A method and system for optimizing operation of an exchange-controlled energy source

Abstract

The application provides a kind of interchange control energy optimization operation method, it is related to energy optimization technical field.The method comprises: obtaining the occupancy rate and charging power of multiple charging piles in multiple partitions at multiple moments in a monitoring period;Obtain the charging times of multiple charging piles in multiple partitions at the end of the monitoring period;Determine the charging demand matching score of each partition, and determine whether the maximum power supply of each partition needs to be adjusted;When adjustment is needed, determine the adjusted maximum power supply of each partition and set the maximum power supply of each partition to the adjusted maximum power supply at the start of the next monitoring period.According to the application, when determining the charging demand matching score, the charging pile occupancy rate, charging power and charging times are comprehensively evaluated, and the adjusted maximum power supply is determined, which does not rely on manual work, improves the accuracy and comprehensiveness of monitoring and adjustment, and improves the efficiency of interchange control energy optimization.

Description

Technical Field

[0001] This invention relates to the field of energy optimization technology, and in particular to a traffic control energy optimization operation method. Background Technology

[0002] When monitoring whether the power supply capacity of charging piles in various regions matches the power demand, staff need to spend a lot of time monitoring and calculating the power consumption in real time. When adjustments are needed, the adjusted power supply is calculated manually. The efficiency of monitoring and adjustment is low, and it relies on manual monitoring and calculation. Therefore, there is a possibility of inaccurate monitoring or adjustment, as well as problems such as low monitoring efficiency. It is difficult to accurately determine whether the power supply capacity of charging piles matches the power demand, and it is also difficult to adjust the power supply in a timely and accurate manner, resulting in low monitoring quality and efficiency of charging pile optimization operation.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a traffic control energy optimization operation method that 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, a traffic control energy optimization operation method is provided, comprising:

[0006] At multiple points in the current monitoring cycle, the occupancy rate and charging power of multiple charging piles in multiple zones are obtained;

[0007] At the end of the current monitoring period, obtain the number of times each charging pile in each zone has been charged;

[0008] Based on the occupancy rate, the charging power, and the number of charging cycles, a charging demand matching score is determined for each zone;

[0009] Based on the charging demand matching score of each zone, determine whether the maximum power supply of each zone needs to be adjusted.

[0010] If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply of each zone shall be determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period.

[0011] At the start of the next monitoring cycle, the maximum power supply for each zone will be set to the adjusted maximum power supply.

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

[0013] The occupancy rate and charging power module can obtain the occupancy rate and charging power of multiple charging piles in multiple zones at multiple moments in the current monitoring period.

[0014] The charging count module obtains the number of times each charging pile in each zone is charged at the end of the current monitoring period.

[0015] The charging demand matching and scoring module determines the charging demand matching score for each zone based on the occupancy rate, the charging power, and the number of charging cycles.

[0016] Whether to adjust the maximum power supply module depends on the matching score of the charging demand of each zone, and whether it is necessary to adjust the maximum power supply of each zone.

[0017] If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply module is determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period.

[0018] 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 cycle.

