of each charging terminal is optimized and calculated using the transformer's safe power threshold as a hard constraint, and an allowable power command for each charging terminal is generated. By performing global optimization of the power allocation of multiple charging terminals under hard constraints, the optimal allocation of limited power resources is achieved under the condition of power distribution capacity shortage. This allows the allocation results of each charging terminal to comprehensively consider the balance between the grid safety boundary and the user's charging demand. Compared with simple start-stop or fixed-ratio power reduction, this significantly improves the overall efficiency of charging services under capacity shortage conditions.

[0103] In some specific embodiments, the multi-objective optimization algorithm constructs a multi-objective function with the optimization objectives of maximizing user satisfaction, maximizing operator revenue, and flattening the power grid load curve; User satisfaction is quantified by a combination of charging completion rate, waiting time, and power satisfaction. The expression for user satisfaction is:

[0104] in, Indicates the first The overall user weight of each charging terminal ; For the first User priority coefficient for each charging terminal; For the first The battery charging urgency coefficient of each charging terminal; Indicates the first Each charging terminal corresponds to the vehicle's state of charge at the end of charging. Indicates the first Each charging terminal corresponds to the target state of charge of the vehicle set by the user; Indicates the first Total grid connection time for each charging terminal; Indicates the first The actual charging time corresponding to each charging terminal; Indicates the first The actual average charging power of each charging terminal; Indicates the first The user requests power for each charging terminal; For the target weight of the state of charge, As a target weight for waiting time, As the target weight for charging power, satisfy , , All are greater than 0 and ; The expression for operator revenue is:

[0105] in, For time period indexes within the scheduling period, Indicates the total number of time periods within the scheduling period; Indicates the first The charging terminal is in the first Charging power during each time period; Indicates the first The duration of each time period; Indicates the first The charging price charged to users for each time period; Indicates the first Electricity purchase price for operators in different time periods; Represents the equipment loss cost function; The expression for the flattest power grid load curve is:

[0106] in, Indicates the first The total community load for each time period, including the base load and the sum of the charging power of all charging terminals; This represents the average load within the scheduling period.

[0107] By employing a multi-objective optimization algorithm, a multi-objective function is constructed with the optimization objectives of maximizing user satisfaction, maximizing operator revenue, and flattening the grid load curve. User satisfaction is comprehensively quantified by weighted coefficients based on charging completion rate, waiting time, and power satisfaction, and user priority coefficient and battery charging urgency coefficient are integrated. Operator revenue considers time-of-use charging prices, electricity purchase prices, and equipment loss costs. Flattening the grid load curve aims at the stability of the total load within the scheduling cycle. This achieves unified modeling and coordinated optimization of the interests of multiple participants and multiple optimization dimensions under the constraint of transformer safe power threshold.

[0108] In some specific embodiments, user priority coefficient The value range is from 0.6 to 2.0; Battery charging urgency level coefficient The value range is from 0.2 to 2.0.

[0109] By setting the user priority coefficient and the battery charging urgency coefficient within a reasonable range, the user satisfaction quantification model provides a clear weight adjustment range for the differentiated needs of different users and the emergency charging needs of different vehicles. This enables the optimization algorithm to assign differentiated weights based on user type and battery status during power allocation, achieving a refined expression of individual user differences.

[0110] In some specific embodiments, user priority coefficient Determined based on at least one of user type, appointment time, or historical response behavior; Battery charging urgency level coefficient It decreases as the state of charge increases.

[0111] By determining the user priority coefficient based on at least one of user type, appointment time, or historical response behavior, and setting the battery charging urgency coefficient to decrease as the state of charge increases, the weight parameters in the user satisfaction model can dynamically reflect the user's true priority characteristics and the real-time charging urgency of the battery. This avoids the problem of optimization results being out of touch with actual needs caused by static weight settings and improves the matching degree between the optimization allocation results and the actual situation.

