A smart car charging pile charging and discharging control method and system

Abstract

The application relates to the technical field of charging pile charging and discharging control, in particular to a smart automobile charging pile charging and discharging control method and system, which comprises the following steps: determining that a smart automobile charging pile in a community power system enters a charging and discharging mode; dividing electric vehicles into charging and discharging nodes; respectively calculating the charging and discharging priorities of the charging and discharging nodes; and controlling the charging and discharging nodes according to the priorities from large to small in the charging and discharging mode of the smart automobile charging pile in the community power system. According to the real-time power demand load condition of residents, the charging and discharging mode of the smart automobile charging pile is adaptively controlled, the superposition with the peak of resident power consumption is reduced, and the power grid operation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of charging pile charging and discharging control, in particular to an intelligent automobile charging pile charging and discharging control method and system. BACKGROUND

[0002] The electric vehicle charging pile is an important supporting facility of new energy vehicles, and the charging and discharging control method is directly related to the stable operation of the power grid and the rational use of energy. The V2G (Vehicle-to-Grid) mode refers to the fact that the electric vehicle can not only receive electric energy for charging during access to the power grid, but also can discharge to the power grid when necessary, forming a two-way flow of energy, that is, the electric vehicle can flexibly adjust the charging power in different periods, and can also feed back the electric energy stored in the battery to the power grid according to the real-time operation of the power grid.

[0003] However, when the electric vehicle load is large-scale accessed, it is easy to cause the load of the community to increase sharply, especially the transformer capacity of the old community is limited and the line is aging, and in severe cases, it will cause the transformer to overload, thereby seriously threatening the safe operation of the community power grid. The existing charging pile mostly adopts the "plug and charge" mode, lacks a global scheduling mechanism, and is often superimposed with the peak of residential life electricity, forming an extreme load curve of "peak on peak", which has a huge impact on the power grid and increases the risk of paralysis of the community power system. SUMMARY

[0004] In order to solve the above technical problems, the application provides an intelligent automobile charging pile charging and discharging control method and system, and the technical scheme adopted is as follows:

[0005] In the first aspect, one embodiment of the application provides an intelligent automobile charging pile charging and discharging control method, which comprises the following steps:

[0006] Predicting the load estimate of all users in the community where the intelligent automobile charging pile is located, and combining the under-voltage user proportion in the community to build a power consumption safety capacity for controlling the intelligent automobile charging pile in the community power system to enter the charging and discharging mode;

[0007] Judging the difference between the state of charge of the electric vehicle and the expected state of charge set by the user to determine the charging and discharging decision value of the electric vehicle, which is used to distinguish the charging and discharging nodes of the electric vehicle;

[0008] Combining the time urgency coefficient of the electric vehicle under the rated charging condition and the charging and discharging decision value, the charging and discharging priority of the charging and discharging node is calculated respectively;

[0009] In the charging and discharging mode of the intelligent automobile charging pile in the community power system, the charging and discharging nodes are controlled according to the priority from large to small.

[0010] Preferably, the power safety capacity is positively correlated with the proportion of undervoltage users and the estimated load of all users' electricity consumption.

[0011] Preferably, the proportion of undervoltage users in the community is determined by calculating the ratio of the number of users whose effective voltage is lower than a preset effective voltage threshold to the total number of users in the community.

[0012] Preferably, the load estimation prediction method is as follows: an LSTM neural network is used to train a community load prediction model, the average load data of all users in the same time slot in the previous 12 hours is obtained in real time as the input of the community load prediction model, the load prediction sequence for the next 1 hour is obtained, and the mean of all data elements in the load prediction sequence is used as the load estimate of the electricity consumption of all users in the community where the charging pile is located at the current time.

[0013] Preferably, the control method for the intelligent vehicle charging piles in the community power system to enter the charging and discharging mode is as follows: utilizing the current transformer capacity of the community. With electrical safety capacity Differences The system determines when a smart car charging station within the community's power system enters charging / discharging mode; when... The system controls the smart car charging piles within the community's power system to enter charging mode when the time is right and discharging mode when the time is not right.

