Traffic control energy optimization operation method and system

Abstract

The invention provides a traffic control energy optimization operation method, and relates to the technical field of energy optimization. The method comprises the following steps: acquiring occupancy rates and charging powers of a plurality of charging piles in a plurality of partitions at a plurality of moments in a monitoring period; obtaining the charging times of the plurality of charging piles in the plurality of subareas at the end of the monitoring period; determining a charging demand matching score of each subarea, and determining whether the maximum power supply power of each subarea needs to be adjusted; and when adjustment is needed, determining the adjusted maximum power supply power of each subarea, and setting the maximum power supply power of each subarea as the adjusted maximum power supply power at the beginning of the next monitoring period. According to the invention, when the charging demand matching score is determined, comprehensive evaluation can be carried out according to the charging pile occupancy rate, the charging power and the number of charging times, the adjusted maximum power supply power is determined, the method does not depend on manpower, the accuracy and comprehensiveness of monitoring and adjustment are improved, and the efficiency of traffic control energy optimization is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and particularly to a traffic control energy optimization operation method. Background Art

[0002] When monitoring whether the power supply capacity of charging piles in each region matches the power supply demand, staff need to spend a lot of time monitoring and calculating the power consumption in real time, and when adjustment is needed, manually calculate the adjusted power supply capacity. The efficiency of monitoring and adjustment is low, and it depends on the monitoring and calculation of personnel. Therefore, there is a possibility of inaccurate monitoring or adjustment, as well as problems such as low monitoring efficiency, making it difficult to accurately judge whether the power supply capacity of charging piles matches the power supply demand, and it is also difficult to accurately adjust the power supply capacity in a timely manner, resulting in low monitoring quality and efficiency of the optimized operation of charging piles.

[0003] The information disclosed in the background art part of this application is only intended to deepen the understanding of the general background art of this application, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a traffic control energy optimization operation method, which can solve the technical problems of low monitoring efficiency and the possibility of inaccurate monitoring or adjustment in related technologies.

[0005] According to a first aspect of the present invention, there is provided a traffic control energy optimization operation method, comprising:

[0006] At multiple moments in the current monitoring period, obtain the occupancy rate and charging power of multiple charging piles in multiple partitions;

[0007] At the end of the current monitoring period, obtain the charging times of each charging pile in each partition;

[0008] According to the occupancy rate, the charging power and the charging times, determine the charging demand matching score of each partition;

[0009] According to the charging demand matching scores of each partition, determine whether it is necessary to adjust the maximum power supply capacity of each partition;

[0010] If it is necessary to adjust the charging power of the charging piles in each partition, then according to the occupancy rate, the charging power, the charging times and the maximum power supply capacity of each partition in the current monitoring period, determine the adjusted maximum power supply capacity of each partition;

[0011] At the beginning of the next monitoring period, set the maximum power supply capacity of each partition to the adjusted maximum power supply capacity.

[0012] According to a second aspect of the present invention, there is provided a traffic control energy optimization operation system, including:

[0013] An occupancy rate and charging power module, which obtains the occupancy rate and charging power of multiple charging piles in multiple partitions at multiple moments in the current monitoring period;

[0014] A charging times module, which obtains the charging times of each charging pile in each partition at the end of the current monitoring period;

[0015] A charging demand matching score module, which determines the charging demand matching score of each partition according to the occupancy rate, the charging power and the charging times;

[0016] A module for determining whether to adjust the maximum power supply, which determines whether it is necessary to adjust the maximum power supply of each partition according to the charging demand matching scores of each partition;

[0017] An adjusted maximum power supply module, if it is necessary to adjust the charging power of the charging piles in each partition, determines the adjusted maximum power supply of each partition according to the occupancy rate, the charging power, the charging times and the maximum power supply of each partition in the current monitoring period;

[0018] A maximum power supply adjustment module, at the beginning of the next monitoring period, sets the maximum power supply of each partition to the adjusted maximum power supply.

[0019] By adopting the above technical solutions, the present invention can achieve the following technical effects:

