A data processing-based intelligent charging station resource scheduling method and system

Abstract

The application relates to a wisdom charging station resource scheduling method and system based on data processing, and relates to the field of vehicle charging technology. The method comprises the following steps: acquiring residual energy storage power; determining required supplementary power according to full energy storage power and residual energy storage power; determining energy storage required time according to energy storage efficiency and required supplementary power; constructing a permitted energy storage time period and dividing a unit interval; constructing a historical interval, and determining a similar interval according to each unit interval in the historical interval, and determining a historical energy storage price in the similar interval; combining each unit interval according to the energy storage required time to construct a simulated energy storage scheme, and determining a scheme overall price according to each historical energy storage price in each simulated energy storage scheme; defining the simulated energy storage scheme corresponding to the scheme overall price with the minimum value as an effective energy storage scheme, and controlling the charging station to perform energy storage scheduling in the unit interval in the effective energy storage scheme. The application has the effect of reducing the power consumption cost of the charging station.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle charging technology, in particular to a smart charging station resource scheduling method and system based on data processing. BACKGROUND

[0002] In recent years, with the popularization of electric vehicles, the construction scale of charging stations has been expanding, and among the operation costs of charging stations, the electricity cost occupies an important part. In order to reduce the electricity cost, the existing technology usually uses an energy storage system (such as a battery energy storage) to charge at a low valley period of electricity price and discharge to supply charging piles at a high peak period of electricity price, so as to realize economic benefits by utilizing the price difference between peak and valley.

[0003] At present, the common energy storage scheduling method usually charges the energy storage based on a preset fixed electricity price threshold or a fixed time period (such as a night low valley period). For example, when it is detected that the real-time electricity price is lower than a certain set threshold, the energy storage charging is started until the energy storage device is fully charged.

[0004] However, the real-time electricity price of the power market is affected by factors such as supply and demand relationship, renewable energy generation, etc., and has dynamic volatility. If the energy storage is only relied on a fixed threshold or a fixed time period, the energy storage device may be fully charged in advance before the real lowest electricity price appears, resulting in the inability to utilize the subsequent lower electricity price period, thereby increasing the electricity cost, and there is still room for improvement. SUMMARY

[0005] In order to reduce the electricity cost of the charging station, the present application provides a smart charging station resource scheduling method and system based on data processing.

[0006] In a first aspect, the present application provides a smart charging station resource scheduling method based on data processing, which adopts the following technical solution:

[0007] A smart charging station resource scheduling method based on data processing, comprising:

[0008] obtaining a remaining energy storage electricity amount;

[0009] performing difference calculation according to a preset full-load energy storage electricity amount and the remaining energy storage electricity amount to determine a required supplementary electricity amount;

[0010] performing calculation according to a preset energy storage efficiency and the required supplementary electricity amount to determine an energy storage required time length;

[0011] constructing a permitted energy storage time period according to a current time point and a preset cut-off time point, and dividing a unit interval according to a preset unit time length in the permitted energy storage time period;

[0012] constructing a history interval with a preset time length on a preset time axis, the history interval having a rear end point of a current time point and a preset history length, and determining a similar interval according to each unit interval in the history interval, and determining a history energy storage electricity price in the similar interval;

[0013] combining each unit interval according to an energy storage demand length to construct a simulated energy storage scheme, and calculating each simulated energy storage scheme according to the history energy storage electricity price of each unit interval to determine a scheme overall electricity price;

[0014] defining the simulated energy storage scheme corresponding to the scheme overall electricity price with the smallest value as an effective energy storage scheme, and controlling the charging station to perform energy storage scheduling in the unit interval in the effective energy storage scheme.

[0015] Optionally, the step of determining the similar interval according to each unit interval in the history interval comprises:

[0016] constructing a nearby interval on the time axis, the nearby interval having a rear end point of the current time point and a preset nearby length, and dividing a first detection interval according to a unit length in the nearby interval;

[0017] randomly dividing a comparison interval with a width consistent with the nearby interval in the history interval, and dividing a second detection interval according to a unit length in the comparison interval;

[0018] obtaining a first real-time electricity price of the first detection interval and a second real-time electricity price of the second detection interval;

[0019] calculating according to all the first real-time electricity prices and the corresponding second real-time electricity prices to determine an interval similarity;

[0020] determining the interval similarity with the largest value according to a preset sorting rule, and defining the comparison interval corresponding to the interval similarity as a previous interval;

[0021] constructing a subsequent interval with a width consistent with the permitted energy storage time period, the subsequent interval having a front end point of a rear end point of the previous interval, and dividing a similar interval corresponding to the unit interval according to a unit length in the subsequent interval.

[0022] Optionally, after the interval similarity is determined, the intelligent charging station resource scheduling method based on data processing further comprises:

[0023] determining whether the interval similarity with the largest value is greater than a preset demand similarity;

[0024] if the interval similarity with the largest value is greater than the demand similarity, determining the previous interval;

[0025] If the interval similarity with the largest value is not greater than the required similarity, then one first detection interval is randomly selected from all first detection intervals and defined as the key interval, and the remaining first detection intervals are defined as secondary intervals.

[0026] The effective similarity is determined by calculating based on the secondary interval and the corresponding second detection interval.

