Multi-element energy storage power station grouping and layering cooperative scheduling method facing wind and light base

Abstract

The invention discloses a multi-element energy storage power station grouping and layering cooperative scheduling method for a wind and light base, and the method comprises the steps: building a day-ahead scheduling optimization model, carrying out the calculation through the day-ahead scheduling optimization model, obtaining a day-ahead scheduling scheme of an energy group, building a prediction control rolling scheduling model, and correcting the day-ahead scheduling scheme, and at the same time, according to the states of the two auxiliary power groups, determining the operation amount of the energy group needing to intervene in real-time frequency modulation, intervening the states of the two auxiliary power groups, and in the real-time operation stage, when the two auxiliary power groups are alternately responsible for stabilizing power rebound and fall-back fluctuation, determining that the two auxiliary power groups are in charge of stabilizing power rebound and fall-back fluctuation. According to the method, the day-ahead energy storage scheduling error is corrected based on the constructed multi-element energy storage power station grouping and layering model, and then the real-time power fluctuation is stabilized through further grouping and labor division of the power groups, so that the real-time frequency modulation is completed. And peak regulation and frequency modulation of wind and light base power generation networking are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy storage operation control and power system peak load and frequency regulation, and in particular to a grouping and layered coordinated dispatching method of multi-energy storage power stations for wind and solar bases. Background Art

[0002] With the deepening of global energy transformation, renewable energy generation technology represented by wind and solar energy has developed rapidly and become an important part of the modern power system. However, the randomness, seasonality, intermittency and volatility of wind and solar power generation have significantly increased the difficulty of grid dispatching, especially in wind and solar bases with a high proportion of renewable energy access. Frequent power fluctuations and prediction deviations have put forward higher requirements for the safe and stable operation of the power system. The development of effective peak-shaving and frequency-regulating technologies is of great significance for realizing the large-scale consumption of renewable energy.

[0003] The existing peak-shaving and frequency-regulation of wind and solar power generation bases mainly rely on multi-energy storage power stations, and the energy storage scheduling methods mainly focus on the regulation of a single time scale. It is difficult to take into account the multi-level regulation preferences and actual needs of operators at the same time, day-ahead, intraday and real-time. Although some studies have considered the complementary and coordinated regulation methods between multi-energy storage for wind and solar peak-shaving and frequency regulation at different time scales, how to manage the complementary regulation plans between multi-energy storage plays a vital role in the peak-shaving and frequency-regulation effects of wind and solar bases. Summary of the invention

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a grouping and layered collaborative scheduling method for multiple energy storage power stations for wind and solar bases, so as to solve the technical problem of how to manage the complementary and coordinated regulation between the multiple energy storages of the wind and solar bases and enhance the peak and frequency regulation capabilities of the wind and solar bases.

[0005] A first aspect of an embodiment of the present invention provides a grouping and hierarchical coordinated scheduling method for a multi-energy storage power station for a wind and solar base, the method comprising: Collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of the multi-energy storage power station, obtain power fluctuation characteristics based on the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's suppression ratio of power fluctuations based on the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model; Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group; A day-ahead dispatch optimization model is constructed based on the day-ahead wind and solar power output forecast results. The day-ahead dispatch optimization model is used to perform calculations to obtain the day-ahead dispatch plan for the energy group. A predictive control rolling dispatch model is constructed, and the deviation of the energy storage capacity between the two power groups is calculated. If the deviation is greater than the preset deviation value, the multiple of the deviation is used as the control amount, and the median is used as the control target. Combined with the energy storage grouping results, the up and down adjustment of the energy group's grid-connected power is obtained, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan.

[0006] The wind and solar base is frequency-regulated according to the revised scheduling scheme, and whether the energy group is involved in the frequency regulation is determined based on the status of each power group to obtain a new frequency regulation strategy. The wind and solar base is frequency-regulated according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy of the frequency fluctuation of the dual power groups and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group are kept unchanged, and real-time scheduling is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output scheduling result; if the third preset condition is met, the energy storage grouping step is returned to the step of re-adjusting the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation on the real-time scale of the day before is started.

[0007] In a possible implementation manner of the first aspect, a mapping relationship between a charging and discharging power configuration of a power group and a suppression ratio of the power group to power fluctuation is constructed according to power fluctuation characteristics and configuration parameters, and a mapping relationship model is obtained, including: According to the power fluctuation characteristics and configuration parameters, a mapping relationship between the charging and discharging power configuration of the power group and the power fluctuation suppression ratio of the power group is constructed to obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

[0008] In a possible implementation of the first aspect, based on a mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain energy storage grouping results, including: Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain an initial energy storage grouping result; The number of days in the preset time period is increased by the preset number of days to obtain new high time resolution data, and the multi-energy storage devices in the multi-energy storage station are grouped using the new high time resolution data to obtain new energy storage grouping results; Calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold. If so, determine that the new energy storage grouping result is the energy storage grouping result; if so, proceed to the step of increasing the number of days in the preset time period by the preset number of days.

