KR102616314B1 - Electric vehicle charger operating device - Google Patents

Abstract

The electric vehicle charger operating device of the present invention may include a data collection unit that collects customer demand data from the electric vehicle charger, and a charging rate proposal unit that suggests an increase or decrease in the charging rate based on the customer demand data to the operator terminal of the electric vehicle charger. In addition, the unit price of the charging fee can be set differently according to customer demand, so that customer demand for electric vehicle chargers can be adjusted.

Description

Electric vehicle charger operating device

The present invention relates to an electric vehicle charger operating device capable of setting electric vehicle charging rates or diagnosing abnormalities in the charger based on customer demand forecast.

The current charging fee for electric vehicle charging stations is the cost received from KEPCO, but the consumer price provided to customers by each business operator is determined by the business operator's discretion.

In addition, there are cases where KEPCO supply costs by time and season are provided when charging consumers decide for each business, but factors that occur during charging station operation, such as power load by system, power demand by region, and number of people waiting at electric vehicle charging stations, are not reflected.

Application number 10-2020-0032685 seeks to generate profits from power reduction by managing charging power demand through usage, reduction amount, reduction amount setting, reduction amount contract, etc., charging using accumulated metering values. A charging power reduction amount calculation unit that calculates the power reduction amount; It is characterized in that it includes a charging power reduction request unit that requests charging power reduction by transmitting charging power reduction information to the demand management service provider server according to the charging power reduction amount calculated by the charging power reduction amount calculation unit.

Existing patents that seek to generate profits through power reduction, such as application number 10-2020-0032685, suggest specific methods for calculating charging power reduction or generating charging power reduction information. It is the majority.

The present invention discloses a specific process in which charging rates can be proposed to the operator according to customer demand when operating an electric vehicle charging station, and charging rates are proposed based on data on customer demand using the charger.

Additionally, the present invention can use customer demand data collected during the operation of an electric vehicle charging station to generate a band that can determine whether there is an abnormality in the electric vehicle charger.

The electric vehicle charger operating device of the present invention may include a data collection unit that collects customer demand data from the electric vehicle charger, and a charging rate proposal unit that suggests an increase or decrease in the charging rate based on the customer demand data to the operator terminal of the electric vehicle charger. In addition, the unit price of the charging fee can be set differently according to customer demand, so that customer demand for electric vehicle chargers can be adjusted.

The electric vehicle charger operating device of the present invention may include a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on customer demand data, and generates a band with a band boundary between the upper band limit and the lower band limit. It may include a unit, and the charger abnormality determination unit may determine normal operation if the customer minimum value or customer maximum value is within the band, and abnormal operation if it is outside the band.

The present invention not only proposes an appropriate charging rate setting for an electric vehicle charger based on customer demand data collected while operating an electric vehicle charging station, but also generates a band from the standard deviation of customer demand to determine whether the charger is abnormal. You can.

As such, the charging rate setting and charger abnormality judgment of the present invention are based on customer demand data obtained while operating an actual electric vehicle charging station, so even though demand is different for the multiple chargers owned by each business operator, a single price is formed for each business operator. , the operation of electric vehicle charging stations can be improved by minimizing the number of chargers that are empty due to lack of demand while there are chargers that are in high demand.

By proposing charging rates based on customer demand and detecting charger abnormalities of the present invention, sales can be increased by reducing the number of idle chargers, and customer waiting time is reduced, thereby increasing the utility of the charging station, reducing inconvenience, and increasing sales. As a result, the business feasibility of electric vehicle chargers can be improved, profitability can be verified, and profitability data objectivity can be secured.

