Dynamic charging distribution method and device based on peak-valley electricity price, and charging pile

Abstract

The invention relates to a dynamic charging distribution method and device based on peak and valley electricity prices and a charging pile. The dynamic charging distribution method based on the peak-valley electricity price comprises the following steps: acquiring battery information of a current new energy automobile, and calculating required charging time; judging whether the required charging time is less than the charging time reserved by the current user; if not, directly starting the charging pile for charging; if yes, constructing a total cost objective function of charging according to the required charging time and the charging time reserved by the current user; and according to the constraint function, a linear programming solver is adopted to minimize the target function of the total cost, the charging time of a plurality of time periods is obtained, and according to the time periods corresponding to the charging time of the plurality of time periods, a charging pile is started to charge the new energy vehicle. According to the dynamic charging distribution method based on the peak-valley electricity price, the problem that the charging distribution template cannot dynamically adjust the charging time period is solved, and finally the optimization of the charging cost and the improvement of the user experience are realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy charging, and in particular to a dynamic charging allocation method, device and charging pile based on peak and valley electricity prices. Background Art

[0002] With the popularization of new energy vehicles, charging piles are gradually laid out and improved, and the charging power of charging piles ranges from 7kW (slow charging) to 120kW (fast charging). When new energy vehicles are charged at high power when the grid load is high, it will affect the stability of the grid. To this end, the country has implemented a peak-valley electricity price policy to guide new energy vehicle users and charging pile operators to charge during low electricity price periods to reduce the burden on the grid. However, the corresponding electricity price list will be adjusted periodically according to regional policies. If users or charging pile operators fail to obtain information on the electricity price list in a timely manner, it may lead to increased charging costs, especially during peak grid load periods.

[0003] Traditional technology constructs a charging strategy template based on the electricity price table. Although it can ensure that users charge when the electricity price is low, when the grid load is too high, the operator may adjust the charging power. At this time, the preset charging strategy template cannot effectively allocate dynamic charging time periods according to actual conditions, which can easily affect the user experience.

[0004] The existing technology uses energy storage charging piles, that is, the charging piles store electrical energy during periods of lower electricity prices. Therefore, even during peak periods or when the temporary load on the power grid is too high, resulting in a reduction in the output power of the output grid, the charging piles can use the stored electrical energy to charge new energy vehicles and ensure the output power of the charging piles, effectively improving the user's charging experience.

[0005] However, energy storage charging piles are generally only built in open areas, because the energy storage equipment of the charging piles needs to store a large amount of electrical energy. Once a short circuit, failure or battery spontaneous combustion occurs, and it is in a relatively closed or poorly ventilated environment, such as underground parking lots in shopping malls, office buildings or residential buildings, it is easy to bring a greater risk of fire spread, and there is even a risk of fire and explosion of the energy storage battery of the charging pile. Due to the large deployment risk of energy storage charging piles, some property companies will also force users of new energy vehicles to leave the charging area only after the charging pile is fully charged or the charging pile is powered off, especially for energy storage charging piles. Therefore, it is difficult for operators of energy storage charging piles to achieve deployment and popularization in underground parking lots such as shopping malls, office buildings or residential buildings.

[0006] Based on this, in the case of underground parking lots such as shopping malls, office buildings or residences where energy storage charging piles cannot be deployed in the existing technology, operators and users can only rely on ordinary charging piles, such as low-power DC charging piles or AC charging piles. Therefore, the charging pile operator needs to construct a charging allocation template for charging allocation strategies in different time periods according to the peak and valley electricity prices in different regions. However, it is difficult for the charging allocation template to effectively allocate dynamic charging time periods according to actual conditions, especially when users return to the parking lot to pick up the car in advance, the fixed charging allocation template may cause the new energy vehicle to fail to reach a healthy level of electricity. Therefore, the charging pile operator of the existing technology cannot flexibly balance the needs of reducing charging costs and improving user experience. Summary of the invention

[0007] Based on this, the object of the present invention is to provide a dynamic charging allocation method based on peak-valley electricity prices.

[0008] A dynamic charging allocation method based on peak-valley electricity prices comprises the following steps:

[0009] S1: Obtain the battery information of the current new energy vehicle and calculate the required charging time;

[0010] S2: Determine whether the required charging time is less than the current user-booked charging time: if not, directly enable the charging pile to charge the new energy vehicle; if yes, execute step S3;

[0011] S3: Construct a total cost objective function of charging based on the required charging time and the current user scheduled charging time;

[0012] S4: Minimizing the objective function of the total cost using a linear programming solver according to the constraint function to obtain charging time for several time periods; wherein the constraint function includes a charging demand constraint, a charging time allocation constraint, and a charging window constraint;

[0013] S5: Activate the charging pile to charge the new energy vehicle according to the time period corresponding to the charging time of the several time periods.

[0014] The dynamic charging allocation method based on peak and valley electricity prices described in the present invention optimizes the charging behavior of each charging period by designing the minimum cost objective function and combining the dynamic allocation of electricity prices and charging time, thereby maximizing the reduction of charging costs. At the same time, the present invention optimizes the charging behavior through charging demand constraints, charging time allocation constraints, and charging window constraints to ensure that while meeting the charging needs of users, the low electricity price period is utilized to the maximum extent and charging during peak electricity prices is avoided. Accordingly, the present invention uses the objective function and the corresponding constraints to achieve effective dynamic charging period allocation according to actual conditions, thereby effectively balancing the charging cost and the user charging experience.

[0015] Furthermore, the total cost objective function of charging is specifically expressed as follows:

[0016]

[0017] In the formula, Cost min is the total charging cost; P charging is the charging power; i represents the charging time T scheduled by the user user The i-th discrete period in time i is the actual charging time in time period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and the electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours;

[0018] The charging demand constraint is specifically expressed as follows:

[0019]

[0020] In the formula, E total It represents the amount of electricity required to fully charge the current battery. The specific calculation is:

[0021] E total =(100%-SoC)×Q full

[0022] In the formula, SoC represents the current battery power, expressed as a percentage; Q full Indicates the rated total capacity of the battery;

[0023] The charging time allocation constraint is specifically expressed as follows:

[0024] 0≤time i ≤T user (i)

[0025] Where i represents the i-th discrete time period in the current user's scheduled charging time; T user (i) represents the maximum available time of the i-th charging time period reserved by the user;

[0026] The charging window constraint is specifically expressed as follows:

[0027] T user (0)≤T user (i)≤T user (n)

[0028] Where, T user (0) indicates the starting time period of the user's scheduled charging time; T user (n) indicates the end time period of the charging time reserved by the user.

