Distribution regulation and control method and system for high-power charging station in transformer area

Abstract

The invention relates to the technical field of power supply and power distribution scheduling, in particular to a power distribution regulation and control method and system for a transformer area high-power charging station. The method is used for managing power distribution of a high-power charging station in a transformer area, a power distribution load model is established by collecting power utilization data of the charging station, power extreme value data is analyzed, and the average frequency is calculated, so that balanced power utilization load evaluation is obtained, and a first power distribution parameter for performing power distribution compensation on a power utilization extreme value is obtained. And predicting the electricity utilization probability of a specific user group in the future through the prediction model, determining a second power distribution parameter for performing redundant power distribution according to the electricity utilization probability, and finally calculating the extra power distribution quantity and the redundant power distribution quantity. The power distribution strategy can be dynamically adjusted according to the power peak value and the predicted power utilization probability, it is ensured that the charging station can stably supply power under the high load and the corresponding prediction probability, and the power distribution efficiency and the power supply reliability are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power supply and distribution scheduling, and specifically to a method and system for power distribution control of high-power charging stations in a distribution area. Background Art

[0002] With the widespread popularity of new energy vehicles, electricity demand has shown an increasingly obvious dynamic change trend in various power supply areas. This change not only challenges the stability of the power supply system, but also puts higher requirements on the rational allocation of power resources. In traditional power supply strategies, power distribution is often based on a static power distribution mode, that is, a fixed amount of power is distributed to each charging station, while ignoring the power consumption data and power consumption mode of the charging station in the past period of time. This static power distribution method cannot adapt to the fluctuations in power demand of charging stations, which may lead to insufficient power supply during certain periods, affecting the normal operation of charging stations and users' charging experience. How to dynamically adjust the power distribution according to the actual power consumption, power consumption mode and other related parameters of the charging station to improve the adequacy and reliability of power supply has become a technical problem that needs to be solved urgently in the field of power supply. The dynamic power distribution strategy needs to be able to monitor and analyze the power consumption data of the charging station in real time, predict future power consumption trends, and adjust the power distribution plan accordingly to ensure that the power demand of the charging station can be met at any time, while optimizing the utilization efficiency of power resources.

[0003] In summary, how to dynamically adjust the power distribution according to parameters such as power consumption to improve power supply adequacy is a technical problem that needs to be solved in this field. Summary of the invention

[0004] In view of this, the present application provides a method and system for controlling power distribution in a high-power charging station in an area, which can dynamically adjust the power distribution according to parameters such as power consumption to improve power supply adequacy.

[0005] In a first aspect, the present application provides a method for controlling power distribution of high-power charging stations in an area, comprising: collecting power consumption data of multiple charging stations in an area; establishing a power distribution load model of the charging station based on the power consumption data, wherein the power distribution load model includes charging power data and power floating data of each charging pile; retrieving power extreme value data of each charging pile within a preset time period from the power distribution load model; the power extreme value data includes maximum peak power and maximum peak power retention time; traversing the power extreme value data within a first historical preset time period, and calculating the extreme value frequency of each charging pile in the first historical preset time period; and calculating the extreme value frequency of each charging pile according to all the power extreme value data within a first historical preset time period. The extreme frequency of the charging pile obtains the corresponding mean frequency; the corresponding first power distribution parameter is obtained according to the mean frequency; a charging probability prediction model for the designated user group of the charging station is established to obtain the power supply probability in a future preset time period; the corresponding second power distribution parameter is obtained according to the power supply probability; based on the first power distribution parameter and the benchmark power distribution corresponding to the charging station, the additional power distribution of the power grid to which the charging station belongs to the charging station in the future preset time period is obtained; and based on the second power distribution parameter and the benchmark power distribution corresponding to the charging station, the redundant power distribution of the power grid to which the charging station belongs to the charging station in the future preset time period is obtained.

[0006] In combination with the first aspect, in a possible implementation method, traversing the power extreme value data within the first historical preset time period and calculating the extreme value frequency of each charging pile in the first historical preset time period includes: obtaining the number of times the charging pile reaches the maximum peak power as the first frequency; obtaining the maximum peak power maintenance time of the charging pile; if the maximum peak power maintenance time is greater than or equal to the frequency calculation period, dividing the maximum peak power maintenance time by the frequency calculation period to obtain the second frequency; and obtaining the extreme value frequency according to the first frequency and the second frequency.

[0007] In combination with the first aspect, in a possible implementation method, obtaining the corresponding first power distribution parameter according to the mean frequency includes: retrieving the maximum frequency and the minimum frequency from the extreme frequencies of all the charging piles; obtaining a frequency gradient according to the maximum frequency and the minimum frequency; dividing the first power distribution parameter into percentage values; matching the frequency gradient and the percentage value with each other; and according to the corresponding frequency of the mean frequency, calling the corresponding percentage value to obtain the first power distribution parameter.

