A Grouping and Layering Cooperative Scheduling Method for a Multi-Energy Storage Power Station Oriented to Wind and Photovoltaic Bases

Abstract

The present invention discloses a method for grouped and hierarchical collaborative scheduling of a multi - energy storage power station for a wind - solar base. By constructing a day - ahead scheduling optimization model, calculating using the day - ahead scheduling optimization model to obtain the day - ahead scheduling plan of the energy group, constructing a predictive control rolling scheduling model to correct the day - ahead scheduling plan, obtaining the corrected day - ahead scheduling plan, and at the same time, judging the operation amount that the energy group needs to intervene in real - time frequency modulation according to the states of two auxiliary power groups, and intervening in the states of the two auxiliary power groups. In the real - time operation stage, when the two auxiliary power groups alternately are responsible for suppressing the power bounce and fall fluctuations, the energy group intelligently intervenes in suppressing the power fluctuations, instantaneously tracks and responds to the power fluctuations to complete real - time frequency modulation. The above - mentioned method corrects the day - ahead energy storage scheduling error by relying on the constructed multi - energy storage power station grouped and hierarchical model, and further groups and divides the work of the power groups to suppress the real - time power fluctuations, so as to realize the peak shaving and frequency modulation of the power generation and grid connection of the wind - solar base.

Description

Technical Field

[0001] The present invention relates to the technical field of multi - energy storage operation control and power system peak shaving and frequency regulation, and particularly relates to a grouped and hierarchical collaborative scheduling method for a multi - energy storage power station facing a wind - solar base. Background Technique

[0002] With the deepening of the global energy transformation, renewable energy power generation technologies represented by wind energy and solar energy have developed rapidly and become an important part of modern power systems. However, the characteristics of randomness, seasonality, intermittency, and volatility of wind - solar power generation have significantly increased the scheduling difficulty of the power grid. Especially in wind - solar bases with a high proportion of renewable energy access, frequent power fluctuations and prediction deviations pose higher requirements for the safe and stable operation of the power system. Developing effective peak - shaving and frequency - regulation technologies is of great significance for realizing the large - scale consumption of renewable energy.

[0003] The existing peak - shaving and frequency - regulation of wind - 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, making it difficult to simultaneously take into account the multi - level regulation preferences and actual needs of operators on a daily, intraday, and real - time basis. Although some studies consider the complementary and coordinated regulation methods among multi - energy storages for wind - solar peak - shaving and frequency - regulation at different time scales, how to manage the complementary regulation plan among multi - energy storages plays a crucial role in the peak - shaving and frequency - regulation effect of wind - solar bases. Summary of the Invention

[0004] To solve the above - mentioned technical problems, an embodiment of the present invention provides a grouped and hierarchical collaborative scheduling method for a multi - energy storage power station facing a wind - solar base, so as to solve the technical problem of how to manage the complementary and coordinated regulation among multi - energy storages in a wind - solar base and enhance the peak - shaving and frequency - regulation ability of the wind - solar base.

[0005] The first aspect of the embodiment of the present invention provides a grouped and hierarchical collaborative scheduling method for a multi - energy storage power station facing a wind - solar base. The method includes:

[0006] Collect high - time - resolution data of wind - solar power generation within a preset time period and the configuration parameters of the multi - energy storage power station, obtain the power fluctuation characteristics according to the high - time - resolution data within the preset time period, and construct a mapping relationship between the charge - discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations based on the power fluctuation characteristics and configuration parameters, so as to obtain a mapping relationship model;

[0007] Based on the mapping relationship model, use the high - time - resolution data within the preset time period to group the multi - energy storage devices in the multi - energy storage station, and obtain a storage grouping result, where the storage grouping result includes two power groups and one energy group;

[0008] Construct a day-ahead scheduling optimization model based on the predicted results of the current wind and light output. Use the day-ahead scheduling optimization model for calculation to obtain the day-ahead scheduling plan for the energy group. Construct a predictive control rolling scheduling model, and calculate the deviation amount of the energy storage capacity between the two power groups. If the deviation amount is greater than the preset deviation value, use the multiple of the deviation amount as the regulation amount, with the median as the regulation target, and combine the energy storage grouping results to obtain the up and down regulation participation scheduling amounts of the grid-connected power of the energy group, and add the scheduling amount as a constraint amount to the feasible boundary of the real-time rolling model to obtain the corrected scheduling plan.

[0009] Perform frequency modulation on the wind and light base according to the corrected scheduling plan, and based on the status of each power group, determine whether to involve the energy group in frequency modulation to obtain a new frequency modulation strategy. Perform frequency modulation on the wind and light base according to the new frequency modulation strategy. If the first preset condition is met, maintain the real-time suppression strategy of the frequency fluctuation of the dual power group and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group unchanged, and perform real-time scheduling; if the second preset condition is met, then transfer to the construction of an intraday predictive control correction model according to the real-time wind and light output scheduling result; if the third preset condition is met, return to the energy storage grouping step to re-adjust the allocation results of the energy group and the power group, and start a new round of peak shaving and frequency modulation of the wind and light base at the day-ahead, intraday, and real-time scales.