[0029] Since most charging piles are generally set to a fixed charging power for safety reasons, the present invention proposes a total cost objective function based on the fixed charging power and by refining the electricity price matrix into minute levels. Accordingly, the present invention only needs to combine the impact of changes in peak and valley electricity prices on the objective function through a constraint function and minimize the objective function, thereby effectively avoiding high electricity price periods and ensuring that charging time is reasonably allocated to meet user needs while reducing charging costs.

[0030] In addition, the present invention uses charging demand constraints to ensure that new energy vehicles can complete the required charging amount within the time set by the user; and charging time allocation constraints to ensure that the actual charging time in each time period will not exceed the maximum available time reserved by the user, ensuring that the charging time allocation is reasonable and meets the user's reservation requirements; and charging window constraints to limit the charging process to within the time range reserved by the user, avoiding charging during unauthorized time by the user, ensuring that the user's time requirements are met; thereby meeting the user's basic needs while reducing the charging cost as much as possible.

[0031] Furthermore, the constraint function also includes a segmented electricity price matrix constraint; the segmented electricity price matrix constraint is specifically expressed as follows:

[0032]

[0033] In the formula, price i ×weight first Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix first After weight calculation, the current SoC of the new energy vehicle is lower than the first threshold SoC Low The weight calculation is triggered when

[0034] price i ×weight sec Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix sec After weight calculation, the current SoC of the new energy vehicle is lower than the second threshold SoC High , and when the remaining allocatable time ΔT is less than the preset time threshold ΔT th The weight calculation is triggered when

[0035] The remaining allocatable total time ΔT is specifically calculated as follows:

[0036] ΔT=(T user (n)-T user (0))-T require

[0037] In the formula, (Tuser (n)-T user (0)) represents the allocatable time; T require is the current required charging time; ΔT th is a preset time threshold;

[0038] First and sec are used to represent the first priority period and the second priority period respectively. The specific calculation is as follows:

[0039] first={T user (i)|T user (i)≤α×(T user (n)-T user (0))}

[0040] Where α is the time interval weight coefficient; T user (i) is the time period in the first α×100% of the allocatable time;

[0041] sec={T user (j)|T user (j)≤(α+β)×(T user (n)-T user (0))}

[0042] Where β is the time interval weight coefficient; T user (j) is the time period that belongs to the j-th time interval (α+β)×100% before the allocatable time.

[0043] The present invention optimizes the allocation of charging time by adjusting the electricity price matrix by weight, thereby ensuring a more economical allocation of charging time periods under different power states and ensuring that the user's charging needs are met, especially when the user may return early, giving priority to ensuring that the user's charging experience is guaranteed.

[0044] Furthermore, the total cost objective function of charging also includes adjustable power and penalty terms, which are specifically expressed as follows:

[0045]

[0046] P charging,i =P option (time i )

[0047] In the formula, Cost min is the total charging cost; P charging,i represents the charging power selected in the i-th period; P option (·) is the charging power selected in the corresponding allocated time period; i represents the charging time T scheduled by the user userThe i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in time period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and its electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; (price i -avg(price)) 2 is the penalty term; avg(price) is the average electricity price of all time periods; λ is the adjustment coefficient;

[0048] The constraint function also includes basic power constraint, maximum charging time constraint and power change constraint;

[0049] The basic power constraint is specifically expressed as follows:

[0050]

[0051] Where P Charging,i The charging power selected for time period i; Option ij It is a binary variable. When its value is 1, it means that power P is selected in time period i. option,j , a is the total number of optional powers;

[0052] The maximum charging time constraint is specifically expressed as follows:

[0053]

[0054] Where, SoC i is the power consumption of new energy vehicles in time period i; time i is the actual charging time in period i; T user (n)-T user (0) represents the total time from the start of charging to the end of charging; γ is the dynamic charging time acceleration coefficient, which is calculated as follows:

[0055] γ=0.95+(100%-SoC i )×β

[0056] In the formula, β is the adjustment factor, which is used to determine the amplitude of the dynamic change of γ;

[0057] The specific expression of the power change constraint is as follows:

[0058]

[0059] Where, SoC i+1 is the power of new energy vehicles in the next period; Q full Indicates the rated total capacity of the battery.

[0060] Compared with fixed power, the present invention introduces adjustable power and penalty terms into the total cost objective function of charging. The introduction of adjustable power enables the system to dynamically select different charging powers at different time periods, especially to select higher power charging during low electricity price periods, thereby significantly reducing charging costs. The introduction of penalty terms enables the system to automatically reduce high-power charging behavior during peak electricity price periods, thereby avoiding unnecessary increases in charging costs.

[0061] In addition, by introducing power changes and charging time constraints, the system can track the battery status in real time and dynamically adjust the charging strategy according to the current power situation, further improving charging efficiency and cost-effectiveness. At the same time, by adding a dynamic charging time acceleration factor to the maximum charging time constraint, the system can flexibly adjust the charging speed according to the current power, ensuring that the charging task is completed efficiently within the specified time, thereby achieving the best balance between charging cost, user experience and efficiency.

[0062] Furthermore, before executing step S3, the following steps are also included:

[0063] Determine the status of the remaining battery power in the current battery information:

[0064] If the remaining battery power is less than the rated capacity of the battery, the SoC Emergency , the charging pile is directly used to charge the new energy vehicle until the power reaches the SoC of the battery's rated capacity Emergency , execute step S3;

[0065] If the remaining battery state is greater than the battery rated capacity SoC Emergency , then directly execute step S3;

[0066] Among them, SoC Emergency Indicates emergency power.

[0067] The present invention ensures that when the user's battery remaining power is low, fast charging is prioritized through the above hard constraints, ensuring that the vehicle obtains basic power in a relatively short time, thereby prioritizing the basic endurance of the user's new energy vehicle in an emergency.