[0008] In combination with the first aspect, in a possible implementation method, the reference range of the percentage value of the first power distribution parameter is 0%~100%; wherein, the power distribution control method of the high-power charging station in the substation area also includes: obtaining the current power distribution peak and valley time period; obtaining the power distribution correction coefficient corresponding to the power distribution peak and valley time period; the power distribution correction coefficient corresponding to the power supply peak period of the power distribution peak and valley time period is less than the power distribution correction coefficient corresponding to the power supply valley period of the power distribution peak and valley time period; and correcting the reference range according to the distribution correction coefficient to obtain the range of the percentage value corresponding to the first power distribution parameter.

[0009] In combination with the first aspect, in a possible implementation method, the establishment of a charging probability prediction model for a designated user group of the charging station to obtain the power supply probability in a future preset time period includes: grading the registered users of the charging station; obtaining the number of charging times and the cumulative charging duration of all the registered users above a preset level in a second historical preset time period; weighting the number of charging times with a first coefficient and weighting the cumulative charging duration with a second coefficient; and predicting the power supply probability based on the number of charging times, the first coefficient, the cumulative charging duration and the second coefficient.

[0010] In combination with the first aspect, in a possible implementation method, predicting the power supply probability based on the number of charging times, the first coefficient, the cumulative charging time and the second coefficient includes: multiplying the number of days corresponding to the second historical preset time period by the number of specific users of the registered users above the preset level to obtain a total reference number of charging times; obtaining a first probability corresponding to the number of charging times based on the ratio of the number of charging times to the total reference number of charging times; and obtaining a first weighted probability corresponding to the number of charging times based on the product of the first probability and the first coefficient.

[0011] In combination with the first aspect, in a possible implementation method, predicting the power supply probability based on the number of charging times, the first coefficient, the cumulative charging time and the second coefficient also includes: multiplying the number of specific users by the standard charging time and the number of days corresponding to the second historical preset time period to obtain a total reference charging time; obtaining a second probability corresponding to the cumulative charging time based on the ratio of the cumulative charging time to the total reference charging time; and obtaining a second weighted probability corresponding to the cumulative charging time based on the product of the second probability and the second coefficient.

[0012] In combination with the first aspect, in a possible implementation method, predicting the power supply probability based on the number of charging times, the first coefficient, the cumulative charging time and the second coefficient also includes: obtaining the power supply probability based on the first weighted probability and the second weighted probability.

[0013] In combination with the first aspect, in a possible implementation, it further includes: obtaining a corresponding first reference power distribution according to the number of the charging piles of the charging station; obtaining a corresponding second reference power distribution according to the power supply priority of the charging station; obtaining a corresponding third reference power distribution according to the power distribution priority specified by the power system for the charging station; obtaining the benchmark power distribution according to the first reference power distribution, the second reference power distribution and the third reference power distribution. .

[0014] In the second aspect, the present application also provides a power distribution control system for high-power charging stations in a substation area, including: a data acquisition module, configured to: collect power consumption data of multiple charging stations in the substation area; establish a distribution load model of the charging station based on the power consumption data, and the distribution load model includes charging power data and power floating data of each charging pile; retrieve the power extreme value data of each charging pile within a preset time period from the distribution load model; the power extreme value data includes the maximum peak power and the duration of the maximum peak power; a frequency calculation module, which is in communication with the data acquisition module, and the frequency calculation module is configured to: traverse the power extreme value data within a first historical preset time period, and calculate the extreme value of each charging pile in the first historical preset time period. frequency; obtaining a corresponding mean frequency according to the extreme frequencies of all the charging piles; a power distribution module, which is in communication with the frequency calculation module, and the power distribution module is configured as follows: obtaining a corresponding first power distribution parameter according to the mean frequency; establishing a charging probability prediction model for a designated user group of the charging station to obtain a power supply probability in a future preset time period; obtaining a corresponding second power distribution parameter according to the power supply probability; obtaining an additional power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period based on the first power distribution parameter and the benchmark power distribution corresponding to the charging station; and obtaining a redundant power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period based on the second power distribution parameter and the benchmark power distribution corresponding to the charging station.

[0015] This application is used to manage the power distribution of high-power charging stations in the substation area. By collecting the power consumption data of the charging stations, a distribution load model is established, the power extreme value data is analyzed and the mean frequency is calculated, so as to obtain a balanced power load assessment and obtain the first distribution parameter for power distribution compensation for the extreme power consumption value. Furthermore, a prediction model is used to predict the power consumption probability of a specific user group in the future, and the second distribution parameter for redundant power distribution based on the power consumption probability is determined accordingly, and finally the additional distribution amount and redundant distribution amount are calculated. This application can dynamically adjust the distribution strategy according to the power peak and predicted power consumption probability, ensuring that the charging station can provide stable power supply under high load and corresponding predicted probability, and improving the distribution efficiency and power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure shows a schematic diagram of the method steps of a method for controlling power distribution in a high-power charging station in an area provided by an embodiment of the present application.

[0017] Figure 2 Shown is the intention of the method steps for calculating the frequency of extreme values.

[0018] Figure 3 Shown is a schematic diagram of the steps for calculating the first power distribution parameter.