[0020] According to the present invention, the occupancy rates, charging powers, and charging times of multiple charging piles in multiple partitions can be obtained separately, so as to determine the charging demand matching scores of each partition, and further determine whether it is necessary to adjust the maximum power supply of each partition. When it is necessary to adjust the charging powers of the charging piles in each partition, the adjusted maximum power supply of each partition is determined, and the maximum power supply of each partition is set to the adjusted maximum power supply. The matching degree of the power consumption demands of each partition can be comprehensively evaluated according to the charging pile occupancy rate, charging power, and charging times, improving the accuracy and comprehensiveness of monitoring and adjustment, and not relying on manual work, thus improving the efficiency of traffic control energy optimization. Based on the occupancy rate, charging power, and charging times of each charging pile in each partition, the usage duration of each charging pile during each charging and the proportion of fast charging demands of the charging pile during multiple chargings can also be determined, and a first preset power and a first duration interval are set to avoid the situation where users misuse the fast charging mode and adjust it to the normal charging mode, improving the accuracy and objectivity of determining the proportion of fast charging demands of the charging pile during multiple chargings, and providing basic data for determining the charging demand matching score. When determining the charging demand matching score, the maximum number of charging piles in each partition can be determined according to the maximum value of the occupancy rate of each partition. The probability of having a fast charging demand is determined according to the proportion of fast charging demands, and the charging power required for the charging piles with fast charging demands is determined based on the maximum value of the charging power. Based on the binomial distribution, the charging power required for the charging piles with normal charging demands can be determined, so as to obtain the total charging power required for the partition demand, and further the charging demand matching score can be obtained. The charging demand matching score can accurately describe the matching degree between the charging power that each partition can provide and the total charging power of the charging demand, improving the accuracy, objectivity, and comprehensiveness of determining the charging demand matching degree. When determining the adjustment conditions, based on the charging demand matching scores of each partition, the gap between the matching degree of the charging power and the required charging power between partitions, the matching degree of the overall charging power and the required charging power of all partitions, and the lowest matching degree of the charging power provided and the required charging power in each partition can be determined, so as to determine multiple adjustment conditions, taking into account the situation that the gap in the charging demand matching degree between partitions is too large, the overall charging matching degree of all partitions is too low, and the charging demand matching degree of a single partition is too low, improving the accuracy, objectivity, and comprehensiveness of determining the adjustment conditions, and being able to accurately judge the partitions that need to be adjusted.When determining the constraint conditions, the charging demand matching scores for each zone based on the adjusted maximum power supply can be determined according to the occupancy rate, charging power, number of charging times, and the maximum power supply of each zone in the current monitoring period. Considering that after increasing the maximum power supply, more users will be attracted to use the fast charging service, the charging demand matching scores for each zone based on the adjusted maximum power supply can accurately describe the matching degree between the charging power provided by each zone after adjusting the maximum power supply and the charging power of the charging demand. At the same time, considering that the power facilities in each zone have not changed, the total power supply value of each zone remains unchanged, the relative gap between the undetermined value of the adjusted maximum power supply of each zone and the maximum power supply in the current monitoring period cannot be too large, the adjusted power supply of each zone cannot be greater than the upper limit of the maximum power supply of this zone, and the power supply cannot be stopped, the constraint conditions of multiple power supply optimization models are determined, ensuring the safety and normal operation of the power supply facilities in each zone during the optimization process. When determining the objective function, considering that reducing the maximum power supply of some zones and increasing the maximum power supply of other zones can make the charging demand matching scores of each zone more balanced and optimize the overall matching degree between the provided charging power and the demanded charging power, the objective function is determined based on this, and then the optimal solution is obtained according to the optimization algorithm as the adjusted maximum power supply of each zone and adjusted. This improves the accuracy and comprehensiveness of the power supply adjustment and enhances the efficiency of the traffic control energy optimization.

[0021] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present invention. According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present invention will become clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can also be obtained based on these drawings.

[0023] Figure 1 Exemplarily shows a flowchart of the traffic control energy optimization operation method according to an embodiment of the present invention;

[0024] Figure 2 Exemplarily shows a block diagram of the traffic control energy optimization operation system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0027] Figure 1 A flowchart showing the traffic control energy optimization operation method according to an embodiment of the present invention is exemplarily shown. The method includes:

[0028] Step S101: Obtain the occupancy rate and charging power of multiple charging piles in multiple zones at multiple moments in the current monitoring period.

[0029] Step S102: Obtain the charging times of each charging pile in each zone at the end of the current monitoring period.

[0030] Step S103: Determine the charging demand matching score of each zone according to the occupancy rate, the charging power, and the charging times.

[0031] Step S104: Determine whether it is necessary to adjust the maximum power supply of each zone according to the charging demand matching scores of each zone.

[0032] Step S105: If it is necessary to adjust the charging power of the charging piles in each zone, determine the adjusted maximum power supply of each zone according to the occupancy rate, the charging power, the charging times, and the maximum power supply of each zone in the current monitoring period.

[0033] Step S106: At the beginning of the next monitoring period, set the maximum power supply of each zone to the adjusted maximum power supply.

[0034] According to the traffic control energy optimization operation method of the embodiments of the present invention, the occupancy rates, charging powers, and charging times of multiple charging piles in multiple zones can be obtained respectively, so as to determine the charging demand matching scores of each zone, and further determine whether it is necessary to adjust the maximum power supply of each zone. When it is necessary to adjust the charging powers of the charging piles in each zone, determine the adjusted maximum power supply of each zone, and set the maximum power supply of each zone to the adjusted maximum power supply. The matching degree of the power consumption demands of each zone can be comprehensively evaluated according to the charging pile occupancy rates, charging powers, and charging times, improving the accuracy and comprehensiveness of monitoring and adjustment, and not relying on manual work, thus improving the efficiency of traffic control energy optimization.

[0035] According to the embodiments of the present invention, in step S101, at multiple moments in the current monitoring period, obtain the occupancy rates and charging powers of multiple charging piles in multiple zones. The monitoring period can be determined as one day, and the time interval between each monitoring moment can be determined as one minute. The present invention does not limit this. The charging pile area in each service area on a highway can be used as a zone, and the charging pile area in each service area in a city can be used as a zone. The present invention does not limit this. In the current monitoring period, obtain the occupancy rates and charging powers of multiple charging piles in multiple zones at each moment. For example, there are 100 charging piles in a certain zone, and 70 of them are in use at a certain moment in this zone. The occupancy rate of the multiple charging piles in this zone at the current moment is 70%.

[0036] According to the embodiments of the present invention, in step S102, at the end moment of the current monitoring period, obtain the charging times of each charging pile in each zone. For example, when the monitoring period is one day, at 24:00 on the same day, it can be determined that within the time period from 0:00 to 24:00, the number of times a certain charging pile in a certain zone charges an electric vehicle is 15 times. Thus, at the end moment of the current monitoring period, the charging times of each charging pile in each zone can be obtained.