[0068] Based on this, the present invention flexibly responds to changes in users' charging needs and peak and valley electricity prices through a combination of soft constraints and hard constraints, thereby solving the problem in the prior art that the charging allocation template cannot dynamically adjust the charging time period, especially the situation that the user may have insufficient power when picking up the car in advance, and ultimately achieves the optimization of charging costs and the improvement of user experience.

[0069] A dynamic charging allocation device based on peak-valley electricity prices, comprising a required charging time acquisition unit, a charging priority judgment unit, an objective function construction unit, a linear programming solution unit and a charging time allocation unit;

[0070] The required charging time acquisition unit is used to acquire the battery information of the current new energy vehicle and calculate the required charging time;

[0071] The charging priority determination unit is used to determine whether the required charging time is less than the charging time currently reserved by the user: if not, directly enable the charging pile to charge the new energy vehicle; if so, call the objective function construction unit;

[0072] The objective function construction unit is used to construct a total cost objective function of charging according to the required charging time and the charging time currently reserved by the user;

[0073] The linear programming solving unit is used to minimize the objective function of the total cost using a linear programming solver according to the constraint function to obtain the charging time of several time periods;

[0074] The charging time allocation unit is used to enable charging piles to charge the new energy vehicle according to the time periods corresponding to the charging times of the several time periods.

[0075] Furthermore, the total cost objective function of charging is specifically expressed as follows:

[0076]

[0077] In the formula, Cost min is the total charging cost; P charging is the charging power; i represents the charging time T scheduled by the user user The i-th discrete period in time i is the actual charging time in time period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and the electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours;

[0078] The charging demand constraint is specifically expressed as follows:

[0079]

[0080] In the formula, E total It represents the amount of electricity required to fully charge the current battery. The specific calculation is:

[0081] E total =(100%-SoC)×q full

[0082] In the formula, SoC represents the current battery power, expressed as a percentage; Q full Indicates the rated total capacity of the battery;

[0083] The charging time allocation constraint is specifically expressed as follows:

[0084] 0≤time i ≤T user (i)

[0085] Where i represents the i-th discrete time period in the current user's scheduled charging time; T user (i) represents the maximum available time of the i-th charging time period reserved by the user;

[0086] The charging window constraint is specifically expressed as follows:

[0087] T user (0)≤T user (i)≤T user (n)

[0088] Where, T user (0) indicates the starting time period of the user's scheduled charging time; T user (n) indicates the end time period of the charging time reserved by the user.

[0089] Furthermore, the constraint function also includes a segmented electricity price matrix constraint; the segmented electricity price matrix constraint is specifically expressed as follows:

[0090]

[0091] In the formula, price i ×weight first Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix first After weight calculation, the current SoC of the new energy vehicle is lower than the first threshold SoC Low The weight calculation is triggered when

[0092] price i ×weight sec Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix sec After weight calculation, the current power SoC of new energy vehicles is lower than the second threshold SoC High , and when the remaining allocatable time ΔT is less than the preset time threshold ΔT th The weight calculation is triggered when

[0093] The remaining allocatable total time ΔT is specifically calculated as follows:

[0094] ΔT=(T user (n)-T user (0))-T require

[0095] In the formula, (T user (n)-T user (0)) represents the allocatable time; T require is the current required charging time; ΔT th is a preset time threshold;

[0096] First and sec are used to represent the first priority period and the second priority period respectively. The specific calculation is as follows:

[0097] first={T user (i)|T user (i)≤α×(T user (n)-T user (0))}

[0098] Where α is the time interval weight coefficient; T user (i) is the time period in the first α×100% of the allocatable time;

[0099] sec={T user (j)|T user (j)≤(α+β)×(T user (n)-T user (0))}

[0100] Where β is the time interval weight coefficient; T user (j) is the time period that belongs to the j-th time interval (α+β)×100% before the allocatable time.

[0101] Furthermore, the total cost objective function of charging also includes adjustable power and penalty terms, which are specifically expressed as follows:

[0102]

[0103] P charging,i =P option (time i )

[0104] In the formula, Cost min is the total charging cost; P charging,i represents the charging power selected in the i-th period; P option (·) is the charging power selected in the corresponding allocated time period; i represents the charging time T scheduled by the user user The i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in time period i; price iis the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and its electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; (price i -avg(price)) 2 is the penalty term; avg(price) is the average electricity price of all time periods; λ is the adjustment coefficient;

[0105] The constraint function also includes basic power constraint, maximum charging time constraint and power change constraint;

[0106] The basic power constraint is specifically expressed as follows:

[0107]

[0108] Where P Charging,i The charging power selected for time period i; Option ij It is a binary variable. When its value is 1, it means that power P is selected in time period i. option,j , a is the total number of optional powers;

[0109] The maximum charging time constraint is specifically expressed as follows:

[0110]

[0111] Where, SoC i is the power consumption of new energy vehicles in time period i; time i is the actual charging time in period i; T user (n)-T user (0) represents the total time from the start of charging to the end of charging; γ is the dynamic charging time acceleration coefficient, which is calculated as follows:

[0112] γ=0.95+(100%-SoC i )×β

[0113] In the formula, β is the adjustment factor, which is used to determine the amplitude of the dynamic change of γ;

[0114] The specific expression of the power change constraint is as follows:

[0115]

[0116] Where, SoC i+1 is the power of new energy vehicles in the next period; Q full Indicates the rated total capacity of the battery.

[0117] A charging pile with dynamic charging distribution, comprising a charging plug of the charging pile, a charging gun and a dynamic charging distribution device;

[0118] The electric plug of the charging pile is connected to the power grid, and is used to convert the current of the power grid into a current form suitable for the battery of the new energy vehicle, and transmit it to the charging gun;

[0119] The charging gun is connected to the charging plug of the charging pile and the charging socket of the new energy vehicle, and is provided with a communication protocol module, which is used to interact with the battery management system of the new energy vehicle through the communication protocol module, obtain the battery information of the new energy vehicle, and transmit the battery information of the new energy vehicle to the dynamic charging distribution device; at the same time, according to the signal sent by the dynamic charging distribution device, the start and stop of the current are controlled, and the electric energy is output to the battery of the new energy vehicle at a fixed power to perform the charging operation;

[0120] The dynamic charging allocation device is used to obtain the battery information of the new energy vehicle and the charging time reserved by the user, and to plan the charging time to obtain the charging time for several time periods, and to send corresponding start signals and stop signals to the charging gun according to the charging time corresponding to the several time periods;

[0121] Among them, the dynamic charging allocation device is the dynamic charging allocation device based on peak and valley electricity prices mentioned above.