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

[0020] According to the present invention, the occupancy rate, charging power, and number of charging cycles of multiple charging piles in multiple zones can be obtained respectively to determine the charging demand matching score of each zone, and then determine whether the maximum power supply of each zone needs to be adjusted. When it is necessary to adjust the charging power of the charging piles in each zone, the maximum power supply of each zone after adjustment is determined, and the maximum power supply of each zone is set as the maximum power supply after adjustment. The matching degree of electricity demand in each zone can be comprehensively evaluated based on the charging pile occupancy rate, charging power, and number of charging cycles, improving the accuracy and comprehensiveness of monitoring and adjustment, and without relying on manual labor, thereby improving the efficiency of traffic control energy optimization. Furthermore, based on the occupancy rate, charging power, and number of charging cycles of each charging pile in each zone, the usage time of each charging pile and the proportion of fast charging demand of the charging pile in multiple charging cycles can be determined, and a first preset power and a first duration interval can be set. This avoids the situation where users mistakenly use the fast charging mode and switch to the normal charging mode, improving the accuracy and objectivity of determining the proportion of fast charging demand of the charging pile in multiple charging cycles, and providing basic data for determining the charging demand matching score. When determining the charging demand matching score, the maximum number of charging stations that can be used in each zone can be determined based on the maximum occupancy rate of each zone. The probability of having fast charging demand is determined based on the proportion of fast charging demand, and the charging power required by charging stations with fast charging demand is determined based on the maximum charging power. Based on a binomial distribution, the charging power required by charging stations with normal charging demand can be determined, thus obtaining the total charging power demand for each zone, and consequently, the charging demand matching score. This allows the charging demand matching score to accurately describe the degree of matching between the charging power that each zone can provide and the total charging power demand, improving the accuracy, objectivity, and comprehensiveness of determining the degree of charging demand matching. When determining adjustment conditions, the charging demand matching score of each zone can be used to determine the gap between the charging power and the required charging power between zones, the overall charging power and the required charging power of all zones, and the minimum matching degree between the charging power provided and the required charging power in each zone. This allows for the determination of multiple adjustment conditions, taking into account situations where the gap in the charging demand matching degree between different zones is too large, the overall charging matching degree of all zones is too low, and the charging demand matching degree of a single zone is too low. This improves the accuracy, objectivity, and comprehensiveness of the adjustment condition determination and enables accurate identification of zones that need adjustment.When determining the constraints, the charging demand matching score for each zone based on the occupancy rate, charging power, number of charging cycles, and the maximum power supply of each zone in the current monitoring period can be determined. This takes into account the possibility that increasing the maximum power supply will attract more users to fast charging services, ensuring that the charging demand matching score for each zone based on the adjusted maximum power supply accurately describes the degree of matching between the charging power provided by each zone and the charging demand after the adjustment. Simultaneously, it considers that the power facilities in each zone remain unchanged, the total power supply of each zone remains constant, the relative difference 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 exceed the upper limit of its maximum power supply, and power supply cannot be stopped. These constraints for multiple power supply optimization models ensure the safety and normal operation of the power supply facilities in each zone during the optimization process. When determining the objective function, the principle of reducing the maximum power supply in some zones and increasing the maximum power supply in others was considered. This approach aims to achieve a more balanced charging demand matching score across zones and optimize the overall matching degree between the supplied charging power and the demanded charging power. Based on this, the objective function was determined, and the optimal solution was then obtained using an optimization algorithm as the adjusted maximum power supply for each zone, which was then adjusted accordingly. This improved the accuracy and comprehensiveness of power supply adjustment and enhanced the efficiency of traffic control energy optimization.

[0021] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0023] Figure 1 An exemplary flowchart of a traffic control energy optimization operation method according to an embodiment of the present invention is shown;

[0024] Figure 2 A block diagram of a traffic control energy optimization operation system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0027] Figure 1 An exemplary flowchart of a traffic control energy optimization operation method according to an embodiment of the present invention is shown, the method comprising:

[0028] Step S101: At multiple moments in the current monitoring cycle, obtain the occupancy rate and charging power of multiple charging piles in multiple zones;

[0029] Step S102: At the end of the current monitoring cycle, obtain the number of times each charging pile in each zone has been charged.

[0030] Step S103: Determine the charging demand matching score for each zone based on the occupancy rate, the charging power, and the number of charging cycles.

[0031] Step S104: Based on the charging demand matching score of each zone, determine whether it is necessary to adjust the maximum power supply of each zone.

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

[0033] Step S106: At the start of the next monitoring cycle, set the maximum power supply of each partition to the adjusted maximum power supply.

[0034] According to an embodiment of the present invention, the traffic control energy optimization operation method can acquire the occupancy rate, charging power, and charging frequency of multiple charging piles in multiple zones, thereby determining the charging demand matching score of each zone and then determining whether the maximum power supply of each zone needs to be adjusted. When it is necessary to adjust the charging power of the charging piles in each zone, the maximum power supply of each zone after adjustment is determined, and the maximum power supply of each zone is set as the adjusted maximum power supply. The matching degree of electricity demand in each zone can be comprehensively evaluated based on the charging pile occupancy rate, charging power, and charging frequency, improving the accuracy and comprehensiveness of monitoring and adjustment, and is independent of manual labor, thus improving the efficiency of traffic control energy optimization.

[0035] According to an embodiment of the present invention, in step S101, the occupancy rate and charging power of multiple charging piles in multiple zones are acquired at multiple moments during the current monitoring period. The monitoring period can be defined as one day, and the duration interval between each monitoring moment can be defined as one minute. The present invention does not impose any limitations on this. The charging pile area within each service area of ​​a highway can be considered as one zone, and the charging pile area within each service area of ​​a city can be considered as one zone; the present invention does not impose any limitations on this. During the current monitoring period, the occupancy rate and charging power of multiple charging piles in multiple zones at various moments are acquired. For example, if there are 100 charging piles in a certain zone, and 70 of the charging piles in that zone are in use at a certain moment, then the occupancy rate of the multiple charging piles in that zone at that moment is 70%.

