A method, apparatus, device, and medium of controlling discharge

Abstract

The application provides a method, device, equipment and medium for controlling discharge, the method comprising: predicting a first predicted power output by a target charging station in a preset time period by using current time information, weather information of the target charging station and a station power time sequence prediction model; adjusting the first predicted power by using at least one charging service already provided by the target charging station to obtain a second predicted power required to be output by the target charging station in the preset time period; and controlling the target charging station to discharge for the charging service in the preset time period based on the second predicted power.

Description

Technical Field

[0001] This application relates to the field of charging station discharge technology, and more specifically, to a method, apparatus, equipment, and medium for controlling discharge. Background Technology

[0002] With societal progress, the number of gasoline-powered vehicles is increasing. However, this increase in the number of gasoline-powered vehicles has led to significant pollution from vehicle exhaust emissions. To improve the environment, electric vehicles, which have a smaller environmental impact, have gradually replaced most gasoline-powered vehicles.

[0003] However, as the number of electric vehicles gradually increases, the problem of electric vehicle charging has gradually emerged. It is difficult for the power supply capacity of charging stations to reach a balance with the charging demand. Once the balance cannot be met, it is easy to put a burden on the power grid and increase the pressure on urban power supply. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, device and medium for controlling discharge, which can solve the problem in the prior art that it is difficult to balance the power supply capacity and charging demand of charging stations by making short-term conformity predictions on currently observed charging events.

[0005] In a first aspect, embodiments of this application provide a method for controlling discharge, including:

[0006] Using the current time information, weather information and power time-series prediction model of the target charging station, the first predicted power output of the target charging station within a preset time period is predicted.

[0007] The first predicted power is adjusted using at least one charging service already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period;

[0008] Based on the second predicted power, the target charging station is controlled to discharge for charging service within a preset time period.

[0009] Optionally, the method further includes:

[0010] The time and weather information of the target charging station are input into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station may provide;

[0011] Using charging information from at least one charging service already provided by the target charging station, determine a second quantity of each type of charging service already provided by the target charging station.

[0012] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0013] Using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service, the first charging power required by the target charging station within a preset time period is obtained;

[0014] Based on the first quantity of each type of charging service that the target charging station may provide and the event power curve of each type of charging service, the second charging power that the target charging station needs to provide within a preset time period is obtained;

[0015] The first difference between the first charging power and the second charging power is added to the first predicted power to obtain the second predicted power.

[0016] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0017] For each type of charging service, calculate a second difference between the second number of charging services of that type already provided by the target charging station and the first number of charging services of that type that the target charging station may provide;

[0018] The first predicted power is adjusted based on the sum of the product of the second difference for each charging service type and the event power curve for that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0019] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0020] For each type of charging service, calculate the average of the first quantity of the charging service type and the second quantity of the charging service type;

[0021] The first predicted power is adjusted based on the sum of the products of the average number of each charging service type and the event power curve of that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0022] Optionally, determining the second quantity of each type of charging service already provided by the target charging station using charging information from at least one charging service already provided by the target charging station includes:

[0023] Input the charging information of at least one charging service already provided by the target charging station into the charging service classification model to determine the charging service type corresponding to each charging service.

[0024] Count the second number of each type of charging service already provided by the target charging station.

[0025] Optionally, the charging service classification model is trained through the following steps:

[0026] Obtain historical charging samples from the target charging station; the historical charging samples contain charging information and charging service types for historical charging orders;

[0027] For each historical charging sample, the charging information of the historical charging sample is used as a positive sample, and the charging service type of the historical charging sample is used as a negative sample. The charging service classification model to be trained is then trained to obtain the trained charging service classification model.

[0028] Optionally, the event power curve for each charging service type is determined through the following steps:

[0029] Obtain the charging power curves corresponding to multiple historical charging orders for each charging service type in the target charging station;

[0030] For each type of charging service, the charging power curves corresponding to multiple historical charging orders of that charging service type are aggregated and averaged to obtain the event power curve of that charging service type.

[0031] Optionally, the charging information includes any one or more of the following: vehicle information for the charging service, charging pile information for the charging service, and interaction information between the vehicle and the charging pile for the charging service.

[0032] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0033] If the second predicted power is greater than the output limit of the target charging station, the output power of the charging piles in the target charging station shall be reduced;

[0034] Based on the output power of the charging piles in the target charging station, a third predicted power is determined;

[0035] Based on the third predicted power, the target charging station is controlled to discharge for charging services within a preset time period.

[0036] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0037] Obtain the second predicted power of other charging stations within the target service area where the target charging station is located;

[0038] Based on the second predicted power of other charging stations within the target service area and the second predicted power of the target charging station, the service information of the target charging station is adjusted to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

[0039] Secondly, embodiments of this application provide a device for controlling discharge, comprising:

[0040] The first prediction module is used to predict the first predicted power output of the target charging station within a preset time period by using the current time information, weather information and station power time-series prediction model of the target charging station.

[0041] An adjustment module is used to adjust the first predicted power using at least one charging service already provided by the target charging station, so as to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0042] The control module is used to control the target charging station to discharge for charging service within a preset time period based on the second predicted power.

[0043] Thirdly, embodiments of this application provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, the computer program being executed by a processor to perform the steps of the method.

[0045] The method for controlling discharge provided in this application firstly predicts the first predicted power output of the target charging station within a preset time period using the current time information, weather information, and station power time-series prediction model of the target charging station; then, it adjusts the first predicted power using at least one charging service already provided by the target charging station to obtain a second predicted power output required by the target charging station within the preset time period; finally, based on the second predicted power, it controls the target charging station to discharge for charging services within the preset time period.