[0009] In a possible implementation of the first aspect, a day-ahead dispatch optimization model is constructed according to a day-ahead wind and solar power output forecast result, including: Taking the weighted sum of the cumulative fluctuations and peak-to-valley differences of wind and solar grid-connected power as the minimum optimization target, the objective function is obtained, where the day-ahead dispatch optimization objective function is: In the formula, For the day before The wind and solar grid power at the moment, the subscript "1" refers to the first-level day-ahead scheduling, and They represent the peak and valley values ​​of wind and solar grid connection in a day. It is a weight coefficient between 0 and 1, which is used to adjust the proportion of cumulative fluctuation and peak-to-valley difference in the objective function; Determine the day-ahead scheduling optimization decision variables and day-ahead scheduling optimization constraints, and build a day-ahead scheduling optimization model based on the decision variables, constraints and objective function. The expression of the day-ahead scheduling optimization decision variables is: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The constraints for day-ahead scheduling optimization are: 0 In the formula, and Respectively expressed in Photovoltaic power generation and wind turbine power generation at each moment, for The storage capacity of the multi-energy storage device of the moment energy group, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. is the maximum charge and discharge power of the power group, Indicates that the energy group multi-energy storage device is The discharge amount at the moment.

[0010] In a possible implementation of the first aspect, building a predictive control rolling scheduling model includes: Determine the scheduling objective function, scheduling decision variables and scheduling constraints, and obtain the predictive control rolling scheduling model, where the scheduling objective function is: In the formula, For the scenery base in Japan The grid-connected power at the time, the subscript "2" refers to the second-level intraday scheduling, For the day before The set of intraday times contained in the moment, is the deviation scaling factor; The current moment when the intraday rolling optimization starts. Indicates the deadline for intraday rolling optimization; The scheduling decision variables are: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The scheduling constraints are: 0 0 In the formula, Respectively represent the time of day Photovoltaic power generation and wind turbine power generation at each moment, is the charge and discharge efficiency of the energy group, for The step length or duration, , are the charging / discharging power and storage energy of the energy group at that time, Prepare the energy group to participate in the real-time frequency regulation of grid-connected power up and down. They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. It is the maximum charge and discharge power of the power group.

[0011] In a possible implementation manner of the first aspect, when frequency modulation is performed on the wind and solar base according to the revised scheduling scheme, the method further includes: When any power group of the two power groups reaches a preset daily threshold, the operation modes of the two power groups are exchanged, wherein the operation modes include a discharge mode and a charge mode.

[0012] In a possible implementation of the first aspect, the first preset condition is a set of time gaps at the preset moments of the day stage that have not been executed, the second preset condition is a set of time gaps at the preset moments of the day stage that have been executed, and a set of time gaps at the remaining moments that have not been executed, and the third preset condition is a set of time gaps at the preset moments of the day stage that have been executed, and the preset moment of the day stage is the last moment of the day.

[0013] In order to solve the same technical problem, a second aspect of an embodiment of the present invention provides a grouping and hierarchical coordinated dispatching system of a multi-energy storage power station for a wind and solar base, the system comprising: The acquisition module is used to collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of the multi-element energy storage power station, obtain power fluctuation characteristics based on the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's suppression ratio of power fluctuations based on the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model; A grouping module, for grouping the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model using the high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group; The correction module is used to build a day-ahead dispatch optimization model based on the day-ahead wind and solar power output forecast results, use the day-ahead dispatch optimization model to calculate, obtain the day-ahead dispatch plan of the energy group, build a predictive control rolling dispatch model, and calculate the deviation of the energy storage capacity between the two power groups. If the deviation is greater than the preset deviation value, the multiple of the deviation is used as the control amount, and the median is used as the control target. Combined with the energy storage grouping results, the up and down adjustment of the energy group's grid-connected power is obtained, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan; The scheduling module is used to adjust the frequency of the wind and solar base according to the revised scheduling plan, and determine whether to include the energy group in the frequency regulation based on the status of each power group, obtain a new frequency regulation strategy, and adjust the frequency of the wind and solar base according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy of the frequency fluctuation of the dual power groups and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group are kept unchanged, and real-time scheduling is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output scheduling result; if the third preset condition is met, the energy storage grouping step is returned to the step of re-adjusting the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation is started on the real-time scale of the day before.

[0014] In a possible implementation manner of the second aspect, the acquisition module includes a mapping relationship model determination unit, wherein: The mapping relationship model determination unit is used to construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's suppression ratio of power fluctuation according to the power fluctuation characteristics and configuration parameters, and obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

[0015] In a possible implementation manner of the second aspect, the grouping module includes an initial grouping unit, an updating unit, and a calculating unit. The initial grouping unit is used to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model using the high time resolution data within a preset time period to obtain the initial energy storage grouping result; The updating unit is used to increase the number of days in the preset time period by the preset number of days to obtain new high time resolution data, and use the new high time resolution data to group the multi-energy storage devices in the multi-energy storage station to obtain new energy storage grouping results; The calculation unit is used to calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold. If so, the new energy storage grouping result is determined to be the energy storage grouping result; if so, the step of increasing the number of days in the preset time period by the preset number of days is performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of a grouping and layered collaborative scheduling method of a multi-energy storage power station for a wind and solar base in an embodiment of the present invention; Figure 2 A schematic diagram of wind-solar base power generation and access to the grid in a method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind-solar bases in an embodiment of the present invention; Figure 3 It is a schematic diagram of the expected operating state of the grouped and hierarchical dispatching power group of the grouped and hierarchical coordinated dispatching method of the multi-energy storage power station for the wind and solar base in an embodiment of the present invention; Figure 4 It is a flowchart of a multi-energy storage power station grouping and hierarchical scheduling method of a multi-energy storage power station grouping and hierarchical collaborative scheduling method for a wind and solar base in an embodiment of the present invention; Figure 5 It is a schematic diagram of the day-ahead, intra-day, and real-time grouping and layering rolling scheduling of a multi-energy storage power station for a wind and solar base in an embodiment of the present invention; Figure 6 It is a structural block diagram of a grouped and hierarchical coordinated dispatching system of a multi-energy storage power station for a wind and solar base in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The embodiment of the present invention provides a method for grouping and layering coordinated scheduling of multiple energy storage power stations for wind and solar bases, such as Figure 1 As shown, Figure 1 This is a flow chart of grouped and hierarchical coordinated dispatching of multiple energy storage power stations for wind and solar bases, including steps S101 to S104, each of which is as follows: S101: Collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of the multi-element energy storage power station, obtain power fluctuation characteristics based on the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's power fluctuation suppression ratio based on the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model.

