Virtual power plant energy storage capacity optimization configuration and dispatch method and system

Abstract

This invention provides a method and system for optimizing the configuration and scheduling of energy storage capacity in virtual power plants, relating to the field of virtual power plant energy storage technology. The method includes extrapolating time-period capacity demand sequences based on historical data, calculating and correcting the dynamic adjustable capacity boundaries of energy storage units. By identifying the temporal complementarity between energy storage units, they are divided into collaborative configuration groups and their capacity is aggregated. Based on the matching degree between the aggregation results and grid commands, power allocation and reserve are performed hierarchically for units within each group. When the capacity within a group is insufficient, complementary time periods across groups are identified, and the reserve capacity of other groups is used for compensatory scheduling. This invention achieves refined configuration and collaborative scheduling of energy storage capacity, improving the responsiveness of virtual power plants to grid demands and enhancing their operational economy.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant energy storage technology, and in particular to a method and system for optimizing the configuration and scheduling of virtual power plant energy storage capacity. Background Technology

[0002] Virtual power plants, as an important form of integrating distributed energy storage resources to participate in grid regulation, require optimized configuration and scheduling of their energy storage capacity as a key technical link to improve overall operational economy and reliability. In existing technologies, capacity planning and scheduling for multiple energy storage units within a virtual power plant typically employ methods based on fixed capacity boundaries or simple time-series overlay. Conventional practices often set static upper and lower limits for charging and discharging power and capacity ranges for each unit based on its rated capacity and historical average load curve. When formulating scheduling plans, each energy storage unit is usually treated as an independent entity, or a simple arithmetic summation of capacity is performed, with the aggregated total capacity boundary responding to grid dispatch commands. These methods focus on meeting macroscopic power balance requirements, rely on forecasting overall load trends, and employ strategies for evenly distributing or allocating power according to a fixed ratio during dispatch execution.

[0003] Static capacity boundary settings fail to adequately consider the dynamic changes in the state of charge and historical behavior patterns of energy storage units during actual operation, leading to overly conservative or overly aggressive boundary settings. Conservative settings limit the dispatchable capacity of energy storage units, resulting in idle resources and wasted investment; overly aggressive settings, on the other hand, can cause command execution failures during dispatch due to insufficient actual available capacity, threatening grid operation safety. Simply aggregating or treating multiple energy storage units independently ignores the temporal complementarity between different units at the adjustable capacity boundary. This neglect prevents the system from tapping into the capacity support potential across units at a finer time scale. When the capacity of a unit or local group is insufficient, there is a lack of effective cross-unit dynamic capacity borrowing and compensation mechanisms, resulting in insufficient overall regulation flexibility and robustness of the virtual power plant, making it difficult to cope with complex grid dispatch demands and random fluctuations in the internal unit states. Summary of the Invention

[0004] This invention provides a method and system for optimizing the configuration and scheduling of energy storage capacity in virtual power plants, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for optimizing the configuration and scheduling of energy storage capacity in a virtual power plant, comprising:

[0006] Based on historical charging and discharging data of virtual power plant energy storage units, the capacity demand for each time period is extrapolated in segments to obtain a time-segmented capacity demand sequence;

[0007] Based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated, and the boundary is corrected according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary;

[0008] Based on power load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units with the temporal complementarity relationship are divided into cooperative configuration groups and their capacities are superimposed to obtain the aggregated capacity configuration result of each cooperative configuration group.

[0009] Based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. According to the matching degree, the energy storage units in the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively.

[0010] When the capacity of any collaborative configuration group is insufficient, the cross-group complementary period of the dynamic capacity adjustable boundary between other collaborative configuration groups is identified, and part of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling during the cross-group complementary period.

[0011] Based on historical charging and discharging data of the virtual power plant's energy storage unit, the capacity demand for each time period is extrapolated in segments, resulting in a time-segmented capacity demand sequence including:

[0012] The historical charging and discharging data of the energy storage unit is divided into time periods. The charging power curve and discharging power curve in each time period are extracted. The peak occurrence time of the charging power curve and the peak occurrence time of the discharging power curve in each time period are counted and the time interval is calculated. The charging and discharging cycle of the energy storage unit in different time periods is identified based on the time interval.

[0013] For each time period, the historical time periods with the same charge-discharge cycle as the current time period are statistically analyzed, the cumulative value of actual charge-discharge capacity within the historical time period is extracted, and the central trend value and dispersion value of the cumulative value of actual charge-discharge capacity are calculated.

[0014] The central trend value is used as the benchmark value for the current period's capacity demand projection. The upper and lower fluctuation ranges of the current period's capacity demand projection are determined based on the dispersion value, thus obtaining the capacity demand projection value for the current period.

[0015] The projected capacity demand values ​​for each time period are arranged in chronological order to form the time-segmented capacity demand sequence.

[0016] Based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated. Boundary corrections are then performed based on the continuity constraints of adjacent time period boundaries to obtain the dynamically adjustable capacity boundaries, including:

[0017] The remaining available capacity and used capacity of the energy storage unit in each time period are calculated based on the state of charge information.

[0018] If the remaining available capacity is greater than the estimated capacity demand value for the corresponding time period in the time-segmented capacity demand sequence, the estimated capacity demand value is used as the upper boundary of the initial available capacity; otherwise, the remaining available capacity is used as the upper boundary of the initial available capacity.

[0019] The lower boundary of the initial required reserve capacity for each time period is determined based on the used capacity of the energy storage unit in each time period and the minimum reserve capacity required for the energy storage unit to maintain normal operation.

[0020] Between adjacent time periods, the difference between the upper boundary of the initial available capacity is extracted as the change in the upper boundary capacity, and the difference between the lower boundary of the initial required reserved capacity is extracted as the change in the lower boundary capacity.

[0021] The maximum allowable capacity change of the energy storage unit between adjacent time periods is calculated based on the rate of change of the maximum charging power and the rate of change of the maximum discharging power of the energy storage unit.

[0022] When the change in the upper boundary capacity or the change in the lower boundary capacity is greater than the maximum allowable change in capacity, the maximum allowable change in capacity is accumulated along the time series direction using the boundary of the previous time period as a reference point to obtain the corrected upper boundary of available capacity or the corrected lower boundary of required reserved capacity, and these are combined to form the dynamic adjustable capacity boundary for each time period.