[0122] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 It is a schematic diagram of the overall structure of the dynamic charging distribution device based on peak-valley electricity prices according to the present invention;

[0124] Figure 2 It is a schematic diagram of the overall process of the dynamic charging allocation method based on peak-valley electricity prices according to the present invention;

[0125] Figure 3 Schematic diagram showing a simple simulation example of charging time allocation corresponding to a fixed power of 11 kWh during the parking time period from 00:32 to 13:47. DETAILED DESCRIPTION

[0126] In order to solve the technical problem that it is difficult to effectively allocate dynamic charging time periods according to actual conditions in the prior art, the present invention obtains the battery information of the user's current new energy vehicle to calculate the required charging time; and determines whether to directly charge the new energy vehicle based on the required charging time and the current user's scheduled charging time, or establishes a total cost objective function for charging allocation based on the required charging time and the scheduled charging time; and uses a linear programming solver to minimize the objective function of the total cost to obtain the charging time for several time periods; and charges the new energy vehicle according to the charging time for the several time periods. Accordingly, the present invention minimizes the objective function of the total cost, thereby realizing flexible response to the user's charging needs and changes in peak and valley electricity prices according to actual conditions, thereby reducing the charging cost and improving the user's charging experience.

[0127] Based on the above design, the present invention proposes a dynamic charging allocation method based on peak-valley electricity prices, and based on this method proposes a dynamic charging allocation device based on peak-valley electricity prices.

[0128] See also Figure 1 and Figure 2 , Figure 1 is a schematic diagram of the overall structure of the dynamic charging allocation device based on peak-valley electricity prices according to the present invention, Figure 2 It is a schematic diagram of the overall process of the dynamic charging allocation method based on peak-valley electricity prices described in the present invention.

[0129] The dynamic charging allocation device based on peak-valley electricity prices comprises a required charging time acquisition unit 1, a charging priority judgment unit 2, an objective function construction unit 3, a linear programming solution unit 4 and a charging time allocation unit 5.

[0130] The required charging time acquisition unit 1 is used to execute step S1: acquiring battery information of the current new energy vehicle and calculating the required charging time.

[0131] Specifically, after the charging pile is connected to the interface of the new energy vehicle, the battery management system (Battery Management System, BMS) of the new energy vehicle is used to obtain battery information; the battery information includes the state of charge (State of Charge, SoC) of the battery, the rated capacity of the battery, the current state of health (State of Health, SoH) of the battery, voltage, temperature and other data;

[0132] Based on this, the required charging time is calculated according to the state of the remaining power of the battery, the rated capacity of the battery and the current output power of the charging pile. The specific calculation is as follows:

[0133]

[0134] Where, T require Indicates the required charging time, which is used to indicate the charging time required for the current battery to charge to the target power target, and its target power target defaults to 100%; SoC indicates the state of the remaining battery power, which is used to indicate the current battery power; Q full Indicates the rated capacity of the battery, that is, the capacity of the battery when fully charged; P charging Indicates the default output power of the current charging pile; a fix and b fix They are correction coefficients corresponding to the current battery power and the rated capacity of the battery, respectively, and are used to indicate the correction of the original predetermined parameters when the battery is aged.

[0135] Among them, a fix The coefficients corresponding to different models can be fitted by combining the charging history data of various models.

[0136] And b fix The current battery health status can be adjusted by obtaining the curve of the actual capacity decay relationship given by the current vehicle model manufacturer, or the coefficients corresponding to different vehicle models can be fitted based on the charging history data or simulation data. Therefore, the present invention does not specifically limit the current battery power SoC and the rated capacity Q of the battery. full The corresponding correction factor a fix 、b fix Method of obtaining .

[0137] Since different underground parking lots have different safety regulations, the maximum output power allowed by the charging piles varies, so the present invention does not specifically limit the selection of the maximum output power.

[0138] Different new energy vehicles have different BMS calculation standards for SoC. To ensure battery safety, some manufacturers may exclude part of the rated capacity when calculating SoC. In addition, as the battery health status decreases, the actual capacity of the battery Q full There may be a certain reduction; and some manufacturers may make additional settings for the BMS for charging safety, by monitoring the battery temperature, voltage and other parameters of the current new energy vehicle, and controlling the input current or voltage to prevent the battery from overcharging; Based on this, the present invention combines the correction factor a fix 、b fix , so that the system can better match the actual battery status and charging needs, fully improving the user's charging experience and the accuracy of charging time estimation.

[0139] The charging priority judgment unit 2 is used to execute step S2: judging whether the required charging time is less than the charging time currently reserved by the user: if not, directly enabling the charging pile to charge the new energy vehicle; if so, calling the objective function construction unit 3.

[0140] Specifically, the charging time reserved by the current user is obtained through an intelligent interaction system between the user and the charging pile;

[0141] The intelligent interactive system may be selected as a mobile phone application, an interactive panel of a charging pile or an in-vehicle system interface, and the user sets the estimated time to leave the parking lot or the maximum allowed charging time.

[0142] Next, compare the required charging time with the current user scheduled charging time:

[0143] If the required charging time is greater than the current user's scheduled charging time, the charging pile is directly used to charge the new energy vehicle, thereby ensuring that the user can obtain as much electricity as possible within the remaining time;

[0144] If the charging time currently reserved by the user is greater than the required charging time, the next step is executed to dynamically allocate the charging time according to time periods with different electricity prices to minimize the charging cost.

[0145] Based on this, the system determines whether to enter the cost optimization process by judging the user's reservation time, thereby ensuring that charging needs are met first within the user's reservation time. When the required time is insufficient, charging is carried out directly to maximize power acquisition. When the reservation time is sufficient, the system will dynamically allocate charging time according to time periods with different electricity prices, reduce charging costs through the cost optimization process, and ensure economical and efficient charging in sufficient time.