[0010] In a possible implementation manner of the first aspect, according to the power fluctuation characteristics and configuration parameters, construct a mapping relationship between the charge and discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations to obtain a mapping relationship model, including:

[0011] According to the power fluctuation characteristics and configuration parameters, construct a mapping relationship between the charge and discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations to obtain a mapping relationship model, where the formula of the mapping relationship model is:

[0012]

[0013] In the formula, is the change of the actual output of the wind and light at time in the real-time stage relative to the predicted value in the intraday operation stage, is the maximum charge and discharge power of the power group.

[0014] In a possible implementation manner of the first aspect, based on the mapping relationship model, use the high-time-resolution data within a preset time period to group the multiple energy storage devices in the multiple energy storage station to obtain an energy storage grouping result, including:

[0015] Based on the mapping relationship model, use the high-time-resolution data within a preset time period to group the multiple energy storage devices in the multiple energy storage station to obtain an initial energy storage grouping result;

[0016] Increase the number of days in the preset time period by a 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 a new energy storage grouping result;

[0017] 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 it is less, determine the new energy storage grouping result as the energy storage grouping result. If it is greater, go to the step of increasing the number of days in the preset time period by a preset number of days.

[0018] In a possible implementation manner of the first aspect, construct a day-ahead scheduling optimization model according to the day-ahead wind and light output prediction results, including:

[0019] Take the weighted sum of the cumulative fluctuations of the wind and light grid-connected power and the peak-valley difference as the minimum optimization objective to obtain the objective function. Among them, the day-ahead scheduling optimization objective function is:

[0020]

[0021] In the formula, is the wind and light grid-connected power at moment, and the subscript "1" refers to the first-layer day-ahead scheduling. and respectively represent the peak and valley values of the wind and light grid connection in a day. is a weight coefficient between 0 and 1, which is used to adjust the proportion of the cumulative fluctuation and the peak-valley difference in the objective function;

[0022] Determine the day-ahead scheduling optimization decision variables and the day-ahead scheduling optimization constraint conditions, and construct a day-ahead scheduling optimization model according to the decision variables, constraint conditions and the objective function. Among them, the expression of the day-ahead scheduling optimization decision variables is:

[0023]

[0024] In the formula, respectively represent the discharge and charge amounts of the multi-energy storage devices in the energy group at moment;

[0025] The day-ahead scheduling optimization constraint conditions are:

[0026]

[0027] 0

[0028]

[0029] In the formula, and respectively represent at Photovoltaic power generation and wind power generation at a certain moment, is The energy storage capacity of the multi - energy - storage device of the energy group at a certain moment, are respectively The maximum energy storage state and the minimum energy storage state of the multi - energy - storage device of the energy group at a certain moment, is the maximum charge - discharge power of the power group, represents the discharge amount of the multi - energy - storage device of the energy group at a certain moment.

[0030] In a possible implementation of the first aspect, a predictive control rolling scheduling model is constructed, including:

[0031] Determine the scheduling objective function, scheduling decision variables, and scheduling constraint conditions to obtain the predictive control rolling scheduling model. Among them, the scheduling objective function is:

[0032]

[0033] In the formula, is the grid - connected power of the wind - solar base at the moment within a day. The subscript "2" refers to the second - layer intraday scheduling, is the set of intraday moments included in the th moment of the day - ahead, is the deviation scaling coefficient; is the current moment when the intraday rolling optimization starts, represents the intraday rolling optimization deadline;

[0034] The scheduling decision variables are:

[0035]

[0036] In the formula, respectively represent the discharge amount and charge amount of the multi - energy - storage device of the energy group at a certain moment;

[0037] The scheduling constraint conditions are:

[0038]

[0039] 0

[0040] 0

[0041]

[0042]

[0043] In the formula, respectively represent within the intraday time The photovoltaic power generation and wind turbine power generation at each moment is the charge-discharge efficiency of the energy group is the step size or duration of 、 The charging / discharging power and stored energy of the energy group at this time are respectively is the up and down regulation participation amount of the grid-connected power of the energy group prepared to participate in real-time frequency modulation are respectively the maximum energy storage state and the minimum energy storage state of the multi-energy storage device of the energy group at the moment is the maximum charge-discharge power of the power group

[0044] In a possible implementation manner of the first aspect, when performing frequency modulation on the wind-solar base according to the corrected scheduling scheme, it further includes:

[0045] When any one of the two power groups reaches the preset threshold within a day, the operating modes of the two power groups are exchanged, where the operating modes include a discharging mode and a charging mode

[0046] In a possible implementation manner of the first aspect, the first preset condition is the set of time intervals of the time gaps at the preset moments within the day that have not been completed, the second preset condition is the set of time intervals of the time gaps at the preset moments within the day that have been completed and the time gaps at the remaining moments that have not been completed, and the third preset condition is the set of time intervals of the time gaps at the preset moments within the day that have been completed and the preset moment within the day is the last moment within the day

[0047] To solve the same technical problem, a second aspect of the embodiments of the present invention provides a multi-energy storage power station grouping and hierarchical collaborative scheduling system for a wind-solar base, and the system includes:

[0048] An acquisition module, configured to acquire high-time-resolution data within a preset time period of wind-solar power generation and configuration parameters of the multi-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 charge-discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations according to the power fluctuation characteristics and configuration parameters, so as to obtain a mapping relationship model