[0027] Determine if there is a situation where the effective similarity is greater than the required similarity;

[0028] If there is no effective similarity greater than the demand similarity, then energy storage scheduling will be carried out according to the preset threshold electricity price.

[0029] If there is a case where the effective similarity is greater than the required similarity, then the preceding interval is determined based on the comparison interval corresponding to the largest effective similarity value.

[0030] Optionally, if there are cases where the effective similarity is greater than the demand similarity, the data processing-based smart charging station resource scheduling method also includes:

[0031] The key interval corresponding to the largest effective similarity is defined as the abnormal interval, and the first real-time electricity price corresponding to the abnormal interval is defined as the abnormal electricity price.

[0032] In the historical period, the second real-time electricity price corresponding to the second detection interval that corresponds to the abnormal interval is defined as the ordinary electricity price;

[0033] The overall stable value is determined by calculations based on abnormal electricity prices and normal electricity prices;

[0034] Determine whether the overall stable value is greater than the preset demand stable value;

[0035] If the overall stationary value is greater than the demand stationary value, then the preceding interval is determined based on the comparison interval corresponding to the largest effective similarity value.

[0036] If the overall stable value is not greater than the stable demand value, then the effective similarity with the largest value is re-determined from the remaining effective similarity values, until the preceding interval is determined or energy storage scheduling is carried out based on the threshold electricity price.

[0037] Optionally, after the overall electricity price of the scheme is determined, the smart charging station resource scheduling method based on data processing also includes:

[0038] Determine whether there exists a simulated energy storage scheme with at least two schemes having the same and lowest overall electricity price;

[0039] If there are no two simulated energy storage schemes with the same and lowest overall electricity price, then the simulated energy storage scheme with the lowest overall electricity price is defined as the effective energy storage scheme.

[0040] If there are at least two schemes with the same and the minimum overall electricity price, the simulated energy storage scheme corresponding to the minimum overall electricity price of the scheme is defined as the alternative energy storage scheme;

[0041] In each alternative energy storage scheme, combinations are made according to consecutive unit intervals to construct continuous combinations, and counting is performed according to the continuous combinations to determine the energy storage times;

[0042] According to the sorting rule, the energy storage times with the minimum value are determined, and the alternative energy storage scheme corresponding to the energy storage times is defined as the effective energy storage scheme.

[0043] Optionally, after the energy storage times are determined, the intelligent charging station resource scheduling method based on data processing further comprises:

[0044] determining whether there are at least two alternative energy storage schemes with the same and the minimum energy storage times;

[0045] If there are not at least two alternative energy storage schemes with the same and the minimum energy storage times, the alternative energy storage scheme corresponding to the minimum energy storage times is defined as the effective energy storage scheme;

[0046] If there are at least two alternative energy storage schemes with the same and the minimum energy storage times, the overall stability value is determined according to the unit interval in each alternative energy storage scheme, and the continuous stability value is determined according to the overall stability value of the unit interval in the continuous combination;

[0047] According to all continuous stability values, the scheme stability value is calculated, and according to the sorting rule, the scheme stability value with the maximum value is determined, and the alternative energy storage scheme corresponding to the scheme stability value is defined as the effective energy storage scheme.

[0048] Optionally, during energy storage scheduling, the intelligent charging station resource scheduling method based on data processing further comprises:

[0049] obtaining the current interval actual electricity price;

[0050] The historical energy storage electricity price corresponding to the current time point is defined as the interval theoretical electricity price, and the predicted deviation electricity price is determined by difference calculation according to the interval actual electricity price and the interval theoretical electricity price;

[0051] determining whether the predicted deviation electricity price is greater than the preset allowable deviation electricity price;

[0052] If the predicted deviation electricity price is not greater than the allowable deviation electricity price, the charging station is controlled to perform energy storage scheduling operation;

[0053] If the predicted deviation price is greater than the permitted deviation price, a unit interval not in the effective energy storage scheme between the current time point and the deadline point is defined as a candidate interval, and the historical energy storage price corresponding to the candidate interval is defined as a candidate theoretical price;

[0054] determining whether there is a candidate interval with a candidate theoretical price less than the actual price of the current interval;

[0055] If there is no candidate interval with a candidate theoretical price less than the actual price of the current interval, the charging station is controlled to perform energy storage scheduling work;

[0056] If there is a candidate interval with a candidate theoretical price less than the actual price of the current interval, the candidate interval is defined as a replacement interval, and a replacement interval is randomly selected to replace the current unit interval in the effective energy storage scheme.

[0057] In a second aspect, the application provides a smart charging station resource scheduling system based on data processing, which adopts the following technical scheme:

[0058] A smart charging station resource scheduling system based on data processing, comprising:

[0059] An acquisition module for acquiring residual energy storage capacity;

[0060] A processing module connected to the acquisition module for storing and processing information;

[0061] The processing module calculates the difference between the preset full-load energy storage capacity and the residual energy storage capacity to determine the required supplemental capacity;

[0062] The processing module calculates the required energy storage duration based on the preset energy storage efficiency and the required supplemental capacity;

[0063] The processing module constructs a permitted energy storage period based on the current time point and the preset deadline point, and divides unit intervals based on the preset unit duration in the permitted energy storage period;

[0064] The processing module constructs a historical interval with the current time point as the rear end point and a preset historical duration on a preset time axis, determines similar intervals based on each unit interval in the historical interval, and determines the historical energy storage price in the similar intervals;

[0065] The processing module combines each unit interval based on the energy storage duration to construct a simulated energy storage scheme, and calculates the overall price of each simulated energy storage scheme based on the historical energy storage price corresponding to each unit interval;

[0066] The processing module defines the simulation energy storage scheme corresponding to the scheme with the minimum integral price as an effective energy storage scheme, and controls the charging station to perform energy storage scheduling in a unit interval in the effective energy storage scheme.