1 is an explanatory diagram of an environment in which the electric vehicle charger operating device and operating method of the present invention is applied.
Figure 2 is a configuration diagram of the electric vehicle charger operating device of the present invention.
Figure 3 is a flowchart of the electric vehicle charger operation method of the present invention.
Figure 4 is a configuration diagram of the charging fee setting step of the present invention.
Figure 5 is an example of data collected by the data collection unit of the present invention.
Figure 6 (a) is an explanatory diagram of the minimum and maximum number of customers calculation step, sampling step, and expected probability code calculation step for the customer maximum value probability distribution of the present invention.
Figure 6 (b) is an explanatory diagram of the minimum and maximum number of customers calculation step, sampling step, and expected probability code calculation step for the customer minimum value probability distribution of the present invention.
Figure 7 is a configuration diagram of the electric vehicle charger abnormality detection step of the present invention.
Figure 8 is an explanatory diagram of the standard deviation calculation step, band creation step, and electric vehicle charger abnormality determination step of the present invention.

With reference to FIGS. 1 to 8, the electric vehicle charger operating device and operating method of the present invention will be described.

Figure 1 illustrates an environment in which the electric vehicle charger operating device and operating method of the present invention can be applied. Each electric vehicle charging station 20 may be equipped with a plurality of electric vehicle chargers 30. Each electric vehicle charging station 20 may belong to the same charging station operator or to different operators.

There may be differences in customer demand for each charger, and charging fees may be set accordingly. In other words, the present invention can set the charging unit price to adjust the customer demand by varying the charging unit price according to customer demand.

The present invention may be proposed between chargers 30 belonging to the same charging station 20 according to the set charging fee, but it provides price information of other nearby electric vehicle charging stations 20 and provides chargers with less customer demand but lower charging fees ( 30) can attract charger users.

At this time, if the separated electric vehicle charging stations 20 belong to the same operator, the electric vehicle charger operating device of the present invention can be used to receive information on all electric vehicle charging stations 20 belonging to the same operator and operate them in an integrated manner.

In addition, even if the separated electric vehicle charging stations 20 belong to different operators, customer demand data for their chargers 30 can be mutually shared through a contract between charging station operators, and charging rates for other charging stations 20 can be shared. By setting fees for information provision and inducement, other charging station operators can also generate profits.

As such, the present invention does not simply attract waiting customers with a large number of charging customers to another nearby electric vehicle charging station 20 with a small number of charging customers, but rather uses predicted or simulated customer demand data from spaced electric vehicle charging stations 20. Since a premium is added to the charging unit price based on the charging fee determined according to the results, it can be beneficial to both electric vehicle charging station operators and electric vehicle driver customers.

Customer demand data collected from a plurality of chargers 30 provided at each electric vehicle charging station 20 may be transmitted to the server 90 or the operator terminal 50. Customer demand data may refer to the number of times charging has been performed at an individual charger 30, and only the complete completion of the charging process from connecting to disconnecting the charging cable may be reflected in the number counting.

The server 90 can provide its own app or a public app to the operator terminal 50 to mutually transmit and receive information such as mutual commands or feedback.

The components included in the electric vehicle charger operating device of the present invention may be determined whether to be provided in the electric vehicle charging station 20 or the server 90 depending on the charging station facility conditions, such as the size of the electric vehicle charging station 20. That is, all of the components included in the electric vehicle charger operating device may be provided in the server 90, or at least some of them may be provided in the server 90.

2 and 3, the electric vehicle charger operating device of the present invention includes a data collection unit 100, a minimum and maximum value calculation unit 210, a probability distribution calculation unit for the number of customers 230, and a charging fee proposal unit 250. , It may include at least one of a server 90, a standard normal distribution calculation unit 310, a band generation unit 330, a charger abnormality determination unit 350, a customer distribution unit 400, and a control unit 420. .

The electric vehicle charger operating method of the present invention may include at least one of a data collection step (S100), a charging fee setting step (S200), a charger abnormality detection step (S300), and a customer distribution step (S400).

In the data collection step (S100), the data collection unit 100 may collect and store customer demand data from the individual charger 30. If it is necessary to separate storage and collection due to the large amount of data, a separate database can be prepared to store customer demand data, and in this case, the data collection unit 100 can only perform the function of collecting customer demand data.