[0112] In some specific embodiments, the multi-objective optimization algorithm uses a linear weighted sum method to transform the multi-objective function into a single-objective function for solving. The transformed single-objective function is:

[0113] in, The objective function is user satisfaction. This represents the theoretical maximum value of the user satisfaction objective function. This is a weighting coefficient for user satisfaction. Let be the objective function for operator revenue. This represents the theoretical maximum value of the operator's revenue objective function. This is the operator's revenue weighting coefficient; Let the objective function be the power grid load curve. This represents the theoretical maximum value of the objective function for the power grid load curve. The weighting coefficient for the power grid load curve; satisfy , , All are greater than 0 and .

[0114] By employing a linear weighted sum method to transform multi-objective functions into single-objective functions for solution, and normalizing each objective function by dividing it by its theoretical maximum value before multiplying it by the corresponding weight coefficient, the three objectives with different dimensions and optimization directions—user satisfaction, operator revenue, and power grid load curve—can be comprehensively weighed under a unified scale. The setting of weight coefficients enables flexible adjustment of the primary and secondary relationships among multiple optimization objectives.

[0115] In some specific embodiments, the hard constraints of the multi-objective optimization algorithm include the transformer safe power threshold constraint, as shown in the formula:

[0116] in, For time period indexes within the scheduling period; Indicates the first Basic load for each time period; Indicates the first The charging terminal is in the first Charging power during each time period; This indicates the safe power threshold for the transformer.

[0117] By using the transformer safety power threshold constraint as a hard constraint condition for the multi-objective optimization algorithm, the algorithm ensures that the sum of the base load and the charging power of all charging terminals in each scheduling period never exceeds the transformer safety power threshold during the optimization process. The power grid safety boundary is rigidly embedded into the optimization model in the form of mathematical constraints, so that the power allocation scheme output by the optimization algorithm meets the requirements for transformer safe operation under any circumstances.

[0118] In some specific embodiments, the hard constraints of the multi-objective optimization algorithm also include: The power constraint of the charging terminal is given by the following formula:

[0119] in, For the first The rated maximum power of each charging terminal; Battery charging constraints, the formula is:

[0120]

[0121] in, Indicates the first The vehicle corresponding to the charging terminal is in the first State of charge at the end of each time period; Indicates the first The vehicle corresponding to the charging terminal is in the first State of charge at the end of each time period; For charging efficiency; For the first The duration of each time period; For the first The vehicle battery capacity corresponding to each charging terminal; This represents the minimum permissible state of charge for the battery. This represents the maximum permissible state of charge of the battery. The continuous charging constraint is given by the following formula:

[0122] in, The maximum allowable power change rate for the charging terminal.

[0123] By setting charging terminal power constraints, battery charging constraints, and continuous charging constraints in the hard constraints of the multi-objective optimization algorithm, constraints are imposed on the optimization allocation scheme from three dimensions: the physical output capability of the charging terminal, the safety boundary of the state of charge of the power battery, and the limit on the rate of change of charging power. This ensures the feasibility and safety of the optimization results at both the device and battery levels.

[0124] In some specific embodiments, the multi-objective optimization algorithm is solved using an improved particle swarm optimization algorithm, specifically including: Initialize the particle swarm, with each particle representing a power allocation scheme; Adaptive inertial weights are used to update particle velocity and position, with the inertial weights decreasing with the number of iterations. The penalty function method is used to handle the transformer safety power threshold constraint; Iterative optimization continues until the maximum number of iterations or the convergence condition is reached, outputting the optimal power allocation scheme.

[0125] By employing an improved particle swarm optimization algorithm to solve multi-objective optimization problems, the following steps are taken: initializing a particle swarm where each particle represents a power allocation scheme; updating particle velocity and position using adaptive inertia weights that decrease with the number of iterations; handling transformer safety power threshold constraints using a penalty function method; and iteratively optimizing until convergence conditions are met, then outputting the optimal power allocation scheme. The parallel search capability of the particle swarm optimization algorithm and the dynamic adjustment characteristics of the adaptive inertia weights improve the convergence speed and global optimization capability of the optimization solution. At the same time, the penalty function method transforms hard constraints into penalty terms of the objective function to ensure the effective satisfaction of the constraints.