[0067] In summary, the present application includes at least one of the following beneficial technical effects:

[0068] When performing energy storage scheduling processing on the charging station, the subsequent electricity price situation is predicted to set an appropriate time period for energy storage operation, thereby realizing energy storage at a lower electricity price to reduce the overall electricity cost.

[0069] By analyzing historical data, time periods with similar electricity price fluctuations can be determined, thereby better predicting the subsequent electricity price situation.

[0070] During the energy storage process of the charging station, if the current electricity price deviates greatly from the predicted electricity price, the energy storage scheme can be corrected to improve the rationality of the charging station energy storage operation. BRIEF DESCRIPTION OF DRAWINGS

[0071] Fig. 1 is a flowchart of the intelligent charging station resource scheduling method based on data processing.

[0072] Fig. 2 is a module flowchart of the intelligent charging station resource scheduling method based on data processing. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings Figs. 1-2 and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0074] The embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0075] The embodiments of the present application disclose an intelligent charging station resource scheduling method based on data processing, which is described with reference to Fig. 1 The method flowchart of the intelligent charging station resource scheduling method based on data processing includes the following steps:

[0076] Step S100: Obtain the remaining energy storage capacity.

[0077] The remaining energy storage capacity is the remaining capacity currently possessed by the charging station.

[0078] Step S101: Calculate the difference between the preset full-load energy storage capacity and the remaining energy storage capacity to determine the required supplementary capacity.

[0079] The full-load energy storage capacity is the maximum energy storage capacity that the charging station can bear, the demand compensation capacity is the capacity value that can be recharged under the theoretical condition, that is, the capacity that needs to be stored in the charging station, and the full-load energy storage capacity is determined by subtracting the residual energy storage capacity.

[0080] Step S102: Calculate the energy storage demand duration according to the preset energy storage efficiency and the demand compensation capacity.

[0081] The energy storage efficiency is the charging efficiency of the charging station under the current charging power, and the energy storage demand duration is the duration required for the charging station to store the demand compensation capacity under the current charging power, which is calculated by dividing the demand compensation capacity by the energy storage efficiency.

[0082] Step S103: Construct the permitted energy storage time period according to the current time point and the preset cutoff time point, and divide the unit interval according to the preset unit duration in the permitted energy storage time period.

[0083] The cutoff time point is the cutoff time point set by the staff for the charging station to perform charging operation every day, which is generally 24:00, that is, all-day charging; the permitted energy storage time period is the time period between the current time point and the cutoff time point, the unit duration is a fixed duration set by the staff, for example, one hour, and the unit interval is the time interval obtained by equally dividing the permitted energy storage time period according to the unit duration. In order to facilitate the division of the unit interval, the current time point is represented by the time period in which the current actual time point is located, for example, the current actual time point is 18:05, the corresponding unit duration is 1 hour, and the current time point can be determined as 18:00. Similarly, when the determined energy storage demand duration is not a multiple of the unit duration, the energy storage demand duration is adjusted upward to be a multiple of the unit duration.

[0084] Step S104: Construct a historical interval with the current time point as the rear end point and the width of the preset historical duration on the preset time axis, and determine the similar interval according to each unit interval in the historical interval, and determine the historical energy storage price in the similar interval.

[0085] The time axis is a coordinate axis formed by combining each time point, which points from the time point that has passed to the time point that has not arrived, wherein the time point that has passed is on the left side of the coordinate axis, and the left side of the coordinate axis is defined as the front side of the time axis; the historical duration is the duration set by the staff for data acquisition of the historical charging condition of the charging station, and the historical interval is constructed to facilitate data acquisition and analysis within the historical duration; the similar interval is a time interval in the historical interval that is similar to the unit interval prediction price, and the specific determination method can refer to steps S200-S205; the historical energy storage price is the actual value presented in the similar interval.

[0086] Step S105: combining each unit interval according to the energy storage demand duration to construct a simulated energy storage scheme, and calculating in each simulated energy storage scheme according to the historical energy storage price corresponding to each unit interval to determine the overall price of the scheme.

[0087] The simulated energy storage scheme is a scheme that randomly selects each unit interval to meet the energy storage requirement. The number of unit intervals required in the entire scheme is obtained by dividing the energy storage demand duration by the unit duration, and then each unit interval is randomly selected according to the number. The overall price of the scheme is the sum of the historical energy storage prices of each unit interval in the simulated energy storage scheme.

[0088] Step S106: defining the simulated energy storage scheme corresponding to the numerically smallest overall price of the scheme as the effective energy storage scheme, and controlling the charging station to perform energy storage scheduling in the unit interval within the effective energy storage scheme.

[0089] The numerically smallest overall price of the scheme indicates that the electricity cost required by the simulated energy storage scheme is the lowest. At this time, it is defined as an effective energy storage scheme for identification, and the charging station is controlled to perform energy storage scheduling in the corresponding unit interval during the energy storage process, so as to reduce the overall electricity cost of the charging station.