[0020] In this embodiment, if Figure 2 As shown in the figure, starting from the power generation of uncontrollable renewable energy bases such as and, the multi-energy storage power station integrates multiple types of energy storage batteries to perform peak and frequency regulation on the wind and solar bases, ensuring that the access to the grid of the wind and solar bases is carried out as much as possible according to the day-ahead dispatch plan, while resisting the impact of wind and solar parameters on the fluctuation of renewable energy generation. The multi-energy storage power station includes energy-type energy storage (pumped storage, electrochemical energy storage group, etc.) and power-type energy storage (flywheel energy storage, electrochemical energy storage, etc.).

[0021] Figure 3 Schematic diagram of the extreme operating state of the multi-element energy storage and the expected operating state of the grouped and layered dispatching power group provided by the present invention, such as Figure 3 As shown, in the intraday time scale and real time scale, if and If the power generation of the solar panel is greater than the power generation predicted by the day-ahead dispatch, the average SOC of the energy storage batteries of the two power groups may be biased towards 1 as a whole, which will result in a decrease in the energy storage capacity of the SOC and will not be able to cope with the sudden increase in wind and solar power generation at subsequent moments. and If the power generation is less than the power generation predicted by the day-ahead dispatch, the average SOC of the two power group energy storage batteries may be biased towards 0 as a whole, which will cause the SOC energy discharge to decrease. When the wind and solar power generation decreases in the subsequent moments, the power group batteries will not have enough power to supply the reduced power generation.

[0022] The multi-energy storage power station grouping and hierarchical coordinated scheduling method proposed in the present invention can stabilize the average SOC of the batteries of the two power groups near the median value. Specifically, Figure 4 As shown in the figure, first divide the multi-energy storage station into three blocks: (single) main energy group and (dual) auxiliary power group according to the historical grid power fluctuation characteristics of wind and solar. The specific steps are: S11: Continuously monitor and record the historical high-time resolution data (such as hours, minutes, and seconds) of wind and solar power generation, and collect the configuration parameters of the multi-energy storage power station. For the next day's scheduling, the wind and solar power output of R days before the "next day" is used as the historical reference data set R. According to the power fluctuation characteristics of the real-time operation stage, the mapping relationship between "the charging and discharging power configuration of the power group" and "the power group's power fluctuation smoothing ratio, CR" is established.

[0023] In one embodiment, according to the power fluctuation characteristics and configuration parameters, a mapping relationship between the charging and discharging power configuration of the power group and the power fluctuation suppression ratio of the power group is constructed to obtain a mapping relationship model, including: According to the power fluctuation characteristics and configuration parameters, a mapping relationship between the charging and discharging power configuration of the power group and the power fluctuation suppression ratio of the power group is constructed to obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

[0024] In this embodiment, a "power group charging and discharging power configuration" is established according to the power fluctuation characteristics in the real-time operation stage. The mapping relationship between "and" the power group's power fluctuation suppression ratio, CR", the model is as follows: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

[0025] S102: Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group.

[0026] In this embodiment, according to the customer's requirements for CR, Based on the data from the energy storage station, the multi-energy storage equipment with higher power density is divided into two power groups, and the rest are used as energy groups with higher energy density, so as to complete the division and grouping of multi-energy storage resources so that the multi-energy storage station can dynamically follow the "wind and solar power output fluctuation law in the last R days".

[0027] In one embodiment, based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain energy storage grouping results, including: Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using high time resolution data within a preset time period to obtain an initial energy storage grouping result; The number of days in the preset time period is increased by the preset number of days to obtain new high time resolution data, and the multi-energy storage devices in the multi-energy storage station are grouped using the new high time resolution data to obtain new energy storage grouping results; Calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold. If so, determine that the new energy storage grouping result is the energy storage grouping result; if so, proceed to the step of increasing the number of days in the preset time period by the preset number of days.

[0028] In this embodiment, based on the mapping relationship model, the high time resolution data within a preset time period is used to group the multi-energy storage devices in the multi-energy storage station, and the specific steps for obtaining the energy storage grouping result are: S12: Based on the customer's requirements for CR, the multi-energy storage equipment with higher power density in the multi-energy storage station is allocated into two power groups, and the remaining ones are used as energy groups with higher energy density, so as to complete the division and grouping of multi-energy storage resources so that the multi-energy storage station can dynamically follow the "wind and solar power output fluctuation law in the last R days".

[0029] +2 = In the formula, and is the total energy storage capacity and power of the multi-energy storage power station, are the capacities allocated to the energy group and the power group respectively, are the powers allocated to the energy group and the power group respectively, and It is the power-capacity ratio of the multi-energy storage resources of the energy group and the power group.