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

[0037] According to an embodiment of the present invention, in step S103, the charging demand matching score can be used to evaluate whether the power supply of a 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 by the product of the number of charging piles and the maximum occupancy rate, and the ratio of the zone's maximum power supply to the aforementioned maximum charging power can be determined to obtain the charging demand matching score. The higher the ratio, the higher the degree to which the power supply meets the charging demand.

[0038] According to an embodiment of the present invention, a charging demand matching score for each zone is determined based on the occupancy rate, the charging power, and the number of charging cycles. This includes: determining the usage duration of the charging pile during each charging cycle based on the charging power and the number of charging cycles; and determining the proportion of fast charging demand for the charging pile in multiple charging cycles based on the charging power and the charging duration. When the charging power is not 0, or falls within a certain range, it can be considered that charging at the charging pile has started, and timing has begun. When the charging power becomes 0, it can be considered that charging at the charging pile has stopped, and timing has stopped, thereby obtaining the duration of this charging cycle.

[0039] According to an embodiment of the present invention, determining the proportion of fast charging demand for the charging pile in multiple charging cycles based on the charging power and the charging duration includes: determining that there is fast charging demand during the time period of the k-th charging cycle when the charging power of the j-th charging pile in the i-th zone at multiple moments during the k-th charging cycle is greater than or equal to a first preset power and the charging duration is within a preset first duration interval; calculating 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; and determining the proportion of fast charging demand for the j-th charging pile in the i-th zone based on the total duration of the time period with fast charging demand and the total usage time of the j-th charging pile in 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, 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 zone at multiple moments during the k-th charging cycle is greater than the first preset power, the charging power of the charging pile is sufficiently large, and it can be considered that the user is using fast charging mode. Since users may mistakenly use fast charging when using charging stations and then switch to slow charging after realizing it, a lower limit for the first duration interval can be set to reduce the possibility of identifying incorrect fast charging mode usage. For example, the first duration interval can be [5, 60] minutes. After the first duration interval ends, the charging station does not need to continue providing fast charging service, thus determining that there is a demand for fast charging during the current charging period. This invention does not impose any limitations on this. The total duration of the time period with fast charging demand for the j-th charging station in the current monitoring period, and the total usage time of the j-th charging station in the current monitoring period are calculated. For example, if 10 vehicles are charging at a certain charging station in the current monitoring period, and 3 of them have undergone a total of 3 hours of fast charging, then the total duration of the time period with fast charging demand for this charging station in the current monitoring period is 3 hours. The total charging time of all 10 vehicles charging in the current monitoring period is 20 hours, and the total usage time of this charging station in the current monitoring period is 20 hours. The fast-charging demand ratio of the j-th charging pile in the i-th zone is the proportion of the total duration of the time period with fast-charging demand for the j-th charging pile in the i-th zone during the current monitoring period to the total usage time of the j-th charging pile during the current monitoring period. For example, if the total duration of the time period with fast-charging demand for a certain charging pile in a certain zone during the current monitoring period is 3 hours, and the total usage time during the current monitoring period is 20 hours, then the fast-charging demand ratio of that 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 zone, the usage time of each charging pile during each charge and the proportion of fast charging demand of the charging pile in multiple charges can be determined. A first preset power and a first duration range can be set to avoid situations where users mistakenly use the fast charging mode and switch back to the normal charging mode. This improves the accuracy and objectivity of determining the proportion of fast charging demand of the charging pile in multiple charges and provides basic data for determining the charging demand matching score.

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

[0042]

[0043] Where, N i Let n be the number of charging stations in the i-th partition. i O is the time period of the current monitoring cycle. i,t Let FC be the occupancy rate of the i-th partition at time t in the current monitoring period. i,j Let P be the proportion of fast charging demand for the j-th charging station in the i-th partition. i,j,t Let P be the charging power of the j-th charging pile in the i-th partition at the t-th moment of the current monitoring period. p,1 For the first preset power, P ps,i Let be the maximum power supply of the i-th partition during the current monitoring period, and max be the function that takes the maximum value, where j ≤ N. i , t≤n i And j, N i , t and n i All are positive integers.