[0037] According to the embodiments of the present invention, in step S103, the charging demand matching score can be used to evaluate whether the power supply of the zone can meet the charging demand. For example, the expected maximum charging power of the zone can be determined by multiplying the maximum charging power of each charging pile, the number of charging piles, and the maximum occupancy rate, and determine the ratio of the maximum power supply of the zone to the above maximum charging power to obtain the charging demand matching score. The higher this ratio is, the higher the degree of satisfaction of the power supply for the charging demand.

[0038] According to an embodiment of the present invention, determining a charging demand matching score for each zone according to the occupancy rate, the charging power, and the number of charging times includes: determining the usage duration of the charging pile during each charging according to the charging power and the number of charging times; determining the fast charging demand ratio of the charging pile during multiple chargings according to the charging power and the charging duration. When the charging power is not 0 or within a certain range, it can be considered that charging using the charging pile starts and the timing begins. When the charging power becomes 0, it can be considered that charging using the charging pile stops and the timing ends, so that the charging duration of this time can be obtained.

[0039] According to an embodiment of the present invention, determining the fast charging demand ratio of the charging pile in multiple chargings according to the charging power and the charging duration includes: when the charging power at multiple moments of the j-th charging pile in the i-th partition during the k-th charging is greater than or equal to a first preset power and the charging duration is within a preset first duration interval, determining that there is a fast charging demand during the time period of the k-th charging; counting the total duration of the time periods with fast charging demand of the j-th charging pile within the current monitoring period, and the total usage duration of the j-th charging pile within the current monitoring period; determining the fast charging demand ratio of the j-th charging pile in the i-th partition according to the total duration of the time periods with fast charging demand and the total usage duration of the j-th charging pile within the current monitoring period. The first preset power is greater than or equal to the charging power during normal charging (i.e., charging at a slower speed). For example, it is 1.1 - 1.2 times the charging power of normal charging. When the charging power of the j-th charging pile in the i-th partition at multiple moments during the k-th charging is greater than the first preset power, the charging power of the charging pile is large enough, and it can be considered that the user uses the fast charging mode for charging. Since when the user uses the charging pile, there may be a situation where the fast charging is misused and then adjusted to slow charging after discovery, therefore, the lower limit of the first duration interval can be set to reduce the possibility of identifying the misuse of the fast charging mode. For example, the first duration interval can be [5, 60] minutes. After the end of the first duration interval, the charging pile does not need to continue to provide fast charging service, and thus it can be determined that there is a fast charging demand during the time period of this charging. The present invention does not limit this. Count the total duration of the time periods with fast charging demand of the j-th charging pile within the current monitoring period, and the total usage duration of the j-th charging pile within the current monitoring period. For example, for a certain charging pile, the number of vehicles charged within the current monitoring period is 10, among which 3 vehicles have a total of 3 hours of fast charging. The total duration of the time periods with fast charging demand of this charging pile within the current monitoring period is 3 hours, and the total charging duration of all 10 vehicles charged within the current monitoring period is 20 hours. The total usage duration of this charging pile within the current monitoring period is 20 hours. The fast charging demand ratio of the j-th charging pile in the i-th partition is the ratio of the total duration of the time periods with fast charging demand of the j-th charging pile in the i-th partition within the current monitoring period to the total usage duration of the j-th charging pile within the current monitoring period. For example, if the total duration of the time periods with fast charging demand of a certain charging pile in a certain partition within the current monitoring period is 3 hours and the total usage duration within the current monitoring period is 20 hours, then the fast charging demand ratio of this charging pile is 3 divided by 20.

[0040] In this way, based on the occupancy rate, charging power, and number of charging times of each charging pile in each partition, the usage duration of each charging pile during each charging and the proportion of fast charging demand of the charging pile during multiple chargings can be determined, and the first preset power and the first duration interval can be set, avoiding the situation where users misuse the fast charging mode and adjusting it to the normal charging mode, improving the accuracy and objectivity of determining the proportion of fast charging demand of the charging pile during multiple chargings, and providing basic data for determining the charging demand matching score.

[0041] According to an embodiment of the present invention, determining the charging demand matching score of each partition according to the occupancy rate, the charging power, and the fast charging demand proportion includes: determining the charging demand matching score CM of the i-th partition according to formula (1) i ,

[0042]

[0043] where N i is the number of charging piles in the i-th partition, n i is the number of moments in the current monitoring period, O i,t is the occupancy rate of the i-th partition at the t-th moment in the current monitoring period, FC i,j is the fast charging demand proportion of the j-th charging pile in the i-th partition, P i,j,t is the charging power of the j-th charging pile in the i-th partition at the t-th moment in the current monitoring period, P p,1 is the first preset power, P ps,i is the maximum power supply of the i-th partition in the current monitoring period, max is the maximum value function, j ≤ N i , t ≤ n i , and j, N i , t, and n i are all positive integers.