[0049] A grouping module, configured to group the multi-energy storage devices in the multi-energy storage station based on the mapping relationship model by using the high-time-resolution data within the preset time period, so as to obtain a storage grouping result, where the storage grouping result includes two power groups and one energy group

[0050] A correction module, which is used to construct a day-ahead scheduling optimization model according to the day-ahead wind and light output prediction results, calculate using the day-ahead scheduling optimization model to obtain the day-ahead scheduling plan of the energy group, construct a predictive control rolling scheduling model, and calculate the deviation of the energy storage capacity between two power groups. If the deviation is greater than the preset deviation value, the multiple of the deviation is used as the regulation amount, the median is used as the regulation target, and the up and down regulation participation scheduling amounts of the grid-connected power of the energy group are obtained in combination with the energy storage grouping result, and the scheduling amount is added as a constraint amount to the feasible boundary of the real-time rolling model to obtain a corrected scheduling plan;

[0051] A scheduling module, which is used to perform frequency modulation on the wind and light base according to the corrected scheduling plan, and judge whether to participate the energy group in frequency modulation based on the states of each power group to obtain a new frequency modulation strategy, and perform frequency modulation on the wind and light base according to the new frequency modulation strategy. If the first preset condition is met, maintain the real-time suppression strategy of the frequency fluctuation of the dual power group and the feasible boundary steps of the real-time scheduling model after the intelligent access of the energy group unchanged, and perform real-time scheduling; if the second preset condition is met, then turn to construct an intraday predictive control correction model according to the real-time wind and light output scheduling result; if the third preset condition is met, return to the energy storage grouping step to re-adjust the allocation results of the energy group and the power group, and start a new round of peak shaving and frequency modulation of the wind and light base under the day-ahead, intraday and real-time scales.

[0052] In a possible implementation manner of the second aspect, the acquisition module includes a mapping relationship model determination unit, where,

[0053] The mapping relationship model determination unit is used to construct a mapping relationship between the charge and discharge power configuration of the power group and the suppression ratio of the power group to power fluctuation according to the power fluctuation characteristics and configuration parameters to obtain a mapping relationship model, where the formula of the mapping relationship model is:

[0054]

[0055] In the formula, is the change of the actual output of the wind and light at time in the real-time stage relative to the predicted value in the intraday operation stage, is the maximum charge and discharge power of the power group.

[0056] In a possible implementation manner of the second aspect, the grouping module includes an initial grouping unit, an update unit and a calculation unit,

[0057] where 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 by using the high-time-resolution data within a preset time period to obtain an initial energy storage grouping result;

[0058] The update unit is used to increase the number of days in a preset time period by a 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 a new energy storage grouping result;

[0059] The calculation unit is used to calculate the change amount between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change amount is less than a preset threshold. If it is less, the new energy storage grouping result is determined as the energy storage grouping result. If it is greater, it goes to the step of increasing the number of days in the preset time period by the preset number of days. Description of the Drawings

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0061] Figure 1 It is a flowchart of the grouping and hierarchical collaborative scheduling method for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention;

[0062] Figure 2 It is a schematic diagram of power generation and grid connection of a wind-solar base in the grouping and hierarchical collaborative scheduling method for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention;

[0063] Figure 3 It is a schematic diagram of the expected operating state of the grouped and hierarchical scheduling power groups in the grouping and hierarchical collaborative scheduling method for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention;

[0064] Figure 4 It is a schematic diagram of the process of the grouping and hierarchical scheduling method for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention;

[0065] Figure 5 It is a schematic diagram of the day-ahead - intra-day - real-time grouped and hierarchical rolling scheduling for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention;

[0066] Figure 6 It is a structural block diagram of the grouping and hierarchical collaborative scheduling system for a multi-energy storage power station facing a wind-solar base in an embodiment of the present invention. Detailed Embodiments

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] The method for group-layer collaborative scheduling of a multi-energy storage power station for a wind-solar base provided by an embodiment of the present invention is as Figure 1 shown. Figure 1 It is a flow chart for group-layer collaborative scheduling of a multi-energy storage power station for a wind-solar base, including steps S101 to S104. The specific steps are as follows:

[0069] S101: Collect high-time-resolution data of wind-solar power generation within a preset time period and configuration parameters of the multi-energy storage power station. Obtain the power fluctuation characteristics based on the high-time-resolution data within the preset time period. According to the power fluctuation characteristics and configuration parameters, construct a mapping relationship between the charge-discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations, and obtain a mapping relationship model.

[0070] In this embodiment, as Figure 2 shown, 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 shaving and frequency modulation on the wind-solar base, ensuring that the wind-solar base goes online as per the day-ahead dispatch plan as much as possible, while resisting the fluctuation impact of wind-solar parameters on renewable energy power generation. The multi-energy storage power station includes two types: energy-type energy storage (such as pumped-storage energy storage, electrochemical energy storage group, etc.) and power-type energy storage (such as flywheel energy storage, electrochemical energy storage, etc.).

[0071] Figure 3 It is a schematic diagram of the extreme operating state of the multi-energy storage and the expected operating state of the power group for group-layer scheduling provided by the present invention. As Figure 3 shown, within the intraday time scale and real-time time scale, if and generate more power 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 overall biased towards 1, which will lead to a decrease in the energy storage capacity of the SOC and an inability to cope with sudden increases in wind-solar power generation at subsequent moments. On the contrary, if and generate less power 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 overall biased towards 0, which will lead to a decrease in the energy release of the SOC, and the power group batteries will not have enough power to supply the reduced part of the power generation when the wind-solar power generation decreases at subsequent moments.