[0046] In some embodiments, the time-series prediction results of the charging station power time-series prediction model and the event prediction results of the event power prediction model are combined. Based on the combined result, the second charging power required by the target charging station in the next preset time period is adjusted. This can compensate for the limitations of time-series prediction, which cannot take into account some sudden events or unpredictable factors, and the limitation of event prediction, which is difficult to accurately predict future trends. The method of controlling the release of electrical energy by the charging station provided in this application minimizes the difference between the electrical energy released by the charging station and the electrical energy required by the current charging event, so that the power supply capacity of the charging station and the charging demand are balanced as much as possible, thereby reducing the impact of this difference on the power grid connected to the charging station. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A schematic flowchart of a method for controlling discharge provided in an embodiment of this application is shown;

[0049] Figure 2 A flowchart illustrating a detailed discharge method provided in an embodiment of this application is shown;

[0050] Figure 3 A schematic diagram of a discharge control device provided in an embodiment of this application is shown;

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0053] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0054] To enable those skilled in the art to utilize the content of this application and in conjunction with the specific application scenario of "charging station discharge," the following implementation method is provided. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application primarily describes charging stations, it should be understood that this is merely an exemplary embodiment.

[0055] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0056] It should be noted that the charging stations in this application mainly refer to places that can charge electric vehicles with electric drive. These vehicles can be bicycles, tricycles, cars, or other electric vehicles. Considering the widespread use of electric vehicles, we can take electric vehicle charging stations as an example below.

[0057] Currently, electric vehicles (EVs) prioritize rapid charging, requiring high charging power. With the increasing number of EVs, direct grid charging would put significant pressure on the power grid. Furthermore, electricity prices vary at different times of day, generally higher during the day than at night. Therefore, energy storage charging stations have emerged. These stations draw power from the grid during the lower-cost nighttime hours and store it. This stored energy can then power EVs with high-power charging, reducing grid impact and avoiding higher charging prices. Multiple vehicles often charge simultaneously at a single charging station. As a car's battery level increases during charging, the required charging power gradually decreases. However, the percentage and duration of this power decrease vary between different cars during each charge. This means that charging stations cannot accurately release the appropriate amount of energy to charge multiple cars, potentially resulting in either excessive or insufficient energy release. In other words, the power supply capacity of the charging station is difficult to balance with the charging demand. Since charging stations are directly connected to the power grid, and there is bidirectional charging and discharging between them, both excessive and insufficient energy release by the charging station will impact the power grid, burdening it and exacerbating the pressure on urban power supply.

[0058] To address the aforementioned shortcomings, this application provides a method for controlling discharge, such as... Figure 1 As shown, it includes:

[0059] S101, using the current time information, weather information and power time-series prediction model of the target charging station, predict the first predicted power output of the target charging station within a preset time period;

[0060] S102, adjust the first predicted power using at least one charging service already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period;

[0061] S103, based on the second predicted power, control the target charging station to discharge for charging service within a preset time period.

[0062] In step S101 above, the target charging station is a location that stores a large amount of electrical energy and can provide high-power charging for multiple electric vehicles. The target charging station is designed to facilitate fast charging for electric vehicles, and therefore, like a gas station, it is typically located in areas where a large number of electric vehicles are likely to be present, such as parking sheds, betting stations, and street communities. Each target charging station has at least one charging pile, which supplies power to the electric vehicles. The charging pile is also directly connected to the power grid. When the power provided by the charging station is insufficient to meet the charging power requirements of the electric vehicles, the power grid will supplement some power for fast charging. Alternatively, when the power provided by the charging station exceeds the charging power required by the electric vehicles, the excess electrical energy will be absorbed by the power grid. The station power time-series prediction model is used to predict the first predicted power that the charging station needs to release within a preset time period after the current moment.

[0063] The power generation time-series prediction model for power stations is trained based on historical environmental information and charging power at historical moments of the target power station. The specific training steps include:

[0064] The historical charging data of the target charging station is obtained; the historical charging data includes multiple charging samples, each of which includes weather information, time information, and the charging power output by the target charging station at that historical moment; each charging sample in the historical charging data is sorted according to the historical moment; wherein, the charging power output by the target charging station at the historical moment is obtained by statistically analyzing the charging power consumed by historical charging orders of the charging services served by the target charging station at that historical moment;

[0065] According to the sorting, each charging point sample is input into the power time series prediction model to be trained, and the power time series prediction model to be trained is trained to obtain the trained power time series prediction model. During the training process, the charging power output by the target charging station in the charging sample is used as a negative sample, and the weather information and time information in the charging sample are used as positive samples.

[0066] In step S102 above, the charging service provided is the service that is being provided at the target charging station.

[0067] In specific implementation, the first predicted power mentioned in step S101 is predicted based on the development trend of the charging power of the target charging station. The charging orders corresponding to the charging services already provided by the target charging station are obtained. Based on the vehicle information in the obtained charging orders, the already provided charging services are determined. These charging services have already occurred and are likely to continue to occur within the next preset time period. However, some of the already occurred charging services may be due to unforeseen factors that do not conform to the development trend. To more accurately determine the charging power that the target charging station will output within the next preset time period, it is necessary to take into account the charging services due to unforeseen factors that do not conform to the development trend. That is, the first predicted power conforming to the development trend needs to be adjusted based on the charging services already provided by the target charging station, thereby accurately predicting the second predicted power that the target charging station needs to output within the preset time period.

[0068] In step S103 above, the target charging station discharges for charging services based on the second predicted power within a preset time period. This method combines the charging services already provided by the target charging station, which may be affected by unforeseen factors, with the time-series prediction results of the charging power obtained from the target charging station power time-series prediction model. The combined result is used as the second charging power required by the target charging station in the next preset time period. This overcomes the limitations of time-series prediction in not considering unforeseen events or unpredictable factors, and the difficulty of accurately predicting future trends in event prediction. The method of controlling the release of electrical energy by the charging station provided in this application minimizes the difference between the electrical energy released by the charging station and the electrical energy required for the upcoming charging event, thus balancing the power supply capacity of the charging station with the charging demand as much as possible and reducing the impact of this difference on the power grid connected to the charging station.

[0069] This application predicts the number of charging services that a target charging station may provide within a preset time period through the following steps; that is, the method further includes:

[0070] Step 104: Input the time information and environmental information of the target charging station into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station may provide.

[0071] In the specific implementation of step 104 above, the charging event prediction model can predict the first number of charging services that may occur for each type of charging service at the target charging station within a certain period after the current time, based on environmental and temporal information that can affect charging demand at the target charging station. Specifically, the output of the charging event prediction model can be the first number of charging services that may occur for each type of charging service at each time point within a certain period after the current time. Based on the first number of charging services that may occur for each type of charging service at each time point within a preset time period, a curve showing the change in the number of charging services for each type of charging service can be constructed.