[0126] In some specific embodiments, the improved particle swarm optimization algorithm employs a model predictive control framework to achieve rolling optimization, specifically including: At the current moment, collect the current total load data, the real-time charging power of each charging terminal, and the charge status of each vehicle; Predict base load for multiple future time periods based on historical data; Using the current state as the initial condition, solve for the power allocation scheme for multiple future time periods; Only the power adjustment command for the next time period is executed; the commands for the remaining time periods are recalculated in the next round of optimization.

[0127] By employing a model predictive control framework to achieve rolling optimization of the improved particle swarm optimization algorithm, the following steps are taken: collecting the current total load data, the real-time charging power of each charging terminal, and the state of charge of each vehicle at the current moment; predicting the base load for multiple future time periods based on historical data; solving the power allocation scheme for multiple future time periods using the current state as the initial condition; executing the power adjustment command for the next time period only, while the commands for the remaining time periods are recalculated in the next round of optimization; and using the rolling optimization mechanism to re-predict and optimize based on the latest real-time data collected in each control cycle, effectively addressing the uncertainties caused by base load prediction errors and dynamic changes in user charging demand.

[0128] Step S5: Send the power allowance command to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the power allowance command. Steps S1 to S5 are executed cyclically to achieve continuous closed-loop control of dynamic power distribution.

[0129] The allowable power command is sent to the corresponding charging terminal and the output voltage or current of the charging terminal is continuously adjusted to make the charging terminal operate according to the allowable power command. The continuous adjustment method realizes the accurate tracking of the charging terminal output power to the allowable power command, avoids the problem of power jump or capacity waste caused by excessive adjustment step size, and ensures that the power allocation scheme generated by the optimization algorithm can be executed accurately, so that the theoretically optimal allocation result can be effectively implemented at the physical device level.

[0130] In some specific embodiments, the output voltage or current of the charging terminal is continuously adjusted, specifically including: After receiving the allowed power command, the charging terminal adjusts the output voltage or current steplessly by adjusting the conduction angle or duty cycle of the power semiconductor switching device, so that the actual output power of the charging terminal continuously tracks the allowed power command.

[0131] After receiving the allowed power command, the charging terminal adjusts the output voltage or current steplessly by adjusting the conduction angle or duty cycle of the power semiconductor switching device. This allows the actual output power of the charging terminal to continuously track the allowed power command. By utilizing the fast response characteristics and continuously adjustable conduction angle or duty cycle of the power semiconductor switching device, the charging terminal's output power is transformed from on / off control or level control to stepless continuous adjustment. This enables the allowed power command generated by the optimization algorithm to be accurately tracked at the physical execution level.

[0132] In some specific embodiments, the charging terminal continuously adjusts its output power within the range of 0 to the rated maximum power, where the rated maximum power is determined according to the type of charging terminal.

[0133] By continuously adjusting the output power of the charging terminal within the range of zero to the rated maximum power, and with the rated maximum power determined according to the type of charging terminal, it is clarified that the charging terminal has the ability to continuously adjust the power across the entire range from zero power to its physical maximum output capability. Furthermore, the rated maximum power is related to the type attribute of the charging terminal itself, enabling charging terminals of different specifications and power levels to achieve fine power adjustment within their respective capability ranges. This provides a wide-range and high-precision execution basis for the differentiated power allocation instructions generated by the multi-objective optimization algorithm.

[0134] The following are embodiments of a community-oriented flexible charging control system provided in this application. This community-oriented flexible charging control system and the flexible charging control methods of the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the flexible charging control system, please refer to the embodiments of the community-oriented flexible charging control method described above.