[0023] Based on electricity load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units exhibiting this temporal complementarity relationship are divided into collaborative configuration groups and their capacities are superimposed. The aggregated capacity configuration results for each collaborative configuration group include:

[0024] Extract the peak load periods and valley load periods from the power load demand data;

[0025] Extract the temporal distribution characteristics of the upper boundary of the available capacity upper boundary and the lower boundary of the required reserved capacity lower boundary in the dynamic capacity adjustable boundary of each energy storage unit.

[0026] The upper boundary time-series distribution characteristics of each energy storage unit are matched with the peak load period, and the lower boundary time-series distribution characteristics of each energy storage unit are matched with the valley load period. The overlapping interval in the time series between the peak period of the upper boundary of the available capacity of any energy storage unit and the valley period of the lower boundary of the required reserved capacity of another energy storage unit is identified.

[0027] Calculate the capacity complementarity between the upper boundary of the available capacity of any energy storage unit and the lower boundary of the required reserved capacity of another energy storage unit within the overlapping interval. When the capacity complementarity meets the preset complementarity condition, it is determined that there is a temporal complementarity relationship between any energy storage unit and another energy storage unit.

[0028] After determining the collaborative configuration group, the upper boundary of the available capacity of each energy storage unit in the collaborative configuration group is superimposed at each time period to obtain the aggregated available capacity upper boundary of the collaborative configuration group. The lower boundary of the required reserved capacity of each energy storage unit in the collaborative configuration group is superimposed to obtain the aggregated required reserved capacity lower boundary of the collaborative configuration group, thus forming the aggregated capacity configuration result of the collaborative configuration group.

[0029] Based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. According to the matching degree, the energy storage units within the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively, including:

[0030] Extract the target dispatch power and target response time period from the power grid dispatch command;

[0031] Calculate the capacity margin between the upper boundary of the aggregated available capacity of each energy storage unit and the target dispatch power during the target response period, and the capacity gap between the lower boundary of the aggregated required reserved capacity of each energy storage unit and the target dispatch power during the target response period.

[0032] Based on the capacity margin and the capacity gap, calculate the matching degree between the dynamic adjustable capacity boundary of each energy storage unit and the grid dispatch command requirements;

[0033] Energy storage units with a matching degree greater than a preset matching threshold are classified as priority scheduling layers, and the remaining energy storage units are classified as standby scheduling layers.

[0034] For the energy storage units within the priority scheduling layer, the allocated power of each energy storage unit is calculated based on the upper boundary of the aggregate available capacity and the target scheduling power of each energy storage unit during the target response period, and is used as the power allocation result of each energy storage unit.

[0035] For the energy storage units within the backup scheduling layer, the reserved capacity of each energy storage unit is calculated as the backup capacity reservation result of each energy storage unit based on the capacity difference between the upper boundary of the aggregated available capacity and the lower boundary of the aggregated required reserved capacity during the target response period.

[0036] When the capacity of any collaborative configuration group is insufficient, the cross-group complementary period of the dynamic capacity adjustable boundary between other collaborative configuration groups is identified. During the cross-group complementary period, a portion of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling, including:

[0037] Calculate the capacity deficit between the upper boundary of the aggregate available capacity of each collaborative configuration group and the target scheduling power during the target response period. When the capacity deficit of any collaborative configuration group is greater than the preset capacity deficit threshold, determine that the capacity of any collaborative configuration group is insufficient and mark it as a collaborative configuration group with insufficient capacity.

[0038] Extract the time-series variation curve of the upper boundary of the aggregated available capacity of the insufficient capacity collaborative configuration group during the target response period, and the time-series variation curve of the lower boundary of the aggregated required retained capacity of other collaborative configuration groups during the target response period. By calculating the capacity difference value of the two time-series variation curves at each time and identifying the inflection point when the capacity difference value turns from negative to positive, determine the time period between adjacent inflection point times as the cross-group complementary time period.

[0039] During the cross-group complementary period, the capacity margin between the reserved capacity and the currently allocated capacity of each energy storage unit in the standby scheduling layer of other collaborative configuration groups is calculated. When the capacity margin is greater than the preset remaining capacity threshold, it is determined that each energy storage unit has remaining adjustable space. Based on the capacity deficit of the insufficient collaborative configuration group and the capacity margin of the energy storage unit with remaining adjustable space, the capacity transfer amount is calculated according to the ratio of capacity deficit to capacity margin. The capacity transfer amount is transferred from the energy storage unit with remaining adjustable space to the insufficient collaborative configuration group as compensation scheduling capacity.

[0040] A second aspect of the present invention provides a virtual power plant energy storage capacity optimization configuration and scheduling system, comprising:

[0041] The segmented extrapolation unit is used to extrapolate the capacity demand for each time period based on the historical charging and discharging data of the virtual power plant energy storage unit, and obtain the time-period capacity demand sequence.

[0042] The boundary calculation unit is used to calculate the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period based on the time-segmented capacity demand sequence and state of charge information, and to perform boundary correction according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary.

[0043] The collaborative configuration unit is used to identify the temporal complementary relationship of the dynamic capacity adjustable boundary between multiple energy storage units based on power load demand data, divide the energy storage units with the temporal complementary relationship into collaborative configuration groups and perform capacity superposition to obtain the aggregated capacity configuration result of each collaborative configuration group.

[0044] The hierarchical scheduling unit is used to calculate the matching degree between the dynamic capacity adjustable boundary of the energy storage unit and the grid scheduling command requirements based on the aggregated capacity configuration results and grid scheduling commands. Based on the matching degree, the energy storage units in the collaborative configuration group are divided into a priority scheduling layer and a standby scheduling layer, and power allocation and standby capacity reservation are performed respectively.

[0045] The cross-group compensation unit is used to identify the cross-group complementary period when the capacity of any collaborative configuration group is insufficient, and to transfer part of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups for compensation scheduling during the cross-group complementary period.