Customer demand data does not simply mean statistical results, but contains extensive information about the charger 30 over a long period of time, so the data collected by the data collection unit 100 is directly sent to the server 90. can be transmitted. However, in case customer demand data cannot always be transmitted in real time due to data transmission failure or equipment inspection, a means for storing at least some data is provided by the electric vehicle charger 30 or charging station ( 30) needs to be provided.

Unless otherwise specified below, the data collection unit 100 may be interpreted as performing both collection and storage functions.

Customer demand data may refer to the number of customers who have completed charging using the charger 30 according to various factors. These factors may include at least one of the following: past weather, day of the week, whether it is a public holiday, or time of day. Weather can mean cloudy, clear, etc., day of the week can mean calendar days of the 1st and 2nd, and time can mean unit time including seconds/minutes/hours, etc.

Basically, the data collection unit 100 can record the number of customers over time, and based on this, it can record the weather by time, and can record customer number information by day of the week or public holiday.

This shows an example of customer demand data provided by the data collection unit 100. Customer demand data can indicate the number of customers according to factors, regardless of the type of factor.

In the data collection step (S100), the customer demand data collected by the data collection unit 100 is sent to at least one of the charging rate setting step (S200), the electric vehicle charger abnormality detection step (S300), and the customer distribution step (S400). It can be used.

In the charging rate setting step (S200), the expected probability distribution of the future number of customers calculated using the customer demand data provided by the data collection unit 100 in the data collection step (S100) is compared with the current number of customers to determine an appropriate charging rate. It may be a suggestion.

The charging rate setting step (S200) includes a minimum and maximum number of customers calculation step (S202), a sampling step (S210), an expected probability distribution calculation step (S230), a comparison step (S250), and a charging rate proposal step (S270). It may include at least one of:

Referring to FIG. 5, in the step of calculating the minimum and maximum number of customers (S202), the minimum and maximum value calculation unit 210 calculates the minimum and maximum values of the number of customers from the remainder excluding outliers from the customer demand data of the number of customers for each factor. The value can be calculated.

At this time, the excluded outliers can be set to the upper and lower X %, and X can be selectively determined according to customer demand data for each charging station/charger. Parameter

The step of calculating the minimum and maximum number of customers (S202) may be the same process as the step of calculating the minimum and maximum number of customers (S302) performed at the beginning of the charger abnormality detection step (S300).

Step S202 of the charging rate setting step (S200) and step S302 of the electric vehicle charger abnormality detection step (S300) are intended to exclude exceptional values that cannot be included in meaningful data before full-scale sampling or band formation. Therefore, if it is determined that such exceptional data has not been collected from the perspective of overall customer demand data, an algorithm in which steps S202 and S302 are excluded and the next step is immediately performed can be applied.

For example, the charging rate setting step (S200) will calculate the expected probability distribution for the minimum value of the number of customers and the expected probability distribution for the maximum value, so the median value can be estimated from these results, and outliers are determined by a predetermined value from the median value. If it falls outside the set range, it may be judged as an outlier. Likewise, in the electric vehicle charger abnormality detection step (S300), an abnormality in the charger is detected by calculating the upper and lower limits of the band for the number of customers, so that the average line of the calculated upper and lower band limits can be estimated, and from this, the abnormality value is determined from the average line. If it is outside the preset range, it may be judged as an outlier.

The customer probability distribution calculation unit 230 may calculate the expected probability distribution of the number of customers in the expected probability distribution calculation step (S230) through sampling in the sampling step (S210).

That is, the customer number probability distribution calculation unit 230 may perform sampling using the results of the minimum and maximum customer number calculation step (S202) in the sampling step (S210), and sample in the expected probability distribution calculation step (S230). The expected probability distribution of the number of customers can be calculated using the sampling result of step S210.

Referring to (a) and (b) of FIG. 6, the sampling in the sampling step (S210) is performed on each of the minimum and maximum customer numbers calculated in the minimum and maximum customer number calculation step (S202). It can be calculated by multiplying an arbitrary value between 1-b) and (1+b), and b can satisfy 0<b<1.