[0014] Preferably, the method for determining the charge / discharge decision value is as follows:

[0015] When the battery state of charge of the nth electric vehicle Greater than or equal to the user-defined desired state of charge At that time, the charging and discharging decision value of the electric vehicle is ;

[0016] Otherwise, the charging / discharging decision value for the nth electric vehicle is... ;

[0017] in, , These are the maximum and minimum battery state of charge preset for electric vehicles, respectively.

[0018] Preferably, electric vehicles with a charge / discharge decision value greater than 0 are designated as discharge nodes of the community power system; electric vehicles with a charge / discharge decision value less than 0 are designated as charging nodes of the community power system; and electric vehicles with a charge / discharge decision value equal to 0 are designated as normal nodes of the community power system.

[0019] Preferably, the method for calculating the charging / discharging priority of the charging / discharging node is as follows:

[0020] For any discharging node, the ratio of the charging and discharging decision value and the time urgency coefficient is calculated as the discharging priority of the discharging node;

[0021] For any charging node, the product of the absolute value of the charging and discharging decision value and the time urgency coefficient is calculated as the charging priority of the charging node.

[0022] Preferably, the time urgency coefficient of the electric vehicle under the rated charging condition is positively correlated with the rated charging time of the electric vehicle, and is negatively correlated with the difference between the expected charging time set by the user of the electric vehicle and the charged time.

[0023] In a second aspect, another embodiment of the present application further provides an intelligent automobile charging pile charging and discharging control system, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the above-mentioned intelligent automobile charging pile charging and discharging control method when executing the computer program.

[0024] The present application has at least the following beneficial effects:

[0025] 1. The present application predicts the load estimate of all users in the community where the intelligent automobile charging pile is located, and quantifies the proportion of users with insufficient voltage in real time, so that the "power consumption safety capacity" can dynamically reflect the real power supply margin of the community, thereby avoiding the risk of transformer overload and persistent insufficient voltage at the end before entering the charging and discharging mode.

[0026] 2. The present application constructs a charging and discharging decision value based on the difference between the state of charge of the electric vehicle battery and the expected state of charge set by the user, which can automatically divide the electric vehicle into three categories of "dischargeable", "chargeable" and "normal", replacing the traditional "plug and charge" disorderly access, providing controllable resources for subsequent global scheduling, and reducing the probability of "peak on peak".

[0027] 3. The present application simultaneously considers "power difference" and "time margin" in the priority formula, so that vehicles with high charging priority (low power and close to expected time) and vehicles with high discharging priority (power surplus and time relaxed) can be quantitatively sorted, ensuring that the most urgent users are charged first and the most surplus vehicles are discharged first under limited capacity, and improving the resource utilization efficiency of the parking space level.

[0028] 4. The present application executes the three-level strategy of "full charging - limited power charging - discharging support" according to the charging and discharging capacity interval of the community power system at the current time, which can fully utilize the remaining capacity at the load low point, and call the discharging node according to the priority at the load peak, realizing the "peak load shifting" within the community, reducing the peak demand of the external power grid, and improving the efficiency of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0030] Figure 1 A flow chart of a smart car charging pile charging and discharging control method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0031] Embodiment 1

[0032] A smart car charging pile charging and discharging control method provided by an embodiment of the present application, for specific reference Figure 1 The method comprises the following steps:

[0033] Step 1: Predict the load estimate of all users in the community where the smart car charging pile is located, and combine the proportion of users with low voltage in the community to build a power consumption safety capacity, which is used to control the smart car charging pile in the community power system to enter the charging and discharging mode.

[0034] The present application uses the communication interface of the smart car charging pile terminal to collect the battery state data and user charging plan of the electric vehicle in real time. The battery state data includes the battery state of charge and the battery health state, and the user charging plan includes the expected charging time and the expected state of charge set by the user.

[0035] In this embodiment, the battery state of charge and the battery health state are collected every 15 minutes, and the expected charging time and the expected state of charge set by the user are collected at the beginning of charging.