[0046] A third aspect of the embodiments of the present invention,

[0047] An electronic device is provided, comprising:

[0048] processor;

[0049] Memory used to store processor-executable instructions;

[0050] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0051] Fourth aspect of the present invention,

[0052] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0053] The beneficial effects of this application are as follows:

[0054] This method significantly improves the overall utilization efficiency and economy of virtual power plant energy storage systems. By segmenting and extrapolating historical charge and discharge data, it can accurately characterize the capacity demand features of different time periods, forming a time-segmented capacity demand sequence. This process effectively avoids the coarseness of capacity allocation based on a single average or peak value, making the prediction of energy storage demand more in line with actual operating conditions and laying a data foundation for subsequent refined capacity management.

[0055] Based on time-segmented capacity demand sequences and real-time state of charge information, the upper boundary of available capacity and the lower boundary of required reserved capacity for each energy storage unit are dynamically calculated and corrected. This dynamic adjustable capacity boundary not only reflects the actual capacity of the energy storage unit at the current moment, but also ensures the smoothness and rationality of the boundary in the time dimension through continuity constraints, preventing drastic fluctuations in dispatch commands. This provides a clear, reliable, and stable capacity adjustment range for energy storage units to participate in grid dispatch.

[0056] By identifying the temporal complementarity between the dynamic adjustable capacity boundaries of different energy storage units, units with synergistic potential are divided into synergistic configuration groups and their capacities are superimposed. The aggregated capacity configuration results realize the spatiotemporal optimization and integration of energy storage resources within the group, amplifying the regulation capability of individual energy storage units. Under the premise of meeting the same grid demand, it can reduce the dependence on the maximum capacity of individual energy storage devices, thereby reducing the initial investment cost.

[0057] Based on the matching degree between the aggregated capacity configuration results and the grid dispatch instructions, the energy storage units within the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer. This hierarchical dispatch mechanism enables differentiated utilization of resources, with the priority dispatch layer undertaking the main regulation tasks and the standby dispatch layer providing buffering and protection. This strategy optimizes power allocation and standby capacity reservation, improving the utilization frequency and efficiency of high-value energy storage resources while ensuring response reliability. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the virtual power plant energy storage capacity optimization configuration and scheduling method according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the hierarchical scheduling and power allocation process for energy storage units. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0062] Figure 1 This is a flowchart illustrating the virtual power plant energy storage capacity optimization configuration and scheduling method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0063] Based on historical charging and discharging data of virtual power plant energy storage units, the capacity demand for each time period is extrapolated in segments to obtain a time-segmented capacity demand sequence;

[0064] Based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated, and the boundary is corrected according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary;

[0065] Based on power load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units with the temporal complementarity relationship are divided into cooperative configuration groups and their capacities are superimposed to obtain the aggregated capacity configuration result of each cooperative configuration group.

[0066] Based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. According to the matching degree, the energy storage units in the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively.

[0067] When the capacity of any collaborative configuration group is insufficient, the cross-group complementary period of the dynamic capacity adjustable boundary between other collaborative configuration groups is identified, and part of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling during the cross-group complementary period.

[0068] In one optional implementation, based on the historical charging and discharging data of the virtual power plant energy storage unit, the capacity demand for each time period is segmented and extrapolated to obtain a time-segmented capacity demand sequence, including:

[0069] The historical charging and discharging data of the energy storage unit is divided into time periods. The charging power curve and discharging power curve in each time period are extracted. The peak occurrence time of the charging power curve and the peak occurrence time of the discharging power curve in each time period are counted and the time interval is calculated. The charging and discharging cycle of the energy storage unit in different time periods is identified based on the time interval.

[0070] For each time period, the historical time periods with the same charge-discharge cycle as the current time period are statistically analyzed, the cumulative value of actual charge-discharge capacity within the historical time period is extracted, and the central trend value and dispersion value of the cumulative value of actual charge-discharge capacity are calculated.

[0071] The central trend value is used as the benchmark value for the current period's capacity demand projection. The upper and lower fluctuation ranges of the current period's capacity demand projection are determined based on the dispersion value, thus obtaining the capacity demand projection value for the current period.

[0072] The projected capacity demand values ​​for each time period are arranged in chronological order to form the time-segmented capacity demand sequence.

[0073] During the optimization and configuration of energy storage capacity in a virtual power plant, historical charging and discharging data of the energy storage units during operation are collected. This data includes the charging power, discharging power, state of charge, and operating duration at each sampling moment. The daily operating time is divided into several time periods, the length of which can be set according to actual dispatching needs. For example, a basic time period unit can be used, such as one hour, or the time period can be further refined to 15 minutes during periods of significant power load fluctuations. For each divided time period, the charging power and discharging power values ​​of all sampling points within that time period are extracted from the historical database, and charging power curves and discharging power curves are plotted respectively.

[0074] In each charging power curve, the point when the power reaches its maximum value is identified by point-by-point comparison, and the peak value is recorded. Similarly, the peak value of the discharge power is located in the discharge power curve. The time difference between the peak charging and peak discharging times within the same time period is calculated; this time difference reflects the charge-discharge conversion characteristics of the energy storage unit during that time period. When the time difference of multiple consecutive time periods exhibits a periodic pattern, the length of time corresponding to this pattern is identified as the charge-discharge cycle of the energy storage unit. For energy storage units dominated by industrial loads, their charge-discharge cycle is related to production shifts; for energy storage units dominated by residential loads, their charge-discharge cycle often coincides with daily work and rest patterns.

[0075] For the current period requiring capacity demand projection, all historical periods with the same charge / discharge cycle as the current period are selected from historical data. Actual charge / discharge records of energy storage units within these historical periods are extracted, and the charging and discharging amounts for each historical period are summed to obtain the cumulative actual charge / discharge capacity for that historical period. A sample set is created from the cumulative capacity values ​​of all selected historical periods, and the central tendency and dispersion values ​​are calculated for this sample set. The central tendency can be represented using the arithmetic mean, weighted average, or median. The weighted average can be weighted according to the time distance between the historical period and the current period, with greater weight given to closer historical periods. The dispersion value can be quantified using the standard deviation or interquartile range. The standard deviation reflects the overall fluctuation characteristics of the sample set, while the interquartile range is more suitable for handling datasets containing outliers.