[0135] like Figure 2 As shown, the flexible charging control system for communities includes: The data acquisition module is used to collect in real time the total load data of the community's main power distribution line, the branch load data of each power distribution branch, the real-time charging power of each charging terminal, and the user charging request data reported by each charging terminal. The dynamic power margin calculation module is used to calculate the current dynamic power margin available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. The dynamic power satisfaction calculation and condition judgment module is used to calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal and the user charging request data, and to judge the comparison relationship between the power satisfaction and the preset first threshold and second threshold. The permissible power command generation module is used to call a multi-objective optimization algorithm, with the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the permissible power command for each charging terminal. The instruction execution module is used to send the allowable power instruction to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the allowable power instruction.

[0136] The flexible charging control system in this embodiment is used to implement a community-oriented flexible charging control method.

[0137] This application also provides an electronic device for implementing the various embodiments of this application. Figure 3 To illustrate the hardware structure of an electronic device according to various embodiments of this application, as shown in the following diagram... Figure 3 As shown, the electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor.

[0138] Those skilled in the art will understand that the electronic device structure involved in the embodiments of this application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0139] In embodiments of this application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0140] In this application embodiment, the processor can be implemented using at least one of an Application-Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such implementations can be implemented within a controller. For software implementations, implementations such as processes or functions can be implemented with separate software modules that allow the performance of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language, and the software code can be stored in memory and executed by the controller.

[0141] In addition, the electronic device includes some functional modules not shown, which will not be described in detail here.

[0142] Those skilled in the art will understand that the various aspects of the electronic device provided in this application can be implemented as a system, method, or program product. Therefore, the various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0143] This application also provides a storage medium storing a program product capable of implementing a community-oriented flexible charging control method. In some possible implementations, various aspects of this application can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the foregoing "Exemplary Methods" section of this specification according to various exemplary embodiments of this application.

[0144] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0145] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A flexible charging control method for communities, characterized in that, include: S1. Real-time collection of total load data of the community's main power distribution line, branch load data of each power distribution branch, real-time charging power of each charging terminal, and user charging request data reported by each charging terminal. Each charging terminal belongs to a power distribution branch, and at least one charging terminal is connected to each power distribution branch. S2. Calculate the dynamic power margin currently available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. S3. Calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal, and user charging request data; If the power satisfaction is less than or equal to the preset first threshold, it is determined to be a state of sufficient capacity, and each charging terminal is allowed to operate according to the corresponding user charging request data. If the power satisfaction is greater than or equal to the preset second threshold, it is determined to be a capacity critical state, and the power of some charging terminals is forcibly reduced. If the power satisfaction is greater than the first threshold and less than the second threshold, it is determined to be a capacity shortage state, and steps S4 to S5 are executed. Wherein, the first threshold is less than the second threshold; S4. Call the multi-objective optimization algorithm, with the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the allowable power command for each charging terminal; S5. Send the power allowance command to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the power allowance command; Repeat steps S1 to S5.

2. The flexible charging control method as described in claim 1, characterized in that, In step S2, the formula for calculating the dynamic power margin is: in, Indicates time Dynamic power margin; This indicates the preset safe power threshold for the transformer; Indicates time The basic load is calculated using the following formula: This represents the total load data at time t; It represents the sum of real-time charging power of charging terminals in all distribution branches at time t. For power distribution branch index, Indicates the first The sum of the real-time charging power of all charging terminals in each power distribution branch.

3. The flexible charging control method as described in claim 1, characterized in that, In step S3, the formula for calculating the dynamic power satisfaction is: in, express Dynamic power satisfaction at any given moment; express The sum of the requested charging power of all charging terminals at any given time, obtained based on user charging request data. An index for the charging terminal; express Dynamic power margin at any given time; Indicates time The sum of real-time charging power of charging terminals in all power distribution branches.

4. The flexible charging control method as described in claim 1, characterized in that, In step S3, the value range of the first threshold is 0.7-0.9; The second threshold ranges from 0.9 to 1.