[0030] S13: gradually increase the R value, repeat steps S12-S13 and observe the changes in the decision results of the multi-energy storage station grouping, until the change in the decision result is less than a certain threshold , the grouping scheme under the relatively stable R value is taken as the final scheme. This step aims to filter out the adverse effects of some extreme weather scenarios on the grouping decision of multiple energy storage stations, and further correct the energy-power capacity division of energy storage stations. The calculation formula of the threshold is: In the formula, is the threshold parameter, is the allocated power group multi-energy storage power when R consists of R days of data, It is the multi-energy storage power of the allocated power group when R-1 is composed of R-1 days of data.

[0031] S103: A day-ahead dispatch optimization model is constructed based on the day-ahead wind and solar power output forecast results. The day-ahead dispatch optimization model is used for calculation to obtain the day-ahead dispatch plan of the energy group. A predictive control rolling dispatch model is constructed, and the deviation of the energy storage capacity between the two power groups is calculated. If the deviation is greater than the preset deviation value, the multiple of the deviation is used as the control amount, and the median is used as the control target. Combined with the energy storage grouping results, the up and down adjustment of the energy group's grid-connected power is obtained, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan.

[0032] In this embodiment, on the day before, a peak load operation optimization model of the energy group is constructed in combination with the wind and solar output forecast results, the charging and discharging plan of the main energy group is optimized and the grid connection plan of the wind and solar base is determined, that is, the day-ahead scheduling plan of the energy group multi-element energy storage. During the day, an energy group prediction and control model is established with the current time to the end of the day as the scheduling window and the goal of minimizing the deviation between the wind and solar grid-connected capacity and the day-ahead planned value; during the day, according to the degree to which the mean state of the dual power groups deviates from the median, it is determined whether to add feasibility constraints to restrict the behavior of the energy group so as to intervene in the real-time frequency regulation task. According to the latest wind and solar forecast data during the day, the day-ahead scheduling plan of the energy group multi-element energy storage is revised to try to keep the actual grid-connected power during the day consistent with the day-ahead scheduling plan.

[0033] In one embodiment, a day-ahead dispatch optimization model is constructed according to the day-ahead wind and solar power output prediction result, including: Taking the weighted sum of the cumulative fluctuations and peak-to-valley differences of wind and solar grid-connected power as the minimum optimization target, the objective function is obtained, where the day-ahead dispatch optimization objective function is: In the formula, For the day before The wind and solar grid power at the moment, the subscript "1" refers to the first-level day-ahead scheduling, and They represent the peak and valley values ​​of wind and solar grid connection in a day. It is a weight coefficient between 0 and 1, which is used to adjust the proportion of cumulative fluctuation and peak-to-valley difference in the objective function; Determine the day-ahead scheduling optimization decision variables and day-ahead scheduling optimization constraints, and build a day-ahead scheduling optimization model based on the decision variables, constraints and objective function. The expression of the day-ahead scheduling optimization decision variables is: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The constraints for day-ahead scheduling optimization are: 0 In the formula, and Respectively expressed in Photovoltaic power generation and wind turbine power generation at each moment, for The storage capacity of the multi-energy storage device of the moment energy group, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. is the maximum charge and discharge power of the power group, Indicates that the energy group multi-energy storage device is The discharge amount at the moment.

[0034] In this embodiment, the day-ahead dispatch takes the minimization of the “weighted sum” of the hourly cumulative fluctuation and peak-to-valley difference of the wind and solar grid-connected power as the optimization goal. By flexibly adjusting the value of the weight coefficient, the optimization direction of the multi-energy storage operation of the day-ahead energy group can be adjusted between the cumulative fluctuation and the peak-to-valley difference to closely match the operator’s regulation preference and actual needs. The specific target model is as follows: In the formula, For the day before The wind and solar grid power at the moment, the subscript "1" refers to the first-level day-ahead scheduling, and They represent the peak and valley values ​​of wind and solar grid connection in a day. It is a weight coefficient between 0 and 1, which is used to adjust the proportion of cumulative fluctuation and peak-to-valley difference in the objective function; Day-ahead dispatch is mainly based on the wind and solar power output forecast results. By adjusting the charging and discharging behavior of the energy group for 24 hours a day, the fluctuation of wind and solar power grid-connected power can be reduced. Therefore, the decision variables can be expressed as: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The constraints of day-ahead dispatch include the power balance at each moment and the energy group multi-storage operation characteristic constraints, as follows: 0 In the formula, and Respectively expressed in Photovoltaic power generation and wind turbine power generation at each moment, for The storage capacity of the multi-energy storage device of the moment energy group, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. is the maximum charge and discharge power of the power group, Indicates that the energy group multi-energy storage device is The discharge amount at the moment.

[0035] In one embodiment, constructing a predictive control rolling scheduling model includes: Determine the scheduling objective function, scheduling decision variables and scheduling constraints, and obtain the predictive control rolling scheduling model, where the scheduling objective function is: In the formula, For the scenery base in Japan The grid-connected power at the time, the subscript "2" refers to the second-level intraday scheduling, For the day before The set of intraday times contained in the moment, is the deviation scaling factor; The current moment when the intraday rolling optimization starts. Indicates the deadline for intraday rolling optimization; The scheduling decision variables are: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The scheduling constraints are: 0 0 In the formula, Respectively represent the time of day Photovoltaic power generation and wind turbine power generation at each moment, is the charge and discharge efficiency of the energy group, for The step length or duration, , are the charging / discharging power and storage energy of the energy group at that time, Prepare the energy group to participate in the real-time frequency regulation of grid-connected power up and down. They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. It is the maximum charge and discharge power of the power group.