[0072] For each type of charging service, the charging power variation under the same conditions is similar. Charging services with similar charging power variation are classified as the same charging service type. The same conditions may include any one or more of the following information: vehicle information, charging pile information, and interaction information between the vehicle and the charging pile. The vehicle information, charging pile information, and interaction information between the vehicle and the charging pile are all charging information recorded in the charging order.

[0073] The charging event prediction model is trained based on historical charging services at the target charging station and environmental information at the time of those services. The training steps include:

[0074] Obtain historical charging orders for the target charging station;

[0075] For each historical moment, based on the charging information recorded in each historical charging order corresponding to that historical moment, determine the quantity corresponding to each type of charging service that occurred at that historical moment;

[0076] For each historical moment, an event training sample is formed based on the quantity of each charging service type corresponding to that historical moment, as well as the time and weather information of that historical moment.

[0077] The event training sample set is composed of event training samples at each time step;

[0078] For each event training sample, the event training sample is input into the charging event prediction model to be trained, and the charging event prediction model to be trained is trained to obtain a trained charging event prediction model. During the training process, the time information and weather information in the charging sample are used as positive samples, and the quantity corresponding to each charging service type in the charging sample is used as negative samples.

[0079] This application can determine the quantity of each type of charging service already provided by the target charging station based on the charging information of the existing charging services. Specifically, this application determines the second quantity corresponding to each type of charging service through the following steps, and the method further includes:

[0080] Step 105: Using the charging information of at least one charging service already provided by the target charging station, determine the second quantity of each type of charging service already provided by the target charging station.

[0081] In the specific implementation of step 105 above, the charging orders of the charging services already provided in the target charging station are obtained, the charging service type of each charging service is determined according to the charging information in the charging order of each charging service, and finally the second quantity of each type of charging service already provided in the target charging station is counted.

[0082] The second quantity for each type of charging service can be obtained by the following steps in this application:

[0083] Step 1051: Input the charging information of at least one charging service already provided by the target charging station into the charging service classification model to determine the charging service type corresponding to each charging service.

[0084] Step 1052: Count the second quantity of each type of charging service already provided by the target charging station.

[0085] In step 1051 above, the charging service classification model can be used to quickly determine the charging service type of a certain charging service. The charging service classification model is trained through the following steps:

[0086] Obtain historical charging samples from the target charging station; the historical charging samples contain charging information and charging service types for historical charging orders;

[0087] For each historical charging sample, the charging information of the historical charging sample is used as a positive sample, and the charging service type of the historical charging sample is used as a negative sample. The charging service classification model to be trained is then trained to obtain the trained charging service classification model.

[0088] In step S102 of this application, the method for adjusting the first predicted power based on at least one charging service already provided by the target charging station may include any of the following:

[0089] The first method involves using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service to obtain the first charging power that the target charging station needs to provide within a preset time period; and then weighting and summing the first charging power and the first predicted power to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0090] In the first adjustment method, corresponding weights can be set for the first charging power and the first predicted power, and the weight values ​​can be determined based on experience.

[0091] The second method involves using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service to obtain the first charging power that the target charging station needs to provide within a preset time period; and determining the average value of the first charging power and the first predicted power as the second predicted power that the target charging station needs to output within the preset time period.

[0092] The third method involves first predicting the charging services that will occur within a preset time period. Then, using these predicted services, the charging power required for each service within the preset time period is predicted. The difference between the charging power required for already occurring services and the charging power required for the upcoming services within the preset time period is calculated, taking into account the impact of unforeseen factors. Based on this difference, the first predicted power is adjusted to obtain a more accurate second predicted power that incorporates the impact of unforeseen events and accurately predicts future trends. This is achieved through the following steps:

[0093] Step 1021: Using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service, obtain the first charging power that the target charging station needs to provide within a preset time period;

[0094] Step 1022: Based on the first quantity of each type of charging service that the target charging station may provide and the event power curve of each type of charging service, obtain the second charging power that the target charging station needs to provide within a preset time period;

[0095] Step 1023: The first difference between the first charging power and the second charging power is superimposed on the first predicted power to obtain the second predicted power.

[0096] In step 1021 above, the event power curve characterizes the power change of a certain type of charging service during the charging process. The specific event power curve can be determined through the following steps:

[0097] Step 10211: Obtain the charging power curves corresponding to multiple historical charging orders for each type of charging service in the target charging station;

[0098] Step 10212: For each type of charging service, aggregate and average the charging power curves corresponding to multiple historical charging orders of the charging service type to obtain the event power curve of the charging service type.

[0099] In step 10211 above, the historical charging orders are classified according to the charging information of each historical charging order in the target charging station. Each historical charging order records the corresponding charging power change, that is, the charging power curve.

[0100] In step 10212 above, for each type of charging service, the multiple charging power curves corresponding to the type of charging service are divided according to each preset time interval. The power values ​​of the multiple charging power curves corresponding to the preset time interval are aggregated, that is, summed, to obtain a sum value. Then, the sum value is averaged to obtain an average value. Finally, the event power curves corresponding to the type of charging service are formed by sorting the average values ​​of each preset time interval according to the time interval.

[0101] In specific implementation, for each type of charging service, the first charging electronic power that the target charging station needs to provide for the charging service type within a preset time period is determined based on the product of the second quantity of that type of charging service already provided by the target charging station and the event power curve of that type of charging service. Based on the sum of the first charging electronic power that the target charging station needs to provide for each type of charging service within the preset time period, the first charging power required by the target charging station within the preset time period is calculated.

[0102] In step 10212 above, in specific implementation, for each type of charging service, based on the product of the first quantity of that type of charging service that the target charging station may provide and the event power curve of that type of charging service, the second charging electronic power that the target charging station needs to provide for the charging service type within a preset time period is determined. Based on the sum of the second charging electronic power that the target charging station needs to provide for each type of charging service within the preset time period, the second charging power required by the target charging station within the preset time period is calculated.