Sampling in the sampling step (S210) may be similar to Monte-Carlo Simulation (MCS) using random number extraction. Due to its nature, MCS is more efficient and can obtain solutions closer to reality as the number of sampling increases. Customer demand data obtained from multiple electric vehicle chargers 30 can be collected in real time, depending on the number of chargers, and a large amount of data can be collected. Sampling in the sampling step (S210) of the present invention is expected to provide fairly accurate data.

By this sampling, the probability distribution of the minimum value of the number of customers or the minimum value of customer demand, and the probability distribution of the maximum value of the number of customers or the maximum value of customer demand can be obtained. The probability distribution of the minimum value of the number of customers and the probability distribution of the maximum value of the number of customers calculated by this method can be used as the expected probability distribution for the future, so that when the charging rate is proposed in the charging rate proposal step (S270), the current number of customers and can be compared.

Multiplying each of the customer count minimums or customer count maximums by an arbitrary value between (1-b) and (1+b) may give the individual values an acceptable variability of 2b intervals. The probability distribution for the minimum number of customers or the maximum number of customers calculated by giving a certain interval of volatility to all individual values has the effect of predicting future customer demand based on customer demand data from the past to the present. I can give it. For example, the expected probability distribution may correspond to the expected demand in 15 minutes. Likewise, the expected probability distribution can represent the expected number of customers for factors including at least one of weather, day of the week, whether it is a public holiday, or time.

In one embodiment, the volatility grant interval may be given as 2b, giving a freedom of variation greater than 0 and less than 2. As in the case of excluding the top and bottom x % when excluding outliers, the parameter b can be arbitrarily changed by various factors such as electric vehicle charging station/electric vehicle charger/region.

The charging rate proposal unit 250 may propose a charging rate based on customer demand to the charging station or charger operator through a comparison step (S250) and a charging rate proposal step (S270).

The charging fee proposal unit 250 may compare the expected probability distribution for the minimum number of customers and the expected probability distribution for the maximum number of customers calculated in the expected probability distribution calculation step (S230) in the comparison step (S250) with the current number of customers. . The current number of customers may be the sum of the current number of users and the number of people waiting for electric vehicle chargers under the management of the operator.

The comparison in the comparison step (S250) may specifically determine whether the current number of customers falls within the upper r% or lower range or the lower r% range of the expected probability distribution.

In the charging fee proposal step (S270), the charging fee proposal unit 250 may propose a reduction in the charging fee if the current number of customers falls within the lower r% range of the expected probability distribution of the minimum number of customers. If the number falls within the upper r% range of the expected probability distribution of the maximum number of customers, an increase in the charging fee can be proposed.

The present invention analyzes data on demand increases and decreases when charging rates change, so that chargers 30 with insufficient demand can attract customers by lowering the price, and charging stations 20 with many waiting customers temporarily receive a premium on the charging unit price. You can expect the effect of dispersing customers by adding . In addition, customer demand can be predicted using machine learning to increase or decrease demand based on past operation data of electric vehicle charging stations, and based on this, the present invention can automatically adjust the charging fee unit price.

Similar to the It can be arbitrarily changed according to customer demand data due to various reasons such as.

The proposal cycle and proposal unit of the charging fee proposal unit 250 may be determined according to the content of the collected customer demand data. For example, the suggestion cycle may be 15 minutes, and the suggestion unit may be increased or decreased by 10 won. The proposal cycle can be shortened as the number of customers using the charger under operation increases. The proposed unit can be adjusted according to changes in the cost of electricity supplied from KEPCO, customer reactions to changes in each proposed unit, etc.

Referring to Figures 7 and 8, the charger abnormality detection step (S300) includes the minimum and maximum number of customers calculation step (S302), standard deviation calculation step (S310), band creation step (S330), and band average calculation step ( It may include at least one of S350), and a charger abnormality determination step (S370).

Step S302 may be the same process as S202 of the charging fee setting step (S200). Step S302 may be to select data that gives valid meaning among customer demand data used after the standard deviation calculation step (S310).

