A storage and charging station soc reservation value correction method and terminal based on historical data

Abstract

This invention discloses a method and terminal for correcting the State of Charge (SOC) reserve value of a power storage and charging station based on historical data. The method inputs the current activity intensity and activity time into a strategy self-adjustment model. The strategy self-adjustment model calculates a calibration coefficient based on historical activity data and historical average daily electricity consumption. Based on the activity time of the current activity, it obtains the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, the power grid strategy is adjusted. This invention takes into account that stations hold marketing activities irregularly. It can calculate the SOC reserve value of the power storage and charging station based on the daily electricity consumption of historical activity days to adjust the power grid strategy. This avoids significant errors caused by relying on daily electricity consumption data for strategy control during activities, effectively reducing the risk of power shortages at the station and achieving better economic benefits.

Description

Technical Field

[0001] This invention relates to the field of storage and charging station strategy adjustment technology, and in particular to a method and terminal for correcting the SOC reservation value of storage and charging stations based on historical data. Background Technology

[0002] Due to the continuous depletion of traditional energy sources and their environmental pollution, the utilization and development of new energy sources have been elevated to a new level. Energy storage systems, as the core component of new energy storage charging stations, have the functions of peak shaving and valley filling, and improving charging power. Since the amount of electricity stored in the energy storage system of an energy storage charging station is limited, strategies need to be developed based on the forecast of the station's charging demand to control the upper and lower limits of the energy storage batteries' charging during each time period, thereby achieving the effect of peak shaving and valley filling.

[0003] However, due to operational needs, stations periodically hold marketing activities to attract customers, which causes significant fluctuations in electricity consumption. Current technology primarily predicts future electricity demand based on historical electricity data, lacking consideration for these random human factors. Therefore, when a station holds marketing activities, the electricity prediction system will generate substantial errors, potentially increasing the risk of power shortages or failing to achieve optimal economic benefits. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for correcting the SOC reservation value of a power storage and charging station based on historical data. The method corrects the SOC reservation value of the power storage and charging station during the activity period, taking into account the impact of the activity, so as to adjust the power storage and charging station strategy, reduce the risk of power shortage and achieve better economic benefits.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for correcting the SOC (State of Charge) reservation value of a storage and charging station based on historical data includes the following steps:

[0007] S1. Input the current activity intensity and activity time into the strategy self-adjustment model;

[0008] S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption.

[0009] S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy.

[0010] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0011] A storage and charging station SOC reservation value correction terminal based on historical data includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0012] S1. Input the current activity intensity and activity time into the strategy self-adjustment model;

[0013] S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption.

[0014] S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy.

[0015] The beneficial effects of the present invention are as follows: The method and terminal for correcting the SOC reserve value of a power storage and charging station based on historical data can calculate the SOC reserve value of the power storage and charging station based on historical activity data and the daily electricity consumption data on recent non-activity days, taking into account that the station holds marketing activities from time to time. This can be used to adjust the power grid strategy, avoid the large errors caused by still using daily electricity consumption data for strategy control during the activity period, effectively reduce the risk of power shortage at the station, and achieve better economic benefits. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for correcting the SOC reservation value of a storage and charging station based on historical data, according to an embodiment of the present invention.

[0017] Figure 2 This is a structural diagram of a storage and charging station SOC reservation value correction terminal based on historical data, according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram illustrating the implementation steps of a method for correcting the SOC reserved value of a storage and charging station based on historical data, according to an embodiment of the present invention.

[0019] Label Explanation:

[0020] 1. A terminal for correcting the SOC reserved value of a storage and charging station based on historical data; 2. Processor; 3. Memory. Detailed Implementation

[0021] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0022] Please refer to Figure 1 as well as Figure 3A method for correcting the SOC (State of Charge) reservation value of a storage and charging station based on historical data, comprising the following steps:

[0023] S1. Input the current activity intensity and activity time into the strategy self-adjustment model;

[0024] S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption.

[0025] S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy.

[0026] As can be seen from the above description, the beneficial effects of the present invention are as follows: The method and terminal for correcting the SOC reserve value of a storage and charging station based on historical data of the present invention, taking into account that the station holds marketing activities from time to time, can calculate the SOC reserve value of the storage and charging station based on the daily electricity consumption of historical activity days, so as to adjust the power grid strategy, avoid the large error caused by still using daily electricity consumption data for strategy control during the activity period, effectively reduce the risk of power shortage at the station, and achieve better economic benefits.