[0044] According to an embodiment of the present invention, in formula (1), This can represent the maximum occupancy rate of the i-th partition at each moment in the current monitoring period, and the maximum occupancy rate can be used to determine the maximum power demand of the partition. The product of the number of charging piles in the i-th partition and the occupancy rate requirement of the i-th partition can be used as the maximum number of charging piles in the i-th partition during the current monitoring period. In formula (1), It can represent the average proportion of fast charging demand for 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 fast charging demand within the current monitoring period. This can represent the maximum charging power of each charging station at all times within the i-th partition. Since fast charging mode has a faster charging speed and higher charging power, this maximum charging power is obtained from the duration of fast charging at each charging station. Because when the charging power reaches the maximum charging power at each time moment within the partition, this maximum charging power can meet the fast charging needs of each charging station within the partition at each time moment, therefore… This can be used as the charging power required by charging piles with fast charging needs in the i-th partition during the current monitoring period, or it can be used to represent the maximum power demand of a charging pile. Since the probability that each charging pile has a fast charging need and the probability that it does not follow a binomial distribution, therefore, based on the binomial distribution... P represents the probability that each charging pile in the i-th partition does not have a fast charging requirement, i.e., the probability that each charging pile in the i-th partition has a slow charging requirement during the current monitoring period. p,1 The first preset power, as defined above, is sufficient to meet the power requirements of normal charging mode. Therefore, This can represent the charging power required by each charging pile in the i-th partition during the current monitoring period. Therefore, P can represent the total charging power required by the i-th partition during the current monitoring period. ps,i The maximum power supply of the i-th partition during the current monitoring period can be represented 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 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 the partition and the required charging power.

[0045] In this way, the maximum number of charging stations in each zone can be determined based on the maximum occupancy rate of each zone. The probability of fast charging demand is determined based on the proportion of fast charging demand, and the required charging power for charging stations with fast charging demand is determined based on the maximum charging power. Based on a binomial distribution, the required charging power for charging stations with normal charging demand can be determined, thus obtaining the total charging power demand for each zone, and subsequently, a charging demand matching score. This allows the charging demand matching score to accurately describe the degree of matching between the charging power that each zone can provide and the total charging power demand, improving the accuracy, objectivity, and comprehensiveness of determining the degree of charging demand matching.

[0046] According to an embodiment of the present invention, in step S104, according to 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: according to the charging demand matching scores of each partition, multiple adjustment conditions are determined; when at least one of the multiple adjustment conditions is satisfied, it is determined that it is necessary to adjust the maximum power supply of each partition.

[0047] According to an embodiment of the present invention, according to the charging demand matching scores of each partition, multiple adjustment conditions are determined, including: determining adjustment condition C1, adjustment condition C2, and adjustment condition C3 according to formula (2),

[0048]

[0049] 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 a preset first threshold, T2 is a preset second threshold, T3 is a preset third threshold, T4 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.

[0050] 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 multiple partitions. In adjustment condition C1, when the above standard deviation is greater than the preset first threshold T1, 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, and further adjustment and optimization are required to make the charging power distribution of each partition more reasonable. In adjustment condition C2, if(CM i < T2, 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, and it can be considered not to match, and the value of the conditional function is 1. On the contrary, 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, and it can be considered to match, and the value of the conditional function is 0. Therefore, This represents the total number of zones where the provided charging power does not match the required charging power, and is the proportion of all zones. This proportion can be used as the overall degree of charging demand mismatch across all zones. When this proportion is greater than or equal to a preset third threshold, the proportion of zones with mismatched charging power is too large, indicating an overall low degree of charging demand mismatch across all zones. Further adjustment and optimization are needed to make the charging power allocation among zones more reasonable. In adjustment condition C3, This can represent the minimum value of the charging demand matching score in each zone. When the minimum value is less than the preset fourth threshold, the charging demand matching score of a certain zone is too low. It can be considered that the charging power provided by the zone is very mismatched with the required charging power, and further adjustment and optimization are needed to increase the charging power of the zone.

[0051] According to an embodiment of the present invention, when the electricity demand matching score of each zone meets at least one adjustment condition, it indicates that the charging power that some or all zones can provide does not match the required charging power, and further adjustment and optimization are needed.