[0072] Through the multi - energy storage power station grouping and hierarchical collaborative scheduling method proposed by the present invention, the average SOC of the two power - group batteries can be stabilized near the median. Specifically, as Figure 4 shown, first, the multi - energy storage station is divided into three blocks, such as a (single) main energy group and a (dual) auxiliary power group, according to the historical grid - connected power fluctuation characteristics of wind and light. The specific steps are as follows:

[0073] S11: Continuously monitor and record the historical high - time - resolution data (such as hours, minutes, seconds) of wind and light power generation, and collect the configuration parameters of the multi - energy storage power station. For the next - day scheduling, taking the wind and light output in the R days before the "next day" as the historical reference data set R, based on the power fluctuation characteristics in the real - time operation stage, establish the mapping relationship between the "charge - discharge power configuration of the power group" and the "fluctuation suppression ratio of the power group to power fluctuations, CR".

[0074] In an embodiment, according to the power fluctuation characteristics and configuration parameters, construct the mapping relationship between the charge - discharge power configuration of the power group and the fluctuation suppression ratio of the power group to power fluctuations, and obtain the mapping relationship model, including:

[0075] According to the power fluctuation characteristics and configuration parameters, construct the mapping relationship between the charge - discharge power configuration of the power group and the fluctuation suppression ratio of the power group to power fluctuations, and obtain the mapping relationship model. Among them, the formula of the mapping relationship model is:

[0076]

[0077] In the formula, is the change in the actual output of wind and light at time t in the real - time stage relative to the predicted value in the intraday operation stage, is the maximum charge - discharge power of the power group.

[0078] In this embodiment, based on the power fluctuation characteristics in the real - time operation stage, establish the mapping relationship between the "charge - discharge power configuration of the power group, " and the "fluctuation suppression ratio of the power group to power fluctuations, CR". The model is as follows:

[0079]

[0080] In the formula, is the change in the actual output of wind and light at time t in the real - time stage relative to the predicted value in the intraday operation stage, is the maximum charge - discharge power of the power group.

[0081] S102: Based on the mapping relationship model, use the high - time - resolution data within a preset time period to group the multi - energy storage devices in the multi - energy storage station, and obtain the energy storage grouping result. Among them, the energy storage grouping result includes two power groups and one energy group.

[0082] In this embodiment, according to the customer's requirements for CR, taking as a reference, the multi-energy storage devices with a larger power density in the multi-energy storage station are divided into two power groups, and the remaining ones are used as the energy group with a larger energy density, completing the division and grouping of the multi-energy storage resources in the multi-energy storage station to dynamically follow the "fluctuation law of wind and light output in the recent R days".

[0083] In one embodiment, based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped by using the high-time-resolution data within a preset time period to obtain the energy storage grouping result, including:

[0084] Based on the mapping relationship model, the multi-energy storage devices in the multi-energy storage station are grouped by using the high-time-resolution data within a preset time period to obtain the initial energy storage grouping result;

[0085] The number of days in the preset time period is increased by a 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 by using the new high-time-resolution data to obtain a new energy storage grouping result;

[0086] Calculate the change amount between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change amount is less than a preset threshold. If it is less, determine the new energy storage grouping result as the energy storage grouping result. If it is greater, go to the step of increasing the number of days in the preset time period by a preset number of days.

[0087] In this embodiment, based on the mapping relationship model, the specific steps for grouping the multi-energy storage devices in the multi-energy storage station by using the high-time-resolution data within a preset time period to obtain the energy storage grouping result are as follows:

[0088] S12: According to the customer's requirements for CR, taking as a reference, the multi-energy storage devices with a larger power density in the multi-energy storage station are divided into two power groups, and the remaining ones are used as the energy group with a larger energy density, completing the division and grouping of the multi-energy storage resources in the multi-energy storage station to dynamically follow the "fluctuation law of wind and light output in the recent R days".

[0089]

[0090] +2 =

[0091]

[0092]

[0093] In the formula, and are 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 is the power-capacity ratio of the multi-energy storage resources in the energy group and the power group.

[0094] S13: Gradually increase the value of R, repeat steps S12 - S13 and observe the change in the grouping decision result of the multi-energy storage station until the change in the decision result is less than a certain threshold , and take the grouping scheme at the R value when it is relatively stable as the final scheme. This step aims to filter out the adverse effects of some extreme weather scenarios on the grouping decision of the multi-energy storage station and further correct the capacity division of the energy-power of the energy storage station. The calculation formula of the threshold is:

[0095]

[0096] In the formula, is the threshold parameter, is the multi-energy storage power of the allocated power group when R is composed of data of R days, is the multi-energy storage power of the allocated power group when R - 1 is composed of data of R - 1 days.