[0044] According to an embodiment of the present invention, in formula (1), can represent the maximum value of the occupancy rates of each moment of the i-th partition in the current monitoring period, and the maximum occupancy rate can be used to determine the maximum demand for power supply of this partition. represents the product of the number of charging piles in the i-th partition and the occupancy rate demand of the i-th partition, and can be used as the maximum number of charging piles used in the i-th partition during the current monitoring period. In formula (1), can represent the average value of the fast charging demand proportions of all charging piles in the i-th partition, and can be used as the probability that each charging pile in the i-th partition has a fast charging demand during the current monitoring period. It can represent the maximum charging power of each charging pile among all moments in the i-th partition. Since the charging speed of the fast charging mode is relatively fast and the charging power is relatively large, the maximum value of the charging power is obtained from the duration of fast charging of each charging pile. Since when the charging power reaches the maximum value of the charging power at each moment in the used partition, the maximum value of the charging power can meet the charging power requirements for fast charging of each charging pile in each moment in the partition, therefore, It can be used as the charging power required by the charging piles with fast charging requirements among the charging piles in the i-th partition during the current monitoring period, and can also be used to represent the maximum power demand of the charging piles. Since the probability of each charging pile having a fast charging requirement and the probability of not having a fast charging requirement conform to the binomial distribution, therefore, based on the binomial distribution, It can be used as the probability that each charging pile in the i-th partition does not have a fast charging requirement, that is, the probability that each charging pile in the i-th partition has a slow charging requirement during the current monitoring period. P p,1 is the first preset power. The definition of the first preset power is as described above and can meet the power requirements in the normal charging mode. Therefore, It can represent the charging power required by each charging pile during charging in the i-th partition during the current monitoring period. Therefore, It can represent the total charging power required by the i-th partition during the current monitoring period. P ps,i It can represent the maximum power supply of the i-th partition during the current monitoring period and can be used as the charging power that the i-th partition can provide during the current monitoring period. Therefore, the ratio of the charging power that the above-mentioned i-th partition can provide during the current monitoring period to the total charging power required by the i-th partition during the current monitoring period can be used as the charging demand matching score. The higher the charging demand matching score, the more the charging power that the i-th partition can provide during the current monitoring period can meet the charging power requirements of the i-th partition during the current monitoring period, and the higher the matching degree between the charging power provided by this partition and the required charging power.

[0046] In this way, the maximum number of charging piles in each partition can be determined according to the maximum value of the occupancy rate of each partition. Determine the probability of having a fast charging requirement according to the fast charging demand ratio, and determine the charging power required by the charging piles with fast charging requirements based on the maximum value of the charging power. Based on the binomial distribution, the charging power required by the charging piles with normal charging requirements can be determined, so as to obtain the total charging power required by the partition, and then the charging demand matching score can be obtained. The charging demand matching score can accurately describe the matching degree between the charging power that each partition can provide and the total charging power of the charging demand, improving the accuracy, objectivity and comprehensiveness of the determination of the charging demand matching degree.

[0047] According to an embodiment of the present invention, in step S104, based on the charging demand matching scores of each partition, it is determined whether it is necessary to adjust the maximum power supply of each partition, including: based on the charging demand matching scores of each partition, a plurality of adjustment conditions are determined; when at least one of the plurality of adjustment conditions is satisfied, it is determined that it is necessary to adjust the maximum power supply of each partition.

[0048] According to an embodiment of the present invention, based on the charging demand matching scores of each partition, a plurality of adjustment conditions are determined, including: determining adjustment condition C1, adjustment condition C2, and adjustment condition C3 according to formula (2),

[0049]

[0050] where CM i is the charging demand matching score of the i-th partition, D(CM i ) is the standard deviation of the charging demand matching scores of a plurality of partitions, T 1 is a preset first threshold, T 2 is a preset second threshold, T 3 is a preset third threshold, T 4 is a preset fourth threshold, N is the number of partitions, min is the minimum value function, if is the conditional function, i ≤ N, and both i and N are positive integers.

[0051] According to an embodiment of the present invention, in formula (2), D(CM i ) is the standard deviation of the charging demand matching scores of a plurality of partitions. In adjustment condition C1, when the above standard deviation is greater than the preset first threshold T 1 , the standard deviation is relatively large, and the gap in the charging demand matching degree between partitions is too large. The charging demand matching degree of some partitions is relatively high, and the charging demand matching degree of some partitions is relatively low, indicating that there are some partitions with overly tight power consumption and insufficient charging power provided, and there are idle charging piles in some partitions with excessive charging power provided. It is necessary to further adjust and optimize to make the charging power distribution of each partition more reasonable. In adjustment condition C2, if(CM i <T 2 ,1,0) is the conditional function, indicating that when the charging demand matching score of the i-th partition is less than the preset second threshold, the charging demand matching score is too low, and the matching degree between the charging power provided by this partition and the required charging power is too low, which can be considered unmatched, and the value of the conditional function is 1. Conversely, the charging demand matching score is high enough, and the matching degree between the charging power provided by this partition and the required charging power is relatively high, which can be considered matched, and the value of the conditional function is 0. Therefore, It can represent the proportion of the total number of partitions in all partitions where the provided charging power of the partitions does not match the required charging power to the total number of partitions, which can be used as the degree of mismatch of the overall charging demand of all partitions. When the value of the above proportion is greater than or equal to the preset third threshold, the proportion of the total number of partitions where the provided charging power of the partitions does not match the required charging power is too large, and the overall charging demand matching degree of all partitions is too low, and further adjustment and optimization are required to make the charging power distribution of each partition more reasonable. In adjustment condition C3, It can represent the minimum value of the charging demand matching score in each partition. When the above minimum value is less than the preset fourth threshold, the charging demand matching score of a certain partition is too low, and it can be considered that the provided charging power of this partition does not match the required charging power very much, and further adjustment and optimization are required to increase the charging power of this partition.