[0019] Figure 4 The figure shows a schematic diagram of the steps for correcting power distribution according to power distribution peaks and valleys.

[0020] Figure 5 Shown is a schematic diagram of the method steps for predicting power supply probability.

[0021] Figure 6 Shown is a schematic diagram of the method steps for predicting the probability corresponding to the number of charging times.

[0022] Figure 7 Shown is a schematic diagram of the method steps for predicting the corresponding probability of the cumulative charging time.

[0023] Figure 8 The figure shows a schematic diagram of the steps of a method for obtaining a predicted probability based on the probability corresponding to the number of charging times and the probability corresponding to the cumulative charging time.

[0024] Fig. 9 Shown is a schematic diagram of the method steps for further additional power distribution.

[0025] Fig.10 The figure shows a schematic diagram of the system structure of a power distribution control system for a high-power charging station in a substation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] An exemplary method for controlling power distribution of a high-power charging station in a substation is as follows: Figure 1 The figure shows a schematic diagram of the method steps of a method for controlling power distribution of a high-power charging station in a substation provided by an embodiment of the present application. The present application provides a method for controlling power distribution of a high-power charging station in a substation. In one embodiment, as Figure 1 As shown, the power distribution control method of the high-power charging station in the area includes: Step 110: Collect electricity usage data of multiple charging stations in the area.

[0028] In this step, for a certain distribution transformer area in a city, there are multiple charging stations, which are used to provide charging power for various new energy vehicles. This step collects the power consumption data of each charging station, which includes multiple parameters of the charging station, including the peak power and charging time of each charging pile.

[0029] Step 120: Establish a power distribution load model of the charging station based on the power consumption data. The power distribution load model includes charging power data and power fluctuation data of each charging pile.

[0030] In this step, the power consumption data of each charging pile in the charging station is summarized and sorted, and a distribution load model is established, which can intuitively and clearly check the power consumption status of each charging pile.

[0031] Step 130: retrieve the power extreme value data of each charging pile within a preset time period from the power distribution load model.

[0032] In this step, the power extreme value data includes the maximum peak power and the duration of the maximum peak power. The distribution load model includes the specific power consumption data of each charging pile, from which the power extreme value data can be extracted. The preset time period can be set to 1 quarter, or half a year, or 1 year, or 2 years.

[0033] Step 140: traverse the power extreme value data in the first historical preset time period, and calculate the extreme value frequency of each charging pile in the first historical preset time period.

[0034] In this step, a certain period of time (for example, 1 week, 1 month, or 1 quarter) is traced back as the first historical preset time period. During the first historical preset time period, the number of times each charging pile reaches the peak power, that is, the extreme frequency, is calculated. Specifically, each time the charging pile reaches the peak power, an extreme frequency is recorded; when the charging pile maintains a peak power greater than the frequency calculation period, the maintenance time is divided by the frequency calculation period to obtain a frequency, and all recorded frequencies are added to obtain the total extreme frequency. The extreme frequency can reflect the high-load power consumption of the charging pile. By distributing power according to the extreme frequency, a more reference-worthy distribution amount can be obtained.

[0035] Step 150: Obtain the corresponding mean frequency according to the extreme frequency of all charging piles.

[0036] In this step, all extreme frequency values ​​are divided by the number of charging piles to obtain the mean frequency, which can reflect the power load status of the charging piles in the charging station in a balanced and objective manner.

[0037] Step 160: Obtain a corresponding first power distribution parameter according to the mean frequency.

[0038] Different mean frequencies are pre-set to correspond to different first power distribution parameters, and the two can be set to be in a positive proportional relationship or a nonlinear proportional relationship. In this step, the corresponding relationship is called to obtain the first power distribution parameter. Specifically, the first power distribution parameter is less than or equal to 1 and is proportional to the extreme value frequency. The greater the extreme value frequency, the greater the first power distribution parameter.

[0039] Step 170: Establish a charging probability prediction model for a designated user group of the charging station to obtain the power supply probability in a future preset time period.

[0040] In this step, the designated user group can be set as registered users who have reached a certain level in the charging station. A common probability prediction model can be used to predict the probability of power consumption of the designated user group in the future preset time period, that is, to obtain the power supply probability of the charging station. The probability prediction model can use a linear regression model, a logistic regression model, a neural network model, etc. In this application, the future preset time period is generally set to 3 days or 7 days, so that the power distribution compensation for the charging station can be more dynamically performed.

[0041] Step 180: Obtain a corresponding second power distribution parameter according to the power supply probability.

[0042] Different power supply probabilities are pre-set to correspond to different second power distribution parameters, and the two can be set to be in a positive proportional relationship or a nonlinear proportional relationship. In this step, the corresponding relationship is called to obtain the second power distribution parameter. Specifically, the second power distribution parameter is less than or equal to 1 and is proportional to the power supply probability. The greater the power supply probability, the greater the second power distribution parameter.

[0043] Step 190: Based on the first power distribution parameter and the reference power distribution corresponding to the charging station, obtain the additional power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period.