[0036] In this embodiment, 15 minutes is used as the scheduling time scale for the intra-day stage, and the time interval from "the current minute-level moment" to "the end of the day" is used as a rolling window. Combined with the latest high-precision (15 min) wind and solar power generation data, the energy group multi-energy storage operation plan is optimized again in a rolling manner, so that the actual grid-connected power of wind and solar power during the day is consistent with the day-ahead planned value, so as to overcome the uncertainty of wind and solar power fluctuations.

[0037] Different from the existing multi-time scale optimization method, which takes the minimization of the sum of the adjustments of the operation of each device in the energy system compared with the day-ahead planned value as the scheduling correction target, the present invention proposes to take the minimization of the difference between the "actual grid-connected power of the wind and solar base during the day" and the "grid-connected power planned and reported day-ahead" as the optimization target, so that the wind and solar grid-connected power is consistent with the planned value. The target model, that is, the scheduling objective function is as follows: In the formula, For the scenery base in Japan The grid-connected power at the time, the subscript "2" refers to the second-level intraday scheduling, For the day before The set of intraday times contained in the moment, is the deviation scaling factor. Increasing the value of this parameter can amplify the impact of the “large deviation moment within the day” on the target, so as to avoid the actual grid-connected power within the day seriously deviating from the day-ahead planned value. The current moment when the intraday rolling optimization starts. Indicates the deadline for intraday rolling optimization (end of day).

[0038] The intraday dispatch is mainly based on the forecast results of wind and solar power output within the day. By adjusting the charging and discharging behavior of the energy group in the rolling window, the charging and discharging behavior is adjusted at the 15-minute resolution level to correct the grid-connected power to be consistent with the planned value of the day before. Therefore, the decision variable, that is, the dispatch decision variable, can be expressed as: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The constraints of intraday dispatch are similar to those of day-ahead dispatch, including power balance and operating characteristics of energy groups. However, the operating range needs to be narrowed to ensure that the energy group has a power of (greater than 0 means that the online power is increased by reducing the energy group charging or increasing its energy release, and less than 0 means the opposite, indicating that the online power is reduced) The up and down adjustment margin of the grid-connected power is used to participate in real-time frequency regulation and replace a power group operation. Therefore, the scheduling constraint within this day is: 0 0 In the formula, Respectively represent the time of day Photovoltaic power generation and wind turbine power generation at each moment, is the charge and discharge efficiency of the energy group, for The step length or duration, , are the charging / discharging power and storage energy of the energy group at that time, The energy group is prepared to participate in the real-time frequency regulation of the grid-connected power up and down adjustment participation amount, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. It is the maximum charge and discharge power of the power group.

[0039] According to the deviation of the mean value of the energy storage capacity of the dual power group equipment relative to the median value, determine To determine the value of (the amount of grid-connected power that the energy group is prepared to participate in real-time frequency regulation), first calculate the deviation of the mean relative to the median of the energy storage of the two power groups ( ), if the deviation is greater than the threshold ( ), then take twice the deviation as the control amount and the median as the control target, combined with the multi-energy storage station grouping scheme, the relevant calculation model is as follows: In the formula, After the equipment group A and equipment group B, which are power groups, are executed at the moment before the intraday time t2, the stored energy value marks and distinguishes the two power groups of the same specification with the symbols "A" and "B". is the deviation, is the deviation threshold, is the maximum charge and discharge power of the power group, It is the maximum charge and discharge power of the energy group.

[0040] In the above model, when the mean state of the two power groups is too large, the energy group needs to participate in the real-time frequency regulation scheduling of "grid-connected power down regulation", thereby reducing the power group charging amount, making the mean state decrease and return to the median. When the mean state of the two power groups is too small, the energy group needs to participate in the real-time frequency regulation scheduling of "grid-connected power up regulation", thereby reducing the power group discharge amount, making the mean state increase and return to the median.

[0041] S104: The wind and solar bases are frequency-regulated according to the revised dispatching scheme, and whether the energy group is to be involved in the frequency regulation is determined based on the status of each power group to obtain a new frequency regulation strategy. The wind and solar bases are frequency-regulated according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy for the frequency fluctuations of the dual power groups and the feasible boundary steps of the real-time dispatching model after the intelligent access of the energy group are kept unchanged, and real-time dispatching is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output dispatching result; if the third preset condition is met, the energy storage grouping step is returned to readjust the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation is started on the real-time scale of the day before.

[0042] In this embodiment, during the real-time operation stage, the two auxiliary power groups are alternately responsible for smoothing power rebound and fallback fluctuations, and the main energy group intelligently intervenes in power fluctuation smoothing, instantaneously tracks and responds to power fluctuations, and completes real-time frequency modulation. Specifically, the two power groups track the changes in the instantaneous power of wind and solar grid connection, alternately charge and discharge, and smooth the rebound and fallback fluctuations of grid-connected power in a division of labor and cooperation, and then the energy group intelligently intervenes in real-time power frequency modulation to replace one power group to smooth power fluctuations, so that the average value of the energy storage of the two power groups is maintained near the median.

[0043] In one embodiment, when the frequency of the wind and solar base is modulated according to the revised scheduling scheme, it also includes: When any power group of the two power groups reaches a preset daily threshold, the operation modes of the two power groups are exchanged, wherein the operation modes include a discharge mode and a charge mode.