[0027] Furthermore, the strategy self-adjustment model stores activity data from the most recent historical activity and the average daily electricity consumption of the most recent preset number of inactive days before the historical activity.

[0028] The activity data includes the activity intensity;

[0029] The value of the activity intensity is the original unit price per kilowatt-hour / the activity unit price;

[0030] The calculation of the calibration coefficient k in step S2 is as follows:

[0031]

[0032] Among them, S m E indicates the intensity of historical activities. mi E represents the electricity consumption of historical activities during the target period i to be adjusted. si This represents the historical average daily electricity consumption.

[0033] As described above, the self-adjusting strategy model only stores relevant data from the most recent historical activity for current activity analysis, effectively ensuring the real-time nature and validity of the reference data while reducing storage pressure. By quantifying the activity intensity through price changes per kilowatt-hour, and combining this with historical activity intensity, electricity consumption, and historical daily electricity consumption to calculate adjustment coefficients, the model can fully reflect the correlation between electricity consumption and activity intensity.

[0034] Further, step S3 includes the following steps:

[0035] S31. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity, and calculate the expected electricity consumption of the target period to be adjusted based on the activity intensity, the calibration coefficient and the average daily electricity consumption.

[0036] S32. Calculate the SOC reserve value based on the estimated power consumption and adjust the power grid strategy accordingly.

[0037] As described above, the expected electricity consumption during the target period of the activity is calculated based on the activity intensity, calibration coefficient, and average daily electricity consumption. The SOC reserve value is calculated based on the expected electricity consumption. The adjustment of the power grid strategy is specifically implemented in the SOC reserve value. As an intermediate parameter of the actual energy storage and charging station strategy, the SOC reserve value can be effectively integrated into the existing energy storage and charging station strategy, thereby adjusting the strategy.

[0038] Furthermore, the estimated power consumption E in step S31 ni The calculation is as follows:

[0039] E ni =k×s×E i ;

[0040] The calculation of the SOC reservation value in step S32 is specifically as follows:

[0041] Based on the projected electricity consumption E ni Calculate the SOC reserve value:

[0042] SOC Reserve Value = F soe (E ni );

[0043] Where k represents the calibration coefficient, and s represents the activity intensity of the current activity; E i F represents the average daily electricity consumption. soe () represents a function that calculates the corresponding SOC based on SOE.

[0044] As described above, calculating the expected electricity consumption for the target period during the activity based on the activity intensity, calibration coefficient, and average daily electricity consumption is more accurate. Furthermore, calculating the SOC reserve value based on the expected electricity consumption using a function of SOC calculated from SOE ensures that the SOC reserve value fully reflects the SOC value corresponding to the expected electricity consumption, which is more reasonable.

[0045] Furthermore, it also includes the following steps:

[0046] S4. Record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the current activity, and feed them back to the strategy self-adjustment model. Mark the archived electricity consumption data according to the activity time of the current activity.

[0047] As described above, the activity data of this event is stored in the strategy self-adjustment model for strategy adjustment in the next event. Since there may be active and inactive periods during a day during the event, it is necessary to mark the active periods to avoid using the activity data in daily strategy adjustments, which could cause large errors in electricity consumption forecasting.

[0048] Please refer to Figure 2 A terminal for correcting the SOC reservation value of a storage and charging station based on historical data includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0049] S1. Input the current activity intensity and activity time into the strategy self-adjustment model;

[0050] S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption.

[0051] S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy.

[0052] As can be seen from the above description, the beneficial effects of the present invention are as follows: The method and terminal for correcting the SOC reserve value of a storage and charging station based on historical data of the present invention, taking into account that the station holds marketing activities from time to time, can calculate the SOC reserve value of the storage and charging station based on the daily electricity consumption of historical activity days, so as to adjust the power grid strategy, avoid the large error caused by still using daily electricity consumption data for strategy control during the activity period, effectively reduce the risk of power shortage at the station, and achieve better economic benefits.

[0053] Furthermore, the strategy self-adjustment model stores activity data from the most recent historical activity and the average daily electricity consumption of the most recent preset number of inactive days before the historical activity.

[0054] The activity data includes the activity intensity;

[0055] The value of the activity intensity is the original unit price per kilowatt-hour / the activity unit price;

[0056] The calculation of the calibration coefficient k in step S2 is as follows:

[0057]

[0058] Among them, S m E indicates the intensity of historical activities. mi E represents the electricity consumption of historical activities during the target period i to be adjusted. si This represents the historical average daily electricity consumption.