[0097] S103: Construct a day-ahead scheduling optimization model according to the day-ahead wind and light output prediction results, calculate using the day-ahead scheduling optimization model to obtain the day-ahead scheduling scheme of the energy group, construct a predictive control rolling scheduling 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, use the multiple of the deviation as the regulation amount, take the median as the regulation target, combine the energy storage grouping result to obtain the up and down regulation participation scheduling amounts of the grid-connected power of the energy group, and add the scheduling amount as a constraint amount to the feasible boundary of the real-time rolling model to obtain the corrected scheduling scheme.

[0098] In this embodiment, on a day-ahead basis, combine the wind and light output prediction results to construct an energy group peak shaving operation optimization model, formulate the charge and discharge plan optimization of the main energy group and determine the grid connection plan of the wind and light base, that is, the day-ahead scheduling scheme of the multi-energy storage of the energy group. During the day, take the time from the current moment to the end of the day as the scheduling window and minimize the deviation between the wind and light grid connection amount and the day-ahead planned value as the goal to establish an energy group predictive control model; during the day, according to the degree of deviation of the double power group mean state from the median, judge whether to add feasibility constraints to limit the behavior of the energy group to intervene in the real-time frequency modulation task. Correct the day-ahead scheduling scheme of the multi-energy storage of the energy group according to the latest wind and light prediction data during the day to make the actual grid-connected power during the day as consistent as possible with the day-ahead scheduling scheme.

[0099] In one embodiment, constructing a day-ahead scheduling optimization model according to the day-ahead wind and light output prediction results includes:

[0100] Taking the weighted sum of the cumulative fluctuations of the wind and light grid-connected power and the peak-valley difference as the minimum optimization objective, the objective function is obtained. Among them, the day-ahead scheduling optimization objective function is:

[0101]

[0102] In the formula, is the wind and light grid-connected power at the day-ahead moment. The subscript "1" refers to the first-layer day-ahead scheduling, and respectively represent the peak and valley values of wind and light grid connection in a day, is a weight coefficient between 0 and 1, which is used to adjust the proportion of the cumulative fluctuation and the peak-valley difference in the objective function;

[0103] Determine the day-ahead scheduling optimization decision variables and day-ahead scheduling optimization constraint conditions, and construct a day-ahead scheduling optimization model according to the decision variables, constraint conditions and objective function. Among them, the expression of the day-ahead scheduling optimization decision variables is:

[0104]

[0105] In the formula, respectively represent the discharge and charge amounts of the energy group multi-energy storage device at moment;

[0106] The day-ahead scheduling optimization constraint conditions are:

[0107]

[0108] 0

[0109]

[0110] In the formula, and respectively represent the photovoltaic power generation and wind turbine power generation at moment, is the stored energy of the energy group multi-energy storage device at moment, are respectively the maximum and minimum energy storage states of the energy group multi-energy storage device at moment, is the maximum charge-discharge power of the power group, represents the discharge amount of the energy group multi-energy storage device at moment.

[0111] In this embodiment, the day-ahead scheduling takes the minimization of the "weighted sum" of two indicators, namely the hourly cumulative fluctuation of the wind-solar grid-connected power and the peak-valley difference, as the optimization objective. By flexibly adjusting the value of the weight coefficient, the optimization direction of the operation of the multi-energy-storage units in the day-ahead energy group can be adjusted between the cumulative fluctuation and the peak-valley difference, so as to closely match the regulation preferences and actual demands of the operator. The specific objective model is as follows:

[0112]

[0113] In the formula, is the wind-solar grid-connected power at the moment. The subscript "1" refers to the first-layer day-ahead scheduling. and respectively represent the peak and valley values of the wind-solar grid connection in a day. is a weight coefficient between 0 and 1, which is used to adjust the proportion of the cumulative fluctuation and the peak-valley difference in the objective function.

[0114] The day-ahead scheduling mainly reduces the fluctuation of the wind-solar grid-connected power by adjusting the charge and discharge behaviors of the energy group in 24 hours of a day according to the predicted results of the wind-solar output. Therefore, the decision variables can be expressed as:

[0115]

[0116] In the formula, respectively represent the discharge amount and charge amount of the multi-energy-storage device of the energy group at the moment.

[0117] The constraints of the day-ahead scheduling include the power balance at each moment and the operation characteristic constraints of the multi-energy-storage device of the energy group, as follows:

[0118]

[0119] 0

[0120]

[0121] In the formula, and respectively represent the photovoltaic power generation and wind turbine power generation at the moment. is the stored energy of the multi-energy-storage device of the energy group at the moment. are respectively the maximum energy storage state and the minimum energy storage state of the multi-energy-storage device of the energy group at the moment. is the maximum charge-discharge power of the power group. represents the discharge amount of the multi-energy-storage device of the energy group at the moment.

[0122] In one embodiment, a predictive control rolling scheduling model is constructed, including:

[0123] Determine the scheduling objective function, scheduling decision variables, and scheduling constraint conditions to obtain the predictive control rolling scheduling model. Among them, the scheduling objective function is:

[0124]

[0125] In the formula, is the grid-connected power of the wind-solar base at the moment within the day. The subscript "2" refers to the second-layer intra-day scheduling. is the set of intra-day moments included in the th moment of the day-ahead. is the deviation scaling coefficient; is the current moment when the intra-day rolling optimization starts. represents the cut-off time of the intra-day rolling optimization;

[0126] The scheduling decision variables are:

[0127]

[0128] In the formula, respectively represent the discharge and charge amounts of the multi-energy storage device of the energy group at the moment;

[0129] The scheduling constraint conditions are:

[0130]

[0131] 0

[0132] 0

[0133]

[0134]

[0135] In the formula, respectively represent the photovoltaic power generation and wind power generation at the intra-day time moment. is the charge-discharge efficiency of the energy group. is the step size or duration of , respectively represent the charge / discharge power and stored energy of the energy group at this time. is the up / down regulation participation amount of the grid-connected power that the energy group is prepared to participate in real-time frequency modulation. respectively represent The maximum energy storage state and the minimum energy storage state of the moment energy group multi - energy storage device is the maximum charge - discharge power of the power group.