[0036] The capacity of the community distribution transformer is limited. If multiple charging piles simultaneously charge electric vehicles, it is easy to cause the overload of the community transformer during the peak period of electricity consumption, resulting in tripping and even equipment damage, which seriously affects the normal electricity consumption of residents. Therefore, with the help of V2G technology, the community power system should feed back the electric energy stored by the electric vehicle to the power grid during the peak load period, reduce the power supply pressure of the power grid, and orderly charge the smart car charging pile during the low load period, effectively utilize the remaining capacity of the power grid.

[0037] In the present application, the community power management system records user load data every preset time interval through the power grid power meter of each household, a total of preset days, calculates the average load of all users in the community in the same time slot (i.e. a preset time interval), uses the average load data of the preset days as the prediction training sample, optionally uses the average load data of the first 12 hours in the prediction training sample as the input vector of the LSTM neural network, and the average load data of the last 1 hour as the prediction label of the input vector. Specifically, the community load prediction model can be trained using the LSTM neural network, the loss function uses mean square error, and the optimizer uses Adam. The training process of the community load prediction model is a known technology, and the specific process will not be described again.

[0038] In the present embodiment, the preset time interval is set to 15 minutes, i.e. recording user load data every 15 minutes, and the preset number of days is set to 60, i.e. recording a total of 60 days.

[0039] The present application obtains the load data of each user in the last 12 hours at the current time through the community power management system, calculates the average load of all users in the community in the same time slot, and forms a historical average load sequence in chronological order. The historical average load sequence is used as the input of the community load prediction model to obtain the load prediction sequence of the next 1 hour. The mean value of all data elements in the load prediction sequence of the next 1 hour is calculated, which is denoted as the load estimate of all users in the community where the charging pile is located at the current time .

[0040] The present application obtains the effective voltage data of all users in the community at the current time, and considers the users with effective voltage data lower than the preset effective voltage threshold (the value in the present embodiment is 198V, i.e. 0.9*220V, which requires that the grid deviation does not exceed ±10%) as the under-voltage users of the community power system, and calculates the ratio of the number of under-voltage users to the total number of users in the community, denoted as the under-voltage user ratio .

[0041] Further, the present application combines the under-voltage user ratio in the community at the current time and the load estimate of all users to construct the power consumption safety capacity, which is used to control the intelligent car charging pile in the community power system to enter the charging and discharging mode. Wherein, the power consumption safety capacity is positively correlated with the under-voltage user ratio and the load estimate of all users.

[0042] It can be understood that the positive correlation means that the dependent variable will increase with the increase of the independent variable, and will decrease with the decrease of the independent variable, which is determined by actual application, and the present application does not make special limitation.

[0043] Specifically, the present embodiment obtains the power consumption safety capacity of the community power system at the current time by the following formula :

[0044]

[0045] In the formula, is the under-voltage user ratio at the current time, reflecting the voltage stability of the cell power system, The larger the value is, the more serious the voltage problem at the user end of the cell is, and more safety capacity should be used to alleviate the voltage problem. The index is introduced for the power system. For the scene with large load fluctuation, it is easy to cause cell load variation after large-scale electric vehicles are accessed, and higher electricity safety capacity is used to improve the robustness of the power system.

[0046] Further, the present application obtains the charging and discharging capacity of the cell power system at the current time through the calculation formula , wherein, is the transformer capacity of the cell at the current time, i.e., the apparent power of the cell distribution transformer, and is the upper limit of the power capacity of the cell power system. In order to ensure that the cell power system still has a low impedance voltage drop at the peak load and avoid persistent under-voltage of the line end user, the electricity safety capacity should be set for the cell power system.

[0047] If the charging and discharging capacity , it indicates that the cell power system has a remaining available capacity at the current time, supporting orderly charging of the automobile charging pile, and then the intelligent automobile charging pile in the cell power system is controlled to enter the charging mode; if the charging and discharging capacity , it indicates that the power supply of the cell power system at the current time is poor, and in order to avoid the voltage quality of the cell end user from being reduced, the intelligent automobile charging pile needs to perform V2G discharging treatment on the electric vehicle, and then the intelligent automobile charging pile in the cell power system is controlled to enter the discharging mode.