[0052] According to the embodiments of the present invention, when the power consumption demand matching scores of each partition satisfy at least one adjustment condition, it indicates that the provided charging power of some partitions or all partitions does not match the required charging power, and further adjustment and optimization are required.

[0053] In this way, based on the charging demand matching scores of each partition, the gap between the matching degrees of the charging power and the required charging power among the partitions, the overall matching degree of the charging power and the required charging power of all partitions, and the lowest matching degree of the provided charging power and the required charging power in each partition can be determined, so as to determine multiple adjustment conditions, taking into account the situation of too large a gap in the charging demand matching degrees among partitions, too low an overall charging matching degree of all partitions, and too low a charging demand matching degree of a single partition, improving the accuracy, objectivity and comprehensiveness of the determination of adjustment conditions, and being able to accurately judge the partitions that need to be adjusted.

[0054] According to the embodiments of the present invention, in step S105, if it is necessary to adjust the charging power of the charging piles in each partition, then according to the occupancy rate, the charging power, the number of charging times, and the maximum power supply of each partition in the current monitoring period, determine the adjusted maximum power supply of each partition, including: determining the constraint conditions of the power supply optimization model according to the occupancy rate, the charging power, the number of charging times, and the maximum power supply of each partition in the current monitoring period; determining the objective function of the power supply optimization model according to the occupancy rate, the charging power, and the number of charging times; and solving the power supply optimization model according to the constraint conditions and the objective function of the power supply optimization model to obtain the adjusted maximum power supply of each partition.

[0055] According to an embodiment of the present invention, the constraint conditions of the power supply optimization model are determined according to the occupancy rate, the charging power, the number of charging times, and the maximum power supply of each partition in the current monitoring period, including: determining the constraint conditions of the power supply optimization model according to formulas (3), (4), (5), and (6).

[0056]

[0057] 0 < P ps,i,ad,pe ≤ P ps,i,max (6)

[0058] where P ps,i,ad,pe is the undetermined value of the adjusted maximum power supply of the i-th partition, P ps,i is the maximum power supply of the i-th partition in the current monitoring period, N i is the number of charging piles in the i-th partition, n i is the number of moments in the current monitoring period, O i,t is the occupancy rate of the i-th partition at the t-th moment in the current monitoring period, FC i,j is the fast charging demand ratio of the j-th charging pile in the i-th partition, P i,j,t is the charging power of the j-th charging pile in the i-th partition at the t-th moment in the current monitoring period, P p,1 is the first preset power, max is the maximum value function, if is the conditional function, T 5 is the fifth preset threshold, N is the number of partitions, P ps,i,max is the upper limit of the maximum power supply of the i-th partition, CM i,ad is the charging demand matching score of the i-th partition based on the adjusted maximum power supply, i ≤ N, j ≤ N i , t ≤ n i , and i, N, j, N i , t, and n i are all positive integers.

[0059] According to an embodiment of the present invention, in formula (3), when P ps,i,ad,pe > P ps,i , it means that the undetermined value of the adjusted maximum power supply of the i-th partition is greater than the maximum power supply of the i-th partition in the current monitoring period, indicating that the maximum power supply before adjustment cannot meet the charging demand power of this partition, and the maximum power supply is adjusted upward. At this time, the charging demand matching score of the i-th partition based on the adjusted maximum power supply is In formula (3), It represents the undetermined value of the adjusted maximum power supply of the i-th partition, which is the ratio to the maximum power supply of the i-th partition during the current monitoring period. Since after the maximum power supply is increased, it will attract more users to use the fast charging service, and the probability of each charging pile having a fast charging demand will also increase at a similar rate as the maximum power supply. Therefore, It can be used as the probability that each charging pile in the i-th partition after adjustment has a fast charging demand. Similar to formula (1), It can represent the total charging power demanded in the i-th partition during the current monitoring period after adjustment. Therefore, the ratio of the undetermined value of the adjusted maximum power supply of the i-th partition to the charging power demanded in the i-th partition during the current monitoring period after the above adjustment can be used as the adjusted charging demand matching score of the i-th partition. Conversely, when P ps,i,ad,pe ≤P ps,i It means that the undetermined value of the adjusted maximum power supply of the i-th partition is less than or equal to the maximum power supply of the i-th partition during the current monitoring period, indicating that the maximum power supply before adjustment can meet the charging demand of this partition, and the maximum power supply is not adjusted or adjusted downward. Since the attraction for users to use the fast charging service will not change after the maximum power supply becomes smaller or remains unchanged, and the probability of each charging pile having a fast charging demand will not change either. Therefore, similar to formula (1), the charging demand matching score of the i-th partition based on the adjusted maximum power supply is

[0060]