[0103] In step 10213 above, a first difference between the first charging power and the second charging power within a preset time period is calculated. This first difference may be positive or negative. Based on the first difference, the difference between the actual charging power consumed by the charging service within the preset time period and the predicted charging power consumed by the possible charging service within the preset time period is determined more accurately. Based on this difference, the first predicted power consumed by the target charging station within the preset time period is corrected to obtain the second predicted power required by the target charging station within the preset time period, thereby improving the accuracy of determining the second predicted power.

[0104] The fourth method involves first predicting the charging services that will occur within a preset time period. Then, using the difference between the predicted number of charging services and the actual number of services that have already occurred (representing the impact of unforeseen factors), the first predicted power is adjusted to obtain a more accurate second predicted power that incorporates the impact of unforeseen events and accurately predicts future trends. This is achieved through the following steps:

[0105] Step 1024: For each type of charging service, calculate the second difference between the second number of charging services of the type already provided by the target charging station and the first number of charging services of the type that the target charging station may provide.

[0106] Step 1025: Adjust the first predicted power based on the sum of the products of the second difference for each charging service type and the event power curve for that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0107] In step 1024 above, specifically, a second difference is calculated between the second quantity of charging services of the target charging station already provided for each type of charging service and the first quantity of the target charging station that may provide the same type of charging service. This second difference may be positive or negative.

[0108] In step 1025 above, for each charging service type, the product of the third difference corresponding to that charging service type and the event power curve of that charging service type is calculated. The products of each charging service type are summed, and the first predicted power is adjusted based on the summed value to obtain the second predicted power required to be output by the target charging station within the preset time period.

[0109] The fifth method involves first predicting the charging services that will occur within a preset time period, then calculating the average between the predicted number of charging services and the actual number of charging services that have already occurred. This average includes both the high-probability scenarios predicted based on historical trends and the impact of unforeseen factors. Based on this average, the first prediction power is adjusted to obtain a more accurate second prediction power that includes the impact of unforeseen events and accurately predicts future trends. This is achieved through the following steps:

[0110] Step 1026: For each type of charging service, calculate the average of the first quantity of the charging service type and the second quantity of the charging service type;

[0111] Step 1027: Adjust the first predicted power based on the sum of the products of the average number of each charging service type and the event power curve of the charging service type to obtain the second predicted power required to be output by the target charging station within the preset time period.

[0112] In steps 1026 and 1027 above, the average of the first and second quantities for each charging service type is calculated. For each charging service type, the product of the average quantity for that charging service type and the event power curve for that charging service type is calculated. The products corresponding to each charging service type are summed to obtain the second predicted power that the target charging station needs to output within a preset time period.

[0113] The target charging station is directly connected to the power grid, which contains transformers with rated power values. If the output power exceeds the rated power, it will affect the stability of the power grid. Therefore, an output limit is set for the target charging station to control its output power. In other words, the method of this application also includes:

[0114] Step 107: If the second predicted power is greater than the output limit of the target charging station, reduce the output power of the charging piles in the target charging station;

[0115] Step 108: Determine the third predicted power based on the output power of the charging piles in the target charging station;

[0116] Step 109: Based on the third predicted power, control the target charging station to discharge for charging service within a preset time period.

[0117] In steps 107 to 109 above, the upper limit of the target charging station's output is the maximum charging power that a target charging station with minimal impact on the power grid can output. The output power of the charging pile is the power that the charging pile can output when charging a vehicle. The third predicted power is determined after adjusting the output power of the charging piles in the target charging station when the second predicted power exceeds the upper limit of the target charging station's output.

[0118] In practice, after predicting the second predicted power that the target charging station may output in the next preset time period, it is necessary to determine whether the second predicted power will exceed the output power of the target charging station. If the second predicted power is greater than the upper limit of the target charging station's output, it means that the target charging station's output power according to the second predicted power may cause grid instability. In order to reduce the impact on the grid, the output power of the charging piles in the target charging station is reduced, and the power of the charging piles is adjusted in a timely manner, which reduces the impact on the grid and can also make full use of the power of the charging piles, reducing the losses caused by unnecessary charging pile power restrictions.

[0119] In this application, the vehicles in the entire system can also be reasonably distributed based on the second predicted power of the target charging station, so that vehicles that need charging can be charged in a timely manner. That is, the method of this application also includes:

[0120] Step 110: Obtain the second predicted power that other charging stations within the target service area where the target charging station is located need to provide within a preset time period;

[0121] Step 111: Based on the second predicted power required by other charging stations within the target service area within a preset time period and the second predicted power required by the target charging station within the preset time period, adjust the service information of the target charging station to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

[0122] In steps 110 and 111 above, the target service area can be a fixed area that includes the target charging station and at least one other charging station. Service information can include the electricity cost required for the target charging station to output power, the output power of the charging pile, etc.

[0123] In practice, the second predicted power required by each charging station within the target service area within a preset time period is obtained. The second predicted power of the target charging station is compared with the second predicted power of other charging stations. Based on the comparison results, the service information of the target charging station is adjusted so that vehicles within the target service area are reasonably diverted between the target charging station and other charging stations.

[0124] For example, if the second predicted power required by other charging stations within the target service area during a preset time period is significantly less than the second predicted power required by the target charging station during the same time period, then the price at the target charging station will be increased during that time period to guide vehicles in the target area to other charging stations with lower predicted power. Conversely, if the second predicted power required by other charging stations within the target service area during a preset time period is significantly greater than the second predicted power required by the target charging station during that time period, then the price at the target charging station will be decreased during that time period to guide vehicles in the target area to the target charging station with lower predicted power.

[0125] For example, if the second predicted power required by other charging stations within the target service area during a preset time period is significantly less than the second predicted power required by the target charging station during the same time period, then the charging power of the charging piles at the target charging station will be reduced during the preset time period, guiding vehicles in the target area to other charging stations for charging. Conversely, if the second predicted power required by other charging stations within the target service area during a preset time period is significantly greater than the second predicted power required by the target charging station during the same time period, then the charging power of the target charging station will be increased during the preset time period, guiding vehicles in the target area to the target charging station for charging.