[0044] The benchmark power distribution is pre-set, and the benchmark power distribution of each charging station is different. The benchmark power distribution is positively correlated with the number of charging piles in the charging station, the historical frequency of use of charging piles (referring to the proportion of charging piles in the charging station that are put into use every day), and the historical average daily power consumption of charging piles (the average daily power consumption of a single charging pile, usually obtained by dividing the daily power consumption of the charging station by the number of charging piles). In the evaluation of the historical frequency of use of charging piles and the historical average daily power consumption of charging piles, the evaluation can be carried out according to the historical time period equal to the length of the future preset time period. For example, if the future preset time period is 3 days, the benchmark power distribution is evaluated based on the historical frequency of use of charging piles and the historical average daily power consumption of charging piles in the past 3 days. The higher the historical frequency of use of charging piles in the past 3 days and the greater the historical average daily power consumption of charging piles, the higher the benchmark power distribution. For example: In a charging station, the average daily power consumption of charging piles in the past three days is 300 kWh, the average daily proportion of charging piles in use in the charging station is 80%, and the number of charging piles is 100. The benchmark power distribution of the charging station is 300*0.7*100=21000. Among them, in the evaluation of the historical usage frequency of charging piles, the evaluation can be carried out according to the usage time. For example, if there are 100 charging piles in the charging station, the full usage time for 3 days is 3*24*100=7200h. The actual usage time collected in the central control system of the charging station for the past three days is 5760h, so the historical usage frequency of the charging piles is 80%. In this step, the benchmark power distribution is not limited to the above-mentioned evaluation method, and can also be obtained by expert experience or other evaluation methods. The first power distribution parameter reflects the frequency of the charging pile reaching peak power. The additional power distribution obtained according to the first power distribution parameter can effectively compensate the charging station for power distribution based on the extreme frequency. The higher the historical extreme frequency, the higher the probability that the charging pile in the charging station will reach peak power supply in the future. The more additional power distribution is obtained, which can effectively compensate for the historical peak situation, so that the charging station can fully cope with future power supply. For example, if the first power distribution parameter is 0.2 and the base power distribution is 21,000 degrees, the additional power distribution is 4,200 degrees.

[0045] Step 200: Based on the second power distribution parameter and the reference power distribution corresponding to the charging station, obtain the redundant power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period.

[0046] In this step, the redundant power distribution amount obtained according to the second power distribution parameter can prepare redundant power distribution for the charging station based on the predicted charging probability to further ensure future power supply. The redundant power distribution amount can be stored in an energy storage device near the charging station for easy access at any time.

[0047] This embodiment is used to manage the power distribution of high-power charging stations in the substation area. By collecting the power consumption data of the charging stations, a distribution load model is established, the power extreme value data is analyzed and the mean frequency is calculated, so as to obtain a balanced power load assessment and obtain the first distribution parameter for power distribution compensation for the extreme power consumption value. Furthermore, the prediction model is used to predict the power consumption probability of a specific user group in the future, and the second distribution parameter for redundant power distribution based on the power consumption probability is determined accordingly, and finally the additional distribution amount and the redundant distribution amount are calculated. This embodiment can dynamically adjust the distribution strategy according to the power peak and the predicted power consumption probability, ensuring that the charging station can provide stable power supply under high load and corresponding predicted probability, and improving the distribution efficiency and power supply reliability.

[0048] Figure 2 Schematic diagram of the method steps for calculating the extreme value frequency. Figure 2 As shown, step 140 includes: Step 141: Obtain the number of times the charging pile reaches the maximum peak power as the first frequency.

[0049] Step 142: Obtain the maximum peak power holding time of the charging pile.

[0050] Step 143: If the maximum peak power holding time is greater than or equal to the frequency calculation period, the maximum peak power holding time is divided by the frequency calculation period to obtain a second frequency.

[0051] Step 144: Obtain an extreme value frequency according to the first frequency and the second frequency.

[0052] In this embodiment, for each charging pile, each time the charging pile reaches the maximum peak power, the frequency is recorded once; when the charging pile maintains the maximum peak power for a long time, the maximum peak power maintenance time is divided by the frequency calculation period to obtain the second frequency. For example: the frequency calculation period is pre-set to 30s, the maximum peak power maintenance time is 3min, and the second frequency is 6 times. Add the first frequency and the second frequency to obtain the extreme frequency of the corresponding charging pile. In the subsequent step 150, the extreme frequencies of all charging piles are added and divided by the number of charging piles to obtain the mean frequency. The extreme frequency calculated in this embodiment can comprehensively take into account the working conditions of reaching the maximum peak power each time, as well as the working conditions of maintaining the maximum peak power for a long time.

[0053] Figure 3 FIG. 1 is a schematic diagram showing the steps of calculating the first power distribution parameter. In one embodiment, Figure 3 As shown, step 160 includes: Step 161: retrieve the maximum frequency and the minimum frequency from the extreme frequencies of all charging piles.