[0044] In this embodiment, two power groups of the same specification are marked and distinguished by the symbols "A" and "B", and the initial energy storage of power groups A and B is set to be in the middle state of capacity. In the initial stage, power group A is only charged to reduce the rebounded grid-connected power to the planned value of the intraday dispatch, and power group B is only discharged to increase the fallen grid-connected power to the planned value of the intraday dispatch, so as to achieve power stability.

[0045] As charging and discharging proceed, the energy storage capacity of power group A or power group B will reach the upper limit ( ) or the lower bound ( ), if the limit is touched, the operation modes of the two power groups are exchanged, power group A starts to enter the discharge mode, power group B starts to enter the charging mode, and so on, alternating charging and discharging to achieve stable grid-connected power. The operation constraints are as follows: In the formula, The power fluctuation occurs during the time gap of the real-time scheduling phase. The subscript "3" refers to the real-time scheduling of the third layer. A value greater than 0 indicates a power rebound and an increase in the grid-connected power. A value less than 0 indicates a power drop and a decrease in the grid-connected power. It is a collection of real-time phase time gaps contained in the intraday phase moments.

[0046] The energy group intelligently intervenes in the real-time power frequency regulation to replace a power group to smooth the power fluctuation, so that the average energy storage of the two power groups is maintained near the median. The intelligent judgment and adjustment strategy is as follows: Case 1: If Greater than 0, If it is less than 0, the energy group will increase the power when discharging, and reduce the discharge of the power group. The energy group and the power group will be in the time gap. The final operation plan is determined by the following model: In the formula, The time slot to which the energy group belongs in the real-time scheduling phase The operation adjustment amount and the adjusted charge and discharge power, and the power group equipment indicated by the superscript "A / B" are determined according to the actual operation modes of the power group A and the power group B determined in step S5.

[0047] Case 2: If Less than 0, If it is greater than 0, the energy group will lower the charging power, reduce the charging of the power group, and the energy group and the power group will be charged in the time gap. The final operation plan is determined by the following model: In the formula, The time slot to which the energy group belongs in the real-time scheduling phase The operating adjustment amount and the adjusted charging and discharging power.

[0048] Case 3: Except for cases 1 and 2, the energy group does not intervene in the frequency modulation optimization in the real-time stage.

[0049] In one embodiment, the first preset condition is a set of time gaps at which the preset moments of the day stage have not been executed, the second preset condition is a set of time gaps at which the preset moments of the day stage have been executed and a set of time gaps at other moments have not been executed, and the third preset condition is a set of time gaps at which the preset moments of the day stage have been executed, and the preset moment of the day stage is the last moment of the day.

[0050] In this embodiment, according to the scheduling progress, the process returns to step S101, S102 or S103, and the time resolution is adjusted according to the scheduling needs to form a scheduling closed loop, as follows: Case 1: If the real-time stage collection ( = ) has not been completed, then , back to S104, the two power groups track the changes in the instantaneous power of wind and solar grid connection, alternately charge and discharge, and work together to smooth the rebound and decline fluctuations of grid-connected power, so as to enter the power smoothing scheduling of the next time interval.

[0051] Case 2: If the real-time stage collection ( = ) is executed, but not executed at each time during the day ( ≠Lprediction), then +1, return to S103 to build a predictive control rolling scheduling model to enter the predictive control rolling scheduling at the next intraday time.

[0052] Case 3: If The last moment of the intraday phase ( = Lprediction), and the real-time stage collection After the execution is completed, the process returns to S101 to enter the next day-ahead scheduling optimization, and adjusts the time resolution of the first layer day-ahead and the second layer intraday according to the actual scheduling needs, operator preferences, etc. (the intraday time resolution should not be lower than the day-ahead).

[0053] The embodiment of the present invention provides a multi-energy storage power station grouping and hierarchical collaborative scheduling system for wind and solar bases, such as Figure 6 As shown, Figure 6 This is a block diagram of the grouped and layered coordinated dispatching system for multiple energy storage power stations for wind and solar bases, including: The acquisition module 601 is used to collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of the multi-element energy storage power station, obtain power fluctuation characteristics according to the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's power fluctuation suppression ratio according to the power fluctuation characteristics and the configuration parameters to obtain a mapping relationship model; A grouping module 602 is used to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model using the high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group; Correction module 603 is used to construct a day-ahead dispatch optimization model according to the day-ahead wind and solar power output forecast results, use the day-ahead dispatch optimization model to perform calculations, obtain the day-ahead dispatch plan of the energy group, construct a predictive control rolling dispatch model, and calculate the deviation of the energy storage capacity between the two power groups. If the deviation is greater than the preset deviation value, the multiple of the deviation is used as the control amount, and the median is used as the control target. Combined with the energy storage grouping results, the up and down adjustment of the energy group's grid-connected power is obtained, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan; The scheduling module 604 is used to adjust the frequency of the wind and solar base according to the revised scheduling plan, and determine whether to include the energy group in the frequency regulation based on the status of each power group, to obtain a new frequency regulation strategy, and to adjust the frequency of the wind and solar base according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy of the frequency fluctuation of the dual power groups and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group are kept unchanged, and real-time scheduling is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output scheduling result; if the third preset condition is met, the energy storage grouping step is returned to the step of re-adjusting the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation is started on the real-time scale of the day before.