[0090] The step of determining similar intervals in the historical interval according to each unit interval includes:

[0091] Step S200: constructing a nearby interval with a preset nearby duration as the rear end point and the current time point on the time axis, and dividing a first detection interval according to the unit duration in the nearby interval.

[0092] The nearby duration is a fixed duration set by the staff. The nearby duration is a multiple of the unit duration. By constructing the nearby interval, data within a period of time before the current time point can be obtained and analyzed. The first detection interval is a time interval obtained by equally dividing the nearby interval according to the unit duration.

[0093] Step S201: randomly dividing a comparison interval with the same width as the nearby interval in the historical interval, and dividing a second detection interval according to the unit duration in the comparison interval.

[0094] The comparison interval is a time interval in the historical interval that can completely coincide with the nearby interval on the same day. For example, if the determined nearby interval is 12:00-18:00, the determined comparison interval is 12:00-18:00 of each day in the historical interval. The second detection interval is a time interval obtained by equally dividing the comparison interval according to the unit duration.

[0095] Step S202: obtaining a first real-time price of the first detection interval and a second real-time price of the second detection interval.

[0096] The first real-time electricity price is the electricity price required when the energy storage is performed at the charging station in the first detection interval, and the second real-time electricity price is the electricity price required when the energy storage is performed at the charging station in the second detection interval.

[0097] Step S203: Calculate according to all the first real-time electricity prices and the corresponding second real-time electricity prices to determine the interval similarity.

[0098] The interval similarity is a value reflecting the similarity of the electricity price fluctuations of the nearby interval and the comparison interval. The larger the value, the more similar the electricity price situation of the two intervals. By comparing the first end of the nearby interval with the first end of the comparison interval and comparing the tail end with the tail end, a one-to-one correspondence between the first detection interval and the second detection interval is achieved. At this time, the first real-time electricity price and the second real-time electricity price of the corresponding two intervals are used for difference calculation to determine the electricity price difference in absolute value. Then, the average value of all electricity price differences is calculated and the reciprocal is obtained to obtain the interval similarity.

[0099] Step S204: Determine the interval similarity with the largest value according to the preset sorting rule, and define the comparison interval corresponding to the interval similarity as the previous interval.

[0100] The sorting rule is a method set by the staff to sort the values, such as the bubble method. The interval similarity with the largest value can be determined through the sorting rule, that is, the electricity price situation of the comparison interval corresponding to this time is the most similar to that of the nearby interval. Therefore, the previous interval is defined to identify the comparison interval, which is convenient for subsequent analysis.

[0101] Step S205: Construct a subsequent interval with the same width as the permitted energy storage period with the rear end point of the previous interval as the front end point, and divide the similar interval corresponding to the unit interval in the subsequent interval according to the unit time length.

[0102] Since the electricity price situation of the previous interval is the most similar to that of the nearby interval, the subsequent electricity price situation at the current time point can be changed according to the electricity price situation after the previous interval at this time. Therefore, the subsequent interval is determined to identify the time interval corresponding to the permitted energy storage period. At this time, the similar interval corresponding to each unit interval can be determined by the way that the front end of the subsequent interval corresponds to the tail end of the permitted energy storage period.

[0103] After the interval similarity is determined, the intelligent charging station resource scheduling method based on data processing further comprises:

[0104] Step S300: Determine whether the interval similarity with the largest value is greater than the preset demand similarity.

[0105] The demand similarity is a minimum interval similarity required to be reached when a staff member determines that two interval electricity prices are similar, and the purpose of the determination is to know whether the interval with the highest similarity meets the requirements.

[0106] Step S3001: If the interval similarity with the largest value is greater than the demand similarity, proceed with the previous interval determination.

[0107] When the interval similarity with the largest value is greater than the demand similarity, it means that the interval with the highest similarity meets the requirements, and the previous interval determination can be performed at this time.

[0108] Step S3002: If the interval similarity with the largest value is not greater than the demand similarity, randomly select a first detection interval from all the first detection intervals to define as a key interval, and define the remaining first detection intervals as secondary intervals.

[0109] When the interval similarity with the largest value is not greater than the demand similarity, it means that the interval with the highest similarity cannot meet the requirements, and there may be a situation where the historical data cannot be referenced due to data abnormalities in part of the first detection intervals, which needs to be further analyzed; the key interval and the secondary intervals are defined to distinguish different first detection intervals, which is convenient for subsequent analysis.

[0110] Step S301: Calculate according to the secondary intervals and the corresponding second detection intervals to determine the effective similarity.

[0111] The effective similarity is a value reflecting the similarity between the nearest interval and the comparison interval after excluding the key interval, and the determination method of the value is consistent with the interval similarity described above, which is not repeated here.

[0112] Step S302: Determine whether there is a case where the effective similarity is greater than the demand similarity.

[0113] The purpose of the determination is to know whether the interval similarity is affected due to the key interval.

[0114] Step S3021: If there is no case where the effective similarity is greater than the demand similarity, perform energy storage scheduling according to the preset threshold electricity price.

[0115] When there is no case where the effective similarity is greater than the demand similarity, it means that there is no situation where the similarity cannot be determined due to data abnormalities in a single key interval, so the historical situation cannot be used to predict the subsequent electricity price situation, and therefore the energy storage scheduling can be performed by setting the corresponding threshold electricity price, wherein the threshold electricity price is set to be the electricity price below which the energy storage operation of the charging station is performed.