[0076] The calculated central trend value is determined as the baseline value for the current period's capacity demand projection. This baseline value represents the most likely capacity demand level for energy storage units under the same charge-discharge cycle conditions. The fluctuation range of capacity demand is set based on the dispersion value. Specifically, a positive offset is added to the baseline value as the upper limit of capacity demand, and a negative offset is subtracted as the lower limit. The magnitude of the offset is proportional to the dispersion value. When historical data fluctuates significantly, a wider fluctuation range is set to improve the fault tolerance of the projection results; when historical data is relatively stable, the fluctuation range can be appropriately narrowed to improve the accuracy of capacity allocation. The baseline value and the fluctuation range are combined to determine the projected capacity demand value for the current period, which is expressed in interval form.

[0077] Following the natural order of time periods, the projected capacity demand values ​​from the first to the last time period are arranged sequentially to form a time-segmented capacity demand sequence covering the entire scheduling cycle. This sequence not only includes the capacity demand values ​​for each time period but also the possible fluctuation range of capacity demand for each time period, providing data support for the subsequent calculation of dynamic capacity boundaries.

[0078] In one optional implementation, based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated, and the boundary is corrected according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary, including:

[0079] The remaining available capacity and used capacity of the energy storage unit in each time period are calculated based on the state of charge information.

[0080] If the remaining available capacity is greater than the estimated capacity demand value for the corresponding time period in the time-segmented capacity demand sequence, the estimated capacity demand value is used as the upper boundary of the initial available capacity; otherwise, the remaining available capacity is used as the upper boundary of the initial available capacity.

[0081] The lower boundary of the initial required reserve capacity for each time period is determined based on the used capacity of the energy storage unit in each time period and the minimum reserve capacity required for the energy storage unit to maintain normal operation.

[0082] Between adjacent time periods, the difference between the upper boundary of the initial available capacity is extracted as the change in the upper boundary capacity, and the difference between the lower boundary of the initial required reserved capacity is extracted as the change in the lower boundary capacity.

[0083] The maximum allowable capacity change of the energy storage unit between adjacent time periods is calculated based on the rate of change of the maximum charging power and the rate of change of the maximum discharging power of the energy storage unit.

[0084] When the change in the upper boundary capacity or the change in the lower boundary capacity is greater than the maximum allowable change in capacity, the maximum allowable change in capacity is accumulated along the time series direction using the boundary of the previous time period as a reference point to obtain the corrected upper boundary of available capacity or the corrected lower boundary of required reserved capacity, and these are combined to form the dynamic adjustable capacity boundary for each time period.

[0085] After obtaining the time-segmented capacity demand sequence, it is necessary to determine the capacity adjustment range of the energy storage unit in each time period based on its actual operating status. The state of charge information of the energy storage unit includes the current charge, rated total capacity, and historical charge and discharge records. The remaining available capacity can be calculated by the difference between the current charge and the rated total capacity. This value represents the maximum amount of electricity that the energy storage unit can theoretically release in the current time period. The used capacity is obtained by comparing the current charge with the historical baseline charge, reflecting the level of electricity that the energy storage unit has already output or stored.

[0086] By comparing the remaining available capacity with the projected capacity demand, when the remaining available capacity is sufficient, i.e. greater than the projected capacity demand for the corresponding time period in the time-segmented capacity demand sequence, it indicates that the energy storage unit has the ability to meet the demand. In this case, the projected capacity demand is used as the upper boundary of the initial available capacity to avoid over-utilizing energy storage resources. Conversely, when the remaining available capacity is insufficient to meet the projected demand, the remaining available capacity is used as the upper boundary of the initial available capacity to ensure that the configuration scheme complies with physical constraints.

[0087] The determination of the initial required reserve capacity lower boundary needs to consider the safe operation requirements of the energy storage unit. The minimum reserve capacity required for the energy storage unit to maintain normal operation is usually set by the battery management system to prevent over-discharge damage. The used capacity at each time period is compared with this minimum reserve capacity, and the larger value is taken as the initial required reserve capacity lower boundary to ensure that the energy storage unit will not fall below the safe operation threshold at any time.

[0088] Because the charging and discharging power of energy storage units is limited by the power electronic converter and battery chemistry, capacity adjustments between adjacent time periods cannot involve excessively large jumps. The difference between adjacent time periods is extracted to obtain the upper boundary capacity change; similarly, the difference between adjacent time periods is extracted to obtain the lower boundary capacity change. These two changes reflect the degree of fluctuation of the boundaries over time.

[0089] The maximum permissible capacity change of an energy storage unit between adjacent time periods is determined by the physical limitations of its charging and discharging power. The maximum charging power change rate and maximum discharging power change rate specified in the energy storage unit's technical parameters are extracted; these rates are typically expressed in power units per time unit. The time interval between adjacent time periods is calculated, i.e., the time difference between the start time of the later time period and the end time of the previous time period. The direction of the current capacity change is determined to be either a charging or discharging process; if it is a charging process, the maximum charging power change rate is used; if it is a discharging process, the maximum discharging power change rate is used. The selected power change rate is multiplied by the time interval to obtain the theoretically adjustable power change of the energy storage unit during the time period transition. The power change is multiplied again by the time interval and the units are converted to obtain the maximum permissible capacity change expressed in capacity units. This value is typically expressed in kilowatt-hours per minute or megawatt-hours per hour, depending on the granularity of the time period division.

[0090] The boundary correction process employs a time-series accumulation method. When the change in upper boundary capacity exceeds the maximum allowable capacity change, it indicates that the initial boundary setting is too aggressive and cannot be achieved through actual charging and discharging operations. In this case, using the upper boundary of available capacity from the previous time period as a reference point, the maximum allowable capacity change is accumulated time-by-time along the time series direction to obtain the corrected upper boundary of available capacity. This correction process ensures that boundary changes comply with power ramping constraints. The correction of the lower boundary follows the same logic: when the change in lower boundary capacity is too large, the maximum allowable capacity change is accumulated and corrected using the lower boundary of required reserved capacity from the previous time period as a reference.

[0091] The revised upper boundary of available capacity and the revised lower boundary of required reserved capacity together constitute the dynamic adjustable capacity boundary for each time period. This boundary forms a capacity adjustment range that changes over time. The upper boundary limits the maximum output capacity of the energy storage unit during that time period, while the lower boundary ensures the bottom line for the safe operation of the energy storage unit. The space between the two is the effective capacity range that can participate in dispatch.