[0059] As described above, the self-adjusting strategy model only stores relevant data from the most recent historical activity for current activity analysis, effectively ensuring the real-time nature and validity of the reference data while reducing storage pressure. By quantifying the activity intensity through price changes per kilowatt-hour, and combining this with historical activity intensity, electricity consumption, and historical daily electricity consumption to calculate adjustment coefficients, the model can fully reflect the correlation between electricity consumption and activity intensity.

[0060] Further, step S3 includes the following steps:

[0061] S31. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity, and calculate the expected electricity consumption of the target period to be adjusted based on the activity intensity, the calibration coefficient and the average daily electricity consumption.

[0062] S32. Calculate the SOC reserve value based on the estimated power consumption and adjust the power grid strategy accordingly.

[0063] As described above, the expected electricity consumption during the target period of the activity is calculated based on the activity intensity, calibration coefficient, and average daily electricity consumption. The SOC reserve value is calculated based on the expected electricity consumption. The adjustment of the power grid strategy is specifically implemented in the SOC reserve value. As an intermediate parameter of the actual energy storage and charging station strategy, the SOC reserve value can be effectively integrated into the existing energy storage and charging station strategy, thereby adjusting the strategy.

[0064] Furthermore, the estimated power consumption E in step S31 ni The calculation is as follows:

[0065] E ni =k×s×E i ;

[0066] The calculation of the SOC reservation value in step S32 is specifically as follows:

[0067] Based on the projected electricity consumption E ni Calculate the SOC reserve value:

[0068] SOC Reserve Value = F soe (E ni );

[0069] Where k represents the calibration coefficient, and s represents the activity intensity of the current activity; E i F represents the average daily electricity consumption. soe () represents a function that calculates the corresponding SOC based on SOE.

[0070] As described above, calculating the expected electricity consumption for the target period during the activity based on the activity intensity, calibration coefficient, and average daily electricity consumption is more accurate. Furthermore, calculating the SOC reserve value based on the expected electricity consumption using a function of SOC calculated from SOE ensures that the SOC reserve value fully reflects the SOC value corresponding to the expected electricity consumption, which is more reasonable.

[0071] Furthermore, it also includes the following steps:

[0072] S4. Record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the current activity, and feed them back to the strategy self-adjustment model. Mark the archived electricity consumption data according to the activity time of the current activity.

[0073] As described above, the activity data of this event is stored in the strategy self-adjustment model for strategy adjustment in the next event. Since there may be active and inactive periods during a day during the event, it is necessary to mark the active periods to avoid using the activity data in daily strategy adjustments, which could cause large errors in electricity consumption forecasting.

[0074] The present invention provides a method and terminal for correcting the SOC reserved value of a charging station based on historical data. This method is applicable to situations where the charging station holds marketing activities from time to time, and corrects the SOC reserved value of the charging station during the activity period in order to adjust the control strategy of the charging station during the activity period.

[0075] Please refer to Figure 1 and Figure 3 Embodiment 1 of the present invention is as follows:

[0076] A method for correcting the SOC (State of Charge) reservation value of a storage and charging station based on historical data includes the following steps:

[0077] S1. Input the current activity intensity and activity time into the strategy self-adjustment model.

[0078] The strategy self-adjustment model stores activity data from the most recent historical event and the average daily electricity consumption of the most recent preset number of inactive days before the historical event.

[0079] In this embodiment, when put into use, the model needs to be self-learned based on an activity, that is, the activity data is collected as the data basis for the next activity.

[0080] Enter the activity intensity, start time, and end time into the system to set it to self-learning mode. Upon entering the preset activity period, the system changes its strategy to a maximum battery charging level of 100% and prioritizes mains power. The EMS system records and saves the electricity consumption for each time period within the activity period; the EMS system also records and saves the average daily electricity consumption for the seven most recent non-activity days for each of the above time periods. Upon leaving the preset activity period, the system exits self-learning mode, and self-learning is complete.

[0081] At this point, the self-adjusting model already stores the activity data of the most recent historical activity and the average daily electricity consumption of the most recent preset number of inactive days before the historical activity. When using it later, you only need to input the activity intensity and activity time of the current activity into the self-adjusting model.

[0082] S2. The self-adjusting model of the strategy calculates the calibration coefficient based on the activity data of historical activities and the average daily electricity consumption of history.

[0083] S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy.