[0061] According to the embodiments of the present invention, in formula (4), P ps,i,ad,pe -P ps,i represents the gap between the undetermined value of the adjusted maximum power supply of the i-th partition and the maximum power supply of the i-th partition during the current monitoring period. Therefore, It can represent the relative gap between the undetermined value of the adjusted maximum power supply of the i-th partition and the maximum power supply of the i-th partition during the current monitoring period. When the above relative gap is greater than the fifth preset threshold, the relative gap between the undetermined value of the adjusted maximum power supply of the i-th partition and the maximum power supply of the i-th partition during the current monitoring period is too large, which may damage the power supply facilities. Therefore, the constraint condition is It means that the above ratio should be less than or equal to the fifth preset threshold to ensure the safety of the power supply facilities. In formula (5), It can represent the total value of the maximum power supplies of each partition after adjusting the maximum power supply. It can represent the total value of the maximum power supply of each sub - area within the current monitoring period. Since the power supply facilities in each sub - area (such as substations, transformers, cables, etc.) have not changed, the total value of the power supply of each sub - area before and after the adjustment remains unchanged. Therefore, the constraint condition is In formula (6), P ps,i,max represents the upper limit of the maximum power supply of the i - th sub - area. Since the power supply facilities in each sub - area have not changed, in order to ensure the safety of the power supply facilities, the maximum power supply of each sub - area after adjustment will not exceed the upper limit of its own maximum power supply, and power supply cannot be stopped. Therefore, the constraint condition is 0 < P ps,i,ad,pe ≤P ps,i,max .

[0062] In this way, according to the occupancy rate, charging power, charging times, and the maximum power supply of each sub - area in the current monitoring period, the charging demand matching score of each sub - area based on the adjusted maximum power supply can be determined. Considering that after increasing the maximum power supply, more users will be attracted to use the fast - charging service, the charging demand matching score of each sub - area based on the adjusted maximum power supply can accurately describe the matching degree between the charging power provided by each sub - area after adjusting the maximum power supply and the charging power of the charging demand. At the same time, considering that the power facilities in each sub - area have not changed, the total power supply value of each sub - area remains unchanged, the relative gap between the undetermined value of the maximum power supply of each sub - area after adjustment and the maximum power supply in the current monitoring period cannot be too large, the power supply of each sub - area after its own adjustment cannot be greater than the upper limit of the maximum power supply of this sub - area, and the power supply cannot be stopped, the constraint conditions of multiple power supply optimization models are determined, ensuring the safety and normal operation of the power supply facilities in each sub - area during the optimization process.

[0063] According to an embodiment of the present invention, determining the objective function of the power supply optimization model according to the occupancy rate, the charging power, and the charging times includes: determining the objective function of the power supply optimization model according to formula (7),

[0064]

[0065] where maximize is the maximization function.

[0066] According to an embodiment of the present invention, in formula (7), It can represent the product of the charging demand matching scores of each partition based on the adjusted maximum power supply. Since under the condition that the total power supply of all partitions remains unchanged, reducing the maximum power supply of some partitions and increasing the maximum power supply of other partitions can make the charging demand matching scores of each partition more balanced, and can increase the product of the charging demand matching scores of each partition based on the adjusted maximum power supply. That is, the overall matching degree of the charging power provided by each partition and the required charging power is optimized. Therefore, the above product can be used as the overall charging demand matching degree after global optimization of the maximum power supply of each partition. Therefore, It can maximize the overall charging demand matching degree, that is, it can be used as the objective function of the power supply optimization model.

[0067] According to an embodiment of the present invention, by solving the power supply optimization model according to the constraint conditions and the objective function of the power supply optimization model, the adjusted maximum power supply of each partition can be obtained. Through optimization algorithms, such as genetic algorithms, simulated annealing algorithms, particle swarm algorithms, etc., the optimal solution of the objective function of the power supply optimization model can be solved under the constraint conditions of the power supply optimization model, that is, the solution that can satisfy the objective described by the objective function to the greatest extent. This solution is the adjusted maximum power supply of each partition. The present invention does not limit this.

[0068] According to an embodiment of the present invention, in step S106, at the beginning of the next monitoring period, the maximum power supply of each partition is set to the adjusted maximum power supply. For example, after determining the adjusted maximum power supply of each partition, the maximum power supply of each partition can be adjusted to the adjusted maximum power supply of each partition as described above.

[0069] In this way, considering the situation of reducing the maximum power supply of some partitions and increasing the maximum power supply of other partitions, the charging demand matching scores of each partition can be made more balanced, and the overall matching degree of the provided charging power and the required charging power can be optimized. Based on this, the objective function is determined, and then the optimal solution is obtained as the adjusted maximum power supply of each partition according to the optimization algorithm and adjusted. This improves the accuracy and comprehensiveness of the power supply adjustment and enhances the efficiency of the traffic control energy optimization.