[0146] Furthermore, if the current user's reservation time is greater than the charging time, the state of the remaining battery power in the current battery information is further determined:

[0147] If the remaining battery power is less than the rated capacity of the battery, the SoC Emergency , the charging pile is directly used to charge the new energy vehicle until the power reaches the SoC of the battery's rated capacity Emergency , proceed to the next step;

[0148] If the remaining battery state is greater than the battery rated capacity SoC Emergency , then proceed directly to the next step.

[0149] Among them, SoC Emergency Indicates emergency power, the default value is 40%, which is used to ensure the battery life.

[0150] Based on this, the above-mentioned hard constraints are used to ensure that low-battery users will not delay charging due to waiting for the best electricity price period, and further ensure the car's endurance requirements when users encounter emergencies, thereby enhancing users' sense of security and charging experience.

[0151] The objective function construction unit 3 is used to execute step S3: constructing an objective function of the total cost of charging according to the required charging time and the charging time currently reserved by the user.

[0152] Specifically, the total cost objective function of charging is specifically expressed as follows:

[0153]

[0154] In the formula, Cost min is the total charging cost; P charging is the charging power, the default is constant slow charging; i represents the charging time T scheduled by the user user The i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in period i. The default value is the time ratio of the current period i. The value range is [0,1]; price i The electricity price for time period i is obtained through the electricity price matrix of the peak and valley electricity price table. The electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours. The minimum time unit of the electricity price is minute by default.

[0155] The present invention constructs a total cost objective function and optimizes the allocation of charging time periods by minimizing the total cost objective function within the charging time range booked by the user, thereby dynamically allocating charging time periods according to the peak and valley electricity prices of the power grid, thereby minimizing charging costs and ensuring that the user's charging experience is not affected.

[0156] In addition, the electricity price matrix of the present invention can more accurately reflect the actual electricity price changes in each period by refining the hourly electricity price to the minute level, thereby optimizing the allocation of charging time and further improving the saving effect of charging costs. Since the time unit can be further refined to seconds for time allocation according to the actual situation of the operator, the present invention does not specifically limit the minimum time unit of its electricity price matrix.

[0157] Furthermore, the total cost objective function of charging is specifically expressed as follows:

[0158]

[0159] P charging,i =P option (time i )

[0160] In the formula, Cost min is the total charging cost; P charging,i represents the charging power selected in the i-th period; P option (·) is the charging power selected in the corresponding allocated time period, and the default allocable power is {21kWh, 11kWh, 7kWh, 3.5kWh}; i represents the charging time T scheduled by the user user The i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in time period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and its electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; (price i -avg(price)) 2 is a penalty term, which greatly increases the cost by using high power when the electricity price is high, thereby guiding the algorithm to choose lower power when the electricity price is high; avg(price) is the average electricity price of all time periods; λ is the adjustment coefficient, which defaults to 0.6.

[0161] Therefore, when the electricity price i When the price is lower than the average electricity price avg(price), the penalty term (price) i -avg(price)) 2 is smaller, therefore, the system tends to choose a higher power for charging, thereby speeding up the charging process, and then completing the charging task faster, reducing the underutilization of low electricity price periods; when the electricity price is higher than the average electricity price, the penalty term is larger, and the system tends to choose a lower power to avoid excessively high charging costs, thereby extending the charging time but reducing the total cost.

[0162] Therefore, by combining the penalty term with dynamic power selection, we can ensure that the power is increased as much as possible when the electricity price is low, and reduce the power selection when the electricity price is high, ultimately minimizing the charging cost.

[0163] The linear programming solving unit 4 is used to execute step S4: according to the constraint function, a linear programming solver is used to minimize the objective function of the total cost to obtain the charging time of several time periods.

[0164] Specifically, the constraint function includes a charging demand constraint, a charging time allocation constraint, a charging window constraint and a segmented electricity price matrix constraint.

[0165] The charging demand constraint is used to ensure that the battery of the new energy vehicle can be fully charged during the entire charging cycle, which is specifically expressed as follows:

[0166]

[0167] In the formula, E total It represents the amount of electricity required to fully charge the current battery. The specific calculation is:

[0168] E total =(100%-SoC)×Q full

[0169] In the formula, SoC represents the current battery power, expressed as a percentage; Q full Indicates the rated total capacity of the battery.

[0170] The charging time allocation constraint is used to ensure that the actual charging time in each time period does not exceed the maximum available time in the time period, which is specifically expressed as follows:

[0171] 0≤time i ≤T user (i)

[0172] Where i represents the i-th discrete time period in the current user's scheduled charging time; T user (i) represents the maximum available time of the i-th charging time period reserved by the user.

[0173] When the user starts charging or the scheduled charging time is not on the hour, the maximum available time for the start time period and the end time period may be shortened.

[0174] The charging window constraint is used to ensure that all allocated actual charging times exist only within the charging time window used for reservation, which is specifically expressed as follows:

[0175] T user (0)≤T user (i)≤T user (n)

[0176] Where, T user (0) indicates the starting time of the user's scheduled charging time, such as the time when the user connects to the charging pile: 12:00; T user (n) indicates the end time period of the charging time reserved by the user, such as the time reserved by the user: 18:00.