[0126] When the stored electricity in the charging station is depleted, the power grid directly connected to the charging pile will directly supply power to the electric vehicle. However, the power grid also needs to supply electricity to individual households, and the charging power required by each household is relatively small compared to the charging power of the electric vehicle. Therefore, to ensure the stability of the power released by the power grid and to avoid significant fluctuations in the power grid when directly supplying power to the electric vehicle, the charging power of the electric vehicle will be appropriately reduced. This may result in some electric vehicles having relatively little remaining charge. If charged directly with high power, they might be fully charged in 1 to 2 minutes, but with the charging power reduced, it might take 10 to 20 minutes to fully charge, significantly increasing the charging time of the electric vehicle. Based on the above reasons, this application provides a discharge method, such as... Figure 2 As shown, it includes:

[0127] S201, when the remaining power of the target charging station is less than the preset power, if the power required by at least one already provided charging service meets the preset requirements, then the second predicted power is increased according to the power required by at least one already provided charging service that meets the preset requirements.

[0128] S202, based on the increased second predicted power, control the target charging station to discharge within a preset time period.

[0129] In step S201 above, the target charging station is equivalent to a large battery. When the power grid charges it, the target charging station stores electrical energy. When the target charging station charges an electric vehicle, it releases electrical energy. The remaining capacity of the target charging station is the electrical energy remaining after the stored electrical energy is consumed by the electric vehicle. The preset capacity can be manually set. The required capacity for the charging service is the electrical energy required for the battery of the electric vehicle corresponding to the charging service to reach a fully charged state. The preset requirement indicates that the electrical energy required for the battery of the electric vehicle corresponding to the charging service to reach a fully charged state is less than the preset capacity. The preset requirement can also be manually set.

[0130] In practice, when the remaining power of the target charging station is less than the preset power, it means that the target charging station is about to run out of power and the power grid will need to charge the electric vehicle, thus reducing the charging power of the electric vehicle. Therefore, at this time, the charging service that requires less power and meets the preset requirements can be selected from the charging services that are currently charging. The second predicted power is increased by using the power required by such charging services, which is equivalent to increasing the charging power of the charging service that requires power that meets the preset requirements.

[0131] In step S202 above, the increased second charging power is used for charging, that is, the increased charging power that meets the preset requirements is used to charge the electric vehicle that meets the preset requirements. This can quickly charge the electric vehicle that is about to be fully charged before the target charging station runs out of power, reducing the charging time of the electric vehicle that is about to be fully charged. It can also quickly leave the target charging station with the fully charged electric vehicle to provide services for other electric vehicles that need to be charged but have not yet been charged.

[0132] This application provides a detailed method for increasing the second predicted power, step S201, including:

[0133] Step 2011: When the remaining power of the target charging station is less than the preset power, obtain the required power of the charging service already provided;

[0134] Step 2012: Based on the required power of each provided charging service, determine whether there is a candidate fast charging service among the provided charging services whose required power meets the preset requirements.

[0135] Step 2013: If there are candidate fast charging services whose required power meets the preset requirements, determine at least one target fast charging service from the candidate fast charging services based on the remaining power and the required power of each candidate fast charging service.

[0136] Step 2014: Increase the second predicted power according to the at least one target accelerated charging service.

[0137] In steps 2011 to 2014 above, when the remaining power of the target charging station is less than the preset power, the required power of the already provided charging services is obtained. Among multiple charging services, it is determined whether there is a charging service whose required power is less than the preset power, that is, a charging service that meets the preset requirements. If so, the charging service that meets the preset requirements is determined as a candidate accelerated charging service. And based on the required power of each candidate accelerated charging service and the remaining power of the target charging station, at least one target accelerated charging service that can ultimately increase the charging power is determined. Then, based on the increased charging power of each target accelerated charging service (it should be noted that the charging power is not increased unconditionally here, and the wear and tear on the electric vehicle's battery needs to be considered. A method that minimizes battery wear can be used to increase the charging power), the second predicted power is increased, so that the selected target accelerated charging services can use the remaining power of the target charging station for fast charging, shorten the charging time, and avoid using the grid for charging.

[0138] Specifically, the target charging acceleration service can be determined based on the following: the sum of the required power of at least one target charging acceleration service is less than or equal to the remaining power.

[0139] When the remaining power at the target charging station is less than the preset power, there may still be many charging services currently charging at the target charging station. After all the required power of the candidate accelerated charging services that meet the preset requirements is exhausted, the remaining power at the target charging station will exceed the remaining power. Therefore, it is necessary to determine the target accelerated charging service that truly needs accelerated charging from the remaining power. Step 2013 includes:

[0140] Step 20131: If there are multiple candidate fast charging services that meet the preset requirements for the required power, sort the multiple candidate fast charging services in ascending order according to the required power.

[0141] Step 20132: Determine at least one candidate fast charging service that ranks highly and whose sum of required power is less than the remaining power as the target fast charging service.

[0142] In steps 20131 and 20132 above, multiple candidate fast charging services are sorted in ascending order according to the amount of electricity required. Then, the electricity required by the top-ranked candidate fast charging services is gradually added together until the sum exceeds the remaining electricity. At this point, the addition of the electricity required by the candidate fast charging services is stopped, and the candidate fast charging services corresponding to the sum of the electricity required before the sum exceeds the remaining electricity are determined as the target fast charging service.

[0143] When sorting candidate fast charging services as described above, there may be multiple candidate fast charging services with the same required power. In this case, the candidate fast charging services with the same required power can be sorted based on the charging start time. That is, step 20131 includes:

[0144] Step 201311: If multiple candidate fast charging services have the same required power, then sort at least one candidate fast charging service with the same required power in ascending order according to the charging start time.

[0145] In step 201311 above, multiple candidate fast charging services are first sorted in ascending order according to the required power consumption. If multiple candidate fast charging services have the same required power consumption, they are then sorted in ascending order according to the start time of each candidate fast charging service. That is, those with earlier start times are ranked first, and those with later start times are ranked last. This sorting method allows the earliest starting charging service to finish as soon as possible when the power stored in the charging station is about to be depleted, thus shortening the charging time.