[0052] In this way, based on the charging demand matching score of each zone, the gap between the charging power and the required charging power between zones, the overall charging power and the required charging power of all zones, and the minimum matching degree between the charging power provided and the required charging power in each zone can be determined. This allows for the determination of multiple adjustment conditions, taking into account situations where the gap in the charging demand matching degree between different zones is too large, the overall charging matching degree of all zones is too low, and the charging demand matching degree of a single zone is too low. This improves the accuracy, objectivity, and comprehensiveness of the adjustment conditions determination and enables accurate identification of zones that need adjustment.

[0053] According to an embodiment of the present invention, in step S105, if it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply of each zone is determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period. This includes: determining the constraints of the power supply optimization model based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period; determining the objective function of the power supply optimization model based on the occupancy rate, the charging power, and the number of charging cycles; and solving the power supply optimization model based on the constraints and objective function to obtain the adjusted maximum power supply of each zone.

[0054] According to an embodiment of the present invention, the constraints of the power supply optimization model are determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each partition in the current monitoring period. This includes determining the constraints of the power supply optimization model according to formulas (3), (4), (5), and (6).

[0055]

[0056] 0 <P ps,i,ad,pe ≤P ps,i,max (6)

[0057] Among them, P ps,i,ad,pe P is the undetermined value of the adjusted maximum power supply for the i-th partition. ps,i N represents the maximum power supply of the i-th partition during the current monitoring period. i Let n be the number of charging stations in the i-th partition. i O is the time period of the current monitoring cycle. i,t Let FC be the occupancy rate of the i-th partition at time t in the current monitoring period. i,j Let P be the proportion of fast charging demand for the j-th charging station in the i-th partition. i,j,t Let P be the charging power of the j-th charging pile in the i-th partition at the t-th moment of the current monitoring period. p,1 The first preset power is given by `max`, which is the function to take the maximum value. `if` is the conditional function. `T5` is the fifth preset threshold. `N` is the number of partitions. `P` is the value of the first preset power. ps,i,max CM is the upper limit of the maximum power supply for the i-th partition. i,ad A score is assigned to match the charging demand of the i-th partition based on the adjusted maximum power supply, where i ≤ N, j ≤ N. i , t≤n i And i, N, j, N i , t and n i All are positive integers.

[0058] According to an embodiment of the present invention, in formula (3), when P ps,i,ad,pe >P ps,i When the value of the undetermined maximum power supply of the i-th partition after adjustment is greater than the maximum power supply of the i-th partition in the current monitoring period, it indicates that the maximum power supply before adjustment cannot meet the charging power demand of the partition. The maximum power supply is then adjusted upwards. At this time, the charging demand matching score of the i-th partition based on the adjusted maximum power supply is... In formula (3), This represents the ratio of the adjusted maximum power supply power of the i-th partition to the maximum power supply power of the i-th partition during the current monitoring period. Since increasing the maximum power supply power will attract more users to use fast charging services, the probability of each charging station having fast charging demand will also increase by a similar proportion to the increase in maximum power supply power. This can be used as the probability that each charging station in the adjusted i-th partition has a fast charging demand. Similar to formula (1), This can represent the total charging power required by the i-th partition after adjustment within the current monitoring period. Therefore, the undetermined value of the maximum power supply of the i-th partition after adjustment, compared with the aforementioned charging power required by the i-th partition after adjustment within the current monitoring period, can be used as the adjusted charging demand matching score for the i-th partition. Conversely, when P... ps,i,ad,pe ≤P ps,i When the 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 in the current monitoring period, it indicates that the maximum power supply before adjustment can meet the charging needs of the partition. The maximum power supply has not been adjusted or has been adjusted downward. Since the attractiveness of the fast charging service to users will not change after the maximum power supply decreases or remains unchanged, and the probability of each charging pile having fast charging needs will not change, similar to formula (1), the charging demand matching score of the i-th partition based on the adjusted maximum power supply is:

[0059]

[0060] According to an embodiment of the present invention, in formula (4), P ps,i,ad,pe -P ps,i This represents the undetermined value of the adjusted maximum power supply of the i-th partition, and the difference between this value and the maximum power supply of the i-th partition during the current monitoring period. Therefore... This can represent the relative difference 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 relative difference exceeds the fifth preset threshold, the relative difference 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... This 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 maximum power supply of each zone after adjusting the maximum power supply. This represents the total maximum power supply of each zone within the current monitoring period. Since the power supply facilities (e.g., substations, transformers, cables, etc.) of each zone remain unchanged, the total power supply of each zone before and after the adjustment remains the same. Therefore, the constraint condition is... In formula (6), P ps,i,max This represents the upper limit of the maximum power supply for the i-th partition. Since the power supply facilities for each partition remain unchanged, to ensure the safety of the power supply facilities, the adjusted maximum power supply for each partition will not exceed its own upper limit, and power supply cannot be stopped. Therefore, the constraint is 0. <P ps,i,ad,pe ≤P ps,i,max .