[0084] The SOC reserve value is an intermediate parameter in the actual energy storage and charging station strategy. For example, during peak power periods, energy storage batteries will be used to supply power first. However, once the battery reaches the lower limit of SOC (here, the lower limit, not the reserve value), in order to prevent the battery from running out of power, it will be supplemented with power from the grid to maintain the SOC value. However, at this time, the electricity price is high, which is not economical. Therefore, a better strategy is to ensure that the battery's SOC is not lower than the SOC reserve value + the lower limit of SOC before the peak power period arrives.

[0085] S4. Record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the current activity, and feed them back to the strategy self-adjustment model. Mark the archived electricity consumption data according to the activity time of the current activity.

[0086] In this embodiment, when adjusting the strategy for the current activity, it is also necessary to record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the activity, and update the model accordingly for use as relevant data for the next activity. Simultaneously, it is necessary to record and archive the actual electricity consumption for each time period within the activity period. Since there may be active and inactive periods within a day during the activity period, it is necessary to mark the active periods to avoid using activity data during routine strategy adjustments, which could lead to significant errors in electricity consumption forecasting.

[0087] Please refer to Figure 1 and Figure 3 Embodiment two of the present invention is as follows:

[0088] A method for correcting the SOC reservation value of a storage and charging station based on historical data, which differs from Embodiment 1 in that the activity data includes the activity intensity;

[0089] The value of the activity intensity is the original unit price per kilowatt-hour / the activity unit price;

[0090] The calculation of the calibration coefficient k in step S2 is as follows:

[0091]

[0092] Among them, S m E indicates the intensity of historical activities. mi E represents the electricity consumption of historical activities during the target period i to be adjusted. si This represents the historical average daily electricity consumption.

[0093] In this embodiment, the system inputs the activity intensity, start time, and end time, and sets the system to automatic adjustment mode.

[0094] After entering the preset activity period, the system will automatically adjust its strategy. For time period i, based on the input activity intensity s, the strategy adjustment process is as follows:

[0095] (1) Read the activity intensity s from the model m ;

[0096] (2) Read the electricity consumption E during time period i when the event is held from the model. mi ;

[0097] (3) Read the average electricity consumption E during the i-th period of the seven most recent inactive days before the event from the model. si ;

[0098] (4) Calculate the calibration coefficient.

[0099] Step S3 includes the following steps:

[0100] S31. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity, and calculate the expected electricity consumption of the target period to be adjusted based on the activity intensity, the calibration coefficient and the average daily electricity consumption.

[0101] The estimated power consumption E in step S31 ni The calculation is as follows:

[0102] E ni =k×s×E i .

[0103] In this embodiment, after obtaining the calibration coefficient:

[0104] (5) Calculate the average electricity consumption during the i-th period of the seven most recent inactive days of the current activity as E. i ;

[0105] (6) Calculate the expected electricity consumption for period i.

[0106] S32. Calculate the SOC reserve value based on the estimated power consumption and adjust the power grid strategy accordingly.

[0107] The calculation of the SOC reservation value in step S32 is specifically as follows:

[0108] Based on the projected electricity consumption E ni Calculate the SOC reserve value:

[0109] SOC Reserve Value = F soe (E ni );

[0110] Where k represents the calibration coefficient, and s represents the activity intensity of the current activity; E i F represents the average daily electricity consumption. soe () represents a function that calculates the corresponding SOC based on SOE.

[0111] In this implementation, according to the above E ni Calculate the SOC reservation value for the current period. This step uses the original EMS strategy calculation method, for example: SOC reservation lower limit = F soe (E ni ), where F soe This is a function for calculating the corresponding SOC using SOE.

[0112] For F soe A simple example is given: SOC = SOE / FCC, where FCC represents the battery's full capacity. However, in practical engineering applications, many more factors will be considered, and the settings will be based on actual needs.

[0113] Please refer to Figure 2 Embodiment 3 of the present invention is as follows:

[0114] A storage and charging station SOC reservation value correction terminal 1 based on historical data includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, it implements the steps in the storage and charging station SOC reservation value correction method based on historical data in one or two of the above embodiments.

[0115] In summary, the present invention provides a method and terminal for correcting the SOC reserve value of a power storage and charging station based on historical data. Considering that the station holds marketing activities from time to time, the method can calculate the SOC reserve value of the power storage and charging station based on the daily electricity consumption of historical activity days, so as to adjust the power grid strategy. This avoids the large errors caused by still relying on daily electricity consumption data for strategy control during the activity period, effectively reducing the risk of power shortage at the station and achieving better economic benefits.