[0146] The situation described in the background art, where the discharge of a charging station affects the power grid, is only possible when there are electric vehicles charging at the charging station. Therefore, it is only meaningful to implement the discharge control method provided in this application when there are charging orders at the charging station.

[0147] Based on the same inventive concept, this application also provides a device for controlling discharge corresponding to the method for controlling discharge. Since the principle of the device in this application is similar to the method for controlling discharge described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0148] Reference Figure 3 The diagram shown is a schematic representation of a controlled discharge according to an embodiment of this application. The device includes:

[0149] The first prediction module 301 is used to predict the first predicted power output of the target charging station within a preset time period by using the current time information, weather information and station power time-series prediction model of the target charging station.

[0150] The adjustment module 302 is used to adjust the first predicted power using at least one charging service already provided by the target charging station, so as to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0151] The control module 303 is used to control the target charging station to discharge for charging service within a preset time period based on the second predicted power.

[0152] Optionally, the device further includes:

[0153] The first determining module is used to input the time information and weather information of the target charging station into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station may provide;

[0154] The second determining module is used to determine a second quantity of each type of charging service already provided by the target charging station by utilizing charging information of at least one charging service already provided by the target charging station.

[0155] Optionally, the adjustment module includes:

[0156] The first calculation unit is used to obtain the first charging power that the target charging station needs to provide within a preset time period by using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service.

[0157] The second calculation unit is used to obtain the second charging power that the target charging station needs to provide within a preset time period based on the first quantity of each type of charging service that the target charging station may provide and the event power curve of each type of charging service.

[0158] The first adjustment unit is used to superimpose the first difference between the first charging power and the second charging power with the first predicted power to obtain the second predicted power.

[0159] Optionally, the adjustment module includes:

[0160] The third calculation unit is used to calculate, for each type of charging service, a second difference between the second number of charging services of the type already provided by the target charging station and the first number of charging services of the type that the target charging station may provide.

[0161] The second adjustment unit is used to adjust the first predicted power based on the sum of the products of the second difference of each charging service type and the event power curve of the charging service type, so as to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0162] Optionally, the adjustment module includes:

[0163] The fourth calculation unit is used to calculate the average of the first quantity and the second quantity of the charging service type for each charging service type.

[0164] The third adjustment unit is used to adjust the first predicted power based on the sum of the products of the average number of each charging service type and the event power curve of the charging service type, so as to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0165] Optionally, the second determining module includes:

[0166] The first determining unit is used to input the charging information of at least one charging service already provided by the target charging station into the charging service classification model to determine the charging service type corresponding to each charging service.

[0167] The statistics unit is used to count the second quantity of each type of charging service already provided by the target charging station.

[0168] Optionally, the device further includes:

[0169] The first acquisition module is used to acquire historical charging samples of the target charging station; the historical charging samples include charging information and charging service type of historical charging orders;

[0170] The training module is used to train the charging service classification model to be trained for each historical charging sample, taking the charging information of the historical charging sample as a positive sample and the charging service type of the historical charging sample as a negative sample, so as to obtain the trained charging service classification model.

[0171] Optionally, the device further includes:

[0172] The second acquisition module is used to acquire the charging power curves corresponding to multiple historical charging orders for each type of charging service in the target charging station.

[0173] The curve processing module is used to aggregate and average the charging power curves corresponding to multiple historical charging orders for each charging service type to obtain the event power curve for that charging service type.

[0174] Optionally, the charging information includes any one or more of the following: vehicle information for the charging service, charging pile information for the charging service, and interaction information between the vehicle and the charging pile for the charging service.

[0175] Optionally, the control module includes:

[0176] The judgment unit is used to reduce the output power of the charging piles in the target charging station if the second predicted power is greater than the output limit of the target charging station.

[0177] The second determining unit is used to determine the third predicted power based on the output power of the charging piles in the target charging station;

[0178] The first control unit is used to control the target charging station to discharge for charging service within a preset time period based on the third predicted power.

[0179] Optionally, the device further includes:

[0180] The third acquisition module is used to acquire the second predicted power of other charging stations within the target service area where the target charging station is located;

[0181] The traffic diversion module is used to adjust the service information of the target charging station based on the second predicted power of other charging stations within the target service area and the second predicted power of the target charging station, so as to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

[0182] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0183] This application embodiment also provides a computer device 40, such as... Figure 4 The diagram shows a schematic of a computer device 40 provided in this embodiment, including a processor 41, a memory 42, and a bus 43. The memory 42 stores machine-readable instructions executable by the processor 41. When the computer device 40 is running, the processor 41 communicates with the memory 42 via the bus 43. When the machine-readable instructions are executed by the processor 41, the following processing is performed:

[0184] Using the current time information, weather information and power time-series prediction model of the target charging station, the first predicted power output of the target charging station within a preset time period is predicted.

[0185] The first predicted power is adjusted using at least one charging service already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period;

[0186] Based on the second predicted power, the target charging station is controlled to discharge for charging service within a preset time period.

[0187] Optionally, the method further includes:

[0188] The time and weather information of the target charging station are input into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station may provide;

[0189] Using charging information from at least one charging service already provided by the target charging station, determine a second quantity of each type of charging service already provided by the target charging station.

[0190] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0191] Using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service, the first charging power required by the target charging station within a preset time period is obtained;

[0192] Based on the first quantity of each type of charging service that the target charging station may provide and the event power curve of each type of charging service, the second charging power that the target charging station needs to provide within a preset time period is obtained;

[0193] The first difference between the first charging power and the second charging power is added to the first predicted power to obtain the second predicted power.

[0194] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0195] For each type of charging service, calculate a second difference between the second number of charging services of that type already provided by the target charging station and the first number of charging services of that type that the target charging station may provide;

[0196] The first predicted power is adjusted based on the sum of the product of the second difference for each charging service type and the event power curve for that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0197] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0198] For each type of charging service, calculate the average of the first quantity of the charging service type and the second quantity of the charging service type;

[0199] The first predicted power is adjusted based on the sum of the products of the average number of each charging service type and the event power curve of that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0200] Optionally, determining the second quantity of each type of charging service already provided by the target charging station using charging information from at least one charging service already provided by the target charging station includes:

[0201] Input the charging information of at least one charging service already provided by the target charging station into the charging service classification model to determine the charging service type corresponding to each charging service.