[0054] Step 162: Obtain a frequency gradient according to the maximum frequency and the minimum frequency.

[0055] Step 163: Divide the first power distribution parameter into percentage values.

[0056] Step 164: Match the frequency gradient and the percentage value with each other.

[0057] Step 165: According to the corresponding frequency of the mean frequency, call the corresponding percentage value to obtain the first power distribution parameter.

[0058] In this embodiment, for all charging piles in the charging station, the maximum frequency and the minimum frequency are taken, and then the frequency gradient corresponding to the difference is calculated based on the difference between the maximum frequency and the minimum frequency. For example, the maximum frequency in the charging station is 560 times, the minimum frequency is 280 times, and the difference between the two is 280. Each integer number of the value 280 is used as a frequency gradient, that is, 280 frequency gradients are obtained. Then the numerical range of the first power distribution parameter is divided into percentage values. For example, if the range of the first power distribution parameter is set to 0.1~0.8, the percentage value corresponding to the first power distribution parameter is 10%~80%. Then the 280 frequency gradients and 10%~80% are matched and corresponded to each other, that is, 10%~80% is evenly divided into 280 percentage gradients, and each percentage gradient is 0.25%. Finally, the correspondence between the frequency gradient and the percentage value is obtained. In step 165, the corresponding percentage value can be obtained according to the calculated mean frequency, that is, the corresponding first power distribution parameter is obtained. For example, if the calculated mean frequency is 435, the percentage value of the corresponding first power distribution parameter is 48.75%, that is, the first power distribution parameter is recorded as 0.4875. This embodiment uses the maximum frequency and the minimum frequency as references to obtain the correspondence between the frequency and the first power distribution parameter. The correspondence is highly correlated with the dynamic range of the extreme frequency, and can objectively evaluate and calibrate the specific value of the first power distribution parameter according to the frequency range in the charging station.

[0059] In some embodiments, if the mean frequency is less than the minimum frequency, the first power distribution parameter is obtained according to the percentage corresponding to the minimum frequency. If the mean frequency is greater than the maximum frequency, the first power distribution parameter is obtained according to the percentage corresponding to the maximum frequency.

[0060] Figure 4 The figure shows a schematic diagram of the steps of correcting the power distribution according to the power distribution peak and valley. In one embodiment, the reference range of the percentage value of the first power distribution parameter is 0%~100%. For example, the maximum frequency in the charging station is 560 times, the minimum frequency is 280 times, and the difference between the two is 280. Each integer number of the value 280 is used as a frequency gradient, that is, 280 frequency gradients are obtained. 0%~100% is divided into 280 percentage gradients, each percentage gradient is 0.357%. In this embodiment, Figure 4As shown, the power distribution control method for the high-power charging station in the substation area also includes: Step 1601: Obtain the current power distribution peak and valley time periods.

[0061] Step 1602: Obtain the power distribution correction coefficient corresponding to the power distribution peak and valley periods.

[0062] In this step, the power distribution correction coefficient corresponding to the power supply peak period in the power distribution peak-valley period is smaller than the power distribution correction coefficient corresponding to the power supply valley period in the power distribution peak-valley period.

[0063] Step 1603: Correct the reference range according to the power distribution correction coefficient to obtain a range of percentage values ​​corresponding to the first power distribution parameter.

[0064] In this embodiment, for example, the power distribution correction coefficient of the power supply peak period is 0.3, and the power distribution correction coefficient of the power supply valley period is 0.7. When the power supply peak period, the range of the percentage value of the first power distribution parameter obtained by correction is 0%~30%, and the range of the percentage value of the first power distribution parameter obtained during the power supply peak period is smaller than that during the power supply valley period, which can be equivalent to reducing the first power distribution parameter finally obtained according to the mean frequency relative to the power supply valley period. Since the power supply during the power supply peak period is relatively abundant and stable, the first power distribution parameter during the power supply peak period can be appropriately adjusted to reduce, so as to obtain a more appropriate additional power distribution, and the power distribution electric energy will not be wasted too much. On the contrary, when the power supply valley period, the range of the percentage value of the first power distribution parameter obtained by correction is 0%~70%, and the range of the percentage value of the first power distribution parameter obtained during the power supply valley period is larger than that during the power supply peak period, which can be equivalent to increasing the first power distribution parameter finally obtained according to the mean frequency relative to the power supply peak period. Since the power supply during the power supply peak period is relatively scarce and unstable, the first power distribution parameter during the power supply peak period can be appropriately adjusted to increase, so as to obtain a more abundant additional power distribution. In some areas, the number of distribution peak and valley periods is 2, 3 or 4, correspondingly there are 2, 3 or 4 distribution correction coefficients.

[0065] Figure 5 The figure shows a schematic diagram of the method steps for predicting power supply probability. Figure 5 As shown, step 170 includes: Step 171: Classify registered users of the charging station.

[0066] Step 172: Obtain the number of charging times and cumulative charging durations of all registered users above the preset level in the second historical preset time period.

[0067] Step 173: weight the first coefficient on the number of charging times, and weight the second coefficient on the accumulated charging time.