[0116] Step S3022: If there is a case where the effective similarity is greater than the required similarity, the previous interval is determined according to the comparison interval corresponding to the maximum value of the effective similarity.

[0117] When there is a case where the effective similarity is greater than the required similarity, it indicates that the interval similarity cannot meet the requirements due to the abnormal data of the key interval. At this time, the previous interval is determined by using the comparison interval corresponding to the maximum value of the effective similarity, which effectively excludes the data interference caused by part of the abnormal data.

[0118] If there is a case where the effective similarity is greater than the required similarity, the intelligent charging station resource scheduling method based on data processing further comprises:

[0119] Step S400: Defining the key interval corresponding to the maximum value of the effective similarity as the abnormal interval, and defining the first real-time electricity price corresponding to the abnormal interval as the abnormal electricity price.

[0120] Defining the abnormal interval and the abnormal electricity price to distinguish different data for subsequent analysis.

[0121] Step S401: Defining the second real-time electricity price corresponding to the second detection interval corresponding to the abnormal interval in the historical interval as the normal electricity price.

[0122] Defining the normal electricity price to distinguish different second real-time electricity prices for subsequent analysis.

[0123] Step S402: Calculating according to the abnormal electricity price and the normal electricity price to determine the overall stability value.

[0124] The overall stability value is a parameter value reflecting the stability of the electricity price at the current time point. The abnormal electricity price is respectively calculated by the difference value, and the reciprocal is determined by taking the absolute value and averaging.

[0125] Step S403: Determine whether the overall stability value is greater than the preset required stability value.

[0126] The required stability value is the minimum overall stability value required by the staff to determine that the abnormal electricity price of the abnormal interval is significantly abnormal compared with the normal electricity price. The purpose of the judgment is to know whether the current abnormal electricity price leads to inaccurate interval similarity.

[0127] Step S4031: If the overall stability value is greater than the required stability value, the previous interval is determined according to the comparison interval corresponding to the maximum value of the effective similarity.

[0128] When the overall stability value is greater than the required stability value, it indicates that the interval similarity cannot meet the requirements completely due to the abnormal interval. At this time, the previous interval is determined normally.

[0129] Step S4032: If the overall stability value is not greater than the demand stability value, re-determine the effective similarity with the largest value in the remaining effective similarities until the previous interval is determined or energy storage scheduling is performed according to the threshold price.

[0130] When the overall stability value is not greater than the demand stability value, it indicates that the current abnormal price is not necessarily the main cause of the interval similarity not meeting the requirements, and therefore a new effective similarity with the largest value is re-determined for continuous analysis.

[0131] After the overall price of the scheme is determined, the intelligent charging station resource scheduling method based on data processing further includes:

[0132] Step S500: Determine whether there are at least two simulation energy storage schemes with the same minimum overall price of the scheme.

[0133] The purpose of the determination is to know whether there are multiple simulation energy storage schemes meeting the requirements, so as to determine the unique effective energy storage scheme.

[0134] Step S5001: If there are not at least two simulation energy storage schemes with the same minimum overall price of the scheme, define the simulation energy storage scheme corresponding to the minimum overall price of the scheme as an effective energy storage scheme.

[0135] When there are not at least two simulation energy storage schemes with the same minimum overall price of the scheme, it indicates that there is only one simulation energy storage scheme meeting the requirements, and therefore the simulation energy storage scheme is determined as an effective energy storage scheme.

[0136] Step S5002: If there are at least two simulation energy storage schemes with the same minimum overall price of the scheme, define the simulation energy storage scheme corresponding to the minimum overall price of the scheme as an alternative energy storage scheme.

[0137] When there are at least two simulation energy storage schemes with the same minimum overall price of the scheme, it indicates that there are multiple simulation energy storage schemes meeting the requirements, and therefore the simulation energy storage schemes are defined as alternative energy storage schemes to distinguish different simulation energy storage schemes and facilitate subsequent analysis.

[0138] Step S501: Combine the alternative energy storage schemes according to continuous unit intervals to construct continuous combinations, and count the continuous combinations to determine the number of energy storage times.

[0139] The continuous combination is a combination of continuous and uninterrupted unit intervals in the alternative energy storage scheme, and the continuous combination is the time interval in which the charging pile needs to perform energy storage operation. The number of energy storage times is the number of times that the charging station needs to output energy storage operation instructions and perform energy storage operation, which is determined by counting the continuous combinations one by one.

[0140] Step S502: determining the energy storage frequency with the minimum value according to the sorting rule, and defining the candidate energy storage scheme corresponding to the energy storage frequency as the effective energy storage scheme.

[0141] The energy storage frequency with the minimum value can be determined through the sorting rule, that is, the number of times of calling the charging station for energy storage operation is the least at this time, which is convenient for the charging station to perform energy storage operation, and therefore the corresponding candidate energy storage scheme is defined as the effective energy storage scheme.

[0142] After the energy storage frequency is determined, the intelligent charging station resource scheduling method based on data processing further comprises:

[0143] Step S600: determining whether there are at least two candidate energy storage schemes with the same and minimum energy storage frequency.