[0177] The segmented electricity price matrix constraint is used to ensure that the battery power can reach a healthy level when the user returns early, and is specifically expressed as follows:

[0178]

[0179] In the formula, price i ×weight first Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix firstCalculate the weight, its weight first The default value is 0.6, which is used to reduce the relative electricity price of the allocatable time interval [0, first] to relatively increase the allocation priority. The current SoC of the new energy vehicle is lower than the first threshold SoC Low The weight calculation is triggered when SoC Low The default value is 60%;

[0180] price i ×weight sec Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix sec Calculate the weight, its weight sec The default value is 0.8, which is used to reduce the relative electricity price of the allocatable time interval [0, sec] to relatively increase the allocation priority. The current SoC of the new energy vehicle is lower than the second threshold SoC High , and when the remaining allocatable time ΔT is less than the preset time threshold ΔT th The weight calculation is triggered when SoC High The default value is 80%;

[0181] The specific calculation of the remaining allocatable total time ΔT is as follows:

[0182] ΔT=(T user (n)-T user (0))-T require

[0183] In the formula, (T user (n)-T user (0)) represents the allocatable time; T require is the current required charging time; ΔT th is a preset time threshold, is a custom threshold, and is used to indicate the buffer threshold at which the user may return early;

[0184] First and sec are used to represent the first priority period and the second priority period respectively. The specific calculation is as follows:

[0185] first={T user (i)|T user (i)≤α×(T user (n)-T user (0))}

[0186] Where α is the time interval weight coefficient, which is 0.4 by default and is used to represent the first 40% of the allocable time interval; T user (i) is the time period that is in the first 40% of the allocatable time;

[0187] sec={T user (j)|T user (j)≤(α+β)×(T user (n)-T user (0))}

[0188] Where β is the time interval weight coefficient, which defaults to 0.2 and is used to represent the first 60% of the allocable time interval; T user (j) is the jth time period in the first 60% of the allocatable time;

[0189] Among them, the linear programming solver can select different solvers according to actual conditions, such as the existence of variables in the objective function: allocable time, adjustable output power, etc., such as Simplex, interior point method, linrog function, or solution in the PuLP library, etc. The present invention does not specifically limit the selection of its linear programming solver.

[0190] Accordingly, the present invention effectively constrains the objective function in the minimization process through charging demand constraints, charging time allocation constraints and charging window constraints, ensuring that charging follows user needs for reasonable time and power allocation.

[0191] The present invention fully considers the user's charging needs and possible early return time through the segmented electricity price matrix constraint. By adjusting the segmented weight of the electricity price matrix, the linear programming solver gives priority to the charging allocation in the lower electricity price period after weight adjustment, thereby realizing dynamic optimization of charging time and ensuring that the battery is charged to a healthy level in critical situations, effectively balancing the user's charging experience and the operator's cost. For details, please refer to Figure 3 , Figure 3 A schematic diagram showing a simple simulation example of charging time allocation corresponding to a parking start time of 00:32 to 13:47, wherein the example specifically shows the allocation of charging time in each time period and the simulated power consumption change.

[0192] Furthermore, when the power P of the charging pile Charging Adjustable, which is specifically expressed as:

[0193] P Charhing,i =P option (time i ) = {21kWh, 11kWh, 7kWh, 3.5kWh}

[0194] Based on this, the actual charging time allocated for each time period i is i Different powers can be selected, and the constraint function also includes a basic power constraint, a maximum charging time constraint and a power change constraint.

[0195] The basic power constraint is used to ensure the actual charging time time of the i-th period i Only a unique corresponding power can be selected to ensure the stability of the charging process, avoid frequent power switching in the same period, and ensure charging efficiency and equipment safety. The specific expression is as follows:

[0196]

[0197] Where P Charging,i The charging power selected for time period i; Option ij It is a binary variable. When its value is 1, it means that power P is selected in time period i. option,j , a is the total number of optional powers, which is 4 by default in this embodiment, and the optional powers are {21kWh, 11kWh, 7kWh, 3.5kWh};

[0198] The maximum charging time constraint is used to ensure that the selected charging power does not cause the charging time to exceed the total allocable time reserved by the user, which is specifically expressed as follows:

[0199]

[0200] Where, SoC i is the power of new energy vehicles in time period i, expressed as a percentage; time i is the actual charging time in period i; T user (n)-T user (0) represents the total time from the start of charging to the end of charging; γ is the dynamic charging time acceleration coefficient, which is used to reduce the allocable charging time so that the algorithm can increase the charging power as much as possible to improve the charging efficiency. The specific calculation is as follows:

[0201] γ=0.95+(100%-SoC i )×β

[0202] Where β is the adjustment factor, which is used to determine the amplitude of the dynamic change of γ, and the default value is 0.2.

[0203] The power change constraint is used to indicate that when the SoC is distributing the adjustable power, the SoC in each period is updated, thereby ensuring the real-time tracking of the charging process and the accuracy of power regulation, which is specifically expressed as follows:

[0204]

[0205] Where, SoC i+1 The power consumption of new energy vehicles in the next period, expressed as a percentage; Q full Indicates the rated total capacity of the battery.

[0206] The basic power constraint provides flexible power selection to ensure that an appropriate charging power is selected for each period, thereby improving the flexibility and charging efficiency of the system; the maximum charging time constraint introduces a dynamic charging time acceleration coefficient γ to adjust the charging speed according to the current power; and the power change constraint enables the algorithm to dynamically change the power during the allocation process, and the maximum charging time constraint selects the power reasonably according to the current power to avoid overcharging or undercharging. Accordingly, through the above constraints, the system can effectively balance power, power selection and cost when allocating charging time, thereby improving charging efficiency and controlling the overall charging cost.

[0207] The charging time allocation unit 5 is used to execute step S5: according to the time periods corresponding to the charging times of the several time periods, the charging piles are enabled to charge the new energy vehicles.

[0208] Specifically, it is determined whether the current time is within the several allocated charging time periods: if so, a signal is sent to enable the charging pile to charge the new energy vehicle; if not, a signal is sent to suspend charging of the charging pile and wait for the next time period.

[0209] Compared with the prior art, the present invention optimizes the charging behavior of each charging period by designing the total charging cost objective function and combining the dynamic allocation of electricity price and charging time, thereby maximizing the reduction of charging costs. At the same time, the present invention constructs a refined electricity price matrix, decomposes the hourly electricity price into minute levels, and prioritizes charging when the electricity price is low, significantly reducing the total charging cost;

[0210] In addition, the present invention optimizes the charging behavior through charging demand constraints, charging time allocation constraints and charging window constraints, while meeting the user's charging needs, making the most of the low electricity price period and avoiding charging during peak electricity prices. In particular, through the segmented electricity price matrix constraints, when the user's new energy vehicle has a low power level, priority is given to charging during low electricity price periods, thereby achieving the best cost and power allocation within a limited time, thereby ensuring that the battery power reaches a healthy level when the user returns early. Accordingly, the present invention flexibly allocates power and time through multiple constraints to adapt to the needs of dynamic power changes, thereby effectively balancing the charging cost and user charging experience.