[0202] Count the second number of each type of charging service already provided by the target charging station.

[0203] Optionally, the charging service classification model is trained through the following steps:

[0204] Obtain historical charging samples from the target charging station; the historical charging samples contain charging information and charging service types for historical charging orders;

[0205] For each historical charging sample, the charging information of the historical charging sample is used as a positive sample, and the charging service type of the historical charging sample is used as a negative sample. The charging service classification model to be trained is then trained to obtain the trained charging service classification model.

[0206] Optionally, the event power curve for each charging service type is determined through the following steps:

[0207] Obtain the charging power curves corresponding to multiple historical charging orders for each charging service type in the target charging station;

[0208] For each type of charging service, the charging power curves corresponding to multiple historical charging orders of that charging service type are aggregated and averaged to obtain the event power curve of that charging service type.

[0209] Optionally, the charging information includes any one or more of the following: vehicle information for the charging service, charging pile information for the charging service, and interaction information between the vehicle and the charging pile for the charging service.

[0210] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0211] If the second predicted power is greater than the output limit of the target charging station, the output power of the charging piles in the target charging station shall be reduced;

[0212] Based on the output power of the charging piles in the target charging station, a third predicted power is determined;

[0213] Based on the third predicted power, the target charging station is controlled to discharge for charging services within a preset time period.

[0214] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0215] Obtain the second predicted power of other charging stations within the target service area where the target charging station is located;

[0216] Based on the second predicted power of other charging stations within the target service area and the second predicted power of the target charging station, the service information of the target charging station is adjusted to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

[0217] This application embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method for controlling discharge.

[0218] Using the current time information, weather information and power time-series prediction model of the target charging station, the first predicted power output of the target charging station within a preset time period is predicted.

[0219] The first predicted power is adjusted using at least one charging service already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period;

[0220] Based on the second predicted power, the target charging station is controlled to discharge for charging service within a preset time period.

[0221] Optionally, the method further includes:

[0222] The time and weather information of the target charging station are input into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station may provide;

[0223] Using charging information from at least one charging service already provided by the target charging station, determine a second quantity of each type of charging service already provided by the target charging station.

[0224] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0225] Using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service, the first charging power required by the target charging station within a preset time period is obtained;

[0226] Based on the first quantity of each type of charging service that the target charging station may provide and the event power curve of each type of charging service, the second charging power that the target charging station needs to provide within a preset time period is obtained;

[0227] The first difference between the first charging power and the second charging power is added to the first predicted power to obtain the second predicted power.

[0228] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0229] For each type of charging service, calculate a second difference between the second number of charging services of that type already provided by the target charging station and the first number of charging services of that type that the target charging station may provide;

[0230] The first predicted power is adjusted based on the sum of the product of the second difference for each charging service type and the event power curve for that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0231] Optionally, the first predicted power is adjusted using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period, including:

[0232] For each type of charging service, calculate the average of the first quantity of the charging service type and the second quantity of the charging service type;

[0233] The first predicted power is adjusted based on the sum of the products of the average number of each charging service type and the event power curve of that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

[0234] Optionally, determining the second quantity of each type of charging service already provided by the target charging station using charging information from at least one charging service already provided by the target charging station includes:

[0235] Input the charging information of at least one charging service already provided by the target charging station into the charging service classification model to determine the charging service type corresponding to each charging service.

[0236] Count the second number of each type of charging service already provided by the target charging station.

[0237] Optionally, the charging service classification model is trained through the following steps:

[0238] Obtain historical charging samples from the target charging station; the historical charging samples contain charging information and charging service types for historical charging orders;

[0239] For each historical charging sample, the charging information of the historical charging sample is used as a positive sample, and the charging service type of the historical charging sample is used as a negative sample. The charging service classification model to be trained is then trained to obtain the trained charging service classification model.

[0240] Optionally, the event power curve for each charging service type is determined through the following steps:

[0241] Obtain the charging power curves corresponding to multiple historical charging orders for each charging service type in the target charging station;

[0242] For each type of charging service, the charging power curves corresponding to multiple historical charging orders of that charging service type are aggregated and averaged to obtain the event power curve of that charging service type.

[0243] Optionally, the charging information includes any one or more of the following: vehicle information for the charging service, charging pile information for the charging service, and interaction information between the vehicle and the charging pile for the charging service.

[0244] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0245] If the second predicted power is greater than the output limit of the target charging station, the output power of the charging piles in the target charging station shall be reduced;

[0246] Based on the output power of the charging piles in the target charging station, a third predicted power is determined;

[0247] Based on the third predicted power, the target charging station is controlled to discharge for charging services within a preset time period.

[0248] Optionally, based on the second predicted power, controlling the target charging station to discharge for charging services within a preset time period includes:

[0249] Obtain the second predicted power of other charging stations within the target service area where the target charging station is located;

[0250] Based on the second predicted power of other charging stations within the target service area and the second predicted power of the target charging station, the service information of the target charging station is adjusted to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

[0251] Specifically, the storage medium can be a general-purpose storage medium, such as a portable disk or hard drive. When the computer program on the storage medium is run, it can execute the aforementioned method of controlling discharge. This addresses the problem in existing technologies where the power supply capacity of charging stations is difficult to balance with charging demand by performing short-term coincidence prediction of currently observed charging events. Furthermore, it combines the time-series prediction results of the station power time-series prediction model with the event prediction results of the event power prediction model. Based on the combined result, it adjusts the second charging power required by the target charging station in the next preset time period. This overcomes the limitations of time-series prediction, which cannot consider some sudden events or unpredictable factors, and the limitation of event prediction, which is difficult to accurately predict future trends. Through the method of controlling the release of electrical energy by the charging station provided in this application, the difference between the electrical energy released by the charging station and the electrical energy required by the current charging event is minimized, so that the power supply capacity of the charging station and the charging demand are balanced as much as possible, thereby reducing the impact of this difference on the power grid connected to the charging station.