[0136] In this embodiment, with 15 minutes as the intra - day stage scheduling time scale, the time interval from the "current minute - level moment" to the "end of the day" is used as a rolling window. Combining the latest high - precision (15 - minute) wind and solar power generation data, the operation plan of the moment energy group multi - energy storage is optimized again to make the actual grid - connected power of wind and solar within the day consistent with the day - ahead planned value, so as to overcome the uncertainty of wind and solar fluctuations.

[0137] Different from the existing multi - time - scale optimization methods, which take the minimization of the sum of the adjustment amounts of the operation of each device in the intra - day energy system compared with the day - ahead plan 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 within the day" and the "grid - connected power planned and reported ahead of schedule" as the optimization target, so that the grid - connected power of wind and solar is consistent with the planned value. The target model, that is, the scheduling target function is as follows:

[0138]

[0139] In the formula, is the grid - connected power of the wind and solar base at the moment within the day. The subscript "2" refers to the second - layer intra - day scheduling. is the set of intra - day moments included in the th moment ahead of schedule. is the deviation scaling coefficient. Increasing the value of this parameter can amplify the influence of the "large - deviation moment within the day" on the target to avoid the serious deviation of the actual grid - connected power within the day from the day - ahead planned value. is the current moment when the intra - day rolling optimization starts. represents the intra - day rolling optimization cut - off time (referring to the end of the day).

[0140] The intra - day scheduling mainly adjusts the charge - discharge behavior of the moment energy group within the rolling window according to the predicted results of wind and solar power output within the day, and adjusts the charge - discharge behavior at the 15 - minute resolution level to correct the grid - connected power to be consistent with the day - ahead planned value. Therefore, the decision variable, that is, the scheduling decision variable can be expressed as:

[0141]

[0142] In the formula, respectively represent the discharge amount and the charge amount of the moment energy group multi - energy storage device at the moment;

[0143] The constraints of the intra - day scheduling are similar to those of the day - ahead scheduling, including two parts: power balance and the operating characteristics of the moment energy group. However, it is also necessary to narrow and limit its operating range to ensure that the moment energy group has a power of The up and down regulation margins of grid-connected power ([greater than 0 indicates an increase in grid-connected power achieved by weakening the energy group charging or enhancing its discharging, while less than 0 indicates the opposite, i.e., a decrease in grid-connected power) are used to participate in real-time frequency modulation and replace the operation of a power group. Therefore, the scheduling constraints within the day are as follows:

[0144]

[0145] 0

[0146] 0

[0147]

[0148]

[0149] In the formula, respectively represent the photovoltaic power generation and wind turbine power generation at the intra-day time moment, is the charge-discharge efficiency of the energy group, is the step size or duration of 、 are respectively the charge / discharge power and stored energy of the energy group at this time, is the up and down regulation participation amount of the grid-connected power of the energy group prepared to participate in real-time frequency modulation, are respectively the maximum energy storage state and minimum energy storage state of the multi-energy storage device of the energy group at the moment, is the maximum charge-discharge power of the power group.

[0150] Based on the deviation of the mean value of the stored energy of the dual-power group device relative to the median, determine the value of (the up and down regulation participation amount of the grid-connected power of the energy group prepared to participate in real-time frequency modulation). First, calculate the deviation amount of the mean value of the stored energy of the two power groups relative to the median ( ). If the deviation is greater than the threshold ( ), then use twice the deviation amount as the regulation amount and the median as the regulation target, and determine it in combination with the grouping scheme of the multi-energy storage station. The relevant calculation model is as follows:

[0151]

[0152]

[0153] In the formula, are respectively the stored energy values of equipment group A and equipment group B of the power group after the execution at the previous moment of intra-day time t2. Two power groups of the same specification are identified and distinguished by the symbols "A" and "B". is the deviation amount, is the deviation threshold, is the maximum charge-discharge power of the power group, is the maximum charge-discharge power of the energy group.

[0154] In the above model, when the average state of the two power groups is on the high side, the energy group needs to participate in the real-time frequency modulation scheduling of "grid-connected power reduction", so as to reduce the charging amount of the power group and make the average state drop to return to the median. When the average state of the two power groups is on the low side, the energy group needs to participate in the real-time frequency modulation scheduling of "grid-connected power increase", so as to reduce the discharge amount of the power group and make the average state rise to return to the median.