[0048] Step two: judge the difference between the battery state of charge of the electric vehicle and the expected state of charge set by the user to determine the charging and discharging decision value of the electric vehicle, which is used to distinguish the electric vehicle as a charging and discharging node.

[0049] The existing charging pile mostly adopts the unordered control mode of "plug and charge", lacks a global scheduling mechanism, and often superimposes on the peak of residential life electricity, forming an extreme load curve of "peak on peak", which has a huge impact on the power grid and increases the risk of paralysis of the cell power system. As a demand side management method, the orderly charging and discharging of the charging pile provides a fast and flexible controllable resource for power grid dispatching and enhances the resilience of the cell power system to deal with supply and demand imbalance.

[0050] The application obtains the number of electric vehicles accessed by the cell power system at the current time, denoted as N, that is, a total of N electric vehicles establish V2G mode connection with the charging pile at the current time, each electric vehicle charging supports setting a user charging plan, and the electric vehicles are orderly charged and discharged. The application obtains the charging and discharging decision value of the nth electric vehicle through the following formula :

[0051]

[0052] In the formula, is the state of charge of the nth electric vehicle, is the desired state of charge set by the user of the nth electric vehicle, , are respectively the maximum battery state of charge and the minimum battery state of charge preset by the electric vehicle, and in this embodiment, 95% and 20% are taken respectively.

[0053] In this embodiment, when the denominator in the formula is 0, a very small positive number is added to the denominator. The very small positive number can be specifically valued by the implementer in combination with the actual situation and the dimension of the denominator.

[0054] If , it indicates that the electric vehicle has sufficient power, the greater, the more the intelligent vehicle charging pile should be based on the V2G mode to control the discharging of the electric vehicle battery, to provide additional power during the peak load period of the cell power system; if , it indicates that the electric vehicle battery is in an underpower state, the smaller, the more the charging pile should be based on the V2G mode to control the charging of the electric vehicle battery, to fully utilize the remaining capacity of the power grid during the trough period of the cell power system.

[0055] According to the above steps, the application obtains the charging and discharging decision value of any electric vehicle in the cell power system. The electric vehicle with a charging and discharging decision value greater than 0 is taken as a discharging node of the cell power system; the electric vehicle with a charging and discharging decision value less than 0 is taken as a charging node of the cell power system; and the electric vehicle with a charging and discharging decision value equal to 0 is taken as a normal node of the cell power system.

[0056] Step three: combine the time urgency coefficient of the electric vehicle under the rated charging condition and the charging and discharging decision value to calculate the charging and discharging priority of the charging and discharging node.

[0057] In the present application, the time urgency coefficient of the electric vehicle under the rated charging condition is positively correlated with the rated charging time of the electric vehicle, and is negatively correlated with the difference between the expected charging time set by the user of the electric vehicle and the charged time.

[0058] It can be understood that the positive correlation means that the dependent variable increases with the increase of the independent variable, and decreases with the decrease of the independent variable; the negative correlation means that the dependent variable decreases with the increase of the independent variable, and increases with the decrease of the independent variable, which is determined by actual application, and the present application does not make special limitation.

[0059] Specifically, the time urgency coefficient of the nth electric vehicle under the rated charging condition is obtained by the following formula :

[0060]

[0061] In the formula, is the time urgency coefficient of the nth electric vehicle under the rated charging condition, reflecting the urgency degree of the charging pile to complete the electric vehicle charging plan, is the expected charging time set by the user of the nth electric vehicle, is the charged time of the nth electric vehicle after connecting the charging pile, is a non-zero factor, which prevents the denominator from being 0, and the present embodiment takes 0.01; is the rated charging time of the nth electric vehicle, indicating the time consumed by the charging pile from the current battery state of charge to the expected state of charge using the rated charging power, which should consider the battery state of charge of the electric vehicle at this time;

[0062] wherein, , in the formula, is a constant, and the present embodiment takes 0.01, is the rated charging power of the nth electric vehicle, is the rated capacity of the battery of the nth electric vehicle. If , it means that the battery state of charge of the electric vehicle exceeds or equals the expected state of charge of the user, and the electric vehicle does not need to be charged; if , it means that the battery state of charge of the electric vehicle has not reached the expected state of charge of the user, and the electric vehicle needs to be charged.