[0054] In one embodiment, the acquisition module 601 includes a mapping relationship model determination unit, wherein: The mapping relationship model determination unit is used to construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's suppression ratio of power fluctuation according to the power fluctuation characteristics and configuration parameters, and obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

[0055] In one embodiment, the grouping module 602 includes an initial grouping unit, an updating unit, and a calculating unit. The initial grouping unit is used to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model using the high time resolution data within a preset time period to obtain the initial energy storage grouping result; The updating unit is used to increase the number of days in the preset time period by the preset number of days to obtain new high time resolution data, and use the new high time resolution data to group the multi-energy storage devices in the multi-energy storage station to obtain new energy storage grouping results; The calculation unit is used to calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold. If so, the new energy storage grouping result is determined to be the energy storage grouping result; if so, the step of increasing the number of days in the preset time period by the preset number of days is performed.

[0056] The specific implementation of the grouped and hierarchical coordinated scheduling system for the multi-energy storage power station for wind and solar bases is basically the same as the specific implementation of the grouped and hierarchical coordinated scheduling method for the multi-energy storage power station for wind and solar bases mentioned above, and will not be repeated here.

[0057] The technical solution of the present invention has the following advantages: The present invention establishes a source-storage coordinated regulation framework, taking the multi-time scale active storage and release of energy of the multi-energy storage power station as the starting point, giving full play to the timing adjustment of the source-side power generation grid-connected power curve of the multi-energy storage power station, and resisting the loss of wind and solar power generation uncertainty, thereby improving the grid's acceptance of the uncontrollable power generation of wind and solar bases and the grid's adaptability to renewable energy, and can guide the formulation of operation plans for distributed new energy power generation grid-connected.

[0058] The present invention also relies on the constructed multi-element energy storage power station grouping and layering model, including the whole-day scheduling of energy-type energy storage power stations in the day-ahead dimension, the rolling scheduling in the intraday dimension to correct the day-ahead energy storage scheduling error, and the further grouping and division of labor of the power groups in the real-time dimension to smooth the real-time power fluctuations. The proposed method can achieve the advantages of day-ahead peak regulation, intraday peak-frequency balancing, and real-time frequency regulation. It has wide adaptability to the uncontrollable power sources of wind and solar power and has strong and flexible energy storage power station management capabilities.

[0059] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A grouping and layering coordinated dispatching method for multiple energy storage power stations for wind and solar bases, characterized in that: include: Collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of the multi-element energy storage power station, obtain power fluctuation characteristics based on the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's power fluctuation suppression ratio according to the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model; Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped using the high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group; A day-ahead dispatch optimization model is constructed according to the day-ahead wind and solar power output forecast results, and the day-ahead dispatch optimization model is used to perform calculations to obtain a day-ahead dispatch plan for the energy group, a predictive control rolling dispatch model is constructed, and the deviation of the energy storage capacity between the two power groups is calculated. If the deviation is greater than a preset deviation value, the multiple of the deviation is used as the control amount, and the median is used as the control target. The up and down adjustment of the grid-connected power of the energy group is obtained in combination with the energy storage grouping results, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan; The wind and solar base is frequency-regulated according to the revised scheduling scheme, and whether the energy group is involved in the frequency regulation is determined based on the status of each power group to obtain a new frequency regulation strategy. The wind and solar base is frequency-regulated according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy of the frequency fluctuation of the dual power groups and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group are kept unchanged, and real-time scheduling is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output scheduling result; if the third preset condition is met, the energy storage grouping step is returned to the step of re-adjusting the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation on the real-time scale of the day before is started.

2. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: The mapping relationship between the charging and discharging power configuration of the power group and the power group's suppression ratio of power fluctuation is constructed according to the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model, including: According to the power fluctuation characteristics and the configuration parameters, a mapping relationship between the charging and discharging power configuration of the power group and the power fluctuation suppression ratio of the power group is constructed to obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

3. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: The method of grouping the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model and using the high time resolution data within a preset time period to obtain energy storage grouping results includes: Based on the mapping relationship model, the multi-element energy storage devices in the multi-element energy storage station are grouped using the high time resolution data within a preset time period to obtain an initial energy storage grouping result; Increasing the number of days in the preset time period by a preset number of days to obtain new high-time resolution data, and using the new high-time resolution data to group the multi-energy storage devices in the multi-energy storage station to obtain new energy storage grouping results; Calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold. If so, determine that the new energy storage grouping result is the energy storage grouping result; if so, proceed to the step of increasing the number of days in the preset time period by a preset number of days.

4. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: The day-ahead dispatch optimization model is constructed according to the day-ahead wind and solar power output prediction result, including: The weighted sum of the cumulative fluctuations and peak-to-valley differences of wind and solar grid-connected power is taken as the minimum optimization target to obtain the objective function, where the day-ahead scheduling optimization objective function is: In the formula, For the day before The wind and solar power grid-connected power at the moment, the subscript "1" refers to the first-layer day-ahead scheduling, and They represent the peak and valley values ​​of wind and solar grid connection in a day. It is a weight coefficient between 0 and 1, which is used to adjust the proportion of cumulative fluctuation and peak-to-valley difference in the objective function; Determine the day-ahead scheduling optimization decision variables and the day-ahead scheduling optimization constraints, and construct a day-ahead scheduling optimization model according to the decision variables, the constraints and the objective function, wherein the expression of the day-ahead scheduling optimization decision variables is: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The day-ahead scheduling optimization constraints are: 0 In the formula, and Respectively expressed in Photovoltaic power generation and wind turbine power generation at all times, for The storage capacity of the multi-energy storage device of the moment energy group, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. is the maximum charge and discharge power of the power group, Indicates that the energy group multi-energy storage device is The discharge amount at the moment.

5. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: The constructing of the predictive control rolling scheduling model includes: Determine the scheduling objective function, scheduling decision variables and scheduling constraints to obtain a predictive control rolling scheduling model, wherein the scheduling objective function is: In the formula, For the scenery base in Japan The grid-connected power at the time, the subscript "2" refers to the second-level intraday scheduling, For the day before The set of intraday times contained in the moment, is the deviation scaling factor; The current moment when the intraday rolling optimization starts. Indicates the deadline for intraday rolling optimization; The scheduling decision variables are: In the formula, Respectively represent the energy group multi-element energy storage device in Discharge and charge at each moment; The scheduling constraints are: 0 0 In the formula, Respectively represent the time of day Photovoltaic power generation and wind turbine power generation at all times, is the charge and discharge efficiency of the energy group, for The step length or duration, , are the charging / discharging power and storage energy of the energy group at that time, The energy group is prepared to participate in the real-time frequency regulation of the grid-connected power up and down adjustment participation amount, They are The maximum and minimum energy storage states of the multi-energy storage devices in the energy group at each moment. It is the maximum charge and discharge power of the power group.

6. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: When the frequency of the wind and solar base is modulated according to the revised scheduling scheme, it also includes: When any one of the two power groups reaches a preset daily threshold, the operation modes of the two power groups are exchanged, wherein the operation modes include a discharge mode and a charge mode.

7. The method for grouping and layering coordinated dispatching of multiple energy storage power stations for wind and solar bases as claimed in claim 1 is characterized in that: The first preset condition is a set of time gaps at the preset moments of the day stage that have not been executed, the second preset condition is a set of time gaps at the preset moments of the day stage that have been executed, and a set of time gaps at the remaining moments that have not been executed, and the third preset condition is a set of time gaps at the preset moments of the day stage that have been executed, and the preset moment of the day stage is the last moment of the day.

8. A multi-energy storage power station grouping and hierarchical coordinated dispatching system for wind and solar bases, characterized in that: include: A collection module is used to collect high-time resolution data of wind and solar power generation within a preset time period and configuration parameters of a multi-element energy storage power station, obtain power fluctuation characteristics based on the high-time resolution data within the preset time period, and construct a mapping relationship between the charging and discharging power configuration of the power group and the power group's power fluctuation suppression ratio according to the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model; A grouping module, used to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model and using the high time resolution data within a preset time period to obtain energy storage grouping results, wherein the energy storage grouping results include two power groups and one energy group; A correction module is used to construct a day-ahead dispatch optimization model according to the day-ahead wind and solar power output forecast results, use the day-ahead dispatch optimization model to perform calculations, obtain the day-ahead dispatch plan of the energy group, construct a predictive control rolling dispatch model, and calculate the deviation of the energy storage capacity between the two power groups. If the deviation is greater than a preset deviation value, then the multiple of the deviation is used as the control amount, and the median is used as the control target. Combined with the energy storage grouping results, the up and down adjustment of the energy group's grid-connected power is obtained, and the dispatch amount is added as a constraint to the feasible boundary of the real-time rolling model to obtain a revised dispatch plan; A scheduling module is used to adjust the frequency of the wind and solar base according to the revised scheduling scheme, and determine whether to include the energy group in the frequency regulation based on the status of each power group, to obtain a new frequency regulation strategy, and to adjust the frequency of the wind and solar base according to the new frequency regulation strategy. If the first preset condition is met, the real-time smoothing strategy of the frequency fluctuation of the dual power groups and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group are kept unchanged, and real-time scheduling is performed; if the second preset condition is met, the intraday prediction control correction model is constructed according to the real-time wind and solar output scheduling result; if the third preset condition is met, the energy storage grouping step is returned to the step of re-adjusting the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation is started on the real-time scale of the day before.

9. The multi-energy storage power station grouping and hierarchical coordinated dispatching system for wind and solar bases as claimed in claim 8 is characterized in that: The acquisition module includes a mapping relationship model determination unit, wherein: The mapping relationship model determination unit is used to construct a mapping relationship between the charging and discharging power configuration of the power group and the power fluctuation suppression ratio of the power group according to the power fluctuation characteristics and the configuration parameters, and obtain a mapping relationship model, wherein the formula of the mapping relationship model is: In the formula, It is the change of the actual output of wind and solar power in real time relative to the estimated value in the daily operation stage. It is the maximum charge and discharge power of the power group.

10. The multi-energy storage power station grouping and hierarchical coordinated dispatching system for wind and solar bases as claimed in claim 8, characterized in that: The grouping module includes an initial grouping unit, an updating unit and a calculating unit. Wherein, the initial grouping unit is used to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model using the high time resolution data within a preset time period to obtain an initial energy storage grouping result; The updating unit is used to increase the number of days in the preset time period by the preset number of days to obtain new high time resolution data, and use the new high time resolution data to group the multi-energy storage devices in the multi-energy storage station to obtain new energy storage grouping results; The calculation unit is used to calculate the change between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change is less than a preset threshold value. If so, determine that the new energy storage grouping result is the energy storage grouping result; if so, go to the step of increasing the number of days in the preset time period by a preset number of days.

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