[0155] S104: Perform frequency modulation on the wind-solar base according to the corrected scheduling plan, and judge whether to involve the energy group in frequency modulation based on the states of each power group to obtain a new frequency modulation strategy. Perform frequency modulation on the wind-solar base according to the new frequency modulation strategy. If the first preset condition is met, maintain the real-time suppression 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 unchanged, and perform real-time scheduling; if the second preset condition is met, then transfer to the construction of the intra-day prediction control correction model according to the real-time wind-solar output scheduling result; if the third preset condition is met, return to the energy storage grouping step to re-adjust the allocation results of the energy group and the power group, and start a new round of peak shaving and frequency modulation of the wind-solar base at the daily and intra-day real-time scales.

[0156] In this embodiment, in the real-time operation stage, the two auxiliary power groups are alternately responsible for suppressing the power rebound and fallback fluctuations, and the main energy group intelligently intervenes in the power fluctuation suppression, instantaneously tracking and responding to the power fluctuation to complete real-time frequency modulation. Specifically, the two power groups track the change of the instantaneous power of the wind-solar grid connection, alternately charge and discharge, and cooperate to suppress the power rebound and fallback fluctuations of the grid connection, and then make the energy group intelligently intervene in the real-time power frequency modulation to replace one power group to suppress the power fluctuation, so that the average value of the stored energy of the two power groups is maintained near the median.

[0157] In one embodiment, when performing frequency modulation on the wind-solar base according to the corrected scheduling plan, it further includes:

[0158] When any one of the two power groups reaches the intra-day preset threshold, exchange the operating modes of the two power groups, where the operating modes include a discharge mode and a charge mode.

[0159] In this embodiment, two power groups of the same specification are identified and distinguished by the symbols "A" and "B", and the initial stored energy of power groups A and B is set in the middle state of the capacity. In each time interval of the third-layer real-time scheduling Initially, only power group A is charged to be responsible for reducing the rebounding grid-connected power to the planned value of the intraday dispatch, and only power group B is discharged to be responsible for increasing the falling grid-connected power to the planned value of the intraday dispatch, so as to achieve power stability.

[0160] As the charging and discharging proceed, the stored energy of power group A or power group B will reach the upper bound ( ), or the lower bound ( ). If the boundary is reached, the operating modes of the two power groups are exchanged. Power group A starts to enter the discharging mode, and power group B starts to enter the charging mode. In this way, it circulates repeatedly to achieve stable grid-connected power with alternating charging and discharging. The operating constraints are as follows:

[0161]

[0162]

[0163]

[0164] In the formula, is the power fluctuation occurring in the time interval of the real-time dispatch stage. The subscript "3" refers to the third layer of real-time dispatch. Greater than 0 indicates power rebound and increasing grid-connected power, and less than 0 indicates power decline and decreasing grid-connected power. is the set of time intervals of the real-time stage included in the intraday stage.

[0165] The energy group intelligently intervenes in the real-time power frequency modulation to replace a power group to suppress the power fluctuation, so that the average value of the stored energy of the two power groups is maintained near the median. The intelligent judgment and adjustment strategy are as follows:

[0166] Case 1: If is greater than 0 and is less than 0, the energy group discharges to increase the power and reduces the discharging of the power group. The final operation plan of the energy group and the power group in the time interval is determined by the following model:

[0167]

[0168] In the formula, is the operation adjustment amount of the energy group in the time interval of the real-time dispatch stage and its adjusted charging and discharging power. The power group equipment referred to by the superscript "A / B" is determined according to the actual operation modes of power group A and power group B determined in step S5.

[0169] Case 2: If is less than 0 and is greater than 0, the energy group charges to reduce the power and reduces the charging of the power group. The energy group and the power group in the time interval The final operation plan is determined by the following model:

[0170]

[0171] Wherein, is the time slot to which the energy group belongs in the real-time scheduling stage is the operation adjustment amount and the adjusted charge and discharge power thereof.

[0172] Case 3: Except for Cases 1 and 2, the energy group does not participate in the frequency modulation optimization in the real-time stage.

[0173] In one embodiment, the first preset condition is the set of time slots at the preset moments in the intraday stage that have not been completed, the second preset condition is the set of time slots at the preset moments in the intraday stage that have been completed and the set of time slots at the remaining moments that have not been completed, and the third preset condition is the set of time slots at the preset moments in the intraday stage that have been completed and the preset moment in the intraday stage is the last moment in the day.

[0174] In this embodiment, according to the scheduling progress, return to steps S101, S102 or S103, adjust the time resolution according to the scheduling needs, and form a scheduling closed loop, specifically as follows:

[0175] Case 1: If the set in the real-time stage ( = ) has not been completed, then , return to S104, and the two power groups track the change of the instantaneous power of the wind-solar grid connection, alternately charge and discharge, and cooperate to suppress the rebound and fall fluctuations of the grid connection power, so as to enter the power suppression scheduling of the next time slot.

[0176] Case 2: If the set in the real-time stage ( = ) has been completed, but each moment in the intraday stage has not been completed ( ≠Lprediction), then +1, return to S103 to construct a predictive control rolling scheduling model to enter the predictive control rolling scheduling of the next intraday moment.

[0177] Case 3: If is the last moment in the intraday stage ( =Lprediction), and the real-time stage set has been completed, then return to S101 to enter the day-ahead scheduling optimization of the next day, and adjust the time resolution of the first layer of day-ahead and the second layer of intraday according to the actual scheduling needs, operator preferences, etc. (the time resolution in the intraday should not be lower than that in the day-ahead).