[0063] When the remaining grid access time of the electric vehicle is sufficient to charge to the expected state of charge at the rated power, i.e. , the time urgency coefficient is lower, and the electric vehicle has surplus time to participate in the V2G mode; if the remaining grid access time is less than the time to charge to the expected state of charge at the rated power, i.e. , then in order to ensure that the charging is completed within the time expected by the user, the time urgency coefficient The greater the time urgency coefficient, the more the electric vehicle can only be charged unidirectionally.

[0064] For any discharging node, the application calculates the ratio of the charging and discharging decision value to the time urgency coefficient as the discharging priority of the discharging node. The more sufficient the battery capacity of the electric vehicle is, the more time the electric vehicle has to participate in the V2G mode, and the higher the discharging priority of the discharging node is.

[0065] For any charging node, the application calculates the product of the absolute value of the charging and discharging decision value and the time urgency coefficient as the charging priority of the charging node. The less the battery capacity of the electric vehicle is, the closer the charging node is to the time expected by the user, and the higher the charging priority of the charging node is. The charging priority of each electric vehicle in the community power system is quantified, and the power system can globally optimize the charging and discharging strategy to avoid "peak on peak" and balance the supply and demand of the power grid.

[0066] Step four: In the charging and discharging mode of the intelligent vehicle charging pile in the community power system, the charging and discharging nodes are controlled according to the priority from high to low.

[0067] The application further utilizes the charging and discharging capacity of the community power system in the charging and discharging mode of the intelligent vehicle charging pile in the community power system. to control the orderly charging and discharging of the charging and discharging nodes of the plurality of intelligent vehicle charging piles in the community, and the specific charging and discharging control method is as follows:

[0068] (1) If , it indicates that the electric energy capacity of the community power system available for electric vehicle charging is relatively large, and at this time it is most likely to be in a load valley period. All intelligent vehicle charging piles can normally charge electric vehicles according to the user charging plan, and the charging power is the rated charging power of the intelligent vehicle charging pile, which fully utilizes the remaining capacity of the community power system. Among them, The application has a value range of [40%, 60%], and the embodiment takes 40%.

[0069] (2) If 0 , it indicates that the electric energy capacity of the community power system available for electric vehicle charging is limited. All charging nodes are extracted from the head according to the charging priority each time charging nodes are controlled by the intelligent vehicle charging pile at the rated power of the charging pile, and the charging nodes not extracted are controlled in the reduced power charging mode. The limited electric energy resources are preferentially allocated to the electric vehicles with the highest charging priority for charging, which not only relieves the peak pressure of the power grid, but also maximizes the guarantee of the emergency charging demand of the user. Among them, the purpose of setting the number is to try to guarantee the emergency charging demand of multiple users, The embodiment takes 5.

[0070] It should be noted that when the charge-discharge decision value of the drawn charge node is greater than or equal to 0, the charge node is set as a normal node and no longer participates in V2G charging.

[0071] (3) If , it indicates that the electric power system of the cell is seriously insufficient in the capacity of electric energy available for charging electric vehicles, and at this time, it is most likely that the period of load peak is being experienced, and all discharge nodes are extracted from the head according to the discharge priority every time. The discharge control of the electric vehicle battery by the intelligent automobile charging pile through the discharge nodes relieves the power supply pressure of the power grid, and all charging nodes should suspend charging and send a short message to the owner. If all are charging nodes and normal nodes at last, at this time, due to the large load of the residents in the cell and the serious shortage of the capacity of electric energy available for charging electric vehicles, the charging nodes all stop charging and wait for the load of the residents to decrease to release more residual capacity of the power grid. The purpose of setting the number is to prevent the harmonic and power quality problems caused by the access of a large number of discharge nodes to the electric power system, The embodiment takes 5.

[0072] It should be noted that when the charge-discharge decision value of the drawn charge node is greater than or equal to 0, the charge node is set as a normal node and no longer participates in V2G charging.

[0073] Embodiment 2

[0074] Another embodiment of the present application further provides an intelligent automobile charging pile charge-discharge control system, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the intelligent automobile charging pile charge-discharge control method described above when executing the computer program.