[0092] In one optional implementation, based on power load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units exhibiting the temporal complementarity relationship are then divided into collaborative configuration groups and their capacities are superimposed. The aggregated capacity configuration result for each collaborative configuration group includes:

[0093] Extract the peak load periods and valley load periods from the power load demand data;

[0094] Extract the temporal distribution characteristics of the upper boundary of the available capacity upper boundary and the lower boundary of the required reserved capacity lower boundary in the dynamic capacity adjustable boundary of each energy storage unit.

[0095] The upper boundary time-series distribution characteristics of each energy storage unit are matched with the peak load period, and the lower boundary time-series distribution characteristics of each energy storage unit are matched with the valley load period. The overlapping interval in the time series between the peak period of the upper boundary of the available capacity of any energy storage unit and the valley period of the lower boundary of the required reserved capacity of another energy storage unit is identified.

[0096] Calculate the capacity complementarity between the upper boundary of the available capacity of any energy storage unit and the lower boundary of the required reserved capacity of another energy storage unit within the overlapping interval. When the capacity complementarity meets the preset complementarity condition, it is determined that there is a temporal complementarity relationship between any energy storage unit and another energy storage unit.

[0097] After determining the collaborative configuration group, the upper boundary of the available capacity of each energy storage unit in the collaborative configuration group is superimposed at each time period to obtain the aggregated available capacity upper boundary of the collaborative configuration group. The lower boundary of the required reserved capacity of each energy storage unit in the collaborative configuration group is superimposed to obtain the aggregated required reserved capacity lower boundary of the collaborative configuration group, thus forming the aggregated capacity configuration result of the collaborative configuration group.

[0098] In the collaborative configuration of energy storage capacity in virtual power plants, extreme characteristic points in the load curve are extracted by performing time-series decomposition on power load demand data. Peak load periods are determined by setting thresholds; for example, consecutive periods where the load value exceeds 120% of the daily average load are marked as peak periods. Valley load periods are consecutive periods where the load value is below 80% of the daily average load. The extraction results are stored as a set of time-stamped data, containing the start time and duration of each extreme period.

[0099] The system collects the trajectory of the upper boundary of available capacity over 24 hours, recording the moment when the boundary value reaches a local peak and its corresponding capacity value. The temporal distribution characteristics of the lower boundary are collected by collecting the trajectory of the lower boundary of the required retained capacity, recording the moment when the boundary value reaches a local trough and its corresponding capacity value. The temporal distribution characteristics are expressed as a discrete time-point sequence, with each time point associated with the boundary capacity value and the rate of change parameter.

[0100] When identifying the temporal complementarity relationship between energy storage units, all energy storage unit pairs are traversed. For any energy storage unit A, the time period during which the upper boundary of its available capacity reaches its peak is compared with the time period during which the lower boundary of its required reserved capacity reaches its trough. When the peak period for energy storage unit A is 8:00-10:00 and the trough period for energy storage unit B is 9:00-11:00, there is an overlap between them from 9:00-10:00. The overlap is determined through temporal intersection operations, and the start and end times and duration of the overlap are recorded.

[0101] Capacity complementarity is calculated using the ratio of capacity difference to capacity demand. Within the overlap region, the upper boundary mean C of the available capacity of energy storage unit A is extracted. A,upper The lower boundary mean of the required retention capacity of energy storage unit B, C B,lower The capacity complementarity is calculated as the ratio of the difference between the two energy storage units to the current load demand. When this ratio is greater than 0.3 and the absolute value of the capacity difference exceeds 50 kWh, the preset complementarity condition is met, confirming a temporal complementary relationship between the two energy storage units. A complementarity matrix is ​​constructed to record the complementary status between all pairs of energy storage units.

[0102] Based on the identified temporal complementarity relationships, a graph clustering algorithm is used to divide energy storage units into cooperative configuration groups. Each energy storage unit is treated as a node, and edges are established between pairs of energy storage units with complementary relationships. The edge weight is set to the capacity complementarity value. A connected component detection algorithm is applied to divide strongly connected sets of energy storage units into the same cooperative configuration group. For isolated nodes or weakly connected nodes, they are supplemented and assigned to the corresponding configuration group based on geographical proximity and similarity of operating modes.

[0103] After the collaborative configuration group is divided, capacity aggregation calculation is performed. Within each scheduling period t, all energy storage units within the collaborative configuration group are traversed, and the upper boundary value of the available capacity of each energy storage unit in that period is accumulated, summing to obtain the aggregated upper boundary of available capacity. Simultaneously, the lower boundary value of the required reserved capacity of each energy storage unit is accumulated, summing to obtain the aggregated lower boundary of required reserved capacity. The aggregation process considers the response latency differences of each energy storage unit. For energy storage units with a response latency exceeding 5 seconds, their boundary values ​​are multiplied by a reduction factor of 0.95 before participating in the aggregation. The aggregated capacity configuration result is stored in a time-series data structure, containing the aggregated upper boundary value, aggregated lower boundary value, and a list of energy storage unit identifiers participating in the aggregation for each period, providing a capacity range basis for subsequent scheduling decisions.

[0104] In one optional implementation, based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. Based on the matching degree, the energy storage units within the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively, including:

[0105] Extract the target dispatch power and target response time period from the power grid dispatch command;

[0106] Calculate the capacity margin between the upper boundary of the aggregated available capacity of each energy storage unit and the target dispatch power during the target response period, and the capacity gap between the lower boundary of the aggregated required reserved capacity of each energy storage unit and the target dispatch power during the target response period.

[0107] Based on the capacity margin and the capacity gap, calculate the matching degree between the dynamic adjustable capacity boundary of each energy storage unit and the grid dispatch command requirements;

[0108] Energy storage units with a matching degree greater than a preset matching threshold are classified as priority scheduling layers, and the remaining energy storage units are classified as standby scheduling layers.

[0109] For the energy storage units within the priority scheduling layer, the allocated power of each energy storage unit is calculated based on the upper boundary of the aggregate available capacity and the target scheduling power of each energy storage unit during the target response period, and is used as the power allocation result of each energy storage unit.

[0110] For the energy storage units within the backup scheduling layer, the reserved capacity of each energy storage unit is calculated as the backup capacity reservation result of each energy storage unit based on the capacity difference between the upper boundary of the aggregated available capacity and the lower boundary of the aggregated required reserved capacity during the target response period.