[0211] Furthermore, the present invention introduces adjustable power into the total charging cost objective function and combines it with a penalty term, thereby optimizing the optional power when performing linear programming. At the same time, through basic power constraints, maximum charging time constraints, and power change constraints, the system can better balance power, charging power, and cost when allocating charging resources, significantly improving the user's charging experience.

[0212] Based on the same inventive concept, the present application also provides an electronic device, which may be a terminal device such as a server, a desktop computing device or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). The device includes one or more processors and a memory, wherein the processor is used to execute a program to implement the dynamic charging allocation method based on peak and valley electricity prices of an embodiment of the present invention; and the memory is used to store a computer program executable by the processor.

[0213] Based on the same inventive concept, the present application also provides a computer-readable storage medium, corresponding to the embodiment of the aforementioned dynamic charging allocation method based on peak-valley electricity prices, and the computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the dynamic charging allocation method based on peak-valley electricity prices recorded in any of the above embodiments.

[0214] The present application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-usable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0215] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, and the present invention is also intended to include these modifications and modifications.

Claims

1. A dynamic charging allocation method based on peak and valley electricity prices, characterized in that: The following steps are involved: S1: Obtain the battery information of the current new energy vehicle and calculate the required charging time; S2: Determine whether the required charging time is less than the current user-booked charging time: if not, directly enable the charging pile to charge the new energy vehicle; if yes, execute step S3; S3: Construct a total cost objective function of charging based on the required charging time and the current user scheduled charging time; S4: Minimizing the objective function of the total cost using a linear programming solver according to the constraint function to obtain charging time for several time periods; wherein the constraint function includes a charging demand constraint, a charging time allocation constraint, and a charging window constraint; S5: Activate the charging pile to charge the new energy vehicle according to the time period corresponding to the charging time of the several time periods.

2. The dynamic charging allocation method based on peak-valley electricity prices according to claim 1 is characterized in that: The specific expression of the total cost objective function of charging is as follows: In the formula, Cost min is the total charging cost; P charging is the charging power; i represents the charging time T scheduled by the user user The i-th discrete period in time i is the actual charging time in period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and the electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; The charging demand constraint is specifically expressed as follows: In the formula, E total It represents the amount of electricity required to fully charge the current battery. The specific calculation is: IN total =(100%)-SoC)×Q full In the formula, SoC represents the current battery power, expressed as a percentage; Q full Indicates the rated total capacity of the battery; The charging time allocation constraint is specifically expressed as follows: 0≤time i ≤T user (i) Where i represents the i-th discrete period of the current user's scheduled charging time; T user (i) represents the maximum available time of the i-th charging time period reserved by the user; The charging window constraint is specifically expressed as follows: T user (0)≤T user (i)≤T user (n) Where, T user (0) indicates the starting time period of the user's scheduled charging time; T user (n) indicates the end time period of the charging time reserved by the user.

3. The dynamic charging allocation method based on peak-valley electricity prices according to claim 2 is characterized in that: The constraint function also includes a segmented electricity price matrix constraint, which is specifically expressed as follows: In the formula, price i ×weight first Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix first After weight calculation, the current SoC of the new energy vehicle is lower than the first threshold SoC Low The weight calculation is triggered when price i ×weight sec Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix sec After weight calculation, the current SoC of the new energy vehicle is lower than the second threshold SoC High , and when the remaining allocatable time ΔT is less than the preset time threshold ΔT th The weight calculation is triggered when The remaining allocatable total time ΔT is specifically calculated as follows: ΔT=(T user (n)-T user (0))-T require In the formula, (T user (n)-T user (0)) represents the allocatable time; T require ΔT is the current required charging time; th is a preset time threshold; First and sec are used to represent the first priority period and the second priority period respectively. The specific calculation is as follows: first={T user (i)|T user (i)≤α×(T user (n)-T user (0))} Where α is the time interval weight coefficient; T user (i) is the time period in the first α×100% of the allocatable time; sec={T user (j)|T user (j)≤(α+β)×(T user (n)-T user (0))} Where β is the time interval weight coefficient; T user (j) is the time period that belongs to the j-th time interval (α+β)×100% before the allocatable time.

4. The dynamic charging allocation method based on peak-valley electricity prices according to claim 3 is characterized in that: The total cost objective function of charging also includes adjustable power and penalty terms, which are specifically expressed as follows: P charging,i =P option (time i ) In the formula, Cost min is the total charging cost; P charging,i represents the charging power selected in the i-th period; P option (·) is the charging power selected in the corresponding allocated time period; i represents the charging time T scheduled by the user user The i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and its electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; (price i -avg(price)) 2 is the penalty term; avg(price) is the average electricity price of all time periods; λ is the adjustment coefficient; The constraint function also includes basic power constraint, maximum charging time constraint and power change constraint; The basic power constraint is specifically expressed as follows: Where P Charging,i The charging power selected for time period i; Option ij It is a binary variable. When its value is 1, it means that power P is selected in time period i. option,j , a is the total number of optional powers; The maximum charging time constraint is specifically expressed as follows: Where, SoC i is the power consumption of new energy vehicles in time period i; time i is the actual charging time in period i; T user (n)-T user (0) represents the total time from the start of charging to the end of charging; γ is the dynamic charging time acceleration coefficient, which is calculated as follows: γ=0.95+(100%-SoC i )×β In the formula, β is the adjustment factor, which is used to determine the amplitude of the dynamic change of γ; The specific expression of the power change constraint is as follows: Where, SoC i+1 is the power of new energy vehicles in the next period; Q full Indicates the rated total capacity of the battery.

5. The dynamic charging allocation method based on peak-valley electricity prices according to any one of claims 2 to 4, characterized in that: Before executing step S3, the following steps are also included: Determine the status of the remaining battery power in the current battery information: If the remaining battery power is less than the rated capacity of the battery, the SoC Emergency , the charging pile is directly used to charge the new energy vehicle until the power reaches the SoC of the battery's rated capacity Emergency , execute step S3; If the remaining battery state is greater than the battery rated capacity SoC Emergency , then directly execute step S3; Among them, SoC Emergency Indicates emergency power.