[0116] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for correcting the SOC (State of Charge) reservation value of a storage and charging station based on historical data, characterized in that, Including the following steps: S1. Input the current activity intensity and activity time into the strategy self-adjustment model; the strategy self-adjustment model stores the activity data of the most recent historical activity and the average daily electricity consumption of the most recent preset number of non-activity days before the historical activity. The activity data includes the activity intensity; The value of the activity intensity is the original unit price per kilowatt-hour / the activity unit price; S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption. The calculation of the calibration coefficient k in step S2 is as follows: ； in, S m Indicates the intensity of historical activities. E mi This indicates the historical activities during the target period to be adjusted. i Electricity consumption E si This represents the historical average daily electricity consumption. S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy, including: S31. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity, and calculate the expected electricity consumption of the target period to be adjusted based on the activity intensity, the calibration coefficient and the average daily electricity consumption. Among them, the estimated electricity consumption E ni The calculation is as follows: ； S32. Calculate the SOC reserve value based on the estimated power consumption and adjust the power grid strategy accordingly; The calculation of the SOC reservation value in step S32 is specifically as follows: Based on the projected electricity consumption E ni Calculate the SOC reserve value: ； in, k Indicates the calibration coefficient. s Indicates the current activity's intensity; E i This represents the average daily electricity consumption. F soe () represents a function that calculates the corresponding SOC based on SOE; The strategy adjustment process is as follows: Read the activity intensity from the model S m ; Read the electricity consumption during time period i when the event is held from the model. E mi ; The model reads the average electricity consumption during time period i of the seven most recent inactive days before the event. E si ; Calculate the calibration coefficient; Calculate the seven most recent inactive days of the current activity. i Average electricity consumption during the period was E i ; Calculate the estimated electricity consumption for period i.

2. The method for correcting the SOC reservation value of a storage and charging station based on historical data according to claim 1, characterized in that, It also includes the following steps: S4. Record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the current activity, and feed them back to the strategy self-adjustment model. Mark the archived electricity consumption data according to the activity time of the current activity.

3. A terminal for correcting the SOC reservation value of a storage and charging station based on historical data, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: S1. Input the current activity intensity and activity time into the strategy self-adjustment model; the strategy self-adjustment model stores the activity data of the most recent historical activity and the average daily electricity consumption of the most recent preset number of non-activity days before the historical activity. The activity data includes the activity intensity; The value of the activity intensity is the original unit price per kilowatt-hour / the activity unit price; S2. The self-adjusting model of the strategy calculates the calibration coefficient based on historical activity data and historical average daily electricity consumption. The calculation of the calibration coefficient k in step S2 is as follows: ； in, S m Indicates the intensity of historical activities. E mi This indicates the historical activities during the target period to be adjusted. i Electricity consumption E si This represents the historical average daily electricity consumption. S3. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity. Based on the activity intensity, the calibration coefficient, and the average daily electricity consumption, calculate the SOC reserve value of the energy storage and charging station for adjusting the power grid strategy, including: S31. Based on the activity time of the current activity, obtain the average daily electricity consumption of a preset number of non-activity days before the activity, and calculate the expected electricity consumption of the target period to be adjusted based on the activity intensity, the calibration coefficient and the average daily electricity consumption. Among them, the estimated electricity consumption E ni The calculation is as follows: ； in, k Indicates the calibration coefficient. s Indicates the intensity of the current activity; E i This represents the average daily electricity consumption. F soe () represents a function that calculates the corresponding SOC based on SOE; S32. Calculate the SOC reserve value based on the estimated power consumption and adjust the power grid strategy accordingly; The calculation of the SOC reservation value in step S32 is specifically as follows: Based on the projected electricity consumption E ni Calculate the SOC reserve value: ； The strategy adjustment process is as follows: Read the activity intensity from the model S m ; Read the electricity consumption during time period i when the event is held from the model. E mi ; The model reads the average electricity consumption during time period i of the seven most recent inactive days before the event. E si ; Calculate the calibration coefficient; Calculate the seven most recent inactive days of the current activity. i Average electricity consumption during the period was E i ; Calculate the estimated electricity consumption for period i.

4. A storage and charging station SOC reserved value correction terminal based on historical data according to claim 3, characterized in that, It also includes the following steps: S4. Record the actual electricity consumption, activity intensity, and average daily electricity consumption for each time period during the current activity, and feed them back to the strategy self-adjustment model. Mark the archived electricity consumption data according to the activity time of the current activity.

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