[0111] like Figure 2 As shown, the method includes:

[0112] After obtaining the aggregated capacity configuration results, it is necessary to complete the hierarchical division and power allocation of energy storage units according to the real-time dispatch instructions issued by the power grid. The power grid dispatch instructions typically include parameters such as target dispatch power, target response time period, and response rate requirements. Among them, the target dispatch power represents the charging or discharging power that the virtual power plant needs to provide to the power grid, and the target response time period specifies the effective time interval of the dispatch instruction.

[0113] Extract the target dispatch power P from the power grid dispatch command. target With the target response time t start , t end For each energy storage unit within the collaborative configuration group, the upper boundary of its aggregated available capacity and the lower boundary of its aggregated required reserved capacity are extracted at each time point within the target response period. The upper boundary of aggregated available capacity represents the maximum adjustable capacity that the energy storage unit can provide without violating safety constraints, while the lower boundary of aggregated required reserved capacity represents the minimum capacity that must be reserved to ensure scheduling continuity in subsequent time periods.

[0114] Calculate the capacity margin of each energy storage unit within the target response period. The capacity margin is defined as the difference between the upper boundary of the aggregated available capacity and the target dispatch power, reflecting the remaining adjustable space of the energy storage unit after responding to the dispatch command. Simultaneously, calculate the capacity gap, i.e., the difference between the target dispatch power and the lower boundary of the aggregated required reserve capacity, representing the degree of risk of the energy storage unit exceeding the safety boundary when responding to the dispatch command.

[0115] A matching degree evaluation index is constructed based on capacity margin and capacity gap. The matching degree is calculated using a weighted combination method. A larger capacity margin indicates that the energy storage unit has sufficient regulation capability, while a smaller capacity gap indicates a higher safety margin in responding to dispatch commands. Specifically, the matching degree can be expressed as the ratio of capacity margin to capacity gap, or it can be comprehensively evaluated using a normalized weighted summation method. The calculated matching degree is compared with a preset matching threshold, which is determined based on historical dispatch experience and grid reliability requirements, and typically ranges from 0.6 to 0.8.

[0116] Energy storage units with a matching degree greater than a preset matching threshold are classified as priority dispatching units. These units have strong dispatching response capabilities and can directly undertake grid dispatching tasks. Energy storage units with a matching degree less than the threshold are classified as backup dispatching units, which serve as capacity reserves to provide supplementary support when the priority dispatching unit's capacity is insufficient or a fault occurs.

[0117] For energy storage units within the priority scheduling layer, power is allocated based on their respective aggregate available capacity upper boundaries. Power allocation follows a capacity proportionality principle, meaning the allocated power for each energy storage unit is proportional to its aggregate available capacity upper boundary, ensuring full utilization of each unit's regulation potential. Specifically, the allocated power for a single energy storage unit equals the target scheduling power multiplied by the proportion of that unit's aggregate available capacity upper boundary to the total available capacity of the priority scheduling layer.

[0118] For each energy storage unit within the standby dispatch layer, its reserved capacity is calculated. The reserved capacity is determined based on the capacity difference between the upper boundary of the aggregated available capacity and the lower boundary of the aggregated required reserved capacity. This difference represents the capacity space available for standby dispatch for each energy storage unit while meeting safety constraints. The reserved capacity results will serve as the basis for capacity allocation in subsequent cross-group compensation dispatch, ensuring that the virtual power plant as a whole possesses sufficient dispatch flexibility and fault response capabilities.

[0119] In one optional implementation, when the capacity of any collaborative configuration group is insufficient, a cross-group complementary period is identified between the dynamic capacity adjustable boundary and other collaborative configuration groups. During the cross-group complementary period, a portion of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling, including:

[0120] Calculate the capacity deficit between the upper boundary of the aggregate available capacity of each collaborative configuration group and the target scheduling power during the target response period. When the capacity deficit of any collaborative configuration group is greater than the preset capacity deficit threshold, determine that the capacity of any collaborative configuration group is insufficient and mark it as a collaborative configuration group with insufficient capacity.

[0121] Extract the time-series variation curve of the upper boundary of the aggregated available capacity of the insufficient capacity collaborative configuration group during the target response period, and the time-series variation curve of the lower boundary of the aggregated required retained capacity of other collaborative configuration groups during the target response period. By calculating the capacity difference value of the two time-series variation curves at each time and identifying the inflection point when the capacity difference value turns from negative to positive, determine the time period between adjacent inflection point times as the cross-group complementary time period.

[0122] During the cross-group complementary period, the capacity margin between the reserved capacity and the currently allocated capacity of each energy storage unit in the standby scheduling layer of other collaborative configuration groups is calculated. When the capacity margin is greater than the preset remaining capacity threshold, it is determined that each energy storage unit has remaining adjustable space. Based on the capacity deficit of the insufficient collaborative configuration group and the capacity margin of the energy storage unit with remaining adjustable space, the capacity transfer amount is calculated according to the ratio of capacity deficit to capacity margin. The capacity transfer amount is transferred from the energy storage unit with remaining adjustable space to the insufficient collaborative configuration group as compensation scheduling capacity.

[0123] In the actual operation of a virtual power plant, when the capacity of a coordinated configuration group cannot meet the grid dispatch requirements due to a sudden load surge or energy storage unit failure, a cross-group capacity compensation mechanism needs to be activated. This mechanism first determines the capacity shortage state through quantitative assessment. For each coordinated configuration group, the upper boundary value of its aggregated available capacity during the target response period is collected. This value is obtained by summing the upper boundaries of the available capacity of all energy storage units within the group. Simultaneously, the target dispatch power command issued by the grid is acquired, and the difference between the two is calculated as the capacity deficit. When the capacity deficit exceeds a preset capacity deficit threshold, the coordinated configuration group is marked as a capacity-deficient coordinated configuration group, and the compensation process is triggered. The capacity deficit threshold is set according to the grid dispatch safety margin, typically taking 5%-10% of the target dispatch power.