[0178] The multi - energy storage power station grouping and hierarchical collaborative scheduling system for a wind - solar base provided by an embodiment of the present invention is as follows Figure 6 as shown Figure 6 in the block diagram of the multi - energy storage power station grouping and hierarchical collaborative scheduling system for a wind - solar base, which includes:

[0179] An acquisition module 601, configured to acquire high - time - resolution data of wind - 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 charge - discharge power configuration of a power group and the suppression ratio of the power group to power fluctuations according to the power fluctuation characteristics and configuration parameters, so as to obtain a mapping relationship model;

[0180] A grouping module 602, configured to group the multi - energy storage devices in the multi - energy storage station based on the mapping relationship model by using the high - time - resolution data within the preset time period, so as to obtain a storage grouping result, where the storage grouping result includes two power groups and one energy group;

[0181] A correction module 603, configured to construct a day - ahead scheduling optimization model according to the day - ahead wind - solar power output prediction result, calculate by using the day - ahead scheduling optimization model to obtain a day - ahead scheduling plan for the energy group, construct a predictive control rolling scheduling model, and calculate the deviation amount of the energy storage capacity between the two power groups. If the deviation amount is greater than a preset deviation value, use the multiple of the deviation amount as the regulation amount, use the median as the regulation target, combine the storage grouping result to obtain the up - and - down regulation participation scheduling amounts of the grid - connected power of the energy group, and add the scheduling amounts as a constraint amount to the feasible boundary of the real - time rolling model to obtain a corrected scheduling plan;

[0182] A scheduling module 604, configured to perform frequency modulation on the wind - solar base according to the corrected scheduling plan, and based on the states of each power group, determine whether to participate in frequency modulation of the energy group to obtain a new frequency modulation strategy, perform frequency modulation on the wind - solar base according to the new frequency modulation strategy. If the first preset condition is met, maintain the real - time suppression strategy of the frequency fluctuations of the double - power group and the feasible boundary steps of the real - time scheduling model after the intelligent access of the energy group, and perform real - time scheduling; if the second preset condition is met, then transfer to constructing an intra - day predictive control correction model according to the real - time wind - solar power output scheduling result within the day; if the third preset condition is met, return to the storage grouping step to re - adjust the distribution results of the energy group and the power group, and start a new round of peak shaving and frequency modulation of the wind - solar base at the day - ahead, intra - day, and real - time scales.

[0183] In one embodiment, the acquisition module 601 includes a mapping relationship model determination unit, where

[0184] The mapping relationship model determination unit is used to construct the mapping relationship between the charge and discharge power configuration of the power group and the suppression ratio of the power group to power fluctuations according to the power fluctuation characteristics and configuration parameters, and obtain the mapping relationship model. The formula of the mapping relationship model is as follows:

[0185]

[0186] In the formula, is the change of the actual output of wind and light at a certain time in the real-time stage relative to the predicted value in the intraday operation stage, is the maximum charge and discharge power of the power group.

[0187] In one embodiment, the grouping module 602 includes an initial grouping unit, an update unit, and a calculation unit.

[0188] Among them, the initial grouping unit is used to group the multi-energy storage devices in the multi-energy storage power station based on the mapping relationship model and using the high-time-resolution data within a preset time period, and obtain the initial energy storage grouping result;

[0189] The update unit is used to increase the number of days in the preset time period by a preset number of days to obtain new high-time-resolution data, and group the multi-energy storage devices in the multi-energy storage power station using the new high-time-resolution data to obtain a new energy storage grouping result;

[0190] The calculation unit is used to calculate the change amount between the new energy storage grouping result and the initial energy storage grouping result, and determine whether the change amount is less than a preset threshold. If it is less, it is determined that the new energy storage grouping result is the energy storage grouping result. If it is greater, it goes to the step of increasing the number of days in the preset time period by a preset number of days.

[0191] The specific implementation manner of the multi-energy storage power station grouping and hierarchical collaborative scheduling system for a wind-solar base is basically the same as the specific embodiment of the above-mentioned multi-energy storage power station grouping and hierarchical collaborative scheduling method for a wind-solar base, and will not be elaborated here.

[0192] The technical solution of the present invention has the following advantages:

[0193] The present invention establishes a source-storage collaborative regulation framework, starting from the multi-time-scale active charging and discharging of the multi-energy storage power station, giving full play to the role of the multi-energy storage power station in adjusting the power curve of power generation and grid connection on the source side in time series and resisting the loss of uncertainty of wind-solar power generation, improving the acceptance degree of the power grid for uncontrollable power generation in the wind-solar base and the adaptability of the power grid to renewable energy, and can guide the formulation of the operation plan for distributed new energy power generation and grid connection.

[0194] 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.

[0195] The technical features of the above embodiments may be arbitrarily combined. 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.

[0196] 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 energy group is intelligently connected 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 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. 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.

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 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.

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 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.

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 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.

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: 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.

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: 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.

7. A multi-energy storage power station grouping and layering 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, 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 suppression 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 remain 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 readjust the energy group and power group allocation results, and a new round of wind and solar base peak regulation and frequency regulation at the real-time scale of the day before is started; 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.

8. The multi-energy storage power station grouping and hierarchical coordinated dispatching system for wind and solar bases as claimed in claim 7 is 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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