[0144] The purpose of the determination is to know whether there are multiple candidate energy storage schemes meeting the requirements, so as to determine the unique effective energy storage scheme.

[0145] Step S6001: if there are not at least two candidate energy storage schemes with the same and minimum energy storage frequency, defining the candidate energy storage scheme corresponding to the minimum energy storage frequency as the effective energy storage scheme.

[0146] When there are not at least two candidate energy storage schemes with the same and minimum energy storage frequency, it is indicated that there is only one candidate energy storage scheme meeting the requirements, and therefore it can be defined as the effective energy storage scheme.

[0147] Step S6002: if there are at least two candidate energy storage schemes with the same and minimum energy storage frequency, determining the overall stability value according to the unit interval in each candidate energy storage scheme, and determining the continuous stability value according to the overall stability value of the unit interval in the continuous combination.

[0148] When there are at least two candidate energy storage schemes with the same and minimum energy storage frequency, it is indicated that there are multiple candidate energy storage schemes meeting the requirements, and therefore further analysis is required; the continuous stability value is a parameter value reflecting the stability degree of the electricity price in the continuous combination, which is obtained by summing all the overall stability values.

[0149] Step S601: calculating the scheme stability value according to all the continuous stability values, determining the scheme stability value with the maximum value according to the sorting rule, and defining the candidate energy storage scheme corresponding to the scheme stability value as the effective energy storage scheme.

[0150] The stable value of the scheme is the parameter value obtained by averaging all continuous stable values. The larger the value, the more stable the electricity price in the scheme, which means that the possibility of abnormal fluctuations in the future is lower, and the prediction result is closer to the actual result. Therefore, by defining the alternative energy storage scheme corresponding to the scheme with the largest stable value as the effective energy storage scheme, low-cost electricity consumption can be achieved in the future.

[0151] In energy storage scheduling, the data processing-based smart charging station resource scheduling method also includes:

[0152] Step S700: Obtain the current actual electricity price for the current range.

[0153] The actual electricity price within a given interval is the energy storage electricity price corresponding to the unit interval when the charging station is within an effective energy storage scheme.

[0154] Step S701: Define the historical energy storage electricity price corresponding to the current time point as the interval theoretical electricity price, and calculate the difference between the interval actual electricity price and the interval theoretical electricity price to determine the prediction deviation electricity price.

[0155] The theoretical electricity price for a given period is the electricity price required at the current point in time when making electricity price predictions. The prediction deviation price is the value of electricity obtained by subtracting the theoretical electricity price for the period from the actual electricity price for that period.

[0156] Step S702: Determine whether the predicted deviation price is greater than the preset permissible deviation price.

[0157] The permissible deviation price is the maximum permissible deviation price set by staff when there is no significant deviation between the predicted result and the actual result. The purpose of the judgment is to determine whether the electricity price at the current point in time meets the prediction result, so as to control whether to carry out energy storage operations at the charging station.

[0158] Step S7021: If the predicted deviation price is not greater than the permissible deviation price, then control the charging station to perform energy storage dispatching operations.

[0159] When the predicted deviation price is not greater than the permissible deviation price, it indicates that there is no significant deviation in the price, and energy storage operations can proceed normally.

[0160] Step S7022: If the predicted deviation price is greater than the permissible deviation price, the unit interval between the current time point and the cutoff time point that is not within the effective energy storage scheme is defined as the candidate interval, and the historical energy storage price corresponding to the candidate interval is defined as the candidate theoretical price.

[0161] When the predicted deviation price is greater than the permissible deviation price, it indicates that there is a significant deviation between the predicted and actual results, requiring further analysis. Defining alternative ranges and alternative theoretical prices helps differentiate between different data points and facilitates subsequent analysis.

[0162] Step S703: judging whether there is an alternative interval with an alternative theoretical electricity price less than the actual electricity price of the current interval.

[0163] The purpose of the judgment is to know whether there is a subsequent time interval to replace the current time interval.

[0164] Step S7031: if there is no alternative interval with an alternative theoretical electricity price less than the actual electricity price of the current interval, the charging station is controlled to perform energy storage scheduling operation.

[0165] When there is no alternative interval with an alternative theoretical electricity price less than the actual electricity price of the current interval, it means that there is no suitable time interval to replace the energy storage operation of the current time interval, so the charging station is normally controlled to perform energy storage operation.

[0166] Step S7032: if there is an alternative interval with an alternative theoretical electricity price less than the actual electricity price of the current interval, the alternative interval is defined as a replacement interval, and a replacement interval is randomly selected to replace the current unit interval in the effective energy storage scheme.

[0167] When there is an alternative interval with an alternative theoretical electricity price less than the actual electricity price of the current interval, it means that there is a time interval that can replace the current time interval, so the replacement interval is defined to identify different alternative intervals, and a replacement interval is randomly selected to replace the current unit interval to achieve low-cost electricity consumption of the charging station; wherein when the replacement interval is selected, the number of energy storages is analyzed to reduce the number of energy storages as much as possible to facilitate the operation control of the charging station.