[0061] In this way, based on occupancy rate, charging power, number of charging sessions, and the maximum power supply of each zone in the current monitoring period, the charging demand matching score for each zone based on the adjusted maximum power supply can be determined. This takes into account the possibility that increasing the maximum power supply will attract more users to fast charging services, ensuring that the charging demand matching score for each zone based on the adjusted maximum power supply accurately describes the degree of matching between the charging power provided by each zone and the charging demand. Simultaneously, it considers that the power facilities in each zone remain unchanged, the total power supply of each zone remains constant, the relative difference 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 exceed the upper limit of its own maximum power supply, and power supply cannot be stopped. These constraints define multiple power supply optimization models, ensuring the safety and normal operation of the power supply facilities in each zone during the optimization process.

[0062] According to an embodiment of the present invention, determining the objective function of the power supply optimization model based on the occupancy rate, the charging power, and the number of charging cycles includes: determining the objective function of the power supply optimization model according to formula (7).

[0063]

[0064] Where maximize is the maximization function.

[0065] According to an embodiment of the present invention, in formula (7), This can be represented as the product of the charging demand matching scores for each zone based on the adjusted maximum power supply. Since, under the condition that the total power supply of all zones remains constant, reducing the maximum power supply of some zones and increasing the maximum power supply of others makes the charging demand matching scores of each zone more balanced. This increases the product of the charging demand matching scores for each zone based on the adjusted maximum power supply, meaning the overall matching degree between the charging power provided and the demanded charging power of each zone is optimized. Therefore, the above product can be used as the overall degree of charging demand matching after globally optimizing the maximum power supply of each zone. It can maximize the matching degree of overall charging demand, that is, it can be used as the objective function of the power supply optimization model.

[0066] According to an embodiment of the present invention, the power supply optimization model is solved based on the constraints and objective function of the power supply optimization model to obtain the adjusted maximum power supply for each partition. The optimal solution to the objective function of the power supply optimization model, i.e., the solution that maximizes the satisfaction of the objective described by the objective function, can be obtained using optimization algorithms such as genetic algorithms, simulated annealing algorithms, and particle swarm optimization algorithms, under the constraints of the power supply optimization model. This solution is the adjusted maximum power supply for each partition. The present invention does not impose any limitations on this.

[0067] According to an embodiment of the present invention, in step S106, at the start of the next monitoring cycle, 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.

[0068] This approach, which considers reducing the maximum power supply of some zones while increasing it of others, can achieve a more balanced charging demand matching score across zones and optimize the overall match between the supplied and demanded charging power. Based on this, an objective function is determined, and the optimal solution is then calculated using an optimization algorithm as the adjusted maximum power supply for each zone, which is then adjusted accordingly. This improves the accuracy and comprehensiveness of power supply adjustment and enhances the efficiency of traffic control energy optimization.