[0068] Step 174: predict the power supply probability according to the number of charging times, the first coefficient, the accumulated charging time, and the second coefficient.

[0069] In this embodiment, the predicted probability is refined, and all registered users in the charging station are screened, and users above a certain level are selected for power supply probability. Registered users above a certain level can more objectively and truly reflect the power consumption of the charging station. The number of times these registered users charge at this charging station can objectively reflect the popularity of the charging station, and can more accurately predict the future power consumption probability. For example, registered users are divided into bronze, silver, gold, platinum, and diamond levels, and the data of registered users above the gold level are taken as a reference. The second historical preset time period is set as needed, which can generally be set to 3 days, 5 days, or 7 days. The larger the second historical preset time period, the larger the amount of reference data obtained, and the power supply probability finally predicted is more valuable for reference.

[0070] Considering that registered users do not need to charge every day, the first coefficient and the second coefficient are weighted to reduce the probability ratio corresponding to the number of charging times and the cumulative charging time. The longer the average endurance time of registered users' vehicles above the preset level, the lower the charging frequency, and the smaller the corresponding first coefficient and second coefficient.

[0071] The first coefficient and the second coefficient are set in advance, and the ratio of the first coefficient and the second coefficient is set as needed. Some charging stations pay more attention to the impact of the number of charging times on the power supply probability, so the first coefficient is appropriately increased; some charging stations pay more attention to the impact of the cumulative charging time on the power supply probability, so the second coefficient is appropriately increased. The impact of the number of charging times or the cumulative charging time on the power supply probability can be adjusted by the first coefficient and the second coefficient. Finally, the power supply probability is calculated, and a corresponding functional relationship can be established between the number of charging times, the first coefficient, the cumulative charging time, the second coefficient and the power supply probability.

[0072] Figure 6 The figure shows a schematic diagram of the method steps for predicting the probability of the number of charging times. Figure 6 As shown, step 174 includes: Step 1741: multiply the number of days corresponding to the second historical preset time period by the number of specific users of registered users above the preset level to obtain a total reference charging number of times.

[0073] Step 1742: Obtain a first probability corresponding to the number of charging times according to the ratio of the number of charging times to the total reference number of charging times.

[0074] Step 1743: Obtain a first weighted probability corresponding to the number of charging times according to the product of the first probability and the first coefficient.

[0075] In this embodiment, for example, the number of specific users of registered users above the preset level is 300, the second historical preset time period is 7 days, and the reference charging number per day is 1, then the total reference charging number is 2100. If all registered users above the preset level are collected, and the charging number in the second historical preset time period in the past 7 days is 1500, then the first probability is 1500 / 2100=71.43%. For example, if the first coefficient is set to 0.7, the first weighted probability is 0.7*71.43%=50%. In this embodiment, the first probability corresponding to the charging number is evaluated with reference to the total reference charging number, and is associated with the charging number data of registered users above the preset level, and the data reliability of the obtained first weighted probability is high.

[0076] Figure 7 The figure shows a schematic diagram of the method steps for predicting the probability corresponding to the cumulative charging time. Figure 7 As shown, step 174 also includes: Step 1744: multiply the number of specific users by the standard charging duration and the number of days corresponding to the second historical preset time period to obtain a total reference charging duration.

[0077] Step 1745: Obtain a second probability corresponding to the cumulative charging time according to the ratio of the cumulative charging time to the total reference charging time.

[0078] Step 1746: Obtain a second weighted probability corresponding to the accumulated charging time according to the product of the second probability and the second coefficient.

[0079] In this embodiment, the standard charging time of new energy vehicles is set to 1.5h. For example, the number of specific users of registered users above the preset level is 300. The total reference charging time of all registered users above the preset level in the second historical preset time period in the past 7 days is 1.5*300*7=3150h. If the accumulated charging time collected is 1400h, the second probability is 1400 / 3150=44.44%. For example, if the second coefficient is set to 0.6, the second weighted probability is 0.6*44.44%=26.67%. In this embodiment, the second probability corresponding to the accumulated charging time is evaluated with reference to the total reference charging time, and is associated with the charging time data of registered users above the preset level, so that the data reliability of the obtained second weighted probability is high.

[0080] Figure 8 The figure shows a schematic diagram of the method steps for obtaining the predicted probability based on the probability corresponding to the number of charging times and the probability corresponding to the cumulative charging time. Figure 8 As shown, step 174 also includes: Step 1747: Obtain the power supply probability according to the first weight probability and the second weight probability.

[0081] In this step, the first weighted probability and the second weighted probability obtained previously are added together to obtain the final predicted power supply probability.

[0082] Fig. 9 Schematic diagram of the method steps for further additional power distribution is shown. In one embodiment, Fig. 9 As shown, the power distribution control method of the high-power charging station in the substation area also includes: Step 210: Obtain a corresponding first reference power distribution amount according to the number of charging piles of the charging station.