[0075] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application embrace any and all variations of the application that fall within the scope of the general inventive concept as defined in the claims and that the application include such variances notwithstanding such variances are not recited in the above description or illustrated in the accompanying drawings.

[0076] It should be understood that the present application is not limited to the precise construction that has been described above and shown in the accompanying drawings and that various modifications and changes can be effected therein by those skilled in the art without departing from the scope of the application.

Claims

1. A method for charging and discharging control of an intelligent automobile charging pile, characterized in that, The method includes the following steps: The load estimate of electricity consumption of all users in the community where the smart car charging station is located is predicted, and the electricity safety capacity is constructed by combining the proportion of undervoltage users in the community, which is used to control the smart car charging station in the community power system to enter the charging and discharging mode. The difference between the battery state of charge of an electric vehicle and the user-defined expected state of charge is determined to identify the charging and discharging decision value of the electric vehicle, which is used to classify the electric vehicle into charging and discharging nodes. Based on the time urgency coefficient and charging / discharging decision value of electric vehicles under rated charging conditions, the charging / discharging priority of charging / discharging nodes is calculated respectively. In the charging and discharging mode of the smart car charging piles within the community's power system, the charging and discharging nodes are controlled according to their priority from highest to lowest. The method for determining the charge / discharge decision value is as follows: When the state of charge of the battery of the nth electric vehicle is greater than or equal to the desired state of charge set by the user , the charge-discharge decision value of the electric vehicle is ; when the state of charge of the battery of the nth electric vehicle is less than the desired state of charge set by the user , the charge-discharge decision value of the electric vehicle is Otherwise, the charge-discharge decision value of the nth electric vehicle is ; wherein, , SoCmax and SoCmin are respectively the maximum and minimum battery state of charge preset for the electric vehicle. Electric vehicles with a charge / discharge decision value greater than 0 are designated as discharge nodes in the community power system; electric vehicles with a charge / discharge decision value less than 0 are designated as charging nodes in the community power system; and electric vehicles with a charge / discharge decision value equal to 0 are designated as normal nodes in the community power system. The method for calculating the charging and discharging priority of the charging and discharging nodes is as follows: For any discharge node, calculate the ratio of the charge / discharge decision value to the time urgency coefficient, which serves as the discharge priority of the discharge node. For any charging node, calculate the product of the absolute value of the charging / discharging decision value and the time urgency coefficient, and use it as the charging priority of the charging node. The time urgency coefficient of the electric vehicle under rated charging conditions is positively correlated with the rated charging time of the electric vehicle, and negatively correlated with the difference between the expected charging time set by the electric vehicle user and the actual charging time.

2. The intelligent charging pile charging and discharging control method of claim 1, wherein, The power safety capacity is positively correlated with the proportion of undervoltage users and the estimated load of all users' electricity consumption. 3.The intelligent charging pile charging and discharging control method of claim 2, wherein, The proportion of undervoltage users in the community is determined by calculating the ratio of users whose effective voltage is lower than a preset effective voltage threshold to the total number of users in the community. 4.The intelligent charging pile charging and discharging control method of claim 2, wherein, The load estimation prediction method is as follows: an LSTM neural network is used to train the community load prediction model. The average load data of all users in the same time slot in the previous 12 hours is obtained in real time as the input of the community load prediction model. The load prediction sequence for the next 1 hour is obtained, and the mean of all data elements in the load prediction sequence is used as the load estimate of the electricity consumption of all users in the community where the charging pile is located at the current time.

5. The intelligent charging pile charging and discharging control method of claim 2, wherein, The control method for the intelligent vehicle charging piles in the community's power system to enter charging and discharging mode is as follows: Utilizing the current transformer capacity of the community... With electrical safety capacity Differences The system determines when a smart car charging station within the community's power system enters charging / discharging mode; when... The system controls the smart car charging piles within the community's power system to enter charging mode when the time is right and discharging mode when the time is not right. 6.A charging and discharging control system of an intelligent automobile charging pile, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements a charging and discharging control method for an intelligent vehicle charging pile as described in any one of claims 1-5.

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