In the standard deviation calculation step (S310), the standard normal distribution calculation unit 310 calculates the standard normal distribution for the customer fluctuation range from customer demand data including the minimum and maximum customer number values in the customer minimum and maximum value calculation step (S302). and standard deviation can be calculated.

The customer fluctuation range can be obtained from the difference between the minimum and maximum values of the number of customers. For example, the customer fluctuation range can be calculated after synchronizing the minimum and maximum values of the number of customers by the average of each section according to each factor including weather, time, etc.

The standard deviation in the standard deviation calculation step (S310) is obtained using the customer fluctuation range itself according to factors such as time, or by deriving the average number of customers by averaging the minimum and maximum number of customers to have the same meaning. It can be obtained using the distance between and the minimum or maximum value.

In the band creation step (S330), the band creation unit 330 may create a band having the upper band limit and the lower band limit as boundaries.

The band upper limit can be given by adding the average line of the number of customers and the value multiplied by the standard deviation calculated in the standard deviation calculation step (S310) and the parameter y. That is, the upper limit of the band may be the average value of the number of customers during a predetermined period + (standard deviation value of the number of customers during a predetermined period) * y.

The lower limit of the band can be given as the mutual difference between the average line of the number of customers and the value multiplied by the standard deviation calculated in the standard deviation calculation step (S310) and the parameter y. That is, the lower limit of the band may be the average value of the number of customers during a predetermined period - (standard deviation value of the number of customers during a predetermined period) * y.

Parameter y may be determined according to the reliability of the band. For example, if the standard normal distribution calculated by the standard normal distribution calculation unit 310 in the expected probability distribution calculation step (S230) is set to have a reliability of 95% or more in the 2σ range (-2σ to +2σ), Parameter y can be set to 2.

The reliability of the band may refer to the accuracy of comparing the difference between the predicted result and the actual result by applying the electric vehicle charger operating device and method of the present invention. For example, having a reliability of over 95% can be interpreted as customer demand data repeating convergence and divergence within a band with a 95% probability.

In the band creation step (S330), the band generated by the band generator 330 can be obtained for each individual charger.

If you want to track the band for each charger, the band creation step (S330) is immediately followed by the charger abnormality determination step (S370), and it is possible to determine whether each charger is operating normally or abnormally.

In the charger abnormality determination step (S370), the charger abnormality determination unit 350 may determine that, for each charger 30, if the charger is within the band, it operates normally, and if it falls outside the band and falls outside the band, it can determine that it operates abnormally. In particular, the charger abnormality determination unit 350 is suspected of parking interference, such as failure, loading in a parking area, parking of an internal combustion engine vehicle, etc., when the customer's maximum or minimum value outside the band lasts longer than a preset period. It is possible to determine abnormal operation of the charger and send a warning.

When the operator of the charging station 20 or charger 30 receives an abnormal operation alert, he or she can monitor the area around the charging station 20 or charger 30 with CCTV or cameras, or deploy personnel to the site to take action to determine the cause of the failure. .

If there are a plurality of chargers 30 under the control of the charging station operator and a plurality of charging stations 20 equipped with a plurality of chargers 30 are managed, a band average obtained by averaging the bands of individual chargers may be useful. The band average obtained from the plurality of chargers 30 in this way can also be used as a standard when operating an additional electric vehicle charger 30. The charger abnormality determination unit 350 may determine normal operation if the customer minimum value or customer maximum value is within the average band, and abnormal operation if it is outside the average band.

Therefore, in the band average calculation step (S350), the control unit 420 can calculate the band average by averaging the individual bands of all chargers 30 under operation, where the band average determines whether the charger is operating normally or abnormally. It can become a standard.

Alternatively, the control unit 420 may obtain a band average obtained by averaging individual bands of only some of the chargers 30 under operation in the band average calculation step (S350). The band average here can be used for inspection before issuing an alarm to the charger 30 where abnormal operation is suspected.

For example, if the charger abnormality determination unit 350 determines that the second charger is operating abnormally in the charger abnormality determination step (S370), the control unit 420 determines that the first charger, which is a peripheral charger disposed adjacent to the second charger, Determine whether to issue a final warning for abnormal operation of the second charger by calculating the average between the band of the charger and the band of the third charger, or the band of the first charger and the third charger, or the average of the first to third chargers. can do.