[0124] Identifying cross-group complementary periods requires time-series matching analysis. The time-series curve of the upper boundary of aggregated available capacity for the undercapacity collaborative configuration group within the target response period is extracted. This curve reflects the maximum available capacity of energy storage resources within the group at different times. Simultaneously, the time-series curve of the lower boundary of aggregated required reserve capacity for other collaborative configuration groups within the same period is extracted. This curve represents the minimum capacity level that other groups must retain to ensure their own scheduling tasks. The two curves are numerically compared at each sampling time, and the capacity difference value is calculated. The capacity difference value is the lower boundary of the aggregated required reserve capacity of other groups minus the upper boundary of the aggregated available capacity of the undercapacity group. When the capacity difference value is negative, it indicates that the required reserve capacity of other groups is lower than the available capacity of the undercapacity group, and other groups have the support capability during this period. When the capacity difference value turns from negative to positive, the corresponding time is the inflection point of the complementary capability boundary. The time period between two adjacent inflection points is the cross-group complementary period, during which other groups have the conditions to provide capacity support to the undercapacity group.

[0125] Within the defined cross-group complementary time period, the transferable capacity resources need to be accurately calculated. This involves iterating through each energy storage unit in the standby scheduling layer of other collaborative configuration groups, reading the reserved capacity configuration value and the currently allocated capacity value for each unit. Reserved capacity is the share of capacity pre-allocated during standby scheduling layer capacity planning to cope with scheduling fluctuations within the group; the currently allocated capacity is the actual scheduling task capacity undertaken by the energy storage unit at the current moment. The difference between the two is the capacity margin, reflecting the remaining adjustable space of the energy storage unit. When the capacity margin is greater than a preset remaining capacity threshold, the energy storage unit is deemed capable of participating in cross-group compensation. The remaining capacity threshold is typically set to more than 10% of the rated capacity of the energy storage unit to ensure that the transfer operation does not affect the scheduling stability of the original group.

[0126] The capacity transfer amount is calculated using a proportional allocation strategy. The total capacity surplus of all energy storage units with remaining adjustable space is calculated, and the proportion of the capacity deficit in the insufficient collaborative configuration group to the total capacity surplus is calculated. For each energy storage unit with remaining adjustable space, its capacity surplus is multiplied by the proportion to obtain the capacity transfer amount for that unit. This allocation method ensures that the capacity transfer burden is reasonably distributed among the energy storage units according to their capabilities. When executing capacity transfer, at the dispatch command level, the corresponding capacity transfer amount is allocated from the reserve capacity of the original collaborative configuration group and redistributed to the insufficient collaborative configuration group as compensation dispatch capacity, realizing dynamic capacity sharing across groups and improving the overall resource utilization efficiency and power supply reliability of the virtual power plant.

[0127] A third aspect of the present invention provides an electronic device, comprising:

[0128] processor;

[0129] Memory used to store processor-executable instructions;

[0130] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0131] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0132] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimal configuration and dispatch of energy storage capacity in a virtual power plant, characterized in that, include: Based on historical charging and discharging data of virtual power plant energy storage units, the capacity demand for each time period is extrapolated in segments to obtain a time-segmented capacity demand sequence; Based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated, and the boundary is corrected according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary; Based on power load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units with the temporal complementarity relationship are divided into cooperative configuration groups and their capacities are superimposed to obtain the aggregated capacity configuration result of each cooperative configuration group. Based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. According to the matching degree, the energy storage units in the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively. When the capacity of any collaborative configuration group is insufficient, the cross-group complementary period of the dynamic capacity adjustable boundary between other collaborative configuration groups is identified, and part of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling during the cross-group complementary period.

2. The method of claim 1, wherein, Based on historical charging and discharging data of the virtual power plant's energy storage unit, the capacity demand for each time period is extrapolated in segments, resulting in a time-segmented capacity demand sequence including: The historical charging and discharging data of the energy storage unit is divided into time periods. The charging power curve and discharging power curve in each time period are extracted. The peak occurrence time of the charging power curve and the peak occurrence time of the discharging power curve in each time period are counted and the time interval is calculated. The charging and discharging cycle of the energy storage unit in different time periods is identified based on the time interval. For each time period, the historical time periods with the same charge-discharge cycle as the current time period are statistically analyzed, the cumulative value of actual charge-discharge capacity within the historical time period is extracted, and the central trend value and dispersion value of the cumulative value of actual charge-discharge capacity are calculated. The central trend value is used as the benchmark value for the current period's capacity demand projection. The upper and lower fluctuation ranges of the current period's capacity demand projection are determined based on the dispersion value, thus obtaining the capacity demand projection value for the current period. The projected capacity demand values ​​for each time period are arranged in chronological order to form the time-segmented capacity demand sequence.

3. The method of claim 1, wherein, Based on the time-segmented capacity demand sequence and state of charge information, the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period are calculated. Boundary corrections are then performed based on the continuity constraints of adjacent time period boundaries to obtain the dynamically adjustable capacity boundaries, including: The remaining available capacity and used capacity of the energy storage unit in each time period are calculated based on the state of charge information. If the remaining available capacity is greater than the estimated capacity demand value for the corresponding time period in the time-segmented capacity demand sequence, the estimated capacity demand value is used as the upper boundary of the initial available capacity; otherwise, the remaining available capacity is used as the upper boundary of the initial available capacity. The lower boundary of the initial required reserve capacity for each time period is determined based on the used capacity of the energy storage unit in each time period and the minimum reserve capacity required for the energy storage unit to maintain normal operation. Between adjacent time periods, the difference between the upper boundary of the initial available capacity is extracted as the change in the upper boundary capacity, and the difference between the lower boundary of the initial required reserved capacity is extracted as the change in the lower boundary capacity. The maximum allowable capacity change of the energy storage unit between adjacent time periods is calculated based on the rate of change of the maximum charging power and the rate of change of the maximum discharging power of the energy storage unit. When the change in the upper boundary capacity or the change in the lower boundary capacity is greater than the maximum allowable change in capacity, the maximum allowable change in capacity is accumulated along the time series direction using the boundary of the previous time period as a reference point to obtain the corrected upper boundary of available capacity or the corrected lower boundary of required reserved capacity, and these are combined to form the dynamic adjustable capacity boundary for each time period.