[0168] Reference Fig. 2 Based on the same inventive concept, the embodiment of the present application provides a smart charging station resource scheduling system based on data processing, comprising:

[0169] The acquisition module is used to acquire the remaining energy storage power;

[0170] The processing module is connected with the acquisition module and is used to store and process information;

[0171] The processing module performs difference calculation on the preset full-load energy storage power and the remaining energy storage power to determine the required supplementary power;

[0172] The processing module performs calculation on the preset energy storage efficiency and the required supplementary power to determine the energy storage demand duration;

[0173] The processing module constructs a permitted energy storage time period according to the current time point and the preset cutoff time point, and divides a unit interval according to the preset unit time in the permitted energy storage time period;

[0174] The processing module constructs a history interval with a current time point as a rear end point and a preset history length as a width on a preset time axis, and determines a similar interval according to each unit interval in the history interval, and determines a history energy storage electricity price in the similar interval;

[0175] The processing module combines each unit interval according to an energy storage demand length to construct a simulated energy storage scheme, and calculates each simulated energy storage scheme according to the corresponding history energy storage electricity price of each unit interval to determine a scheme overall electricity price;

[0176] The processing module defines the simulated energy storage scheme corresponding to the scheme overall electricity price with the smallest value as an effective energy storage scheme, and controls the charging station to perform energy storage scheduling in the unit interval in the effective energy storage scheme;

[0177] The similar interval determination module is configured to determine the similar interval of each unit interval;

[0178] The preceding interval determination module is configured to analyze the interval similarity to determine a more suitable preceding interval;

[0179] The abnormal situation analysis module is configured to analyze the situation that the effective similarity is greater than the demand similarity;

[0180] The simulated energy storage scheme screening module is configured to screen a plurality of simulated energy storage schemes meeting the requirements;

[0181] The alternative energy storage scheme screening module is configured to screen a plurality of alternative energy storage schemes meeting the requirements;

[0182] The energy storage scheme adjustment module is configured to adjust the energy storage scheme according to the electricity price during the energy storage operation of the charging station.

[0183] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

Claims

1. A data processing-based intelligent charging station resource scheduling method, characterized in that, The method comprises the following steps: acquiring a remaining energy storage power; calculating a demand supplement power by subtracting the remaining energy storage power from a preset full-load energy storage power; calculating an energy storage demand duration based on a preset energy storage efficiency and the demand supplement power; constructing an energy storage permission time period based on a current time point and a preset deadline time point, and dividing a unit interval based on a preset unit time in the energy storage permission time period; constructing a history interval with the current time point as a rear end point and a preset history time length as a width on a time axis, and determining a similar interval based on each unit interval in the history interval, and determining a historical energy storage power in the similar interval; combining each unit interval based on the energy storage demand duration to construct a simulated energy storage scheme, and calculating a scheme overall power based on the historical energy storage power corresponding to each unit interval in each simulated energy storage scheme; defining a simulated energy storage scheme corresponding to a scheme overall power with a minimum value as an effective energy storage scheme, and controlling the charging station to perform energy storage scheduling in the unit interval in the effective energy storage scheme; the step of determining the similar interval based on each unit interval in the history interval comprises: constructing a nearby interval with the current time point as a rear end point and a preset nearby time length as a width on the time axis, and dividing a first detection interval based on the unit time in the nearby interval; randomly dividing a comparison interval with a width consistent with the nearby interval in the history interval, and dividing a second detection interval based on the unit time in the comparison interval; acquiring a first real-time power of the first detection interval and a second real-time power of the second detection interval; calculating an interval similarity based on all the first real-time powers and the corresponding second real-time powers; determining an interval similarity with a maximum value based on a preset sorting rule, and defining the comparison interval corresponding to the interval similarity as a previous interval; constructing an after interval with a width consistent with the energy storage permission time period with the rear end point of the previous interval as a front end point, and dividing a similar interval corresponding to the unit interval based on the unit time in the after interval. 2.The data processing based intelligent charging station resource scheduling method according to claim 1, characterized in that, After the interval similarity is determined, the data processing-based intelligent charging station resource scheduling method further comprises: determining whether the interval similarity with the maximum value is greater than a preset demand similarity; if the interval similarity with the maximum value is greater than the demand similarity, determining the previous interval; if the interval similarity with the maximum value is not greater than the demand similarity, randomly selecting a first detection interval from all the first detection intervals as a key interval, and defining the remaining first detection intervals as secondary intervals; calculating an effective similarity based on the secondary intervals and the corresponding second detection intervals; determining whether there is a case that the effective similarity is greater than the demand similarity; if there is no case that the effective similarity is greater than the demand similarity, performing energy storage scheduling based on a preset threshold power; if there is a case that the effective similarity is greater than the demand similarity, determining the previous interval based on the comparison interval corresponding to the effective similarity with the maximum value. 3.The data processing based intelligent charging station resource scheduling method according to claim 2, characterized in that, if there is a case that the effective similarity is greater than the demand similarity, the data processing-based intelligent charging station resource scheduling method further comprises: Define the key interval corresponding to the maximum effective similarity as an abnormal interval, and define the first real-time electricity price corresponding to the abnormal interval as an abnormal electricity price; Define the second real-time electricity price corresponding to the second detection interval corresponding to the abnormal interval in the historical interval as a normal electricity price; Calculate according to the abnormal electricity price and the normal electricity price to determine the overall smooth value; Determine whether the overall smooth value is greater than the preset demand smooth value; If the overall smooth value is greater than the demand smooth value, determine the previous interval according to the comparison interval corresponding to the maximum effective similarity; If the overall smooth value is not greater than the demand smooth value, determine the maximum effective similarity in the remaining effective similarity until the previous interval is determined or the threshold electricity price is used for energy storage scheduling. 4.The data processing based intelligent charging station resource scheduling method according to claim 3, characterized in that, After determining the overall electricity price of the scheme, the intelligent charging station resource scheduling method based on data processing further includes: Determine whether there are at least two simulation energy storage schemes with the same minimum overall electricity price of the scheme; If there are not at least two simulation energy storage schemes with the same minimum overall electricity price of the scheme, define the simulation energy storage scheme corresponding to the minimum overall electricity price of the scheme as an effective energy storage scheme; If there are at least two simulation energy storage schemes with the same minimum overall electricity price of the scheme, define the simulation energy storage scheme corresponding to the minimum overall electricity price of the scheme as an alternative energy storage scheme; Combine according to the continuous unit interval in each alternative energy storage scheme to construct a continuous combination, and count according to the continuous combination to determine the energy storage times; Determine the minimum energy storage times according to the sorting rule, and define the alternative energy storage scheme corresponding to the energy storage times as an effective energy storage scheme. 5.The data processing based intelligent charging station resource scheduling method according to claim 4, characterized in that, After determining the energy storage times, the intelligent charging station resource scheduling method based on data processing further includes: Determine whether there are at least two alternative energy storage schemes with the same minimum energy storage times; If there are not at least two alternative energy storage schemes with the same minimum energy storage times, define the alternative energy storage scheme corresponding to the minimum energy storage times as an effective energy storage scheme; If there are at least two alternative energy storage schemes with the same minimum energy storage times, determine the overall smooth value according to the unit interval in each alternative energy storage scheme, and determine the continuous smooth value according to the overall smooth value of the unit interval in the continuous combination; Calculate all continuous smooth values to determine the scheme smooth value, determine the maximum scheme smooth value according to the sorting rule, and define the alternative energy storage scheme corresponding to the scheme smooth value as an effective energy storage scheme. 6.The data processing based intelligent charging station resource scheduling method according to claim 1, characterized in that, When energy storage is scheduled, the intelligent charging station resource scheduling method based on data processing further includes: Obtain the current interval actual electricity price; Define the historical energy storage electricity price corresponding to the current time point as the interval theoretical electricity price, and calculate the prediction deviation electricity price by difference according to the interval actual electricity price and the interval theoretical electricity price; Determine whether the prediction deviation electricity price is greater than the preset allowable deviation electricity price; If the prediction deviation electricity price is not greater than the allowable deviation electricity price, control the charging station to perform energy storage scheduling operation; If the predicted deviation price is greater than the permitted deviation price, a unit interval not in the effective energy storage scheme between the current time point and the deadline time point is defined as a candidate interval, and a historical energy storage price corresponding to the candidate interval is defined as a candidate theoretical price; It is judged whether there is a candidate interval with a candidate theoretical price less than the actual price of the current interval; If there is no candidate interval with a candidate theoretical price less than the actual price of the current interval, the charging station is controlled to perform energy storage scheduling operation; If there is a candidate interval with a candidate theoretical price less than the actual price of the current interval, the candidate interval is defined as a replacement interval, and a replacement interval is randomly selected to replace the current unit interval in the effective energy storage scheme.