1.

5. The flexible charging control method as described in claim 1, characterized in that, In step S3, when the system is determined to be in a critical capacity state, the specific rules for forcibly reducing the power of some charging terminals are as follows: The charging terminals are sorted according to their battery state of charge. The power of charging terminals with higher state of charge is reduced or their charging is suspended first, while charging terminals with state of charge below the preset charge threshold are kept at the basic charging power.

6. The flexible charging control method as described in claim 1, characterized in that, In step S4, the multi-objective optimization algorithm constructs a multi-objective function with the optimization objectives of maximizing user satisfaction, maximizing operator revenue, and flattening the power grid load curve; User satisfaction is quantified by a combination of charging completion rate, waiting time, and power satisfaction. The expression for user satisfaction is: in, Indicates the first The overall user weight of each charging terminal ; For the first User priority coefficient for each charging terminal; For the first The battery charging urgency coefficient of each charging terminal; Indicates the first Each charging terminal corresponds to the vehicle's state of charge at the end of charging. Indicates the first Each charging terminal corresponds to the target state of charge of the vehicle set by the user; Indicates the first Total grid connection time for each charging terminal; Indicates the first The actual charging time corresponding to each charging terminal; Indicates the first The actual average charging power of each charging terminal; Indicates the first The user requests power for each charging terminal; For the target weight of the state of charge, As a target weight for waiting time, As the target weight for charging power, satisfy , , All are greater than 0 and ; The expression for operator revenue is: in, For time period indexes within the scheduling period, Indicates the total number of time periods within the scheduling period; Indicates the first The charging terminal is in the first Charging power during each time period; Indicates the first The duration of each time period; Indicates the first The charging price charged to users for each time period; Indicates the first Electricity purchase price for operators in different time periods; Represents the equipment loss cost function; The expression for the flattest power grid load curve is: in, Indicates the first The total community load for each time period, including the base load and the sum of the charging power of all charging terminals; This represents the average load within the scheduling period.

7. The flexible charging control method as described in claim 6, characterized in that, In step S4, the multi-objective optimization algorithm uses the linear weighted sum method to transform the multi-objective function into a single-objective function for solution. The transformed single-objective function is: in, The objective function is user satisfaction. This represents the theoretical maximum value of the user satisfaction objective function. This is a weighting coefficient for user satisfaction. Let be the objective function for operator revenue. This represents the theoretical maximum value of the operator's revenue objective function. This is the operator's revenue weighting coefficient; Let the objective function be the power grid load curve. This represents the theoretical maximum value of the objective function for the power grid load curve. The weighting coefficient for the power grid load curve; satisfy , , All are greater than 0 and .

8. A flexible charging control system for communities, characterized in that, To implement the flexible charging control method as described in any one of claims 1-7, comprising: The data acquisition module is used to collect in real time the total load data of the community's main power distribution line, the branch load data of each power distribution branch, the real-time charging power of each charging terminal, and the user charging request data reported by each charging terminal. The dynamic power margin calculation module is used to calculate the current dynamic power margin available for charging based on the total load data, the real-time charging power of each charging terminal, and the preset transformer safety power threshold. The dynamic power satisfaction calculation and condition judgment module is used to calculate the dynamic power satisfaction based on the dynamic power margin, the real-time charging power of each charging terminal and the user charging request data, and to judge the comparison relationship between the power satisfaction and the preset first threshold and second threshold. The permissible power command generation module is used to call a multi-objective optimization algorithm, with the transformer safety power threshold as a hard constraint, to optimize the power allocation of each charging terminal and generate the permissible power command for each charging terminal. The instruction execution module is used to send the allowable power instruction to the corresponding charging terminal and continuously adjust the output voltage or current of the charging terminal so that the charging terminal operates according to the allowable power instruction.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the steps of the flexible charging control method as described in any one of claims 1-7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the flexible charging control method as described in any one of claims 1-7.

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