In this way, the operation of the control unit 420 in the band average calculation step (S350) not only uses the average of the bands for each surrounding charger to determine abnormal operation, but also uses the normal operation pattern of chargers surrounding the charger suspected of abnormal operation or This may include identifying band patterns.

In the customer distribution step (S400), the customer distribution unit 400 may interconnect the charging rate proposal of the charging rate proposal unit 250 and the failure prediction or failure detection of the charger abnormality determination unit 350. For example, the charger 30, which is predicted to reach the end of its life or break down by the charger abnormality determination unit 350, is replaced by another charger 30 by increasing the unit price of the charging fee by the charging fee proposal unit 250. ), the effect of dispersing customers can be achieved by inducing charging.

Accordingly, the present invention functions as an integrated platform where the charging station operator not only monitors changes in the number of customers according to operating hours, but also receives unit price suggestions for charging charges or is provided with information on the occurrence of charger abnormalities or the probability of abnormalities occurring. You can.

10... Electric vehicles
20... Electric vehicle charging station
30... Electric vehicle charger
50... Operator terminal
90...server
100... Data collection department
210... Minimum and maximum value calculation unit
230... Number of customers probability distribution calculation unit
250... Charging fee proposal
310... Standard normal distribution calculation unit
330... Band creation unit
350... Charger error judgment unit
400... Customer Distribution Department
420... Control unit
S100... data collection phase
S200... Charging fee setting steps
S202... Step of calculating minimum and maximum number of customers
S210... Sampling stage
S230... Expected probability distribution calculation step
S250... comparison steps
S270... Charging fee proposal stage
S300... Charger abnormality detection stage
S302... Step of calculating minimum and maximum number of customers
S310... Standard deviation calculation step
S330... Band creation step
S350... band averaging step
S370... Determination stage of charger abnormality
S400... Customer distribution phase

Claims (16)