4. The method of claim 1, wherein, Based on electricity load demand data, the temporal complementarity relationship of the dynamic capacity adjustable boundary among multiple energy storage units is identified. Energy storage units exhibiting this temporal complementarity relationship are divided into collaborative configuration groups and their capacities are superimposed. The aggregated capacity configuration results for each collaborative configuration group include: Extract the peak load periods and valley load periods from the power load demand data; Extract the temporal distribution characteristics of the upper boundary of the available capacity upper boundary and the lower boundary of the required reserved capacity lower boundary in the dynamic capacity adjustable boundary of each energy storage unit. The upper boundary time-series distribution characteristics of each energy storage unit are matched with the peak load period, and the lower boundary time-series distribution characteristics of each energy storage unit are matched with the valley load period. The overlapping interval in the time series between the peak period of the upper boundary of the available capacity of any energy storage unit and the valley period of the lower boundary of the required reserved capacity of another energy storage unit is identified. Calculate the capacity complementarity between the upper boundary of the available capacity of any energy storage unit and the lower boundary of the required reserved capacity of another energy storage unit within the overlapping interval. When the capacity complementarity meets the preset complementarity condition, it is determined that there is a temporal complementarity relationship between any energy storage unit and another energy storage unit. After determining the collaborative configuration group, the upper boundary of the available capacity of each energy storage unit in the collaborative configuration group is superimposed at each time period to obtain the aggregated available capacity upper boundary of the collaborative configuration group. The lower boundary of the required reserved capacity of each energy storage unit in the collaborative configuration group is superimposed to obtain the aggregated required reserved capacity lower boundary of the collaborative configuration group, thus forming the aggregated capacity configuration result of the collaborative configuration group.

5. The method of claim 4, wherein, Based on the aggregated capacity configuration results and grid dispatch instructions, the matching degree between the dynamic adjustable capacity boundary of the energy storage unit and the grid dispatch instruction requirements is calculated. According to the matching degree, the energy storage units within the collaborative configuration group are divided into a priority dispatch layer and a standby dispatch layer, and power allocation and standby capacity reservation are performed respectively, including: Extract the target dispatch power and target response time period from the power grid dispatch command; Calculate the capacity margin between the upper boundary of the aggregated available capacity of each energy storage unit and the target dispatch power during the target response period, and the capacity gap between the lower boundary of the aggregated required reserved capacity of each energy storage unit and the target dispatch power during the target response period. Based on the capacity margin and the capacity gap, calculate the matching degree between the dynamic adjustable capacity boundary of each energy storage unit and the grid dispatch command requirements; Energy storage units with a matching degree greater than a preset matching threshold are classified as priority scheduling layers, and the remaining energy storage units are classified as standby scheduling layers. For the energy storage units within the priority scheduling layer, the allocated power of each energy storage unit is calculated based on the upper boundary of the aggregate available capacity and the target scheduling power of each energy storage unit during the target response period, and is used as the power allocation result of each energy storage unit. For the energy storage units within the backup scheduling layer, the reserved capacity of each energy storage unit is calculated as the backup capacity reservation result of each energy storage unit based on the capacity difference between the upper boundary of the aggregated available capacity and the lower boundary of the aggregated required reserved capacity during the target response period.

6. The method of claim 1, wherein, When the capacity of any collaborative configuration group is insufficient, the cross-group complementary period of the dynamic capacity adjustable boundary between other collaborative configuration groups is identified. During the cross-group complementary period, a portion of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups is transferred for compensation scheduling, including: Calculate the capacity deficit between the upper boundary of the aggregate available capacity of each collaborative configuration group and the target scheduling power during the target response period. When the capacity deficit of any collaborative configuration group is greater than the preset capacity deficit threshold, determine that the capacity of any collaborative configuration group is insufficient and mark it as a collaborative configuration group with insufficient capacity. Extract the time-series variation curve of the upper boundary of the aggregated available capacity of the insufficient capacity collaborative configuration group during the target response period, and the time-series variation curve of the lower boundary of the aggregated required retained capacity of other collaborative configuration groups during the target response period. By calculating the capacity difference value of the two time-series variation curves at each time and identifying the inflection point when the capacity difference value turns from negative to positive, determine the time period between adjacent inflection point times as the cross-group complementary time period. During the cross-group complementary period, the capacity margin between the reserved capacity and the currently allocated capacity of each energy storage unit in the standby scheduling layer of other collaborative configuration groups is calculated. When the capacity margin is greater than the preset remaining capacity threshold, it is determined that each energy storage unit has remaining adjustable space. Based on the capacity deficit of the insufficient collaborative configuration group and the capacity margin of the energy storage unit with remaining adjustable space, the capacity transfer amount is calculated according to the ratio of capacity deficit to capacity margin. The capacity transfer amount is transferred from the energy storage unit with remaining adjustable space to the insufficient collaborative configuration group as compensation scheduling capacity.

7. A virtual power plant energy storage capacity optimization configuration and dispatch system for implementing the method of any one of claims 1-6, characterized in that, include: The segmented extrapolation unit is used to extrapolate the capacity demand for each time period based on the historical charging and discharging data of the virtual power plant energy storage unit, and obtain the time-period capacity demand sequence. The boundary calculation unit is used to calculate the upper boundary of the available capacity and the lower boundary of the required reserved capacity of the energy storage unit in each time period based on the time-segmented capacity demand sequence and state of charge information, and to perform boundary correction according to the continuity constraint of the boundary of adjacent time periods to obtain the dynamic adjustable capacity boundary. The collaborative configuration unit is used to identify the temporal complementary relationship of the dynamic capacity adjustable boundary between multiple energy storage units based on power load demand data, divide the energy storage units with the temporal complementary relationship into collaborative configuration groups and perform capacity superposition to obtain the aggregated capacity configuration result of each collaborative configuration group. The hierarchical scheduling unit is used to calculate the matching degree between the dynamic capacity adjustable boundary of the energy storage unit and the grid scheduling command requirements based on the aggregated capacity configuration results and grid scheduling commands. Based on the matching degree, the energy storage units in the collaborative configuration group are divided into a priority scheduling layer and a standby scheduling layer, and power allocation and standby capacity reservation are performed respectively. The cross-group compensation unit is used to identify the cross-group complementary period when the capacity of any collaborative configuration group is insufficient, and to transfer part of the capacity of energy storage units with remaining adjustable space in the standby scheduling layer of other collaborative configuration groups for compensation scheduling during the cross-group complementary period.

8. An electronic device, comprising: include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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