[0070] According to the traffic control energy optimization operation method of the embodiments of the present invention, the occupancy rate, charging power, and charging times of multiple charging piles in multiple partitions can be obtained respectively, so as to determine the charging demand matching score of each partition, and further determine whether it is necessary to adjust the maximum power supply of each partition. When it is necessary to adjust the charging power of the charging piles in each partition, determine the adjusted maximum power supply of each partition, and set the maximum power supply of each partition to the adjusted maximum power supply. The matching degree of the electricity consumption demand of each partition can be comprehensively evaluated according to the charging pile occupancy rate, charging power, and charging times, improving the accuracy and comprehensiveness of monitoring and adjustment, and not relying on manual work, thus improving the efficiency of traffic control energy optimization. Based on the occupancy rate, charging power, and charging times of each charging pile in each partition, the usage duration of each charging pile during each charging and the proportion of fast charging demand in multiple chargings of the charging pile can be determined, and a first preset power and a first duration interval can be set to avoid the situation where users misuse the fast charging mode and adjust it to the normal charging mode, improving the accuracy and objectivity of determining the proportion of fast charging demand in multiple chargings of the charging pile, and providing basic data for determining the charging demand matching score. When determining the charging demand matching score, the maximum number of charging piles in each partition can be determined according to the maximum value of the occupancy rate of each partition. Determine the probability of having a fast charging demand according to the proportion of fast charging demand, and determine the charging power required for the charging piles with fast charging demand based on the maximum value of the charging power. Based on the binomial distribution, the charging power required for the charging piles with normal charging demand can be determined, so as to obtain the total charging power required for the partition demand, and further obtain the charging demand matching score. The charging demand matching score can accurately describe the matching degree between the charging power that each partition can provide and the total charging power of the charging demand, improving the accuracy, objectivity, and comprehensiveness of determining the charging demand matching degree. When determining the adjustment conditions, based on the charging demand matching scores of each partition, determine the gap between the matching degree of the charging power and the required charging power between partitions, the matching degree of the overall charging power and the required charging power of all partitions, and the lowest matching degree of the charging power provided and the required charging power in each partition, so as to determine multiple adjustment conditions, taking into account the situation where the gap in the charging demand matching degree between partitions is too large, the overall charging matching degree of all partitions is too low, and the charging demand matching degree of a single partition is too low, improving the accuracy, objectivity, and comprehensiveness of determining the adjustment conditions, and being able to accurately judge the partitions that need to be adjusted.When determining the constraint conditions, the charging demand matching scores for each zone based on the adjusted maximum power supply can be determined according to the occupancy rate, charging power, number of charging times, and the maximum power supply of each zone in the current monitoring period. Considering that after increasing the maximum power supply, more users will be attracted to use the fast charging service, the charging demand matching scores for each zone based on the adjusted maximum power supply can accurately describe the matching degree between the charging power provided by each zone after adjusting the maximum power supply and the charging demand. At the same time, considering that the power facilities in each zone have not changed, the total power supply value of each zone remains unchanged, the relative gap between the undetermined value of the adjusted maximum power supply of each zone and the maximum power supply in the current monitoring period cannot be too large, the adjusted power supply of each zone cannot be greater than the upper limit of the maximum power supply of this zone, and power supply cannot be stopped, the constraint conditions of multiple power supply optimization models are determined to ensure the safety and normal operation of the power supply facilities in each zone during the optimization process. When determining the objective function, considering that reducing the maximum power supply of some zones and increasing the maximum power supply of other zones can make the charging demand matching scores of each zone more balanced and optimize the overall matching degree between the provided charging power and the required charging power, the objective function is determined based on this, and then the optimal solution is obtained according to the optimization algorithm as the adjusted maximum power supply of each zone and adjusted. This improves the accuracy and comprehensiveness of the power supply adjustment and enhances the efficiency of the traffic control energy optimization.

[0071] Figure 2 Exemplarily, a block diagram of a traffic control energy optimization operation system according to an embodiment of the present invention is shown. The system includes:

[0072] Occupancy rate and charging power module, which obtains the occupancy rate and charging power of multiple charging piles in multiple zones at multiple moments in the current monitoring period;

[0073] Number of charging times module, which obtains the number of charging times of each charging pile in each zone at the end of the current monitoring period;

[0074] Charging demand matching score module, which determines the charging demand matching score for each zone according to the occupancy rate, the charging power, and the number of charging times;

[0075] Whether to adjust the maximum power supply module, which determines whether it is necessary to adjust the maximum power supply of each zone according to the charging demand matching scores of each zone;

[0076] Adjusted maximum power supply module, if it is necessary to adjust the charging power of the charging piles in each zone, then according to the occupancy rate, the charging power, the number of charging times, and the maximum power supply of each zone in the current monitoring period, determine the adjusted maximum power supply of each zone;

[0077] The maximum power supply adjustment module sets the maximum power supply of each partition to the adjusted maximum power supply at the start of the next monitoring period.

[0078] The present invention may be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present invention.

[0079] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The object of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and described in the embodiments, and without departing from the said principles, the embodiments of the present invention may have any deformation or modification.

[0080] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A traffic control energy optimization operation method, characterized in that: include: At multiple moments in the current monitoring cycle, obtain the occupancy rate and charging power of multiple charging piles in multiple partitions; At the end of the current monitoring cycle, the number of charging times of each charging pile in each partition is obtained; Determining a charging demand matching score for each partition according to the occupancy rate, the charging power, and the number of charging times; According to the charging demand matching score of each partition, determine whether the maximum power supply power of each partition needs to be adjusted; If it is necessary to adjust the charging power of the charging piles in each partition, determine the adjusted maximum power supply power of each partition according to the occupancy rate, the charging power, the number of charging times and the maximum power supply power of each partition in the current monitoring period; At the beginning of the next monitoring cycle, the maximum power supply power of each partition is set to the adjusted maximum power supply power.

2. The traffic control energy optimization operation method according to claim 1 is characterized in that: Determining a charging demand matching score for each partition according to the occupancy rate, the charging power, and the number of charging times, including: Determining the usage time of the charging pile during each charging according to the charging power and the number of charging times; Determining a fast charging demand ratio of the charging pile in multiple charging processes according to the charging power and the charging duration; A charging demand matching score of each partition is determined according to the occupancy rate, the charging power and the fast charging demand ratio.