[0069] According to an embodiment of the present invention, the traffic control energy optimization operation method can acquire the occupancy rate, charging power, and number of charging times of multiple charging piles in multiple zones, thereby determining the charging demand matching score of each zone and then determining whether the maximum power supply of each zone needs to be adjusted. When it is necessary to adjust the charging power of the charging piles in each zone, the maximum power supply of each zone after adjustment is determined, and the maximum power supply of each zone is set as the maximum power supply after adjustment. The matching degree of electricity demand in each zone can be comprehensively evaluated based on the charging pile occupancy rate, charging power, and number of charging times, improving the accuracy and comprehensiveness of monitoring and adjustment, and is independent of manual labor, thus improving the efficiency of traffic control energy optimization. Furthermore, based on the occupancy rate, charging power, and number of charging times of each charging pile in each zone, the usage time of each charging pile during charging and the proportion of fast charging demand in multiple charging sessions can be determined, and a first preset power and a first duration interval can be set. This avoids situations where users mistakenly use the fast charging mode and switch to the normal charging mode, improving the accuracy and objectivity of determining the proportion of fast charging demand in multiple charging sessions, and providing basic data for determining the charging demand matching score. When determining the charging demand matching score, the maximum number of charging stations that can be used in each zone can be determined based on the maximum occupancy rate of each zone. The probability of having fast charging demand is determined based on the proportion of fast charging demand, and the charging power required by charging stations with fast charging demand is determined based on the maximum charging power. Based on a binomial distribution, the charging power required by charging stations with normal charging demand can be determined, thus obtaining the total charging power demand for each zone, and consequently, the charging demand matching score. This allows the charging demand matching score to accurately describe the degree of matching between the charging power that each zone can provide and the total charging power demand, improving the accuracy, objectivity, and comprehensiveness of determining the degree of charging demand matching. When determining adjustment conditions, the charging demand matching score of each zone can be used to determine the gap between the charging power and the required charging power between zones, the overall charging power and the required charging power of all zones, and the minimum matching degree between the charging power provided and the required charging power in each zone. This allows for the determination of multiple adjustment conditions, taking into account situations where the gap in the charging demand matching degree between different zones is too large, the overall charging matching degree of all zones is too low, and the charging demand matching degree of a single zone is too low. This improves the accuracy, objectivity, and comprehensiveness of the adjustment condition determination and enables accurate identification of zones that need adjustment.When determining the constraints, the charging demand matching score for each zone based on the occupancy rate, charging power, number of charging cycles, and the maximum power supply of each zone in the current monitoring period can be determined. This takes into account the possibility that increasing the maximum power supply will attract more users to fast charging services, ensuring that the charging demand matching score for each zone based on the adjusted maximum power supply accurately describes the degree of matching between the charging power provided by each zone and the charging demand after the adjustment. Simultaneously, it considers that the power facilities in each zone remain unchanged, the total power supply of each zone remains constant, the relative difference 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 exceed the upper limit of its maximum power supply, and power supply cannot be stopped. These constraints for multiple power supply optimization models ensure the safety and normal operation of the power supply facilities in each zone during the optimization process. When determining the objective function, the principle of reducing the maximum power supply in some zones and increasing the maximum power supply in others was considered. This approach aims to achieve a more balanced charging demand matching score across zones and optimize the overall matching degree between the supplied charging power and the demanded charging power. Based on this, the objective function was determined, and the optimal solution was then obtained using an optimization algorithm as the adjusted maximum power supply for each zone, which was then adjusted accordingly. This improved the accuracy and comprehensiveness of power supply adjustment and enhanced the efficiency of traffic control energy optimization.

[0070] Figure 2 An exemplary block diagram of a traffic control energy optimization operation system according to an embodiment of the present invention is shown, the system comprising:

[0071] The occupancy rate and charging power module can obtain the occupancy rate and charging power of multiple charging piles in multiple zones at multiple moments in the current monitoring period.

[0072] The charging count module obtains the number of times each charging pile in each zone is charged at the end of the current monitoring period.

[0073] The charging demand matching and scoring module determines the charging demand matching score for each zone based on the occupancy rate, the charging power, and the number of charging cycles.

[0074] Whether to adjust the maximum power supply module depends on the matching score of the charging demand of each zone, and whether it is necessary to adjust the maximum power supply of each zone.

[0075] If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply module is determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period.

[0076] 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 cycle.

[0077] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0078] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to 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 points in the current monitoring cycle, the occupancy rate and charging power of multiple charging piles in multiple zones are obtained; At the end of the current monitoring period, obtain the number of times each charging pile in each zone has been charged; Based on the occupancy rate, the charging power, and the number of charging cycles, a charging demand matching score is determined for each zone; Based on the charging demand matching score of each zone, determine whether the maximum power supply of each zone needs to be adjusted. If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply of each zone shall be determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period. At the start of the next monitoring cycle, the maximum power supply of each zone will be set to the adjusted maximum power supply. If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply of each zone is determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period, including: Based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period, the constraints of the power supply optimization model are determined. The objective function of the power supply optimization model is determined based on the occupancy rate, the charging power, and the number of charging cycles. Based on 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 for each partition. Based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone in the current monitoring period, the constraints of the power supply optimization model are determined, including: 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 P is the undetermined value of the adjusted maximum power supply for the i-th partition. ps,i N represents the maximum power supply of the i-th partition during the current monitoring period. i Let n be the number of charging stations in the i-th partition. i O is the time period of the current monitoring cycle. i,t Let FC be the occupancy rate of the i-th partition at time t in the current monitoring period. i,j Let P be the proportion of fast charging demand for the j-th charging station in the i-th partition. i,j,t Let P be the charging power of the j-th charging pile in the i-th partition at the t-th moment of the current monitoring period. p,1 The first preset power is given, max is the maximum value function, if is the conditional function, T5 is the fifth preset threshold, N is the number of partitions, and P... ps,i,max CM is the upper limit of the maximum power supply for the i-th partition. i,ad A score is assigned to match the charging demand of the i-th partition based on the adjusted maximum power supply, where i ≤ N, j ≤ N. i , t≤n i And i, N, j, N i , t and n i All are positive integers; The objective function of the power supply optimization model is determined based on the occupancy rate, the charging power, and the number of charging cycles, including: According to the formula Determine the objective function of the power supply optimization model, where maximize is the maximization function.