6. A dynamic charging distribution device based on peak and valley electricity prices, characterized in that: It includes a required charging time acquisition unit, a charging priority judgment unit, an objective function construction unit, a linear programming solution unit and a charging time allocation unit; The required charging time acquisition unit is used to acquire the battery information of the current new energy vehicle and calculate the required charging time; The charging priority determination unit is used to determine whether the required charging time is less than the charging time currently reserved by the user: if not, directly enable the charging pile to charge the new energy vehicle; if so, call the objective function construction unit; The objective function construction unit is used to construct a total cost objective function of charging according to the required charging time and the charging time currently reserved by the user; The linear programming solving unit is used to minimize the objective function of the total cost using a linear programming solver according to the constraint function to obtain the charging time of several time periods; The charging time allocation unit is used to enable charging piles to charge the new energy vehicle according to the time periods corresponding to the charging times of the several time periods.

7. The dynamic charging distribution device based on peak and valley electricity prices according to claim 6 is characterized in that: The specific expression of the total cost objective function of charging is as follows: In the formula, Cost min is the total charging cost; P charging is the charging power; i represents the charging time T scheduled by the user user The i-th discrete period in time i is the actual charging time in period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and the electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; The charging demand constraint is specifically expressed as follows: In the formula, E total It represents the amount of electricity required to fully charge the current battery. The specific calculation is: IN total =(100%)-SoC)×Q full In the formula, SoC represents the current battery power, expressed as a percentage; Q full Indicates the rated total capacity of the battery; The charging time allocation constraint is specifically expressed as follows: 0≤time i ≤T user (i) Where i represents the i-th discrete period of the current user's scheduled charging time; T user (i) represents the maximum available time of the i-th charging time period reserved by the user; The charging window constraint is specifically expressed as follows: T user (0)≤T user (i)≤T user (n) Where, T user (0) indicates the starting time period of the user's scheduled charging time; T user (n) indicates the end time period of the charging time reserved by the user.

8. The dynamic charging distribution device based on peak-valley electricity prices according to claim 7 is characterized in that: The constraint function also includes a segmented electricity price matrix constraint; the segmented electricity price matrix constraint is specifically expressed as follows: In the formula, price i ×weight first Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix first After weight calculation, the current SoC of the new energy vehicle is lower than the first threshold SoC Low The weight calculation is triggered when price i ×weight sec Represents the electricity price and weight coefficient weight of the i-th period of the current electricity price matrix sec After weight calculation, the current SoC of the new energy vehicle is lower than the second threshold SoC High , and when the remaining allocatable time ΔT is less than the preset time threshold ΔT th The weight calculation is triggered when The remaining allocatable total time ΔT is specifically calculated as follows: ΔT=(T user (n)-T user (0))-T require In the formula, (T user (n)-T user (0)) represents the allocatable time; T require ΔT is the current required charging time; th is a preset time threshold; First and sec are used to represent the first priority period and the second priority period respectively. The specific calculation is as follows: first={T user (i)|T user (i)≤α×(T user (n)-T user (0))} Where α is the time interval weight coefficient; T user (i) is the jth time period in the first α×100% of the allocatable time; sec={T user (j)|T user (j)≤(α+β)×(T user (n)-T user (0))} Where β is the time interval weight coefficient; T user (j) is the time period that belongs to the j-th time interval (α+β)×100% before the allocatable time.

9. The dynamic charging distribution device based on peak and valley electricity prices according to claim 8, characterized in that: The total cost objective function of charging also includes adjustable power and penalty terms, which are specifically expressed as follows: In the formula, Cost min is the total charging cost; P charging,i represents the charging power selected in the i-th period; P option (·) is the charging power selected in the corresponding allocated time period; i represents the charging time T scheduled by the user user The i-th discrete period in time, each of which has a default duration of 1 hour; i is the actual charging time in period i; price i is the electricity price of time period i, obtained through the electricity price matrix of the peak and valley electricity price table, and its electricity price matrix is ​​used to represent the electricity price corresponding to the i-th time period in 24 hours; (price i -avg(price)) 2 is the penalty term; avg(price) is the average electricity price of all time periods; λ is the adjustment coefficient; The constraint function also includes basic power constraint, maximum charging time constraint and power change constraint; The basic power constraint is specifically expressed as follows: Where P Charging,i The charging power selected for time period i; Option ij It is a binary variable. When its value is 1, it means that power P is selected in time period i. option,j , a is the total number of optional powers; The maximum charging time constraint is specifically expressed as follows: Where, SoC i is the power consumption of new energy vehicles in time period i; time i is the actual charging time in period i; T user (n)-T user (0) represents the total time from the start of charging to the end of charging; γ is the dynamic charging time acceleration coefficient, which is calculated as follows: γ=0.95+(100%-SoC i )×β In the formula, β is the adjustment factor, which is used to determine the amplitude of the dynamic change of γ; The specific expression of the power change constraint is as follows: Where, SoC i+1 is the power of new energy vehicles in the next period; Q full Indicates the rated total capacity of the battery.

10. A charging pile with dynamic charging allocation, characterized in that: Including charging plug, charging gun and dynamic charging distribution device of charging pile; The electric plug of the charging pile is connected to the power grid, and is used to convert the current of the power grid into a current form suitable for the battery of the new energy vehicle, and transmit it to the charging gun; The charging gun is connected to the charging plug of the charging pile and the charging socket of the new energy vehicle, and is provided with a communication protocol module, which is used to interact with the battery management system of the new energy vehicle through the communication protocol module, obtain the battery information of the new energy vehicle, and transmit the battery information of the new energy vehicle to the dynamic charging distribution device; at the same time, according to the signal sent by the dynamic charging distribution device, the start and stop of the current are controlled, and the electric energy is output to the battery of the new energy vehicle at a fixed power to perform the charging operation; The dynamic charging allocation device is used to obtain the battery information of the new energy vehicle and the charging time reserved by the user, and to plan the charging time to obtain the charging time for several time periods, and to send corresponding start signals and stop signals to the charging gun according to the charging time corresponding to the several time periods; Wherein, the dynamic charging allocation device is a dynamic charging allocation device based on peak-valley electricity prices as described in any one of claims 6-9 above.

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