3. The traffic control energy optimization operation method according to claim 2 is characterized in that: Determining the fast charging demand ratio of the charging pile in multiple charging processes according to the charging power and the charging duration includes: When the charging power of the j-th charging pile in the i-th partition at multiple times of the k-th charging is greater than or equal to the first preset power, and the charging duration is within the preset first duration interval, it is determined that there is a fast charging demand within the time period of the k-th charging; Count the total duration of the time period with fast charging demand for the j-th charging pile in the current monitoring period, and the total usage time of the j-th charging pile in the current monitoring period; The fast charging demand ratio of the jth charging pile in the ith partition is determined according to the total duration of the time period with fast charging demand and the total usage time of the jth charging pile in the current monitoring cycle.

4. The traffic control energy optimization operation method according to claim 2 is characterized in that: Determining a charging demand matching score for each partition according to the occupancy rate, the charging power, and the fast charging demand ratio includes: According to the formula Determine the charging demand matching score CM of the i-th partition i , where N i is the number of charging piles in the ith partition, n i is the time number of the current monitoring cycle, O i,t is the occupancy rate of the i-th partition at the t-th moment of the current monitoring period, FC i,j is the fast charging demand ratio of the jth charging pile in the i-th partition, P i,j,t is the charging power of the jth charging pile in the i-th partition at the t-th moment of the current monitoring period, P p,1 is the first preset power, P ps,i is the maximum power supply of the ith partition in the current monitoring period, max is the maximum value function, j≤N i , t≤n i , and j, N i , t and n i All are positive integers.

5. The traffic control energy optimization operation method according to claim 1 is characterized in that: According to the charging demand matching score of each zone, determine whether the maximum power supply power of each zone needs to be adjusted, including: Determine multiple adjustment conditions based on the charging demand matching score of each zone; When at least one of the plurality of adjustment conditions is satisfied, it is determined that the maximum power supply of each partition needs to be adjusted.

6. The traffic control energy optimization operation method according to claim 5 is characterized in that: Based on the charging demand matching score of each zone, multiple adjustment conditions are determined, including: According to the formula Determine adjustment conditions C1, C2 and C3, where CM i is the charging demand matching score of the i-th partition, D(CM i ) is the standard deviation of the charging demand matching scores of multiple partitions, T1 is the preset first threshold, T2 is the preset second threshold, T3 is the preset third threshold, T4 is the preset fourth threshold, N is the number of partitions, min is the minimum value function, if is the conditional function, i≤N, and i and N are both positive integers.

7. The traffic control energy optimization operation method according to claim 1, characterized in that: If the charging power of the charging piles in each partition needs to be adjusted, the adjusted maximum power supply power of each partition is determined according to the occupancy rate, the charging power, the number of charging times and the maximum power supply power of each partition in the current monitoring period, including: Determine the constraint conditions of the power supply optimization model according to the occupancy rate, the charging power, the number of charging times and the maximum power supply power of each partition in the current monitoring period; Determine an objective function of a power supply optimization model according to the occupancy rate, the charging power and the number of charging times; According to the constraints and objective function of the power supply optimization model, the power supply optimization model is solved to obtain the adjusted maximum power supply power of each partition.

8. The traffic control energy optimization operation method according to claim 7 is characterized in that: Determining the constraint conditions of the power supply optimization model according to the occupancy rate, the charging power, the number of charging times, and the maximum power supply power of each partition in the current monitoring period includes: According to the formula 0<P ps,i,ad,pe ≤P ps,i,max Determine the constraints of the power supply optimization model, where P ps,i,ad,pe is the undetermined value of the adjusted maximum power supply of the ith partition, P ps,i is the maximum power supply power of the ith partition in the current monitoring period, N i is the number of charging piles in the ith partition, n i is the time number of the current monitoring cycle, O i,t is the occupancy rate of the i-th partition at the t-th moment of the current monitoring period, FC i,j is the fast charging demand ratio of the jth charging pile in the i-th partition, P i,j,t is the charging power of the jth charging pile in the i-th partition at the t-th moment of the current monitoring period, P p,1 is the first preset power, max is the maximum value function, if is the conditional function, T5 is the fifth preset threshold, N is the number of partitions, P ps,i,max is the upper limit of the maximum power supply of the ith partition, CM i,ad is the charging demand matching score of the ith partition based on the adjusted maximum power supply, i≤N, j≤N i , t≤n i , and i, N, j, N i , t and n i All are positive integers.

9. The traffic control energy optimization operation method according to claim 8, characterized in that: Determining an objective function of a power supply optimization model according to the occupancy rate, the charging power, and the number of charging times includes: According to the formula Determine the objective function of the power supply optimization model, where maximize is the maximization function.

10. A traffic control energy optimization operation system, characterized in that: include: The occupancy rate and charging power module obtains the occupancy rate and charging power of multiple charging piles in multiple partitions at multiple times in the current monitoring cycle; The charging times module obtains the charging times of each charging pile in each partition at the end of the current monitoring cycle; A charging demand matching scoring module, which determines a charging demand matching score for each partition according to the occupancy rate, the charging power and the number of charging times; Whether to adjust the maximum power supply module, according to the charging demand matching score of each partition, determine whether to adjust the maximum power supply of each partition; The adjusted maximum power supply module, if the charging power of the charging piles of each partition needs to be adjusted, determines the adjusted maximum power supply power of each partition according to the occupancy rate, the charging power, the number of charging times and the maximum power supply power of each partition in the current monitoring period; The maximum power supply adjustment module sets the maximum power supply of each partition to the adjusted maximum power supply at the beginning of the next monitoring cycle.

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