[0252] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0253] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0254] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0255] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0256] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling discharge, characterized in that, include: Using the current time information, weather information, and power time-series prediction model of the target charging station, the first predicted power output of the target charging station within a preset time period is predicted according to the historical development trend of the charging power of the target charging station. The first predicted power is adjusted using at least one charging service already provided by the target charging station to obtain the second predicted power required by the target charging station within the preset time period. The at least one charging service already provided refers to at least one charging service currently charging at the target charging station. The second predicted power indicates the sum of the charging power required by the target charging station for charging services that conform to the historical development trend and the charging power required by charging services caused by unforeseen factors within the preset time period. Based on the second predicted power, the target charging station is controlled to discharge for charging service within a preset time period; The second predicted power is obtained by: Predict the charging services that will occur at the target charging station within the preset time period, in line with the historical development trend; Determine the charging power difference, quantity difference, or average quantity between the upcoming charging services that conform to the historical trend and the already provided charging services, wherein the charging power difference indicates the charging power required for the charging service due to unforeseen factors, the quantity difference indicates the number of charging services due to unforeseen factors, and the average quantity includes the number of upcoming charging services that conform to the historical trend and the number of charging services due to unforeseen factors; and Based on the differences in charging power, quantity, or average quantity, the first predicted power is adjusted to obtain the second predicted power.

2. The method according to claim 1, characterized in that, The method further includes: The time and weather information of the target charging station are input into the charging event prediction model to predict the first quantity of each type of charging service that the target charging station can provide; Using charging information from at least one charging service already provided by the target charging station, determine a second quantity of each type of charging service already provided by the target charging station.

3. The method according to claim 2, characterized in that, Adjusting the first predicted power using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period includes: Using the second quantity of each type of charging service already provided by the target charging station and the event power curve of each type of charging service, the first charging power required by the target charging station within a preset time period is obtained; Based on the first quantity of each type of charging service that the target charging station can provide and the event power curve of each type of charging service, the second charging power that the target charging station needs to provide within a preset time period is obtained; The first difference between the first charging power and the second charging power is added to the first predicted power to obtain the second predicted power.

4. The method according to claim 2, characterized in that, Adjusting the first predicted power using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period includes: For each type of charging service, calculate a second difference between the second number of charging services of that type already provided by the target charging station and the first number of charging services of that type that the target charging station can provide; The first predicted power is adjusted based on the sum of the product of the second difference for each charging service type and the event power curve for that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

5. The method according to claim 2, characterized in that, Adjusting the first predicted power using the charging services already provided by the target charging station to obtain the second predicted power that the target charging station needs to output within the preset time period includes: For each type of charging service, calculate the average of the first quantity of the charging service type and the second quantity of the charging service type; The first predicted power is adjusted based on the sum of the products of the average number of each charging service type and the event power curve of that charging service type, to obtain the second predicted power that the target charging station needs to output within the preset time period.

6. The method according to claim 2, characterized in that, The step of determining a second quantity of each type of charging service already provided by the target charging station using charging information from at least one charging service already provided by the target charging station includes: The charging information of at least one charging service already provided by the target charging station is input into the charging service classification model to determine the charging service type corresponding to each charging service. Count the second number of each type of charging service already provided by the target charging station.

7. The method according to claim 6, characterized in that, The charging service classification model is trained through the following steps: Obtain historical charging samples from the target charging station; the historical charging samples contain charging information and charging service types for historical charging orders; For each historical charging sample, the charging information of the historical charging sample is used as a positive sample, and the charging service type of the historical charging sample is used as a negative sample. The charging service classification model to be trained is then trained to obtain the trained charging service classification model.

8. The method according to any one of claims 3 or 4, characterized in that, The event power curve for each charging service type is determined through the following steps: Obtain the charging power curves corresponding to multiple historical charging orders for each charging service type in the target charging station; For each type of charging service, the charging power curves corresponding to multiple historical charging orders of that charging service type are aggregated and averaged to obtain the event power curve of that charging service type.

9. The method according to claim 2, characterized in that, The charging information includes any one or more of the following: vehicle information for the charging service, charging pile information for the charging service, and interaction information between the vehicle and the charging pile for the charging service.

10. The method according to claim 1, characterized in that, Based on the second predicted power, the target charging station is controlled to discharge for charging services within a preset time period, including: If the second predicted power is greater than the output limit of the target charging station, the output power of the charging piles in the target charging station shall be reduced; Based on the output power of the charging piles in the target charging station, a third predicted power is determined; Based on the third predicted power, the target charging station is controlled to discharge for charging services within a preset time period.

11. The method according to claim 1, characterized in that, Also includes: Obtain the second predicted power of other charging stations within the target service area where the target charging station is located; Based on the second predicted power of other charging stations within the target service area and the second predicted power of the target charging station, the service information of the target charging station is adjusted to guide vehicles within the target service area to be reasonably diverted between the target charging station and other charging stations.

12. A device for controlling discharge, characterized in that, include: The prediction module is used to predict the first predicted power output of the target charging station within a preset time period by using the current time information, weather information and station power time series prediction model of the target charging station, according to the historical development trend of the charging power of the target charging station. An adjustment module is used to adjust the first predicted power using at least one charging service already provided by the target charging station to obtain a second predicted power that the target charging station needs to output within the preset time period. The at least one charging service already provided refers to at least one charging service that is currently charging at the target charging station. The second predicted power indicates the sum of the charging power required by the target charging station for charging services that conform to the historical development trend and the charging power required by charging services caused by unforeseen factors within the preset time period. The control module is used to control the target charging station to discharge for charging service within a preset time period based on the second predicted power. The adjustment module is further configured as follows: Predict the charging services that will occur at the target charging station within the preset time period, in line with the historical development trend; Determine the charging power difference, quantity difference, or average quantity between the charging service that is about to occur and the charging service that has already been provided, wherein the charging power difference indicates the charging power required for the charging service due to unforeseen factors, the quantity difference indicates the number of charging services due to unforeseen factors, and the average quantity includes the number of charging services that are about to occur and the number of charging services due to unforeseen factors. as well as Based on the differences in charging power, quantity, or average quantity, the first predicted power is adjusted to obtain the second predicted power.

13. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 11.

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