[0083] In this step, power is distributed according to the number of charging piles. For example, if the average daily power consumption of each charging pile is 300 kWh and there are 100 charging piles in the charging station, the first reference power distribution is 30,000 kWh.

[0084] Step 220: Obtain a corresponding second reference power distribution amount according to the power supply priority of the charging station.

[0085] In this step, the nature of the charging station itself determines the power supply priority. For example, the power supply priorities of civil charging stations, engineering charging stations, and emergency charging stations are different, and they correspond to different second reference power distributions. For example, the power supply priority of civil charging stations is the lowest, and the corresponding second reference power distribution is set to 5000 degrees; the power supply priority of engineering charging stations is higher than that of civil charging stations, and the corresponding second reference power distribution is set to 8000 degrees; emergency charging stations generally refer to charging stations set up during crisis periods such as disasters, and their power supply priority is higher than the first two, and the corresponding second reference power distribution is set to 15000 degrees.

[0086] Step 230: Obtain a corresponding third reference power distribution amount according to the power distribution priority specified by the power system for the charging station.

[0087] In this step, the power system can also set priorities for charging stations individually. For example, in the event of an emergency disaster, the power system can increase the power distribution priority of a charging station, and different power distribution priorities correspond to different third reference power distribution quantities. For example, when the charging station responds to an emergency disaster, the third reference power distribution quantity corresponding to the power distribution priority is set to 15,000 degrees.

[0088] Step 240: Obtain a reference power distribution according to the first reference power distribution, the second reference power distribution, and the third reference power distribution.

[0089] In this step, the first reference power distribution amount, the second reference power distribution amount and the third reference power distribution amount calculated previously are added together to obtain the reference power distribution amount.

[0090] Fig.10 The figure shows a schematic diagram of the system structure of a high-power charging station distribution control system in a substation area provided by an embodiment of the present application. The present application also provides a high-power charging station distribution control system in a substation area, such as Fig.10 As shown, the system includes a data acquisition module 101 , a frequency calculation module 102 , and a power distribution module 103 .

[0091] The data acquisition module 101 is configured to: collect power consumption data of multiple charging stations in the substation area; establish a distribution load model of the charging station based on the power consumption data, and the distribution load model includes the charging power data and power floating data of each charging pile; retrieve the power extreme value data of each charging pile within a preset time period from the distribution load model; the power extreme value data includes the maximum peak power and the duration of maintaining the maximum peak power.

[0092] The frequency calculation module 102 is communicatively connected with the data acquisition module 101, and the frequency calculation module 102 is configured to: traverse the power extreme value data within the first historical preset time period, calculate the extreme value frequency of each charging pile in the first historical preset time period; and obtain the corresponding average frequency according to the extreme value frequency of all the charging piles.

[0093] The power distribution module 103 is communicatively connected with the frequency calculation module 102, and the power distribution module 103 is configured as follows: obtaining a corresponding first power distribution parameter according to the mean frequency; establishing a charging probability prediction model for a designated user group of the charging station to obtain a power supply probability in a future preset time period; obtaining a corresponding second power distribution parameter according to the power supply probability; obtaining an additional power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period based on the first power distribution parameter and a benchmark power distribution corresponding to the charging station; and obtaining a redundant power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period based on the second power distribution parameter and the benchmark power distribution corresponding to the charging station.

[0094] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0095] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0096] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0097] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0098] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling power distribution in a high-power charging station in a substation, characterized in that: include: Collect electricity consumption data of multiple charging stations in the area; Establishing a power distribution load model of the charging station according to the power consumption data, wherein the power distribution load model includes charging power data and power floating data of each charging pile; Retrieving the power extreme value data of each charging pile within a preset time period from the power distribution load model; the power extreme value data includes the maximum peak power and the duration of the maximum peak power maintenance; Traversing the power extreme value data in a first historical preset time period, and calculating the extreme value frequency of each charging pile in the first historical preset time period; Obtaining the corresponding mean frequency according to the extreme frequency of all the charging piles; Obtaining a corresponding first power distribution parameter according to the mean frequency; Establishing a charging probability prediction model for a designated user group of the charging station to obtain the power supply probability in a future preset time period; Obtaining a corresponding second power distribution parameter according to the power supply probability; Based on the first power distribution parameter and the reference power distribution corresponding to the charging station, obtaining the additional power distribution of the power grid to which the charging station belongs to the charging station in a preset time period in the future; as well as Based on the second power distribution parameter and the reference power distribution corresponding to the charging station, the redundant power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period is obtained.

2. The power distribution control method for a high-power charging station in an area according to claim 1 is characterized in that: The traversing the power extreme value data in the first historical preset time period and calculating the extreme value frequency of each charging pile in the first historical preset time period includes: Obtaining the number of times the charging pile reaches the maximum peak power as a first frequency; Obtaining the maximum peak power holding time of the charging pile; If the maximum peak power holding time is greater than or equal to the frequency calculation period, dividing the maximum peak power holding time by the frequency calculation period to obtain a second frequency; and The extreme value frequency is obtained according to the first frequency and the second frequency.