2. The traffic control energy optimization operation method according to claim 1, characterized in that, Based on the occupancy rate, the charging power, and the number of charging cycles, a charging demand matching score is determined for each zone, including: The usage time of the charging pile during each charging session is determined based on the charging power and the number of charging sessions. Based on the charging power and the charging duration, determine the proportion of fast charging demand for the charging pile in multiple charging sessions; A charging demand matching score is determined for each zone based on the occupancy rate, the charging power, and the proportion of fast charging demand.

3. The traffic control energy optimization operation method according to claim 2, characterized in that, Based on the charging power and the charging duration, the proportion of fast charging demand for the charging pile in multiple charging cycles is determined, including: If the charging power of the j-th charging pile in the i-th partition is greater than or equal to the first preset power at multiple moments during the k-th charging, and the charging time is within the preset first time interval, it is determined that there is a fast charging demand during the time period of the k-th charging. Calculate the total duration of the time period with fast charging demand for the j-th charging pile during the current monitoring period, and the total usage time of the j-th charging pile during the current monitoring period; Based on the total duration of the time period with fast charging demand and the total usage time of the j-th charging pile in the current monitoring period, determine the fast charging demand ratio of the j-th charging pile in the i-th zone.

4. The traffic control energy optimization operation method according to claim 2, characterized in that, Based on the occupancy rate, the charging power, and the proportion of fast charging demand, a charging demand matching score is determined for each zone, including: According to the formula Determine the charging demand matching score (CM) for the i-th partition. i , where N i Let n be the number of charging stations in the i-th partition. i O is the time period of the current monitoring cycle. i,t Let FC be the occupancy rate of the i-th partition at time t in the current monitoring period. i,j Let P be the proportion of fast charging demand for the j-th charging station in the i-th partition. i,j,t Let P be the charging power of the j-th charging pile in the i-th partition at the t-th moment of the current monitoring period. p,1 For the first preset power, P ps,i Let be the maximum power supply of the i-th partition during the current monitoring period, and max be the function that takes the maximum value, where 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, characterized in that, Based on the charging demand matching score of each zone, determine whether the maximum power supply of each zone needs to be adjusted, including: Based on the charging demand matching score of each zone, multiple adjustment conditions are determined; If at least one of the multiple adjustment conditions is met, determine the maximum power supply that needs to be adjusted for each zone.

6. The traffic control energy optimization operation method according to claim 5, characterized in that, Based on the charging demand matching score for each zone, several adjustment conditions are determined, including: According to the formula Determine adjustment conditions C1, C2, and C3, where CM i To match a score to the charging demand of the i-th partition, D(CM) i ) represents the standard deviation of the charging demand matching score for 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. A traffic control energy optimization operation system, said system being used to execute the method as described in any one of claims 1-6, characterized in that, include: The occupancy rate and charging power module can obtain the occupancy rate and charging power of multiple charging piles in multiple zones at multiple moments in the current monitoring period. The charging count module obtains the number of times each charging pile in each zone is charged at the end of the current monitoring period. The charging demand matching and scoring module determines the charging demand matching score for each zone based on the occupancy rate, the charging power, and the number of charging cycles. Whether to adjust the maximum power supply module depends on the matching score of the charging demand of each zone, and whether it is necessary to adjust the maximum power supply of each zone. If it is necessary to adjust the charging power of the charging piles in each zone, the adjusted maximum power supply module is determined based on the occupancy rate, the charging power, the number of charging cycles, and the maximum power supply of each zone 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 start of the next monitoring cycle.

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