7. A data processing-based intelligent charging station resource scheduling system, characterized in that, It comprises: An acquisition module is configured to acquire the remaining energy storage capacity; A processing module is connected with the acquisition module and is configured to store and process information; The processing module performs difference calculation on the preset full-load energy storage capacity and the remaining energy storage capacity to determine the required supplementary capacity; The processing module performs calculation on the preset energy storage efficiency and the required supplementary capacity to determine the energy storage requirement duration; The processing module constructs a permitted energy storage time period according to the current time point and the preset deadline time point, and divides unit intervals according to the preset unit time length in the permitted energy storage time period; The processing module constructs a historical interval with the current time point as the rear end point and a preset historical time length as the width on a preset time axis, and determines similar intervals according to each unit interval in the historical interval, and determines the historical energy storage price in the similar interval; The processing module combines each unit interval according to the energy storage requirement duration to construct a simulated energy storage scheme, and calculates the overall price of each simulated energy storage scheme according to the historical energy storage price corresponding to each unit interval; The processing module defines the simulated energy storage scheme corresponding to the overall price with the smallest value as the effective energy storage scheme, and controls the charging station to perform energy storage scheduling in the unit interval in the effective energy storage scheme; The step of determining similar intervals according to each unit interval in the historical interval comprises: The processing module constructs a nearby interval with the current time point as the rear end point and a preset nearby time length as the width on the time axis, and divides a first detection interval according to the unit time length in the nearby interval; The processing module randomly divides a comparison interval with the same width as the nearby interval in the historical interval, and divides a second detection interval according to the unit time length in the comparison interval; The acquisition module acquires a first real-time price of the first detection interval and a second real-time price of the second detection interval; The processing module calculates the interval similarity according to all the first real-time prices and the corresponding second real-time prices; The processing module determines the interval similarity with the largest value according to the preset sorting rule, and defines the comparison interval corresponding to the interval similarity as a previous interval; The processing module constructs a subsequent interval with the same width as the permitted energy storage time period with the rear end point of the previous interval as the front end point, and divides a similar interval corresponding to the unit interval according to the unit time length in the subsequent interval.

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