3. The power distribution control method for a high-power charging station in a substation according to claim 1 is characterized in that: The obtaining of the corresponding first power distribution parameter according to the mean frequency includes: Retrieving the maximum frequency and the minimum frequency from the extreme frequencies of all the charging piles; Obtaining a frequency gradient according to the maximum frequency and the minimum frequency; dividing the first power distribution parameter into percentage values; matching the frequency gradient and the percentage value to each other; and According to the corresponding frequency of the mean frequency, the corresponding percentage value is called to obtain the first power distribution parameter.

4. The power distribution control method for a high-power charging station in an area according to claim 3 is characterized in that: The reference range of the percentage value of the first power distribution parameter is 0%~100%; The power distribution control method for high-power charging stations in the substation area also includes: Get the current peak and valley periods of power distribution; Obtaining a power distribution correction coefficient corresponding to the power distribution peak-valley period; the power distribution correction coefficient corresponding to the power supply peak period of the power distribution peak-valley period is smaller than the power distribution correction coefficient corresponding to the power supply valley period of the power distribution peak-valley period; and The reference range is corrected according to the power distribution correction coefficient to obtain a range of percentage values ​​corresponding to the first power distribution parameter.

5. The method for controlling power distribution of a high-power charging station in a substation according to claim 1, characterized in that: The step of establishing a charging probability prediction model for a designated user group of the charging station to obtain the power supply probability in a future preset time period includes: Classifying registered users of the charging station; Obtaining the number of charging times and cumulative charging durations of all registered users above a preset level in a second historical preset time period; A first coefficient is weighted on the number of charging times, and a second coefficient is weighted on the accumulated charging time; The power supply probability is predicted according to the number of charging times, the first coefficient, the accumulated charging time, and the second coefficient.

6. The method for controlling power distribution of a high-power charging station in a substation according to claim 5, characterized in that: The predicting the power supply probability according to the number of charging times, the first coefficient, the accumulated charging time and the second coefficient includes: The number of days corresponding to the second historical preset time period is multiplied by the number of specific users of the registered users above the preset level to obtain a total reference charging number; Obtaining a first probability corresponding to the number of charging times according to a ratio of the number of charging times to the total reference number of charging times; and A first weighted probability corresponding to the number of charging times is obtained according to the product of the first probability and the first coefficient.

7. The method for controlling power distribution of a high-power charging station in a substation according to claim 6, characterized in that: The predicting the power supply probability according to the number of charging times, the first coefficient, the accumulated charging time and the second coefficient further includes: Multiply the number of specific users by the standard charging duration and the number of days corresponding to the second historical preset time period to obtain a total reference charging duration; Obtaining a second probability corresponding to the cumulative charging time according to a ratio of the cumulative charging time to the total reference charging time; and A second weighted probability corresponding to the accumulated charging duration is obtained according to the product of the second probability and the second coefficient.

8. The method for controlling power distribution of a high-power charging station in a substation according to claim 7, characterized in that: The predicting the power supply probability according to the number of charging times, the first coefficient, the accumulated charging time and the second coefficient further includes: The power supply probability is obtained according to the first weighted probability and the second weighted probability.

9. The method for controlling power distribution of a high-power charging station in a substation according to claim 1, characterized in that: Also includes: According to the number of the charging piles of the charging station, a corresponding first reference power distribution is obtained; According to the power supply priority of the charging station, a corresponding second reference power distribution is obtained; Obtaining a corresponding third reference power distribution amount according to a power distribution priority specified by the power system for the charging station; The reference power distribution amount is obtained according to the first reference power distribution amount, the second reference power distribution amount and the third reference power distribution amount.

10. A power distribution control system for a high-power charging station in an area, characterized in that: include: The data acquisition module is configured to: collect power consumption data of multiple charging stations in the area; A power distribution load model of the charging station is established according to the power consumption data, wherein the power distribution load model includes charging power data and power floating data of each charging pile; power extreme value data of each charging pile within a preset time period is retrieved from the power distribution load model; the power extreme value data includes maximum peak power and maximum peak power retention time; A frequency calculation module is communicatively connected to the data acquisition module, and the frequency calculation module is configured to: traverse the power extreme value data within a first historical preset time period, calculate the extreme value frequency of each charging pile in the first historical preset time period; and obtain the corresponding average frequency according to the extreme value frequencies of all the charging piles; A power distribution module is communicatively connected to the frequency calculation module, and the power distribution module is configured to: obtain a corresponding first power distribution parameter according to the mean frequency; establish a charging probability prediction model for a designated user group of the charging station, and obtain a power supply probability in a future preset time period; Obtain a corresponding second power distribution parameter according to the power supply probability; obtain an additional power distribution of the power grid to which the charging station belongs to the charging station in a preset time period in the future based on the first power distribution parameter and a reference power distribution corresponding to the charging station; And based on the second power distribution parameter and the reference power distribution corresponding to the charging station, the redundant power distribution of the power grid to which the charging station belongs to the charging station in a future preset time period is obtained.

Images