A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
The above charging fee proposal section,
Compare the expected probability distribution for the minimum number of customers and the expected probability distribution for the maximum number of customers with the current number of customers,
Determine whether the current number of customers falls within the upper r% or lower r% range of each expected probability distribution,
If the current number of customers falls below the bottom r% of the expected probability distribution of the minimum number of customers, a reduction in the charging fee is proposed,
An electric vehicle charger operating device that proposes an increase in charging fees when the current number of customers falls within the upper r% range of the expected probability distribution of the maximum number of customers.
According to claim 1,
A customer probability distribution calculation unit that samples using the customer demand data and calculates an expected probability distribution of the number of customers using an electric vehicle charger using the sampling,
The sampling is an electric vehicle charger operating device using a Monte-Carlo Simulation method using random number extraction.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a minimum and maximum value calculation unit that calculates the minimum number of customers and the maximum number of customers from the remainder excluding outliers from the customer demand data,
A customer probability distribution calculation unit that performs sampling using the customer demand data and calculates an expected probability distribution of the number of customers using an electric vehicle charger using the sampling,
The sampling is calculated by multiplying each of the minimum number of customers or the maximum number of customers calculated in the minimum and maximum value calculation unit by a random value between (1-b) and (1+b), where the parameter b is 0< An electric vehicle charger operating device that satisfies b<1.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on the customer demand data,
It includes a band generator that generates a band bordering the upper band limit and the lower band limit,
The charger abnormality determination unit determines normal operation if the customer minimum value or customer maximum value is within the band, and abnormal operation if it is outside the band.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a standard normal distribution calculation unit that calculates the standard normal distribution and standard deviation for the customer fluctuation range from customer demand data including the minimum and maximum values of the number of customers,
It includes a band generator that generates a band bordering the upper band limit and the lower band limit,
The upper band limit is calculated by adding the average line of the number of customers multiplied by the standard deviation and parameter y,
The lower limit of the band is an electric vehicle charger operating device calculated by subtracting the average line of the number of customers multiplied by the standard deviation and parameter y.
According to clause 5,
The parameter y is determined according to the reliability of the band,
The reliability of the band means that the customer demand data repeats convergence and divergence within the band with a probability greater than reliability.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
A customer probability distribution calculation unit that performs sampling using the customer demand data and calculates an expected probability distribution of the number of customers using an electric vehicle charger using the sampling,
The charging fee proposal unit compares the expected probability distribution of the minimum number of customers and the expected probability distribution of the maximum number of customers calculated by the probability distribution calculation unit of the number of customers with the current number of customers,
An electric vehicle charger operating device where the current number of customers is the sum of the current number of users and the number of people waiting for the electric vehicle charger.
delete According to claim 1,
The above customer demand data refers to the number of customers who completed charging using an electric vehicle charger according to factors,
An electric vehicle charger operating device wherein the factors include at least one of weather, day of the week, whether it is a public holiday, or time.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a minimum and maximum value calculation unit that calculates the minimum number of customers and the maximum number of customers from the remainder excluding outliers from the customer demand data,
Excluded outliers are set to top and bottom X%,
Parameter
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on the customer demand data,
The charger abnormality determination unit is an electric vehicle charger operating device including a standard normal distribution calculation unit that calculates a standard normal distribution and standard deviation for the customer fluctuation range from customer demand data including the minimum and maximum values of the number of customers.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a standard normal distribution calculation unit that calculates the standard normal distribution and standard deviation for the customer fluctuation range from customer demand data including the minimum and maximum values of the number of customers,
The standard deviation is,
It can be obtained using the customer fluctuation range itself according to factors including time, or
It is obtained using the distance between the average number of customers and the minimum customer value, or the distance between the average number of customers and the maximum customer value,
Here, the average number of customers is an electric vehicle charger operating device derived by averaging the minimum and maximum values of the number of customers.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on the customer demand data,
It includes a band generator that generates a band bordering the upper band limit and the lower band limit,
The band generator creates a band for each charger,
A control unit that calculates a band average obtained by averaging the bands of a plurality of chargers under the management of an operator,
The band average serves as a standard for normal operation of a plurality of chargers under the operator's management,
The charger abnormality determination unit determines normal operation if the customer minimum value or customer maximum value is within the average band, and abnormal operation if it is outside the average band.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on the customer demand data,
It includes a band generator that generates a band bordering the upper band limit and the lower band limit,
The charger abnormality determination unit determines normal operation if the customer minimum value or customer maximum value is within the band, and abnormal operation if it is outside the band,
An electric vehicle charger operating device comprising a control unit that determines whether to issue a final warning for the abnormal operation by determining the band pattern of other adjacent chargers that are judged to be abnormal when the charger abnormality determination unit determines that the operation is abnormal.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
A customer probability distribution calculation unit that performs sampling using the customer demand data and calculates an expected probability distribution of the number of customers using an electric vehicle charger using the sampling,
The sampling is,
Variability is given at a predetermined interval to all individual values including the minimum number of customers and the maximum number of customers of the customer demand data,
An electric vehicle charger operating device that predicts future customer demand based on the customer demand data from the past to the present.
A data collection unit that collects customer demand data from electric vehicle chargers; and
A charging rate proposal unit that proposes an increase or decrease in the charging rate based on the customer demand data in the operator terminal of the electric vehicle charger; Including,
By varying the unit price of the charging fee according to customer demand, the unit price of the charging fee is set to adjust customer demand for electric vehicle chargers.
It includes a charger abnormality determination unit that determines whether each charger is operating normally or abnormally based on the customer demand data,
It includes a customer distribution unit that interconnects the charging fee proposal of the charging fee proposal unit and the failure prediction or failure detection of the charger abnormality determination unit,
The customer distribution unit is an electric vehicle charger operating device that induces charging with another charger by causing the charging fee proposal unit to suggest an increase in the charging fee when a predetermined charger is predicted to reach the end of its life or break down by the charger abnormality determination unit. .

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